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Jodern | a name jodern a jodern this is our project for the mobile development course at hcmus a name navigation a navigation jodern jodern navigation navigation features features login login onboarding onboarding shopping time shopping time product details product details lookup lookup image search image search text search text search voice search voice search share product share product profile page profile page wishlist wishlist cart system cart system personal cart personal cart shared cart shared cart on checkout on checkout delivery order delivery order pick up order pick up order order confirmation mail order confirmation order details order details map map technologies technologies frontend frontend backend backend demo demo features a name login a login p align center img style width 240 px height 480px margin 10px src readme resources images login 1 png img style width 240 px height 480px margin 10px src readme resources images login 2 png p a name onboard a onboarding p align center img style width 240 px height 480px margin 10px src readme resources images onboard 1 png img style width 240 px height 480px margin 10px src readme resources images onboard 2 png p a name shopping time a shopping time p align center img style width 240 px height 400px margin 10px src readme resources images product list 1 png img style width 240 px height 480px margin 10px src readme resources images shopping time 1 png img style width 240 px height 480px margin 10px src readme resources images shopping time 2 png img style width 240 px height 480px margin 10px src readme resources images shopping time 3 png p a name product details a product details p align center img style width 240 px height 480px margin 10px src readme resources images product details 1 png img style width 240 px height 480px margin 10px src readme resources images product details 2 png p a name searching a lookup a name text searching a text search p align center img style width 240 px height 480px margin 10px src readme resources images text search png img style width 240 px height 480px margin 10px src readme resources images text search result png p a name voice searching a voice search p align center img style width 240 px height 480px margin 10px src readme resources images voice search 1 png img style width 240 px height 480px margin 10px src readme resources images voice search result png p a name image searching a image search p align center img style height 400px margin 10px src readme resources images image search 1 png img style height 400px margin 10px src readme resources images image search 2 png img style height 400px margin 10px src readme resources images image search 3 png p a name share product a share product p align center img style width 240 px height 400px margin 10px src readme resources images share product png p a name profile a profile page p align center img style width 240 px height 480px margin 10px src readme resources images profile page png p a name wishlist a wishlist p align center img style width 240 px height 480px margin 10px src readme resources images wishlist 1 png p a name cart system a cart system p align center img style width 240 px height 480px margin 10px src readme resources images carts system png p a name personal cart a personal cart p align center img style width 240 px height 400px margin 10px src readme resources images personal cart 1 png p a name personal cart a shared cart p align center img style width 240 px height 480px margin 10px src readme resources images share cart 1 png img style width 240 px height 480px margin 10px src readme resources images share cart 2 png img style width 240 px height 480px margin 10px src readme resources images create a shared cart png img style width 240 px height 480px margin 10px src readme resources images shared cart sharing png img style width 240 px height 480px margin 10px src readme resources images shared cart joining png p a name checkout a on checkout a name delivery order a delivery order p align center img style width 240 px height 480px margin 10px src readme resources images information form png img style width 240 px height 480px margin 10px src readme resources images checkout successfully png p a name pick up order a pick up order p align center img style width 240 px height 480px margin 10px class app src readme resources images pickup order png img style width 240 px height 480px margin 10px class app src readme resources images pickup order 1 png img style width 240 px height 480px margin 10px class app src readme resources images pick up order 1 png p a name order confirmation a order confirmation mail p align center img src readme resources images order confirmation png style width 422px img src readme resources images mail order 1 png style width 422px p a name order detail a order details p align center img style width 240 px height 480px margin 10px src readme resources images order detail 1 png p p align center img style width 240 px height 480px margin 10px src readme resources images order tracking png p a name map a map p align center img style width 240 px height 480px margin 10px src readme resources images map 1 png img style width 240 px height 480px margin 10px src readme resources images map 2 png p a name technologies a technologies a name frontend a frontend android studio java a name backend a backend microsoft azue services sql server django framework deep learning models swin transformer v2 tiny vietnamese sbert faiss library for efficient searching a name demo a demo link video youtube https youtu be for1isa6ehq | android-app django image-retrieval shoping | front_end |
turnip_cli | turnip cli an extensible c cli command line interface designed for embedded systems this example is built with freertos libopencm3 stm32f1 however it s straight forward to rip out the cli part please note the assets inside the lib folder are not covered under mit they have their own licences creating commands each command is a seperate h cpp pair or just h please see inc hellocmd h for an example each command class should implement c virtual const char parse char input the parse function what gets called when the user enters the command the class should also inherit from from the cmd class just look at the code you should be able to figure it out registering commands commands are dynamically registered like so c cmd menu new hellocmd hello uart1 0 running the cli just call present it will not return the first argument is the menu shown above which is built at runtime c cli present menu building bash git clone https github com machine hum turnip cli cd turnip cli git submodule update init recursive cd lib libopencm3 make cd make make flash | os |
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spring-boot-angular-maven-build | spring boot angular maven build architecture overview prerequisites java 8 download and install the jdk 8 node js download and install angular cli used to bootstrap the angular application you can install it using npm as a default package manager for the javascript runtime environment node js in the example that i m detailing below we used spring boot 2 2 1 release version define project modules the first step of the implementation is to create a multi module spring boot application initially we create a parent module spring boot angular maven build which will contain the backend module and the frontend module assuming that you created the backend and frontend projects in the specified modules backend will contain the backend project frontend will contain the frontend project then we need to create the modules using maven build tool we add the modules in the main pom xml modules module backend module module frontend module modules also here we have to specify the packaging to serve as a container for our sub modules packaging pom packaging implementing the back end side in the backend module we implement the parent section parent groupid com groupid artifactid frakton artifactid version 0 0 1 snapshot version parent artifactid backend artifactid version 0 0 1 snapshot version next we implement the maven resources plugin this plugin is used to execute our generated frontend module in the output directory section we select the project build directory and the resources from our dist frakton generated folder in the snippet below we have added also some plugins such as maven failsafe plugin is used and designed for integration tests maven surefire plugin is used to run unit tests spring boot maven plugin this plugin provides several usages allowing us to package executable jar or war archives and run the application build plugins plugin artifactid maven resources plugin artifactid executions execution id copy resources id phase validate phase goals goal copy resources goal goals configuration outputdirectory project build directory classes resources outputdirectory resources resource directory project parent basedir frontend dist frakton directory resource resources configuration execution executions plugin plugin groupid org apache maven plugins groupid artifactid maven failsafe plugin artifactid plugin plugin groupid org apache maven plugins groupid artifactid maven surefire plugin artifactid plugin plugin groupid org springframework boot groupid artifactid spring boot maven plugin artifactid plugin plugins build after implementing the plugins section we need to include the frontend dependency this will make sure the frontend is included in the final executable jar dependencies dependency groupid com groupid artifactid frontend artifactid version project version version scope runtime scope dependency other dependencies dependencies implement front end side next we need to implement the pom xml in frontend by adding the parent section parent groupid com groupid artifactid frakton artifactid version 0 0 1 snapshot version parent artifactid frontend artifactid version 0 0 1 snapshot version to execute some of npm commands we need the frontend maven plugin this plugin comes with a set of built in commands which we can use for triggering npm commands build plugins plugin groupid com github eirslett groupid artifactid frontend maven plugin artifactid version 1 7 6 version configuration workingdirectory workingdirectory nodeversion v10 16 0 nodeversion npmversion 6 10 2 npmversion configuration executions execution id install node and npm id goals goal install node and npm goal goals execution execution id npm install id goals goal npm goal goals execution execution id npm run build id goals goal npm goal goals configuration arguments run build arguments configuration execution executions plugin plugins build in the configuration tag we implement the working directory and we select the node and npm versions also in the build section we need to define the resources by specifying the directory the dist frakton folder is used to be placed inside the build output resources resource directory dist frakton directory targetpath static targetpath resource resources testing back end and front end after completing the configurations steps we make sure our instances are working correctly before the build first we can run the backend project by using mvn spring boot run also make sure you execute the command inside the backend module next we can start our angular project using ng serve build project if everything seems to work correctly we can build the project using mvn clean install make sure you are executing the command in the spring boot angular maven build parent module after building the application in the backend is generated the target folder which contains the jar backend 0 0 1 snapshot jar and in the frontend is generated the dist folder and node modules if you want to run the executable jar open terminal and add java jar backend 0 0 1 snapshot jar allow angular to handle routes after running the jar file when accessing https localhost 8080 we will have a whitelabel error page this happens because angular by default all paths are supported and accessible but with the usage of maven spring boot tries to manage paths by itself to fix this we need to add some configurations create a package config and create webconfig java this class has to implement webmvcconfigurer and to use resourcehandlers override public void addresourcehandlers resourcehandlerregistry registry registry addresourcehandler addresourcelocations classpath static resourcechain true addresolver new pathresourceresolver override protected resource getresource string resourcepath resource location throws ioexception resource requestedresource location createrelative resourcepath return requestedresource exists requestedresource isreadable requestedresource new classpathresource static index html the pattern is matched by antpathmatcher to directories in the path so the configuration will be applied to our project routes also the pathresourceresolver will try to find any resource under the location given so all the requests that are not handled by spring boot will be redirected to static index html giving access to angular to manage them next we just build our project and generate the jar file once the application has started we will be able to see the welcome page | front_end |
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ml-forex-prediction | predicting financial time series data with machine learning this is an example that predicts future prices from past price movements here we implement it with eur usd rate as an example and you can also predict stock prices by changing symbol backtest example for eur usd using daily close prices from 2008 to 2016 first 95 for training and last 5 for testing green and red vertical lines represent winning trade and losing trade respectively equity equity png installation to run this demo you need following environment and libraries python 2 7 not tested on 3 x jupyter notebook scikit learn numpy pandas matplotlib seaborn note you may need extra libraries to install above license mit license copyright c 2017 | machine-learning ml python scikit-learn forex-prediction | ai |
CarSimulatorRTOS | simulation of car operation using a real time operating system rtos description a real time operating system is created in order so simulate a number of car processes these include brake acceleration odometry speed average speed side lights engine state and indicators method a thread scheduallar is used in order to carry out multiple processes the maximum number of additional threads we were able to use was 10 as a result several processes with the same repetition rate had to go under a single thread 4 semaphores are used which allows controlled access between these processes authors roshenac mitchell march 2016 | rtos embedded car mbed | os |
Open-Prompt-Research | agi llm llm llm voyager voyager minedojo org gpt 4 minecraft voyager voyager prompt gpt 4 3 5 text explain plan 1 2 3 code javascript helper functions only if needed try to avoid them main function after the helper functions async function yourmainfunctionname bot prompt explain plan code javascript async async 0 await await async promise promise await async await try catch async await promise api async await promise javascript function resolveafter2seconds return new promise resolve settimeout resolve resolved 2000 async function asynccall console log calling const result await resolveafter2seconds console log result expected output resolved asynccall prompt text you are a helpful assistant that writes mineflayer javascript code to complete any minecraft task specified by me here are some useful programs written with mineflayer apis programs at each round of conversation i will give you code from the last round execution error chat log biome time nearby blocks nearby entities nearest to farthest health hunger position equipment inventory xx 36 chests task context critique you should then respond to me with explain if applicable are there any steps missing in your plan why does the code not complete the task what does the chat log and execution error imply plan how to complete the task step by step you should pay attention to inventory since it tells what you have the task completeness check is also based on your final inventory code 1 write an async function taking the bot as the only argument 2 reuse the above useful programs as much as possible use mineblock bot name count to collect blocks do not use bot dig directly use craftitem bot name count to craft items do not use bot craft or bot recipesfor directly use smeltitem bot name count to smelt items do not use bot openfurnace directly use placeitem bot name position to place blocks do not use bot placeblock directly use killmob bot name timeout to kill mobs do not use bot attack directly 3 your function will be reused for building more complex functions therefore you should make it generic and reusable you should not make strong assumption about the inventory as it may be changed at a later time and therefore you should always check whether you have the required items before using them if not you should first collect the required items and reuse the above useful programs 4 functions in the code from the last round section will not be saved or executed do not reuse functions listed there 5 anything defined outside a function will be ignored define all your variables inside your functions 6 call bot chat to show the intermediate progress 7 use exploreuntil bot direction maxdistance callback when you cannot find something you should frequently call this before mining blocks or killing mobs you should select a direction at random every time instead of constantly using 1 0 1 8 maxdistance should always be 32 for bot findblocks and bot findblock do not cheat 9 do not write infinite loops or recursive functions 10 do not use bot on or bot once to register event listeners you definitely do not need them 11 name your function in a meaningful way can infer the task from the name you should only respond in the format as described below response format response format prompt llm javascript llm code from the last round execution error chat log biome time nearby blocks nearby entities nearest to farthest health hunger position equipment inventory xx 36 chests task context critique explain are there any steps missing in your plan why does the code not complete the task what does the chat log and execution error imply plan how to complete the task step by step you should pay attention to inventory since it tells what you have the task completeness check is also based on your final inventory code text 1 write an async function taking the bot as the only argument 2 reuse the above useful programs as much as possible use mineblock bot name count to collect blocks do not use bot dig directly use craftitem bot name count to craft items do not use bot craft or bot recipesfor directly use smeltitem bot name count to smelt items do not use bot openfurnace directly use placeitem bot name position to place blocks do not use bot placeblock directly use killmob bot name timeout to kill mobs do not use bot attack directly 3 your function will be reused for building more complex functions therefore you should make it generic and reusable you should not make strong assumption about the inventory as it may be changed at a later time and therefore you should always check whether you have the required items before using them if not you should first collect the required items and reuse the above useful programs 4 functions in the code from the last round section will not be saved or executed do not reuse functions listed there 5 anything defined outside a function will be ignored define all your variables inside your functions 6 call bot chat to show the intermediate progress 7 use exploreuntil bot direction maxdistance callback when you cannot find something you should frequently call this before mining blocks or killing mobs you should select a direction at random every time instead of constantly using 1 0 1 8 maxdistance should always be 32 for bot findblocks and bot findblock do not cheat 9 do not write infinite loops or recursive functions 10 do not use bot on or bot once to register event listeners you definitely do not need them 11 name your function in a meaningful way can infer the task from the name llm llm llm text you are an assistant that assesses my progress of playing minecraft and provides useful guidance you are required to evaluate if i have met the task requirements exceeding the task requirements is also considered a success while failing to meet them requires you to provide critique to help me improve i will give you the following information biome the biome after the task execution time the current time nearby blocks the surrounding blocks these blocks are not collected yet however this is useful for some placing or planting tasks health my current health hunger my current hunger level for eating task if my hunger level is 20 0 then i successfully ate the food position my current position equipment my final equipment for crafting tasks i sometimes equip the crafted item inventory xx 36 my final inventory for mining and smelting tasks you only need to check inventory chests if the task requires me to place items in a chest you can find chest information here task the objective i need to accomplish context the context of the task you should only respond in json format as described below reasoning reasoning success boolean critique critique ensure the response can be parsed by python json loads e g no trailing commas no single quotes etc here are some examples input inventory 2 36 oak log 2 spruce log 2 task mine 3 wood logs response reasoning you need to mine 3 wood logs you have 2 oak logs and 2 spruce logs which add up to 4 wood logs success true critique llm prompt text you are a helpful assistant that tells me the next immediate task to do in minecraft my ultimate goal is to discover as many diverse things as possible accomplish as many diverse tasks as possible and become the best minecraft player in the world i will give you the following information question 1 answer question 2 answer question 3 answer biome time nearby blocks other blocks that are recently seen nearby entities nearest to farthest health higher than 15 means i m healthy hunger higher than 15 means i m not hungry position equipment if i have better armor in my inventory you should ask me to equip it inventory xx 36 chests you can ask me to deposit or take items from these chests there also might be some unknown chest you should ask me to open and check items inside the unknown chest completed tasks so far failed tasks that are too hard you must follow the following criteria 1 you should act as a mentor and guide me to the next task based on my current learning progress 2 please be very specific about what resources i need to collect what i need to craft or what mobs i need to kill 3 the next task should follow a concise format such as mine quantity block craft quantity item smelt quantity item kill quantity mob cook quantity food equip item etc it should be a single phrase do not propose multiple tasks at the same time do not mention anything else 4 the next task should not be too hard since i may not have the necessary resources or have learned enough skills to complete it yet 5 the next task should be novel and interesting i should look for rare resources upgrade my equipment and tools using better materials and discover new things i should not be doing the same thing over and over again 6 i may sometimes need to repeat some tasks if i need to collect more resources to complete more difficult tasks only repeat tasks if necessary 7 do not ask me to build or dig shelter even if it s at night i want to explore the world and discover new things i don t want to stay in one place 8 tasks that require information beyond the player s status to verify should be avoided for instance placing 4 torches and dig a 2x1x2 hole are not ideal since they require visual confirmation from the screen all the placing building planting and trading tasks should be avoided do not propose task starting with these keywords you should only respond in the format as described below response format reasoning based on the information i listed above do reasoning about what the next task should be task the next task here s an example response reasoning the inventory is empty now chop down a tree to get some wood task obtain a wood log llm llm prompt text you are a helpful assistant that asks questions to help me decide the next immediate task to do in minecraft my ultimate goal is to discover as many things as possible accomplish as many tasks as possible and become the best minecraft player in the world i will give you the following information biome time nearby blocks other blocks that are recently seen nearby entities nearest to farthest health hunger position equipment inventory xx 36 chests completed tasks so far failed tasks that are too hard you must follow the following criteria 1 you should ask at least 5 questions but no more than 10 questions to help me decide the next immediate task to do each question should be followed by the concept that the question is about 2 your question should be specific to a concept in minecraft bad example the question is too general question what is the best way to play minecraft concept unknown bad example axe is still general you should specify the type of axe such as wooden axe what are the benefits of using an axe to gather resources concept axe good example question how to make a wooden pickaxe concept wooden pickaxe 3 your questions should be self contained and not require any context bad example the question requires the context of my current biome question what are the blocks that i can find in my current biome concept unknown bad example the question requires the context of my current inventory question what are the resources you need the most currently concept unknown bad example the question requires the context of my current inventory question do you have any gold or emerald resources concept gold bad example the question requires the context of my nearby entities question can you see any animals nearby that you can kill for food concept food bad example the question requires the context of my nearby blocks question is there any water source nearby concept water good example question what are the blocks that i can find in the sparse jungle concept sparse jungle 4 do not ask questions about building tasks such as building a shelter since they are too hard for me to do let s say your current biome is sparse jungle you can ask questions like question what are the items that i can find in the sparse jungle concept sparse jungle question what are the mobs that i can find in the sparse jungle concept sparse jungle let s say you see a creeper nearby and you have not defeated a creeper before you can ask a question like question how to defeat the creeper concept creeper let s say you last completed task is craft a wooden pickaxe you can ask a question like question what are the suggested tasks that i can do after crafting a wooden pickaxe concept wooden pickaxe here are some more question and concept examples question what are the ores that i can find in the sparse jungle concept sparse jungle the above concept should not be ore because i need to look up the page of sparse jungle to find out what ores i can find in the sparse jungle question how can you obtain food in the sparse jungle concept sparse jungle the above concept should not be food because i need to look up the page of sparse jungle to find out what food i can obtain in the sparse jungle question how can you use the furnace to upgrade your equipment and make useful items concept furnace question how to obtain a diamond ore concept diamond ore question what are the benefits of using a stone pickaxe over a wooden pickaxe concept stone pickaxe question what are the tools that you can craft using wood planks and sticks concept wood planks you should only respond in the format as described below response format reasoning question 1 concept 1 question 2 concept 2 question 3 concept 3 question 4 concept 4 question 5 concept 5 llm llm prompt text you are a helpful assistant that answer my question about minecraft i will give you the following information question you will answer the question based on the context only if available and helpful and your own knowledge of minecraft 1 start your answer with answer 2 answer answer unknown if you don t know the answer llm llm prompt text you are a helpful assistant that generates a curriculum of subgoals to complete any minecraft task specified by me i ll give you a final task and my current inventory you need to decompose the task into a list of subgoals based on my inventory you must follow the following criteria 1 return a python list of subgoals that can be completed in order to complete the specified task 2 each subgoal should follow a concise format such as mine quantity block craft quantity item smelt quantity item kill quantity mob cook quantity food equip item 3 include each level of necessary tools as a subgoal such as wooden stone iron diamond etc you should only respond in json format as described below subgoal1 subgoal2 subgoal3 ensure the response can be parsed by python json loads e g no trailing commas no single quotes etc llm llm text you are a helpful assistant that writes a description of the given function written in mineflayer javascript code 1 do not mention the function name 2 do not mention anything about bot chat or helper functions 3 there might be some helper functions before the main function but you only need to describe the main function 4 try to summarize the function in no more than 6 sentences 5 your response should be a single line of text for example if the function is async function minecobblestone bot check if the wooden pickaxe is in the inventory if not craft one let woodenpickaxe bot inventory findinventoryitem mcdata itemsbyname wooden pickaxe id if woodenpickaxe bot chat crafting a wooden pickaxe await craftwoodenpickaxe bot woodenpickaxe bot inventory findinventoryitem mcdata itemsbyname wooden pickaxe id equip the wooden pickaxe if it exists if woodenpickaxe await bot equip woodenpickaxe hand explore until we find a stone block await exploreuntil bot new vec3 1 1 1 60 const stone bot findblock matching mcdata blocksbyname stone id maxdistance 32 if stone return true mine 8 cobblestone blocks using the wooden pickaxe bot chat found a stone block mining 8 cobblestone blocks await mineblock bot stone 8 bot chat successfully mined 8 cobblestone blocks save the event of mining 8 cobblestone bot save cobblestone mined else bot chat failed to craft a wooden pickaxe cannot mine cobblestone the main function is minecobblestone then you would write the function is about mining 8 cobblestones using a wooden pickaxe first check if a wooden pickaxe is in the inventory if not craft one if the wooden pickaxe is available equip the wooden pickaxe in the hand next explore the environment until finding a stone block once a stone block is found mine a total of 8 cobblestone blocks using the wooden pickaxe llm | ai |
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node-webkit-agent | node webkit agent gitter https badges gitter im join chat svg https gitter im c4milo node webkit agent utm source badge utm medium badge utm campaign pr badge utm content badge stories in ready https badge waffle io c4milo node webkit agent png label ready https waffle io c4milo node webkit agent this module is an implementation of chrome developer tools protocol https developer chrome com devtools docs protocol 1 0 index it is still pretty much a work in progress and only heap and cpu profilers are working right now help is wanted to finish implementing debugger networking and console agents as well as a implementing from scratch a flamegraphs agent features this module allows you to debug and profile remotely your nodejs applications leveraging the following features by re using the built in devtools front end https developer chrome com devtools that comes with any webkit based browsers such as chrome or safari remote heap and cpu profiling more agents are coming installation npm install webkit devtools agent usage from within your node application just require the module as usual and start the agent for example javascript var agent require webkit devtools agent agent start once the agent is initiated use any of the following hosted devtools uis to profile your application node v0 6 x http c4milo github io node webkit agent 19 0 1084 46 inspector html host localhost 9999 page 0 node v0 8 x and v0 10 x http c4milo github io node webkit agent 26 0 1410 65 inspector html host localhost 9999 page 0 you can also change the agent port and binding address where it listen to by setting up the following parameters port the port for the devtools ui to connect to using websockets set to 9999 by default bind to the host or ip address where the websockets service is going to be bound to set to 127 0 0 1 by default ipc port ipc port for internal use set to 3333 by default verbose whether to log more information or not set to false by default see the example below to understand better how to set these parameters example a more elaborated example looks like javascript var agent require index assume this http service is your service var http require http http createserver function req res console log boooo res writehead 200 content type text plain res end hello world n listen 9000 127 0 0 1 console log server running at http 127 0 0 1 9000 pid process pid now let s have a signal handler for sigusr2 that is going to activate the devtools agent you can use any other means to activate the agent not just signal handlers process on sigusr2 function if agent server agent stop else agent start port 9999 bind to 0 0 0 0 ipc port 3333 verbose true abi compatibility abi http en wikipedia org wiki application binary interface compatibility breaks between node v0 6 x and v0 8 x therefore if you switch node versions you would have to re install webkit devtools agent again see issue 11 https github com c4milo node webkit agent issues 11 screenshots cpu profiling screenshot https i cloudup com yysnmmge3a png heap profiling screenshot https i cloudup com wr5mkg6i02 png for detailed information on debugging memory leaks using devtools heap comparisons using this module follow this tutorial https developer chrome com devtools docs javascript memory profiling happy debugging license the mit license copyright 2014 camilo aguilar all rights reserved permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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BlockchainAspNetCoreMssql | blockchainaspnetcoremssql blockchain with asp net core and mssql | blockchain |
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RaLLe | r ll r ll is an accessible framework for developing and evaluating retrieval augmented large language models r llms an overview of the main uses of r ll on gui is presented in this video https youtu be jybm75qnftg r ll is developed at the institute of memory technology research and development https www kioxia com en jp rd organization memory technology rd html of kioxia corporation https www kioxia com en jp top html key features easy development and testing users can easily select combine and test various retrievers and llms including open source models within a graphical interface objective evaluation of r llms r ll provides reproducible experiments with objective benchmarks metrics enabling objective assessments of r llm performance transparent prompt engineering all input prompts and output of an llm are visible to the user allowing for easy exploration and optimization of prompts usage getting started installation instruction install md install md document indexing here docs indexing md using custom datasets here docs using custom datasets md using r ll guides on gui here docs gui usage md evaluation with a python script here docs evaluation md review the evaluation results with mlflow here docs mlflow md note evaluation experiments can be performed both through the gui docs gui usage md evaluate and save config and using the script reference news our paper has been accepted by emnlp 2023 system demonstrations reference to cite when you use r ll in a research paper https arxiv org abs 2308 10633 bibtex misc ralle title ralle a framework for developing and evaluating retrieval augmented large language models author yasuto hoshi and daisuke miyashita and youyang ng and kento tatsuno and yasuhiro morioka and osamu torii and jun deguchi url https arxiv org abs 2308 10633 year 2023 eprint 2308 10633 publisher arxiv license r ll is mit licensed refer to the license file license for more details | large-language-models prompt-engineering retrieval-augmented-generation retrieval-augmented-qa retrieval-based-chatbots | ai |
id-ruby | id ruby heart you hi all blue heart green heart purple heart yellow heart ruby adalah salah satu bahasa pemrograman expresif dengan sintaks yang bersih id ruby adalah komunitas yang mewadahi pengguna ruby di indonesia langkah instalasi bundle install bin jekyll serve watch untuk menjalankan sistem di local bin jekyll serve watch langkah penambahan logo perusahaan startup 1 tambahkan file logo anda di folder images dengan format logo nama entitas png 2 buka file data companies yml 3 tambahkan entitas anda sesuai urutan abjad 4 tambahkan deskripsi sesingkat mungkin berikut penjelasan singkat bagaimana ruby digunakan di perusahaan startup organisasi anda 5 buat pull request 6 tunggu di merge tada cara rebuild rake site publish | front_end |
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Cloud-Ghost | cloud ghost cloud computing applications and services project master of software engineering at universidade do minho code structure create gke cluster yml ansible playbook to create a gke cluster destroy gke cluster yml ansible playbook to destroy a gke cluster deploy app yml ansible playbook to install and configure the application the application is installed in the cluster created by the create gke cluster yml playbook this playbook can be executed in a new installation or in an existing one assuming the persistent data was not deleted in a new installation the playbook should automatically create an administrator with the following credentials username ascn password ascn123 email ascn example com undeploy ghost yml terminates the current installation of the application only when flag e delete data true is passed the persistent installation data is deleted test all yml ansible playbook to run all the automatic tests roles contains the roles used by the playbooks both gke cluster create and destroy roles need a service account with google cloud cli to access the gke api the test role tests the availability of the application by for example sending http requests to the application inventory contains the inventory files used by the playbooks the gcp yml file contains variables used by the previous roles variables starting with gcp are used by the gke cluster roles gcp project identifies the name of the google cloud project gcp cred file identifies the path to the json file containting the service account credentials include this file in gitignore ghost ip identifies the public ip address of the application ghost port identifies the port of the application reminder shutdown the cluster when not in use to avoid incurring in costs start and shutdown virtual machines in the cluster gcloud container clusters resize ascn cluster node pool default pool num nodes 0 zone europe southwest1 a run playbooks ansible playbook vault password file pass txt deploy ghost yml i inventory gcp yml ansible playbook undeploy ghost yml i inventory gcp yml ansible playbook undeploy ghost yml i inventory gcp yml e delete data true other commands kubectl get pods n namespace kubectl exec it pod name n namespace mysql u root p ansible vault encrypt string vault password file pass txt pass to encrypt name name of the variable out txt | cloud-computing | cloud |
serverless-pipeline | project overview the goal of this project is to reconstruct the serverless data pipeline in aws described in the figure below in summary a producer lambda function will read from a dyanmodb table every minute and send the contents to a queue in sqs a consumer lambda function will be triggered by messages filling the queue and will look up the contents of message on the relevant wikipedia article aws comprehend will be used to perform sentiment analysis and entity extraction and the results will be uploaded to an s3 bucket primary reference awslambda tutorial by noah gift https github com noahgift awslambda serverless pipeline diagram https camo githubusercontent com bb29cd924f9eb66730bbf7b0ed069a6ae03d2f1a 68747470733a2f2f757365722d696d616765732e67697468756275736572636f6e74656e742e636f6d2f35383739322f35353335343438332d62616537616638302d353437612d313165392d393930392d6135363231323531303635622e706e67 potential disk space issue after you create the producer function and begin creating the consumer function it is possible that you will not have enough space if you are using a t 2 micro instance to free space it is recommended to delete some default docker images in cloud9 run the following commands docker image ls docker image rm image id replace image id above with the proper docker image id recommended images for removal are python 2 7 python 3 6 and all nodejs images procedure dyanmodb 1 create a table in dynamodb if the name of the table is not fang be sure to change the table name in the producer py function 2 make sure to set the unique identifier to name to be consistent with the variable in the producer py function 3 add items to the list such as company or food names sqs 1 create a queue with the default settings in sqs make sure that its name corresponds to the main one in the producer py function s3 1 create an s3 bucket and make sure the name is the same as in the consumer py function lambda functions producer 1 on the aws cloud9 ide navigate to the left side and click the aws explorer button 2 create an aws lambda function and choose python 3 7 and the hello world sam application 3 if you create the function in the main environment folder a new folder with the function name will be generated 4 in the hello world subfolder replace the app py code with the code from producer py 5 change region and table variables as necessary in the producer py code 6 enter the following commands to deploy the lambda application sam build use container sam deploy guided 7 after filling out the fields in step 6 the lambda application will be deployed and you can check it in the console 8 adjust the role in the lambda application to have adminstrator access go to iam create a role for lambda use cases and attach the adminstratoraccess policy 9 for the lambda function check the permissions tab under configuration and change the execution role to the one you just created 10 apply a eventbridge cloudwatch trigger to the function and in the schedule expression field input rate 1 minute consumer 1 repeat steps 1 9 above if you do not have space when at step 6 refer to the potential disk space issue section above 2 apply an sqs trigger to the consumer lambda function and select the queue you created earlier running the pipeline 1 enable the trigger for both the producer and consumer lambda functions 2 within a minute you should see messages populating the queue in sqs they may also disappear quickly once the consumer runs these messages will contain items from your table 3 disable the trigger of the producer lambda function 4 disable the trigger of the consumer lambda function 5 check the cloudwatch logs and the s3 bucket to see if appropriate results were generated and or if there were any errors | cloud |
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BLE-environment-sensor | nightly ci status master master ci badge master ci link irc irc badge irc link zzzzzz zzzzzzzzzzzz zzzzzzzzzzzzzzzz zzzzzzz zzzzzz zzzzzz zzzzz zzzzz zzzz zzzz zzzzz zzzz zzzz zzzz zzzzz zzzz zzzzzz zzzz zzzzzzzz 777 7777 7777777777 zz zzzz zzzzzzzz 777 77777777 77777777777 zzzzzzz zzzz zzzzzzz 777 7777 7777 777 zzzzzzzzz zzzz z 777 777 777 777 zzzzzz zzzz 777 777 777 777 zzzzz zzzz 777 777 777 777 zzzzz zzzzz zzzz 777 777 777 777 zzzz zzzzz zzzzz 777 777 777 777 zzzz zzzzz zzzzz 777 777 777 777 zzzz zzzz zzzzz 777 777 777 777 zzzzz zzzzz zzzzz 777 777 777 777 zzzzzz zzzzzz zzzzz 777 7777777777 777 zzzzzzzzzzzzzzz zzzz 777 77777777 777 zzzzzzzzzzz z zzzzz the friendly operating system for iot riot is a real time multi threading operating system that supports a range of devices that are typically found in the internet of things iot 8 bit 16 bit and 32 bit microcontrollers riot is based on the following design principles energy efficiency real time capabilities small memory footprint modularity and uniform api access independent of the underlying hardware this api offers partial posix compliance riot is developed by an international open source community which is independent of specific vendors e g similarly to the linux community riot is licensed with lgplv2 1 a copyleft license which fosters indirect business models around the free open source software platform provided by riot e g it is possible to link closed source code with the lgpl code features riot is based on a microkernel architecture and provides features including but not limited to a preemptive tickless scheduler with priorities flexible memory management high resolution long term timers support 100 boards based on avr msp430 esp8266 esp32 mips risc v arm7 and arm cortex m the native port allows to run riot as is on linux bsd and macos multiple instances of riot running on a single machine can also be interconnected via a simple virtual ethernet bridge ipv6 6lowpan rfc4944 rfc6282 and rfc6775 udp rpl storing mode p2p mode coap ccn lite sigfox lorawan getting started you want to start the riot just follow our quickstart guide https doc riot os org index html the quickest start or try this tutorial https github com riot os tutorials blob master readme md for specific toolchain installation follow instructions in the getting started https doc riot os org getting started html page the riot api itself can be built from the code using doxygen the latest version of the documentation is uploaded daily to riot os org api https riot os org api using the native port with networking if you compile riot for the native cpu and include the netdev tap module you can specify a network interface like this port tap0 make term setting up a tap network there is a shell script in riot dist tools tapsetup called tapsetup which you can use to create a network of tap interfaces usage to create a bridge and two or count at your option tap interfaces dist tools tapsetup tapsetup c count contribute to contribute something to riot please refer to our contributing document contributing md mailing lists riot os kernel developers list devel riot os org https lists riot os org mailman listinfo devel riot os users list users riot os org https lists riot os org mailman listinfo users riot commits commits riot os org https lists riot os org mailman listinfo commits github notifications notifications riot os org https lists riot os org mailman listinfo notifications license most of the code developed by the riot community is licensed under the gnu lesser general public license lgpl version 2 1 as published by the free software foundation some external sources especially files developed by sics are published under a separate license all code files contain licensing information for more information see the riot website https www riot os org master ci badge https ci riot os org riot os riot master latest badge svg master ci link https ci riot os org nightlies html master irc badge https img shields io badge irc join 20chat 20 e2 86 92 blue svg irc link https webchat freenode net channels 23riot os | os |
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semantic-kernel-docs | microsoft semantic kernel documentation this is the github repository for the technical product documentation for semantic kernel this documentation is published at microsoft semantic kernel documentation https learn microsoft com semantic kernel how to contribute thanks for your interest in contributing https learn microsoft com home of technical content for microsoft products and services to learn how to make contributions to the content in this repository start with our docs contributor guide https learn microsoft com contribute code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | ai |
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bootstrap-gulp-starter-template | bootstrap gulp starter template all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 1 orange svg style flat square contributors all contributors badge end note the docs folder is for demo purposes only changes should be commited to src files only the dist folder is not kept in source control how to use the template is built with sass and gulp build system with these features handlebars html templates with panini a super simple flat file generator for use with gulp it compiles a series of html pages using a common layout these pages can also include html partials external handlebars helpers or external data as json or yaml sass compilation prefixing with autoprefixer and javascript concatenation built in browsersync server will automatically reload your page when files are changed it also live injects css changes when you save a sass file this task runs continuously and defaults to localhost for production builds css compression javascript compression image compression and more requirements to use this template your computer needs node js is used to run the build processes https nodejs org en download run node v in the terminal npm node comes with npm installed so you should have a version of npm used to manage development dependencies run npm v in the terminal gulp task runner npm install g gulp run gulp v in the terminal installing clone this repo git clone https github com johndavemanuel bootstrap gulp starter template git navigate into the repo directory cd bootstrap gulp starter template install all node packages npm install run gulp development your site is now viewable at this url http localhost 3000 to create compressed production ready assets run gulp production this will delete everything in the dist folder and recreate all of your complied files never make updates directly into the dist folder as these files get overridden each time folder structure dist compiled distribution files node modules front end dependencies src contains all of your core working files static assets pages templates etc src assets scss files js files images and fonts src data external data json or yaml src layouts html layouts templates src pages site pages src partials handlebars partials files gulpfile js all task definitions package json handles the project dependencies htmllintrc handles the html lint rules sass lint yml handles the scss lint rules reports txt generated file for accessibility issues includes fontawesome 5 https fontawesome com animate css https animate style rtl support https github com tysonmatanich directional scss gulp plugins gulp accessibility https www npmjs com package gulp accessibility gulp autoprefixer https www npmjs com package gulp autoprefixer gulp compile handlebars https www npmjs com package gulp compile handlebars gulp concat https www npmjs com package gulp concat gulp cssmin https www npmjs com package gulp cssmin gulp gh pages https www npmjs com package gulp gh pages gulp html replace https www npmjs com package gulp html replace gulp htmllint https www npmjs com package gulp htmllint gulp imagemin https www npmjs com package gulp imagemin gulp jshint https www npmjs com package gulp jshint gulp newer https www npmjs com package gulp newer gulp pretty html https www npmjs com package gulp pretty html gulp purgecss https www npmjs com package gulp purgecss gulp remove code https www npmjs com package gulp remove code gulp remove logging https www npmjs com package gulp remove logging gulp rename https www npmjs com package gulp rename gulp sass https www npmjs com package gulp sass gulp sass lint https www npmjs com package gulp sass lint gulp sourcemaps https www npmjs com package gulp sourcemaps gulp uglify https www npmjs com package gulp uglify gulp babel https www npmjs com package gulp babel additional resources sass syntactically awesome style sheets http sass lang com bootstrap https getbootstrap com handlebars http handlebarsjs com panini https github com zurb panini gulp https gulpjs org getting started license mit license contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href https github com tommix95 img src https avatars2 githubusercontent com u 44686328 v 4 width 100px alt br sub b tommix95 b sub a br a href https github com johndavemanuel bootstrap gulp starter template commits author tommix95 title code a td tr table markdownlint enable prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | gulp workflow front-end-development automation template starter panini sass handlebars bootstrap starter-project front-end boilerplate | front_end |
us-twitter-election-2016-nlp | twitter presidential election 2016 usa project melissa k jan 2017 table of content motivation motivation data collection data collection preprocessing steps tweets preprocessing steps tweets word2vec embedding word2vec embedding classification classification deep learning deep learning what i learned what i learned motivation the u s presidential election on nov 8 2016 donald trump vs hillary clinton has been a historical event where the outcome turned out to be contrary to most expert predictions the collected twitter streaming data has the potential to uncover a shift in opinions and reactions around the election day data collection prior to the election day i wrote a pretty standard python module based on the tweepy http docs tweepy org en v3 5 0 library i started data collection on nov 7 around 3 pm and stopped the live stream on nov 9 around 1pm i collected 40gb of raw twitter streaming data this was probably just a very tiny fraction of all tweets each tweet was saved in json format the twitter streaming api https dev twitter com streaming overview had the advantage of collecting a much higher volume of data than querying the twitter rest api https dev twitter com rest public using a regular free account i collected tweets in english german french and spanish en de fr es and tracked the following tags trump realdonaldtrump always trump makeamericagreatagain hillaryclinton votehillary2016 hillaryclinton clintonnews as a next step i parsed the raw tweets and saved the lang text and created at values in tabular format simple delimiter separated file with header in different sub folders according to the language tag this way the data volume was reduced to around 1 5gb subsequent data analysis focused on english however i plan to extend my nlp capabilities to more languages in the future preprocessing steps tweets a tweet text has max 140 characters and there are a multitude of decisions to be made in terms of preprocessing before each tweet is formalized enough for mathematical modeling i came up with the following steps see preprocesstweets py twitterelection preprocesstweets py 1 removing several characters such as mentions mentions rt everything following an apostrophe some custom characters such as numbers special characters e g or urls via regex matching 2 emojis were left as they are for fun there is a function that extracts emojis 3 tokenizing words using nltk s tweettokenizer http www nltk org api nltk tokenize html note smiley strings like or p are tokenized to one token while repeated characters are reduced to one neat feature of tweettokenizer 4 removing stopwords and only now removing single or or etc characters 5 spell corrections simple naiv approach using first suggestion of pyenchant http pythonhosted org pyenchant 6 lastly stemming words using nltk s snowballstemmer http www nltk org modules nltk stem snowball html more details on how i handled above steps can be found in the source code see preprocesstweets py twitterelection preprocesstweets py below is a random test tweet i fabricated to debug the code you can see the results of the preprocessing procedures there are certainly pros and cons for my approach and i really believe it s an artwork at the end also some cases are not fully accounted for e g emoji got split into separate tokens and makesme did not get separated into two words not to mention some funny spell corrections however these steps may have helped to more uniformly derive one token for a multitude of variations of the same word python original tweet rt election2016 realdonaldtrump victory p hillaryclinton or nyse that s 4 x gt 2 amp i luv this makesme sooooo haaaaaapppy all of us https www twitter com tokenized and preprocessed tweet victori p x lug makes m sou happi us emojis extracted word2vec embedding word2vec is truly booming not only in natural language processing it is an embedding approach with the goal of deriving a continuous vector space where semantically similar words are mapped to nearby points are embedded nearby each other instead of representing words as unique discrete ids tensorflow word2vec https www tensorflow org tutorials word2vec explains the background extremely well highly recommend reading their documentation as i am not going into further details here as opposed to tensorflow i wanted to explore gensim word2vec https radimrehurek com gensim models word2vec html here in this project see embedding py twitterelection embedding py following parameters were set method continuous bag of words model cbow embedding dimension 200 in gensim size 200 no maximum vocabulary size note this is limit ram and not max words no maximum word length as opposed to different approaches not needed here as i took averages of the embedding vectors corresponding to each token of one tweet therefore the number of tokens did not matter see below other approaches define a maximum sentence length and flatten the embedding vectors which however results in an extremely high feature space remaining parameters pretty much default i believe that when it gets to embeddings it is most important to track the dimensions of the data matrices in the process here there are 5370247 tweets in gensim language this would be 5370247 sentences the tweets are stored as a python list of tweets whereas each tweet is a list of tokens therefore list of lists i didn t set a ram limit to the vocabulary size and ended up with 14202 words that make up the vocabulary therefore the resulting model matrix of the word2vec has dimensions 14202 200 python total number of tweets 5370247 size of tweets in memory 43281112 length of word2vec vocabulary 14202 dimension of embedding max vocab size x dim 14202 200 the next step is to transform the same 5370247 tweets using this model see embedding py twitterelection embedding py for each tweet each token will be compared with the model vocabulary and if the token is contained within the vocabulary the respective vector will be extracted otherwise a vector of zeros with the same size 200 will be extracted then as a simple approach the average of all of the tokens of one tweet was taken the final feature matrix x thus has dimensions tweets x dim 5370247 200 below i also show some neat features of the word embedding such as quantifying the similarity of two words or finding vocabulary that is close to a specific word python dimension of x 5370247 200 most similar to trump clinton 0 4256572127342224 hillari 0 3691369891166687 would 0 3448511064052582 thu 0 33279508352279663 say 0 32681870460510254 popular 0 3263714611530304 candid 0 32262635231018066 drum pf 0 3211328685283661 peopl 0 3207015097141266 whether 0 31549662351608276 most similar to poepl pol 0 7333731651306152 everyon 0 4832013249397278 voter 0 4737037420272827 anyon 0 4535198211669922 support 0 4492112994194031 guy 0 4350971579551697 folk 0 4311058819293976 realli 0 4233342409133911 person 0 40902650356292725 women 0 3935454785823822 similarity hillary and trump 0 369136993042 classification instead of performing a classic sentiment analysis i labeled each tweet as hillary encoded as int 0 trump encoded as int 1 or neutral encoded as int 2 based on whether and mentions of the respective tweet were dominated by tags pointing to either trump or hillary if none was the case the neutral label got assigned see preprocesstweets py twitterelection preprocesstweets py this approach resembles a topic classification note that all and mentions were removed after labeling the respective tweet in the course of preprocessing the tweet so they were not words used as features for the classification task solely tweets labeled as hillary or clinton were used note that tweets about trump were much more frequent to have a balanced and manageable data set i selected only a subset of labeled tweets with an even amount of samples for each of the two labels topicmodel py twitterelection topicmodel py contains a class that wraps around the complete machine learning classification task main steps in bullet points input data feature matrix x and target vector for topic classification the feature matrix x numpy nd array samples x features here 157982 200 was derived using the embedding model word2vec embedding word2vec embedding 200 was the chosen embedding dimension the target vector containing the label for each sample is a numpy 1d vector of shape samples here 157982 machine learning procedures train optimize and predict 3 fold cross validation was done using sklearn s model selection stratifiedshufflesplit http scikit learn org stable modules generated sklearn model selection stratifiedshufflesplit html selected best model from the following three models naivebayesclassifier http scikit learn org stable modules generated sklearn naive bayes gaussiannb html randomforestclassifier http scikit learn org stable modules generated sklearn ensemble randomforestclassifier html adaboostclassifier http scikit learn org stable modules generated sklearn ensemble adaboostclassifier html as first step parameters for each of these three models were optimized using the less computationally expensive model selection randomizedsearchcv http scikit learn org stable modules generated sklearn model selection randomizedsearchcv html from the sklearn library to further optimize this step a custom scorer metrics make scorer http scikit learn org stable modules generated sklearn metrics make scorer html that maximizes the minimum of precision and recall was used instead of accuracy as performance metric when looping over the three classifiers clfs using their best parameters metrics were stored to finally derive a confidence interval ci for each of the classifiers if lower and upper ends of two classifiers don t overlap the models are said to be significantly different from one another accuracy on the validation set was used to determine final model performance output of main twitterelection classification py main twitterelection classification py total number of tweets 5370247 total number of filtered and labeled tweets 157982 shapes of x and target 157982 200 157982 parameter search parameter search of randomforestclassifier bootstrap true class weight none criterion gini max depth none max features auto max leaf nodes none min impurity split 1e 07 min samples leaf 1 min samples split 2 min weight fraction leaf 0 0 n estimators 10 n jobs 1 oob score false random state 42 verbose 0 warm start false parameter search of adaboostclassifier algorithm samme r base estimator none learning rate 1 0 n estimators 50 random state none best estimators gaussiannb priors none randomforestclassifier bootstrap false class weight none criterion entropy max depth 1 max features 2 max leaf nodes none min impurity split 1e 07 min samples leaf 9 min samples split 6 min weight fraction leaf 0 0 n estimators 31 n jobs 1 oob score false random state 42 verbose 0 warm start false adaboostclassifier algorithm samme r base estimator none learning rate 0 8 n estimators 100 random state none cross validation using following classifier gaussiannb priors none accuracy 0 671588 precision 0 682339 recall 0 671588 f1 score 0 666677 dtype float64 confidence interval ci accuracy 0 66781321431938101 0 67536298135630035 cross validation using following classifier randomforestclassifier bootstrap false class weight none criterion entropy max depth 1 max features 2 max leaf nodes none min impurity split 1e 07 min samples leaf 9 min samples split 6 min weight fraction leaf 0 0 n estimators 31 n jobs 1 oob score false random state 42 verbose 0 warm start false accuracy 0 647788 precision 0 656927 recall 0 647788 f1 score 0 642584 dtype float64 confidence interval ci accuracy 0 64072072917132639 0 65485525762045982 cross validation using following classifier adaboostclassifier algorithm samme r base estimator none learning rate 0 8 n estimators 100 random state none accuracy 0 731352 precision 0 732381 recall 0 731352 f1 score 0 731054 dtype float64 confidence interval ci accuracy 0 72825247897875744 0 73445172349092558 visualizations all classifiers roc curves img src output all clfs roc curve png width 400 all classifiers confusion matrix img src output all clfs confusion matrix png width 800 best classifier adaboost confusion matrix in large img src output adaboostclassifier confusion matrix png width 400 conclusion machine learning topic classification adaboost showed best accuracy on validation data set preprocessing and word2vec embedding dimensions are parameters that have been left out to further fine tune about 73 accuracy doesn t sound too impressive but it appears that humans as well only agree about 70 of the time on topics auch as sentiment analysis which is similar to the topic modeling approach used here see wikipedia sentiment analysis https en wikipedia org wiki sentiment analysis only a subset of all data could be processed in a reasonable time on the local computer all metrics aka precision recall and accuracy were similar pointing out that there were no misleading high false positives and false negatives hidden by a higher accuracy score it appears that the word embedding aka features were better predictors of tweets about trump one reason for this could have been that the original data set all tweets was used to train the word2vec embedding model and that data set was somewhat unbalanced in that in general more tweets were referencing trump deep learning intend to experiment with recurrent neural network rnn using the long short term memory lstm architecture what i learned preprocessing tweets is an artwork explored gensim word2vec embedding tested twitter topic modeling in the context of u s presidential election explored deep learning lstm | ai |
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IoT-Security-Wiki | demo iot security wiki https iot security wiki gitbook https iot security wiki https iot security wiki https github com iot security wiki https github com iot security wiki iot security wiki iot security wiki 0 3 iot security wiki https iot security wiki iot security iot security org https iot security org img src https cdn bg7ywl com iot security wiki author png yaseng https www yaseng org bg7ywl https www bg7ywl com info iot security wiki iot security shop img src https cdn bg7ywl com iot security wiki shop1 png iot security shop https iot security taobao com qq img src https cdn bg7ywl com iot security wiki qq png 306482276 https qm qq com cgi bin qm qr k 8uiqa4vgqktiqc4a x 7mihjlxljbwa jump from webapi https github com iot security wiki iot security wiki | server |
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Deep-Learning-for-Computer-Vision | deep learning for computer vision code repository for deep learning for computer vision by packt this is the code repository for deep learning for computer vision https www packtpub com big data and business intelligence deep learning computer vision utm source github utm medium repository utm campaign 9781788295628 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book deep learning has shown its power in several application areas of artificial intelligence especially in computer vision computer vision is the science of understanding and manipulating images and finds enormous applications in the areas of robotics automation and so on this book will also show you with practical examples how to develop computer vision applications by leveraging the power of deep learning instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 the code will look like the following merged summary operation tf summary merge all train summary writer tf summary filewriter tmp train session graph test summary writer tf summary filewriter tmp test the examples covered in this book can be run with windows ubuntu or mac all the installation instructions are covered basic knowledge of python and machine learning is required it s preferable that the reader has gpu hardware but it s not necessary related products deep learning with keras https www packtpub com big data and business intelligence deep learning keras utm source github utm medium repository utm campaign 9781787128422 tensorflow 1 x deep learning cookbook https www packtpub com big data and business intelligence tensorflow 1x deep learning cookbook utm source github utm medium repository utm campaign 9781788293594 deep learning with tensorflow https www packtpub com big data and business intelligence deep learning tensorflow utm source github utm medium repository utm campaign 9781786469786 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781788295628 https packt link free ebook 9781788295628 a p | ai |
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InfoTec | infotec tabla de contenido 1 info general info general 2 tecnolog as tecnolog as 3 colaboraci n colaboraci n 4 faqs faqs info general este es un proyecto acad mico de desarrollo web donde implementamos nuestros conocimientos en lenguaje html css y javascript todo esto basado en las ense anzas presentadas en clase screenshot imagen de la portada de la p gina web imagenes infotec sitio png tecnolog as una lista de tecnolog as utilizadas dentro del proyecto git https git scm com github https github com github desktop https desktop github com azure devops https azure microsoft com es mx visual studio code https code visualstudio com version 1 56 2 html version html5 css version css3 javascript version ecmascript 2016 colaboraci n desarrolladores involucrados en el desarrollo de este proyecto anderson david mendoza ramos aldo cupe mej a jos humberto requejo guti rrez faqs una lista de preguntas frecuentes 1 cu l es el prop sito de crear esta p gina web consideramos esta creaci n como una prueba de inicio a nuestra etapa de desarrolladores webs 2 les parece complicado iniciar en el mundo del desarrollo web no consideramos este proceso como algo dificil ya que solo necesitas estos rquisitos para poder internarte en este mundo del desarrollo curiosidad por aprender nuevas cosas creatividad atracci n hacia el mundo de la tecnolog a compromiso | server |
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food-ordering-app | this project was bootstrapped with create react app https github com facebook create react app below you will find some information on how to perform common tasks br you can find the most recent version of this guide here https github com facebook create react app blob master packages react scripts template readme md table of contents updating to new releases updating to new releases sending feedback sending feedback folder structure folder structure available scripts available scripts npm start npm start npm test npm test npm run build npm run build npm run eject npm run eject supported browsers supported browsers supported language features supported language features syntax highlighting in the editor syntax highlighting in the editor displaying lint output in the editor displaying lint output in the editor debugging in the editor debugging in the editor formatting code automatically formatting code automatically changing the page title changing the page title installing a dependency installing a dependency importing a component importing a component code splitting code splitting adding a stylesheet adding a stylesheet adding a css modules stylesheet adding a css modules stylesheet adding a sass stylesheet adding a sass stylesheet post processing css post processing css adding images fonts and files adding images fonts and files adding svgs adding svgs using the public folder using the public folder changing the html changing the html adding assets outside of the module system adding assets outside of the module system when to use the public folder when to use the public folder using global variables using global variables adding bootstrap adding bootstrap using a custom theme using a custom theme adding flow adding flow adding relay adding relay adding a router adding a router adding custom environment variables adding custom environment variables referencing environment variables in the html referencing environment variables in the html adding temporary environment variables in your shell adding temporary environment variables in your shell adding development environment variables in env adding development environment variables in env can i use decorators can i use decorators fetching data with ajax requests fetching data with ajax requests integrating with an api backend integrating with an api backend node node ruby on rails ruby on rails proxying api requests in development proxying api requests in development invalid host header errors after configuring proxy invalid host header errors after configuring proxy configuring the proxy manually configuring the proxy manually using https in development using https in development generating dynamic meta tags on the server generating dynamic meta tags on the server pre rendering into static html files pre rendering into static html files injecting data from the server into the page injecting data from the server into the page running tests running tests filename conventions filename conventions command line interface command line interface version control integration version control integration writing tests writing tests testing components testing components using third party assertion libraries using third party assertion libraries initializing test environment initializing test environment focusing and excluding tests focusing and excluding tests coverage reporting coverage reporting continuous integration continuous integration disabling jsdom disabling jsdom snapshot testing snapshot testing editor integration editor integration debugging tests debugging tests debugging tests in chrome debugging tests in chrome debugging tests in visual studio code debugging tests in visual studio code developing components in isolation developing components in isolation getting started with storybook getting started with storybook getting started with styleguidist getting started with styleguidist publishing components to npm publishing components to npm making a progressive web app making a progressive web app why opt in why opt in offline first considerations offline first considerations progressive web app metadata progressive web app metadata analyzing the bundle size analyzing the bundle size deployment deployment static server static server other solutions other solutions serving apps with client side routing serving apps with client side routing building for relative paths building for relative paths customizing environment variables for arbitrary build environments customizing environment variables for arbitrary build environments azure azure firebase firebase github pages github pages heroku heroku netlify netlify now now s3 and cloudfront s3 and cloudfront surge surge advanced configuration advanced configuration troubleshooting troubleshooting npm start doesn t detect changes npm start doesnt detect changes npm test hangs or crashes on macos sierra npm test hangs or crashes on macos sierra npm run build exits too early npm run build exits too early npm run build fails on heroku npm run build fails on heroku npm run build fails to minify npm run build fails to minify moment js locales are missing momentjs locales are missing alternatives to ejecting alternatives to ejecting something missing something missing updating to new releases create react app is divided into two packages create react app is a global command line utility that you use to create new projects react scripts is a development dependency in the generated projects including this one you almost never need to update create react app itself it delegates all the setup to react scripts when you run create react app it always creates the project with the latest version of react scripts so you ll get all the new features and improvements in newly created apps automatically to update an existing project to a new version of react scripts open the changelog https github com facebook create react app blob master changelog md find the version you re currently on check package json in this folder if you re not sure and apply the migration instructions for the newer versions in most cases bumping the react scripts version in package json and running npm install or yarn install in this folder should be enough but it s good to consult the changelog https github com facebook create react app blob master changelog md for potential breaking changes we commit to keeping the breaking changes minimal so you can upgrade react scripts painlessly sending feedback we are always open to your feedback https github com facebook create react app issues folder structure after creation your project should look like this my app readme md node modules package json public index html favicon ico src app css app js app test js index css index js logo svg for the project to build these files must exist with exact filenames public index html is the page template src index js is the javascript entry point you can delete or rename the other files you may create subdirectories inside src for faster rebuilds only files inside src are processed by webpack br you need to put any js and css files inside src otherwise webpack won t see them only files inside public can be used from public index html br read instructions below for using assets from javascript and html you can however create more top level directories br they will not be included in the production build so you can use them for things like documentation available scripts in the project directory you can run npm start runs the app in the development mode br open http localhost 3000 http localhost 3000 to view it in the browser the page will reload if you make edits br you will also see any lint errors in the console npm test launches the test runner in the interactive watch mode br see the section about running tests running tests for more information npm run build builds the app for production to the build folder br it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes br your app is ready to be deployed see the section about deployment deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it supported browsers by default the generated project supports all modern browsers br support for internet explorer 9 10 and 11 requires polyfills https github com facebook create react app blob master packages react app polyfill readme md supported language features this project supports a superset of the latest javascript standard br in addition to es6 https github com lukehoban es6features syntax features it also supports exponentiation operator https github com rwaldron exponentiation operator es2016 async await https github com tc39 ecmascript asyncawait es2017 object rest spread properties https github com tc39 proposal object rest spread es2018 dynamic import https github com tc39 proposal dynamic import stage 3 proposal class fields and static properties https github com tc39 proposal class public fields part of stage 3 proposal jsx https facebook github io react docs introducing jsx html and flow https flow org syntax learn more about different proposal stages https babeljs io docs plugins presets stage x experimental presets while we recommend using experimental proposals with some caution facebook heavily uses these features in the product code so we intend to provide codemods https medium com cpojer effective javascript codemods 5a6686bb46fb if any of these proposals change in the future note that this project includes no polyfills https github com facebook create react app blob master packages react app polyfill readme md by default if you use any other es6 features that need runtime support such as array from or symbol make sure you are including the appropriate polyfills manually https github com facebook create react app blob master packages react app polyfill readme md or that the browsers you are targeting already support them syntax highlighting in the editor to configure the syntax highlighting in your favorite text editor head to the relevant babel documentation page https babeljs io docs editors and follow the instructions some of the most popular editors are covered displaying lint output in the editor note this feature is available with react scripts 0 2 0 and higher br it also only works with npm 3 or higher some editors including sublime text atom and visual studio code provide plugins for eslint they are not required for linting you should see the linter output right in your terminal as well as the browser console however if you prefer the lint results to appear right in your editor there are some extra steps you can do you would need to install an eslint plugin for your editor first then add a file called eslintrc to the project root js extends react app now your editor should report the linting warnings note that even if you edit your eslintrc file further these changes will only affect the editor integration they won t affect the terminal and in browser lint output this is because create react app intentionally provides a minimal set of rules that find common mistakes if you want to enforce a coding style for your project consider using prettier https github com jlongster prettier instead of eslint style rules debugging in the editor this feature is currently only supported by visual studio code https code visualstudio com and webstorm https www jetbrains com webstorm visual studio code and webstorm support debugging out of the box with create react app this enables you as a developer to write and debug your react code without leaving the editor and most importantly it enables you to have a continuous development workflow where context switching is minimal as you don t have to switch between tools visual studio code you would need to have the latest version of vs code https code visualstudio com and vs code chrome debugger extension https marketplace visualstudio com items itemname msjsdiag debugger for chrome installed then add the block below to your launch json file and put it inside the vscode folder in your app s root directory json version 0 2 0 configurations name chrome type chrome request launch url http localhost 3000 webroot workspaceroot src sourcemappathoverrides webpack src webroot note the url may be different if you ve made adjustments via the host or port environment variables advanced configuration start your app by running npm start and start debugging in vs code by pressing f5 or by clicking the green debug icon you can now write code set breakpoints make changes to the code and debug your newly modified code all from your editor having problems with vs code debugging please see their troubleshooting guide https github com microsoft vscode chrome debug blob master readme md troubleshooting webstorm you would need to have webstorm https www jetbrains com webstorm and jetbrains ide support https chrome google com webstore detail jetbrains ide support hmhgeddbohgjknpmjagkdomcpobmllji chrome extension installed in the webstorm menu run select edit configurations then click and select javascript debug paste http localhost 3000 into the url field and save the configuration note the url may be different if you ve made adjustments via the host or port environment variables advanced configuration start your app by running npm start then press d on macos or f9 on windows and linux or click the green debug icon to start debugging in webstorm the same way you can debug your application in intellij idea ultimate phpstorm pycharm pro and rubymine formatting code automatically prettier is an opinionated code formatter with support for javascript css and json with prettier you can format the code you write automatically to ensure a code style within your project see the prettier s github page https github com prettier prettier for more information and look at this page to see it in action https prettier github io prettier to format our code whenever we make a commit in git we need to install the following dependencies sh npm install save husky lint staged prettier alternatively you may use yarn sh yarn add husky lint staged prettier husky makes it easy to use githooks as if they are npm scripts lint staged allows us to run scripts on staged files in git see this blog post about lint staged to learn more about it https medium com okonetchnikov make linting great again f3890e1ad6b8 prettier is the javascript formatter we will run before commits now we can make sure every file is formatted correctly by adding a few lines to the package json in the project root add the following field to the package json section diff husky hooks pre commit lint staged next we add a lint staged field to the package json for example diff dependencies lint staged src js jsx json css prettier single quote write git add scripts now whenever you make a commit prettier will format the changed files automatically you can also run node modules bin prettier single quote write src js jsx to format your entire project for the first time next you might want to integrate prettier in your favorite editor read the section on editor integration https prettier io docs en editors html on the prettier github page changing the page title you can find the source html file in the public folder of the generated project you may edit the title tag in it to change the title from react app to anything else note that normally you wouldn t edit files in the public folder very often for example adding a stylesheet adding a stylesheet is done without touching the html if you need to dynamically update the page title based on the content you can use the browser document title https developer mozilla org en us docs web api document title api for more complex scenarios when you want to change the title from react components you can use react helmet https github com nfl react helmet a third party library if you use a custom server for your app in production and want to modify the title before it gets sent to the browser you can follow advice in this section generating dynamic meta tags on the server alternatively you can pre build each page as a static html file which then loads the javascript bundle which is covered here pre rendering into static html files installing a dependency the generated project includes react and reactdom as dependencies it also includes a set of scripts used by create react app as a development dependency you may install other dependencies for example react router with npm sh npm install save react router dom alternatively you may use yarn sh yarn add react router dom this works for any library not just react router dom importing a component this project setup supports es6 modules thanks to webpack br while you can still use require and module exports we encourage you to use import and export http exploringjs com es6 ch modules html instead for example button js js import react component from react class button extends component render export default button don t forget to use export default dangerbutton js js import react component from react import button from button import a component from another file class dangerbutton extends component render return button color red export default dangerbutton be aware of the difference between default and named exports http stackoverflow com questions 36795819 react native es 6 when should i use curly braces for import 36796281 36796281 it is a common source of mistakes we suggest that you stick to using default imports and exports when a module only exports a single thing for example a component that s what you get when you use export default button and import button from button named exports are useful for utility modules that export several functions a module may have at most one default export and as many named exports as you like learn more about es6 modules when to use the curly braces http stackoverflow com questions 36795819 react native es 6 when should i use curly braces for import 36796281 36796281 exploring es6 modules http exploringjs com es6 ch modules html understanding es6 modules https leanpub com understandinges6 read leanpub auto encapsulating code with modules code splitting instead of downloading the entire app before users can use it code splitting allows you to split your code into small chunks which you can then load on demand this project setup supports code splitting via dynamic import http 2ality com 2017 01 import operator html loading code on demand its proposal https github com tc39 proposal dynamic import is in stage 3 the import function like form takes the module name as an argument and returns a promise https developer mozilla org en us docs web javascript reference global objects promise which always resolves to the namespace object of the module here is an example modulea js js const modulea hello export modulea app js js import react component from react class app extends component handleclick import modulea then modulea use modulea catch err handle failure render return div button onclick this handleclick load button div export default app this will make modulea js and all its unique dependencies as a separate chunk that only loads after the user clicks the load button you can also use it with async await syntax if you prefer it with react router if you are using react router check out this tutorial http serverless stack com chapters code splitting in create react app html on how to use code splitting with it you can find the companion github repository here https github com anomalyinnovations serverless stack demo client tree code splitting in create react app also check out the code splitting https reactjs org docs code splitting html section in react documentation adding a stylesheet this project setup uses webpack https webpack js org for handling all assets webpack offers a custom way of extending the concept of import beyond javascript to express that a javascript file depends on a css file you need to import the css from the javascript file button css css button padding 20px button js js import react component from react import button css tell webpack that button js uses these styles class button extends component render you can use them as regular css styles return div classname button this is not required for react but many people find this feature convenient you can read about the benefits of this approach here https medium com seek blog block element modifying your javascript components d7f99fcab52b however you should be aware that this makes your code less portable to other build tools and environments than webpack in development expressing dependencies this way allows your styles to be reloaded on the fly as you edit them in production all css files will be concatenated into a single minified css file in the build output if you are concerned about using webpack specific semantics you can put all your css right into src index css it would still be imported from src index js but you could always remove that import if you later migrate to a different build tool adding a css modules stylesheet note this feature is available with react scripts 2 0 0 and higher this project supports css modules https github com css modules css modules alongside regular stylesheets using the name module css file naming convention css modules allows the scoping of css by automatically creating a unique classname of the format filename classname hash tip should you want to preprocess a stylesheet with sass then make sure to follow the installation instructions adding a sass stylesheet and then change the stylesheet file extension as follows name module scss or name module sass css modules let you use the same css class name in different files without worrying about naming clashes learn more about css modules here https css tricks com css modules part 1 need button module css css error background color red another stylesheet css css error color red button js js import react component from react import styles from button module css import css modules stylesheet as styles import another stylesheet css import regular stylesheet class button extends component render reference as a js object return button classname styles error error button button result no clashes from other error class names html this button has red background but not red text button class button error ax7yz div this is an optional feature regular link stylesheets and css files are fully supported css modules are turned on for files ending with the module css extension adding a sass stylesheet note this feature is available with react scripts 2 0 0 and higher generally we recommend that you don t reuse the same css classes across different components for example instead of using a button css class in acceptbutton and rejectbutton components we recommend creating a button component with its own button styles that both acceptbutton and rejectbutton can render but not inherit https facebook github io react docs composition vs inheritance html following this rule often makes css preprocessors less useful as features like mixins and nesting are replaced by component composition you can however integrate a css preprocessor if you find it valuable to use sass first install node sass bash npm install node sass save or yarn add node sass now you can rename src app css to src app scss and update src app js to import src app scss this file and any other file will be automatically compiled if imported with the extension scss or sass to share variables between sass files you can use sass imports for example src app scss and other component style files could include import shared scss with variable definitions this will allow you to do imports like scss import styles colors scss assuming a styles directory under src import nprogress nprogress importing a css file from the nprogress node module tip you can opt into using this feature with css modules adding a css modules stylesheet too note you must prefix imports from node modules with as displayed above post processing css this project setup minifies your css and adds vendor prefixes to it automatically through autoprefixer https github com postcss autoprefixer so you don t need to worry about it support for new css features like the all property https developer mozilla org en us docs web css all break properties https www w3 org tr css break 3 breaking controls custom properties https developer mozilla org en us docs web css using css variables and media query ranges https www w3 org tr mediaqueries 4 range context are automatically polyfilled to add support for older browsers you can customize your target support browsers by adjusting the browserslist key in package json accoring to the browserslist specification https github com browserslist browserslist readme for example this css app display flex flex direction row align items center becomes this css app display webkit box display ms flexbox display flex webkit box orient horizontal webkit box direction normal ms flex direction row flex direction row webkit box align center ms flex align center align items center if you need to disable autoprefixing for some reason follow this section https github com postcss autoprefixer disabling css grid layout https developer mozilla org en us docs web css css grid layout prefixing is disabled by default but it will not strip manual prefixing if you d like to opt in to css grid prefixing first familiarize yourself about its limitations https github com postcss autoprefixer does autoprefixer polyfill grid layout for ie br to enable css grid prefixing add autoprefixer grid on to the top of your css file adding images fonts and files with webpack using static assets like images and fonts works similarly to css you can import a file right in a javascript module this tells webpack to include that file in the bundle unlike css imports importing a file gives you a string value this value is the final path you can reference in your code e g as the src attribute of an image or the href of a link to a pdf to reduce the number of requests to the server importing images that are less than 10 000 bytes returns a data uri https developer mozilla org en us docs web http basics of http data uris instead of a path this applies to the following file extensions bmp gif jpg jpeg and png svg files are excluded due to 1153 https github com facebook create react app issues 1153 here is an example js import react from react import logo from logo png tell webpack this js file uses this image console log logo logo 84287d09 png function header import result is the url of your image return img src logo alt logo export default header this ensures that when the project is built webpack will correctly move the images into the build folder and provide us with correct paths this works in css too css logo background image url logo png webpack finds all relative module references in css they start with and replaces them with the final paths from the compiled bundle if you make a typo or accidentally delete an important file you will see a compilation error just like when you import a non existent javascript module the final filenames in the compiled bundle are generated by webpack from content hashes if the file content changes in the future webpack will give it a different name in production so you don t need to worry about long term caching of assets please be advised that this is also a custom feature of webpack it is not required for react but many people enjoy it and react native uses a similar mechanism for images br an alternative way of handling static assets is described in the next section adding svgs note this feature is available with react scripts 2 0 0 and higher one way to add svg files was described in the section above you can also import svgs directly as react components you can use either of the two approaches in your code it would look like this js import reactcomponent as logo from logo svg const app div logo is an actual react component logo div this is handy if you don t want to load svg as a separate file don t forget the curly braces in the import the reactcomponent import name is special and tells create react app that you want a react component that renders an svg rather than its filename using the public folder note this feature is available with react scripts 0 5 0 and higher changing the html the public folder contains the html file so you can tweak it for example to set the page title changing the page title the script tag with the compiled code will be added to it automatically during the build process adding assets outside of the module system you can also add other assets to the public folder note that we normally encourage you to import assets in javascript files instead for example see the sections on adding a stylesheet adding a stylesheet and adding images and fonts adding images fonts and files this mechanism provides a number of benefits scripts and stylesheets get minified and bundled together to avoid extra network requests missing files cause compilation errors instead of 404 errors for your users result filenames include content hashes so you don t need to worry about browsers caching their old versions however there is an escape hatch that you can use to add an asset outside of the module system if you put a file into the public folder it will not be processed by webpack instead it will be copied into the build folder untouched to reference assets in the public folder you need to use a special variable called public url inside index html you can use it like this html link rel shortcut icon href public url favicon ico only files inside the public folder will be accessible by public url prefix if you need to use a file from src or node modules you ll have to copy it there to explicitly specify your intention to make this file a part of the build when you run npm run build create react app will substitute public url with a correct absolute path so your project works even if you use client side routing or host it at a non root url in javascript code you can use process env public url for similar purposes js render note this is an escape hatch and should be used sparingly normally we recommend using import for getting asset urls as described in adding images and fonts above this section return img src process env public url img logo png keep in mind the downsides of this approach none of the files in public folder get post processed or minified missing files will not be called at compilation time and will cause 404 errors for your users result filenames won t include content hashes so you ll need to add query arguments or rename them every time they change when to use the public folder normally we recommend importing stylesheets adding a stylesheet images and fonts adding images fonts and files from javascript the public folder is useful as a workaround for a number of less common cases you need a file with a specific name in the build output such as manifest webmanifest https developer mozilla org en us docs web manifest you have thousands of images and need to dynamically reference their paths you want to include a small script like pace js http github hubspot com pace docs welcome outside of the bundled code some library may be incompatible with webpack and you have no other option but to include it as a script tag note that if you add a script that declares global variables you also need to read the next section on using them using global variables when you include a script in the html file that defines global variables and try to use one of these variables in the code the linter will complain because it cannot see the definition of the variable you can avoid this by reading the global variable explicitly from the window object for example js const window this makes it obvious you are using a global variable intentionally rather than because of a typo alternatively you can force the linter to ignore any line by adding eslint disable line after it adding bootstrap you don t have to use reactstrap https reactstrap github io together with react but it is a popular library for integrating bootstrap with react apps if you need it you can integrate it with create react app by following these steps install reactstrap and bootstrap from npm reactstrap does not include bootstrap css so this needs to be installed as well sh npm install save reactstrap bootstrap 4 alternatively you may use yarn sh yarn add bootstrap 4 reactstrap import bootstrap css and optionally bootstrap theme css in the beginning of your src index js file js import bootstrap dist css bootstrap css put any other imports below so that css from your components takes precedence over default styles import required reactstrap components within src app js file or your custom component files js import button from reactstrap now you are ready to use the imported reactstrap components within your component hierarchy defined in the render method here is an example app js https gist githubusercontent com zx6658 d9f128cd57ca69e583ea2b5fea074238 raw a56701c142d0c622eb6c20a457fbc01d708cb485 app js redone using reactstrap using a custom theme note this feature is available with react scripts 2 0 0 and higher sometimes you might need to tweak the visual styles of bootstrap or equivalent package br as of react scripts 2 0 0 you can import scss files this makes it possible to use a package s built in sass variables for global style preferences to customize bootstrap create a file called src custom scss or similar and import the bootstrap source stylesheet add any overrides before the imported file s you can reference bootstrap s documentation http getbootstrap com docs 4 1 getting started theming css variables for the names of the available variables scss override default variables before the import body bg 000 import bootstrap and its default variables import bootstrap scss bootstrap scss note you must prefix imports from node modules with as displayed above finally import the newly created scss file instead of the default bootstrap css in the beginning of your src index js file for example javascript import custom scss adding flow flow is a static type checker that helps you write code with fewer bugs check out this introduction to using static types in javascript https medium com preethikasireddy why use static types in javascript part 1 8382da1e0adb if you are new to this concept recent versions of flow https flow org work with create react app projects out of the box to add flow to a create react app project follow these steps 1 run npm install save flow bin or yarn add flow bin 2 add flow flow to the scripts section of your package json 3 run npm run flow init or yarn flow init to create a flowconfig file https flow org en docs config in the root directory 4 add flow to any files you want to type check for example to src app js now you can run npm run flow or yarn flow to check the files for type errors you can optionally use an ide like nuclide https nuclide io docs languages flow for a better integrated experience in the future we plan to integrate it into create react app even more closely to learn more about flow check out its documentation https flow org adding relay relay is a framework for building data driven react applications powered by graphql the current release candidate of relay works with create react app projects out of the box using babel macros simply set up your project as laid out in the relay documentation https facebook github io relay then make sure you have a version of the babel plugin providing the macro to add it run sh npm install save dev babel plugin relay dev alternatively you may use yarn sh yarn upgrade babel plugin relay dev then wherever you use the graphql template tag import the macro js import graphql from babel plugin relay macro instead of import graphql from babel plugin relay graphql query userquery viewer id to learn more about relay check out its documentation https facebook github io relay adding a router create react app doesn t prescribe a specific routing solution but react router https reacttraining com react router web is the most popular one to add it run sh npm install save react router dom alternatively you may use yarn sh yarn add react router dom to try it delete all the code in src app js and replace it with any of the examples on its website the basic example https reacttraining com react router web example basic is a good place to get started note that you may need to configure your production server to support client side routing serving apps with client side routing before deploying your app adding custom environment variables note this feature is available with react scripts 0 2 3 and higher your project can consume variables declared in your environment as if they were declared locally in your js files by default you will have node env defined for you and any other environment variables starting with react app the environment variables are embedded during the build time since create react app produces a static html css js bundle it can t possibly read them at runtime to read them at runtime you would need to load html into memory on the server and replace placeholders in runtime just like described here injecting data from the server into the page alternatively you can rebuild the app on the server anytime you change them note you must create custom environment variables beginning with react app any other variables except node env will be ignored to avoid accidentally exposing a private key on the machine that could have the same name https github com facebook create react app issues 865 issuecomment 252199527 changing any environment variables will require you to restart the development server if it is running these environment variables will be defined for you on process env for example having an environment variable named react app secret code will be exposed in your js as process env react app secret code there is also a special built in environment variable called node env you can read it from process env node env when you run npm start it is always equal to development when you run npm test it is always equal to test and when you run npm run build to make a production bundle it is always equal to production you cannot override node env manually this prevents developers from accidentally deploying a slow development build to production these environment variables can be useful for displaying information conditionally based on where the project is deployed or consuming sensitive data that lives outside of version control first you need to have environment variables defined for example let s say you wanted to consume a secret defined in the environment inside a form jsx render return div small you are running this application in b process env node env b mode small form input type hidden defaultvalue process env react app secret code form div during the build process env react app secret code will be replaced with the current value of the react app secret code environment variable remember that the node env variable will be set for you automatically when you load the app in the browser and inspect the input you will see its value set to abcdef and the bold text will show the environment provided when using npm start html div small you are running this application in b development b mode small form input type hidden value abcdef form div the above form is looking for a variable called react app secret code from the environment in order to consume this value we need to have it defined in the environment this can be done using two ways either in your shell or in a env file both of these ways are described in the next few sections having access to the node env is also useful for performing actions conditionally js if process env node env production analytics disable when you compile the app with npm run build the minification step will strip out this condition and the resulting bundle will be smaller referencing environment variables in the html note this feature is available with react scripts 0 9 0 and higher you can also access the environment variables starting with react app in the public index html for example html title react app website name title note that the caveats from the above section apply apart from a few built in variables node env and public url variable names must start with react app to work the environment variables are injected at build time if you need to inject them at runtime follow this approach instead generating dynamic meta tags on the server adding temporary environment variables in your shell defining environment variables can vary between oses it s also important to know that this manner is temporary for the life of the shell session windows cmd exe cmd set react app secret code abcdef npm start note quotes around the variable assignment are required to avoid a trailing whitespace windows powershell powershell env react app secret code abcdef and npm start linux macos bash bash react app secret code abcdef npm start adding development environment variables in env note this feature is available with react scripts 0 5 0 and higher to define permanent environment variables create a file called env in the root of your project react app secret code abcdef note you must create custom environment variables beginning with react app any other variables except node env will be ignored to avoid accidentally exposing a private key on the machine that could have the same name https github com facebook create react app issues 865 issuecomment 252199527 changing any environment variables will require you to restart the development server if it is running env files should be checked into source control with the exclusion of env local what other env files can be used note this feature is available with react scripts 1 0 0 and higher env default env local local overrides this file is loaded for all environments except test env development env test env production environment specific settings env development local env test local env production local local overrides of environment specific settings files on the left have more priority than files on the right npm start env development local env development env local env npm run build env production local env production env local env npm test env test local env test env note env local is missing these variables will act as the defaults if the machine does not explicitly set them br please refer to the dotenv documentation https github com motdotla dotenv for more details note if you are defining environment variables for development your ci and or hosting platform will most likely need these defined as well consult their documentation how to do this for example see the documentation for travis ci https docs travis ci com user environment variables or heroku https devcenter heroku com articles config vars expanding environment variables in env note this feature is available with react scripts 1 1 0 and higher expand variables already on your machine for use in your env file using dotenv expand https github com motdotla dotenv expand for example to get the environment variable npm package version react app version npm package version also works react app version npm package version or expand variables local to the current env file domain www example com react app foo domain foo react app bar domain bar can i use decorators some popular libraries use decorators https medium com google developers exploring es7 decorators 76ecb65fb841 in their documentation br create react app intentionally doesn t support decorator syntax at the moment because it is an experimental proposal and is subject to change in fact it has already changed once and will change again most libraries currently support only the old version of the proposal which will never be a standard however in many cases you can rewrite decorator based code without decorators just as fine br please refer to these two threads for reference 214 https github com facebook create react app issues 214 411 https github com facebook create react app issues 411 create react app will add decorator support when the specification advances to a stable stage fetching data with ajax requests react doesn t prescribe a specific approach to data fetching but people commonly use either a library like axios https github com axios axios or the fetch api https developer mozilla org en us docs web api fetch api provided by the browser the global fetch function allows you to easily make ajax requests it takes in a url as an input and returns a promise that resolves to a response object you can find more information about fetch here https developer mozilla org en us docs web api fetch api using fetch a promise represents the eventual result of an asynchronous operation you can find more information about promises here https www promisejs org and here https developer mozilla org en us docs web javascript reference global objects promise both axios and fetch use promises under the hood you can also use the async await https davidwalsh name async await syntax to reduce the callback nesting make sure the fetch api https developer mozilla org en us docs web api fetch api and promises https developer mozilla org en us docs web javascript reference global objects promise are available in your target audience s browsers for example support in internet explorer requires a polyfill https github com facebook create react app blob master packages react app polyfill readme md you can learn more about making ajax requests from react components in the faq entry on the react website https reactjs org docs faq ajax html integrating with an api backend these tutorials will help you to integrate your app with an api backend running on another port using fetch to access it node check out this tutorial https www fullstackreact com articles using create react app with a server you can find the companion github repository here https github com fullstackreact food lookup demo ruby on rails check out this tutorial https www fullstackreact com articles how to get create react app to work with your rails api you can find the companion github repository here https github com fullstackreact food lookup demo rails api platform php and symfony api platform https api platform com is a framework designed to build api driven projects it allows to create hypermedia and graphql apis in minutes it is shipped with an official progressive web app generator as well as a dynamic administration interface both built for create react app check out this tutorial https api platform com docs distribution proxying api requests in development note this feature is available with react scripts 0 2 3 and higher people often serve the front end react app from the same host and port as their backend implementation br for example a production setup might look like this after the app is deployed static server returns index html with react app todos static server returns index html with react app api todos server handles any api requests using the backend implementation such setup is not required however if you do have a setup like this it is convenient to write requests like fetch api todos without worrying about redirecting them to another host or port during development to tell the development server to proxy any unknown requests to your api server in development add a proxy field to your package json for example js proxy http localhost 4000 this way when you fetch api todos in development the development server will recognize that it s not a static asset and will proxy your request to http localhost 4000 api todos as a fallback the development server will only attempt to send requests without text html in its accept header to the proxy conveniently this avoids cors issues http stackoverflow com questions 21854516 understanding ajax cors and security considerations and error messages like this in development fetch api cannot load http localhost 4000 api todos no access control allow origin header is present on the requested resource origin http localhost 3000 is therefore not allowed access if an opaque response serves your needs set the request s mode to no cors to fetch the resource with cors disabled keep in mind that proxy only has effect in development with npm start and it is up to you to ensure that urls like api todos point to the right thing in production you don t have to use the api prefix any unrecognized request without a text html accept header will be redirected to the specified proxy the proxy option supports http https and websocket connections br if the proxy option is not flexible enough for you alternatively you can configure the proxy yourself configuring the proxy manually enable cors on your server here s how to do it for express http enable cors org server expressjs html use environment variables adding custom environment variables to inject the right server host and port into your app invalid host header errors after configuring proxy when you enable the proxy option you opt into a more strict set of host checks this is necessary because leaving the backend open to remote hosts makes your computer vulnerable to dns rebinding attacks the issue is explained in this article https medium com webpack webpack dev server middleware security issues 1489d950874a and this issue https github com webpack webpack dev server issues 887 this shouldn t affect you when developing on localhost but if you develop remotely like described here https github com facebook create react app issues 2271 you will see this error in the browser after enabling the proxy option invalid host header to work around it you can specify your public development host in a file called env development in the root of your project host mypublicdevhost com if you restart the development server now and load the app from the specified host it should work if you are still having issues or if you re using a more exotic environment like a cloud editor you can bypass the host check completely by adding a line to env development local note that this is dangerous and exposes your machine to remote code execution from malicious websites note this is dangerous it exposes your machine to attacks from the websites you visit dangerously disable host check true we don t recommend this approach configuring the proxy manually note this feature is available with react scripts 2 0 0 and higher if the proxy option is not flexible enough for you you can get direct access to the express app instance and hook up your own proxy middleware you can use this feature in conjunction with the proxy property in package json but it is recommended you consolidate all of your logic into src setupproxy js first install http proxy middleware using npm or yarn bash npm install http proxy middleware save or yarn add http proxy middleware next create src setupproxy js and place the following contents in it js const proxy require http proxy middleware module exports function app you can now register proxies as you wish here s an example using the above http proxy middleware js const proxy require http proxy middleware module exports function app app use proxy api target http localhost 5000 note you do not need to import this file anywhere it is automatically registered when you start the development server note this file only supports node s javascript syntax be sure to only use supported language features i e no support for flow es modules etc note passing the path to the proxy function allows you to use globbing and or pattern matching on the path which is more flexible than the express route matching using https in development note this feature is available with react scripts 0 4 0 and higher you may require the dev server to serve pages over https one particular case where this could be useful is when using the proxy feature proxying api requests in development to proxy requests to an api server when that api server is itself serving https to do this set the https environment variable to true then start the dev server as usual with npm start windows cmd exe cmd set https true npm start note the lack of whitespace is intentional windows powershell powershell env https true and npm start linux macos bash bash https true npm start note that the server will use a self signed certificate so your web browser will almost definitely display a warning upon accessing the page generating dynamic meta tags on the server since create react app doesn t support server rendering you might be wondering how to make meta tags dynamic and reflect the current url to solve this we recommend to add placeholders into the html like this html doctype html html lang en head meta property og title content og title meta property og description content og description then on the server regardless of the backend you use you can read index html into memory and replace og title og description and any other placeholders with values depending on the current url just make sure to sanitize and escape the interpolated values so that they are safe to embed into html if you use a node server you can even share the route matching logic between the client and the server however duplicating it also works fine in simple cases pre rendering into static html files if you re hosting your build with a static hosting provider you can use react snapshot https www npmjs com package react snapshot or react snap https github com stereobooster react snap to generate html pages for each route or relative link in your application these pages will then seamlessly become active or hydrated when the javascript bundle has loaded there are also opportunities to use this outside of static hosting to take the pressure off the server when generating and caching routes the primary benefit of pre rendering is that you get the core content of each page with the html payload regardless of whether or not your javascript bundle successfully downloads it also increases the likelihood that each route of your application will be picked up by search engines you can read more about zero configuration pre rendering also called snapshotting here https medium com superhighfives an almost static stack 6df0a2791319 injecting data from the server into the page similarly to the previous section you can leave some placeholders in the html that inject global variables for example js doctype html html lang en head script window server data server data script then on the server you can replace server data with a json of real data right before sending the response the client code can then read window server data to use it make sure to sanitize the json before sending it to the client https medium com node security the most common xss vulnerability in react js applications 2bdffbcc1fa0 as it makes your app vulnerable to xss attacks running tests note this feature is available with react scripts 0 3 0 and higher br read the migration guide to learn how to enable it in older projects https github com facebook create react app blob master changelog md migrating from 023 to 030 create react app uses jest https facebook github io jest as its test runner to prepare for this integration we did a major revamp https facebook github io jest blog 2016 09 01 jest 15 html of jest so if you heard bad things about it years ago give it another try jest is a node based runner this means that the tests always run in a node environment and not in a real browser this lets us enable fast iteration speed and prevent flakiness while jest provides browser globals such as window thanks to jsdom https github com tmpvar jsdom they are only approximations of the real browser behavior jest is intended to be used for unit tests of your logic and your components rather than the dom quirks we recommend that you use a separate tool for browser end to end tests if you need them they are beyond the scope of create react app filename conventions jest will look for test files with any of the following popular naming conventions files with js suffix in tests folders files with test js suffix files with spec js suffix the test js spec js files or the tests folders can be located at any depth under the src top level folder we recommend to put the test files or tests folders next to the code they are testing so that relative imports appear shorter for example if app test js and app js are in the same folder the test just needs to import app from app instead of a long relative path colocation also helps find tests more quickly in larger projects command line interface when you run npm test jest will launch in the watch mode every time you save a file it will re run the tests just like npm start recompiles the code the watcher includes an interactive command line interface with the ability to run all tests or focus on a search pattern it is designed this way so that you can keep it open and enjoy fast re runs you can learn the commands from the watch usage note that the watcher prints after every run jest watch mode http facebook github io jest img blog 15 watch gif version control integration by default when you run npm test jest will only run the tests related to files changed since the last commit this is an optimization designed to make your tests run fast regardless of how many tests you have however it assumes that you don t often commit the code that doesn t pass the tests jest will always explicitly mention that it only ran tests related to the files changed since the last commit you can also press a in the watch mode to force jest to run all tests jest will always run all tests on a continuous integration continuous integration server or if the project is not inside a git or mercurial repository writing tests to create tests add it or test blocks with the name of the test and its code you may optionally wrap them in describe blocks for logical grouping but this is neither required nor recommended jest provides a built in expect global function for making assertions a basic test could look like this js import sum from sum it sums numbers expect sum 1 2 toequal 3 expect sum 2 2 toequal 4 all expect matchers supported by jest are extensively documented here https facebook github io jest docs en expect html content br you can also use jest fn and expect fn tobecalled https facebook github io jest docs en expect html tohavebeencalled to create spies or mock functions testing components there is a broad spectrum of component testing techniques they range from a smoke test verifying that a component renders without throwing to shallow rendering and testing some of the output to full rendering and testing component lifecycle and state changes different projects choose different testing tradeoffs based on how often components change and how much logic they contain if you haven t decided on a testing strategy yet we recommend that you start with creating simple smoke tests for your components js import react from react import reactdom from react dom import app from app it renders without crashing const div document createelement div reactdom render app div this test mounts a component and makes sure that it didn t throw during rendering tests like this provide a lot of value with very little effort so they are great as a starting point and this is the test you will find in src app test js when you encounter bugs caused by changing components you will gain a deeper insight into which parts of them are worth testing in your application this might be a good time to introduce more specific tests asserting specific expected output or behavior if you d like to test components in isolation from the child components they render we recommend using shallow rendering api http airbnb io enzyme docs api shallow html from enzyme http airbnb io enzyme to install it run sh npm install save enzyme enzyme adapter react 16 react test renderer alternatively you may use yarn sh yarn add enzyme enzyme adapter react 16 react test renderer as of enzyme 3 you will need to install enzyme along with an adapter corresponding to the version of react you are using the examples above use the adapter for react 16 the adapter will also need to be configured in your global setup file initializing test environment src setuptests js js import configure from enzyme import adapter from enzyme adapter react 16 configure adapter new adapter note keep in mind that if you decide to eject before creating src setuptests js the resulting package json file won t contain any reference to it read here initializing test environment to learn how to add this after ejecting now you can write a smoke test with it js import react from react import shallow from enzyme import app from app it renders without crashing shallow app unlike the previous smoke test using reactdom render this test only renders app and doesn t go deeper for example even if app itself renders a button that throws this test will pass shallow rendering is great for isolated unit tests but you may still want to create some full rendering tests to ensure the components integrate correctly enzyme supports full rendering with mount http airbnb io enzyme docs api mount html and you can also use it for testing state changes and component lifecycle you can read the enzyme documentation http airbnb io enzyme for more testing techniques enzyme documentation uses chai and sinon for assertions but you don t have to use them because jest provides built in expect and jest fn for spies here is an example from enzyme documentation that asserts specific output rewritten to use jest matchers js import react from react import shallow from enzyme import app from app it renders welcome message const wrapper shallow app const welcome h2 welcome to react h2 expect wrapper contains welcome tobe true expect wrapper contains welcome toequal true all jest matchers are extensively documented here http facebook github io jest docs en expect html br nevertheless you can use a third party assertion library like chai http chaijs com if you want to as described below additionally you might find jest enzyme https github com blainekasten enzyme matchers helpful to simplify your tests with readable matchers the above contains code can be written more simply with jest enzyme js expect wrapper tocontainreact welcome to enable this install jest enzyme sh npm install save jest enzyme alternatively you may use yarn sh yarn add jest enzyme import it in src setuptests js initializing test environment to make its matchers available in every test js import jest enzyme using third party assertion libraries we recommend that you use expect for assertions and jest fn for spies if you are having issues with them please file those against jest https github com facebook jest issues new and we ll fix them we intend to keep making them better for react supporting for example pretty printing react elements as jsx https github com facebook jest pull 1566 however if you are used to other libraries such as chai http chaijs com and sinon http sinonjs org or if you have existing code using them that you d like to port over you can import them normally like this js import sinon from sinon import expect from chai and then use them in your tests like you normally do initializing test environment note this feature is available with react scripts 0 4 0 and higher if your app uses a browser api that you need to mock in your tests or if you just need a global setup before running your tests add a src setuptests js to your project it will be automatically executed before running your tests for example src setuptests js js const localstoragemock getitem jest fn setitem jest fn clear jest fn global localstorage localstoragemock note keep in mind that if you decide to eject before creating src setuptests js the resulting package json file won t contain any reference to it so you should manually create the property setuptestframeworkscriptfile in the configuration for jest something like the following js jest setuptestframeworkscriptfile rootdir src setuptests js focusing and excluding tests you can replace it with xit to temporarily exclude a test from being executed br similarly fit lets you focus on a specific test without running any other tests coverage reporting jest has an integrated coverage reporter that works well with es6 and requires no configuration br run npm test coverage note extra in the middle to include a coverage report like this coverage report http i imgur com 5bfhnts png note that tests run much slower with coverage so it is recommended to run it separately from your normal workflow configuration the default jest coverage configuration can be overridden by adding any of the following supported keys to a jest config in your package json supported overrides collectcoveragefrom https facebook github io jest docs en configuration html collectcoveragefrom array coveragereporters https facebook github io jest docs en configuration html coveragereporters array string coveragethreshold https facebook github io jest docs en configuration html coveragethreshold object snapshotserializers https facebook github io jest docs en configuration html snapshotserializers array string example package json json name your package jest collectcoveragefrom src js jsx rootdir node modules rootdir path to dir coveragethreshold global branches 90 functions 90 lines 90 statements 90 coveragereporters text snapshotserializers my serializer module continuous integration by default npm test runs the watcher with interactive cli however you can force it to run tests once and finish the process by setting an environment variable called ci when creating a build of your application with npm run build linter warnings are not checked by default like npm test you can force the build to perform a linter warning check by setting the environment variable ci if any warnings are encountered then the build fails popular ci servers already set the environment variable ci by default but you can do this yourself too on ci servers travis ci 1 following the travis getting started https docs travis ci com user getting started guide for syncing your github repository with travis you may need to initialize some settings manually in your profile https travis ci org profile page 1 add a travis yml file to your git repository language node js node js 8 cache directories node modules script npm run build npm test 1 trigger your first build with a git push 1 customize your travis ci build https docs travis ci com user customizing the build if needed circleci follow this article https medium com knowbody circleci and zeits now sh c9b7eebcd3c1 to set up circleci with a create react app project on your own environment windows cmd exe cmd set ci true npm test cmd set ci true npm run build note the lack of whitespace is intentional windows powershell powershell env ci true and npm test powershell env ci true and npm run build linux macos bash bash ci true npm test bash ci true npm run build the test command will force jest to run tests once instead of launching the watcher if you find yourself doing this often in development please file an issue https github com facebook create react app issues new to tell us about your use case because we want to make watcher the best experience and are open to changing how it works to accommodate more workflows the build command will check for linter warnings and fail if any are found disabling jsdom if you know that none of your tests depend on jsdom https github com tmpvar jsdom you can safely set env node and your tests will run faster diff scripts start react scripts start build react scripts build test react scripts test test react scripts test env node to help you make up your mind here is a list of apis that need jsdom any browser globals like window and document reactdom render https facebook github io react docs top level api html reactdom render testutils renderintodocument https facebook github io react docs test utils html renderintodocument a shortcut https github com facebook react blob 34761cf9a252964abfaab6faf74d473ad95d1f21 src test reacttestutils js l83 l91 for the above mount http airbnb io enzyme docs api mount html in enzyme http airbnb io enzyme index html in contrast jsdom is not needed for the following apis testutils createrenderer https facebook github io react docs test utils html shallow rendering shallow rendering shallow http airbnb io enzyme docs api shallow html in enzyme http airbnb io enzyme index html finally jsdom is also not needed for snapshot testing http facebook github io jest blog 2016 07 27 jest 14 html snapshot testing snapshot testing is a feature of jest that automatically generates text snapshots of your components and saves them on the disk so if the ui output changes you get notified without manually writing any assertions on the component output read more about snapshot testing http facebook github io jest blog 2016 07 27 jest 14 html editor integration if you use visual studio code https code visualstudio com there is a jest extension https github com orta vscode jest which works with create react app out of the box this provides a lot of ide like features while using a text editor showing the status of a test run with potential fail messages inline starting and stopping the watcher automatically and offering one click snapshot updates vs code jest preview https cloud githubusercontent com assets 49038 20795349 a032308a b7c8 11e6 9b34 7eeac781003f png debugging tests there are various ways to setup a debugger for your jest tests we cover debugging in chrome and visual studio code https code visualstudio com note debugging tests requires node 8 or higher debugging tests in chrome add the following to the scripts section in your project s package json json scripts test debug react scripts inspect brk test runinband place debugger statements in any test and run bash npm run test debug this will start running your jest tests but pause before executing to allow a debugger to attach to the process open the following in chrome about inspect after opening that link the chrome developer tools will be displayed select inspect on your process and a breakpoint will be set at the first line of the react script this is done simply to give you time to open the developer tools and to prevent jest from executing before you have time to do so click the button that looks like a play button in the upper right hand side of the screen to continue execution when jest executes the test that contains the debugger statement execution will pause and you can examine the current scope and call stack note the runinband cli option makes sure jest runs test in the same process rather than spawning processes for individual tests normally jest parallelizes test runs across processes but it is hard to debug many processes at the same time debugging tests in visual studio code debugging jest tests is supported out of the box for visual studio code https code visualstudio com use the following launch json https code visualstudio com docs editor debugging launch configurations configuration file version 0 2 0 configurations name debug cra tests type node request launch runtimeexecutable workspaceroot node modules bin react scripts args test runinband no cache cwd workspaceroot protocol inspector console integratedterminal internalconsoleoptions neveropen developing components in isolation usually in an app you have a lot of ui components and each of them has many different states for an example a simple button component could have following states in a regular state with a text label in the disabled mode in a loading state usually it s hard to see these states without running a sample app or some examples create react app doesn t include any tools for this by default but you can easily add storybook for react https storybook js org source https github com storybooks storybook or react styleguidist https react styleguidist js org source https github com styleguidist react styleguidist to your project these are third party tools that let you develop components and see all their states in isolation from your app storybook for react demo http i imgur com 7ciawpb gif you can also deploy your storybook or style guide as a static app this way everyone in your team can view and review different states of ui components without starting a backend server or creating an account in your app getting started with storybook storybook is a development environment for react ui components it allows you to browse a component library view the different states of each component and interactively develop and test components first install the following npm package globally sh npm install g storybook cli then run the following command inside your app s directory sh getstorybook after that follow the instructions on the screen learn more about react storybook learn storybook tutorial https learnstorybook com documentation https storybook js org basics introduction github repo https github com storybooks storybook snapshot testing ui https github com storybooks storybook tree master addons storyshots with storybook addon storyshot getting started with styleguidist styleguidist combines a style guide where all your components are presented on a single page with their props documentation and usage examples with an environment for developing components in isolation similar to storybook in styleguidist you write examples in markdown where each code snippet is rendered as a live editable playground first install styleguidist sh npm install save react styleguidist alternatively you may use yarn sh yarn add react styleguidist then add these scripts to your package json diff scripts styleguide styleguidist server styleguide build styleguidist build start react scripts start then run the following command inside your app s directory sh npm run styleguide after that follow the instructions on the screen learn more about react styleguidist github repo https github com styleguidist react styleguidist documentation https react styleguidist js org docs getting started html publishing components to npm create react app doesn t provide any built in functionality to publish a component to npm if you re ready to extract a component from your project so other people can use it we recommend moving it to a separate directory outside of your project and then using a tool like nwb https github com insin nwb react components and libraries to prepare it for publishing making a progressive web app the production build has all the tools necessary to generate a first class progressive web app https developers google com web progressive web apps but the offline cache first behavior is opt in only by default the build process will generate a service worker file but it will not be registered so it will not take control of your production web app in order to opt in to the offline first behavior developers should look for the following in their src index js src index js file js if you want your app to work offline and load faster you can change unregister to register below note this comes with some pitfalls learn more about service workers http bit ly cra pwa serviceworker unregister as the comment states switching serviceworker unregister to serviceworker register will opt you in to using the service worker why opt in offline first progressive web apps are faster and more reliable than traditional web pages and provide an engaging mobile experience all static site assets are cached so that your page loads fast on subsequent visits regardless of network connectivity such as 2g or 3g updates are downloaded in the background your app will work regardless of network state even if offline this means your users will be able to use your app at 10 000 feet and on the subway on mobile devices your app can be added directly to the user s home screen app icon and all this eliminates the need for the app store however they can make debugging deployments more challenging https github com facebook create react app issues 2398 so starting with create react app 2 service workers are opt in the workbox webpack plugin https developers google com web tools workbox modules workbox webpack plugin is integrated into production configuration and it will take care of generating a service worker file that will automatically precache all of your local assets and keep them up to date as you deploy updates the service worker will use a cache first strategy https developers google com web fundamentals instant and offline offline cookbook cache falling back to network for handling all requests for local assets including navigation requests https developers google com web fundamentals primers service workers high performance loading first what are navigation requests for your html ensuring that your web app is consistently fast even on a slow or unreliable network offline first considerations if you do decide to opt in to service worker registration please take the following into account 1 service workers require https https developers google com web fundamentals getting started primers service workers you need https although to facilitate local testing that policy does not apply to localhost http stackoverflow com questions 34160509 options for testing service workers via http 34161385 34161385 if your production web server does not support https then the service worker registration will fail but the rest of your web app will remain functional 1 service workers are not supported https jakearchibald github io isserviceworkerready moar in older web browsers service worker registration won t be attempted src registerserviceworker js on browsers that lack support 1 the service worker is only enabled in the production environment deployment e g the output of npm run build it s recommended that you do not enable an offline first service worker in a development environment as it can lead to frustration when previously cached assets are used and do not include the latest changes you ve made locally 1 if you need to test your offline first service worker locally build the application using npm run build and run a simple http server from your build directory after running the build script create react app will give instructions for one way to test your production build locally and the deployment instructions deployment have instructions for using other methods be sure to always use an incognito window to avoid complications with your browser cache 1 users aren t always familiar with offline first web apps it can be useful to let the user know https developers google com web fundamentals instant and offline offline ux inform the user when the app is ready for offline consumption when the service worker has finished populating your caches showing a this web app works offline message and also let them know when the service worker has fetched the latest updates that will be available the next time they load the page showing a new content is available please refresh message showing this messages is currently left as an exercise to the developer but as a starting point you can make use of the logic included in src registerserviceworker js src registerserviceworker js which demonstrates which service worker lifecycle events to listen for to detect each scenario and which as a default just logs appropriate messages to the javascript console 1 by default the generated service worker file will not intercept or cache any cross origin traffic like http api requests integrating with an api backend images or embeds loaded from a different domain progressive web app metadata the default configuration includes a web app manifest located at public manifest json public manifest json that you can customize with details specific to your web application when a user adds a web app to their homescreen using chrome or firefox on android the metadata in manifest json public manifest json determines what icons names and branding colors to use when the web app is displayed the web app manifest guide https developers google com web fundamentals engage and retain web app manifest provides more context about what each field means and how your customizations will affect your users experience progressive web apps that have been added to the homescreen will load faster and work offline when there s an active service worker that being said the metadata from the web app manifest will still be used regardless of whether or not you opt in to service worker registration analyzing the bundle size source map explorer https www npmjs com package source map explorer analyzes javascript bundles using the source maps this helps you understand where code bloat is coming from to add source map explorer to a create react app project follow these steps sh npm install save source map explorer alternatively you may use yarn sh yarn add source map explorer then in package json add the following line to scripts diff scripts analyze source map explorer build static js main start react scripts start build react scripts build test react scripts test then to analyze the bundle run the production build then run the analyze script npm run build npm run analyze deployment npm run build creates a build directory with a production build of your app set up your favorite http server so that a visitor to your site is served index html and requests to static paths like static js main hash js are served with the contents of the static js main hash js file static server for environments using node https nodejs org the easiest way to handle this would be to install serve https github com zeit serve and let it handle the rest sh npm install g serve serve s build the last command shown above will serve your static site on the port 5000 like many of serve https github com zeit serve s internal settings the port can be adjusted using the p or port flags run this command to get a full list of the options available sh serve h other solutions you don t necessarily need a static server in order to run a create react app project in production it works just as fine integrated into an existing dynamic one here s a programmatic example using node https nodejs org and express http expressjs com javascript const express require express const path require path const app express app use express static path join dirname build app get function req res res sendfile path join dirname build index html app listen 9000 the choice of your server software isn t important either since create react app is completely platform agnostic there s no need to explicitly use node the build folder with static assets is the only output produced by create react app however this is not quite enough if you use client side routing read the next section if you want to support urls like todos 42 in your single page app serving apps with client side routing if you use routers that use the html5 pushstate history api https developer mozilla org en us docs web api history api adding and modifying history entries under the hood for example react router https github com reacttraining react router with browserhistory many static file servers will fail for example if you used react router with a route for todos 42 the development server will respond to localhost 3000 todos 42 properly but an express serving a production build as above will not this is because when there is a fresh page load for a todos 42 the server looks for the file build todos 42 and does not find it the server needs to be configured to respond to a request to todos 42 by serving index html for example we can amend our express example above to serve index html for any unknown paths diff app use express static path join dirname build app get function req res app get function req res res sendfile path join dirname build index html if you re using apache http server https httpd apache org you need to create a htaccess file in the public folder that looks like this options multiviews rewriteengine on rewritecond request filename f rewriterule index html qsa l it will get copied to the build folder when you run npm run build if you re using apache tomcat http tomcat apache org you need to follow this stack overflow answer https stackoverflow com a 41249464 4878474 now requests to todos 42 will be handled correctly both in development and in production on a production build and when you ve opted in why opt in a service worker https developers google com web fundamentals primers service workers will automatically handle all navigation requests like for todos 42 by serving the cached copy of your index html this service worker navigation routing can be configured or disabled by eject ing npm run eject and then modifying the navigatefallback https github com googlechrome sw precache navigatefallback string and navigatefallbackwhitelist https github com googlechrome sw precache navigatefallbackwhitelist arrayregexp options of the swpreacheplugin configuration config webpack config prod js when users install your app to the homescreen of their device the default configuration will make a shortcut to index html this may not work for client side routers which expect the app to be served from edit the web app manifest at public manifest json public manifest json and change start url to match the required url scheme for example js start url building for relative paths by default create react app produces a build assuming your app is hosted at the server root br to override this specify the homepage in your package json for example js homepage http mywebsite com relativepath this will let create react app correctly infer the root path to use in the generated html file note if you are using react router 4 you can root link s using the basename prop on any router br more information here https reacttraining com react router web api browserrouter basename string br br for example js browserrouter basename calendar link to today renders a href calendar today serving the same build from different paths note this feature is available with react scripts 0 9 0 and higher if you are not using the html5 pushstate history api or not using client side routing at all it is unnecessary to specify the url from which your app will be served instead you can put this in your package json js homepage this will make sure that all the asset paths are relative to index html you will then be able to move your app from http mywebsite com to http mywebsite com relativepath or even http mywebsite com relative path without having to rebuild it customizing environment variables for arbitrary build environments you can create an arbitrary build environment by creating a custom env file and loading it using env cmd https www npmjs com package env cmd for example to create a build environment for a staging environment 1 create a file called env staging 1 set environment variables as you would any other env file e g react app api url http api staging example com 1 install env cmd https www npmjs com package env cmd sh npm install env cmd save or yarn add env cmd 1 add a new script to your package json building with your new environment json scripts build staging env cmd env staging npm run build now you can run npm run build staging to build with the staging environment config you can specify other environments in the same way variables in env production will be used as fallback because node env will always be set to production for a build azure https azure microsoft com see this https medium com to pe deploying create react app on microsoft azure c0f6686a4321 blog post on how to deploy your react app to microsoft azure see this https medium com strid host create react app on azure 986bc40d5bf2 pycfnafbg blog post or this https github com ulrikaugustsson azure appservice static repo for a way to use automatic deployment to azure app service firebase https firebase google com install the firebase cli if you haven t already by running npm install g firebase tools sign up for a firebase account https console firebase google com and create a new project run firebase login and login with your previous created firebase account then run the firebase init command from your project s root you need to choose the hosting configure and deploy firebase hosting sites and choose the firebase project you created in the previous step you will need to agree with database rules json being created choose build as the public directory and also agree to configure as a single page app by replying with y sh project setup first let s associate this project directory with a firebase project you can create multiple project aliases by running firebase use add but for now we ll just set up a default project what firebase project do you want to associate as default example app example app fd690 database setup firebase realtime database rules allow you to define how your data should be structured and when your data can be read from and written to what file should be used for database rules database rules json database rules for example app fd690 have been downloaded to database rules json future modifications to database rules json will update database rules when you run firebase deploy hosting setup your public directory is the folder relative to your project directory that will contain hosting assets to uploaded with firebase deploy if you have a build process for your assets use your build s output directory what do you want to use as your public directory build configure as a single page app rewrite all urls to index html yes wrote build index html i writing configuration info to firebase json i writing project information to firebaserc firebase initialization complete important you need to set proper http caching headers for service worker js file in firebase json file or you will not be able to see changes after first deployment issue 2440 https github com facebook create react app issues 2440 it should be added inside hosting key like next hosting headers source service worker js headers key cache control value no cache now after you create a production build with npm run build you can deploy it by running firebase deploy sh deploying to example app fd690 i deploying database hosting database rules ready to deploy i hosting preparing build directory for upload uploading 75 hosting build folder uploaded successfully hosting 8 files uploaded successfully i starting release process may take several minutes deploy complete project console https console firebase google com project example app fd690 overview hosting url https example app fd690 firebaseapp com for more information see add firebase to your javascript project https firebase google com docs web setup github pages https pages github com note this feature is available with react scripts 0 2 0 and higher step 1 add homepage to package json the step below is important br if you skip it your app will not deploy correctly open your package json and add a homepage field for your project json homepage https myusername github io my app or for a github user page json homepage https myusername github io or for a custom domain page json homepage https mywebsite com create react app uses the homepage field to determine the root url in the built html file step 2 install gh pages and add deploy to scripts in package json now whenever you run npm run build you will see a cheat sheet with instructions on how to deploy to github pages to publish it at https myusername github io my app https myusername github io my app run sh npm install save gh pages alternatively you may use yarn sh yarn add gh pages add the following scripts in your package json diff scripts predeploy npm run build deploy gh pages d build start react scripts start build react scripts build the predeploy script will run automatically before deploy is run if you are deploying to a github user page instead of a project page you ll need to make two additional modifications 1 first change your repository s source branch to be any branch other than master 1 additionally tweak your package json scripts to push deployments to master diff scripts predeploy npm run build deploy gh pages d build deploy gh pages b master d build step 3 deploy the site by running npm run deploy then run sh npm run deploy step 4 ensure your project s settings use gh pages finally make sure github pages option in your github project settings is set to use the gh pages branch img src http i imgur com hujer9l png width 500 alt gh pages branch setting step 5 optionally configure the domain you can configure a custom domain with github pages by adding a cname file to the public folder your cname file should look like this mywebsite com notes on client side routing github pages doesn t support routers that use the html5 pushstate history api under the hood for example react router using browserhistory this is because when there is a fresh page load for a url like http user github io todomvc todos 42 where todos 42 is a frontend route the github pages server returns 404 because it knows nothing of todos 42 if you want to add a router to a project hosted on github pages here are a couple of solutions you could switch from using html5 history api to routing with hashes if you use react router you can switch to hashhistory for this effect but the url will be longer and more verbose for example http user github io todomvc todos 42 k yknaj read more https reacttraining com react router web api router about different history implementations in react router alternatively you can use a trick to teach github pages to handle 404 by redirecting to your index html page with a special redirect parameter you would need to add a 404 html file with the redirection code to the build folder before deploying your project and you ll need to add code handling the redirect parameter to index html you can find a detailed explanation of this technique in this guide https github com rafrex spa github pages troubleshooting dev tty no such a device or address if when deploying you get dev tty no such a device or address or a similar error try the following 1 create a new personal access token https github com settings tokens 2 git remote set url origin https user token github com user repo 3 try npm run deploy again cannot read property email of null if when deploying you get cannot read property email of null try the following 1 git config global user name your name 2 git config global user email your email 3 try npm run deploy again heroku https www heroku com use the heroku buildpack for create react app https github com mars create react app buildpack br you can find instructions in deploying react with zero configuration https blog heroku com deploying react with zero configuration resolving heroku deployment errors sometimes npm run build works locally but fails during deploy via heroku following are the most common cases module not found error cannot resolve file or directory if you get something like this remote failed to create a production build reason remote module not found error cannot resolve file or directory mydirectory in tmp build 1234 src it means you need to ensure that the lettercase of the file or directory you import matches the one you see on your filesystem or on github this is important because linux the operating system used by heroku is case sensitive so mydirectory and mydirectory are two distinct directories and thus even though the project builds locally the difference in case breaks the import statements on heroku remotes could not find a required file if you exclude or ignore necessary files from the package you will see a error similar this one remote could not find a required file remote name index html remote searched in tmp build a2875fc163b209225122d68916f1d4df public remote remote npm err linux 3 13 0 105 generic remote npm err argv tmp build a2875fc163b209225122d68916f1d4df heroku node bin node tmp build a2875fc163b209225122d68916f1d4df heroku node bin npm run build in this case ensure that the file is there with the proper lettercase and that s not ignored on your local gitignore or gitignore global netlify https www netlify com to do a manual deploy to netlify s cdn sh npm install netlify cli g netlify deploy choose build as the path to deploy to setup continuous delivery with this setup netlify will build and deploy when you push to git or open a pull request 1 start a new netlify project https app netlify com signup 2 pick your git hosting service and select your repository 3 click build your site support for client side routing to support pushstate make sure to create a public redirects file with the following rewrite rules index html 200 when you build the project create react app will place the public folder contents into the build output now https zeit co now now offers a zero configuration single command deployment you can use now to deploy your app for free 1 install the now command line tool either via the recommended desktop tool https zeit co download or via node with npm install g now 2 build your app by running npm run build 3 move into the build directory by running cd build 4 run now name your project name from within the build directory you will see a now sh url in your output like this ready https your project name tpspyhtdtk now sh copied to clipboard paste that url into your browser when the build is complete and you will see your deployed app details are available in this article https zeit co blog unlimited static s3 https aws amazon com s3 and cloudfront https aws amazon com cloudfront see this blog post https medium com omgwtfmarc deploying create react app to s3 or cloudfront 48dae4ce0af on how to deploy your react app to amazon web services s3 and cloudfront surge https surge sh install the surge cli if you haven t already by running npm install g surge run the surge command and log in you or create a new account when asked about the project path make sure to specify the build folder for example sh project path path to project build note that in order to support routers that use html5 pushstate api you may want to rename the index html in your build folder to 200 html before deploying to surge this ensures that every url falls back to that file https surge sh help adding a 200 page for client side routing advanced configuration you can adjust various development and production settings by setting environment variables in your shell or with env adding development environment variables in env variable development production usage browser white check mark x by default create react app will open the default system browser favoring chrome on macos specify a browser https github com sindresorhus opn app to override this behavior or set it to none to disable it completely if you need to customize the way the browser is launched you can specify a node script instead any arguments passed to npm start will also be passed to this script and the url where your app is served will be the last argument your script s file name must have the js extension host white check mark x by default the development web server binds to localhost you may use this variable to specify a different host port white check mark x by default the development web server will attempt to listen on port 3000 or prompt you to attempt the next available port you may use this variable to specify a different port https white check mark x when set to true create react app will run the development server in https mode public url x white check mark create react app assumes your application is hosted at the serving web server s root or a subpath as specified in package json homepage building for relative paths normally create react app ignores the hostname you may use this variable to force assets to be referenced verbatim to the url you provide hostname included this may be particularly useful when using a cdn to host your application ci large orange diamond white check mark when set to true create react app treats warnings as failures in the build it also makes the test runner non watching most cis set this flag by default react editor white check mark x when an app crashes in development you will see an error overlay with clickable stack trace when you click on it create react app will try to determine the editor you are using based on currently running processes and open the relevant source file you can send a pull request to detect your editor of choice https github com facebook create react app issues 2636 setting this environment variable overrides the automatic detection if you do it make sure your systems path https en wikipedia org wiki path variable environment variable points to your editor s bin folder you can also set it to none to disable it completely chokidar usepolling white check mark x when set to true the watcher runs in polling mode as necessary inside a vm use this option if npm start isn t detecting changes generate sourcemap x white check mark when set to false source maps are not generated for a production build this solves oom issues on some smaller machines node path white check mark white check mark same as node path in node js https nodejs org api modules html modules loading from the global folders but only relative folders are allowed can be handy for emulating a monorepo setup by setting node path src troubleshooting npm start doesn t detect changes when you save a file while npm start is running the browser should refresh with the updated code br if this doesn t happen try one of the following workarounds if your project is in a dropbox folder try moving it out if the watcher doesn t see a file called index js and you re referencing it by the folder name you need to restart the watcher https github com facebook create react app issues 1164 due to a webpack bug some editors like vim and intellij have a safe write feature that currently breaks the watcher you will need to disable it follow the instructions in adjusting your text editor https webpack js org guides development adjusting your text editor if your project path contains parentheses try moving the project to a path without them this is caused by a webpack watcher bug https github com webpack watchpack issues 42 on linux and macos you might need to tweak system settings https github com webpack docs wiki troubleshooting not enough watchers to allow more watchers if the project runs inside a virtual machine such as a vagrant provisioned virtualbox create an env file in your project directory if it doesn t exist and add chokidar usepolling true to it this ensures that the next time you run npm start the watcher uses the polling mode as necessary inside a vm if none of these solutions help please leave a comment in this thread https github com facebook create react app issues 659 npm test hangs or crashes on macos sierra if you run npm test and the console gets stuck after printing react scripts test to the console there might be a problem with your watchman https facebook github io watchman installation as described in facebook create react app 713 https github com facebook create react app issues 713 we recommend deleting node modules in your project and running npm install or yarn if you use it first if it doesn t help you can try one of the numerous workarounds mentioned in these issues facebook jest 1767 https github com facebook jest issues 1767 facebook watchman 358 https github com facebook watchman issues 358 ember cli ember cli 6259 https github com ember cli ember cli issues 6259 it is reported that installing watchman 4 7 0 or newer fixes the issue if you use homebrew http brew sh you can run these commands to update it watchman shutdown server brew update brew reinstall watchman you can find other installation methods https facebook github io watchman docs install html build install on the watchman documentation page if this still doesn t help try running launchctl unload f library launchagents com github facebook watchman plist there are also reports that uninstalling watchman fixes the issue so if nothing else helps remove it from your system and try again npm run build exits too early it is reported that npm run build can fail on machines with limited memory and no swap space which is common in cloud environments even with small projects this command can increase ram usage in your system by hundreds of megabytes so if you have less than 1 gb of available memory your build is likely to fail with the following message the build failed because the process exited too early this probably means the system ran out of memory or someone called kill 9 on the process if you are completely sure that you didn t terminate the process consider adding some swap space https www digitalocean com community tutorials how to add swap on ubuntu 14 04 to the machine you re building on or build the project locally npm run build fails on heroku this may be a problem with case sensitive filenames please refer to this section resolving heroku deployment errors moment js locales are missing if you use a moment js https momentjs com you might notice that only the english locale is available by default this is because the locale files are large and you probably only need a subset of all the locales provided by moment js https momentjs com multiple locale support to add a specific moment js locale to your bundle you need to import it explicitly br for example js import moment from moment import moment locale fr if you are importing multiple locales this way you can later switch between them by calling moment locale with the locale name js import moment from moment import moment locale fr import moment locale es moment locale fr this will only work for locales that have been explicitly imported before npm run build fails to minify before react scripts 2 0 0 this problem was caused by third party node modules using modern javascript features because the minifier couldn t handle them during the build this has been solved by compiling standard modern javascript features inside node modules in react scripts 2 0 0 and higher if you re seeing this error you re likely using an old version of react scripts you can either fix it by avoiding a dependency that uses modern syntax or by upgrading to react scripts 2 0 0 and following the migration instructions in the changelog alternatives to ejecting ejecting npm run eject lets you customize anything but from that point on you have to maintain the configuration and scripts yourself this can be daunting if you have many similar projects in such cases instead of ejecting we recommend to fork react scripts and any other packages you need this article https auth0 com blog how to configure create react app dives into how to do it in depth you can find more discussion in this issue https github com facebook create react app issues 682 something missing if you have ideas for more how to recipes that should be on this page let us know https github com facebook create react app issues or contribute some https github com facebook create react app edit master packages react scripts template readme md | front_end |
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blockchainSecurityDB | blockchain security db blockchain security db is an open source database created by consensys diligence for all things blockchain security the database contains a catalog of blockchain projects with details pertaining to their security including audits bounties and security contacts click on the name of the project in the project column to see more details about a project to add or update a project see this guide https github com consensys blockchainsecuritydb add or update a project setup pip install mkdocs mkdocs serve visit localhost 8000 add or update a project in the projects directory create a json file named after your project note check to see if your project already exists before creating a new one using the following template fill in project data project project name project url project url description description of your project bounty bounty program url bounty max max bounty payout security contact security contact email audits repos url url of the repo pertaining to audit title audit title date mm yy auditor name of auditor url audit url effort person weeks effort of audit run npm run generate markdown | blockchain |
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code-quality-assurance | code quality assurance introduction purpose implemented and integrated a recipe app website using java and spring boot as the backend the main focus of the project was to write maintainable code using best backend practices like solid principles design patterns refactoring techniques test driven development continuous integration jenkins github and azure as a medium for providing continuous integration we created two branches on github namely develop test from production master default branch and mapped azure environment with these branches namely dev environment test environment and production environment further we mapped the environment with jenkins we had three setups on jenkins such that whenever there is a change in develop branch of the project jenkins would pick up the change and run it by checking the unit test cases against the code if everything was successful jenkins deploys the application to azure link the complete report can be accessed here https github com navneetprakashsingh code quality assurance blob master group3 report pdf | server |
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applied-ml | applied ml curated papers articles and blogs on data science machine learning in production contributions welcome https img shields io badge contributions welcome brightgreen svg style flat contributing md summaries https img shields io badge summaries in 20tweets 2300acee svg style flat https twitter com eugeneyan status 1350509546133811200 hitcount http hits dwyl com eugeneyan applied ml svg figuring out how to implement your ml project learn how other organizations did it how the problem is framed e g personalization as recsys vs search vs sequences what machine learning techniques worked and sometimes what didn t why it works the science behind it with research literature and references what real world results were achieved so you can better assess roi p s want a summary of ml advancements ml surveys https github com eugeneyan ml surveys p p s looking for guides and interviews on applying ml applyingml https applyingml com table of contents 1 data quality data quality 2 data engineering data engineering 3 data discovery data discovery 4 feature stores feature stores 5 classification classification 6 regression regression 7 forecasting forecasting 8 recommendation recommendation 9 search ranking search ranking 10 embeddings embeddings 11 natural language processing natural language processing 12 sequence modelling sequence modelling 13 computer vision computer vision 14 reinforcement learning reinforcement learning 15 anomaly detection anomaly detection 16 graph graph 17 optimization optimization 18 information extraction information extraction 19 weak supervision weak supervision 20 generation generation 21 audio audio 22 privacy preserving machine learning privacy preserving machine learning 23 validation and a b testing validation and ab testing 24 model management model management 25 efficiency efficiency 26 ethics ethics 27 infra infra 28 mlops platforms mlops platforms 29 practices practices 30 team structure team structure 31 fails fails data quality 1 reliable and scalable data ingestion at airbnb https www slideshare net hadoopsummit reliable and scalable data ingestion at airbnb 63920989 airbnb 2016 2 monitoring data quality at scale with statistical modeling https eng uber com monitoring data quality at scale uber 2017 3 data management challenges in production machine learning https research google pubs pub46178 paper https thodrek github io cs839 spring18 papers p1723 polyzotis pdf google 2017 4 automating large scale data quality verification https www amazon science publications automating large scale data quality verification paper https assets amazon science a6 88 ad858ee240c38c6e9dce128250c0 automating large scale data quality verification pdf amazon 2018 5 meet hodor gojek s upstream data quality tool https www gojek io blog meet hodor gojeks upstream data quality tool gojek 2019 6 data validation for machine learning https research google pubs pub47967 paper https mlsys org conferences 2019 doc 2019 167 pdf google 2019 6 an approach to data quality for netflix personalization systems https www youtube com watch v t7vhpa39txm netflix 2020 7 improving accuracy by certainty estimation of human decisions labels and raters https research fb com blog 2020 08 improving the accuracy of community standards enforcement by certainty estimation of human decisions paper https research fb com wp content uploads 2020 08 clara confidence of labels and raters pdf facebook 2020 data engineering 1 zipline airbnb s machine learning data management platform https www youtube com watch v tg5vemesc 0 airbnb 2018 2 sputnik airbnb s apache spark framework for data engineering https www youtube com watch v bqumogsbsuw airbnb 2020 3 unbundling data science workflows with metaflow and aws step functions https netflixtechblog com unbundling data science workflows with metaflow and aws step functions d454780c6280 netflix 2020 4 how doordash is scaling its data platform to delight customers and meet growing demand https doordash engineering 2020 09 25 how doordash is scaling its data platform doordash 2020 5 revolutionizing money movements at scale with strong data consistency https eng uber com money scale strong data uber 2020 6 zipline a declarative feature engineering framework https www youtube com watch v ljckcm0g oy airbnb 2020 7 automating data protection at scale part 1 https medium com airbnb engineering automating data protection at scale part 1 c74909328e08 part 2 https medium com airbnb engineering automating data protection at scale part 2 c2b8d2068216 airbnb 2021 8 real time data infrastructure at uber https arxiv org pdf 2104 00087 pdf uber 2021 9 introducing fabricator a declarative feature engineering framework https doordash engineering 2022 01 11 introducing fabricator a declarative feature engineering framework doordash 2022 10 functions dags introducing hamilton a microframework for dataframe generation https multithreaded stitchfix com blog 2021 10 14 functions dags hamilton stitch fix 2021 11 optimizing pinterest s data ingestion stack findings and learnings https medium com pinterest engineering optimizing pinterests data ingestion stack findings and learnings 994fddb063bf pinterest 2022 12 lessons learned from running apache airflow at scale https shopifyengineering myshopify com blogs engineering lessons learned apache airflow scale shopify 2022 13 understanding data storage and ingestion for large scale deep recommendation model training https arxiv org abs 2108 09373v4 meta 2022 14 data mesh a data movement and processing platform netflix https netflixtechblog com data mesh a data movement and processing platform netflix 1288bcab2873 netflix 2022 15 building scalable real time event processing with kafka and flink https doordash engineering 2022 08 02 building scalable real time event processing with kafka and flink doordash 2022 data discovery 1 apache atlas data goverance and metadata framework for hadoop https atlas apache org code https github com apache atlas apache 2 collect aggregate and visualize a data ecosystem s metadata https marquezproject github io marquez code https github com marquezproject marquez wework 3 discovery and consumption of analytics data at twitter https blog twitter com engineering en us topics insights 2016 discovery and consumption of analytics data at twitter html twitter 2016 4 democratizing data at airbnb https medium com airbnb engineering democratizing data at airbnb 852d76c51770 airbnb 2017 5 databook turning big data into knowledge with metadata at uber https eng uber com databook uber 2018 6 metacat making big data discoverable and meaningful at netflix https netflixtechblog com metacat making big data discoverable and meaningful at netflix 56fb36a53520 code https github com netflix metacat netflix 2018 7 amundsen lyft s data discovery metadata engine https eng lyft com amundsen lyfts data discovery metadata engine 62d27254fbb9 lyft 2019 8 open sourcing amundsen a data discovery and metadata platform https eng lyft com open sourcing amundsen a data discovery and metadata platform 2282bb436234 code https github com lyft amundsen lyft 2019 9 datahub a generalized metadata search discovery tool https engineering linkedin com blog 2019 data hub code https github com linkedin datahub linkedin 2019 10 amundsen one year later https eng lyft com amundsen 1 year later 7b60bf28602 lyft 2020 11 using amundsen to support user privacy via metadata collection at square https developer squareup com blog using amundsen to support user privacy via metadata collection at square square 2020 12 turning metadata into insights with databook https eng uber com metadata insights databook uber 2020 13 datahub popular metadata architectures explained https engineering linkedin com blog 2020 datahub popular metadata architectures explained linkedin 2020 14 how we improved data discovery for data scientists at spotify https engineering atspotify com 2020 02 27 how we improved data discovery for data scientists at spotify spotify 2020 15 how we re solving data discovery challenges at shopify https engineering shopify com blogs engineering solving data discovery challenges shopify shopify 2020 16 nemo data discovery at facebook https engineering fb com data infrastructure nemo facebook 2020 17 exploring data netflix https netflixtechblog com exploring data netflix 9d87e20072e3 code https github com netflix nf data explorer netflix 2021 feature stores 1 distributed time travel for feature generation https netflixtechblog com distributed time travel for feature generation 389cccdd3907 netflix 2016 2 building the activity graph part 2 feature storage section https engineering linkedin com blog 2017 07 building the activity graph part 2 linkedin 2017 3 fact store at scale for netflix recommendations https www youtube com watch v diwkg8kynvu netflix 2018 4 zipline airbnb s machine learning data management platform https www youtube com watch v tg5vemesc 0 airbnb 2018 5 feature store the missing data layer for machine learning pipelines https www hopsworks ai post feature store the missing data layer in ml pipelines hopsworks 2018 6 introducing feast an open source feature store for machine learning https cloud google com blog products ai machine learning introducing feast an open source feature store for machine learning code https github com feast dev feast gojek 2019 7 michelangelo palette a feature engineering platform at uber https www infoq com presentations michelangelo palette uber uber 2019 8 the architecture that powers twitter s feature store https www youtube com watch v unailxoiiry twitter 2019 9 accelerating machine learning with the feature store service https technology condenast com story accelerating machine learning with the feature store service cond nast 2019 10 feast bridging ml models and data https www gojek io blog feast bridging ml models and data gojek 2020 11 building a scalable ml feature store with redis binary serialization and compression https doordash engineering 2020 11 19 building a gigascale ml feature store with redis doordash 2020 12 rapid experimentation through standardization typed ai features for linkedin s feed https engineering linkedin com blog 2020 feed typed ai features linkedin 2020 13 building a feature store https nlathia github io 2020 12 building a feature store html monzo bank 2020 14 butterfree a spark based framework for feature store building https medium com quintoandar tech blog butterfree a spark based framework for feature store building 48c3640522c7 code https github com quintoandar butterfree quintoandar 2020 15 building riviera a declarative real time feature engineering framework https doordash engineering 2021 03 04 building a declarative real time feature engineering framework doordash 2021 16 optimal feature discovery better leaner machine learning models through information theory https eng uber com optimal feature discovery ml uber 2021 17 ml feature serving infrastructure at lyft https eng lyft com ml feature serving infrastructure at lyft d30bf2d3c32a lyft 2021 18 near real time features for near real time personalization https engineering linkedin com blog 2022 near real time features for near real time personalization linkedin 2022 19 building the model behind doordash s expansive merchant selection https doordash engineering 2022 04 19 building merchant selection doordash 2022 20 open sourcing feathr linkedin s feature store for productive machine learning https engineering linkedin com blog 2022 open sourcing feathr linkedin s feature store for productive m linkedin 2022 21 evolution of ml fact store https netflixtechblog com evolution of ml fact store 5941d3231762 netflix 2022 22 developing scalable feature engineering dags https outerbounds com blog developing scalable feature engineering dags metaflow hamilton via outerbounds 2022 23 feature store design at constructor https medium com constructor engineering feature store design at constructor 330b65f64b18 constructor io 2023 classification 1 prediction of advertiser churn for google adwords https research google pubs pub36678 paper https storage googleapis com pub tools public publication data pdf 36678 pdf google 2010 2 high precision phrase based document classification on a modern scale https engineering linkedin com research 2011 high precision phrase based document classification on a modern scale paper http web stanford edu gavish documents phrase based pdf linkedin 2011 3 chimera large scale classification using machine learning rules and crowdsourcing https dl acm org doi 10 14778 2733004 2733024 paper http pages cs wisc edu 7eanhai papers chimera vldb14 pdf walmart 2014 4 large scale item categorization in e commerce using multiple recurrent neural networks https www kdd org kdd2016 subtopic view large scale item categorization in e commerce using multiple recurrent neur paper https www kdd org kdd2016 papers files adf0392 haaemb pdf naver 2016 5 learning to diagnose with lstm recurrent neural networks https arxiv org abs 1511 03677 paper https arxiv org pdf 1511 03677 pdf google 2017 6 discovering and classifying in app message intent at airbnb https medium com airbnb engineering discovering and classifying in app message intent at airbnb 6a55f5400a0c airbnb 2019 7 teaching machines to triage firefox bugs https hacks mozilla org 2019 04 teaching machines to triage firefox bugs mozilla 2019 8 categorizing products at scale https engineering shopify com blogs engineering categorizing products at scale shopify 2020 9 how we built the good first issues feature https github blog 2020 01 22 how we built good first issues github 2020 10 testing firefox more efficiently with machine learning https hacks mozilla org 2020 07 testing firefox more efficiently with machine learning mozilla 2020 11 using ml to subtype patients receiving digital mental health interventions https www microsoft com en us research blog a path to personalization using ml to subtype patients receiving digital mental health interventions paper https jamanetwork com journals jamanetworkopen fullarticle 2768347 microsoft 2020 12 scalable data classification for security and privacy https engineering fb com security data classification system paper https arxiv org pdf 2006 14109 pdf facebook 2020 13 uncovering online delivery menu best practices with machine learning https doordash engineering 2020 11 10 uncovering online delivery menu best practices with machine learning doordash 2020 14 using a human in the loop to overcome the cold start problem in menu item tagging https doordash engineering 2020 08 28 overcome the cold start problem in menu item tagging doordash 2020 15 deep learning product categorization and shelving https medium com walmartglobaltech deep learning product categorization and shelving 630571e81e96 walmart 2021 16 large scale item categorization for e commerce https dl acm org doi 10 1145 2396761 2396838 paper https www 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requests html twitter 2020 3 open sourcing riskquant a library for quantifying risk https netflixtechblog com open sourcing riskquant a library for quantifying risk 6720cc1e4968 code https github com netflix skunkworks riskquant netflix 2020 4 solving for unobserved data in a regression model using a simple data adjustment https doordash engineering 2020 10 14 solving for unobserved data in a regression model doordash 2020 forecasting 1 engineering extreme event forecasting at uber with rnn https eng uber com neural networks uber 2017 2 forecasting at uber an introduction https eng uber com forecasting introduction uber 2018 3 transforming financial forecasting with data science and machine learning at uber https eng uber com transforming financial forecasting machine learning uber 2018 4 under the hood of gojek s automated forecasting tool https www gojek io blog under the hood of gojeks automated forecasting tool gojek 2019 5 bustr predicting bus travel times from real time traffic 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recommendations https static googleusercontent com media research google com en pubs archive 45530 pdf youtube 2016 9 e commerce in your inbox product recommendations at scale https arxiv org abs 1606 07154 paper https arxiv org pdf 1606 07154 pdf yahoo 2016 10 to be continued helping you find shows to continue watching on netflix https netflixtechblog com to be continued helping you find shows to continue watching on 7c0d8ee4dab6 netflix 2016 11 personalized recommendations in linkedin learning https engineering linkedin com blog 2016 12 personalized recommendations in linkedin learning linkedin 2016 12 personalized channel recommendations in slack https slack engineering personalized channel recommendations in slack slack 2016 13 recommending complementary products in e commerce push notifications https arxiv org abs 1707 08113 paper https arxiv org pdf 1707 08113 pdf alibaba 2017 14 artwork personalization at netflix https netflixtechblog com artwork personalization c589f074ad76 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discovery with uber eats recommending for the marketplace https eng uber com uber eats recommending marketplace uber 2018 20 explore exploit and explain personalizing explainable recommendations with bandits https dl acm org doi 10 1145 3240323 3240354 paper https static1 squarespace com static 5ae0d0b48ab7227d232c2bea t 5ba849e3c83025fa56814f45 1537755637453 bartrecsys pdf spotify 2018 21 talent search and recommendation systems at linkedin practical challenges and lessons learned https arxiv org abs 1809 06481 paper https arxiv org pdf 1809 06481 pdf linkedin 2018 21 behavior sequence transformer for e commerce recommendation in alibaba https arxiv org abs 1905 06874 paper https arxiv org pdf 1905 06874 pdf alibaba 2019 22 sdm sequential deep matching model for online large scale recommender system https arxiv org abs 1909 00385 paper https arxiv org pdf 1909 00385 pdf alibaba 2019 23 multi interest network with dynamic routing for recommendation at tmall https arxiv org abs 1904 08030 paper https arxiv org pdf 1904 08030 pdf alibaba 2019 24 personalized recommendations for experiences using deep learning https www tripadvisor com engineering personalized recommendations for experiences using deep learning tripadvisor 2019 25 powered by ai instagram s explore recommender system https ai facebook com blog powered by ai instagrams explore recommender system facebook 2019 26 marginal posterior sampling for slate bandits https www ijcai org proceedings 2019 308 paper https www ijcai org proceedings 2019 0308 pdf netflix 2019 27 food discovery with uber eats using graph learning to power recommendations https eng uber com uber eats graph learning uber 2019 28 music recommendation at spotify http sigir org afirm2019 slides 16 20friday 20 20music 20recommendation 20at 20spotify 20 20ben 20carterette pdf spotify 2019 29 using machine learning to predict what file you need next part 1 https dropbox tech machine learning content suggestions machine learning dropbox 2019 30 using machine learning to predict what file you need next part 2 https dropbox tech machine learning using machine learning to predict what file you need next part 2 dropbox 2019 31 learning to be relevant evolution of a course recommendation system https dl acm org doi pdf 10 1145 3357384 3357817 paper needed linkedin 2019 32 temporal contextual recommendation in real time https www amazon science publications temporal contextual recommendation in real time paper https assets amazon science 96 71 d1f25754497681133c7aa2b7eb05 temporal contextual recommendation in real time pdf amazon 2020 33 p companion a framework for diversified complementary product recommendation https www amazon science publications p companion a principled framework for diversified complementary product recommendation paper https assets amazon science d5 16 3f7809974a899a11bacdadefdf24 p companion a principled framework for diversified complementary product recommendation pdf amazon 2020 34 deep interest with 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only personalizing spotify home with machine learning https engineering atspotify com 2020 01 16 for your ears only personalizing spotify home with machine learning spotify 2020 41 reach for the top how spotify built shortcuts in just six months https engineering atspotify com 2020 04 15 reach for the top how spotify built shortcuts in just six months spotify 2020 42 contextual and sequential user embeddings for large scale music recommendation https dl acm org doi 10 1145 3383313 3412248 paper https dl acm org doi pdf 10 1145 3383313 3412248 spotify 2020 43 the evolution of kit automating marketing using machine learning https engineering shopify com blogs engineering evolution kit automating marketing machine learning shopify 2020 44 a closer look at the ai behind course recommendations on linkedin learning part 1 https engineering linkedin com blog 2020 course recommendations ai part one linkedin 2020 45 a closer look at the ai behind course recommendations on linkedin learning part 2 https engineering linkedin com blog 2020 course recommendations ai part two linkedin 2020 46 building a heterogeneous social network recommendation system https engineering linkedin com blog 2020 building a heterogeneous social network recommendation system linkedin 2020 47 how tiktok recommends videos foryou https newsroom tiktok com en us how tiktok recommends videos for you bytedance 2020 48 zero shot heterogeneous transfer learning from recsys to cold start search retrieval https arxiv org abs 2008 02930 paper https arxiv org pdf 2008 02930 pdf google 2020 49 improved deep cross network for feature cross learning in web scale ltr systems https arxiv org abs 2008 13535 paper https arxiv org pdf 2008 13535 pdf google 2020 50 mixed negative sampling for learning two tower neural networks in recommendations https research google pubs pub50257 paper https storage googleapis com pub tools public publication data pdf b9f4e78a8830fe5afcf2f0452862fb3c0d6584ea pdf google 2020 51 future data helps training modeling future contexts for session based recommendation https arxiv org pdf 1906 04473 pdf paper https arxiv org pdf 1906 04473 pdf tencent 2020 52 a case study of session based recommendations in the home improvement domain https dl acm org doi 10 1145 3383313 3412235 paper https dl acm org doi pdf 10 1145 3383313 3412235 home depot 2020 53 balancing relevance and discovery to inspire customers in the ikea app https dl acm org doi 10 1145 3383313 3411550 paper https dl acm org doi pdf 10 1145 3383313 3411550 ikea 2020 54 how we use automl multi task learning and multi tower models for pinterest ads https medium com pinterest engineering how we use automl multi task learning and multi tower models for pinterest ads db966c3dc99e pinterest 2020 55 multi task learning for related products recommendations at pinterest https medium com pinterest engineering multi task learning for related products recommendations at pinterest 62684f631c12 pinterest 2020 56 improving the quality of recommended pins with lightweight ranking https medium com pinterest engineering improving the quality of recommended pins with lightweight ranking 8ff5477b20e3 pinterest 2020 57 multi task learning and calibration for utility based home feed ranking https medium com pinterest engineering multi task learning and calibration for utility based home feed ranking 64087a7bcbad pinterest 2020 57 personalized cuisine filter based on customer preference and local popularity https doordash engineering 2020 01 27 personalized cuisine filter doordash 2020 58 how we built a matchmaking algorithm to cross sell products https www gojek io blog how we built a matchmaking algorithm to cross sell products gojek 2020 59 lessons learned addressing dataset bias in model based candidate generation https arxiv org abs 2105 09293 paper https arxiv org pdf 2105 09293 pdf twitter 2021 60 self supervised learning for large scale item recommendations https arxiv org abs 2007 12865 paper https arxiv org pdf 2007 12865 pdf google 2021 61 deep retrieval end to end learnable structure model for large scale recommendations https arxiv org abs 2007 07203 paper https arxiv org pdf 2007 07203 pdf bytedance 2021 62 using ai to help health experts address the covid 19 pandemic https ai facebook com blog using ai to help health experts address the covid 19 pandemic facebook 2021 63 advertiser recommendation systems at pinterest https medium com pinterest engineering advertiser recommendation systems at pinterest ccb255fbde20 pinterest 2021 64 on youtube s recommendation system https blog youtube inside youtube on youtubes recommendation system youtube 2021 65 are you sure preliminary insights from scaling product comparisons to multiple shops https arxiv org abs 2107 03256 coveo 2021 66 mozrt a deep learning recommendation system empowering walmart store associates https medium com walmartglobaltech mozrt a deep learning recommendation system empowering walmart store associates with a 5d42c08d88da walmart 2021 67 understanding data storage and ingestion for large scale deep recommendation model training https arxiv org abs 2108 09373 paper https arxiv org pdf 2108 09373 pdf meta 2021 67 the amazon music conversational recommender is hitting the right notes https www amazon science latest news how amazon music uses recommendation system machine learning amazon 2022 68 personalized complementary product recommendation https www amazon science publications personalized complementary product recommendation paper https assets amazon science 6c d9 a0ec3eda4f0fb4312ce0ada41771 personalized complementary product recommendation pdf amazon 2022 69 building a deep learning based retrieval system for personalized recommendations https tech ebayinc com engineering building a deep learning based retrieval system for personalized recommendations ebay 2022 70 how we built an early stage machine learning model for recommendations https www onepeloton com press articles how we built machine learning peloton 2022 71 lessons learned from building out context aware recommender systems https www onepeloton com press articles lessons learned from building context aware recommender systems peloton 2022 72 beyond matrix factorization using hybrid features for user business recommendations https engineeringblog yelp com 2022 04 beyond matrix factorization using hybrid features for user business recommendations html yelp 2022 73 improving job matching with machine learned activity features https engineering linkedin com blog 2022 improving job matching with machine learned activity features linkedin 2022 74 understanding data storage and ingestion for large scale deep recommendation model training https arxiv org abs 2108 09373v4 meta 2022 75 blueprints for recommender system architectures 10th anniversary edition https amatriain net blog recsysarchitectures xavier amatriain 2022 76 how pinterest leverages realtime user actions in recommendation to boost homefeed engagement volume https medium com pinterest engineering how pinterest leverages realtime user actions in recommendation to boost homefeed engagement volume 165ae2e8cde8 pinterest 2022 77 recsysops best practices for operating a large scale recommender system https netflixtechblog medium com recsysops best practices for operating a large scale recommender system 95bbe195a841 netflix 2022 78 recommend api unified end to end machine learning infrastructure to generate recommendations https slack engineering recommend api slack 2022 79 evolving doordash s substitution recommendations algorithm https doordash engineering 2022 09 08 evolving doordashs substitution recommendations algorithm doordash 2022 80 homepage recommendation with exploitation and exploration https doordash engineering 2022 10 05 homepage recommendation with exploitation and exploration doordash 2022 81 gpu accelerated ml inference at pinterest https medium com pinterest engineering gpu accelerated ml inference at 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arxiv org abs 1605 04624 paper https arxiv org pdf 1605 04624 pdf linkedin 2016 5 using deep learning at scale in twitter s timelines https blog twitter com engineering en us topics insights 2017 using deep learning at scale in twitters timelines html twitter 2017 6 an ensemble based approach to click through rate prediction for promoted listings at etsy https arxiv org abs 1711 01377 paper https arxiv org pdf 1711 01377 pdf etsy 2017 7 powering search recommendations at doordash https doordash engineering 2017 07 06 powering search recommendations at doordash doordash 2017 8 applying deep learning to airbnb search https arxiv org abs 1810 09591 paper https arxiv org pdf 1810 09591 pdf airbnb 2018 9 in session personalization for talent search https arxiv org abs 1809 06488 paper https arxiv org pdf 1809 06488 pdf linkedin 2018 10 talent search and recommendation systems at linkedin https arxiv org abs 1809 06481 paper https arxiv org pdf 1809 06481 pdf linkedin 2018 11 food discovery with uber eats building a query understanding engine https eng uber com uber eats query understanding uber 2018 12 globally optimized mutual influence aware ranking in e commerce search https arxiv org abs 1805 08524 paper https arxiv org pdf 1805 08524 pdf alibaba 2018 13 reinforcement learning to rank in e commerce search engine https arxiv org abs 1803 00710 paper https arxiv org pdf 1803 00710 pdf alibaba 2018 14 semantic product search https arxiv org abs 1907 00937 paper https arxiv org pdf 1907 00937 pdf amazon 2019 15 machine learning powered search ranking of airbnb experiences https medium com airbnb engineering machine learning powered search ranking of airbnb experiences 110b4b1a0789 airbnb 2019 16 entity personalized talent search models with tree interaction features https arxiv org abs 1902 09041 paper https arxiv org pdf 1902 09041 pdf linkedin 2019 17 the ai behind linkedin recruiter search and recommendation systems https engineering linkedin com blog 2019 04 ai behind linkedin recruiter search and recommendation systems linkedin 2019 18 learning hiring preferences the ai behind linkedin jobs https engineering linkedin com blog 2019 02 learning hiring preferences the ai behind linkedin jobs linkedin 2019 19 the secret sauce behind search personalisation https www gojek io blog the secret sauce behind search personalisation gojek 2019 20 neural code search ml based code search using natural language queries https ai facebook com blog neural code search ml based code search using natural language queries facebook 2019 21 aggregating search results from heterogeneous sources via reinforcement learning https arxiv org abs 1902 08882 paper https arxiv org pdf 1902 08882 pdf alibaba 2019 22 cross domain attention network with wasserstein regularizers for e commerce search https dl acm org doi 10 1145 3357384 3357809 alibaba 2019 23 understanding searches better than ever before https www blog google products search search language understanding bert 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learning for airbnb search https arxiv org abs 2002 05515 paper https arxiv org pdf 2002 05515 pdf airbnb 2020 30 quality matches via personalized ai for hirer and seeker preferences https engineering linkedin com blog 2020 quality matches via personalized ai linkedin 2020 31 understanding dwell time to improve linkedin feed ranking https engineering linkedin com blog 2020 understanding feed dwell time linkedin 2020 32 ads allocation in feed via constrained optimization https dl acm org doi abs 10 1145 3394486 3403391 paper https dl acm org doi pdf 10 1145 3394486 3403391 video https crossminds ai video 5f33697a0576dd25aef288ea linkedin 2020 33 understanding dwell time to improve linkedin feed ranking https engineering linkedin com blog 2020 understanding feed dwell time linkedin 2020 34 ai at scale in bing https blogs bing com search 2020 05 ai at scale in bing microsoft 2020 35 query understanding engine in traveloka universal search https medium com traveloka engineering query understanding engine in traveloka universal search 410ad3895db7 traveloka 2020 36 bayesian product ranking at wayfair https tech wayfair com data science 2020 01 bayesian product ranking at wayfair wayfair 2020 37 cold towards the next generation of pre ranking system https arxiv org abs 2007 16122 paper https arxiv org pdf 2007 16122 pdf alibaba 2020 38 shop the look building a large scale visual shopping system at pinterest https dl acm org doi abs 10 1145 3394486 3403372 paper https dl acm org doi pdf 10 1145 3394486 3403372 video https crossminds ai video 5f3369790576dd25aef288d7 pinterest 2020 39 driving shopping upsells from pinterest search https medium com pinterest engineering driving shopping upsells from pinterest search d06329255402 pinterest 2020 40 gdmix a deep ranking personalization framework https engineering linkedin com blog 2020 gdmix a deep ranking personalization framework code https github com linkedin gdmix linkedin 2020 41 bringing personalized search to etsy https codeascraft com 2020 10 29 bringing personalized search to etsy etsy 2020 42 building a better search engine for semantic scholar https medium com ai2 blog building a better search engine for semantic scholar ea23a0b661e7 allen institute for ai 2020 43 query understanding for natural language enterprise search https arxiv org abs 2012 06238 paper https arxiv org pdf 2012 06238 pdf salesforce 2020 44 things not strings understanding search intent with better recall https doordash engineering 2020 12 15 understanding search intent with better recall doordash 2020 45 query understanding for surfacing under served music content https research atspotify com publications query understanding for surfacing under served music content paper https labtomarket files wordpress com 2020 08 cikm2020 pdf spotify 2020 46 embedding based retrieval in facebook search https arxiv org abs 2006 11632 paper https arxiv org pdf 2006 11632 pdf facebook 2020 47 towards personalized and semantic retrieval for e commerce search via embedding learning https arxiv org abs 2006 02282 paper https arxiv org pdf 2006 02282 pdf jd 2020 48 queen neural query rewriting in e commerce https www amazon science publications queen neural query rewriting in e commerce paper https assets amazon science f9 78 dda8f1e143dba8ca96e43ec487c6 queen neural query rewriting in ecommerce pdf amazon 2021 49 using learning to rank to precisely locate where to deliver packages https www amazon science blog using learning to rank to precisely locate where to deliver packages paper https www amazon science publications getting your package to the right place supervised machine learning for geolocation amazon 2021 50 seasonal relevance in e commerce search https www amazon science publications seasonal relevance in e commerce search paper https assets amazon science ac 5e d47612a846d6bec15738d7c8ab40 seasonal relevance in ecommerce search pdf amazon 2021 51 graph intention network for click through rate prediction in sponsored search https arxiv org abs 2103 16164 paper https arxiv org pdf 2103 16164 pdf alibaba 2021 52 how we built a context specific bidding system for etsy ads https codeascraft com 2021 03 23 how we built a context specific bidding system for etsy ads etsy 2021 53 pre trained language model based ranking in baidu search https arxiv org abs 2105 11108 paper https arxiv org pdf 2105 11108 pdf baidu 2021 54 stitching together spaces for query based recommendations https multithreaded stitchfix com blog 2021 08 13 stitching together spaces for query based recommendations stitch fix 2021 55 deep natural language processing for linkedin search systems https arxiv org abs 2108 08252 paper https arxiv org pdf 2108 08252 pdf linkedin 2021 56 siamese bert based model for web search relevance ranking https arxiv org abs 2112 01810 paper https arxiv org pdf 2112 01810 pdf code https github com seznam dareczech seznam 2021 57 searchsage learning search query representations at pinterest https 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messages linkedin 2017 4 how natural language processing helps linkedin members get support easily https engineering linkedin com blog 2019 04 how natural language processing help support linkedin 2019 5 gmail smart compose real time assisted writing https arxiv org abs 1906 00080 paper https arxiv org pdf 1906 00080 pdf google 2019 6 goal oriented end to end conversational models with profile features in a real world setting https www amazon science publications goal oriented end to end chatbots with profile features in a real world setting paper https assets amazon science 47 03 e0d14dc34d3eb6e0d4ec282067bd goal oriented end to end chatbots with profile features in a real world setting pdf amazon 2019 7 give me jeans not shoes how bert helps us deliver what clients want https multithreaded stitchfix com blog 2019 07 15 give me jeans stitch fix 2019 8 detext a deep nlp framework for intelligent text understanding https engineering linkedin com blog 2020 open sourcing detext code https 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https ai facebook com blog a highly efficient real time text to speech system deployed on cpus facebook 2020 16 deep learning to translate between programming languages https ai facebook com blog deep learning to translate between programming languages paper https arxiv org abs 2006 03511 code https github com facebookresearch transcoder facebook 2020 17 deploying lifelong open domain dialogue learning https arxiv org abs 2008 08076 paper https arxiv org pdf 2008 08076 pdf facebook 2020 18 introducing dynabench rethinking the way we benchmark ai https ai facebook com blog dynabench rethinking ai benchmarking facebook 2020 19 how gojek uses nlp to name pickup locations at scale https www gojek io blog nlp cartobert gojek 2020 20 the state of the art open domain chatbot in chinese and english http research baidu com blog index view id 142 paper https arxiv org pdf 2006 16779 pdf baidu 2020 21 pegasus a state of the art model for abstractive text summarization https ai googleblog com 2020 06 pegasus state of art model for html paper https arxiv org pdf 1912 08777 pdf code https github com google research pegasus google 2020 22 photon a robust cross domain text to sql system https www aclweb org anthology 2020 acl demos 24 paper https www aclweb org anthology 2020 acl demos 24 pdf demo http naturalsql com salesforce 2020 23 gedi a powerful new method for controlling language models https blog einstein ai gedi paper https arxiv org abs 2009 06367 code https github com salesforce gedi salesforce 2020 24 applying topic modeling to improve call center operations https www youtube com watch v kzrr8ojf ei t 2s ricoh 2020 25 widetext a multimodal deep learning framework https medium com airbnb engineering widetext a multimodal deep learning framework 31ce2565880c airbnb 2020 26 dynaboard moving beyond accuracy to holistic model evaluation in nlp https ai facebook com blog dynaboard moving beyond accuracy to holistic model evaluation in nlp code https github com facebookresearch dynalab fbclid iwar3qcv7qk2uxm4s4m0xuoqqo4i2desdy0lzfkxsqchhp 3hf6fr2 ndfwx8 facebook 2021 27 how we reduced our text similarity runtime by 99 96 https medium com data science at microsoft how we reduced our text similarity runtime by 99 96 e8e4b4426b35 microsoft 2021 28 textless nlp generating expressive speech from raw audio https ai facebook com blog textless nlp generating expressive speech from raw audio part 1 https arxiv org abs 2102 01192 part 2 https arxiv org abs 2104 00355 part 3 https arxiv org abs 2109 03264 code and pretrained models https github com pytorch fairseq tree master examples textless nlp facebook 2021 29 grammar correction as you type on pixel 6 https ai googleblog com 2021 10 grammar correction as you type on pixel html google 2021 30 auto generated summaries in google docs https ai googleblog com 2022 03 auto generated summaries in google docs html google 2022 31 ml enhanced code completion improves developer productivity https ai googleblog com 2022 07 ml enhanced code completion improves html google 2022 32 words all the way down conversational sentiment analysis https medium com paypal tech words all the way down conversational sentiment analysis afe0165b84db paypal 2022 sequence modelling 1 doctor ai predicting clinical events via recurrent neural networks https arxiv org abs 1511 05942 paper https arxiv org pdf 1511 05942 pdf sutter health 2015 2 deep learning for understanding consumer histories https engineering zalando com posts 2016 10 deep learning for understanding consumer histories html paper https doogkong github io 2017 papers paper2 pdf zalando 2016 3 using recurrent neural network models for early detection of heart failure onset https www ncbi nlm nih gov pmc articles pmc5391725 paper https www ncbi nlm nih gov pmc articles pmc5391725 pdf ocw112 pdf sutter health 2016 4 continual prediction of notification attendance with classical and deep networks https arxiv org abs 1712 07120 paper https arxiv org pdf 1712 07120 pdf telefonica 2017 5 deep learning for electronic health records https ai googleblog com 2018 05 deep learning for electronic health html paper https www nature com articles s41746 018 0029 1 pdf google 2018 6 practice on long sequential user behavior modeling for click through rate prediction https arxiv org abs 1905 09248 paper https arxiv org pdf 1905 09248 pdf alibaba 2019 7 search based user interest modeling with sequential behavior data for ctr prediction https arxiv org abs 2006 05639 paper https arxiv org pdf 2006 05639 pdf alibaba 2020 8 how duolingo uses ai in every part of its app https venturebeat com 2020 08 18 how duolingo uses ai in every part of its app duolingo 2020 9 leveraging online social interactions for enhancing integrity at facebook https research fb com blog 2020 08 leveraging online social interactions for enhancing integrity at facebook paper https research fb com wp content uploads 2020 08 ties temporal interaction embeddings for enhancing social media integrity at facebook pdf video https crossminds ai video 5f3369780576dd25aef288cf facebook 2020 10 using deep learning to detect abusive sequences of member activity https engineering linkedin com blog 2021 using deep learning to detect abusive sequences of member activi video https exchange scale com public videos using deep learning to detect abusive sequences of member activity on linkedin linkedin 2021 computer vision 1 creating a modern ocr pipeline using computer vision and deep learning https dropbox tech machine learning creating a modern ocr pipeline using computer vision and deep learning dropbox 2017 2 categorizing listing photos at airbnb https medium com airbnb engineering categorizing listing photos at airbnb f9483f3ab7e3 airbnb 2018 3 amenity detection and beyond new frontiers of computer vision at airbnb https medium com airbnb engineering amenity detection and beyond new frontiers of computer vision at airbnb 144a4441b72e airbnb 2019 4 how we improved computer vision metrics by more than 5 only by cleaning labelling errors https deepomatic com en how we improved computer vision metrics by more than 5 percent only by cleaning labelling errors deepomatic 5 making machines recognize and transcribe conversations in meetings using audio and video https www microsoft com en us research blog making machines recognize and transcribe conversations in meetings using audio and video microsoft 2019 6 powered by ai advancing product understanding and building new shopping experiences https ai facebook com blog powered by ai advancing product understanding and building new shopping experiences facebook 2020 7 a neural weather model for eight hour precipitation forecasting https ai googleblog com 2020 03 a neural weather model for eight hour html paper https arxiv org pdf 2003 12140 pdf google 2020 8 machine learning based damage assessment for disaster relief https ai googleblog com 2020 06 machine learning based damage html paper https arxiv org pdf 1910 06444 pdf google 2020 9 repnet counting repetitions in videos https ai googleblog com 2020 06 repnet counting repetitions in videos html paper https openaccess thecvf com content cvpr 2020 papers dwibedi counting out time class agnostic video repetition counting in the cvpr 2020 paper pdf google 2020 10 converting text to images for product discovery https www amazon science blog converting text to images for product discovery paper https assets amazon science 4c 76 5830542547b7a11089ce3af943b4 scipub 972 pdf amazon 2020 11 how disney uses pytorch for animated character recognition https medium com pytorch how disney uses pytorch for animated character recognition a1722a182627 disney 2020 12 image captioning as an assistive technology https www ibm com blogs research 2020 07 image captioning assistive technology video https ivc ischool utexas edu yz9244 vizwiz workshop videos mmteam oral mp4 ibm 2020 13 ai for ag production machine learning for agriculture https medium com pytorch ai for ag production machine learning for agriculture e8cfdb9849a1 blue river 2020 14 ai for full self driving at tesla https youtu be hx7bxih7zx8 t 513 tesla 2020 15 on device supermarket product recognition https ai googleblog com 2020 07 on device supermarket product html google 2020 16 using machine learning to detect deficient coverage in colonoscopy screenings https ai googleblog com 2020 08 using machine learning to detect html paper https ieeexplore ieee org stamp stamp jsp tp arnumber 9097918 google 2020 17 shop the look building a large scale visual shopping system at pinterest https dl acm org doi abs 10 1145 3394486 3403372 paper https dl acm org doi pdf 10 1145 3394486 3403372 video https crossminds ai video 5f3369790576dd25aef288d7 pinterest 2020 18 developing real time automatic sign language detection for video conferencing https ai googleblog com 2020 10 developing real time automatic sign html paper https storage googleapis com pub tools public publication data pdf 2eaf0d18ec6bef00d7dd88f39dd4f9ff13eeeeb2 pdf google 2020 19 vision based price suggestion for online second hand items https arxiv org abs 2012 06009 paper https arxiv org pdf 2012 06009 pdf alibaba 2020 20 new ai research to help predict covid 19 resource needs from x rays https ai facebook com blog new ai research to help predict covid 19 resource needs from a series of x rays paper https arxiv org pdf 2101 04909 pdf model https github com facebookresearch covidprognosis facebook 2021 21 an efficient training approach for very large scale face recognition https arxiv org abs 2105 10375 paper https arxiv org pdf 2105 10375 alibaba 2021 22 identifying document types at scribd https tech scribd com blog 2021 identifying document types html scribd 2021 23 semi supervised visual representation learning for fashion compatibility https arxiv org pdf 2109 08052 pdf paper https arxiv org pdf 2109 08052 pdf walmart 2021 24 recognizing people in photos through private on device machine learning https machinelearning apple com research recognizing people photos apple 2021 25 deepfusion lidar camera deep fusion for multi modal 3d object detection https arxiv org pdf 2203 08195 pdf google 2022 26 contrastive language and vision learning of general fashion concepts https www nature com articles s41598 022 23052 9 paper https www nature com articles s41598 022 23052 9 pdf coveo 2022 27 leveraging computer vision for search ranking https arize com resource bazaarvoice leveraging computer vision models for search ranking bazaarvoice 2023 reinforcement learning 1 deep reinforcement learning for sponsored search real time bidding https arxiv org abs 1803 00259 paper https arxiv org pdf 1803 00259 pdf alibaba 2018 2 budget constrained bidding by model free reinforcement learning in display advertising https arxiv org abs 1802 08365 paper https arxiv org pdf 1802 08365 pdf alibaba 2018 3 reinforcement learning for on demand logistics https doordash engineering 2018 09 10 reinforcement learning for on demand logistics doordash 2018 4 reinforcement learning to rank in e commerce search engine https arxiv org abs 1803 00710 paper https arxiv org pdf 1803 00710 pdf alibaba 2018 5 dynamic pricing on e commerce platform with deep reinforcement learning https arxiv org abs 1912 02572 paper https arxiv org pdf 1912 02572 pdf alibaba 2019 6 productionizing deep reinforcement learning with spark and mlflow https www youtube com watch v hy w69zf4oo zynga 2020 7 deep reinforcement learning in production part1 https towardsdatascience com deep reinforcement learning in production 7e1e63471e2 part 2 https towardsdatascience com deep reinforcement learning in production part 2 personalizing user notifications 812a68ce2355 zynga 2020 8 building ai trading systems https dennybritz com blog ai trading denny britz 2020 9 shifting consumption towards diverse content via reinforcement learning https research atspotify com shifting consumption towards diverse content via reinforcement learning paper https dl acm org doi 10 1145 3437963 3441775 spotify 2022 10 bandits for online calibration an application to content moderation on social media platforms https arxiv org abs 2211 06516 meta 2022 11 how to optimise rankings with cascade bandits https medium com expedia group tech how to optimise rankings with cascade bandits 5d92dfa0f16b expedia 2022 12 selecting the best image for each merchant using exploration and machine learning https doordash engineering 2023 01 04 selecting the best image for each merchant using exploration and machine learning doordash 2023 anomaly detection 1 detecting performance anomalies in external firmware deployments https netflixtechblog com detecting performance anomalies in external firmware deployments ed41b1bfcf46 netflix 2019 2 detecting and preventing abuse on linkedin using isolation forests https engineering linkedin com blog 2019 isolation forest code https github com linkedin isolation forest linkedin 2019 3 deep anomaly detection with spark and tensorflow https databricks com session eu19 deep anomaly detection from research to production leveraging spark and tensorflow hopsworks video https www youtube com watch v tgxvu8dsycq swedbank hopsworks 2019 4 preventing abuse using unsupervised learning https www youtube com watch v sfrrfwynaui linkedin 2020 5 the technology behind fighting harassment on linkedin https engineering linkedin com blog 2020 fighting harassment linkedin 2020 6 uncovering insurance fraud conspiracy with network learning https arxiv org abs 2002 12789 paper https arxiv org pdf 2002 12789 pdf ant financial 2020 7 how does spam protection work on stack exchange https stackoverflow blog 2020 06 25 how does spam protection work on stack exchange stack exchange 2020 8 auto content moderation in c2c e commerce https www usenix org conference opml20 presentation ueta mercari 2020 9 blocking slack invite spam with machine learning https slack engineering blocking slack invite spam with machine learning slack 2020 10 cloudflare bot management machine learning and more https blog cloudflare com cloudflare bot management machine learning and more cloudflare 2020 11 anomalies in oil temperature variations in a tunnel boring machine https www youtube com watch v yv ullhprak sener 2020 12 using anomaly detection to monitor low risk bank customers https www youtube com watch v mexokmm bp4 t 3s rabobank 2020 13 fighting fraud with triplet loss https tech olx com fighting fraud with triplet loss 86e5f79c7a3e olx group 2020 14 facebook is now using ai to sort content for quicker moderation https www theverge com 2020 11 13 21562596 facebook ai moderation alternative https venturebeat com 2020 11 13 facebooks redoubled ai efforts wont stop the spread of harmful content facebook 2020 15 how ai is getting better at detecting hate speech part 1 https ai facebook com blog how ai is getting better at detecting hate speech part 2 https ai facebook com blog heres how were using ai to help detect misinformation part 3 https ai facebook com blog training ai to detect hate speech in the real world part 4 https ai facebook com blog how facebook uses super efficient ai models to detect hate speech facebook 2020 16 using deep learning to detect abusive sequences of member activity https engineering linkedin com blog 2021 using deep learning to detect abusive sequences of member activi video https exchange scale com public videos using deep learning to detect abusive sequences of member activity on linkedin linkedin 2021 17 project radar intelligent early fraud detection system with humans in the loop https eng uber com project radar intelligent early fraud detection uber 2022 18 graph for fraud detection https engineering grab com graph for fraud detection grab 2022 19 bandits for online calibration an application to content moderation on social media platforms https arxiv org abs 2211 06516 meta 2022 20 evolving our machine learning to stop mobile bots https blog cloudflare com machine learning mobile traffic bots cloudflare 2022 21 improving the accuracy of our machine learning waf using data augmentation and sampling https blog cloudflare com data generation and sampling strategies cloudflare 2022 22 machine learning for fraud detection in streaming services https netflixtechblog com machine learning for fraud detection in streaming services b0b4ef3be3f6 netflix 2022 23 pricing at lyft https eng lyft com pricing at lyft 8a4022065f8b lyft 2022 graph 1 building the linkedin knowledge graph https engineering linkedin com blog 2016 10 building the linkedin knowledge graph linkedin 2016 2 scaling knowledge access and retrieval at airbnb https medium com airbnb engineering scaling knowledge access and retrieval at airbnb 665b6ba21e95 airbnb 2018 3 graph convolutional neural networks for web scale recommender systems https arxiv org abs 1806 01973 paper https arxiv org pdf 1806 01973 pdf pinterest 2018 4 food discovery with uber eats using graph learning to power recommendations https eng uber com uber eats graph learning uber 2019 5 aligraph a comprehensive graph neural network platform https arxiv org abs 1902 08730 paper https arxiv org pdf 1902 08730 pdf alibaba 2019 6 contextualizing airbnb by building knowledge graph https medium com airbnb engineering contextualizing airbnb by building knowledge graph b7077e268d5a airbnb 2019 7 retail graph walmart s product knowledge graph https medium com walmartlabs retail graph walmarts product knowledge graph 6ef7357963bc walmart 2020 8 traffic prediction with advanced graph neural networks https deepmind com blog article traffic prediction with advanced graph neural networks deepmind 2020 9 simclusters community based representations for recommendations https dl acm org doi 10 1145 3394486 3403370 paper https dl acm org doi pdf 10 1145 3394486 3403370 video https crossminds ai video 5f3369790576dd25aef288d5 twitter 2020 10 metapaths guided neighbors aggregated network for heterogeneous graph reasoning https arxiv org abs 2103 06474 paper https arxiv org pdf 2103 06474 pdf alibaba 2021 11 graph intention network for click through rate prediction in sponsored search https arxiv org abs 2103 16164 paper https arxiv org pdf 2103 16164 pdf alibaba 2021 12 jel applying end to end neural entity linking in jpmorgan chase https ojs aaai org index php aaai article view 17796 paper https www aaai org aaai21papers iaai 21 dingw pdf jpmorgan chase 2021 13 how aws uses graph neural networks to meet customer needs https www amazon science blog how aws uses graph neural networks to meet customer needs amazon 2022 14 graph for fraud detection https engineering grab com graph for fraud detection grab 2022 optimization 1 matchmaking in lyft line part 1 https eng lyft com matchmaking in lyft line 9c2635fe62c4 part 2 https eng lyft com matchmaking in lyft line 691a1a32a008 part 3 https eng lyft com matchmaking in lyft line part 3 d8f9497c0e51 lyft 2016 2 the data and science behind grabshare carpooling https ieeexplore ieee org document 8259801 part 1 https engineering grab com the data and science behind grabshare part i paper needed grab 2017 3 how trip inferences and machine learning optimize delivery times on uber eats https eng uber com uber eats trip optimization uber 2018 4 next generation optimization for dasher dispatch at doordash https doordash engineering 2020 02 28 next generation optimization for dasher dispatch at doordash doordash 2020 5 optimization of passengers waiting time in elevators using machine learning https www youtube com watch v vxndcc89bcw t 4s thyssen krupp ag 2020 6 think out of the package recommending package types for e commerce shipments https www amazon science publications think out of the package recommending package types for e commerce shipments paper https assets amazon science 0c 6c 9d0986b94bef92d148f0ac0da1ea think out of the package recommending package types for e commerce shipments pdf amazon 2020 7 optimizing doordash s marketing spend with machine learning https doordash engineering 2020 07 31 optimizing marketing spend with ml doordash 2020 8 using learning to rank to precisely locate where to deliver packages https www amazon science blog using learning to rank to precisely locate where to deliver packages paper https assets amazon science 69 8d 2249945a4e10ba8fc758f7523b0c getting your package to the right place supervised machine learning for geolocation pdf amazon 2021 information extraction 1 unsupervised extraction of attributes and their values from product description https www aclweb org anthology i13 1190 paper https www aclweb org anthology i13 1190 pdf rakuten 2013 2 using machine learning to index text from billions of images https dropbox tech machine learning using machine learning to index text from billions of images dropbox 2018 3 extracting structured data from templatic documents https ai googleblog com 2020 06 extracting structured data from html paper https www aclweb org anthology i13 1190 pdf google 2020 4 autoknow self driving knowledge collection for products of thousands of types https www amazon science publications autoknow self driving knowledge collection for products of thousands of types paper https arxiv org pdf 2006 13473 pdf video https crossminds ai video 5f3369730576dd25aef288a6 amazon 2020 5 one shot text labeling using attention and belief propagation for information extraction https arxiv org abs 2009 04153 paper https arxiv org pdf 2009 04153 pdf alibaba 2020 6 information extraction from receipts with graph convolutional networks https nanonets com blog information extraction graph convolutional networks nanonets 2021 weak supervision 1 snorkel drybell a case study in deploying weak supervision at industrial scale https dl acm org doi abs 10 1145 3299869 3314036 paper https dl acm org doi pdf 10 1145 3299869 3314036 google 2019 2 osprey weak supervision of imbalanced extraction problems without code https dl acm org doi abs 10 1145 3329486 3329492 paper https ajratner github io assets papers osprey deem pdf intel 2019 3 overton a data system for monitoring and improving machine learned products https arxiv org abs 1909 05372 paper https arxiv org pdf 1909 05372 pdf apple 2019 4 bootstrapping conversational agents with weak supervision https www aaai org ojs index php aaai article view 5011 paper https arxiv org pdf 1812 06176 pdf ibm 2019 generation 1 better language models and their implications https openai com blog better language models paper https cdn openai com better language models language models are unsupervised multitask learners pdf openai 2019 2 image gpt https openai com blog image gpt paper https cdn openai com papers generative pretraining from pixels v2 pdf code https github com openai image gpt openai 2019 3 language models are few shot learners https arxiv org abs 2005 14165 paper https arxiv org pdf 2005 14165 pdf gpt 3 blog post https openai com blog openai api openai 2020 4 deep learned super resolution for feature film production https graphics pixar com library superresolution paper https graphics pixar com library superresolution paper pdf pixar 2020 5 unit test case generation with transformers https arxiv org pdf 2009 05617 pdf microsoft 2021 audio 1 improving on device speech recognition with voicefilter lite https ai googleblog com 2020 11 improving on device speech recognition html paper https arxiv org pdf 2009 04323 pdf google 2020 2 the machine learning behind hum to search https ai googleblog com 2020 11 the machine learning behind hum to html google 2020 privacy preserving machine learning 1 federated learning collaborative machine learning without centralized training data https ai googleblog com 2017 04 federated learning collaborative html paper https arxiv org pdf 1602 05629 google 2017 2 federated learning with formal differential privacy guarantees https ai googleblog com 2022 02 federated learning with formal html paper https arxiv org pdf 2103 00039 google 2022 3 mpc based machine learning achieving end to end privacy preserving machine learning https research facebook com blog 2022 10 mpc based machine learning achieving end to end privacy preserving machine learning paper https research facebook com file 455681589729383 private computation framework 2 0 white paper pdf facebook 2022 validation and a b testing 1 overlapping experiment infrastructure more better faster experimentation https research google pubs pub36500 paper https storage googleapis com pub tools public publication data pdf 36500 pdf google 2010 2 the reusable holdout preserving validity in adaptive data analysis https ai googleblog com 2015 08 the reusable holdout preserving html paper https science sciencemag org content sci 349 6248 636 full pdf google 2015 3 twitter experimentation technical overview https blog twitter com engineering en us a 2015 twitter experimentation technical overview html twitter 2015 4 it s all a bout testing the netflix experimentation platform https netflixtechblog com its all a bout testing the netflix experimentation platform 4e1ca458c15 netflix 2016 5 building pinterest s a b testing platform https medium com pinterest engineering building pinterests a b testing platform ab4934ace9f4 pinterest 2016 6 experimenting to solve cramming https blog twitter com engineering en us topics insights 2017 experimenting to solve cramming html twitter 2017 7 building an intelligent experimentation platform with uber engineering https eng uber com experimentation platform uber 2017 8 scaling airbnb s experimentation platform https medium com airbnb engineering https medium com jonathan parks scaling erf 23fd17c91166 airbnb 2017 9 meet wasabi an open source a b testing platform https www intuit com blog technology engineering meet wasabi an open source ab testing platform code https github com intuit wasabi intuit 2017 10 analyzing experiment outcomes beyond average treatment effects https eng uber com analyzing experiment outcomes uber 2018 11 under the hood of uber s experimentation platform https eng uber com xp uber 2018 12 constrained bayesian optimization with noisy experiments https research fb com publications constrained bayesian optimization with noisy experiments paper https arxiv org pdf 1706 07094 pdf facebook 2018 13 reliable and scalable feature toggles and a b testing sdk at grab https engineering grab com feature toggles ab testing grab 2018 14 modeling conversion rates and saving millions using kaplan meier and gamma distributions https better engineering modeling conversion rates and saving millions of dollars using kaplan meier and gamma distributions code https github com better convoys better 2019 15 detecting interference an a b test of a b tests https engineering linkedin com blog 2019 06 detecting interference an a b test of a b tests linkedin 2019 16 announcing a new framework for designing optimal experiments with pyro https eng uber com oed pyro release paper https papers nips cc paper 9553 variational bayesian optimal experimental design pdf paper https arxiv org pdf 1911 00294 pdf uber 2020 17 enabling 10x more experiments with traveloka experiment platform https medium com traveloka engineering enabling 10x more experiments with traveloka experiment platform 8cea13e952c traveloka 2020 18 large scale experimentation at stitch fix https multithreaded stitchfix com blog 2020 07 07 large scale experimentation paper http proceedings mlr press v89 schmit19a schmit19a pdf stitch fix 2020 19 multi armed bandits and the stitch fix experimentation platform https multithreaded stitchfix com blog 2020 08 05 bandits stitch fix 2020 20 experimentation with resource constraints https multithreaded stitchfix com blog 2020 11 18 virtual warehouse stitch fix 2020 21 computational causal inference at netflix https netflixtechblog com computational causal inference at netflix 293591691c62 paper https arxiv org pdf 2007 10979 pdf netflix 2020 22 key challenges with quasi experiments at netflix https netflixtechblog com key challenges with quasi experiments at netflix 89b4f234b852 netflix 2020 23 making the linkedin experimentation engine 20x faster https engineering linkedin com blog 2020 making the linkedin experimentation engine 20x faster linkedin 2020 24 our evolution towards t rex the prehistory of experimentation infrastructure at linkedin https engineering linkedin com blog 2020 our evolution towards t rex the prehistory of experimentation i linkedin 2020 25 how to use quasi experiments and counterfactuals to build great products https engineering shopify com blogs engineering using quasi experiments counterfactuals shopify 2020 26 improving experimental power through control using predictions as covariate https doordash engineering 2020 06 08 improving experimental power through control using predictions as covariate cupac doordash 2020 27 supporting rapid product iteration with an experimentation analysis platform https doordash engineering 2020 09 09 experimentation analysis platform mvp doordash 2020 28 improving online experiment capacity by 4x with parallelization and increased sensitivity https doordash engineering 2020 10 07 improving experiment capacity by 4x doordash 2020 29 leveraging causal modeling to get more value from flat experiment results https doordash engineering 2020 09 18 causal modeling to get more value from flat experiment results doordash 2020 30 iterating real time assignment algorithms through experimentation https doordash engineering 2020 12 08 optimizing real time algorithms experimentation doordash 2020 31 spotify s new experimentation platform part 1 https engineering atspotify com 2020 10 29 spotifys new experimentation platform part 1 part 2 https engineering atspotify com 2020 11 02 spotifys new experimentation platform part 2 spotify 2020 32 interpreting a b test results false positives and statistical significance https netflixtechblog com interpreting a b test results false positives and statistical significance c1522d0db27a netflix 2021 33 interpreting a b test results false negatives and power https netflixtechblog com interpreting a b test results false negatives and power 6943995cf3a8 netflix 2021 34 running experiments with google adwords for campaign optimization https doordash engineering 2021 02 05 google adwords campaign optimization doordash 2021 35 the 4 principles doordash used to increase its logistics experiment capacity by 1000 https doordash engineering 2021 09 21 the 4 principles doordash used to increase its logistics experiment capacity by 1000 doordash 2021 36 experimentation platform at zalando part 1 evolution https engineering zalando com posts 2021 01 experimentation platform part1 html zalando 2021 37 designing experimentation guardrails https medium com airbnb engineering designing experimentation guardrails ed6a976ec669 airbnb 2021 38 how airbnb measures future value to standardize tradeoffs https medium com airbnb engineering how airbnb measures future value to standardize tradeoffs 3aa99a941ba5 airbnb 2021 38 network experimentation at scale https research fb com publications network experimentation at scale paper https arxiv org abs 2012 08591 facebook 2021 39 universal holdout groups at disney streaming https medium com disney streaming universal holdout groups at disney streaming 2043360def4f disney 2021 40 experimentation is a major focus of data science across netflix https netflixtechblog com experimentation is a major focus of data science across netflix f67923f8e985 netflix 2022 41 search journey towards better experimentation practices https engineering atspotify com 2022 02 search journey towards better experimentation practices spotify 2022 42 artificial counterfactual estimation machine learning based causal inference at airbnb https medium com airbnb engineering artificial counterfactual estimation ace machine learning based causal inference at airbnb ee32ee4d0512 airbnb 2022 43 beyond a b test speeding up airbnb search ranking experimentation through interleaving https medium com airbnb engineering beyond a b test speeding up airbnb search ranking experimentation through interleaving 7087afa09c8e airbnb 2022 44 challenges in experimentation https eng lyft com challenges in experimentation be9ab98a7ef4 lyft 2022 45 overtracking and trigger analysis reducing sample sizes while increasing sensitivity https booking ai overtracking and trigger analysis how to reduce sample sizes and increase the sensitivity of 71755bad0e5f booking 2022 46 meet dash ab the statistics engine of experimentation at doordash https doordash engineering 2022 05 24 meet dash ab the statistics engine of experimentation at doordash doordash 2022 47 comparing quantiles at scale in online a b testing https engineering atspotify com 2022 03 comparing quantiles at scale in online a b testing spotify 2022 48 accelerating our a b experiments with machine learning https dropbox tech machine learning accelerating our a b experiments with machine learning xr dropbox 2023 49 supercharging a b testing at uber https www uber com blog supercharging a b testing at uber uber model management 1 operationalizing machine learning managing provenance from raw data to predictions https vimeo com 274396495 comcast 2018 2 overton a data system for monitoring and improving machine learned products https arxiv org abs 1909 05372 paper https arxiv org pdf 1909 05372 pdf apple 2019 3 runway model lifecycle management at netflix https www usenix org conference opml20 presentation cepoi netflix 2020 4 managing ml models scale intuit s ml platform https www usenix org conference opml20 presentation wenzel intuit 2020 5 ml model monitoring 9 tips from the trenches https building nubank com br ml model monitoring 9 tips from the trenches nubank 2021 6 dealing with train serve skew in real time ml models a short guide https building nubank com br dealing with train serve skew in real time ml models a short guide nubank 2023 efficiency 1 groknet unified computer vision model trunk and embeddings for commerce https ai facebook com research publications groknet unified computer vision model trunk and embeddings for commerce paper https scontent sea1 1 xx fbcdn net v t39 8562 6 99353320 565175057533429 3886205100842024960 n pdf nc cat 110 nc sid ae5e01 nc ohc wqbazy1gnmuax8ecqtt nc ht scontent sea1 1 xx oh cab2f11dd9154d817149cb73e8b692a8 oe 5f5a3778 facebook 2020 2 how we scaled bert to serve 1 billion daily requests on cpus https blog roblox com 2020 05 scaled bert serve 1 billion daily requests cpus roblox 2020 3 permute quantize and fine tune efficient compression of neural networks https arxiv org abs 2010 15703 paper https arxiv org pdf 2010 15703 pdf uber 2021 4 gpu accelerated ml inference at pinterest https medium com pinterest engineering gpu accelerated ml inference at pinterest ad1b6a03a16d pinterest 2022 ethics 1 building inclusive products through a b testing https engineering linkedin com blog 2020 building inclusive products through a b testing paper https arxiv org pdf 2002 05819 pdf linkedin 2020 2 lift a scalable framework for measuring fairness in ml applications https engineering linkedin com blog 2020 lift addressing bias in large scale ai applications paper https arxiv org pdf 2008 07433 pdf linkedin 2020 3 introducing twitter s first algorithmic bias bounty challenge https blog twitter com engineering en us topics insights 2021 algorithmic bias bounty challenge twitter 2021 4 examining algorithmic amplification of political content on twitter https blog twitter com en us topics company 2021 rml politicalcontent twitter 2021 5 a closer look at how linkedin integrates fairness into its ai products https engineering linkedin com blog 2022 a closer look at how linkedin integrates fairness into its ai pr linkedin 2022 infra 1 reengineering facebook ai s deep learning platforms for interoperability https ai facebook com blog reengineering facebook ais deep learning platforms for interoperability facebook 2020 2 elastic distributed training with xgboost on ray https eng uber com elastic xgboost ray uber 2021 mlops platforms 1 meet michelangelo uber s machine learning platform https eng uber com michelangelo machine learning platform uber 2017 2 operationalizing machine learning managing provenance from raw data to predictions https vimeo com 274396495 comcast 2018 3 big data machine learning platform at pinterest https www slideshare net alluxio pinterest big data machine learning platform at pinterest pinterest 2019 4 core modeling at instagram https instagram engineering com core modeling at instagram a51e0158aa48 instagram 2019 5 open sourcing metaflow a human centric framework for data science https netflixtechblog com open sourcing metaflow a human centric framework for data science fa72e04a5d9 netflix 2019 6 managing ml models scale intuit s ml platform https www usenix org conference opml20 presentation wenzel intuit 2020 7 real time machine learning inference platform at zomato https www youtube com watch v 0 3es1vzw14 zomato 2020 8 introducing flyte cloud native machine learning and data processing platform https eng lyft com introducing flyte cloud native machine learning and data processing platform fb2bb3046a59 lyft 2020 9 building flexible ensemble ml models with a computational graph https doordash engineering 2021 01 26 computational graph machine learning ensemble model support doordash 2021 10 lyftlearn ml model training infrastructure built on kubernetes https eng lyft com lyftlearn ml model training infrastructure built on kubernetes aef8218842bb lyft 2021 11 you don t need a bigger boat a full data pipeline built with open source tools https github com jacopotagliabue you dont need a bigger boat paper https arxiv org abs 2107 07346 coveo 2021 12 mlops at greensteam shipping machine learning https neptune ai blog mlops at greensteam shipping machine learning case study greensteam 2021 13 evolving reddit s ml model deployment and serving architecture https www reddit com r redditeng comments q14tsw evolving reddits ml model deployment and serving reddit 2021 14 redesigning etsy s machine learning platform https www etsy com codeascraft redesigning etsys machine learning platform etsy 2021 15 understanding data storage and ingestion for large scale deep recommendation model training https arxiv org abs 2108 09373 paper https arxiv org pdf 2108 09373 pdf meta 2021 15 building a platform for serving recommendations at etsy https www etsy com codeascraft building a platform for serving recommendations at etsy etsy 2022 16 intelligent automation platform empowering conversational ai and beyond at airbnb https medium com airbnb engineering intelligent automation platform empowering conversational ai and beyond at airbnb 869c44833ff2 airbnb 2022 17 darwin data science and artificial intelligence workbench at linkedin https engineering linkedin com blog 2022 darwin data science and artificial intelligence workbench at li linkedin 2022 18 the magic of merlin shopify s new machine learning platform https shopify engineering merlin shopify machine learning platform shopify 2022 19 zalando s machine learning platform https engineering zalando com posts 2022 04 zalando machine learning platform html zalando 2022 20 inside meta s ai optimization platform for engineers across the company https ai facebook com blog looper meta ai optimization platform for engineers paper https arxiv org pdf 2110 07554 pdf meta 2022 21 monzo s machine learning stack https monzo com blog 2022 04 26 monzos machine learning stack monzo 2022 22 evolution of ml fact store https netflixtechblog com evolution of ml fact store 5941d3231762 netflix 2022 23 using mlops to build a real time end to end machine learning pipeline https www binance com en blog all using mlops to build a realtime endtoend machine learning pipeline 3820048062346322706 binance 2022 24 serving machine learning models efficiently at scale at zillow https www zillow com tech serving machine learning models efficiently at scale at zillow zillow 2022 25 didact ai the anatomy of an ml powered stock picking engine https principiamundi com posts didact anatomy utm campaign data elixir utm source data elixir 407 didact ai 2022 26 deployment for free a machine learning platform for stitch fix s data scientists https multithreaded stitchfix com blog 2022 07 14 deployment for free stitch fix 2022 27 machine learning operations mlops overview definition and architecture https arxiv org abs 2205 02302 paper https arxiv org ftp arxiv papers 2205 2205 02302 pdf ibm 2022 practices 1 practical recommendations for gradient based training of deep architectures https arxiv org abs 1206 5533 paper https arxiv org pdf 1206 5533 pdf yoshua bengio 2012 2 machine learning the high interest credit card of technical debt https research google pubs pub43146 paper https storage googleapis com pub tools public publication data pdf 43146 pdf paper https papers nips cc paper 5656 hidden technical debt in machine learning systems pdf google 2014 3 rules of machine learning best practices for ml engineering https developers google com machine learning guides rules of ml google 2018 4 on challenges in machine learning model management http sites computer org debull a18dec p5 pdf amazon 2018 5 machine learning in production the booking com approach https booking ai https booking ai machine learning production 3ee8fe943c70 booking 2019 6 150 successful machine learning models 6 lessons learned at booking com https booking ai 150 successful machine learning models 6 lessons learned at booking com 681e09107bec paper https dl acm org doi pdf 10 1145 3292500 3330744 booking 2019 7 successes and challenges in adopting machine learning at scale at a global bank https www youtube com watch v qyqkg5ocwei rabobank 2019 8 challenges in deploying machine learning a survey of case studies https arxiv org abs 2011 09926 paper https arxiv org pdf 2011 09926 pdf cambridge 2020 9 reengineering facebook ai s deep learning platforms for interoperability https ai facebook com blog reengineering facebook ais deep learning platforms for interoperability facebook 2020 10 the problem with ai developer tools for enterprises https towardsdatascience com the problem with ai developer tools for enterprises and what ikea has to do with it b26277841661 databricks 2020 11 continuous integration and deployment for machine learning online serving and models https eng uber com continuous integration deployment ml uber 2021 12 tuning model performance https eng uber com tuning model performance uber 2021 13 maintaining machine learning model accuracy through monitoring https doordash engineering 2021 05 20 monitor machine learning model drift doordash 2021 14 building scalable and performant marketing ml systems at wayfair https www aboutwayfair com careers tech blog building scalable and performant marketing ml systems at wayfair wayfair 2021 15 our approach to building transparent and explainable ai systems https engineering linkedin com blog 2021 transparent and explainable ai systems linkedin 2021 16 5 steps for building machine learning models for business https shopify engineering building business machine learning models shopify 2021 17 data is an art not just a science and storytelling is the key https shopifyengineering myshopify com blogs engineering data storytelling shopify shopify 2022 18 best practices for real time machine learning alerting https building nubank com br best practices for real time machine learning alerting nubank 2022 19 automatic retraining for machine learning models tips and lessons learned https building nubank com br automatic retraining for machine learning models nubank 2022 20 recsysops best practices for operating a large scale recommender system https netflixtechblog medium com recsysops best practices for operating a large scale recommender system 95bbe195a841 netflix 2022 21 ml education at uber frameworks inspired by engineering principles https www uber com en pl blog ml education at uber uber 2022 team structure 1 what is the most effective way to structure a data science team https towardsdatascience com what is the most effective way to structure a data science team 498041b88dae udemy 2017 1 engineers shouldn t write etl a guide to building a high functioning data science department https multithreaded stitchfix com blog 2016 03 16 engineers shouldnt write etl stitch fix 2016 2 building the analytics team at wish https medium com wish engineering scaling analytics at wish 619eacb97d16 wish 2018 3 beware the data science pin factory the power of the full stack data science generalist https multithreaded stitchfix com blog 2019 03 11 fullstackds generalists stitch fix 2019 4 cultivating algorithms how we grow data science at stitch fix https cultivating algos stitchfix com stitch fix 5 analytics at netflix who we are and what we do https netflixtechblog com analytics at netflix who we are and what we do 7d9c08fe6965 netflix 2020 6 building a data team at a mid stage startup a short story https erikbern com 2021 07 07 the data team a short story html erikbern 2021 7 a behind the scenes look at how postman s data team works https entrepreneurshandbook co a behind the scenes look at how postmans data team works fded0b8bfc64 postman 2021 8 data scientist x machine learning engineer roles how are they different how are they alike https building nubank com br data scientist x machine learning engineer roles how are they different how are they alike nubank 2022 fails 1 when it comes to gorillas google photos remains blind https www wired com story when it comes to gorillas google photos remains blind google 2018 2 160k high school students will graduate only if a model allows them to http positivelysemidefinite com 2020 06 160k students html international baccalaureate 2020 3 an algorithm that predicts criminality based on a face sparks a furor https www wired com story algorithm predicts criminality based face sparks furor harrisburg university 2020 4 it s hard to generate neural text from gpt 3 about muslims https twitter com abidlabs status 1291165311329341440 openai 2020 5 a british ai tool to predict violent crime is too flawed to use https www wired co uk article police violence prediction ndas united kingdom 2020 6 more in awful ai https github com daviddao awful ai 7 ai incident database https incidentdatabase ai partnership on ai 2022 br p s want a summary of ml advancements get up to speed with survey papers ml surveys https github com eugeneyan ml surveys | applied-machine-learning production applied-data-science machine-learning data-science reinforcement-learning data-engineering recsys search deep-learning data-quality data-discovery computer-vision natural-language-processing | ai |
palava-portal | palava portal palava tv https palava tv is simplistic video communication with your friends and colleagues from within your web browser it is build on top of the webrtc https webrtc org technology no registration or browser plugin required see palavatv palava https github com palavatv palava for an overview of all parts of palava tv the palava portal is the single page web application which runs palava it is built using the palava client https github com palavatv palava client the react https facebook github io react javascript library and compiled for the web by middleman http middlemanapp com this application is not part of the palava tv stack anymore it was replaced by palavatv palava web https github com palavatv palava web setup clone this directory and checkout the submodule git clone https github com palavatv palava portal cd palava portal git submodule init git submodule update make sure you have ruby and bundler installed maybe you also need to export the path manually ruby v after cloning this repo and in the new directory run bundle install make also sure that you also have install nodejs and run npm install in another tab start the palavamachine https github com palavatv palava machine or signaltower https github com farao signaltower start the middleman development server on http localhost 4567 middleman in case middleman leads to errors due to some missing components try running bundle update and then re run middleman you can build a static version of the page using middleman build configure using environment variables you can set the addresses of the rtc and stun server via environment variables the defaults are a local rtc server ws localhost 4233 and the palava stun server stun stun palava tv use ws for unsecured and wss for ssl secured websocket connections export palava rtc address ws some ip some port export palava stun address stun some ip some port export palava base address https your domain com middleman defaults palava base address localhost 4567 palava stun address stun stun palava tv palava rtc address ws localhost 4233 middleman use with palava tv palava base address palava tv palava stun address stun stun palava tv palava rtc address wss machine palava tv middleman credits mit license part of the palava project https palava tv copyright c 2014 2020 palava e v contact palava tv copyright c 2013 jan lelis hi ruby consulting copyright c 2013 marius melzer marius rasumi net copyright c 2013 stephan thamm stephan innovailable eu copyright c 2013 kilian ulbrich kilian innovailable eu | palava webrtc react | front_end |
GunMetal | gunmetal mobile development | front_end |
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bitloops-language | bitloops https storage googleapis com bitloops github assets github readme image png p align center a href https bitloops com docs bitloops language category quick start quick start a a href https github com bitloops bitloops language what are the benefits of using bitloops language benefits a a href https github com bitloops bitloops language ef b8 8f why build the bitloops language why a a href https github com bitloops bitloops language language goals goals a a href https github com bitloops bitloops language project status project status a a href https discord gg vj8edzx8gk discord a a href https github com bitloops bitloops language discussions github discussions a a href https github com bitloops bitloops language issues github issues a a href https github com bitloops bitloops language blob main contributing md contributing a p build great modular monoliths or microservices faster much faster bitloops language bl is a high productivity domain specific language dsl that helps you focus on the business logic of your application which is what really matters it incorporates software development best practices and design methodologies such as ddd https bitloops com docs bitloops language learning domain driven design bdd https en wikipedia org wiki behavior driven development and layered hexagonal architecture https alistair cockburn us hexagonal architecture the bitloops language guides and empowers any software developer to write clean code and build high quality well designed software this is particularly relevant for server application software that has complex and frequently changing business requirements with bl developers can build software using principles such as separation of concerns loose coupling high cohesion and command query responsibility segregation cqrs which ensure systems are easier to understand maintain and change with bitloops language developers are able to 1 write clean code in an intuitive and structured approach 2 follow best practices to ensure the code and software can be easily understood by other developers 3 create objects with high cohesion and loose coupling between each other 4 separate the busienss logic from the technical aspects which leads to more robust and flexible systems 5 focus on the core domain or problem and not worry about boilerplate code and implementation details in essence software developers can focus on what they do best solving problems with the bitloops language developers write code that will allow other developers and even themselves after 6 months to easily understand and build on top of that code part of the bitloops language project under the gpl 3 0 license see license for license information spdx license identifier gpl 3 0 only the gpl 3 0 license does not cover the use of bitloops trademarks and logos nbsp please keep in mind that the bitloops language is in its early stages and under very active development expect bugs and limitations any backward compatibility is not guaranteed before reaching v1 0 0 nbsp quick start the best and fastest way to understand how the bitloops language helps you write clean code and great software is to follow the instructions below with this tutorial you will run and execute a bitloops todo app learn how bitloops works and see the output files in typescript and appreciate how the bitloops language works if you face any issues especially with windows check the common issues common issues section below 1 install the transpiler bitloops still hasn t created binaries so the best way to install and run the transpiler is to install the bitloops language cli as a global npm package copy the following and run it in your ide console npm install g bitloops bitloops language cli alternatively you can use yarn console yarn global add bitloops bitloops language cli 2 clone the todo app example repo the bitloops todo app https github com bitloops bitloops language tree main examples todo bl source is readily available for cloning you can extract the files following the link or clone it using the command below console git clone https github com bitloops bitloops language git 3 run the bitloops transpiler the next step is to transpile the todo app code from bitloops language code into typescript code transpile comes from the word transcompile and means the translation of code from one programming language to another macos linux console bl transpile s bitloops language examples todo bl source t output windows console bl transpile s bitloops language examples todo bl source t output or console bitloops language transpile that s it you can now run the following to see how many lines of code you saved yourself spoiler alert 77 or 2141 loc console bl analyze lines bl bitloops language examples todo bl source ts output ok now you have all your business logic beautifully transpiled into well structured typescript code but wait what about the server and all the infrastructure code how am i supposed to run this the scope of the bitloops language ends here but using bitloops you can automatically generate everything else using bitloops magic and ai bitloops will generate for you a nest js project and all the required infrastructure grpc or rest controllers repository adapters for postgresql or mongo etc as well as docker or k8s files to deploy your system to learn more about the automatic bitloops process click here alternatively you are free to use any framework you like or just an express server or fastify server etc common issues in windows if you are still using powershell you will probably come across the error running scripts is disabled on this system to fix this you can do the following 1 open powershell with run as administrator right click on the icon and select the option from the menu 2 run the following command in powershell console set executionpolicy executionpolicy remotesigned 3 type y and press enter what are the benefits of using bitloops language software you ll be proud of well designed and written easy to understand and follow high productivity by focusing on the core domain problem having less boilerplate code to build manage and debug and more quickly being able to develop new features easy to learn and intuitive syntax learn about key software development best practices patterns and methodologies such as domain driven design ddd https bitloops com docs bitloops language learning domain driven design behavior driven development bdd https en wikipedia org wiki behavior driven development test driven development tdd http agiledata org essays tdd html and layered hexagonal architecture https alistair cockburn us hexagonal architecture testing is treated as a 1st class citizen in the software development process switch between modular monolith or microservices architecture within minutes as all messages are moved through either in memory or distributed message buses depending on your deployment choice significantly reduce the amount of boilerplate code you need to write maintain and debug improved alignment between business and engineering with a natural ubiquitous language ability to transpiles to widely used programming languages for maximum compatibility with existing code currently typescript https github com microsoft typescript support kotlin c go java c or maybe even rust or carbon https github com carbon language carbon lang to follow in the future why build the bitloops language there are numerous great programming languages out there with massive and growing codebases and investments however the most common problem faced by organizations that build and maintain systems with teams of developers working on them is good architecture and design of an interconnected system of services designing a complex system so that it can last through time and will allow developers existing and new joiners to work on its codebase with steady or hopefully increasing productivity is very difficult good testing is an additional major requirement of long lasting products which is also made possible by good architecture and design unfortunately there aren t enough knowledgeable and experienced senior engineers around the world to build and maintain great systems for all who need them even when a company is lucky enough to have some it is unable to hire more junior engineers than the senior ones can review their work and guide in order to make sure the system does not degrade over time due to bad design decisions the bitloops language is the first programming language that aims to address these issues by making it much easier to adopt important software engineering principles and patterns such as domain driven design https bitloops com docs bitloops language learning domain driven design and behavior driven development without requiring many years of experience to do so successfully as a result the work of senior engineers can be further leveraged and the contributions of junior engineers significantly boosted language goals every software engineer has a common goal we want to write better code and build better software and we want to do this faster however this can only be achieved with significant and continuous dedication learning and experience which takes a lot of time bitloops wants to significantly reduce the time it takes a developer to start building high quality software and we have built the bitloops language that already incorporates many of the software development best practices and design methodologies ultimately bl s goals are to put the focus on the domain and you business logic and automate everything else as much as possible delay infrastructure work and decisions as much as possible as these are implementation details increase developer productivity significantly not only at the start of a project but for the entire lifecycle reduce the time and cost to build new features and products even on large complex projects reduce the learning curve specifically of ddd bdd and layered hexagonal architecture needed to build and maintain great software empower many more software developers to learn adopt these important principles and best practices reduce the dependency on developer discipline during the code development process with a structured process make testing a more integral collaborative useful and fun process make domain logic timeless as well as platform language independent eliminate the need for boilerplate code enable the reuse of existing packages writen in any language empower software engineers to postpone the need for a microservices architecture until it is strictly needed from a infrastructure perspective to better manage computing and engineering resources reduce intial costs allow systems to be converted from a modular monolith to a microservices architecture in hours not months the bitloops language aims to define and retain a simplistic syntax that will be as close to human language and business logic as possible that will become timeless helping adopt and use a ubiquitous language within each module or bounded context it is then bl s job to transpile into modern and up to date syntax of your target language making the bitloops language a transpiled language was a core decision to achieve exactly this by allowing oraganisations to write their business logic in a timeless language that can be transpiled to powerful but also changing target languages without burdening the users of the bitloops language with this task the bitloops language will make sure it transpiles to optimized code of relevant up to date and right for the task languages project status the bitloops language is currently in early stages its transpiler has been created as a proof of concept and is not meant to cover the full range of developer creative code writing at this stage we want to better understand whether we can build a language that meets your needs and whether we can gather a critical mass of interest within the ddd community and outside of it there are many things we want to add in the future including x cqrs support event sourcing support kotlin target language c target language golang target language and many more if you re interested in contributing we would love your help bitloops typescript transpilation if you are already aware of the ddd concepts aggregates value objects use cases controller etc and know how to code in any modern programming language it should be really easy to pick up the bitloops language it is built out of a consistent set of language constructs that should feel familiar and be easy to read and understand while bitloops is an object oriented language it doesn t have a generic class specific bitloops classes are build in as follows valueobject entity root entity command commandhandler query queryhandler dto props ok applicationerror domainerror error etc bitloops language code like this 11 lines node bitloops language rule titleoutofboundsrule title string throws domainerrors titleoutofboundserror isbrokenif title length 150 or title length 4 props titleprops string title valueobject titlevo constructor props titleprops ok titlevo errors domainerrors titleoutofboundserror applyrules titleoutofboundsrule props title transpiles to this typescript code 28 lines node typescript import domain either ok fail from bitloops bl boilerplate core import domainerrors from errors export class titleoutofboundsrule implements domain irule constructor private title string public error new domainerrors titleoutofbounds this title public isbrokenif boolean return this title length 150 this title length 4 export namespace rules export class titleoutofbounds extends titleoutofboundsrule interface titleprops title string export class titlevo extends domain valueobject titleprops get title string return this props title private constructor props titleprops super props public static create props titleprops either titlevo domainerrors titleoutofbounds const res domain applyrules new rules titleoutofbounds props title if res return fail res return ok new titlevo props questions for questions and support please use our official discord channel https discord gg vj8edzx8gk feel free to join if you re looking to learn more about software development design patterns contributing we are a small team on a mission to democratize well designed code and high quality software and we ll take all the help we can get if you d like to get involved please check out our contribution guidelines https github com bitloops bitloops language blob main contributing md to learn how and where we could use your help we aim to identify good first issues so you can quickly start contributing and learning issues please make sure to read our reporting issues https github com bitloops bitloops language blob main github security md guidelines before opening an issue issues not conforming to the guidelines may be closed immediately community support staying in touch for general help using bitloops language please use one of these channels community discord https discord gg vj8edzx8gk for live discussion with the community and bitloops team forum discussions https github com bitloops bitloops language discussions for deeper conversations about features connectors or problems contributions github https github com bitloops bitloops language bug reports contributions demonstrations one to one discussion https calendly com bitloops 30min month 2022 11 live informal 30 minute video call sessions with the bitloops team documentation bitloops docs https bitloops com docs bitloops language category introduction all available documentation regarding bitloops language we re working hard on releasing more website www bitloops com https bitloops org bitloops language the official bitloops website with additional information and documentation license bitloops language has a gnu general public license v3 0 see the license https github com bitloops bitloops language blob main license file for licensing information recruitment bitloops is always looking for great people to contribute to the bitloops language and platform if you re keen on joining a team that wants to radically improve how software is built going forward then send your cv cover letter very important github profile to careers bitloops com like what you are seeing show us your appreciation p align center img src https storage googleapis com bitloops github assets star us gif | ddd ddd-architecture ddd-example transpiler bitloops bitloops-language bdd bdd-tests behavior-driven-development domain-driven-design domain-model test-driven-development ubiquitous-language programming-language contributions-welcome developer-tools open-source opensource typescript clean-architecture | os |
c-basic-programs | c basic programs what is c c is pronounced c sharp it is an object oriented programming language created by microsoft that runs on the net framework c has roots from the c family and the language is close to other popular languages like c and java the first version was released in year 2002 the latest version c 8 was released in september 2019 c is a modern object oriented programming language developed in 2000 by anders hejlsberg the principal designer and lead architect at microsoft it is pronounced as c sharp inspired by the musical notation which stands for a note with a slightly higher pitch as it s considered an incremental compilation of the c language the name c sharp seemed most appropriate the sharp symbol however has been replaced by the keyboard friendly as a suffix to c for purposes of programming although the code is very similar to c c is newer and has grown fast with extensive support from microsoft the fact that it s so similar to java syntactically helps explain why it has emerged as one of the most popular programming languages today c is pronounced c sharp it is an object oriented programming language created by microsoft that runs on the net framework c has roots from the c family and the language is close to other popular languages like c and java the first version was released in year 2002 the latest version c 8 was released in september 2019 c is used for mobile applications desktop applications web applications web services web sites games vr database applications and much much more an introduction to c programming c is a general purpose object oriented programming language that is structured and easy to learn it runs on microsoft s net framework and can be compiled on a variety of computer platforms as the syntax is simple and easy to learn developers familiar with c c or java have found a comfort zone within c c is a boon for developers who want to build a wide range of applications on the net framework windows applications web applications and web services in addition to building mobile apps windows store apps and enterprise software it is thus considered a powerful programming language and features in every developer s cache of tools although first released in 2002 when it was introduced with net framework 1 0 the c language has evolved a great deal since then the most recent version is c 8 0 available in preview as part of visual studio to get access to all of the new language features you would need to install the latest preview version of net core 3 0 c is used for mobile applications desktop applications web applications web services web sites games vr database applications and much much more why use c it is one of the most popular programming language in the world it is easy to learn and simple to use it has a huge community support c is an object oriented language which gives a clear structure to programs and allows code to be reused lowering development costs as c is close to c c and java it makes it easy for programmers to switch to c or vice versa the c environment you need the net framework and an ide integrated development environment to work with the c language the net framework the net framework platform of the windows os is required to write web and desktop based applications using not only c but also visual basic and jscript as the platform provides language interoperability besides the net framework allows c to communicate with any of the other common languages such as c jscript cobol and so on ides microsoft provides various ides for c programming visual studio 2010 vs visual studio express visual web developer visual studio code vsc the c source code files can be written using a basic text editor like notepad and compiled using the command line compiler of the net framework alternative open source versions of the net framework can work on other operating systems as well for instance the mono has a c compiler and runs on several operating systems including linux mac android bsd ios windows solaris and unix this brings enhanced development tools to the developer as c is part of the net framework platform it has access to its enormous library of codes and components such as common language runtime clr the net framework class library common language specification common type system metadata and assemblies windows forms asp net and asp net ajax windows workflow foundation wf windows communication foundation wcf and linq c and java c and java are high level programming languages that share several similarities as well as many differences they are both object oriented languages much influenced by c but while c is suitable for application development in the microsoft ecosystem from the front java is considered best for client side web applications also while c has many tools for programming java has a larger arsenal of tools to choose from in ides and text editors c is used for virtual reality projects like games mobile and web applications it is built specifically for microsoft platforms and several non microsoft based operating systems like the mono project that works with linux and os x java is used for creating messaging applications and developing web based and enterprise based applications in open source ecosystems both c and java support arrays however each language uses them differently in c arrays are a specialization of the system in java they are a direct specialization of the object the c programming language executes on the clr the source code is interpreted into bytecode which is further compiled by the clr java runs on any platform with the assistance of jre java runtime environment the written source code is first compiled into bytecode and then converted into machine code to be executed on a jre c and c although c and c are both c based languages with similar code there are some differences for one c is considered a component oriented programming language while c is a partial object oriented language also while both languages are compiled languages c compiles to clr and is interpreted by net but c compiles to machine code the size of binaries in c is much larger than in c other differences between the two include the following c gives compiler errors and warnings but c doesn t support warnings which may cause damage to the os c runs in a virtual machine for automatic memory management c requires you to manage memory manually c can create windows net web desktop and mobile applications but not stand alone apps c can create server side stand alone and console applications as it can work directly with the hardware c can be used on any platform while c is targeted toward windows os generally c being faster than c the former is preferred for applications where performance is essential features of c the c programming language has many features that make it more useful and unique when compared to other languages including object oriented language being object oriented c allows the creation of modular applications and reusable codes an advantage over c as an object oriented language c makes development and maintenance easier when project size grows it supports all three object oriented features data encapsulation inheritance interfaces and polymorphism simplicity c is a simple language with a structured approach to problem solving unsafe operations like direct memory manipulation are not allowed speed the compilation and execution time in c is very powerful and fast a modern programming language c programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection error handling debugging and robust security it has built in support for a web service to be invoked from any app running on any platform type safe arrays and objects are zero base indexed and bound checked there is an automatic checking of the overflow of types the c type safety instances support robust programming interoperability language interoperability of c maximizes code reuse for the efficiency of the development process c programs can work upon almost anything as a program can call out any native api consistency its unified type system enables developers to extend the type system simply and easily for consistent behavior updateable c is automatically updateable its versioning support enables complex frameworks to be developed and evolved component oriented c supports component oriented programming through the concepts of properties methods events and attributes for self contained and self describing components of functionality for robust and scalable applications structured programming language the structured design and modularization in c break a problem into parts using functions for easy implementation to solve significant problems rich library c has a standard library with many inbuilt functions for easy and fast development prerequisites for learning c basic knowledge of c or c or any programming language or programming fundamentals additionally the oop concept makes for a short learning curve of c advantages of c there are many advantages to the c language that makes it a useful programming language compared to other languages like java c or c these include being an object oriented language c allows you to create modular maintainable applications and reusable codes familiar syntax easy to develop as it has a rich class of libraries for smooth implementation of functions enhanced integration as an application written in net will integrate and interpret better when compared to other net technologies as c runs on clr it makes it easy to integrate with components written in other languages it s safe with no data loss as there is no type conversion so that you can write secure codes the automatic garbage collection keeps the system clean and doesn t hang it during execution as your machine has to install the net framework to run c it supports cross platform strong memory backup prevents memory leakage programming support of the microsoft ecosystem makes development easy and seamless low maintenance cost as c can develop ios android and windows phone native apps the syntax is similar to c c and java which makes it easier to learn and work with c useful as it can develop ios android and windows phone native apps with the xamarin framework c is the most powerful programming language for the net framework fast development as c is open source steered by microsoft with access to open source projects and tools on github and many active communities contributing to the improvement what can c sharp do for you c can be used to develop a wide range of windows client applications windows libraries and components windows services web applications native ios and android mobile apps azure cloud applications and services gaming consoles and gaming systems video and virtual reality games interoperability software like sharepoint enterprise software backend services and database programs ai and ml applications distributed applications hardware level programming virus and malware software gui based applications iot devices blockchain and distributed ledger technology c programming for beginners introduction features and applications by simplilearn last updated on jan 20 2020674 c programming for beginners as a programmer you re motivated to master the most popular languages that will give you an edge in your career there s a vast number of programming languages that you can learn but how do you know which is the most useful if you know c and c do you need to learn c as well how similar is c to java does it become more comfortable for you to learn c if you already know java every developer and wannabe programmer asks these types of questions so let us explore c programming how it evolved as an extension of c and why you need to learn it as a part of the master s program in integrated devops for server side execution are you a web developer or someone interested to build a website enroll for the javascript certification training check out the course preview now what is c c is a modern object oriented programming language developed in 2000 by anders hejlsberg the principal designer and lead architect at microsoft it is pronounced as c sharp inspired by the musical notation which stands for a note with a slightly higher pitch as it s considered an incremental compilation of the c language the name c sharp seemed most appropriate the sharp symbol however has been replaced by the keyboard friendly as a suffix to c for purposes of programming although the code is very similar to c c is newer and has grown fast with extensive support from microsoft the fact that it s so similar to java syntactically helps explain why it has emerged as one of the most popular programming languages today an introduction to c programming c is a general purpose object oriented programming language that is structured and easy to learn it runs on microsoft s net framework and can be compiled on a variety of computer platforms as the syntax is simple and easy to learn developers familiar with c c or java have found a comfort zone within c c is a boon for developers who want to build a wide range of applications on the net framework windows applications web applications and web services in addition to building mobile apps windows store apps and enterprise software it is thus considered a powerful programming language and features in every developer s cache of tools although first released in 2002 when it was introduced with net framework 1 0 the c language has evolved a great deal since then the most recent version is c 8 0 available in preview as part of visual studio to get access to all of the new language features you would need to install the latest preview version of net core 3 0 the c environment you need the net framework and an ide integrated development environment to work with the c language the net framework the net framework platform of the windows os is required to write web and desktop based applications using not only c but also visual basic and jscript as the platform provides language interoperability besides the net framework allows c to communicate with any of the other common languages such as c jscript cobol and so on ides microsoft provides various ides for c programming visual studio 2010 vs visual studio express visual web developer visual studio code vsc the c source code files can be written using a basic text editor like notepad and compiled using the command line compiler of the net framework alternative open source versions of the net framework can work on other operating systems as well for instance the mono has a c compiler and runs on several operating systems including linux mac android bsd ios windows solaris and unix this brings enhanced development tools to the developer as c is part of the net framework platform it has access to its enormous library of codes and components such as common language runtime clr the net framework class library common language specification common type system metadata and assemblies windows forms asp net and asp net ajax windows workflow foundation wf windows communication foundation wcf and linq c and java c and java are high level programming languages that share several similarities as well as many differences they are both object oriented languages much influenced by c but while c is suitable for application development in the microsoft ecosystem from the front java is considered best for client side web applications also while c has many tools for programming java has a larger arsenal of tools to choose from in ides and text editors c is used for virtual reality projects like games mobile and web applications it is built specifically for microsoft platforms and several non microsoft based operating systems like the mono project that works with linux and os x java is used for creating messaging applications and developing web based and enterprise based applications in open source ecosystems both c and java support arrays however each language uses them differently in c arrays are a specialization of the system in java they are a direct specialization of the object the c programming language executes on the clr the source code is interpreted into bytecode which is further compiled by the clr java runs on any platform with the assistance of jre java runtime environment the written source code is first compiled into bytecode and then converted into machine code to be executed on a jre c and c although c and c are both c based languages with similar code there are some differences for one c is considered a component oriented programming language while c is a partial object oriented language also while both languages are compiled languages c compiles to clr and is interpreted by net but c compiles to machine code the size of binaries in c is much larger than in c other differences between the two include the following c gives compiler errors and warnings but c doesn t support warnings which may cause damage to the os c runs in a virtual machine for automatic memory management c requires you to manage memory manually c can create windows net web desktop and mobile applications but not stand alone apps c can create server side stand alone and console applications as it can work directly with the hardware c can be used on any platform while c is targeted toward windows os generally c being faster than c the former is preferred for applications where performance is essential features of c the c programming language has many features that make it more useful and unique when compared to other languages including object oriented language being object oriented c allows the creation of modular applications and reusable codes an advantage over c as an object oriented language c makes development and maintenance easier when project size grows it supports all three object oriented features data encapsulation inheritance interfaces and polymorphism simplicity c is a simple language with a structured approach to problem solving unsafe operations like direct memory manipulation are not allowed speed the compilation and execution time in c is very powerful and fast a modern programming language c programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection error handling debugging and robust security it has built in support for a web service to be invoked from any app running on any platform type safe arrays and objects are zero base indexed and bound checked there is an automatic checking of the overflow of types the c type safety instances support robust programming interoperability language interoperability of c maximizes code reuse for the efficiency of the development process c programs can work upon almost anything as a program can call out any native api consistency its unified type system enables developers to extend the type system simply and easily for consistent behavior updateable c is automatically updateable its versioning support enables complex frameworks to be developed and evolved component oriented c supports component oriented programming through the concepts of properties methods events and attributes for self contained and self describing components of functionality for robust and scalable applications structured programming language the structured design and modularization in c break a problem into parts using functions for easy implementation to solve significant problems rich library c has a standard library with many inbuilt functions for easy and fast development full stack java developer course the gateway to master web developmentexplore coursefull stack java developer course prerequisites for learning c basic knowledge of c or c or any programming language or programming fundamentals additionally the oop concept makes for a short learning curve of c advantages of c there are many advantages to the c language that makes it a useful programming language compared to other languages like java c or c these include being an object oriented language c allows you to create modular maintainable applications and reusable codes familiar syntax easy to develop as it has a rich class of libraries for smooth implementation of functions enhanced integration as an application written in net will integrate and interpret better when compared to other net technologies as c runs on clr it makes it easy to integrate with components written in other languages it s safe with no data loss as there is no type conversion so that you can write secure codes the automatic garbage collection keeps the system clean and doesn t hang it during execution as your machine has to install the net framework to run c it supports cross platform strong memory backup prevents memory leakage programming support of the microsoft ecosystem makes development easy and seamless low maintenance cost as c can develop ios android and windows phone native apps the syntax is similar to c c and java which makes it easier to learn and work with c useful as it can develop ios android and windows phone native apps with the xamarin framework c is the most powerful programming language for the net framework fast development as c is open source steered by microsoft with access to open source projects and tools on github and many active communities contributing to the improvement what can c sharp do for you c can be used to develop a wide range of windows client applications windows libraries and components windows services web applications native ios and android mobile apps azure cloud applications and services gaming consoles and gaming systems video and virtual reality games interoperability software like sharepoint enterprise software backend services and database programs ai and ml applications distributed applications hardware level programming virus and malware software gui based applications iot devices blockchain and distributed ledger technology who should learn the c programming language and why c is one of the most popular programming languages as it can be used for a variety of applications mobile apps game development and enterprise software what s more the c 8 0 version is packed with several new features and enhancements to the c language that can change the way developers write their c code the most important new features available are null reference types enhanced pattern matching and async streams that help you to write more reliable and readable code as you re exposed to the fundamental programming concepts of c in this course you can work on projects that open the doors for you as a full stack java developer so upskill and master the c language for a faster career trajectory and salary scope | server |
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mooc | massive open online course coursera edx descriptions and proof of accomplishments category course statement of accomplishment certificates introduction to big data with apache spark https www edx org course introduction big data apache spark uc berkeleyx cs100 1x certificate https github com beaglebagel mooc blob master certificates edx 20introduction 20to 20big 20data 20with 20apache 20spark pdf scalable machine learning https www edx org course scalable machine learning uc berkeleyx cs190 1x certificate https github com beaglebagel mooc blob master certificates edx 20scalable 20machine 20learning pdf introduction to natural language processing https www coursera org course nlpintro certificate https github com beaglebagel mooc blob master certificates coursera 20nlpintro 202016 pdf mining massive datasets https www coursera org course mmds certificate https github com beaglebagel mooc blob master certificates coursera 20mmds 202015 pdf computer networks https www coursera org course comnetworks certificate https github com beaglebagel mooc blob master certificates coursera 20comnetworks 202015 pdf analytics edge https www edx org course analytics edge mitx 15 071x 0 certificate https github com beaglebagel mooc blob master certificates edx 20the 20analytics 20edge pdf applied logistic regression https www coursera org course logisticregression certificate https github com beaglebagel mooc blob master certificates coursera 20logisticregression 202015 pdf finding hidden messages in dna bioinformatics i https www coursera org course hiddenmessages certificate https github com beaglebagel mooc blob master certificates coursera 20hiddenmessages 202015 pdf genome sequencing bioinformatics ii https www coursera org course assembly certificate https github com beaglebagel mooc blob master certificates coursera 20assembly 202015 pdf comparing genes proteins and genomes bioinformatics iii https www coursera org course comparinggenomes certificate https github com beaglebagel mooc blob master certificates coursera 20comparinggenomes 202015 pdf cloud computing specialization https www coursera org specializations cloudcomputing certificate https github com beaglebagel mooc blob master certificates coursera cloud computing 2016 pdf cloud computing concepts https www coursera org course cloudcomputing certificate https github com beaglebagel mooc blob master certificates coursera 20cloudcomputing 202015 pdf cloud computing concepts2 https www coursera org course cloudcomputing2 certificate https github com beaglebagel mooc blob master certificates coursera 20cloudcomputing2 202015 pdf cloud computing applications https www coursera org course cloudapplications certificate https github com beaglebagel mooc blob master certificates coursera 20cloudapplications 202015 pdf cloud networking https www coursera org course cloudnetworking certificate https github com beaglebagel mooc blob master certificates coursera 20cloudnetworking 202015 pdf | mooc cloud-computing data data-science data-engineering | cloud |
namada | namada license gpl v3 https img shields io badge license gplv3 blue svg license ci status https github com anoma namada actions workflows build and test yml badge svg branch main overview namada http namada net is a proof of stake l1 for interchain asset agnostic privacy namada uses cometbft consensus and enables multi asset shielded transfers for any native or non native asset namada features full ibc protocol support a natively integrated ethereum bridge a modern proof of stake system with automatic reward compounding and cubic slashing and a stake weighted governance signalling mechanism users of shielded transfers are rewarded for their contributions to the privacy set in the form of native protocol tokens a multi asset shielded transfer wallet is provided in order to facilitate safe and private user interaction with the protocol blogpost introducing namada interchain asset agnostic privacy https blog namada net introducing namada interchain asset agnostic privacy docs dev docs built from dev mdbook documentation dev warning here lay dragons this codebase is still experimental try at your own risk installing there is a single command to build and install namada executables from source the node the client and the wallet this command will also verify that a compatible version of cometbft dependencies is available and if not attempt to install it note that currently at least 16gb ram is needed to build from source shell make install after installation the main namada executable will be available on path to find how to use it check out the user guide section of the docs https docs namada net user guide index html for more detailed instructions and more install options see the install section https docs namada net user guide install index html of the user guide development shell build the provided validity predicate and transaction wasm modules make build wasm scripts docker development debug build namada which includes a validator and some default accounts whose keys and addresses are available in the wallet namada dev true make before submitting a pr pls make sure to run the following shell format the code make fmt lint the code make clippy logging to change the log level set namada log environment variable to one of error warn info debug trace the default is set to info for all the modules expect for combetbft abci which has a lot of debug logging for more fine grained logging levels settings please refer to the tracing subscriber docs https docs rs tracing subscriber 0 2 18 tracing subscriber struct envfilter html directives for more information to switch on logging in tests that use test macro from test log test use rust log with e g rust log info cargo test nocapture how to contribute please see the contributing page contributing md dependencies the ledger currently requires cometbft v0 37 2 https github com cometbft cometbft releases tag v0 37 2 is installed and available on path this can be achieved through following these instructions https github com cometbft cometbft blob main docs guides install md | blockchain privacy rust zkp | blockchain |
MacYTDL | macytdl macytdl is a utility which downloads videos using the youtube dl https github com ytdl org youtube dl and yt dlp https github com yt dlp yt dlp video downloader scripts youtube dl and yt dlp are able to download videos from a great many web sites macytdl runs on any apple mac with macos 10 10 or later macytdl has been developed mostly in applescript the code is not accessable from within the applet however text exports of the code are in the code folder above and can be opened in script editor macytdl is code signed and notarized download from here https github com section83 macytdl releases download 1 25 macytdl v1 25 dmg main dailog img src https github com section83 macytdl blob master images main png width 480 height 374 features download individual videos and playlists download multiple videos separately in parallel or in one process sequentially works with all sites supported by yt dlp https github com yt dlp yt dlp can switch between youtube dl and yt dlp on macs running macos from 10 15 to 12 2 1 can cancel individual downloads can pause resume downloads by default will resume interrupted downloads if passed the url can pass through any custom settings to youtube dl and yt dlp settings for level of youtube dl feedback download folder file format remuxing format download speed proxy url quicktime compatibility custom file name template etc batch downloads download a text description of the video option to choose from available download formats switch between stable and nightly builds of yt dlp localisation currently spanish italian french and german are available switching languages is supported in macos 10 15 and later localisation is not yet up to date download and embed subtitles in chosen format and language including auto generated captions from youtube download and optionally embed thumbnail images and metadata download or extract audio only files in chosen format optional macos service which gets the current text selection clipboard contents or active web browser url switches to macytdl and pastes url of video to be downloaded the service can be set to automatically download from the url of the current web page without showing the main dialog settings can be saved restored and rest to default all components downloaded and or installed by macytdl which can be controlled by the user component updates available in the app issues notification via alerter https github com vjeantet alerter when download finished with option to play the video a separate log file is retained for each download enabling problem solving if a download fails has a built in uninstaller which moves all components to trash includes a range of simple utilities uses arm64 or x86 64 code according to user s mac requirements an apple mac running macos 10 10 yosemite and higher is required to use macytdl macytdl works in parallels virtual machines yt dlp is the default for all macs python 3 8 is built into the yt dlp executable however homebrew and macports installs can be used detail is in the help https github com section83 macytdl blob master images help pdf youtube dl can be used with all versions of macos up to monterey 12 2 1 how to install for the first time download and open the dmg file https github com section83 macytdl releases download 1 25 macytdl v1 25 dmg browse the help file click and drag macytdl to any location it s best to use the applications folder open macytdl click on yes to install various components and create a preferences folder when asked provide administrator credentials when asked wait macytdl main dialog is displayed how to update download and open the dmg file https github com section83 macytdl releases download 1 25 macytdl v1 25 dmg click and drag macytdl to your usual location make sure to replace the old version open macytdl components such as the preferences file are updated as required more detail is available in the help file https github com section83 macytdl blob master images help pdf 3 97mb bugs problems questions to report bugs problems etc get a github account click on the issues tab above and open a new issue alternatively open an item in discussions above or send an email to macytdl gmail com acknowledgements macytdl would be useless without youtube dl https github com ytdl org youtube dl and yt dlp https github com yt dlp yt dlp they are remarkable feature rich tools maintained by the most dedicated group of volunteers it should be noted that youtube dl https github com ytdl org youtube dl has not been updated since december 2021 yt dlp https github com yt dlp yt dlp however is in active development much is owed to shane stanley for his many contributions solving problems with macytdl shane developed dialog toolkit plus https latenightsw com support freeware which provides the dialogs in macytdl ideas for this gui front end came from many sources including adam albrec author of ppc media centre michael page http techion com au author of the video hoarder automator script kopurando https github com kopurando author of the virga downloader tombs https forum videohelp com members 235982 tombs an active contributor to whirlpool www whirlpool net au and author of the urldown dropper utility for windows xplorr https forum videohelp com members 268051 xplorr author of tvdownloader anonymous https cresstone com apps youtubedlfrontend author of youtubedlfrontend frank geoff john santo trevor and walter fellow members of the act apple users group https www actapple org au macytdl users 1alessandro1 11lucasarr11 adam airmarty alex alphabitnz andy andyrb412 artcore c barney1903 ba ar bigjoe309 bovirus brandon cdrspock dantha darbid defcon5at didier euronymousdeadohlin ericthederek frissonlabs grantgochnauer gustavosaez hamza heviiguy hunterbr3193 itsmorepaul jack janvdvelde jeremydouglass kuglee l kiewa labhansh sharma leonardomaracino malebenso martinsstuff meiwechner michel gh mike mmaslar mrjmpl3 nellio nottooloud onaforeignshore pedrocadiz13 peter qaqde rick raymond adams roest01 swineburglar ted tenz14 thejasonparker tht7 tigrr tobias tom tomascarlson upekshapriya vinsamlegast78 watto23 woolfy025 zxzzz8 macytdl is free macytdl is a retirement project for me and will always be free please consider sponsoring the yt dlp https github com yt dlp yt dlp team detail on sponsorships is available here https github com yt dlp yt dlp blob master collaborators md collaborators if you use other shareware or open source software consider making a donation to the developers let them know they are appreciated | youtube-dl applescript macos gui download-videos macos-gui-front dialogs video | front_end |
E6156_Microservices_Cloud_Applications_New | br br br br microservices and cloud native applications notes 1 this is the fall 2019 material 2 i am reorganizing the repository and content to clean up the material lectures example etc overview this course covers core fundamental concepts in developing modern cloud native application development the core topics include microservices content distribution networks infrastructure as a service platform as a service api as a service api management and gateways using cloud apis serverless and function as a service message drive architectures event driven architectures rest oauth2 social media integration multi tenancy sql nosql and cloud databases service orchestration application and systems management application development best practices students will learn the material by building an application in small teams the application will be a simple cloud application for playing fantasy baseball the application includes the basics of user interface microservices social networking sql and nosql databases rest apis service composition data analysis and simple machine learning there will be lectures and meetings with the professor that explain and cover implementation technology and review the projects teams will use amazon web services for the cloud platform but the course concepts apply to most modern cloud platforms some of the specific aws technologies the course will cover are ec2 elastic beanstalk ecs lambda functions sqs sms s3 neptune rds dynamodb documentdb elasicache cloudwatch cloudtrail cloudformation cloudsearch data pipeline iam cognito waf cloudfront route 53 api gateway code commit code build sage maker the projects will also require using external cloud apis e g smartystreets https smartystreets com for address verification students may optionally choose to use additional or different enablement technology the final grade is based on the review of the project there will be interim reviews with the professor and with the class project overview simplistically there are three parts to an application 1 user interface 2 application business logic 3 data the project will use a microservice architecture to implement the application and business logic and also for implementing infrastructure and middle ware services wikipedia https en wikipedia org wiki microservices provides a simple definition of microservices and links to related topics the course material will provide examples links to additional material etc microservices are a software development technique a variant of the service oriented architecture soa architectural style that structures an application as a collection of loosely coupled services in a microservices architecture services are fine grained and the protocols are lightweight the benefit of decomposing an application into different smaller services is that it improves modularity this makes the application easier to understand develop test and become more resilient to architecture erosion it parallelizes development by enabling small autonomous teams to develop deploy and scale their respective services independently it also allows the architecture of an individual service to emerge through continuous refactoring microservices based architectures enable continuous delivery and deployment baseballinfo data microservice allows users to query and explore data about players and their performance date comes from lahman2018 baseball database http www seanlahman com baseball archive statistics the microservice will require rds data pipeline and elastic beanstalk fantasy baseball data microservice allows users to create an account define a fantasy team define a fantasy league assign players to teams etc the database is a newly design database and data model the microservice will require lambda functions dynamodb cognito and social media integration and using external web apis social interaction microservice allows users to follow like comment etc on teams players users the microservice will require neptune or neo4j discussion comment data microservice stores comments responses discussion threads etc uses documentdb analytics machine learning data microservice a database of analyzed and processed baseball performance interface that enables analysis and prediction using machine learning uses data pipeline rds and sage maker application business logic microservice implement business logic and rules primarily governing correct operation of the fantasy league and rules uses lambda functions sqs sms and step functions caching microservice rest api and data access response cache to optimize performance uses elasticache static content web server delivers html css images javascript etc to web browser ui uses s3 cloudfront cloud search route 53 etc user interface browser application using angularjs lecture material being cleaned up and simplified come back hopefully soon examples being cleaned up and simplified come back hopefully soon | cloud |
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FOD-data | a name overview foreign object debris in airports fod a dataset overview the fod a dataset consists of images of common foreign object debris fod with a runway or taxiway background while the main annotation style consists of bounding boxes fod a also includes seperate light level and weather categorization annotations fod a is designed to be easily expanded using a command line tool developed alongside the dataset the instructions for this process are contained in a pdf file included in this repository p align center img alt annotationexamples src examples annotationexamples png p p align center img alt examples src examples manyex png p p align center img alt instances src examples annotationinstancesv2 1 png p a name download instructions download it is recommended to use the pascal voc format for experimentation and the original format for dataset extension a train and validation split is provided in the pascal voc version experiments in the original paper used pascal voc version 2 1 300x300 resolution with the splits provided in that version tools for resizing the annotations and converting the original format to the pascal voc format are included in the tools folder of this repository most current version download fod a version 2 1 original format 8 3 gb 400x400 image size https drive google com file d 1dka pgbhdzqk4jzrspmzb54 xcrbnl4b view usp sharing fod a version 2 1 pascal voc format 412 mb 300x300 image size https drive google com file d 1xiqogkkpxrpj xr hkytqrxue2ftpktu view usp sharing a name citation citation if you find this dataset beneficial to your work consider citing the paper travis munyer pei chi huang chenyu huang and xin zhong 2021 fod a a dataset for foreign object debris in airports https arxiv org abs 2110 03072 travis munyer daniel brinkman chenyu huang and xin zhong 2021 integrative use of computer vision and unmanned aircraft technologies in public inspection foreign object debris image collection arxiv https arxiv org abs 2106 00161 | ai |
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Basic_Banking_App | basic banking app mobile development | front_end |
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blockchain-java | java build status https travis ci org wangweix blockchain java svg branch master https travis ci org wangweix blockchain java https github com jeiwan blockchain go maven 3 2 5 jdk 1 7 idea lombok https wangwei one posts 917fb1e0 html https wangwei one posts build blockchain in java base prototype html https wangwei one posts build blockchain in java proof of work html https wangwei one posts build blockchain in java data persistence html utxo https wangwei one posts build blockchain in java transaction utxo html https wangwei one posts build blockchain in java wallet address html merkle tree https wangwei one posts build blockchain in java transaction merkle tree html https wangwei one | blockchain blockchain-technology blockchain-java-programming bitcoin utxo pow merkle-tree bitcoin-script bitcoin-wallet bitcoin-address bitcoin-transaction bitcoin-mining bitcoin-miner bitcoin-proof bitcoin-persistence bitcoin-network bitcoin-java blockchain-java p2p-network netty-p2p | blockchain |
sec-ml | sec ml security machine learning machine learning for cyber security https github com wtsxdev machine learning for cyber security collection of security and network data resources http www covert io data links the definitive security data science and machine learning guide http www covert io the definitive security datascience and machinelearning guide deep learning security papers http www covert io deep learning security papers machine learning for cyber security https github com jivoi awesome ml for cybersecurity datasets | ai |
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LeafSnap | leafsnap this project presents a computer vision based mobile plant classification application the objectives to develop such a mobile application is to utilize smartphones and benefit from the photographs taken by their camera in order to perform environmental monitoring moreover enabling individuals to use such a technnology is expected to increase individuals awareness to their environment the developed application identifies the plants by processing a photograph of its leaf the main processing steps of the system are determining whether there is a leaf in the photo detecting whether there is a blur leaf segmentation feature extraction and classification each building block of the system is tested separately and promising results have been obtained in the experiments br br img src https raw github com cihanturkay leafsnap master assets content png important notes this project need a server to run the server includes computer vision operations br img src https raw github com cihanturkay leafsnap master assets flow png br more information about rest api you can check a href https github com aksakalli graduation project this project a dependencies a href https github com jgilfelt android mapviewballoons mapview balloons a br a href https github com koush urlimageviewhelper urlimageviewhelper a br a href https github com jakewharton actionbarsherlock actionbarsherlock a br related work a href http leafsnap com leaf snap a | ai |
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spectrum-dsp | adobe spectrum dsp beta adobe spectrum design system package dsp beta based on spectrum adobe com https spectrum adobe com including design tokens typography collections of tokens and components with spectrum react https react spectrum adobe com react spectrum index html code snippets installing to install adobe spectrum dsp in your project please run npm install adobe spectrum dsp feedback for questions issues or feedback please file an issue here https github com demianborba spectrum dsp issues how can i use the package please use the adobe xd extension for vs code https letsxd com vscode to open this and other dsps after installing the adobe spectrum dsp with npm open the adobe xd extension for vs code and select load package then navigate to your node modules folder and select the adobe spectrum dsp folder dsps contain a dsp json file at the root level if possible watch the short adobe xd extension for vs code learn videos https letsxd com vscode to know how to consume what is available in the dsp package what is in the package compiled design tokens available in dist as android css flutter dart ios objective c ios swift javascript and scss code snippets available as spectrum css spectrum web components html and react spectrum code assets code completion png code assets code snippets png | os |
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RescueRobot | cpre 288 final project networking commands new command end of command f move foward b move back l turn left r turn right m play a song s do a scanner sweep x bot x location y bot y location a bot angle p initiate sweep o object detected | os |
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SQLEmployeeAnalysis | employee database engineering and analysis sql i did data modeling engineering and analysis on six different csv files using sql background there are six csv files that remain on the database of the employees between the 1980s and 1990s i have been asked to design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data what i did data modeling inspected the csvs and sketched out an erd of the tables i used http www quickdatabasediagrams com http www quickdatabasediagrams com to sketch it out employeesql quickdbd export png data engineering used the information to create a table schema for each of the six csv files images createtables png imported each csv file into the corresponding sql table images viewtables png data analysis 1 listed the following details of each employee employee number last name first name sex and salary images 1 png 2 listed first name last name and hire date for employees who were hired in 1986 images 2 png 3 listed the manager of each department with the following information department number department name the manager s employee number last name first name images 3 png 4 listed the department of each employee with the following information employee number last name first name and department name images 4 png 5 listed first name last name and sex for employees whose first name is hercules and last names begin with b images 5 png 6 listed all employees in the sales department including their employee number last name first name and department name images 6 png 7 listed all employees in the sales and development departments including their employee number last name first name and department name images 7 png 8 in descending order listed the frequency count of employee last names i e how many employees share each last name images 8 png the data is provided by ucsd extension data science and visualization bootcamp contact email arcebri1 gmail com | server |
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Introduction-to-Computer-Vision | introduction to computer vision a python version in this project you will find notes screenshots and python implementations of all the concepts discussed in the lovely free ucacity course introduction to computer vision thankfully produced by georgia institute of technology and presented by prof aaron bobick irfan essa and arpan chakraborty this course provides an introduction to computer vision including fundamentals and methods for application and machine learning classification project instructions 1 the code in the notebooks is based on python 3 python 2 will fail in several cases 2 the notebooks essentially depend on the following libraries numpy scipy cv2 and pil 3 you will likely need to install more pip packages to run the notebooks we assume that you have enough proficiency to install them as needed content 1a l1 introduction images https jalalirs github io introduction to computer vision l2 l2 html 2a l1 images as functions 2a l2 filtering 2a l3 linearity and convolution 2a l4 filters as templates 2a l5 edge detection gradients 2a l6 edge detection 2d operators 2b l1 hough transform lines 2b l2 hough transform circles 2b l3 generalized hough transform 2c l1 fourier transform 2c l2 convolution in frequency domain 2c l3 aliasing camera calibration https jalalirs github io introduction to computer vision l3 l3 html 3a l1 cameras and images 3a l2 perspective imaging 3b l1 stereo geometry 3b l2 epipolar geometry 3b l3 stereo correspondence 3c l1 extrinsic camera parameters 3c l2 instrinsic camera parameters 3c l3 calibrating cameras 3d l1 image to image projections 3d l2 homographies and mosaics 3d l3 projective geometry 3d l4 essential matrix 3d l5 fundamental matrix visual feature https jalalirs github io introduction to computer vision l4 l4 html 4a l1 introduction to features 4a l2 finding corners 4a l3 scale invariance 4b l1 sift descriptor 4b l2 matching feature points a little 4c l1 robust error functions 4c l2 ransac photometry https jalalirs github io introduction to computer vision l5 l5 html 5a l1 photometry 5b l1 lightness 5c l1 shape from shading motion https jalalirs github io introduction to computer vision l6 l6 html 6a l1 introduction to motion 6b l1 dense flow brightness constraint 6b l2 dense flow lucas and kanade 6b l3 hierarchical lk 6b l4 motion models tracking https jalalirs github io introduction to computer vision l7 l7 html 7a l1 introduction to tracking 7b l1 tracking as inference 7b l2 the kalman filter 7c l1 bayes filters 7c l2 particle filters 7c l3 particle filters for localization 7c l4 particle filters for real 7d l1 tracking considerations recognition https jalalirs github io introduction to computer vision l8 l8 html 8a l1 introduction to recognition 8b l1 classification generative models 8b l2 principle component analysis 8b l3 appearance based tracking 8c l1 discriminative classifiers 8c l2 boosting and face detection 8c l3 support vector machines 8c l4 bag of visual words 8d l1 introduction to video analysis 8d l2 activity recognition 8d l3 hidden markov models colors https jalalirs github io introduction to computer vision l9 l9 html 9a l1 color spaces 9a l2 segmentation 9a l3 mean shift segmentation 9a l4 segmentation by graph partitioning 9b l1 binary morphology 9c l1 3d perception human vision system https jalalirs github io introduction to computer vision l10 l10 html 10a l1 the retina 10b l1 vision in the brain we re done | computer-vision udacity-nanodegree | ai |
js13k-ecs | js13k ecs a 1kb entity component system http entity systems wikidot com designed for js13k npm version https img shields io npm v js13k ecs svg style flat square https www npmjs com package js13k ecs build status https img shields io travis kutuluk js13k ecs master svg style flat square https travis ci org kutuluk js13k ecs tiny weighs about one kilobyte gzipped install this project uses node http nodejs org and npm https npmjs com go check them out if you don t have them locally installed sh npm install js13k ecs then with a module bundler like rollup http rollupjs org or webpack https webpack js org use as you would anything else javascript using es6 modules import ecs from js13k ecs using commonjs modules var ecs require js13k ecs the umd https github com umdjs umd build is also available on unpkg https unpkg com html script src https unpkg com js13k ecs dist ecs umd js script you can find the library on window ecs usage see example folder live example https kutuluk github io js13k ecs api ecs register components registers components for use by the library components must be classes or constructor functions no other requirements are imposed on the components note that there is currently a limit of 32 registered components process systems adds systems for use by the library systems must be instances of classes or objects systems must implement the update method create id creates the entity with the specified id if id is not specified serial numbers starting with 1 are generated and encoded in base36 returns the created entity get id returns the entity with the specified id or undefined if it is not present select components returns a selection of entities that have the specified set of components the sample is updated real time and always relevant the selector has the length property which stores the number of entities in the sample and the iterate fn method with which you can loop through all entities update delta successively calls update methods on all systems passing them the delta parameter returns the object that contains the duration of the execution of the systems entity add components adds the components to the entity remove components removes components of the components class from the entity calls the destructor method of each component if it is present has component returns true if the entity has a component of the component class and false otherwise get component returns a component of the component class or undefined if it is not present eject removes the entity from all selectors and sets its id to zero calls the destructor method of each component if it is present | ecs entity-component-system gamedev | os |
Front-end-Developer-Interview-Answers | front end job interview questions this repo contains a number of front end interview questions that can be used when vetting potential candidates it is by no means recommended to use every single question here on the same candidate that would take hours choosing a few items from this list should help you vet the intended skills you require rebecca murphey http rmurphey com s baseline for front end developers http rmurphey com blog 2012 04 12 a baseline for front end developers is also a great resource to read up on before you head into an interview note keep in mind that many of these questions are open ended and could lead to interesting discussions that tell you more about the person s capabilities than a straight answer would a name toc table of contents a 1 original contributors contributors 1 general questions general 1 html questions html 1 css questions css 1 js questions js 1 jquery questions jquery 1 coding questions jscode 1 fun questions fun a name contributors original contributors a the majority of the questions were plucked from an oksoclap http oksoclap com thread created originally by paul irish http paulirish com paul irish http twitter com paul irish and contributed to by the following individuals bentruyman http twitter com bentruyman http bentruyman com cowboy http twitter com cowboy http benalman com ajpiano http ajpiano http ajpiano com slexaxton http twitter com slexaxton http alexsexton com boazsender http twitter com boazsender http boazsender com miketaylr http twitter com miketaylr http miketaylr com vladikoff http twitter com vladikoff http vladfilippov com gf3 http twitter com gf3 http gf3 ca jon neal http twitter com jon neal http twitter com jon neal wookiehangover http twitter com wookiehangover http wookiehangover com iansym http twitter com iansym http twitter com iansym toc a name general general questions a what did you learn yesterday this week depends on person me vertical align by line height what excites or interests you about coding depends on person me i like bring life to interfaces talk about your preferred development environment os editor browsers tools etc windows because of ie webstorm chrome terminal can you describe your workflow when you create a web page write html add css add js run web storm and chrome can you describe the difference between progressive enhancement and graceful degradation progressive enhancement from less functionality growing to full functional graceful degradation made full functional and then made fallbacks bonus points for describing feature detection check is feture is exist use fallback if not explain what semantic html means html consist of only of information all styling is on css how would you optimize a websites assets resources minimize http calls use cdn set expires headers use gzip put css on top put scripts on bottom make javascript and css external minify javascript and css avoid redirects reduce dom size dont scale images on client why is it better to serve site assets from multiple domains how many resources will a browser download from a given domain at a time depends on browser 2 8 name 3 ways to decrease page load perceived or actual load time if you jumped on a project and they used tabs and you used spaces what would you do suggest the project utilize something like editorconfig http editorconfig org conform to the conventions stay consistent issue retab command me change settings in my ide write a simple slideshow page bonus points if it does not use js what tools do you use to test your code s performance profiler jsperf dromaeo google dev tools if you could master one technology this year what would it be advanced algorithm s explain the importance of standards and standards bodies without standards you cant be sure that you code or design works as you expect to what is fouc how do you avoid fouc flash of unstyled content put css in head you can also made loader but i did not like it toc a name html html questions a what s a doctype do it tells the browser which version of html or xml it needs to use while rendering the page what s the difference between standards mode and quirks mode quirks mode is used for legacy browsers while standards mode follows the w3c specification what are the limitations when serving xhtml pages more strict that classic html xhtml does not have good browser support are there any problems with serving pages as application xhtml xml ie 8 and older does not handle it how do you serve a page with content in multiple languages from html point of view use utf mostly server side problem what kind of things must you be wary of when design or developing for multilingual sites right to left languages what are data attributes good for store custom data in dom node consider html5 as an open web platform what are the building blocks of html5 localstorage video data canvas describe the difference between cookies sessionstorage and localstorage cookies only 4kb and send allways sessionstorage 5mb for session localstorege 5mb toc a name css css questions a describe what a reset css file does and how it s useful set default s to main elements like marhin paddings border to 0 describe floats and how they work align element on right or left side and text will flow around it what are the various clearing techniques and which is appropriate for what context explain css sprites and how you would implement them on a page or site css sprite is set of small images stored in one image background property will help you what are your favourite image replacement techniques and which do you use when css property hacks conditionally included css files or something else how do you serve your pages for feature constrained browsers what techniques processes do you use what are the different ways to visually hide content and make it available only for screen readers media queries have you ever used a grid system and if so what do you prefer foundation have you used or implemented media queries or mobile specific layouts css used any familiarity with styling svg how do you optimize your webpages for print media query what are some of the gotchas for writing efficient css what are the advantages disadvantages of using css preprocessors sass compass stylus less if so describe what you like and dislike about the css preprocessors you have used how would you implement a web design comp that uses non standard fonts webfonts font services like google webfonts typekit etc explain how a browser determines what elements match a css selector explain your understanding of the box model and how you would tell the browser in css to render your layout in different box models box model it s how you calc size of box box sizing can help you set different aproaches toc a name js js questions a explain event delegation event delegation it s when parant recieve a event explain how this works in javascript depends on how it s call explain how prototypal inheritance works how do you go about testing your javascript qunit jasmine and bunch of staff amd vs commonjs what s a hashtable datasctructure explain why the following doesn t work as an iife function foo what needs to be changed to properly make it an iife what s the difference between a variable that is null undefined or undeclared how would you go about checking for any of these states what is a closure and how why would you use one what s a typical use case for anonymous functions callback explain the javascript module pattern and when you d use it bonus points for mentioning clean namespacing what if your modules are namespace less how do you organize your code module pattern classical inheritance what s the difference between host objects and native objects difference between javascript function person var person person var person new person what s the difference between call and apply explain function prototype bind when do you optimize your code can you explain how inheritance works in javascript when would you use document write most generated ads still utilize document write although its use is frowned upon what s the difference between feature detection feature inference and using the ua string explain ajax in as much detail as possible explain how jsonp works and how it s not really ajax have you ever used javascript templating if so what libraries have you used mustache js handlebars etc explain hoisting describe event bubbling what s the difference between an attribute and a property why is extending built in javascript objects not a good idea why is extending built ins a good idea difference between document load event and document ready event what is the difference between and explain how you would get a query string parameter from the browser window s url explain the same origin policy with regards to javascript describe inheritance patterns in javascript make this work javascript 1 2 3 4 5 duplicate 1 2 3 4 5 1 2 3 4 5 describe a strategy for memoization avoiding calculation repetition in javascript why is it called a ternary expression what does the word ternary indicate what is the arity of a function what is use strict what are the advantages and disadvantages to using it toc a name jquery jquery questions a explain chaining explain deferreds what are some jquery specific optimizations you can implement what does end do how and why would you namespace a bound event handler name 4 different values you can pass to the jquery method selector string html string callback function htmlelement object array element array jquery object etc what is the effects or fx queue what is the difference between get and eq what is the difference between bind live and delegate what is the difference between and fn or just what is fn optimize this selector javascript foo div bar eq 0 difference between delegate and live toc a name jscode code questions a javascript 3 14 question what value is returned from the above statement answer 3 javascript i m a lasagna hog split reverse join question what value is returned from the above statement answer goh angasal a m i javascript window foo window foo bar question what is the value of window foo answer bar only if window foo was falsey otherwise it will retain its value javascript var foo hello function var bar world alert foo bar alert foo bar question what is the outcome of the two alerts above answer hello world referenceerror bar is not defined javascript var foo foo push 1 foo push 2 question what is the value of foo length answer 2 javascript var foo foo bar hello question what is the value of foo length answer undefined toc a name fun fun questions a what s the coolest thing you ve ever coded what are you most proud of what are your favorite parts about the developer tools you use do you have any pet projects what kind what s your favorite feature of internet explorer toc | front_end |
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ITMO-Embedding-System-Design | embedding system design id 192050191 name wang jiayi br br this is my lab work of embedding system design here is the link of reports lab 2 report https github com joey9503 itmo embedding system design tree master lab 2 readme md br br lab 3 report https github com joey9503 itmo embedding system design tree master lab 3 readme md br br lab 4 report https github com joey9503 itmo embedding system design tree master lab 4 readme md | os |
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BioM-Transformers | biom transformers building large biomedical language models with bert albert and electra paper link https www aclweb org anthology 2021 bionlp 1 24 abstract the impact of design choices on the performance of biomedical language models recently has been a subject for investigation in this paper we empirically study biomedical domain adaptation with large transformer models using different design choices we evaluate the performance of our pretrained models against other existing biomedical language models in the literature our results show that we achieve state of the art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature our findings highlight the significant effect of design choices on improving the performance of biomedical language models pre trained models pytorch biom electra base discriminator link https huggingface co sultan biom electra base discriminator biom electra base generator link https huggingface co sultan biom electra base generator biom electra large discriminator link https huggingface co sultan biom electra large discriminator biom electra large generator link https huggingface co sultan biom electra large generator biom bert pubmed pmc large link sultan biom bert pubmed pmc large biom albert xxlarge link https huggingface co sultan biom albert xxlarge biom albert xxlarge pmc link https huggingface co sultan biom albert xxlarge pmc pmc for 64k steps biom electra large squad2 link https huggingface co sultan biom electra large squad2 biom albert xxlarge squad2 link https huggingface co sultan biom albert xxlarge squad2 pre trained lm models tensorflow model corpus vocab batch size training steps link biom electra base pubmed abstracts 29k pubmed 1024 500k link https drive google com file d 1 dobjaim8mhqwsobplov6inyhvdxg6ru view usp sharing biom electra large pubmed abstracts 29k pubmed 4096 434k link https drive google com file d 1 60kzbf7x8y5xizpdniqhql82zpoyene view usp sharing biom bert large pubmed abstracts pmc 30k en wiki books corpus 4096 690k link https drive google com file d 1 fxvp98uipgbnamojahpirbcyhokvaws view usp sharing biom albert xxlarge pubmed abstracts 30k pubmed 8192 264k link https drive google com file d 1 artcfguej8x9feuk8adcslsshtcopvr view usp sharing biom albert xxlarge pmc pubmed abstracts pmc 30k pubmed 8192 64k link https drive google com file d 1 8os2gv97wufje52yscfcork2zx8kznu view usp sharing squad fine tuned checkpoints tensorflow model exact match em f1 score link biom electra base squad2 81 35 84 20 link https drive google com file d 1z0mndygveg7ntbr7szjvt3ozwlbwi7c4 view usp sharing biom electra large squad2 85 48 88 27 link https drive google com file d 1 kvvn 0tjkmmxcrbrgiuo5ln5kbjj47e view usp sharing biom electra large mnli squad2 85 24 88 01 link https drive google com file d 18bw62owaawh4gyo1htwresd3rdsfbfeo view usp sharing biom albert xxlarge squad2 83 86 86 99 link https drive google com file d 1 hhlpxiypm fxtnq cxnosidux8kwkgp view usp sharing biom albert xxlarge mnli squad2 84 35 87 31 link https drive google com file d 1 g793o1jtfpagqtjig nug4z q8zt4gw view usp sharing we implement transferability between mnli and squad which was explained in details by jeong et al 2020 https arxiv org abs 2007 00217 we detailed our particpiation in bioasq9b in this paper http ceur ws org vol 2936 paper 14 pdf to check the performance of our systems udel lab from the official bioasq leaderboard visit http participants area bioasq org results 9b phaseb gluonnlp mxnet checkpoints more information about glounnlp https github com dmlc gluon nlp model link biom electra base link https drive google com file d 1kebw cfkw31ukhplp53h8uulqzaiqpwl view usp sharing biom electra large link https drive google com file d 1u49i9h8fah1m5dgtzwbzbai iqtlwb6b view usp sharing model exact match em f1 score link biom electra base squad2 80 93 83 86 link https drive google com file d 1xfetcucddn66461ggdhfxcuevbhy1tck view usp sharing biom electra large squad2 85 34 88 09 link https drive google com file d 1eulgkhsn8vpczv3dxx1ckl8mx9 gpy2 view usp sharing colab notebook examples biom electra large on ner and chemprot task open in colab colab https colab research google com github salrowili biom transformers blob main examples example of ner and chemprot task on tpu ipynb biom electra large on squad2 0 and bioasq7b factoid tasks open in colab colab https colab research google com github salrowili biom transformers blob main examples example of squad2 0 and bioasq7b tasks with biom electra large on tpu ipynb biom albert xxlarge on squad2 0 and bioasq7b factoid tasks open in colab colab https colab research google com github salrowili biom transformers blob main examples example of squad2 0 and bioasq7b tasks with biom albert xxlarge on tpu ipynb text classification task with huggingface transformers and pytorchxla on free tpu open in colab colab https colab research google com github salrowili biom transformers blob main examples fine tuning biomedical models on text classification task with huggingface transformers and pytorch xla ipynb fine tunning biom transformers on question answering dataset with tpu and torch xla open in colab colab https github com salrowili biom transformers blob main examples fine tuning biom transformers on squad on tpu with pytorch xla ipynb reproducing our blurb results with jax open in colab colab https colab research google com github salrowili biom transformers blob main examples blurb leaderboard with tpu vm ipynb acknowledgment we would like to acknowledge the support we have from tensorflow research cloud tfrc team to grant us access to tpuv3 units citation biom transfomers model bibtex inproceedings alrowili shanker 2021 biom title b io m transformers building large biomedical language models with bert albert and electra author alrowili sultan and shanker vijay booktitle proceedings of the 20th workshop on biomedical language processing month jun year 2021 address online publisher association for computational linguistics url https www aclweb org anthology 2021 bionlp 1 24 pages 221 227 abstract the impact of design choices on the performance of biomedical language models recently has been a subject for investigation in this paper we empirically study biomedical domain adaptation with large transformer models using different design choices we evaluate the performance of our pretrained models against other existing biomedical language models in the literature our results show that we achieve state of the art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature our findings highlight the significant effect of design choices on improving the performance of biomedical language models bibtex article alrowili2021large title large biomedical question answering models with albert and electra author alrowili sultan and shanker k url http ceur ws org vol 2936 paper 14 pdf journal clef working notes year 2021 colab https colab research google com assets colab badge svg | ai |
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tp-itba-python-applications | solution for building a data system with airflow generic badge https img shields io badge apache airflow 2 2 2 blue svg https airflow apache org generic badge https img shields io badge python 3 6 blue svg https www python org generic badge https img shields io badge postgresql 13 blue svg https www postgresql org generic badge https img shields io badge pytest 6 2 5 blue svg https pytest org generic badge https img shields io badge apache superset 1 5 0 blue svg https superset apache org generic badge https img shields io badge docker 20 10 6 blue svg https www docker com the goal of this practice is to build a system that logs the daily price of different stocks exercise 1 the first thing we have to do is downloading the official docker compose yaml file obtained from here https airflow apache org docs apache airflow stable start docker html docker compose yaml by running teh following curl lfo https airflow apache org docs apache airflow 2 2 2 docker compose yaml we change one parameter to to disable the automatic loading of examples to avoid ui clutter airflow core load examples true to airflow core load examples false after this we set the user by generating the airflow uid and store it on the env file by running the follwoing command echo e airflow uid id u nairflow gid 0 env now we are ready to build and initiate airflow docker compose up airflow init docker compose up after this initial setup an airflow instance should be reachable in http localhost 8080 home the default username and password are airflow airflow exercise 2 add a postgresql database on the docker compose yaml file that we are going to use for storing the stocks data for this we are adding a new postgres service on the docker compose yaml called postgres local postgres local image postgres 13 environment postgres user python postgres password python postgres db python container name postgres local healthcheck test cmd pg isready u python interval 5s retries 5 restart always also i added some environment variables on the x airflow common part postgres host postgres local postgres user python postgres db python postgres password python so if we build and run again the docker compose yaml file we are going to have this new database available exercise 3 to develop the data model to store the daily stock data symbol date open high low close i modified both dags models py and dags create tables py files on the first file i define the behaviour and columns of stock value table using sqlalchemy s declarative base and on the second file i create the code that creates the table on the database to create the table i added a new service on the docker compose yaml called create stocks db which executes a dockerfile that runs the dags create tables py script so by building and running again the docker compose yaml we are going to have the table created on the database exercise 4 created the dags sqlite cli py file to connect with the connect with the postgres db exercise 5 created the dags stocks data py script which has the dag definition to make requests to https www alphavantage co api searching for google goog microsoft msft and amazon amzn data and stores it on our local postgres the database we added on the docker compose yaml on the exercise 2 the implemented dag called stocks dag has currently only one task get daily data which is in charge of making the request to the api and storing the returned data to the postgresql database assets exercise five png some important mentions around the dag definition first i added a 60 second time sleep at the beginning of the request api task to prevent the api returning an error on frequent requests 5 per minute when reading the dag definition we find the following with dag stocks dag schedule interval 0 0 1 5 start date datetime 2021 10 1 catchup true max active runs 1 default args default args as dag this means that we are only running the dag on weekdays schedule interval 0 0 1 5 beacause there is no price information on weekends also we are backfilling our database with values from 2021 10 01 and the backfills are run sequentially to prevent the frequency cap on requests max active runs 1 once the ui reads the dag all the dag runs since 2021 10 01 are queued and executed sequentially exercise 6 create a second task on the stocks dag dag that depends on the first task this second task is in charge of pulling data from the postregsql database creating a plot with that data and storing it on dags tmp files stocks plots directory the plot consist of a 7 day rolling average using 7 calendar consecutive days i am not skipping the days where i don t have any information on the db of each stock closing price so we are generating one plot per stock symbol per day plotting the 7 day rolling average of the price with data since 2021 10 01 to the dag run date the stocks dag dag graph is modified as you can see on the following image assets exercise six png here we also modified the env file adding matplotlib 3 3 4 as a dependency if not airflow is not going to be able to create the necessary plots by adding it there we ensure that matplotlib will be installed when building and running the docker compose yaml file so now our env file has the following inside airflow uid 502 airflow gid 0 pip additional requirements matplotlib 3 3 4 exercise 7 added two unit tests one that tests the extraction and formatting before storing it on the database and the other one that tests the values before creating the plot to accomplish this i am going to use pytest https docs pytest org by adding pytest and pytest mock on our env file so the dependencies can be installed also i am mocking both the api response and the postgresql query response these tests are stored on dags test test functions py directory and can be executed on the terminal as follows docker exec it tp itba python applications airflow webserver 1 bin bash default ec9b4f842bdd opt airflow pytest dags we are going to see the following response on the terminal to check that both tests have passed assets exercise seven png exercise 8 implement a ci step using github actions https docs github com en actions to run the unit tests using pytest each time a commit is pushed to a branch in a pr we should see these in case the ci ran ok assets exercise eight a png assets exercise eight b png bonus points 1 added the configurations for pylint https pylint org on pylintrc file and black https black readthedocs io en stable on pyroject toml file and added a new workflow on github actions https docs github com en actions to run both each time a commit is pushed to a branch in a pr we should see these in case the ci ran ok assets bonus points one a png assets bonus points one b png bonus points 2 another way to display the 7 day rolling window average plots is by displaying them on a bi tool here we are going to build apache superset https superset apache org and build a dashboard there to show the 7 day rolling window average closing price evolution for each of the three stocks we are going to use the offcial docker compose yaml file to build locally apache superset we can get the yaml file from here https github com apache superset blob master docker compose non dev yml now we have to make a small modification on docker compose non dev yml file mainly to add the same network as our airflow project so we can access to the postgresql database and then run the docker compose non dev yml file so to add our created network is as simple as adding this command on docker compose non dev yml file networks default external true name tp itba python applications default once we added this we can run the docker compose non dev yml file cd superset docker compose f docker compose non dev yml up once everytihing is up and running we can reach superset ui in http localhost 8088 the default username and password are admin admin there we can go to data databases and create a connection to our postgresql databses that has the stocks information afert doing that we can set up the dashbords plotting the 7 day moving average closing price evolution and enable the user to select which stock symbol he wants to analyse assets bonus points two a png assets bonus points two b png assets bonus points two c png | airflow docker postgresql pytest python supertest | cloud |
React-Native | react native app screen development using react native for frontend and json as backend if paid by you show it in green color and sign with it else show it in red color and sign with it total payable sum amount where paid by you sum amount where paid by you if total payable is positive show it in green card else show it in red card without any sign if total payable is positive show get credit button else show settle up button whatsapp image 2022 01 21 at 7 30 03 pm https user images githubusercontent com 54104463 150539722 939097ec ae5f 49fd ab19 62ba0bdf0738 jpeg screenshot of the app screen | server |
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geogalactica | div style text align center centering img src https big cheng com geogal geogal orange png alt geogal logo width 400 h2 a larger foundation language model in geoscience h2 div a href img src https img shields io badge paper arxiv c71585 a a href https huggingface co geobrain ai geogalactica img src https img shields io badge f0 9f a4 97 20hugging face geogalactica red img a a href https huggingface co datasets daven3 geosignal img src https img shields io badge dataset geosignal 4169e1 img a a href https huggingface co datasets daven3 geobench img src https img shields io badge dataset geobench 4169e1 img a technical report is on the way the data pre processing toolkits are open sourced on sciparser https github com davendw49 sciparser introduction coming soon resources code https github com geobrain ai geogalactica data https huggingface co datasets daven3 geobench https huggingface co datasets daven3 geosignal and https github com zthang geotools model https huggingface co geobrain ai geogalactica plot https github com dbylynn geogalactica analysis sciparser https github com davendw49 sciparser quick start coming soon code coming soon cases coming soon contributors this project was founded by acemap at shanghai jiao tong university leading by zhouhan lin https hantek github io and a group of students including cheng deng https big cheng com student leader le zhou https github com lzhou1998 tianhang zhang https github com zthang yi xu https github com xyjigsaw yutong xu https github com xyt fe beiya dai https github com dbylynn qiyuan chen mailto q224chen uwaterloo ca yuanyuan shi https github com syy yoyo and zhongmou he https github com twelfth star supervised by zhouhan lin https hantek github io junxian he https jxhe github io and xinbing wang the whole project is supported by chenghu zhou and the institute of geographical science natural resources research chinese academy of sciences and deep time digital earth big science project https www iugs org dde acknowledgements geogalactica has referred to the following open source projects we want to express our gratitude and respect to the researchers of the projects facebook galactica https galactica org facebook llama https github com facebookresearch llama stanford alpaca https github com tatsu lab stanford alpaca alpaca lora by tloen https github com tloen alpaca lora alpaca gp4 by chansung park https github com tloen alpaca lora issues 340 k2 by cheng deng https github com davendw49 k2 geogalactica is under the support of chenghu zhou and the institute of geographical science natural resources research chinese academy of sciences and deep time digital earth big science project https www iugs org dde we would also like to express our appreciation for the effort of data processing and annotation from the students in cas to do series of applications with geogalactica license geogalactica is a research preview intended for non commercial use only subject to the model license of galactica and the terms of use of the data generated by openai please contact us if you find any potential violations the code is released under the apache license 2 0 the data geosignal and geobench is open sourced by k2 https github com davendw49 k2 if you want to subscribe the data you can emaill us davendw sjtu edu cn mailto davendw sjtu edu cn citation if you use the code or data of geogalactica please declare the reference with the following coming soon | ai |
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azure-iot-pcs-device-simulation | build build badge build url issues issues badge issues url gitter gitter badge gitter url azure iot device simulation note as of may 6th 2021 the device simulation solution accelerator is no longer supported this repository has been archived overview as iot is growing so is the need for technologies to help accelerate the development and testing of new and existing iot solutions azure iot device simulation helps you build simulated devices that look and behave like the real thing enabling powerful testing scenarios with this project you can gather process analyze and act on data from simulated devices allowing you to test your solutions near end to end you can evaluate how well your solution processes manages and presents the data being generated by your simulated devices get up and running instantly by using sample devices and sample simulations when you care less about the actual data and just want to see data flowing through the system get a prototype up and running quickly and iterate at amazing speeds by adjusting simulated device behavior on the fly prove out the idea before investing in costly hardware you can create custom devices through the ui to generate a prototype device in seconds validate the solution works as expected from device to solution by simulating real world device behaviors you can script complex device behaviors using javascript to ensure the most accurate simulated device telemetry possible build confidence in your solution by simulating both normal peak and beyond peak load conditions make device simulation your own by modifying our open source code to meet your specific needs device simulation screen shot docs simulationscreenshot png common scenarios 1 load testing customers will be able to perform large scale tests using device simulation individual simulations will support up to 20 000 simulated devices 2 iot demos device simulation now includes sample simulations that can be started with a click this allows for instant demos showcasing device data flowing to an iot hub 3 simple pocs this scenario was supported in the initial release of device simulation however there are more capabilities here customers can create device models and re use them for easily repeatable simulations individual simulations can also now support a mix of multiple device types 4 advanced device behaviors customers will be able to write custom json device definition files and script device behaviors using javascript this allows for very complex scenarios allowing a device to mimic the behavior of the real thing e g vehicles following roads interior temperature varying based on exterior temperature etc 5 simulation driven development customers can use device simulation to generate both expected and unexpected telemetry for continuous testing while developing their solution on top of azure iot getting started to get started with this project deploy it to your azure subscription follow the deployment instructions deployment readme md to get started project structure the solution contains the following projects and folders webservice asp net web api exposing a restful api for simulation functionality e g start stop add devices etc this is also the service entry point starting all the main threads pcs config dotnet configuration library used to create and store configuration settings specific to your deployment pcs diagnostics dotnet diagnostics library used to collect metrics on how the application is used and optionally send this data to microsoft simulation service microservice that manages all simulations storage service microservice that allows management of key values abstracting the storage semantic and store data of any format using azure cosmosdb webui the web user interface for device simulation the service provides a restful endpoint to configure the simulation details to start and stop the simulation to add and remove virtual devices and more deployment scripts to configure and deploy other resources device models specification https github com azure device simulation dotnet wiki device models simulation service api specs https github com azure device simulation dotnet wiki 5bapi specifications 5d service device models api specs https github com azure device simulation dotnet wiki 5bapi specifications 5d device models simulations api specs https github com azure device simulation dotnet wiki 5bapi specifications 5d simulations contributing refer to our contribution guidelines docs contributing md license copyright c microsoft corporation all rights reserved licensed under the mit license license build badge https img shields io travis azure azure iot pcs simulation svg build url https travis ci org azure azure iot pcs simulation issues badge https img shields io github issues azure azure iot pcs simulation svg issues url https github com azure azure iot pcs simulation issues gitter badge https img shields io gitter room azure iot solutions js svg gitter url https gitter im azure iot solutions | server |
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llllm | llllm a suite of tools to perform geospatial operations using large language models llllm stands for lat lng large language model you can call it as el el el el emm or l4m setup 1 create the llllm env mamba env create f environment yaml 2 set your openai api key as an environment variable export openai api key your key here getting started adding a new tool tools are ways the agent can use to interact with the outside world you can find more information on how llms use tools to solve new tasks at scale in this paper toolformer https arxiv org pdf 2302 04761 pdf langchain comes bundled with a set of tools like google search wikipedia python repl shell wolfram alpha several others find the list of pre built tools here https python langchain com en latest modules agents tools html creating a new tool is simple in langchain you can create a custom tool by using the tool dataclass or inhering from the basetool class both these methods need to provide name unique referred to by llm while executing the chains description detailed description of when how the llm should use this tool args schema arguments to the tool barebones of how to define a custom tool by inheriting from the basetool class python from langchain tools import basetool class yourcustomtool basetool name name description detailed description args schema pydanticmodel def run self query functionality of the tool pass def arun self query async implementation of the tool optional raise notimplementederror we have a few tools available in the llllm toolkit that you can use for reference mercantiletool tools mercantile tool py geopytool tools geopy osmnxtool tools osmnx learn more about creating custom tools by reading this blog on building custom tools for llm agents from pinecone https www pinecone io learn langchain tools or documention from langchain https python langchain com en latest modules agents tools custom tools html creating an agent agents act like routers using llms to decide which tools to use for the task at hand there are different types of agents available in langchain react https python langchain com en latest modules agents agents examples react html reason act agents are optimized for picking tools for best response read more about them in this paper https react lm github io conversation agent https python langchain com en latest modules agents agents examples chat conversation agent html they are ideal to use in a conversational setting structured tool chat agent https python langchain com en latest modules agents agents examples structured chat html use this agent if you have tools that expect multi input parameters check the osmnx or geopy tool for reference usage python from agents l4m agent import base agent agent base agent llm llm object instance of an llm like gpt 3 5 turbo or gpt 4 tools tool1 tool1 list of tools the agent has access to perform its tasks name name of agent agent type for eg zero shot react description or structure chat zero shot react description for multi input tools creating notebooks to test import the tools create an agent in jupyter notebook to interact with them please find example notebooks here nbs running the streamlit app streamlit run app py | ai |
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augemented-analysis-for-industrial-data | augemented analysis for industrial data development of automatic data semantic information composition expression technology based on augmented analysis for diagnosing industrial data status and maximizing improvement research area data visualization visual analytics augemented analysis eye gaze tracking virtual reality deep learning volume rendering data quality causal modeling and analysis traffic analysis eeg analysis knowledge map members yun jang jiyun jung sangbong yoo changyoung yoon soobin yim changyoung jung seokhwan choi seongbum seo seongwon choi instituions sejong university seoul south korea contacts vis sejong ac kr | server |
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Blockchain | nitdablockchainscholarship non officially study plan if you fail to plan you are planning to fail benjamin franklin success is no accident it is hard work perseverance learning studying sacrifice and most of all love of what you are doing or learning to do img align center alt coding src https media giphy com media btrtnpmpq8uorcrbwg giphy gif onboarding programme will start on 8 december 2022 where you will delve deeper into the programme and complete a series of assignments if you finish among the top 30 of the class you will be onboarded into the main blockchain programme img align center alt coding src https media giphy com media kfhbj8jn52ucpspcyi giphy gif core curriculum must prove your capabilities in the first 2 weeks by getting a cumulative score that puts you in the 30th percentile in all quizzes and projects in that period in order to progress in the programme if you don t meet this requirement you will no longer be part of the programme after 2 weeks pre requisites git https adamsgeeky github io blockchain docs recources tutorial basics gitgithub github https adamsgeeky github io blockchain docs recources tutorial basics github difference between web1 web2 web3 https geekink hashnode dev blockchain technologies details summary the first 2 weeks summary bitcoin theory introduction https adamsgeeky github io blockchain docs category bitcoin theory how bitcoin works https adamsgeeky github io blockchain docs recources btheory intro objective chapter 1 abstract objective x peer to peer cash x digital signatures and trusted third parties x peer to peer network x time chain and proof of work x cpu power x cooperation in the network x network structure x messaging between nodes chapter 2 introduction objective details summary commerce on the internet summary has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments while the system works well enough for most transaction it still suffers from the inherent weaknesses of te the trust based model such as hight transaction cost transaction par day limit details details summary non reversible transactions summary completely non reversible transaction are not possible since financials instaituion cannot avoid mediating disputes the cost of mediating increases transactions costs limiting the minimum practical transaction size and cutting off the possibility fr small casual transactions and there is a broader cost in the loss of ability to make non reversible payments for non reversible services details details summary privacy in commerce summary with the possibility of reversal the need for trust spreads merchants who be wary of their customers hassling them for move information than they would otherwise need this bring a huge problem for privacy of the good actor within the system as their identity details often end up being stored in large merchant database with their corresponding payment details details details summary the paradigm of fraud acceptance summary a certain percentage of fraud is accepted as unavoidable traditional payment system there cost and payment uncertainties can be avoided in person by using physical currency but no mechanism exists to make payments over a communications channel without a trusted party details bitcoin solve all the above mention problems details summary what is needed summary is an electronic payment system based on cryptographic proof instead of trust allowing any two willing parties to transact directly with each other without the need of trusted third party bank bitcoin achieve this by using digital signature and a simple but fully feature scripting language by using bitcoin receive can quickly and simply validate that funds were indeed controlled that is they own it by the sending party and that the transaction correctly allocate the amount to their control without additional validation by third parties details details summary protecting sellers from fraud summary details details summary proposed solution summary details details summary security and honesty summary details chapter 3 transactions objective x electronic coins is the chain of digital signature spending a coin payee verification existing solutions first seen rule broadcasting transactions achieving consensus proof of acceptance chapter 4 timestamp server objective timestamped hashes a chain of timestamped hashes timestamp server video chapter 5 proof of work objective hashcash scanning random space nonce immutable work chained effort one cpu one vote the majority decision the honest chain attacking the longest chain controlling the block discovery rate chapter 6 network objective section read through running the network the longest chain simultaneous blocks breaking the tie missed messages chapter 7 incentive objective the coinbase transaction coin distribution mining analogy transaction fees the end of inflation encouraging honesty the attacker s dilemma incentive video chapter 8 reclaiming disk space objective spent transactions the merkle tree compacting blocks block headers chapter 9 simplified payment verification objective full network nodes merkle branches transaction acceptance verification during attack situations maintaining an attack invalid block relay system businesses running nodes chapter 10 combining and splitting value objective dynamically sized coins inputs and outputs a typical example fan out chapter 11 privacy objective traditional models privacy in bitcoin public records stock exchange comparison key re use linking inputs chapter 12 calculations objective attacking the chain things the attacker cannot achieve the only thing the attacker can achieve the binomial random walk the gambler s ruin exponential odds waiting for confirmation attack via proof of work vanishing probabilities details pre requisites what is program programming programming language compiler and interpreter https geekink hashnode dev programming fundamentals javascript objectives identify interactions on web pages created with javascript articulate in general terms the importance of how javascript was developed and how that impacts the way javascript is written identify properly formed semantic html articulate major concepts in css identify properly formed css syntax write simple javascript statements in the web browser console assign and retrieve values from variables and arrays in javascript course outline 1 introduction to javascript https adamsgeeky github io blockchain docs recources js intro 2 javascript variables 3 javascript arrays 4 module summary golang course overview this course covers the fundamental elements of go data types protocols formats and writing code that incorporates rfcs and json details summary an introduction of go why go is worth learning summary golang build simple secure scalable systems x what is go go or golang is a programming language developed at google it has received a lot of acclaim from developers for its speed and straightforward syntax an open source programming language supported by google easy to learn and great for teams built in concurrency and a robust standard library large ecosystem of partners communities and tools x why go is worth learning golang is a compiled multi threaded programming language based on open source c and developed internally at google it is a single language that allows different processes to run simultaneously which means simultaneous programming extremely fast easy to maintain and efficient golang has all the advantages needed for distributed systems because it can handle multiple parts of the blockchain concurrently the language was developed for high performance programs running on modern distributed systems and multicore processors market participants perceived the launch of golang as an attempt to create a replacement for c c today the hyperledger fabric blockchain platform uses this programming language it is often used in nft marketplace development examples of blockchain projects that use golang gochain dero loom network ethereum hyperledger fabric details course outline overview objects concurrency installing go https adamsgeeky github io blockchain docs recources golang local setting up go locally how to compile and run go programs https adamsgeeky github io blockchain docs recources golang structure workspaces packages go tool variables https adamsgeeky github io blockchain docs recources golang varibles variable initialization https adamsgeeky github io blockchain docs recources golang varibles data types pointers https adamsgeeky github io blockchain docs recources golang datatype variable scope http localhost 3000 blockchain docs recources golang varibles deallocating memory garbage collection comments printing integers ints floats strings https adamsgeeky github io blockchain docs recources golang datatype string packages constants https adamsgeeky github io blockchain docs recources golang varibles control flow control flow scan composite data types arrays https adamsgeeky github io blockchain docs recources golang datatype slices https adamsgeeky github io blockchain docs recources golang datatype variable slices https adamsgeeky github io blockchain docs recources golang datatype hash tables maps https adamsgeeky github io blockchain docs recources golang datatype structs https adamsgeeky github io blockchain docs recources golang datatype protocols and formats rfcs json file access ioutil file access os become familiar with go code introduction of source code elements keywords and identifiers basic types and their value constants and variables also introduces untyped values and type deductions common operators also introduces more type deduction rules function declarations and calls code packages and package imports expressions statements and simple statements basic control flows goroutines deferred function calls go type system go type system overview a must read to master go programming pointers structs value parts to gain a deeper understanding into go values arrays slices and maps first class citizen container types strings functions function types and values including variadic functions channels the go way to do concurrency synchronizations methods interfaces value boxes used to do reflection and polymorphism type embedding type extension in the go way type unsafe pointers generics use and read composite types reflections the reflect standard package some special topics line break rules more about deferred function calls some panic recover use cases explain panic recover mechanism in detail also explains exiting phases of function calls code blocks and identifier scopes expression evaluation orders value copy costs in go bounds check elimination concurrent programming concurrency synchronization overview channel use cases how to gracefully close channels other concurrency synchronization techniques the sync standard package atomic operations the sync atomic standard package memory order guarantees in go common concurrent programming mistakes memory related memory blocks memory layouts memory leaking scenarios details details summary bitcoin blockchain for 5 weeks summary details details summary project for 5 weeks summary details img align center alt coding src https media giphy com medial4jyy0qtljtlczowm giphy gif | blockchain blockchain-technology nigeria bsv-blockchain | blockchain |
Web_Development | web development materials related to web development github repo https github com riti2409 web development source https www youtube com playlist list plu0w 9lii9agicuzyrsvtgtxdxkzpyitg code with harry react https www youtube com playlist list plu0w 9lii9agx66oznt6iyhcmibumnmdt pdf https drive google com file d 1pdqyp3nigf4hwou2b2nmraz8nesiyn8r view usp sharing | front_end |
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SkinGPT-4 | pre trained multimodal large language model enhances dermatological diagnosis using skingpt 4 juexiao zhou https www joshuachou ink xiaonan he liyuan sun jiannan xu xiuying chen yuetan chu longxi zhou xingyu liao bin zhang xin gao king abdullah university of science and technology kaust a href skingpt 4 manuscript v7 pdf img src https img shields io badge paper pdf red a installation conda env create f environment yml conda activate skingpt4 conda install c conda forge mamba 1 4 7 pip install einops mamba install pytorch 2 0 0 torchvision 0 15 0 torchaudio 2 0 0 pytorch cuda 11 8 c pytorch c nvidia download our trained weights our previous trained weights for skin disease diagnosis with only step 1 dataset and vicuna could be downloaded at skingpt v1 pth https drive google com file d 1pgbmbioipgxn5yfx6okx4bgypbm1praf view usp sharing the latest model trained with both public datasets and the proprietary dataset based on falcon 40b instruct and llama 2 13b chat hf are not publicly available currently please feel free to keep in touch with xin gao kaust edu sa and juexiao zhou kaust edu sa for potential collaboration launching demo locally python demo py cfg path eval configs skingpt4 eval vicuna yaml gpu id 0 python demo py cfg path eval configs skingpt4 eval falcon40b yaml gpu id 0 python demo py cfg path eval configs skingpt4 eval llama2 13bchat yaml gpu id 0 illustraion of skingpt 4 fig1 https cdn jsdelivr net gh joshuachou2018 oss main upic adrerl fig1 png examples of skin disease diagnosis fig3 https cdn jsdelivr net gh joshuachou2018 oss main upic fig3 uigbum png clinical evaluation fig4 https cdn jsdelivr net gh joshuachou2018 oss main upic b40u3b fig4 png acknowledgement minigpt 4 https minigpt 4 github io this repo is developped on minigpt 4 an awesome repo for vision language chatbot lavis vicuna falcon llama 2 citation if you re using skingpt 4 in your research or applications please cite skingpt 4 using this bibtex misc zhou2023skingpt title skingpt 4 an interactive dermatology diagnostic system with visual large language model author juexiao zhou and xiaonan he and liyuan sun and jiannan xu and xiuying chen and yuetan chu and longxi zhou and xingyu liao and bin zhang and xin gao year 2023 eprint 2304 10691 archiveprefix arxiv primaryclass eess iv | ai |
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nlp-class | a hands on introduction to natural language processing nlp about this course this course was created by prof mohammad ghassemi https ghassemi xyz in fall of 2020 as part of the cse 842 class at michigan state university the course provides a step by step guide to nlp and makes no assumptions that you have a background in the material nlp or machine learning the content in this repository will teach you 1 how to collect and process text data 2 how to generate text using language models 3 how to classify text using machine learning 4 how to use and tune state of the art sequence to sequence models including transformers 5 how to process speech signals all lectures are hosted on youtube and can be consumed at your own pace see links below at the end of most every lecture there is a tutorial homework assignment that will demonstrate how to perform nlp tasks in python the python notebooks are available through the links below and in the homework folder introduction u lectures u a introduction to the course https youtu be 4osuti8oke b overview of syllabus https youtu be 92gdhhgmjeo c overview of course tools https youtu be xgnys9rdesq d overview of the course project https youtu be jmdo5yjpioo hw0 setting up your notebook and gitlab repo homework hw0 instructions md project guidelines project instructions md nlp fundamentals and n gram language models u optional readings u chapter 2 https web stanford edu jurafsky slp3 2 pdf and chapter 3 https web stanford edu jurafsky slp3 3 pdf u lecture u introduction https youtu be lwzyb0zendm a language and its properties https youtu be 662hoepxevo b regular expressions https youtu be xv51z73yayu c text normalization https youtu be afuxv7 wufm d edit distance https youtu be snez5bees k e n gram language models https youtu be p3d7d78cj1e f generalization and smoothing https youtu be vpj iw63cfu hw1 tutorial https youtu be jdtuvnohczm hw1 and code tutorial basic data manipulations representations and statistics homework hw1 assignment ipynb niave bayes sentiment classification logistic regression u optional readings u chapter 4 https web stanford edu jurafsky slp3 4 pdf and chapter 5 https web stanford edu jurafsky slp3 5 pdf u lecture u introduction https youtu be nmvswvhqatm overview https youtu be wckpjnkcygu a numerical representations of text https youtu be qzyouqf45sq b naive bayes https youtu be tlnjxai7f m c logistic regression https youtu be tbjuazlhjvc d performance metrics https youtu be wfjk 9xrgnc hw2 tutorial https youtu be xvf1vcieqvi hw2 and code tutorial supervised language classification models and their assessment homework hw2 assignment ipynb vector semantics embeddings neural language models u optional readings u chapter 6 https web stanford edu jurafsky slp3 6 pdf and chapter 7 https web stanford edu jurafsky slp3 7 pdf pca https builtin com data science step step explanation principal component analysis word2vec https lilianweng github io lil log 2017 10 15 learning word embedding html u lecture u introduction https youtu be oc jqimrkas overview https youtu be nfw8yjo kis a simple contextual representations https youtu be lgjszcvbt3g b gradient descent https youtu be pfjime8ed g c neural networks https youtu be 9ejcknljvs4 d neural langauge models https youtu be y4zdz3mwrew hw3 tutorial https youtu be jlx 2x7gb8o hw3 and code tutorial embeddings and neural networks homework hw3 modeling text as a sequence u optional readings u chapter 8 https web stanford edu jurafsky slp3 8 pdf and chapter 9 https web stanford edu jurafsky slp3 9 pdf u lecture u overview https youtu be 5hjf1c9 icq introduction https youtu be vtzjvqhxy1w a hidden markov models https youtu be 7ak1 zdugeg b maximum entropy markov models https youtu be ftxrkzlknpq c recurrent neural networks https youtu be rmlw bky lk hw4 tutorial https youtu be jp8vuzsid4 hw4 and code tutorial sequence models homework hw4 encoder decoder models attention and transformers u optional readings u chapter 10 https web stanford edu jurafsky slp3 10 pdf viswani 2017 https arxiv org pdf 1706 03762 pdf u lecture u overview https youtu be m3d7merbsrc introduction https youtu be aj9ar9nqg1e a encoder decoder networks https youtu be xe8y1emjlmk b attention https youtu be dzght1jsjee c transformers https youtu be gyc1lvt3urw hw5 tutorial https youtu be wd14dradnry hw5 and code tutorial transformers homework hw5 constituencies parsing and dependency u optional readings u chapter 12 https web stanford edu jurafsky slp3 12 pdf chapter 13 https web stanford edu jurafsky slp3 13 pdf chapter 14 https web stanford edu jurafsky slp3 14 pdf chapter 15 https web stanford edu jurafsky slp3 15 pdf u lecture u overview https youtu be pxtry2gpmn0 introduction https youtu be el4r91dgctq a context free grammar https youtu be kq4auyzllb0 b constituency parsing https youtu be opooiyszra c statistical consituency parsing https youtu be avmq ovgoom d dependency parsing https youtu be 2jlk93iiyrw hw6 and code tutorial context free grammar homework hw6 speech processing u optional readings u chapter 27 https web stanford edu jurafsky slp3 27 pdf u lecture u overview https youtu be yz 3ff9ecgk introduction https youtu be 6l1xbid5aws a phonetics https youtu be y8kewd2djnw b speech signals https youtu be rphrxrs wby hw7 tutorial https youtu be 29p7q84xgls hw7 and code tutorial speech analysis homework hw7 | machine-learning natural-language-processing python3 nlp python speech neural-network pytorch jupyter-notebook lecture-material tutorial-code | ai |
InstagramClone | project 4 instagram clone instagram clone is a photo sharing app using parse as its backend time spent 14 hours spent in total user stories the following required functionality is completed x user can sign up to create a new account using parse authentication x user can log in and log out of his or her account x the current signed in user is persisted across app restarts x user can take a photo add a caption and post it to instagram x user can view the last 20 posts submitted to instagram x user can pull to refresh the last 20 posts submitted to instagram x user can tap a post to view post details including timestamp and caption the following optional features are implemented x run your app on your phone and use the camera to take the photo user can load more posts once he or she reaches the bottom of the feed using infinite scrolling show the username and creation time for each post x user can use a tab bar to switch between a home feed tab all posts and a profile tab only posts published by the current user user profiles x allow the logged in user to add a profile photo x display the profile photo with each post tapping on a post s username or profile photo goes to that user s profile page after the user submits a new post show a progress hud while the post is being uploaded to parse user can comment on a post and see all comments for each post in the post details screen user can like a post and see number of likes for each post in the post details screen style the login page to look like the real instagram login page style the feed to look like the real instagram feed implement a custom camera view the following additional features are implemented x error handling through ios alerts please list two areas of the assignment you d like to discuss further with your peers during the next class examples include better ways to implement something how to extend your app in certain ways etc 1 how they went about implementing comments on the backend 2 how they went about implementing a custom camera view video walkthrough here s a walkthrough of implemented user stories gif1 gif gif2 gif gif3 gif gif4 gif credits list an 3rd party libraries icons graphics or other assets you used in your app afnetworking https github com afnetworking afnetworking networking task library parse library to interact with database notes the main problem i had was learning to work with parse and how to call data from the database i had to use some time to focus on reading documentation to understand what was going on with the calls we were making to parse especially when it came to how we were uploading the images speaking of images the image picker was also something that i had to take a moment to read several times through the code to make sure i understood how that was working license copyright 2021 sebasti n salda a licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | os |
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vitamin-compose | important this current version of vitamin will no longer evolve and only accept bug fixes from now on more details here https github com decathlon vitamin design blob main important note md br p align center img width 300px src https user images githubusercontent com 9600228 102414461 e3b92b00 3ff6 11eb 9c96 5f37c4d5e02c png gh light mode only alt vitamin decathlon design system logo img width 300px src https user images githubusercontent com 9600228 147513091 66fcc204 279b 4140 9be5 c16744c0f637 png gh dark mode only alt vitamin decathlon design system logo p h1 align center vitamin compose h1 p align center decathlon design system libraries for android applications p p align center a href https www decathlon design website a p p align center a aria label contributors graph href https github com decathlon vitamin compose graphs contributors img src https img shields io github contributors decathlon vitamin compose svg a a aria label last commit href https github com decathlon vitamin compose commits img alt src https img shields io github last commit decathlon vitamin compose svg a a aria label license href https github com decathlon vitamin compose blob main license img src https img shields io github license decathlon vitamin compose svg alt a a aria label slack href https join slack com t decathlon design shared invite zt 13kxb50ar ihzqv olsu4 nckepj5c4g img src https img shields io badge slack decathlon 20design 20system purple svg logo slack alt a p compose decathlon design system is based on material design compose artifact described on the official documentation https material io and maintained by google developers and designers but these native components are overridden to respect decathlon s visual identity you ll find the design specifications and technical information for supported platforms by decathlon on decathlon design https www decathlon design if you are interested by a sample you can install a demo with sample module in the technical project getting started a vitamintheme is available in foundation artifact from where you can get all colors typographies and shapes in your composable hierarchy note that this theme is mandatory if you want to use any composable available in other vitamin compose artifacts if you don t use it an error will be triggered at the runtime kotlin vitamintheme your composable declarations download release artifacts are available on maven central check the release page https github com decathlon vitamin compose releases to know what is the latest release version of vitamin artifacts kotlin repositories mavencentral implementation com decathlon vitamin compose component last version if you want to test latest changes merge in main branch you can test vitamin android from snapshot artifacts but we don t recommend to use theses artifacts in production where you can have regressions or breaking changes until the next official release kotlin repositories maven url uri https oss sonatype org content repositories snapshots implementation com decathlon vitamin compose component latest major 1 0 0 snapshot if you have any question about the versioning of this project you can just read our documentation https github com decathlon vitamin compose tree main versioning md about it special thanks thank you to the contributors https github com decathlon vitamin compose graphs contributors involved in these vitamin compose libraries a href https github com decathlon vitamin compose graphs contributors img src https contrib rocks image repo decathlon vitamin compose a thank you also remix icon https remixicon com because vitamix icons is the official decathlon icon library based on their open source icon library https github com remix design remixicon remix design 2020 this original library is under the license apache 2 0 and has been modified by decathlon learn more https www decathlon design 726f8c765 p 58575f vitamix license license copyright 2022 decathlon licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | components compose decathlon design-system vitamin | os |
phala-blockchain | phala blockchain img align right width 320 src docs static web3 foundation grants badge black svg alt funded by the web3 foundation rust https github com phala network phala blockchain workflows build badge svg phala network is a decentralized offchain computing protocol aiming to build the web3 cloud this repo includes node the main development blockchain built on substrate standalone pherry the message relayer to connect the blockchain and pruntime standalone pruntime the contract execution kernel running inside tee enclave overview docs static phala design png the blockchain is the central component of the system it records commands confidential contract invocation serves as the pruntime registry runs the native token and on chain governance modules pherry is the message relayer it connects the blockchain and pruntime it passes the block data from the chain to pruntime and passes pruntime side effects back to the chain a multi client version of the runtime bridge is being developed here https github com phala network runtime bridge and now in alpha version pruntime phala network secure enclave runtime is a runtime to execute confidential smart contracts based on confidential computing native build dependencies details summary expand summary system dependencies ubuntu tested with 22 04 bash apt install y build essential pkg config libssl dev protobuf compiler macos bash brew install protobuf see here https grpc io docs protoc installation for more protobuf installation options rust bash curl https sh rustup rs ssf sh substrate dependencies bash git submodule update init sh scripts init sh llvm 14 bash wget https apt llvm org llvm sh chmod x llvm sh llvm sh 14 details build the blockchain and bridge make sure you have rust and llvm 10 installed note for mac users you also need llvm and binutils from homebrew or macport and to add their binaries to your path run git submodule update init to fetch submodules before build if you haven t add option recursive when clone code bash cargo build release the build script enforces llvm 10 or newer is used llvm 10 is needed because of the wasm port of rust crypto library ring we have to compile the c code into wasm while keeping the compatibility with the current rustc run please refer to the run scripts scripts run sub pages rpc docs rpc md rpc documentations test docs test md how to test the components external resources phala wiki https github com phala network phala wiki the technical documentation phala docker https github com phala network phala docker the production dockerfiles including the blockchain pherry and pruntime code bounty program https forum phala network t topic 2045 responsible disclosure docs responsible disclosure md | blockchain |
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Embedded-Systems-IOT-and-PCB-Design | embedded systems iot and pcb design embedded systems iot and pcb design | os |
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docking | docking development of ros package that provides generalized docking solution for lidar equipped mobile robots read the design report https github com rwbot docking blob master docking design report pdf | front_end |
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go-web | go web source code for the book web development with go published by apress taskmanager restful api sample rest api app with go and mongodb for chapter 9 updates on 22 jun 2016 in taskmanager app 1 upgraded the package jwt go and modified the source in common auth go 2 authentication middleware now sets the user name into the http request context using package github com gorilla context 3 added a logging implementation in common logger go 4 minor code refactoring in the app gokit check out github com shijuvar gokit https github com shijuvar gokit for further examples on go | golang | front_end |
SevSU_IS | sevsu is sevastopol state university information systems and technologies hello world it s one o clock in the morning on december 5 2021 with the help of my best friend i learned the basics of using the git i will now pour everything here | server |
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daily-stock-forecast | daily stock forecast daily stock forecasts optimizes and ranks machine learning models to predict the intraday movement of the stock market for the top 10 us equities by market cap and a number of popular indicies http daily stock forecast com screenshots of daily stock forecast live and in action br https github com dmtsource daily stock forecast blob master daily stock forecast png features every trading day dsf builds a number of classification models using historical candle volume data each model s hyperparameters are optimized as well as the length of the lookback period per sample classification reprots are generated using test data the f1 score is used to rank models file structure key files in the application hierarchy polymer site a simple polymer starter kit is used to build a responsive website backend forecast generation script helpers installation the frontend runs on a google app engine instance it utilizes python webapp2 jinja2 templating jquery google charts and soon polymer and web components the backend and analysis can run locally if the datastore writing is disabled but the current datastore exchange expects that the forecast is performed inside the project on a google compute engine instance with the ability to securely access the datastore dependencies python 2 7 numpy pandas pandas datastore 0 5 0 pytz scikit learn polymer 2 usage python daily stock forecast py credits contact daily stock forecast was developed by derek m tishler br https www linkedin com in derekmtishler | stock machine-learning datastore polymer python pandas sklearn forecast google-charts appengine-python | ai |
kubehost | kubehost kubehost helps you expose services directly on nodes of your google kubernetes engine gke cluster the common way to expose a service and get an external ip is kubectl expose deployment type loadbalancer which will expose your deployment on a production grade google cloud load balancer sometimes you just want to expose a service on your vm directly like during development where uptime and reliability are not as important that s where kubehost comes in kubehost uses existing features of gke to expose your service directly onto one of the vms in your cluster by creating a pod that runs on the vm s network and forwards traffic to your in cluster clusterip service and creating firewall rules to permit external traffic while you could do this manually kubehost takes the toil out of managing this configuration by automating the necessary actions br warning for development use only kubehost is not designed for production use nodes in gke are designed to be redundant meaning they can fail when the node on which your service is exposed via kubehost fails or is upgraded your service will experience several minutes of downtime by comparison if you use a production grade google cloud load balancer and you have enough replicas of your pod spread over multiple nodes with properly implemented health and readiness checks then a node can fail with only minimal impact to the availability of your service at any time you can upgrade to a google cloud load balancer with the kubehost upgrade command installation kubehost is a bash script to install clone this repository and add it to your path or copy kubehost to your usr local bin you may need to set the executable permission i e chmod x kubehost configuration before using kubehost you need to ensure both gcloud and kubectl are configured with your desired project cluster 1 run gcloud init to select your account project and region containing the gke cluster 2 run get credentials https cloud google com sdk gcloud reference container clusters get credentials to configure kubectl exposing a deployment with kubehost 1 create your deployment like normal 2 create a clusterip service for your deployment this is the default service type so no need to specify any type on your desired external port 3 run kubehost bind service where service is the name of the kubernetes service you created at step 2 what this does is create some glue in the form of a hostport deployment so that your service is bound to port you specified in the service on your node s external ip read under the hood for a longer technical description it also opens the necessary gcp firewall rules to undo kubehost unbind service complete example bash kubectl run hello image gcr io google samples hello app 1 0 port 8080 kubectl expose deployment hello port 80 target port 8080 name hello service kubehost bind hello service cleanup bash kubehost unbind hello service kubectl delete deployment hello kubectl delete service hello service switching between hostport and a load balancer upgrading to a load balancer from hostport is your app ready for prime time remove the hostport pod glue and convert your service into one backed by a google cloud load balancer https cloud google com load balancing with one simple command bash kubehost upgrade service where service is the name of your cluster ip service downgrading a load balancer to hostport did you already expose your service with a load balancer and found it s more than you needed convert it to an internal clusterip service and expose it on a host in one command with bash kubehost downgrade service where service is the name of your kubernetes service of type loadbalancer limitations kubehost currently works with services that have a single port if you need to expose two ports create two clusterip services kubehost is not designed for production usage see the note above kubehost doesn t give you a static ip the ip address of node may change which will affect your service you can create a static ip and use the kubeip https github com doitintl kubeip operator to keep it assigned through node maintenance events under the hood what kubehost is doing when you call bind is creating a kubernetes deployment with a single replica of a pod that uses hostport to bind onto the host s network interface the container in this pod forwards traffic to your clusterip service while you could instead change your deployment to use hostport directly we think this approach is superior as 1 it s closer to the production kubernetes experience where deployments have a matching service to receive traffic 2 it s easier to switch between this and a production setup by changing the service type to loadbalancer and removing the hostport deployment and vice versa no need to modify your application deployment 3 your deployment s replica count isn t limited by available ports | front_end |
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astronomicalalgorithms | astronomical algorithms export aaplus as library for mobile development initially for android how to use it add this line into your build gradle inside dependencies implementation org cjrams astronomical algorithms 0 4 0 the you can use the mpv version to get sun rise set and transition for any date and location calendar calendar calendar getinstance calendar set 2017 6 15 12 0 0 sun sun new sun calendar float 59 3293 float 18 0686 dateformat dateformat new simpledateformat yyyy mm dd hh mm ss log d test sun rise dateformat format sun getrise gettimeinmillis using flavors in order to reduce the resulting versions the recommendation is to use product flavors libray is release with all actual abi binaries but the recommendation is to use only one for distribution apk also if your application don t need to build specifics for any abi while main ones are compatible use only arm v7a or also add x86 here is a snipped to add to the build gradle application file to make smaller packages that follows the previous comments productflavors arm7 dimension abi ndk abifilters armeabi v7a x86 dimension abi ndk abifilters x86 fat dimension abi ndk abifilters x86 armeabi v7a | front_end |
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Turnout | turnout ios app githel suico video demo watch video https youtu be moybizq6yxy https youtu be moybizq6yxy table of contents 1 overview overview 2 product spec product spec 3 wireframes wireframes 4 schema schema overview description turnout is an ios mobile app designed to bring light to its users civil liberties and rights as a voter voter suppression and the lack of voter turnout has been a pressing issue throughout election periods therefore turnout serves as a one stop point to provide information on voter registration steps polling station locations dates https www democracy works elections api details on running candidates and data party past projects etc on your state municipal national government officials https developers google com civic information the main functionality is a reporting system allowing users to post any instances of voter suppression station location date changes longer lines etc to a public livefeed app evaluation category news mobile on the go aspect allows users to report an incident immediately and access information anywhere whether at a polling station registering to vote anywhere story recent elections have seen a decrease in millenial political engagement therefore turnout tackles voter suppression and the decreasing turnout rate by providing information on voter registration ballot access and candidate details with turnout users can report false information about voting procedures candidates etc market first time voters those who want to learn more about how they can be more involved voters in general habit users would often use this app to inform themselves of their civic rights not just during election seasons scope possible in four weeks mvp is fetching data on election dates locations and candidates from the api s as well as creating a user login through the facebook sdk and the ability to post ui consists of several table view cells app requirements multiple views interacts with a database e g parse log in log out of your app as a user sign up with a new user profile camera to take a picture users can post their incidents with pictures integrates with a sdk facebook sdk app contains at least one more complex algorithm data sharding https blog yugabyte com how data sharding works in a distributed sql database gesture recognizers animation e g fade in out e g animating a view growing and shrinking incorporates an external library to add visual polish product spec 1 user stories required and optional required must have stories users can login sign up through facebook sdk take into account the user s zip code if new user users can post incidents to the livefeed posts incorporate camera and photos and save to database livefeed section users can view the posted reports of other user s incorporate complex algorithm with how to sort this data users can like a report profile section users can view their own past reports users can save details from the information section to view on their profile voter guide section fetches from google civic info api and directs user to vote org s links for voter registration elections presents list of upcoming elections with their dates locations for polling station also presents running candidates proposals list of representatives based on zipcod tapping on details leads to info on rep bills they cosponsor bills list of bills and tapping on cell redirects to its details representatives cosponsoring it optional nice to have stories the ability to share posts to other social media services users can comment on a post posting videos and using a media player to view them for each government candidate users can leave a comment on their page almost like a reviews section calendar a user can save another users posts in their profile section the ability for a user to change their zip code settings page 2 screen archetypes login sign up users can login sign up through facebook sdk onboarding first time user signs up redirect them to modally presented view that requires a zipcode stream information section livefeed section of posted reports creation users can post incidents to the livefeed posts incorporate camera and photos and save to database detail each posts has details a user can view including texts and photo profile users can view their own past reports users can save details from the information section to view on their profile 3 navigation tab navigation tab to screen livefeed section profile section information section flow navigation screen to screen livefeed section each post leads to a detail screen top right navigation button leads to creation screen profile section each post leads to a detail screen top right navigation button leads to creation screen information sections each post leads to a detail screen wireframes hand sketched wire frames img src https i imgur com h4roblg jpg width 800 digital wireframes mockups figma wireframes https www figma com file dfhajo78qqzboufkvo3dqc turnout ios app node id 0 3a1 img src https i imgur com saclvcb png width 150 img src https i imgur com 5zos3lt png width 150 img src https i imgur com g5pko9m png width 150 img src https i imgur com vvy2rmh png width 150 img src https i imgur com fbg8hmv png width 150 img src https i imgur com zonoosk png width 150 img src https i imgur com 8biwyf3 png width 150 img src https i imgur com ipxh5ju png width 150 interactive prototype figma prototype https www figma com proto dfhajo78qqzboufkvo3dqc turnout ios app node id 4 3a63 scaling scale down img src https media1 giphy com media kfjnd2d7tqf0dxqush giphy gif width 250 img src https media2 giphy com media dt5wf69i8ixkud2qbn giphy gif width 250 br img src https media0 giphy com media dwhy61cfcgfyfm6lmp giphy gif width 250 img src https media1 giphy com media f5mn72etestttv6dno giphy gif width 250 schema models parse classes nsobjects user property type description objectid string unique id for the username string name for the user zipcode pointer to zipcode zipcode of post s user post property type description objectid string unique id for the user post default field author pointer to user image author context relation image file image attached to user posts status string text status by author likescount number number of likes for the post createdat datetime date when post is created default field updatedat datetime date when post is last updated default field zipcode pointer to zipcode zipcode of post s user reference current user s bookmarked data from api only save api data into parse db when user adds it to bookmarks saved property type description objectid string unique id for reference tile string title for the reference fetched from the api endpoint info string list of strings for website urls photourl string url of the fetched reference s image date string date of event if applicable location string location of event if applicable createdat datetime date when post is created default field links array list of strings for website urls supporters array list of strings for website urls savedbyuser boolean whether current user has saved the reference a different iteration property type description objectid string unique id for reference fetcheddata object nsdictionary of the fetched data from the api savers array list of user objects that have bookmarked the reference zipcode property type description objectid string unique id for reference zipcode string current zipcode neighbors array list of neighboring zipcodes assoc property type description objectid string unique id for reference typeid string whether it is like or bookmark infoboxid string id to indicate if obj is a reference from api or a user post networking list of network requests by screen parse db live feed screen read get query all posts sorted in weights of likescount and zipcdoe create post create a new like on a post delete delete existing like creation screen create post create a new post object index screen create post create a new reference when user bookmarks a reference from the index screen delete when user unbookmarks a reference remove user from array property and delete row if array count is now 0 profile screen read get query logged in user object read get query all posts where user is author read get query all references where user is inside the savers property array existing api endpoints google maps geocoding api base url http maps googleapis com maps api http maps googleapis com maps api http verb endpoint description get geocode json address zipcode get zipcode lat lng coord from the key location google civic information api base url https www googleapis com civicinfo v2 https www googleapis com civicinfo v2 http verb endpoint description get elections list of available elections to query get voterinfo looks up information relevant to a voter based on the voter s registered address get representatives looks up political geography and representative information for a single address openfec api base url https api open fec gov https api open fec gov http verb endpoint description get candidates candidate id fetch most recent information about that candidate get candidates candidate id history fetches a candidate s characteristics over time propublica congress api base url https api propublica org congress v1 https api propublica org congress v1 http verb endpoint description get congress chamber members json get a list of members of a particular chamber in a particular congress get members member id bills type json get recent bills by a specific member | os |
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CS224n_NLP | hits https hitcounter pythonanywhere com count tag svg url https 3a 2f 2fgithub com 2fskksaikia 2fcs224n nlp cs224n natural language processing with deep learning http web stanford edu class cs224n img src https github com skksaikia cs224n nlp blob master nlp png cs224n is a nlp deep learning course at stanford this course is open and you ll find everything in their course website gotta learn this course and start my nlp journey the notes are amazing the course is amazing let s get started b book b foundations of statistical natural language processing chris manning hinrich sch tze https github com skksaikia cs224n nlp blob master ed3book pdf speech and language processing 3rd edition in making dan jurafsky james h martin https github com skksaikia cs224n nlp blob master manningschutze 1999 foundationsofstatisticalnaturallanguageprocessing pdf natural language processing with python https github com skksaikia cs224n nlp blob master natural 20language 20processing 20with 20python pdf nltk essentials https github com skksaikia cs224n nlp blob master nltk 20essentials pdf p align justify natural language processing nlp is one of the most important technologies of the information age understanding complex language utterances is also a crucial part of artificial intelligence applications of nlp are everywhere because people communicate most everything in language web search advertisement emails customer service language translation radiology reports etc there are a large variety of underlying tasks and machine learning models behind nlp applications recently deep learning approaches have obtained very high performance across many different nlp tasks these can solve tasks with single end to end models and do not require traditional task specific feature engineering in this winter quarter course students will learn to implement train debug visualize and invent their own neural network models the course provides a thorough introduction to cutting edge research in deep learning applied to nlp on the model side we will cover word vector representations window based neural networks recurrent neural networks long short term memory models recursive neural networks convolutional neural networks as well as some recent models involving a memory component through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems p the winter 2017 version lectures are available here at youtube https www youtube com watch v oqq w 63ugq list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 assignments http web stanford edu class cs224n assignments html assignment1 https github com skksaikia cs224n nlp tree master assignment assignment1 solution https github com skksaikia cs224n nlp blob master assignment assignment1 assignment1 solution pdf assignment2 https drive google com drive folders 1iwvt1fd5jd07k2hntv iflfa5cqdkqoh usp sharing solution https drive google com file d 1vnoetmz vbi4fwxjec1vqx0jvm3unyp6 view usp sharing assignment3 https github com skksaikia cs224n nlp tree master assignment assignment3 solution https github com skksaikia cs224n nlp blob master assignment assignment3 assignment3 soln pdf course http web stanford edu class cs224n syllabus html h2 b natural language processing b h2 introduction to nlp and deep learning word vectors neural networks backpropagation and project advice introduction to tensorflow dependency parsing recurrent neural networks and language models vanishing gradients fancy rnns machine translation seq2seq and attention advanced attention transformer networks and cnns coreference resolution tree recursive neural networks and constituency parsing advanced architectures and memory networks reinforcement learning for nlp guest lecture semi supervised learning for nlp future of nlp models multi task learning and qa systems the notes and slides are available in the course website here http web stanford edu class cs224n syllabus html exams 2018 winter midterm https github com skksaikia cs224n nlp blob master midterm cs224n midterm 2018 pdf solution https github com skksaikia cs224n nlp blob master midterm cs224n midterm 2018 solution pdf 2017 winter midterm https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 3 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 3 sol pdf 2017 winter prac mid1 https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 1 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 1 pdf 2017 winter prac mid2 https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 2 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 2 sol pdf udacity nlp nanodegree https github com skksaikia nlpnanod nlp dan jurafsky christopher manning https www youtube com watch v 3dt yh1mf u list plqiyvnmpdlknzybtuolsi9mi9waerftfm nltk with python 3 for natural language processing https www youtube com watch v flzvoksckxy list plqvvvaa0qudf2jswnfigklibinznic4hl nltk https www nltk org how to solve 90 of nlp problems a step by step guide https blog insightdatascience com how to solve 90 of nlp problems a step by step guide fda605278e4e natural language processing is fun https medium com ageitgey natural language processing is fun 9a0bff37854e a practitioner s guide to nlp i https towardsdatascience com a practitioners guide to natural language processing part i processing understanding text 9f4abfd13e72 final project past projects http web stanford edu class cs224n reports html as a part of this course i did this project | ai |
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iot-java | java for ibm watson iot platform build status https travis ci org ibm watson iot iot java svg branch master https travis ci org ibm watson iot iot java coverage status https coveralls io repos github ibm watson iot iot java badge svg branch master https coveralls io github ibm watson iot iot java branch master github issues https img shields io github issues ibm watson iot iot java svg https github com ibm watson iot iot java issues github https img shields io github license ibm watson iot iot java svg https github com ibm watson iot iot java blob master license maven central https img shields io maven central v com ibm wiotp com ibm wiotp sdk svg https search maven org search q g com ibm wiotp product withdrawal notice per the september 8 2020 announcement https www 01 ibm com common ssi cgi bin ssialias subtype ca infotype an appname isource supplier 897 letternum enus920 136 rprodnx ibm watson iot platform 5900 a0n has been withdrawn from marketing effective december 9 2020 as a result updates to this project will be limited overview logging powered by slf4j https www slf4j org manual html http api support is provided by swagger codegen https github com swagger api swagger codegen see com ibm wiotp sdk swagger https github com ibm watson iot swagger java documentation https ibm watson iot github io iot java https ibm watson iot github io iot java usage add a dependency on com ibm wiotp sdk to your project s pom xml xml dependency groupid com ibm wiotp groupid artifactid com ibm wiotp sdk artifactid version 0 4 1 version dependency applications simple example that initialises an application client from environment variables connects sends 1 command to a device and then disconnects java import com google gson jsonobject import com ibm wiotp sdk app applicationclient import com ibm wiotp sdk codecs jsoncodec class simpletest public static void main string args applicationclient appclient new applicationclient appclient registercodec new jsoncodec appclient connect jsonobject data new jsonobject data addproperty distance 10 appclient publishcommand mydevicetype mydeviceid mycommand data appclient disconnect devices simple example that initialises a device client from environment variables connects sends 1 event and then disconnects java import com google gson jsonobject import com ibm wiotp sdk device deviceclient import com ibm wiotp sdk codecs jsoncodec class simpletest public static void main string args deviceclient deviceclient new deviceclient deviceclient registercodec new jsoncodec deviceclient connect jsonobject data new jsonobject data addproperty distance 10 deviceclient publishevent myevent data deviceclient disconnect | iot java sdk | server |
ece155 | ece155 ece 155 engineering design with embedded systems official university of waterloo calendar description as of fall 2014 introduction to embedded systems review of engineering design and analysis principles software development life cycle integrated development environments use of software requirements and specifications unified modelling language and documentation event handling simulation project management project scheduling testing verification and maintenance considerations | os |
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Thamod_HelloWorldLabs | thamod helloworldlabs first repository of digital design and embedded systems | os |
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huspacy | div align center markdown project logo https raw githubusercontent com huspacy huspacy develop github resources logo png python version https img shields io badge python 3e 3 7 blue https github com huspacy huspacy spacy https img shields io badge built 20with spacy 09a3d5 svg https spacy io pypi wheel https img shields io pypi wheel huspacy pypi version https badge fury io py huspacy svg https pypi org project huspacy demo https img shields io badge try 20the demo important https huggingface co spaces huspacy demo br build https github com huspacy huspacy actions workflows build yml badge svg https github com huspacy huspacy actions workflows build yml models https github com huspacy huspacy actions workflows test models yml badge svg https github com huspacy huspacy actions workflows test models yml downloads https static pepy tech personalized badge huspacy period month units international system left color grey right color green left text downloads month https pepy tech project huspacy downloads https static pepy tech personalized badge huspacy period total units international system left color grey right color green left text downloads https pepy tech project huspacy hits https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2fhuspacy 2fhuspacy count bg 2379c83d title bg 23555555 icon icon color 23e7e7e7 title hits edge flat true https hits seeyoufarm com stars https img shields io github stars huspacy huspacy style social https github com huspacy huspacy div huspacy is a spacy https spacy io library providing industrial strength hungarian language processing facilities through spacy models the released pipelines consist of a tokenizer sentence splitter lemmatizer tagger predicting morphological features as well dependency parser and a named entity recognition module word and phrase embeddings are also available through spacy s api all models have high throughput decent memory usage and close to state of the art accuracy a live demo is available here https huggingface co spaces huspacy demo model releases are published to hugging face hub https huggingface co huspacy this repository contains material to build huspacy and all of its models in a reproducible way installation to get started using the tool first we need to download one of the models the easiest way to achieve this is to install huspacy from pypi https pypi org project huspacy and then fetch a model through its api bash pip install huspacy python import huspacy download the latest cpu optimized model huspacy download install the models directly you can install the latest models directly from hugging face hub cpu optimized large model https huggingface co huspacy hu core news lg pip install https huggingface co huspacy hu core news lg resolve main hu core news lg any py3 none any whl gpu optimized transformers model https huggingface co huspacy hu core news trf pip install https huggingface co huspacy hu core news trf resolve main hu core news trf any py3 none any whl to speed up inference on gpus cuda should be installed as described in https spacy io usage https spacy io usage read more on the models here https huspacy github io models quickstart huspacy is fully compatible with spacy s api https spacy io api doc newcomers can easily get started with spacy 101 https spacy io usage spacy 101 guide although huspacy models can be loaded with spacy load the tool provides convenience methods to easily access downloaded models python load the model using spacy load import spacy nlp spacy load hu core news lg python load the default large model if downloaded import huspacy nlp huspacy load python load the model directly as a module import hu core news lg nlp hu core news lg load to process texts you can simply call the loaded model i e the nlp callable object https spacy io api language call pytest codeblocks cont python doc nlp csiribiri csiribiri zabszalma n gy csillag k zt alszom ma as huspacy is built on spacy the returned doc document https spacy io api doc title contains all the annotations given by the pipeline components api documentation is available in our website https huspacy github io models overview we provide several pretrained models 1 hu core news lg https huggingface co huspacy hu core news lg is a cnn based large model which achieves a good balance between accuracy and processing speed this default model provides tokenization sentence splitting part of speech tagging ud labels w detailed morphosyntactic features lemmatization dependency parsing and named entity recognition and ships with pretrained word vectors 2 hu core news trf https huggingface co huspacy hu core news trf is built on hubert https huggingface co sztaki hlt hubert base cc and provides the same functionality as the large model except the word vectors it comes with much higher accuracy in the price of increased computational resource usage we suggest using it with gpu support 3 hu core news md https huggingface co huspacy hu core news md greatly improves on hu core news lg s throughput by loosing some accuracy this model could be a good choice when processing speed is crucial 4 hu core news trf xl https huggingface co huspacy hu core news trf xl is an experimental model built on xlm roberta large https huggingface co xlm roberta large it provides the same functionality as the hu core news trf model however it comes with slightly higher accuracy in the price of significantly increased computational resource usage we suggest using it with gpu support huspacy s model versions follows spacy s versioning scheme https spacy io models model versioning a demo of the models is available at hugging face spaces https huggingface co spaces huspacy demo to read more about the model s architecture we suggest reading the relevant sections from spacy s documentation https spacy io models design comparison models md lg trf trf xl embeddings 100d floret 300d floret transformer br hubert https huggingface co sztaki hlt hubert base cc transformer br xlm roberta large https huggingface co xlm roberta large target hardware cpu cpu gpu gpu accuracy resource usage citation if you use huspacy or any of its models please cite it as arxiv http img shields io badge cs cl arxiv 3a2308 12635 b31b1b svg https arxiv org abs 2308 12635 bibtex inproceedings huspacy 2023 author orosz gy o rgy and szab o gerg h o and berkecz p e ter and sz a nt o zsolt and farkas rich a rd editor ek v s tein kamil and p a rtl franti v s ek and konop i k miloslav title advancing hungarian text processing with huspacy efficient and accurate nlp pipelines booktitle text speech and dialogue year 2023 publisher springer nature switzerland address cham pages 58 69 isbn 978 3 031 40498 6 arxiv http img shields io badge cs cl arxiv 3a2201 01956 b31b1b svg https arxiv org abs 2201 01956 bibtex inproceedings huspacy 2021 title huspacy an industrial strength hungarian natural language processing toolkit booktitle xviii magyar sz a m i t o g e pes nyelv e szeti konferencia author orosz gy o rgy and sz a nt o zsolt and berkecz p e ter and szab o gerg h o and farkas rich a rd location szeged pages 59 73 year 2022 contact for feature requests issues and bugs please use the github issue tracker https github com huspacy huspacy issues otherwise reach out to us in the discussion forum https github com huspacy huspacy discussions authors huspacy is implemented in the szegedai https szegedai github io team coordinated by orosz gy rgy mailto gyorgy orosz link in the hungarian ai national laboratory milab https mi nemzetilabor hu program license this library is released under the apache 2 0 license https github com huspacy huspacy blob master license trained models have their own license cc by sa 4 0 https creativecommons org licenses by sa 4 0 as described on the models page https huggingface co huspacy | hungarian hunlp nlp natural-language-processing spacy spacy-models pos-tagger dependency-parsing universal-dependencies information-extraction spacy-pipeline ner named-entity-recognition lemmatization huspacy morphological-analysis machine-learning python text-mining | ai |
Supply-Chain-Management-BlockChain-UseCase | supply chain management blockchain usecase supply chain management system supply chain management using this supply chain management a user can view the journey of mobile phone from the manufacturer to it s user through blockchain technology we can create a trusted transparent system of this supply chain the api performs following functions registering stakeholders manufacturer distributor and user by providing name password and type it returns userid login user with the user id and password register the mobile for the auction view the mobile and track the owners and journey in the supply chain transferring the ownership of the mobiles tech stack this supply chain management system uses multiple technologies ethereum blockchain https geth ethereum org downloads ethereum blockchain stack tool used geth vs code https code visualstudio com text editor for writing code twitter bootstrap great ui boilerplate for modern web apps node js evented i o for the backend express fast node js network app framework tjholowaychuk web3 js https web3js readthedocs io en 1 0 getting started html ethereum javascript api jquery and of course dillinger itself is open source with a public repository dill on github installation node js https nodejs org v6 to run sh curl sl https deb nodesource com setup 6 x sudo e bash sudo apt get install y nodejs installing ethereum package sh sudo apt get install software properties common sudo add apt repository y ppa ethereum ethereum sudo apt get update sudo apt get install ethereum testing want to contribute great try these commands to work on this open your favorite terminal and run these commands create local chain sh geth rpc rpcport 8085 rpcapi db eth net web3 personal datadir data networkid 123 nodiscover maxpeers 0 init genesis json launch geth console sh geth rpc rpcport 8085 rpcapi db eth net web3 personal datadir data networkid 123 nodiscover maxpeers 0 console create new account in geth console personal newaccount passphrase mine some ether miner setetherbase personal listaccounts 0 miner start deploy the smart contract loadscript mobilechain js p s mobilechain js file is in the directory and it s the web3 deploy form of smart contract unlock account 0th using this syntax personal unlockaccount address password timeout p s you can also use eth accounts 0 in place of address for the 0th account in the geth running node server instance using another terminal window sh nodemon server js p s here i m using nodemon for testing purpose verify the deployment by navigating to your server address in your preferred browser sh 127 0 0 1 5000 todos write more tests be happy to add functionalities and create prs license gnu free software hell yeah these are reference links used in the body of this note and get stripped out when the markdown processor does its job there is no need to format nicely because it shouldn t be seen thanks so http stackoverflow com questions 4823468 store comments in markdown syntax dill https github com joemccann dillinger git repo url https github com joemccann dillinger git john gruber http daringfireball net df1 http daringfireball net projects markdown markdown it https github com markdown it markdown it ace editor http ace ajax org node js http nodejs org twitter bootstrap http twitter github com bootstrap jquery http jquery com tjholowaychuk http twitter com tjholowaychuk express http expressjs com angularjs http angularjs org gulp http gulpjs com pldb https github com joemccann dillinger tree master plugins dropbox readme md plgh https github com joemccann dillinger tree master plugins github readme md plgd https github com joemccann dillinger tree master plugins googledrive readme md plod https github com joemccann dillinger tree master plugins onedrive readme md plme https github com joemccann dillinger tree master plugins medium readme md plga https github com rahulhp dillinger blob master plugins googleanalytics readme md | ethereum ethereum-dapp ethereum-blockchain nodejs blockchain geth geth-console dapp blockchain-technology supply-chain-management supplychain usecase | blockchain |
Lethe | project lethe privacy preserving indoor localization system animation summary of lethe system https github com salahassana lethe blob master md files figures lethe animation gif named after the river lethe https en wikipedia org wiki lethe from greek mythology project lethe is a privacy preserving indoor localization system designed to allow for room level localization of individuals in a home or building the system preserves user anonymity by using a novel detection algorithm that limits both the memory available onboard the camera and the data rate of camera communication to prevent a full image from being extracted briefly the algorithm detects the direction of movement by breaking the camera s fov into three quadrants and tracking the order in which the person enters and exits each quadrant system design lethe consists of both a custom built hardware system and a novel data processing pipeline hardware components flir thermal sensor the system utilizes a flir lepton thermal imaging module https www sparkfun com products 16465 as the main component of its detection module the thermal imager captures infrared radiation input in its nominal response wavelength band from 8 to 14 microns and has a thermal sensitivity of 50 milli kelvins the imager has an effective frame rate of 8 6 hz and a resolution of 60 w 80 h active pixels each 17 m in size and covering a 0 6375 degree angle breakout board the thermal imager is embedded in a breakout board which provides the socket for the lepton on board power supplies 25mhz reference clock can be by passed power efficient 1 2v core voltage can be by passed dual low noise ldo for 2 8v voltage can be by passed 100 mil header for use in a breadboard or wiring to any host system a few things to consider about this kit the breakout board will accept a 3 5 5v input and regulate it to what the lepton wants to read an image from the lepton module all you need is an spi port and to configure the camera settings you also need an i2c port although this is not required raspberry pi 1 frames obtained by the thermal imager are sent over an spi port to the pi where each pixel is processed independently and then permanently deleted the stream of pixel values is processed by a novel algorithm that determines the direction of the crossing software components customized driver the flir thermal module is operated using a custom written driver which uses a limited memory buffer to transfer one pixel at a time presence detection we detect the presence of a person in the field of view based on their temperature indoor environments are typically air conditioned to a 20 c to 22 c range and most objects in the environment conform to this temperature human skin temperature ranges between 32 c and 34 c additionally hair and clothing tend to absorb the skin s heat and retain a temperature lower than skin temperature but higher than the surrounding environment e g 25 c to 30 c hence a person can be detected in the view of a thermal camera by identifying pixels that are warmer than the background temperature in lethe we determine the background temperature https latex codecogs com svg image r back as the average temperature of the last frame absent human presence this value is updated with each new human less frame to adjust the background temperature to changes in the environment temperature we detect a human in a pixel whenever that pixel has a value https latex codecogs com svg image r thresh degrees higher than the background reference https latex codecogs com svg image r back if at any point in the streaming evaluation of a frame a pixel passes this threshold the frame is declared as having a presence if this is the beginning of an event https latex codecogs com svg image t s is updated once a person is detected we consider all frames 0 366 seconds after the last frame with a presence https latex codecogs com svg image t last to be part of the event this aims to cover any gap caused by lost frames in an event without merging two events in succession this lag time value is conservatively based on the average walking speed of a person 1 39 m s and the average shoulder span of an adult male 0 508 m direction detection once presence detection has ended an event with the creation of the https latex codecogs com svg image t s https latex codecogs com svg image t e tuple direction detection either assigns a direction d to that event or removes that event as a non crossing interaction direction detection collects the information needed to make this determination in parallel to presence detection each streaming pixel evaluated by presence detection is also used by direction direction detection uses the same https latex codecogs com svg image r back and https latex codecogs com svg image r thresh as presence to determine if someone is present in a pixel however direction detection goes beyond presence to use this information to determine a person s location in the frame and how that location changes over time when a person crosses through the field of view they first appear on one side of the frame and then progress in intervals to the other side before leaving the field of view a canonical example of this behavior can be seen in the figure above lethe captures the progress of the person over time through the field of view by leftmost sector and rightmost sector location of the person in frame https latex codecogs com svg image i if the pixels of a frame are processed from left to right this means lethe sets https latex codecogs com svg image loc to the column of the first pixel with a human presence and loci to the column of the last pixel with a human presence in this way lethe identifies the region a person is located in for each frame privacy preserving algorithm the lethe thermal camera requires only 21 values in memory for it s operation for the detection algorithms 4 values are required for presence detection https latex codecogs com svg image t s https latex codecogs com svg image t e https latex codecogs com svg image r back https latex codecogs com svg image t last 12 values for direction https latex codecogs com svg image loc l1 i https latex codecogs com svg image loc r1 i https latex codecogs com svg image loc l1 i 1 https latex codecogs com svg image loc r1 i 1 https latex codecogs com svg image d ind1 https latex codecogs com svg image d sum1 https latex codecogs com svg image loc l2 i https latex codecogs com svg image loc r2 i https latex codecogs com svg image loc l2 i 1 https latex codecogs com svg image loc r2 i 1 https latex codecogs com svg image d ind2 https latex codecogs com svg image d sum2 and a single value for pixel height detection https latex codecogs com svg image h pixel additionally the thermal camera requires space to store the current pixel temperature the current location of the pixel row and column and the current time of the device reference information such as the height and width of the image the lag time allowed in frames and the https latex codecogs com svg image r thresh value can be kept statically many of the dynamic values depending on the size of the image can be represented using a single byte only the timestamps require a larger storage space at 4 bytes a piece hence the memory requirement for the lethe thermal camera is only https latex codecogs com svg image m 33 bytes in the 60x80 pixel thermal camera used in this work that means only 33 pixels 0 69 of the image can ever be stored on the device cross detection examples of non crossing events lethe can distinguish https github com salahassana lethe blob master md files figures noncrossings experiment png once presence detection has ended an event with the creation of the https latex codecogs com svg image t s https latex codecogs com svg image t e tuple direction detection either assigns a direction https latex codecogs com svg image d to that event or removes that event as a non crossing interaction direction detection collects the information needed to make this determination in parallel to presence detection each streaming pixel evaluated by presence detection is also used by direction direction detection uses the same https latex codecogs com svg image r back and https latex codecogs com svg image r thresh as presence to determine if someone is present in a pixel however direction detection goes beyond presence to use this information to determine a person s location in the frame and how that location changes over time when a person crosses through the field of view they first appear on one side of the frame and then progress in intervals to the other side before leaving the field of view a canonical example of this behavior can be seen in the figure above lethe captures the progress of the person over time through the field of view by leftmost https latex codecogs com svg image loc l i and rightmost https latex codecogs com svg image loc r i the location of the person in frame https latex codecogs com svg image i if the pixels of a frame are processed from left to right this means lethe sets https latex codecogs com svg image loc l i to the column of the first pixel with a human presence and https latex codecogs com svg image loc r i to the column of the last pixel with a human presence in this way lethe identifies the region a person is located in for each frame once https latex codecogs com svg image loc l i and https latex codecogs com svg image loc r i for a human region have been identified the challenge becomes turning this information into a direction using limited memory while we could retain each region for every frame and process the information once the event is over this would mean storing values double the number of frames in an event in memory since there is no limit on the number of frames in an event a person could stand in the doorway for multiple minutes before walking through this would not allow lethe to maintain memory limitations instead lethe accumulates information about crossing direction by comparing only two regions at any given time each frame s region is compared to the stored region of the previous frame and assigned a direction indicator value 1 if the region has shifted to the right 1 if it has shifted to the left lethe retains only the sum of these direction indicators https latex codecogs com svg image d sum over multiple frames and assigns a direction either left or right based on the negative or positive value of https latex codecogs com svg image d sum respectively at the end of the event to detect and remove non crossing events lethe identifies https latex codecogs com svg image d sum 0 as an event where a person entered and left from the same side of the field of view however the basic direction algorithm described above will fail in two scenarios variable walking speeds and non crossing events with a person on either side of the door in the case of variable speed events a person might walk slowly into the door and then quickly out the same side here https latex codecogs com svg image d sum might equal https latex codecogs com svg image 1 1 1 1 1 1 1 1 20 202 as the first five frame pairs indicated right and only the last three indicated left hence the event would be mislabeled as a rightward crossing through the frame to prevent this only direction indicators that are different from the last indicator https latex codecogs com svg image d ind are added to https latex codecogs com svg image d sum in our example https latex codecogs com svg image 1 1 1 1 1 1 1 1 20 202 would become https latex codecogs com svg image 1 1 0 and the event would be correctly identified as a non crossing this makes the algorithm agnostic to walking speed non crossing events with a person on either side of the doorway are more complex an example of such an event where two people walk into the field of view to chat across the doorway can be seen in the figure above to identify these non crossing events we split the direction detection algorithm into two and determine a direction for each side of the doorway independently each side detects human regions and accumulates a direction sum i e https latex codecogs com svg image d sum1 or https latex codecogs com svg image d sum2 if https latex codecogs com svg image d sum1 and https latex codecogs com svg image d sum2 both indicate left or both indicate right the event is given a direction if both direction sums are 0 or different directions then lethe dismisses the event as a non crossing event hence lethe now retains 12 pieces of information for any pair of frames https latex codecogs com svg image loc l1 i https latex codecogs com svg image loc r1 i https latex codecogs com svg image loc l1 i 1 https latex codecogs com svg image loc r1 i 1 https latex codecogs com svg image d ind1 https latex codecogs com svg image d sum1 https latex codecogs com svg image loc l2 i https latex codecogs com svg image loc r2 i https latex codecogs com svg image loc l2 i 1 https latex codecogs com svg image loc r2 i 1 https latex codecogs com svg image d ind2 https latex codecogs com svg image d sum2 with this information lethe determines the direction of crossing events and detects when a person has passed through the field of view but has not crossed the threshold applications lethe is an adaptive system that could be utilized for multipule purposes occupancy monitoring a simple application of the lethe would be to use it to monitor the occupancy level of any multiroom building for example a hospital or clinic could use lethe to quickly determine which rooms are available for use smart buildings smart buildings would benefit from a room level occupancy monitoring system while existing systems such as motion detectors could provide binary information on which rooms have occupants lethe provides a quantitative value this allows buildings to go beyond simply turning lights on and off and would allow for dynamically setting hvac usage limitations future works while the lethe prototype shows the potential of privacy preserving image processing there is still work to be done false detections while our experiments found a fairly uniform background temperature that was not disturbed by the floor to ceiling window in its field of view the background may not be as uniform in all environments non human hot objects such as computers or laptops may present a challenge in in situ environments they could either raise the average background temperature or mistakenly be identified as people furthermore lethe s use of thermal cameras limits its use to environments that are cooler than human temperatures limited resolution due to thermal cameras only recently being made available for public domain use they have a limited resolution future work would include testing lethe with a higher resolution and higher frame rate camera to see if our predictions of increased accuracy hold true a 3 or greater camera system as opposed to lethe s two could also provide an increase in the accuracy of the system multi person crossings all testing was performed with a single person crossing through the sensors field of view multi user occlusion also presents a limitation for lethe where people walking side by side or in quick succession are mislabeled as only a single crossing for lethe 0 366 seconds must pass before another person can cross the doorway and be accurately detected further sensing from multiple viewpoints may mitigate this problem for example a camera placed at the top of the doorway could identify when two people cross side by side work in this direction may also lead towards solutions to multi user phenomena such as a person sitting in the field of view of the cameras while another person crosses through the doorway an initial two object expansion to tracking in lethe where the recorded values in memory are doubled could be the first step in that direction virtual barriers in our testing the sensor was mounted to the side of a door and tested to detect the crossing of a physical barrier such as doorways however the system could also be modified to detect a crossing inside of a room by limiting the sensors field of view and creating a virtual barrier multi sensor system just as the two binocular cameras in lethe can localize a person s heights future work may look at localizing a person using a privacy preserving image process in a space using cameras on two or more walls finally would we be able to detect other human movements such as coarse or fine grained gestures with this approach to provide privacy preservation to existing thermal imaging technologies publications griffiths erin salah assana and kamin whitehouse privacy preserving image processing with binocular thermal cameras proceedings of the acm on interactive mobile wearable and ubiquitous technologies 1 4 2018 1 25 pdf https web archive org web 20190727022605id https dam prod media mit edu x 2018 10 17 a133 griffiths n3mim1w pdf citing article griffiths2018privacy title privacy preserving image processing with binocular thermal cameras author griffiths erin and assana salah and whitehouse kamin journal proceedings of the acm on interactive mobile wearable and ubiquitous technologies volume 1 number 4 pages 1 25 year 2018 publisher acm new york ny usa | sensors thermal-camera privacy indoor-localisation smart-home | os |
farsinlp.github.io | datasets for farsi persian natural language processing nlp farsi persian dependency parsing farsi dependency parsing md irony detection farsi irony detection md lexical database farsi lexical database md named entity recognition farsi named entity recognition md natural language inference farsi natural language inference md parallel corpora farsi parallel corpora md part of speech tagging farsi part of speech tagging md pre trained embeddings farsi pre trained embeddings md pre trained lm farsi pre trained lm md question answering farsi question answering md raw text corpora farsi raw text corpora md sentiment analysis farsi sentiment analysis md spell checking farsi spell checking md text classification farsi text classification md text summarization farsi text summarization md word similarity farsi word similarity md nlp tools farsi nlp tools md this website aims at listing datasets and tools for research and development in farsi natural language processing nlp if you would like to find this document again in the future just go to farsinlp github io https farsinlp github io in your browser contribute adding a new dataset or task if you would like to add a new dataset or edit an existing one you can just click on the small edit button in the top right corner of the corresponding md file for the task in the github repository https github com farsinlp farsinlp github io this allows you to edit the file in markdown simply add a row to the corresponding table in the same format after you ve made your change make sure that the table still looks ok by clicking on the preview changes tab at the top of the page if everything looks good go to the bottom of the page where you see a form add a name for your proposed change an optional description indicate that you would like to create a new branch for this commit and start a pull request and click on propose file change you can also use this google form to contribute https forms gle hojfmiamzoeuu93m6 https forms gle hojfmiamzoeuu93m6 about a project initiated by dadmatech http dadmatech ir with the help of mohammad taher pilehvar director https pilehvar github io mohsen fayyaz https github com mohsenfayyaz alireza moradi https github com alireza1044 zhivar sourati https github com zhpinkman sepehr babapour parisa yalsavar https github com parisays ehsan aghazadeh https github com ehsanaghazadeh | ai |
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primitive | primitive ui license mit https img shields io badge license mit blue svg https opensource org licenses mit primitive ui on npm https img shields io npm v primitive ui svg color green label primitive ui https www npmjs com package primitive ui a front end design toolkit built with sass for developing responsive web apps primitive also provides helpful browser consistent styling for default html elements buttons forms tables lists and typography view documentation https taniarascia github io primitive installation css quick start easy download the stylesheet https taniarascia github io primitive css main css or use the cdn url https unpkg com primitive ui dist css main css https unpkg com primitive ui dist css main css save the stylesheet and link to it in the head of your project html link rel stylesheet href https unpkg com primitive ui dist css main css it s all set to go and your html elements will be given sensible default styling sass integration recommended the beauty of primitive is the ease with which you can create unique designs in a beautiful simple system bash clone the repo git clone https github com taniarascia primitive git watch for file changes npm run sass watch build a minified production build npm run sass build now you can begin modifying variables in variables scss this file will define your colors typography sizes breakpoints buttons borders and more define all your variables here to keep your project organized you can view dist test html or docs template html to see some example elements as you make changes gulp usage if you use would prefer to use gulp for compiling the option is available watch modifications and recompile yarn gulp watch npm run gulp watch build css yarn gulp css npm run gulp css acknowledgements dave gamache for building skeleton css http getskeleton com the original inspiration for building primitive and understanding responsive css contributing please feel free to fork comment critique or submit a pull request author tania rascia https www taniarascia com license this project is open source and available under the mit license license md | framework sass css css-framework custom workflow boilerplate template starter-kit scss frontend | front_end |
serving | tensorflow serving ubuntu build status https storage googleapis com tensorflow serving kokoro build badges bucket ubuntu svg https storage googleapis com tensorflow serving kokoro build badges bucket ubuntu html ubuntu build status at tf head https storage googleapis com tensorflow serving kokoro build badges bucket ubuntu tf head svg https storage googleapis com tensorflow serving kokoro build badges bucket ubuntu tf head html docker cpu nightly build status https storage googleapis com tensorflow serving kokoro build badges bucket docker cpu nightly svg docker gpu nightly build status https storage googleapis com tensorflow serving kokoro build badges bucket docker gpu nightly svg tensorflow serving is a flexible high performance serving system for machine learning models designed for production environments it deals with the inference aspect of machine learning taking models after training and managing their lifetimes providing clients with versioned access via a high performance reference counted lookup table tensorflow serving provides out of the box integration with tensorflow models but can be easily extended to serve other types of models and data to note a few features can serve multiple models or multiple versions of the same model simultaneously exposes both grpc as well as http inference endpoints allows deployment of new model versions without changing any client code supports canarying new versions and a b testing experimental models adds minimal latency to inference time due to efficient low overhead implementation features a scheduler that groups individual inference requests into batches for joint execution on gpu with configurable latency controls supports many servables tensorflow models embeddings vocabularies feature transformations and even non tensorflow based machine learning models serve a tensorflow model in 60 seconds bash download the tensorflow serving docker image and repo docker pull tensorflow serving git clone https github com tensorflow serving location of demo models testdata pwd serving tensorflow serving servables tensorflow testdata start tensorflow serving container and open the rest api port docker run t rm p 8501 8501 v testdata saved model half plus two cpu models half plus two e model name half plus two tensorflow serving query the model using the predict api curl d instances 1 0 2 0 5 0 x post http localhost 8501 v1 models half plus two predict returns predictions 2 5 3 0 4 5 end to end training serving tutorial refer to the official tensorflow documentations site for a complete tutorial to train and serve a tensorflow model https www tensorflow org tfx tutorials serving rest simple documentation set up the easiest and most straight forward way of using tensorflow serving is with docker images we highly recommend this route unless you have specific needs that are not addressed by running in a container install tensorflow serving using docker tensorflow serving g3doc docker md recommended install tensorflow serving without docker tensorflow serving g3doc setup md not recommended build tensorflow serving from source with docker tensorflow serving g3doc building with docker md deploy tensorflow serving on kubernetes tensorflow serving g3doc serving kubernetes md use export your tensorflow model in order to serve a tensorflow model simply export a savedmodel from your tensorflow program savedmodel https github com tensorflow tensorflow blob master tensorflow python saved model readme md is a language neutral recoverable hermetic serialization format that enables higher level systems and tools to produce consume and transform tensorflow models please refer to tensorflow documentation https www tensorflow org guide saved model save and restore models for detailed instructions on how to export savedmodels configure and use tensorflow serving follow a tutorial on serving tensorflow models tensorflow serving g3doc serving basic md configure tensorflow serving to make it fit your serving use case tensorflow serving g3doc serving config md read the performance guide tensorflow serving g3doc performance md and learn how to use tensorboard to profile and optimize inference requests tensorflow serving g3doc tensorboard md read the rest api guide tensorflow serving g3doc api rest md or grpc api definition https github com tensorflow serving tree master tensorflow serving apis use savedmodel warmup if initial inference requests are slow due to lazy initialization of graph tensorflow serving g3doc saved model warmup md if encountering issues regarding model signatures please read the signaturedef documentation tensorflow serving g3doc signature defs md if using a model with custom ops learn how to serve models with custom ops tensorflow serving g3doc custom op md extend tensorflow serving s architecture is highly modular you can use some parts individually e g batch scheduling and or extend it to serve new use cases ensure you are familiar with building tensorflow serving tensorflow serving g3doc building with docker md learn about tensorflow serving s architecture tensorflow serving g3doc architecture md explore the tensorflow serving c api reference https www tensorflow org tfx serving api docs cc create a new type of servable tensorflow serving g3doc custom servable md create a custom source of servable versions tensorflow serving g3doc custom source md contribute if you d like to contribute to tensorflow serving be sure to review the contribution guidelines contributing md for more information please refer to the official tensorflow website http tensorflow org for more information | machine-learning deep-learning deep-neural-networks python cpp neural-network ml serving tensorflow | ai |
System-Design | system design introduction what is system a system is a loosely used term for an architecture or collection of software or technology that communicate with each other or interact with each other in order to serve a certain set of users with a certain set of requirements a system can be defined and built keeping these three factors in mind 1 the user of the systems 2 the requirements of those users and 3 the components that are chosen in order to build that system to serve those users and their requirements what is design design is a process of understanding the user requirements and selecting the components modules and software technologies how they are going to be intertwined and communicating with each other to actually serve the need of the system in order to understand and develop this skill of designing certain kind of systems which serve to larger scale and larger users the process of system design comes into the picture hr components of system design components are the basic building blocks of system components it could be divided into two parts 1 logical entities data databases applications cache message queues infra communication protocol requests api rpc etc 2 tangible entities text images videos mongodb mysql cassandra java golang python react redis memecache kafka rabbitmq aws gcp azure apis rpcs messages hr client server architecture client server architecture is a computing model in which the server hosts delivers and manages most ofthe resources and services to be consumed by the client client a piece ofsoftware or application that takes the input and sends request to the servers server a piece ofsoftware that receives and processes requestsfrom clients load balancer responsible for distributing incoming network traffic across a group of backend servers to optimize resource usage a typical topological data flow goes as follows 1 client requests data from server 2 load balancer routes the request to the appropriate server 3 server processes the request client 4 server queries appropriate database for some data 5 database returns the queried data back to the server 6 the server processes the data and sends the data back to the client 7 this process repeats types of architecture thin client a thin client is designed to be especially small so that the bulk of the data processing occurs on the server example ecommerce streaming applications thick client a thick client fat client is one that will perform the bulk of the processing in client server applications with thick clients there is no need for continuous server communications as it is mainly communicating archival storage information to the server example gaming apps video editing software tier based architecture 1 tier it is the simplest one as it is equivalent to running the application on the personal computer 2 tier it is like client server architecture where communication takes place between client and server 3 tier the 3 tier architecture has three different layers presentation layer business logic layer database layer n tier an n tier application program is one that is distributed among three or more separate computers in a distributed network hr proxies proxy is an intermediary server between client and the internet proxy servers allow to hide conceal and make your network id anonymous by hiding your ip address proxy servers offers the following basic functionalities firewall and network data filtering network connection sharing data caching types of proxy forward proxy in this the client requests its internal network server to forward to the internet reverse proxy in this the requests are forwarded to one or more proxy servers and the response from the proxy server is retrieved as if it came directly from the original server hr data and data flow different formats of data representation in business layer texts videos images etc in application layer json xml in data stores databases tables indexes cache queues etc network layer packets hardware layer 0s and 1s data generation users internal data system populates on their own insights data flow methods apis messages events factors to be considered type of data volume consumption retrieval security types of system examples authorization system streaming system transactional system heavy compute system hr databases types of databases if we consider data as people in terms of buildings then the way those buildings house people can be said as databases some common types of databases are relational non relational file type network etc hr anatomy of applications and services applications or services performs certain tasks and at different layers they have different responsibility tech stack all the codes in applications are written in some languages using some frameworks any application can be written solely with the use of language but frameworks do most of the bootstrapping so we can use this feature to make an application on the top of the framework responsibilities client app render ui handle interactions collect data communicate with backend api to fetch and store data render static data informations backend app expose api endpoints house business logics handle data modelling transformation interact with data stores interact with other services elements factors of application development feature requirements layer tech stack code structure design pattern data store interactions performance cost deployment monitoring operational excellence reliability hr application programming interface api an api is a set of defined rules that explain how computers or applications communicate with one another advantages communication abstraction platform agnostic examples private apis the hidden apis not accessible to everyone public apis available to public ex google maps api web apis superset of public and private apis sdk library apis factors to consider api contracts documentation data format security standards rest stands for representational state transfer guidelines client server cacheable layered stateless uniform interface code on demand rpc soap hr caching a hardware or software component which helps in serving the data which is either frequently requested or it is expensive to compute on so cache stores the computed response and saves the cost of computing cache hit if a response for a request is available in cache memory it is called a cache hit cache miss if a response for a request is not available in cache memory it is called a cache miss invalidation and eviction invalidation the data that is kept in cache is not there for forever it is volatile the data is going to change at some point of time hence we need to update the cache as well the process of updating the data in cache by replacing the old value with new value is called data invalidation methods to invalidate cache expiry ttl time to live remove the cache when a new request come cache miss will happen and data will be fetched update the cache eviction a cache eviction algorithm is a way of deciding which element to evict when the cache is full catch eviction methods fifo lru lfu cache patterns cache aside strategy pattern a pattern in which cache never talks to db only the application code talks to cache advantages if cache fails data can still be served disadvantage to decide the expiry time for data or write the logic to update the cache whenever data is changed read through strategy pattern in this pattern cache sits between application and db hence application always talks to cache and never to db advantage supports read heavy workloads disadvantage first request will always be a cache miss solved sometimes by pre heating the cache write through strategy pattern it is similar to read through pattern there is an extra layer of latency while writing app to cahce then to db write around strategy pattern similar to write through only difference being app directly writes to db but for reading it reads from the cache write back strategy pattern all the write requested are stored at cache after some time these writes are sent in bulk to the db untitled https user images githubusercontent com 55986045 122691774 41049200 d24f 11eb 9c77 4230023b1d51 png where can be keep cache browser level proxy level application level outside application level hr message queue image https user images githubusercontent com 55986045 122849696 a2009880 d329 11eb 96a8 16a8d578aa20 png a href https medium datadriveninvestor com what is message queue b5468ff6db50 message queue a hr performance metrics used to evaluate how good the system is performing throughput throughput is the number of actions executed or results produced in a certain amount of time in system design throughput comes into picutre when we need to understand how many api calls are being served in a particular amount of time bandwidth bandwidth is the maximum amount of data that can travel through a channel latency latency is the time required to perform some action or to produce some result response time response time is the time between a client sending a request and receiving a response it is the sum of round trip latency and service time examples analogies water analogy latency is the amount of time it takes to travel through the tube bandwidth is how wide the tube is the amount of water flow will be your throughput vehicle analogy vehicle travel time from source to destination is latency types of roadways are bandwidth number of vehicles traveling is throughput hr performance metrics of different components applications api response time throughput of apis error occurences bug defect in the code databases time taken by various database queries number of queries executed per unit time or throughput memory caches latency of writing to cache number of cache eviction and invalidation memory of cache instance message queues rate of production and consumption fraction of stale or unprocessed messages number of consumers affects bandwidth and throughput workers time taken for job completion resources used in processing server instances memory ram cpu hr fault v s failure fault is the cause failure is the effect hr scaling the ability to handle more request by buying more machines bigger machines key features able to handle the increased load not complex to implement and maintain performance shouldn takr a hit or rather performance should increase vertical scaling when we increase the capacity of existing resource it is vertical scaling horizontal scaling when we increase the number of resources it is horizontal scaling horizontal v s vertical scaling horizontal vertical need load balancers load balancers not needed resilient single point of failure slow remote procedure calls fast inter process communication data inconsistency data consistent scales well as uses increases hardware limit hr database replication replication to have a copy having exact copy of data present in other databases in other machines the database that has main source of writes updates becomes the primary db master the database which has the copies from the primary db s is called the secondary database slave why do we need replication having replicas helps in tolerating faults having replicas helps in reducing latency replica databases can be used for read queries whereas the primary one can be used for write queries gain application performance replication lag the time it takes for the value to be copied from the primary to secondary database if replication lag is huge then it becomes a problem replicas will give inconsistent data to overcome this there are several consistenct models synchronous replication all replicas have to be updated before host is acknowledged advantages no lag data is always consistent disadvantage performance might take a hit because every write will have to wait for all replicas to get updated as well as acknowledge high latency if any replica goes down and couldn t give any acknowledgement write will fail asynchronous replication host is acknowledged after primary database is updated replicas update asynchronously advantage write opeartion becomes faster disadvantage if any replica fails system will be in a inconsistent state semi synchronous replication whenever a new write is issued the primary database will update the value to all the replicas and will wait for one of the replicas to acknowledge cap consistency availability and partitioning consistency in a consistent system once a client writes a value to any server and gets a response it expects to get that value or a fresher value back from any server it reads from availability in an available system if the client sends a request to a server and the server has not crashed then the server must eventually respond to the client the server is not allowed to ignore the client s requests partitioning the system continues to function and upholds its consistency guarantees in spite of network partitions if we can tolerate the partition and even though if partition happens and system can still be available and consistent is called partition tolerance cap theorem brewer theorem any network shared system wants to have these three properties in such a system having all three properties is nearly impossible we need to sacrifice one of them partition tolerance happens due to network failures and we do not have complete control over network failures hence partition tolerance becomes a mandatory property to support database sharding | system-design low-level-design high-level-design lld microservices hld | os |
gst-shark | gstshark a front end for gsttracer description gstshark leverages gstreamers newest instrumentation subsystem by installing a set of custom hooks to the trace points a variety of data analytics are extracted and presented to the user in a graphical friendly way the complete official documentation is held at ridgerun s developer s wiki pages gstshark user guide http developer ridgerun com wiki index php title gstshark gstshark examples http developer ridgerun com wiki index php title gstshark examples | front_end |
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awesome-blockchain | awesome blockchain awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of blockchain resources media coindesk http www coindesk com cointelegraph http cointelegraph com bitcoin magazine http bitcoinmagazine com coinjournal http coinjournal net coinfox http coinfox info bitcointalk press links https bitcointalk org index php board 77 0 8btc http www 8btc com blockchain chainbcom http chainb com courses bitcoin and cryptocurrency technologies princeton university https www coursera org course bitcointech reports imf virtual currencies and beyond initial considerations http www imf org external pubs ft sdn 2016 sdn1603 pdf santanderinnoventures the fintech 2 0 paper rebooting financial services http santanderinnoventures com wp content uploads 2015 06 the fintech 2 0 paper pdf deloitte blockchain disrupting the financial services industry http www2 deloitte com content dam deloitte ie documents financialservices ie cons blockchain 1015 pdf uk distributed ledger technology blackett review https www gov uk government uploads system uploads attachment data file 492972 gs 16 1 distributed ledger technology pdf un how can cryptocurrency and blockchain technology play a role in building social and solidarity finance http www unrisd org 80256b3c005bccf9 28httpauxpages 29 196aef663b617144c1257f550057887c file brett 20scott pdf dtcc embracing disruption tapping the potential of distributed ledgers to improve the post trade landscape http www dtcc com media files pdfs dtcc embracing disruption pdf 2016 160323 http www microbell com docdetail 1789967 html 160322 http www microbell com docdetail 1787568 html visa 160321 http www microbell com docdetail 1786071 html 160224 http www microbell com docdetail 1768223 html 160224 http www microbell com docdetail 1767179 html 160201 http www microbell com docdetail 1758348 html 160129 http www microbell com docdetail 1755862 html 160129 http www microbell com docdetail 1755860 html 160125 http www microbell com docdetail 1754164 html 160124 http www microbell com docdetail 1752061 html 160124 http www microbell com docdetail 1751968 html 160118 http www microbell com docdetail 1748256 html 160108 http www microbell com docdetail 1745069 html 160112 http www microbell com docdetail 1745038 html 151220 http www microbell com docdetail 1732241 html books bitcoin and cryptocurrency technologies textbook https d28rh4a8wq0iu5 cloudfront net bitcointech readings princeton bitcoin book pdf blockchain revolution how the technology behind bitcoin is changing money business and the world http www amazon com blockchain revolution technology changing business dp 1101980133 blockchain blueprint for a new economy http www amazon com blockchain blueprint economy melanie swan dp 1491920491 decentralized applications harnessing bitcoin s blockchain technology http www amazon com decentralized applications harnessing blockchain technology dp 1491924543 mastering bitcoin unlocking digital cryptocurrencies http www amazon com mastering bitcoin unlocking digital cryptocurrencies dp 1449374042 the age of cryptocurrency how bitcoin and digital money are challenging the global economic order http www amazon com the age cryptocurrency challenging economic dp 1250065631 http item jd com 11862992 html http item jd com 11501298 html http item jd com 11821366 html http item jd com 11368879 html http item jd com 11388432 html meetings 2015 http www hbrchina org 150916 blockchain as a service baas ibm blockchain http www ibm com blockchain microsoft azure https azure microsoft com en us documentation templates bitshares https azure microsoft com en us documentation templates bitshares ubuntu vm blockapps strato https azure microsoft com en us marketplace partners consensys blockapps strato go ethereum on ubuntu https azure microsoft com en us documentation templates go ethereum on ubuntu eris https azure microsoft com en us documentation templates eris platform openchain https azure microsoft com en us documentation templates openchain blockchain coinprism baas http baas blockchainlabs org bitcoin ethereum bitshares factom proof of existence assets factom http www factom org a scalable data layer for the blockchain ascribe http ascribe io lock in attribution securely share and trace where your digital work spreads proof of existence http proofofexistence com select a document and have it certified in the bitcoin blockchain bitproof io https bitproof io bitproof created geniusx a tool that lets you protect your intellectual property blocknotary http www blocknotary com blockchain securely timestamps every interview and protects it from tampering stampd http stampd io entrust the blockchain to notarize proof of ownership of any digital creation stampery https stampery co create an immutable record of existence integrity and ownership of your documents business processes and communications lapreuve http www lapreuve net explication html broken link eternitywall http eternitywall it messages lasting forever tierion https tierion com tierion is an engine for collecting data and recording it in the blockchain virtual notary http virtual notary org a free and secure electronic attestation service cryptopublicnotary http www cryptopublicnotary com block chain provenance for digital documents colu https www colu co your assets made digital guardtime https guardtime com a blockchain approach to cybersecurity that ensures the integrity of systems networks and data at industrial scale monegraph https monegraph com a content monetization platform using blockchain uproov https uproov com prove your image video audio or other files chronicled http www chronicled com 2015 09 raised 150m smart tags for authentication sneakers blockai https blockai com claim ownership of your digital creations coinspark http coinspark org upgrade your bitcoin with messaging and assets coinprism https www coinprism com use the bitcoin blockchain with any kind of asset everledger http www everledger io an online reputation system for diamonds blockverify io http blockverify io blockchain based anti counterfeit solution blockcypher assets api http dev blockcypher com asset api assets api using openassets protocol http www alstru com online identity passcard https passcard info onename https onename com shocard http www shocard com blockchainme http blockchainme com timestamp btproof https www btproof com oringstamp http www originstamp org document signing blocksign https blocksign com ssl certificates revoke ssl https github com christophera revocable self signed tls certificates hack analytics numisigh http numisight com numisight gives you the tools you need to view the forest the trees and all the levels of detail in between blockseer https www blockseer com bitcoin blockchain analytics other tradle http tradle io extending the bitcoin blockchain to non financial applications private blockchain multichain http www multichain com open platform for building blockchains openchain http openchain org blockchain technology for the enterprise eris industries https erisindustries com eris is free software that allows anyone to build their own secure low cost run anywhere data infrastructure using blockchain and smart contract technology linux foundation blockchain https blockchain linuxfoundation org backed by ibm applied blockchain http appliedblockchain com consulting company openblockchain https github com openblockchain blockchain fabric code storage sia http sia tech enterprise grade collaborative cloud for data storage storj io http storj io decentralized cloud storage storj is based on blockchain technology and peer to peer protocols to provide the most secure private and encrypted cloud storage maidsafe http maidsafe net safe secure access for everyone network a new secure way to access a world of existing apps where the security of your data is put above all else ipfs https ipfs io the interplanetary file system ipfs is a new hypermedia distribution protocol addressed by content and identities ipfs enables the creation of completely distributed applications it aims to make the web faster safer and more open lbry http lbry io lbry is a decentralized censorship resistant open source peer to peer information marketplace and discovery protocol filecoin http filecoin io filecoin is a data storage network and electronic currency based on bitcoin authentication bitid https github com bitid bitid the connect with bitcoin open protocol bitauth https github com bitpay bitauth authenticate with web services utilizing the same strategy as bitcoin social communication getgems http getgems org getgems is a free social messaging app that rewards you for your activity twister http twister net co fully decentralized p2p microblogging platform dns blockstack https blockstack org decentralized dns for blockchain applications marketplace rein http reinproject org decentralized professional services market ribbit me http ribbit me loyalty solution based on blockchain | blockchain |
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blockchain-core | helium blockchain core the helium blockchain core is a library that other helium blockchain applications such as miner https github com helium miner and blockchain api https github com helium blockchain api depend on build status https badge buildkite com 0d404ddd91fd8bafd867bfe487014413864965073506866b14 svg branch master https buildkite com helium blockchain core codecov https codecov io gh helium blockchain core branch master graph badge svg token wm3wklxpir https codecov io gh helium blockchain core for a detailed explanation of how the network works please see the helium network guide https www helium com technology | blockchain |
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MediaManagementSystem | html head head body h1 web app media management system h1 p strong public area strong this is a content management system web application p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner png p strong private area strong admins can add delete view orders inventory strong p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner2 png p strong about strong p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner3 png html tech stack description html to add content images and description css bootstrap add style to page javascript add dynamic effects to page php send and get data from mysql db mysql database to store data phpmyadmin manage mysql through interface apache server server used for server side php logic | server |
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SprinklerApp | smarthome this project is a cumulation of a nodemcu board incorporated into a circuit to control and manage the sprinkler sytems at my residence involving temperature and humidity sensors as extra features and an android app encoded in java to control the same thingspeak is being used as the backend api to communicate with the app and the development board both ways elaborate documentation will be added later | server |
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spor | spor this is the monorepo containing vy s design system spor and its supporting applications what s inside this repo includes the following apps docs a remix https remix run app documenting and demoing the design system available spor vy no https spor vy no studio a sanity https sanity io studio app for creating and editing documentation content available spor vy no studio https spor vy no studio this repo also includes a bunch of packages the ones you need to know about are vygruppen spor react the react component library vygruppen spor design tokens design tokens for react react native and elm vygruppen spor icon all svg icons vygruppen spor icon react all svg icons as react components vygruppen spor icon react native all svg icons as react components vygruppen spor loader all lottie loading animation data documentation and demos feel free to visit our documentation website on spor vy no https spor vy no you ll find live versions of all components including extensive documentation and the website is built by dogfooding the react component library if you want you can also test it out in a codesandbox https codesandbox io s demo spor b137ig usage react to get started you ll need to install the required packages bash npm install vygruppen spor react or yarn add vygruppen spor react next wrap your entire application in the sporprovider component you ll need to pass the current language as well tsx in your src index tsx file for instance import sporprovider language from vygruppen spor react import createroot from react dom import app from app const root createroot document getelementbyid root root render sporprovider language language english app sporprovider now you can start importing components across your app for instance to use the button component tsx import button from vygruppen spor react import editoutline24icon from vygruppen spor icon react export const app return button variant primary lefticon editoutline24icon edit me button you ll find lots of components and extensive documentation on the documentation site https spor vy no komponenter usage react native the react native version of spor lives in the app repo https github com nsbno salgsapp react native tree master app spor the apis should be pretty much the same as in react but there might be some differences look at the relevant documentation for more information setup to develop locally clone the repository and run npm install at the root level if you want to run the docs website locally you ll need a few secrets follow the instructions in each app s readme to discover and set them locally then run npm run build to build all artifacts for the first time finally run npm run dev to start the development servers build to build all apps and packages run the following command npm run build develop to develop all apps and packages run the following command npm run dev this will start all apps and packages in development mode you can then visit the following urls localhost 3000 http localhost 3000 the local version of the docs website localhost 3333 http localhost 3333 the local version of the sanity studio | react design-system | os |
astro | astro a design system by magnetis build status https travis ci org magnetis astro svg branch main https travis ci org magnetis astro table of contents introduction introduction supported libraries supported libraries adding astro to your project adding astro to your project using astro using astro contributing contributing troubleshooting troubleshooting contact us contact us introduction made for magnetis designers developers product managers data analysts data scientists stakeholders and enthusiasts astro helps us protect the brand concepts elevate consistency of user experience and increase the speed and efficiency of how we design and build products for our customers we believe that a good user experience is built with consistent design well defined visual and interaction patterns carefully built emotions and made for all therefore astro is essentially atomic astro is built based on atomic design so our designers and developers can use its style set and components to enhance our product interface as a modular system that can be easily managed and updated open magnetis believes that good design and experiences can impact the product market and society itself our design and code assets are open so we can contribute to the community and help new product teams accessible design is meant to be for all astro style and components follow basic recommendations from the wcag guidelines for accessibility with tested contrast ratios and good practices in legibility and screen reading solutions see wcag https www w3 org tr wcag21 supported libraries for now astro only supports react as its official library adding astro to your project installing the dependency install astro via terminal run yarn add magnetis astro or npm install magnetis astro using via cdn html link href https unpkg com magnetis astro dist astro css rel stylesheet type text css using astro ready to start coding with astro follow the instructions in the docs astro magnetis com br https astro magnetis com br to learn how to use our components you ll also see how to apply attribute modifiers such as classes to your html elements and react components here s a quick example if you d like to create a display text styled in astro typography 1 complete all install steps listed above 2 import astro in your file through one of the following methods inside the head tag html head link href node modules magnetis astro dist astro css rel stylesheet type text css head with es6 modules import magnetis astro with commonjs require magnetis astro 3 go to astro s typography page https astro magnetis com br typography 4 see that the display section guides you to create a p element with a specific text display class 5 replicate the instructions in your page file and make sure the correct styles have been applied 6 if your styles aren t rendered correctly make sure astro has been successfully installed into your project tree and astro css is correctly imported although our css variables are available in the bundle it s important to emphasize that you should use astro classes whenever possible instead of using the variables directly always look for the element you re creating in the docs before you start to build it from scratch in other words avoid this css paragraph font family var font primary font weight 600 font size 24px line height 1 5 color var color moon 900 html p class paragraph don t do this p in the example above all these properties could be replaced with using the a text large class in the paragraph element like so html p class a text large do this p customizing styles in case you absolutely have to customize one or more properties in an astro component you should still use the corresponding astro class that s closer to your goal and override it with your custom properties you can do this css my paragraph color var color moon 700 override default astro color html p class a text large my paragraph customize like this p contributing it s awesome that you want to contribute to astro please see contributing md contributing md to learn how it works troubleshooting if you copy the markup from an example in our documentation don t forget to change the attributes classname to class and htmlfor to for if you re not in a react environment like our docs are contact us found a bug please see if it was already reported on our issues page https github com magnetis astro issues if you can t find a matching report you can open a new issue https github com magnetis astro issues new including as much information as you can gather we appreciate it any lingering questions you can contact us at dev magnetis com br mailto dev magnetis com br thank you for using astro | design-system design-systems component-library hacktoberfest | os |
coursera-full-stack | coursera full stack coursera full stack web development specialization from hkust | front_end |
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Django-Web-Development-with-Python | django web development with python this is the code repository for django web development with python https github com packtpublishing django web development with python published by packt it contains all the supporting project files necessary to work through the book from start to finish what you will learn share c code across platforms and call native objective c or java libraries from c submit your app to the apple app store and google play use the out of the box services to support third party libraries find out how to get feedback while your application is used by your users create shared data access using a local sqlite database and a rest service test and monitor your applications gain memory management skills to avoid memory leaks and premature code cycles while decreasing the memory print of your applications integrate network resources with cross platform applications design and implement eye catching and reusable ui components without compromising nativity in mobile applications instructions and navigation all of the code is organized into folders each folder starts with number followed by the application name for example chapter02 you will see code something similar to the following if name main os environ setdefault django settings module work manager settings from django core management import execute from command line execute from command line sys argv related django products web development with django cookbook https www packtpub com web development web development django cookbook utm source github utm medium repository utm campaign 9781783286898 django 1 0 website development https www packtpub com web development django 10 website development utm source github utm medium repository utm campaign 9781847196781 django javascript integration ajax and jquery https www packtpub com web development django javascript integration ajax and jquery utm source github utm medium repository utm campaign 9781849510349 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781787121386 https packt link free ebook 9781787121386 a p | front_end |
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Fregata | fregata machine learning github license http og41w30k3 bkt clouddn com apache2 svg license fregata http talkingdata com is a light weight super fast large scale machine learning library based on apache spark http spark apache org and it provides high level apis in scala more accurate for various problems fregata can achieve higher accuracy compared to mllib higher speed for generalized linear model fregata often converges in one data epoch for a 1 billion x 1 billion data set fregata can train a generalized linear model in 1 minute with memory caching or 10 minutes without it usually fregata is 10 100 times faster than mllib parameter free fregata uses gsa http arxiv org abs 1611 03608 sgd optimization which dosen t require learning rate tuning because we found a way to calculate appropriate learning rate in the training process when confronted with super high dimension problem fregata calculates remaining memory dynamically to determine the sparseness of the output balancing accuracy and efficiency automatically both features enable fregata to be treated as a standard module in data processing for different problems lighter weight fregata just uses spark s standard api which allows it to be integrated into most business data processing flow on spark quickly and seamlessly architecture this documentation is about fregata version 0 1 core mainly implements stand alone algorithms based on gsa including classification font color 808080 regression font and font color 808080 clustering font classification supports both binary and multiple classification regression will release later clustering will release later spark mainly implements large scale machine learning algorithms based on spark by wrapping core jar and supplies the corresponding algorithms fregata supports spark 1 x and 2 x with scala 2 10 and scala 2 11 algorithms trillion lr docs largescale lr md trillion softmax docs largescale softmax md logistic regression docs logistic regression md combine freatures logistic regression docs clr md softmax docs softmax md rdt docs rdt md installation two ways to get fregata by maven or sbt maven s pom xml xml dependency groupid com talkingdata fregata groupid artifactid core artifactid version 0 0 3 version dependency dependency groupid com talkingdata fregata groupid artifactid spark artifactid version 0 0 3 version dependency sbt s build sbt scala if you deploy to local mvn repository please add resolvers resolver mavenlocal librarydependencies com talkingdata fregata core 0 0 3 librarydependencies com talkingdata fregata spark 0 0 3 if you want to manual deploy to local maven repository as follow git clone https github com talkingdata fregata git cd fregata mvn clean package install quick start suppose that you re familiar with spark the example below shows how to use fregata s logistic regression and experimental datas can be obtained on libsvm data https www csie ntu edu tw cjlin libsvmtools datasets adding fregata into project by maven or sbt referring to the downloading part importing packages scala import fregata spark data libsvmreader import fregata spark metrics classification areaunderroc accuracy import fregata spark model classification logisticregression import org apache spark sparkconf sparkcontext loading training datas by fregata s libsvmreader api scala val traindata libsvmreader read sc trainpath numfeatures toint val testdata libsvmreader read sc testpath numfeatures toint building logsitic regression model by trainging datas scala val model logisticregression run traindata predicting the scores of instances scala val pd model classpredict testdata evaluating the quality of predictions of the model by auc or other metrics scala val auc areaunderroc of pd map case x l p c p l input data format fregata s training api needs rdd fregata vector fregata num predicting api needs the same or rdd fregata vector without label scala import breeze linalg vector bvector sparsevector bsparsevector densevector bdensevector import fregata vector sparsevector vsparsevector package object fregata type num double type vector bvector num type sparsevector bsparsevector num type sparsevector2 vsparsevector num type densevector bdensevector num def zeros n int bdensevector zeros num n def norm x vector breeze linalg norm x 2 0 def asnum v double num v if the data format is libsvm then fregata s libsvmreader read api can be used directly scala sc is spark context path is the location of input datas on hdfs numfeatures is the number of features for single instance minpartitions is the minimum number of partitions for the returned rdd pointing the input datas read sc sparkcontext path string numfeatures int 1 minpartition int 1 int rdd fregata vector fregata num else some constructions are needed using sparsevector scala indices is an 0 based array and the index th feature is not equal to zero values is an array storing the corresponding value of indices length is the total features of each instance label is the instance s label input datas with label sc textfile input map val indicies val values val label new sparsevector indices values length asinstanceof vector asnum label input datas without label just for predicting api sc textfile input map val indicies val values new sparsevector indices values length asinstanceof vector using densevector scala datas is the value of each feature label is the instance s label input datas with label sc textfile input map val datas val label new densevector datas asinstanceof vector asnum label input datas without label just for predicting api sc textfile input map val datas new densevector indices values length asinstanceof vector maillist yongjun tian tendcloud com haijun liu tendcloud com xiatian zhang tendcloud com fan yao tendcloud com contributors contributed by talkingdata https github com talkingdata fregata contributors | ai |
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llm | llm large language models | ai |
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DevOps-Crash-Course | devops crash course this beginner s devops course is your first step toward a devops engineering this repo contains cloud native theory workshops guides cool cloud native projects and students contribution area this repository contains the content of a devops course to send pr or make forks with the idea to gather knowledge from different countries and mindsets it is also intended to be a guide to understand learn and contribute towards the use of cloud native technologies focusing specifically on the cncf https landscape cncf io projects finally you will find in this repository cloud native workshops guides and a contribution area for students to promote cncf tecnologies in general this content is based on the syllabus of the current course from f a s t university https www nu edu pk of pakistan cncf os projects cncf projects repositories repositories docs docs communities communities devops roadmap devops roadmap news news slides slides podcasts podcasts course progress course progress devops youtube devops youtube devops workshop devops workshop cloud native students days cloud native students days cncf projects kubernetes https github com kubernetes kubernetes linkerd https github com linkerd linkerd2 prometheus https github com prometheus prometheus keda https github com kedacore keda helm https github com helm helm kyevrno https github com kyverno kyverno opa https github com open policy agent opa keptn https github com keptn keptn paralus https github com paralus paralus k3s https github com k3s io k3s kind https github com kubernetes sigs kind microk8s https github com canonical microk8s k0s https github com k0sproject k0s cartographer https github com vmware tanzu cartographer ko https github com google ko kaniko https github com googlecontainertools kaniko istio https github com istio istio knative https github com knative docs grafana https github com grafana grafana devops roadmap 90daysofdevops https github com michaelcade 90daysofdevops roadmap sh https roadmap sh devops communities cloud native islamabad https community cncf io islamabad join cn islamabad discord server https discord gg frbkhfn8h9 devops workshop docker 101 https www youtube com watch v 0mxhs7h6bxm t 1s a guide to devops road map https www youtube com watch v hucbo4ssuws t 4s devops youtube viktor farcic https www youtube com c devopstoolkit bret fisher https www youtube com c bretfisherdockeranddevops david mccay https www youtube com c rawkode techworldwithnana https www youtube com c techworldwithnana sid palas https www youtube com c devopsdirective saiyam pathak https www youtube com c saiyam911 kunal kushwaha https www youtube com c kunalkushwaha soulman iqbal https www youtube com c soulmaniqbal podcasts cloudnativefm https www youtube com channel uc7b9fl8jq8tedocypf4g3wg beaming ortelius https beaminortelius buzzsprout com kubernetes podcast https kubernetespodcast com kube cuddle https share transistor fm s f0891ceb devops paradox https www devopsparadox com slides docker deep dive https www slideshare net mehwishhayat3 docker deep dive understanding docker engine docker for devops qid 0d2f5734 7fce 4f5c 8c0d c555d6e53608 v b from search 1 course progress docs sheet for students medium blog https docs google com spreadsheets d 1wcnekapgqr bnxgg3jizk5pzi7emzq3hjvm a4uq1 g edit gid 0 students contribution area students contribution md cloud native students days event planning and hosting cnsdays eventplanning md | cloud |
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polyaxon | license apache 2 https img shields io badge license apache2 blue svg style flat longcache true license polyaxon api https img shields io docker pulls polyaxon polyaxon api https hub docker com r polyaxon polyaxon api slack https img shields io badge slack 1 5k 20members blue svg style flat logo slack longcache true https polyaxon com slack docs https img shields io badge docs stable brightgreen svg style flat longcache true https polyaxon com docs release https img shields io badge release v1 20 0 brightgreen svg longcache true https polyaxon com docs releases 1 20 github https img shields io badge issue tracker github blue style flat logo github longcache true https github com polyaxon polyaxon issues github https img shields io badge roadmap github blue style flat logo github longcache true https github com orgs polyaxon projects 5 cli https github com polyaxon polyaxon actions workflows cli yml badge svg https github com polyaxon polyaxon actions workflows cli yml haupt https github com polyaxon polyaxon actions workflows haupt yml badge svg https github com polyaxon polyaxon actions workflows haupt yml hypertune https github com polyaxon polyaxon actions workflows hypertune yml badge svg https github com polyaxon polyaxon actions workflows hypertune yml traceml https github com polyaxon polyaxon actions workflows traceml yml badge svg https github com polyaxon polyaxon actions workflows traceml yml codacy badge https api codacy com project badge grade 90c05b6b112548c1a88b950beceacb69 https www codacy com app polyaxon polyaxon utm source github com amp utm medium referral amp utm content polyaxon polyaxon amp utm campaign badge grade a href https polyaxon com img src https raw githubusercontent com polyaxon polyaxon master artifacts logo vector primary white default monochrome svg width 125 height 125 align right a reproduce automate scale your data science welcome to polyaxon a platform for building training and monitoring large scale deep learning applications we are making a system to solve reproducibility automation and scalability for machine learning applications polyaxon deploys into any data center cloud provider or can be hosted and managed by polyaxon and it supports all the major deep learning frameworks such as tensorflow mxnet caffe torch etc polyaxon makes it faster easier and more efficient to develop deep learning applications by managing workloads with smart container and node management and it turns gpu servers into shared self service resources for your team or organization br p align center img src https raw githubusercontent com polyaxon polyaxon master artifacts demo gif alt demo width 80 p br install tl dr install cli bash install polyaxon cli pip install u polyaxon create a deployment bash create a namespace kubectl create namespace polyaxon add polyaxon charts repo helm repo add polyaxon https charts polyaxon com deploy polyaxon polyaxon admin deploy f config yaml access api polyaxon port forward please check polyaxon installation guide https polyaxon com docs setup quick start tl dr start a project bash create a project polyaxon project create name quick start description polyaxon quick start train and track logs resources bash upload code and start experiments polyaxon run f experiment yaml u l dashboard bash start polyaxon dashboard polyaxon dashboard dashboard page will now open in your browser continue y n y br p align center img src https raw githubusercontent com polyaxon polyaxon master artifacts compare png alt compare width 400 img src https raw githubusercontent com polyaxon polyaxon master artifacts dashboards png alt dashboards width 400 p br notebook bash start jupyter notebook for your project polyaxon run hub notebook br p align center img src https raw githubusercontent com polyaxon polyaxon master artifacts notebook png alt compare width 400 p br tensorboard bash start tensorboard for a run s output polyaxon run hub tensorboard p uuid uuid br p align center img src https raw githubusercontent com polyaxon polyaxon master artifacts tensorboard png alt tensorboard width 400 p br please check our quick start guide https polyaxon com docs intro quick start to start training your first experiment distributed job polyaxon supports and simplifies distributed jobs depending on the framework you are using you need to deploy the corresponding operator adapt your code to enable the distributed training and update your polyaxonfile here are some examples of using distributed training distributed tensorflow https polyaxon com docs experimentation distributed tf jobs distributed pytorch https polyaxon com docs experimentation distributed pytorch jobs distributed mpi https polyaxon com docs experimentation distributed mpi jobs horovod https polyaxon com integrations horovod spark https polyaxon com docs experimentation distributed spark jobs dask https polyaxon com docs experimentation distributed dask jobs hyperparameters tuning polyaxon has a concept for suggesting hyperparameters and managing their results very similar to google vizier called experiment groups an experiment group in polyaxon defines a search algorithm a search space and a model to train grid search https polyaxon com docs automation optimization engine grid search random search https polyaxon com docs automation optimization engine random search hyperband https polyaxon com docs automation optimization engine hyperband bayesian optimization https polyaxon com docs automation optimization engine bayesian optimization hyperopt https polyaxon com docs automation optimization engine hyperopt custom iterative optimization https polyaxon com docs automation optimization engine iterative parallel executions you can run your processing or model training jobs in parallel polyaxon provides a mapping https polyaxon com docs automation mapping abstraction to manage concurrent jobs dags and workflows polyaxon dags https polyaxon com docs automation flow engine is a tool that provides container native engine for running machine learning pipelines a dag manages multiple operations with dependencies each operation is defined by a component runtime this means that operations in a dag can be jobs services distributed jobs parallel executions or nested dags architecture polyaxon architecture artifacts polyaxon architecture png documentation check out our documentation https polyaxon com docs to learn more about polyaxon dashboard polyaxon comes with a dashboard that shows the projects and experiments created by you and your team members to start the dashboard just run the following command in your terminal bash polyaxon dashboard y project status polyaxon is stable and it s running in production mode at many startups and fortune 500 companies contributions please follow the contribution guide line contribute to polyaxon contributing md research if you use polyaxon in your academic research we would be grateful if you could cite it feel free to contact us mailto contact polyaxon com we would love to learn about your project and see how we can support your custom need | deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ml k8s jupyter notebook jupyterlab pipelines workflow mlops hyperparameter-optimization | ai |
jobseeker | jobseeker this is the source code and issue board for the bots for https remoted io remoted is an aggregator for remote jobs for it professionals software engineering database cloud security you are more than welcome to participate to help making https remoted io the best remote job board give feedback report bugs suggest improvements and ask questions here https github com remoted io jobseeker issues other useful links remoted io https remoted io remoted graphql api https remoted io graphql remoted io source code https github com remoted io remoted jobseeker is licensed as agpl https github com remoted io jobseeker blob master license md made with by andre pena https twitter com andrerpena | server |
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fish-feeder | fish feeder using mbed os to build a tiny web service env ide uvision5 development board stm32 nucleo f767zi https developer mbed org platforms st nucleo f767zi i o servomotor three dupont lines team member e212156822000 https github com e212156822000 jxcodetw https github com jxcodetw | os |
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docs-searchbar.js | p align center img src https github com meilisearch integration guides blob main assets logos logo svg alt meilisearch width 200 height 200 p h1 align center deprecated docs searchbar js h1 deprecation warning dear community we d like to share some updates regarding the future maintenance of this repository our team is small and our availability will be reduced in the upcoming times as such we decided to deprecate this repository we invite you into using tauri s meilisearch docsearch https github com tauri apps meilisearch docsearch instead of this one we still accept bug fixes from the community but no more enhancements seeking immediate support please join us on our discord channel https discord meilisearch com h4 align center a href https github com meilisearch meilisearch meilisearch a a href https www meilisearch com cloud utm campaign oss utm source github utm medium docs searchbar js meilisearch cloud a a href https www meilisearch com docs documentation a a href https discord meilisearch com discord a a href https roadmap meilisearch com tabs 1 under consideration roadmap a a href https www meilisearch com website a a href https www meilisearch com docs faq faq a h4 p align center a href https www npmjs com package docs searchbar js img src https img shields io npm v docs searchbar js svg alt npm version a a href https github com meilisearch docs searchbar js actions img src https github com meilisearch docs searchbar js workflows tests badge svg alt test a a href https github com meilisearch docs searchbar js blob main license img src https img shields io badge license mit informational alt license a a href https ms bors herokuapp com repositories 11 img src https bors tech images badge small svg alt bors enabled a p docs searchbar js is a front end sdk for meilisearch providing a search bar for your documentation docs searchbar js comes with a css template the default styling of this library fits a documentation search bar but you can customize it to make it work you need to have your documentation s content in a meilisearch instance if not already the case you can achieve this using docs scraper https github com meilisearch docs scraper meilisearch is an open source search engine discover what meilisearch is https github com meilisearch meilisearch docs searchbar js example assets docs searchbar example png if you use vuepress for your website you should check out our vuepress plugin for meilisearch https github com meilisearch vuepress plugin meilisearch table of contents omit in toc supercharge your meilisearch experience supercharge your meilisearch experience installation installation getting started getting started customization customization compatibility with meilisearch compatibility with meilisearch development workflow and contributing ef b8 8f development workflow and contributing credits credits supercharge your meilisearch experience say goodbye to server deployment and manual updates with meilisearch cloud https www meilisearch com cloud utm campaign oss utm source github utm medium docs searchbar js get started with a 14 day free trial no credit card required installation with npm we only guarantee that the package works with node 12 and node 15 sh with npm npm install docs searchbar js with yarn yarn add docs searchbar js in your html add the following script into your html file html script src https cdn jsdelivr net npm docs searchbar js latest dist cdn docs searchbar min js script run meilisearch omit in toc there are many easy ways to download and run a meilisearch instance https www meilisearch com docs reference features installation html download and launch for example using the curl command in your terminal https itconnect uw edu learn workshops online tutorials web publishing what is a terminal bash install meilisearch curl l https install meilisearch com sh launch meilisearch meilisearch master key masterkey nb you can also download meilisearch from homebrew or apt or even run it using docker index your data omit in toc the goal of this library is to provide a front end search bar into your own documentation to make that possible you need to gather your website content in advance and index it in a meilisearch instance luckily we provide all the tools that you need and can help you through the whole process if you follow this guide https www meilisearch com docs create how to search bar for docs html note if you want to try out docs searchbar js as a first introduction try out our playground contributing md playground use your own scraper omit in toc we recommend using the docs scraper tool https github com meilisearch docs scraper to scrape your website but this is not mandatory if you already have your own scraper but you still want to use meilisearch and docs searchbar js check out this discussion https github com meilisearch docs searchbar js issues 40 getting started es module omit in toc add an input tag with the attribute id search bar input in one of your html file html input type search id search bar input then import docs searchbar js and run the docssearchbar function for more explaination of the required parameters see next section js import docssearchbar from docs searchbar js docssearchbar hosturl https mymeilisearch com apikey xxx indexuid docs inputselector search bar input html omit in toc add the following code to one of your html files html doctype html html head link rel stylesheet href https cdn jsdelivr net npm docs searchbar js latest dist cdn docs searchbar min css head body input type search id search bar input script src https cdn jsdelivr net npm docs searchbar js latest dist cdn docs searchbar min js script script docssearchbar hosturl https mymeilisearch com apikey xxx indexuid docs inputselector search bar input debug true set debug to true if you want to inspect the dropdown script body html the hosturl and the apikey optional fields are the credentials of your meilisearch instance br indexuid is the index identifier in your meilisearch instance in which your website content is stored br inputselector is the id attribute of the html search input tag as an alternative the dom element can be supplied with inputelement directly your documentation content is not indexed yet check out this tutorial https www meilisearch com docs create how to search bar for docs html warning we recommend providing the meilisearch public key which is enough to perform search requests br read more about meilisearch authentication https www meilisearch com docs reference features authentication html styling docs searchbar js comes with a css template it has to be added in your project in the following way in an es environment js import docs searchbar js dist cdn docs searchbar css in a html file the link tag should be added in your header html link rel stylesheet href https cdn jsdelivr net npm docs searchbar js latest dist cdn docs searchbar min css customization the default behavior of this library fits perfectly for a documentation search bar but you might need some customizations optional parameters optional parameters when calling docssearchbar method styling styling with css optional parameters omit in toc when calling the docssearchbar method you can add optional fields queryhook omit in toc queryhook takes a callback function as value this function will be called on every keystroke to transform the typed keywords before querying meilisearch by default it does not do anything but it is the perfect place for you to add some preprocessing or custom functionality transformdata omit in toc transformdata takes a callback function as value this function will be called on every hit before displaying them by default it does not do anything but it lets you add any post processing around the data you received from meilisearch handleselected omit in toc handleselected takes a callback function a value this function is called when a suggestion is selected either from a click or a keystroke by default it displays anchor links to the results page here is an example to override this behavior javascript docssearchbar handleselected function input event suggestion datasetnumber context prevents the default behavior on click and rather opens the suggestion in a new tab if context selectionmethod click input setval const windowreference window open suggestion url blank windowreference focus note that by default you can already open a new tab thanks to the cmd ctrl click action the function is called with the following arguments input a reference to the search input element it comes with the open close getval and setval methods event the actual event triggering the selection suggestion the object representing the current selection it contains a url key representing the destination datasetnumber this should always be equal to 1 as docs searchbar js is searching into one dataset at a time you can ignore this attribute context additional information about the selection contains a selectionmethod key that can be either click enterkey tabkey or blur depending on how the suggestion was selected meilisearchoptions omit in toc you can forward search parameters to the meilisearch api by using the meilisearchoptions key checkout out the meilisearch documentation about search parameters https www meilisearch com docs reference api search search parameters search parameters for example you might want to increase the number of results displayed in the dropdown javascript docssearchbar meilisearchoptions limit 10 enabledarkmode omit in toc allows you to display the searchbar in dark mode it is useful if your website has dark mode support and you also want the searchbar to appear in a dark version you can always edit the style of the searchbar to match the style of your website when the option enabledarkmode is set to auto the searchbar automatically sets the mode to the system mode enabledarkmode has three possible states false enforce light mode true enforce dark mode auto system mode light or dark example javascript docssearchbar enabledarkmode auto dark mode with enabledarkmode set to auto and system mode set to dark docs searchbar with dark mode assets dark mode png enhancedsearchinput omit in toc when set to true a theme is applied to the search box to improve its appearance it adds the searchbox class which can be used to further customise the search box example javascript docssearchbar enhancedsearchinput true more examples omit in toc here is a basic html file playground index html used in the playground of this repository as a more concrete example you can check out the configuration https github com meilisearch vuepress plugin meilisearch blob main meilisearchbox vue l60 applied in the meilisearch plugin for vuepress styling omit in toc css main dropdown wrapper meilisearch autocomplete dsb dropdown menu width 500px main category meilisearch autocomplete docs searchbar suggestion category header color darkgray border 1px solid gray category meilisearch autocomplete docs searchbar suggestion subcategory column color gray title meilisearch autocomplete docs searchbar suggestion title font weight bold color black description meilisearch autocomplete docs searchbar suggestion text font size 0 8rem color gray highlighted text meilisearch autocomplete docs searchbar suggestion highlight color blue tips when inspecting the dropdown markup with your browser tools you should add debug true to your docssearchbar call to prevent it from closing on inspection more examples omit in toc here is the css customization https github com meilisearch vuepress plugin meilisearch blob main meilisearchbox vue l82 applied in the meilisearch plugin for vuepress compatibility with meilisearch this package guarantees compatibility with version v1 x of meilisearch https github com meilisearch meilisearch releases latest but some features may not be present please check the issues https github com meilisearch docs searchbar js issues q is 3aissue is 3aopen label 3a 22good first issue 22 label 3aenhancement for more info development workflow and contributing any new contribution is more than welcome in this project if you want to know more about the development workflow or want to contribute please visit our contributing guidelines contributing md for detailed instructions credits based on algolia docsearch repository https github com algolia docsearch from this commit https github com algolia docsearch commit 4c32b6f80b753f592de83351116664bf74b10297 br due to a lot of future changes in this repository compared to the original one we don t maintain it as an official fork hr meilisearch provides and maintains many sdks and integration tools like this one we want to provide everyone with an amazing search experience for any kind of project if you want to contribute make suggestions or just know what s going on right now visit us in the integration guides https github com meilisearch integration guides repository | meilisearch integration search-bar | front_end |
Efficient-Embedded-Systems-Design-Education-Kit | efficient embedded systems design education kit welcome to our efficient embedded systems design education kit download the edkit here https github com arm university efficient embedded systems design education kit archive refs heads main zip our flagship offering to universities worldwide is the arm university program education kit series these self contained educational materials offered exclusively and at no cost to academics and teaching staff worldwide they re designed to support your day to day teaching on core electronic engineering and computer science subjects you have the freedom to choose which modules to teach you can use all the modules in the education kit or only those that are most appropriate to your teaching outcomes our efficient embedded systems design education kit covers the fundamentals of embedded systems design including how the theories and concepts can be applied in the design and efficient programming of embedded processor interfacing with both analog and digital input and output peripehrals a full description of the education kit can be found here https www arm com resources education education kits efficient embedded systems kit specification a full set of lecture slides ready for use in a typical 10 12 week undergraduate course full syllabus below recent updates to the edkit include updated material on armv8 m architecture and the cortex m33 processor a dedicated module on arm s system wide trustzone security technology and refreshed lab exercises on the st nucleo l552ze q board lab manual with solutions for faculty labs are based on low cost yet powerful arm based hardware platforms donated by partners subject to availability prerequisites basics of c and assembly programming course aim to produce students who can design and efficiently program arm based embedded systems syllabus 1 introduction to embedded systems design 1 software design basics 1 cortex m4 m33 processor core part 1 cortex m4 for nucleo f401re version while cortex m33 for nucleo l552ze q version 1 cortex m4 m33 processor core part 2 cortex m4 for nucleo f401re version while cortex m33 for nucleo l552ze q version 1 c as implemented in assembly language 1 interrupts and low power features 1 general purpose i o 1 analog interfacing 1 timer peripherals 1 serial communications 1 programming for power efficient computing high level techniques 1 programming for power efficient computing low level techniques 1 trustzone features in cortex m33 available only on nucleo l552ze q version license you are free to fork or clone this material see license md https github com arm university efficient embedded systems design education kit blob main license license md for the complete license inclusive language commitment arm is committed to making the language we use inclusive meaningful and respectful our goal is to remove and replace non inclusive language from our vocabulary to reflect our values and represent our global ecosystem arm is working actively with our partners standards bodies and the wider ecosystem to adopt a consistent approach to the use of inclusive language and to eradicate and replace offensive terms we recognise that this will take time this course has been updated to replace references to non inclusive language we recognise that some of you will be accustomed to using the previous terms and may not immediately recognise their replacements please refer to the following example when introducing the amba 3 ahb lite protocols we will use the term manager instead of master and subordinate instead of slave this course may still contain other references to non inclusive language it will be updated with newer terms as those terms are agreed and ratified with the wider community contact us at education arm com with questions or comments about this course you can also report non inclusive and offensive terminology usage in arm content at terms arm com | embedded embedded-systems arm-cortex-m4 arm-cortex-m33 embedded-system-design arm cortex-m hardware-designs st-nucleo-f401re trustzone st-nucleo-l552ze amba discovery-f4 armv8 keil-mdk | os |
web-llm | discord url https discord gg 9xpy2hgbud web llm npm package https www npmjs com package mlc ai web llm get started get started examples examples documentation https mlc ai mlc llm docs deploy javascript html mlc llm https github com mlc ai mlc llm discord discord url webllm is a modular customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration everything runs inside the browser with no server support and accelerated with webgpu we can bring a lot of fun opportunities to build ai assistants for everyone and enable privacy while enjoying gpu acceleration check out our demo webpage to try out https webllm mlc ai you can use webllm as a base npm package https www npmjs com package mlc ai web llm and build your own web application on top of it by following the documentation https mlc ai mlc llm docs deploy javascript html and checking out get started get started this project is a companion project of mlc llm https github com mlc ai mlc llm our companion project that runs llms natively on iphone and other native local environments img src site img fig demo gif get started webllm offers a minimalist and modular interface to access the chatbot in the browser the webllm package itself does not come with ui and is designed in a modular way to hook to any of the ui components the following code snippet demonstrate a simple example that generates a streaming response on a webpage you can check out examples get started examples get started to see the complete example typescript import as webllm from mlc ai web llm we use label to intentionally keep it simple function setlabel id string text string const label document getelementbyid id if label null throw error cannot find label id label innertext text async function main create a chatmodule const chat new webllm chatmodule this callback allows us to report initialization progress chat setinitprogresscallback report webllm initprogressreport setlabel init label report text you can also try out redpajama incite chat 3b v1 q4f32 0 await chat reload llama 2 7b chat hf q4f32 1 const generateprogresscallback step number message string setlabel generate label message const prompt0 what is the capital of canada setlabel prompt label prompt0 const reply0 await chat generate prompt0 generateprogresscallback console log reply0 const prompt1 can you write a poem about it setlabel prompt label prompt1 const reply1 await chat generate prompt1 generateprogresscallback console log reply1 console log await chat runtimestatstext main using web worker webllm comes with api support for webworker so you can hook the generation process into a separate worker thread so that the compute in the webworker won t disrupt the ui we first create a worker script that created a chatmodule and hook it up to a handler that handles requests typescript worker ts import chatworkerhandler chatmodule from mlc ai web llm hookup a chat module to a worker handler const chat new chatmodule const handler new chatworkerhandler chat self onmessage msg messageevent handler onmessage msg then in the main logic we create a chatworkerclient that implements the same chatinterface the rest of the logic remains the same typescript main ts import as webllm from mlc ai web llm async function main use a chat worker client instead of chatmodule here const chat new webllm chatworkerclient new worker new url worker ts import meta url type module everything else remains the same build a chatapp you can find a complete a complete chat app example in examples simple chat examples simple chat customized model weights webllm works as a companion project of mlc llm https github com mlc ai mlc llm it reuses the model artifact and builds flow of mlc llm please check out mlc llm document on how to build new model weights and libraries mlc llm document will come in the incoming weeks to generate the wasm needed by webllm you can run with target webgpu in the mlc llm build there are two elements of the webllm package that enables new models and weight variants model url contains a url to model artifacts such as weights and meta data model lib the web assembly libary that contains the executables to accelerate the model computations both are customizable in the webllm typescript async main const myllamaurl url to my llama const appconfig model list model url myllamaurl local id myllama 3b v1 q4f32 0 model lib map llama v1 3b q4f32 0 url to myllama3b wasm override default const chatopts repetition penalty 1 01 const chat new chatmodule load a prebuilt model with a chat option override and app config under the hood it will load the model from myllamaurl and cache it in the browser cache let us assume that myllamaurl mlc config json contains a model lib field that points to llama v1 3b q4f32 0 then chat module will initialize with these information await chat reload myllama 3b v1 q4f32 0 chatopts appconfig in many cases we only want to supply the model weight variant but not necessarily a new model in such cases we can reuse the model lib in such cases we can just pass in the model list field and skip the model lib and make sure the mlc chat config json in the model url has a model lib that points to a prebuilt version right now the prebuilt lib includes llama 2 7b chat hf q4f32 1 llama 7b models redpajama incite chat 3b v1 q4f32 0 redpajama 3b variant use webllm package you can directly use webllm in your package via npm checkout instructions in the following project get started examples get started minimum get started example web worker examples web worker get started with web worker backed chat simple chat examples simple chat a mininum and complete chat app build webllm package from source note you don t need to build by yourself unless you would like to change the webllm package follow use webllm use web llm package instead webllm package is a web runtime designed for mlc llm https github com mlc ai mlc llm 1 install all the prerequisites for compilation 1 emscripten https emscripten org it is an llvm based compiler that compiles c c source code to webassembly follow the installation instruction https emscripten org docs getting started downloads html installation instructions using the emsdk recommended to install the latest emsdk source emsdk env sh by source path to emsdk env sh so that emcc is reachable from path and the command emcc works 4 install jekyll by following the official guides https jekyllrb com docs installation it is the package we use for website this is not needed if you re using nextjs see next simple chat in the examples 5 install jekyll remote theme by command try gem mirror https gems ruby china com if install blocked shell gem install jekyll remote theme we can verify the successful installation by trying out emcc and jekyll in terminal respectively 2 setup necessary environment prepare all the necessary dependencies for web build shell scripts prep deps sh 3 buld webllm package shell npm run build 4 validate some of the sub packages you can then go to the subfolders in examples examples to validate some of the sub packages we use parcelv2 for bundling although parcel is not very good at tracking parent directory changes sometimes when you make a change in the webllm package try to edit the package json of the subfolder and save it which will trigger parcel to rebuild links demo page https webllm mlc ai if you want to run llm on native runtime check out mlc llm https github com mlc ai mlc llm you might also be interested in web stable diffusion https github com mlc ai web stable diffusion acknowledgement this project is initiated by members from cmu catalyst uw sampl sjtu octoml and the mlc community we would love to continue developing and supporting the open source ml community this project is only possible thanks to the shoulders open source ecosystems that we stand on we want to thank the apache tvm community and developers of the tvm unity effort the open source ml community members made these models publicly available pytorch and hugging face communities make these models accessible we would like to thank the teams behind vicuna sentencepiece llama alpaca we also would like to thank the webassembly emscripten and webgpu communities finally thanks to dawn and webgpu developers | deep-learning llm tvm webgpu webml chatgpt language-model | ai |
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indulge in this journey of open source the main aim is to provide an efficient and beginner friendly projects that would help you in mastering the ml ai algorithms and make you familiar turn yourself into pro with all the hands on that got you covered join here anyone related to technology who are looking to contribute to open source are all invited to hop in this place has task for everyone machine learning deep learning natural language processing computer vision frontend designers backend developers ui ux designers data analysts frame the problem get explore and prepare the data machine learning deep learning computer vision natural language processing enthusiasts try different algorithms build model optimize the model front end designers design or code the webpage designed by designers back end developers create backend for the model using flask or django ui ux designers design dashboards forms and webpages for the model if you had worked on or want to initiate a unique project and want to share it with the world you can do that through here go through the contributing guidelines in contributing https github com prathimacode hub ml projectkart blob main contributing md when issue is raised from your end or taken it from issues tab to add a project elaborate as much as you could and as well notify us about the task you will be working that might be model creation or data analysis or front end or back end or ui ux design subsequently also go through the github documentation on creating a pull request https help github com en github collaborating with issues and pull requests creating a pull request look through the kart of amazing projects s no project name description 01 advertisement click prediction https github com prathimacode hub ml projectkart tree main advertisement 20click 20prediction the goal of this project is to make a prediction model using the advertisement classification algortihms which will predict the desired ad as per the user information 02 advertisement success prediction https github com prathimacode hub ml projectkart tree main advertisement 20success 20prediction in this project we will be working with an advertising data set indicating whether or not a particular internet user clicked on an advertisement 03 age gender and ethnicity prediction https github com prathimacode hub ml projectkart tree main age 2c 20gender 20and 20ethnicity 20prediction the goal of this project is to be able to predict age gender and ethnicity of a person just by looking at an image of a person 04 air quality predication https github com prathimacode hub ml projectkart tree main air 20quality 20prediction the goal of this project is to predict air quality index aqi form features as particulate matter pm2 5 and pm10 nitrogen oxide no nitric dioxide no2 carbon monoxide co sulphur dioxide so2 ozone o3 etc 05 airbnb price prediction https github com prathimacode hub ml projectkart tree main airbnb 20price 20prediction the goal of this project is to make a prediction model which will predict the prices of the airbnb hotels using different parameters 06 airline passenger satistfaction https github com prathimacode hub ml projectkart tree main airline 20passenger 20satisfaction the aim of this project is to perform analysis of the passenger satisfaction data and data preprocessing to prepare the data and predict whether the passenger is satisfied or not with the airline services 07 alpaca identification https github com prathimacode hub ml projectkart tree main alpaca 20identification the goal of this project is to make an identification cum classification model using deep neural networks which will identify the images of alpaca from the user given input 08 amazon alexa reviews https github com prathimacode hub ml projectkart tree main amazon 20alexa 20reviews this model predicts the feedback of amazon alexa users based on the features such as ratings variations and verified reviews 09 amazon books analysis https github com prathimacode hub ml projectkart tree main amazon 20books 20analysis prediction on whether books will be fiction or non fiction using goodreads 10 amazon data analysis https github com prathimacode hub ml projectkart tree main amazon 20data 20analysis the goal of this project is to predict the upvote of the customer on the basis of reviews text and score of the cuustomer given in the dataset 11 amazon mobile phones reviews analysis https github com prathimacode hub ml projectkart tree main amazon 20mobile 20phones 20reviews 20analysis this is one of the major techniques used to analyze customer reviews on different products and find necessary insights from reviews the major idea is to classify reviews and determine how satisfied customers were regarding the product 12 amazon products review classification https github com prathimacode hub ml projectkart tree main amazon 20products 20reviews 20classification the goal of this project is to make a classifiation model which will classify the products review enlisted in the amazon inc website so that it can help the company for their betterment and also they can rectify their faults depending on the users experience 13 american sign language asl recognition https github com prathimacode hub ml projectkart tree main american 20sign 20language 20 asl 20recognition t he aim of this project is to recognize what the person is trying to convey using different hand gestures the dataset contains 29 classes which comprises of a to z alphabets nothing space and delete hand gestures 14 animate me https github com prathimacode hub ml projectkart tree main animate 20me it is a simple opencv project that converts input image to a cartoon equivalent 15 appliances energy prediction https github com prathimacode hub ml projectkart tree main appliances 20energy 20prediction the goal of this project is to make a prediction model which will predict the enery consumption by the appliances based on the given dataset 16 automated essay grading https github com prathimacode hub ml projectkart tree main automated 20essay 20grading the goal of this project is to make a prediction model which will give scoring to student written essays 17 avito product analysis and price prediction https github com prathimacode hub ml projectkart tree main avito 20product 20analysis 20and 20price 20prediction perform exploratory data analysis on the dataset of products from avito advertising website and develop a ml model to predict prices of the other products in the website 18 balls image classification https github com prathimacode hub ml projectkart tree main balls 20image 20classification the goal of this project is to make a deep learning model which will classify the images of different types of balls using the convoolution neural network to be precise the mobilenet architecture 19 bank customers prediction https github com prathimacode hub ml projectkart tree main bank 20customers 20prediction to predict the customers who are withdrawing their account from the bank due to some loss and other issues 20 bike crash analysis https github com prathimacode hub ml projectkart tree main bike 20crash 20analysis the goal of this project to analyze the dataset on various factors and depending on the various factors making of a prediction model which will predict the accident prone sights 21 bike rental demand analysis https github com prathimacode hub ml projectkart tree main bike 20rental 20demand 20analysis the aim of the project is to make a forecast of the demand in bike rental services this project predicts the upcoming nature of the customer request demand 22 bike sharing prediction https github com prathimacode hub ml projectkart tree main bike 20sharing 20prediction the main goal of the project is to analyse the bike share count in case of different seasons weekdays weathers etc and based on that train a model to predict the number of bike users under a given circumstance 23 bird species classification and recognition https github com prathimacode hub ml projectkart tree main bird 20species 20classification 20and 20recognition the goal is to complete an end to end computer vision problem with basic parts such as bulding an input pipeline compose the model data augmentation etc 24 bitcoin price prediction https github com prathimacode hub ml projectkart tree main bitcoin 20price 20prediction the main purpose of this project is to predict the price of bitcoin 25 black friday sales analysis and prediction https github com prathimacode hub ml projectkart tree main black 20friday 20sales 20analysis 20and 20prediction the goal of this project is to analyse and predict purchases in the black friday sales from features as age group gender occupation product category etc 26 board game review prediction https github com prathimacode hub ml projectkart tree main board 20game 20review 20prediction the goal of this project are what are the categories of game that are the most popular can we build a model with the available data that predicts user rating what factors make for the best modern board game 27 body fat prediction https github com prathimacode hub ml projectkart tree main body 20fat 20prediction the goal is to predict percentage of body fat of a person 28 book genre prediction https github com prathimacode hub ml projectkart tree main book 20genre 20prediction the goal of this project is to make a prediction model which will predict genre of the book 29 book recommendation systems https github com prathimacode hub ml projectkart tree main book 20recommendation 20systems the goal of this project is to make a recommendation system which will recommend the user a book based on the search results given input by the users 30 brain tumor detection and classification https github com prathimacode hub ml projectkart tree main brain 20tumor 20detection 20and 20classification this model detects the presence of brain tumor by processing mri scans of the patient 31 brain weight prediction https github com prathimacode hub ml projectkart tree main brain 20weight 20prediction the goal of this project is to make a prediction model which will predict the weight of the human brain depending on the head size 32 brazil fires prediction https github com prathimacode hub ml projectkart tree main brazil 20fires 20prediction to predict the number of the fires taken place statewise in brazil 33 breast cancer prediction https github com prathimacode hub ml projectkart tree main breast 20cancer 20prediction the goal is to predict breast cancer 34 campus placement prediction https github com prathimacode hub ml projectkart tree main campus 20placement 20prediction the goal is to predict if a student is placed or not according to his her percentage and other factors 35 campus recruitment analysis and prediction https github com prathimacode hub ml projectkart tree main campus 20recruitment 20 20analysis 20and 20prediction the goal of this project is to analyze the factors that can effect the campus recruitment and also creating a model which will predict the chances of getting placed depending on various factors 36 captcha decoding https github com prathimacode hub ml projectkart tree main captcha 20decoding the goal of this project is to create a deep learning model which will recognize the captcha letters 37 car brand classification https github com prathimacode hub ml projectkart tree main car 20brand 20classification this project classifies the car brand using machine learning 38 car prices prediction https github com prathimacode hub ml projectkart tree main car 20prices 20prediction the main goal is to predict the price of cars with the available independent variables 39 cartier jewelry classification https github com prathimacode hub ml projectkart tree main cartier 20jewelry 20classification the goal of this project is to make a classification model which will classify the jewelries based on the various features 40 cats vs dogs classification https github com prathimacode hub ml projectkart tree main cats 20vs 20dogs 20classification the goal of this project is to classify whether the image is of a dog or a cat 41 cervical cancer risk prediction https github com prathimacode hub ml projectkart tree main cervical 20cancer 20risk 20prediction the business objective is to build a machine learning prediction model that predicts the result of biopsy test and thereby confirming the presence non presence of cervical cancer in the patients 42 churn risk score prediction https github com prathimacode hub ml projectkart tree main churn 20risk 20score 20prediction this project is used to predict the churn score for a website based on the related features 43 classification of images using mnist and cifar10 https github com prathimacode hub ml projectkart tree main classification 20of 20images 20using 20mnist 20and 20cifar10 classifying and predicting images with accuracy 44 coffee production prediction https github com prathimacode hub ml projectkart tree main coffee 20production 20prediction the goal of this project is to make a prediction model which will give total production of the coffee from year 1990 2018 45 colour identification using machine learning https github com prathimacode hub ml projectkart tree main colour 20identification 20using 20machine 20learning this project defines us the colour we have asked to see instead of showing the color object 46 concrete strength prediction https github com prathimacode hub ml projectkart tree main concrete 20strength 20prediction the aim of this project is to predict strength of the concrete from features as fly ash blast furnamce slag water fine aggregate coarse aggregate cement age etc 47 cotton disease prediction https github com prathimacode hub ml projectkart tree main cotton 20disease 20prediction this project predicts the disease of cotton tree using transfer learning with resnet152v2 model 48 covid19 data analysis https github com prathimacode hub ml projectkart tree main covid19 data analysis the goal is to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of great help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient 49 covid 19 data analysis https github com prathimacode hub ml projectkart tree main covid 19 20data 20analysis the goal of this project is to analyze the situation caused due to covid 19 pandemic using data visualization 50 credit card fraud detection https github com prathimacode hub ml projectkart tree main credit 20card 20fraud 20detection the aim of the project is to predict fraudulent credit card transactions using machine learning models 51 crop recommendation system https github com prathimacode hub ml projectkart tree main crop 20recommendation 20system the goal of this project is to build the recommendation model using the crop recommendation dataset 52 crop yield prediction https github com prathimacode hub ml projectkart tree main crop 20yield 20prediction this dataset is focused on the different crops which can reflect the price area and other attributes from that one can predict for future price yield by using some better attributes 53 cryotherapy analysis https github com prathimacode hub ml projectkart tree main cryotherapy 20analysis in this project we are going to analyze the cryotherapy dataset and will deploy several machine learning algorithm models 54 customer region classification https github com prathimacode hub ml projectkart tree main customer 20region 20classification the goal is to predict customer region using random forest classifier model 55 dna sequencing classification https github com prathimacode hub ml projectkart tree main dna 20sequencing 20classification in this project we will understand how to interpret a dna structure and how machine learning algorithms can be used to build a prediction model on dna sequence data 56 dance form classification https github com prathimacode hub ml projectkart tree main dance 20form 20classification the goal of this project is to classify the images of various dance forms and prepare a classification model using deep learning methods 57 dandelion recognition https github com prathimacode hub ml projectkart tree main dandelion 20recognition the goal of this project is to create classification model which will identify the dandelion from the images given to the model 58 data analysis of meteorological data https github com prathimacode hub ml projectkart tree main data 20analysis 20of 20meteorological 20data it analyzes the meteorological weather data of the last 10yrs from 2006 to 2016 at finland to check an increase of global warming 59 diabetes prediction https github com prathimacode hub ml projectkart tree main diabetes 20prediction the goal is to predict if a person is having diabetes or not using logistic regression and svm models 60 diamond price prediction https github com prathimacode hub ml projectkart tree main diamond 20price 20prediction the goal is to predict price of diamond 61 disease symptom prediction https github com prathimacode hub ml projectkart tree main disease 20symptom 20prediction the goal of this project is predict symptom of disease using the information contained in this dataset 62 disneyland reviews analysis https github com prathimacode hub ml projectkart tree main disneyland 20reviews 20analysis the aim of this project is to analyse the reviews given by visitors from different countries of the world using nlp 63 dog breed identification https github com prathimacode hub ml projectkart tree main dog 20breed 20identification this project is about predicting the breed of a dog this can be helpful to people who are not expert in the field 64 dogecoin price prediction https github com prathimacode hub ml projectkart tree main dogecoin 20price 20prediction the goal of this project is to make a prediction model which will predict the price of the dogecoin in the future times depending on the previous parameters 65 driver drowsiness detection system https github com prathimacode hub ml projectkart tree main driver 20drowsiness 20detection 20system the main purpose of this project is to awake the driver s if they fall asleep in order to avoid drowsiness related road accidents 66 dry beans classification https github com prathimacode hub ml projectkart tree main dry 20beans 20classification the goal is to predict dry beans 67 email classification https github com prathimacode hub ml projectkart tree main email classification this project classify emails as spam or not spam on the basis of the message 68 emoji classification using opencv https github com prathimacode hub ml projectkart tree main emoji 20classification 20using 20opencv the goal of this project is to make a model which will predict the emotion shown using the input image and also it will classify the particular emotion shown in the given image 69 emotion classification https github com prathimacode hub ml projectkart tree main emotion 20classification the goal of this project is to create a model which will classify different emotions based on the text provided in the dataset 70 emotion recognition using nlp https github com prathimacode hub ml projectkart tree main emotion 20recognition 20based 20on 20nlp an intelligent emotion predictor which uses nlp technique by sending text input to determine whether data is positive negative or neutral 71 employee retention prediction https github com prathimacode hub ml projectkart tree main employee 20retention 20project the project revolves around the idea to give basic modelling techniques used to classify if the employee will leave the company or not based on certain features in the datset 72 english alphabet classification https github com prathimacode hub ml projectkart tree main english 20alphabet 20classification model the aim of this project is to recognize the computerised generated images of the english apphabets the dataset contains 26 classes which comprises of a to z alphabets each class containing 100 images 73 exports classification https github com prathimacode hub ml projectkart tree main exports 20classification the goal of this project is to build a classification model using regression algorithms such as linear regression random forest regression decision tree regressor and many more algorithms 74 face generation using dcgan https github com prathimacode hub ml projectkart tree main face 20generation 20using 20dcgan the main goal of this project is to get a generator network to generate new images of faces that look as realistic as possible using a dcgan on a dataset of faces 75 face mask detection using opencv https github com prathimacode hub ml projectkart tree main face 20mask 20detection 20using 20opencv this is a face mask detection project that uses both haar cascades and caffe framework approaches for face detection and a finetuned mobilenetv2 model to detect masks on face taking real time video stream as input 76 face detection using pca https github com prathimacode hub ml projectkart tree main face 20detection 20using 20pca implement face detection using principal component analysis pca to reduce the dimensionality of large data sets 77 fake currency prediction https github com prathimacode hub ml projectkart tree main fake 20currency 20prediction the goal is to predict whether a given note is fake or not using machine learning models 78 fake news detection https github com prathimacode hub ml projectkart tree main fake 20news 20detection the aim of the project is to detect whether the news is real or fake using different text extraction nlp techniques 79 fake job posting prediction https github com prathimacode hub ml projectkart tree main fake 20job 20posting 20prediction the goal of this project is to predict the whether the employee with jods has fake job or real 80 fashion mnist classification https github com prathimacode hub ml projectkart tree main fashion 20mnist 20classification to classify various clothings into groups 81 fish weight prediction https github com prathimacode hub ml projectkart tree main fish 20weight 20prediction the main goal of this project to predict the weight of the any fish using linear regression model 82 flight delay prediction https github com prathimacode hub ml projectkart tree main flight 20delay 20prediction the main purpose of this project is to predict future delays in flights using machine learning models 83 flight fare prediction https github com prathimacode hub ml projectkart tree main flight 20fare 20prediction the aim of the project is to predict the fare of different airlines covering different routes using machine learning models 84 flowers recognition https github com prathimacode hub ml projectkart tree main flowers 20recognition the goal of this project is to identify the flower name from an image uploaded by the user 85 football match prediction https github com prathimacode hub ml projectkart tree main football 20match 20prediction the goal of this project is to predict the match winner according to the prediction model 86 football team rating prediction https github com prathimacode hub ml projectkart tree main football 20team 20rating 20prediction the goal is to predict rating of football team 87 forest cover type classification https github com prathimacode hub ml projectkart tree main forest 20cover 20type 20classification the main goal of this project to classify the cover type of forest with the dataset using random forest classification model 88 forest fire prediction https github com prathimacode hub ml projectkart tree main forest 20fire 20prediction to predict the area burned in the forest fire 89 fragnance price prediction https github com prathimacode hub ml projectkart tree main fragnance 20price 20prediction to predict the price of the fragnances 90 fresher s salary prediction https github com prathimacode hub ml projectkart tree main fresher s 20salary 20prediction the main goal of this project to predict the salary of fresher students on the basis of subjects degree type percentages etc 91 fuel consumption analysis https github com prathimacode hub ml projectkart tree main fuel 20consumption 20analysis the aim of this project is to predict the consumption depending on the gas type it can be used to determine the effect of weather speed or gas type on the car consumption 92 got episodes imdb rating prediction https github com prathimacode hub ml projectkart tree main got 20episodes 20imdb 20rating 20prediction the goal of this project is to create a prediction model which will predict the imdb ratings of the episodes of the game of thrones series depending on the views and season of the got 93 gender classification using dl https github com prathimacode hub ml projectkart tree main gender 20classification 20using 20dl gender classification aims to recognize a person s gender based on the characteristics that differentiate masculinity and femininity 94 german traffic sign classification https github com prathimacode hub ml projectkart tree main german 20traffic 20sign 20classification the aim is to automatically classify traffic signs 95 glass classification https github com prathimacode hub ml projectkart tree main glass 20classification the aim of this project is to classify the glass type using percentage of minerals present in each class of glass 96 gold price prediction https github com prathimacode hub ml projectkart tree main graduate 20admission 20prediction the aim of this project is to perform analysis the gold price using different eda techniques and eventually train different model to predict the price of gold 97 graduate admission prediction https github com prathimacode hub ml projectkart tree main glass 20classification to predict about the graduate admissions from an indian perspective 98 handwritten digit recognition https github com prathimacode hub ml projectkart tree main handwritten 20digit 20recognition the main purpose of this project is to recognize handwritten digits by humans 99 heart attack analysis https github com prathimacode hub ml projectkart tree main heart 20attack 20analysis the goal of this exercise is to identify the parameters that influences the heart attack and build a ml model for the prediction of heart attack 100 heart disease prediction https github com prathimacode hub ml projectkart tree main heart 20disease 20prediction this project predicts heart disease based on features like age cholesterol level blood sugar level etc 101 heights and weights prediction https github com prathimacode hub ml projectkart tree main heights 20and 20weights 20prediction the goal of this project is to build the prediction model using the regression algorithms to predict height and weight 102 honey bee pollen detection https github com prathimacode hub ml projectkart tree main honey 20bee 20pollen 20detection the aim of this project is to classify the images of the bees to detect whether they are carrying pollen grains or not 103 horses or humans classification https github com prathimacode hub ml projectkart tree main horses 20or 20humans 20classification the aim of this project is to create a model able to classify the horses and humans 104 hotel booking prediction https github com prathimacode hub ml projectkart tree main hotel 20booking 20prediction the goal is to predict the possibility of the booking whether the booking is successfull or not 105 hotel rating prediction https github com prathimacode hub ml projectkart tree main hotel 20rating 20prediction the goal of this project is to build the model for the hotel rating prediction using nine machine learning models 106 house prices prediction https github com prathimacode hub ml projectkart tree main house 20price 20prediction this project predicts the prices of houses located in the cities of the us with the help of essential features 107 human activity recognition using smartphones https github com prathimacode hub ml projectkart tree main human 20activity 20recognition 20using 20smartphones the aim of this project is to build a model that predicts the human activities such as walking walking upstairs walking downstairs sitting standing or laying using data recorded by multiple smartphone sensors 108 imdb sentiment analysis https github com prathimacode hub ml projectkart tree main imdb 20sentiment 20analysis the main purpose of this project is to predict the sentiment of movie reviews on imdb 109 ipl score prediction https github com prathimacode hub ml projectkart tree main ipl 20score 20prediction the goal of this project is to predict the score of an innings in an ipl match 110 ipl winner prediction https github com prathimacode hub ml projectkart tree main ipl 20winner 20prediction the aim of this project is to perform analysis of the ipl data and predict the winner of the ipl matches 111 iris flower classification https github com prathimacode hub ml projectkart tree main iris 20flower 20classification the project is to classify the flowers among three 3 different types 112 ice cream revenue prediction https github com prathimacode hub ml projectkart tree main ice 20cream 20revenue 20prediction this is an icecream revenue prediction which predicts the daily revenue of icecreams depending on the temperature feature 113 image compression using clustering https github com prathimacode hub ml projectkart tree main image 20compression 20using 20clustering this project helps in compressing the image using k means clustering 114 imagenet classification https github com prathimacode hub ml projectkart tree main imagenet 20classification imagenet is an image database organized according to the wordnet hierarchy currently only the nouns in which each node of the hierarchy is depicted by hundreds and thousands of images 115 income prediction web app https github com prathimacode hub ml projectkart tree main income 20prediction 20web 20app the main goal of this project to find out whether a person earn more than 50k dollar 50k and less than or equal to 50k dollar 50k based on the given presonal information about person 116 insurance claim prediction https github com prathimacode hub ml projectkart tree main insurance 20claim 20prediction the goal of this project is to classify insurance claim as 0 or 1 if the policy holder will claim insurance or not from features as age number of children bmi residential region etc 117 kidney stone prediction https github com prathimacode hub ml projectkart tree main kidney 20stone 20prediction the goal of this project is to create a prediction model which will predict the success rate of kidney stone operation based on the stone s size and type of treatment 118 lego minifigures https github com prathimacode hub ml projectkart tree main lego 20minifigures this project contains the dataset of a lot of pictures of various lego minifigures in different poses or with different environments for the image classification tasks 119 laptop prices prediction https github com prathimacode hub ml projectkart tree main laptop 20prices 20prediction the goal of this project is to make a prediction model which will predict the prices of the laptops depending on various factors such as size company set up and many more things 120 license plate detection and recognition https github com prathimacode hub ml projectkart tree main license 20plate 20detection 20and 20recognition this project is to detect and identify the license plates of a vehicles 121 loan eligibility prediction https github com prathimacode hub ml projectkart tree main loan 20eligibility 20prediction the main goal of the project is to predict the eligibility of a customer for getting a loan from the company 122 mba specialization classification https github com prathimacode hub ml projectkart tree main mba 20specialization 20classification the goal of this project is to find out the mba students who will be having good scores in their mba career based on past activities using classification algorithms 123 malaria disease detection https github com prathimacode hub ml projectkart tree main malaria 20disease 20detection the aim of this project is to recognize whether the image of the human cell is infected with malaria disease or not 124 male female eyes classification https github com prathimacode hub ml projectkart tree main male 20 26 20female 20eyes 20classification the goal of this project is to create a classification model which will classify the genders based on the eyes images as per given in the dataset for this we are going to use different architectures of convolution neural network 125 mall customer segmentation https github com prathimacode hub ml projectkart tree main mall 20customer 20segmentation this model will segment the customers based on the parameters 126 marathon time prediction https github com prathimacode hub ml projectkart tree main marathon 20time 20prediction the goal of this project is to predict the time for the marathon using the given details in dataset 127 marble surface anomaly detection https github com prathimacode hub ml projectkart tree main marble 20surface 20anomaly 20detection the goal of this project is to make a detection model which will detect which type of marble is present 128 medical cost analysis for smokers and non smokers https github com prathimacode hub ml projectkart tree main medical 20cost 20analysis 20for 20smokers 20and 20non smokers this project consists of the fact that the average medicine charges can be changed for the smokers and non smokers 129 mempool prediction https github com prathimacode hub ml projectkart tree main mempool 20prediction to predict about the memepool in detail 130 mobile price range classification https github com prathimacode hub ml projectkart tree main mobile 20price 20range 20classification in this project we do not have to predict actual price but a price range indicating how high the price is 131 movie oscar win prediction https github com prathimacode hub ml projectkart tree main movie 20oscar 20win 20prediction the goal of this project is to make a prediction model which will predict the chances of winning the oscar award 132 movie recommendation system https github com prathimacode hub ml projectkart tree main movie 20recommendation 20system this project will help one to recommend the movies will provide the movies names 133 mushroom classification https github com prathimacode hub ml projectkart tree main mushroom 20classification this project is all about deploying classification algorithms and comparing the models 134 music genre classification https github com prathimacode hub ml projectkart tree main music 20genre 20classification the goal of this project is to create a model which will classify all the 10 genres based on the song selected the song can be selected from the dataset or any other source the song format must be in wav format and for 30 sec in duration 135 nasa asteroids classification https github com prathimacode hub ml projectkart tree main nasa 20asteroids 20classification this project helps in classifying the nasa asteroids and checks for features responsible for claiming whether the asteroid is hazardous 136 nba analysis and prediction https github com prathimacode hub ml projectkart tree main nba analysis 20and 20prediction to predict number of points that were scored in the season 2013 2014 by the nba players 137 natural images classification https github com prathimacode hub ml projectkart tree main natural 20images 20classification the goal of this project is to build the classification model for natural images using the neural networks and deep learning 138 netflix eda and recommedation system https github com prathimacode hub ml projectkart tree main netflix 20eda 20and 20recommedation 20system the project aims to gain insights from detailed eda and recommend which movie to watch next 139 object detection using opencv https github com prathimacode hub ml projectkart tree main olympic 20medal 20prediction to understand computer vision libraries like opencv and detect various objects using deep learning models with pretrained weights 140 olympic medal prediction https github com prathimacode hub ml projectkart tree main mushroom 20classification the goal of this project is to make a prediction model which will predict whether an athlete will win medal or not 141 organ donors prediction https github com prathimacode hub ml projectkart tree main organ 20donors 20prediction the goal of this project is to predict donors using the information contained in this dataset 142 paris housing classification https github com prathimacode hub ml projectkart tree main paris 20housing 20classification the goal is to predict paris housing 143 parkinson s disease prediction https github com prathimacode hub ml projectkart tree main parkinson s 20disease 20prediction this project helps in finding the reasons for parkinson s disease and who are predicted to have this disease 144 password strength classifier https github com prathimacode hub ml projectkart tree main password 20strength 20classifier the goal of this project is to classify and predict the strength of the password given in the dataset 145 persian license plate characters identification https github com prathimacode hub ml projectkart tree main persian 20license 20plate 20characters 20identification the goal of this project is to make a detection model which will detect different characters from the persian number plate 146 phishing website detection https github com prathimacode hub ml projectkart tree main phishing 20website 20detection the goal of this project is to make a detection model which will detect the phishing websites depending on various factors using machine learning algorithms 147 plant disease prediction https github com prathimacode hub ml projectkart tree main plant 20disease 20prediction goal of this project is to identify the condition of a plant by looking at the image provided 148 plant seedlings classification https github com prathimacode hub ml projectkart tree main plant 20seedlings 20classification the goal of this project is to build the plant seedlings classification model the architectures used here are resnet alexnet vgg inception mobilenet squeezenet densenet to deploy the classification model 149 predict future sales https github com prathimacode hub ml projectkart tree main predict 20future 20sales to predict whether the future sales 150 private companies prediction https github com prathimacode hub ml projectkart tree main private 20companies 20prediction to predict the number of the private limited companies 151 productivity prediction https github com prathimacode hub ml projectkart tree main productivity 20prediction it is highly desirable among the decision makers in the garments industry to track analyse and predict the productivity performance of the working teams in their factories 152 railway track fault detection https github com prathimacode hub ml projectkart tree main railway 20track 20fault 20detection to predict railway track faults 153 rain prediction https github com prathimacode hub ml projectkart tree main rain 20prediction raintomorrow is the target variable to predict on the basis of some given features 154 reddit tweets prediction https github com prathimacode hub ml projectkart tree main reddit 20tweets 20prediction to predict about the different reddit tweets in detail 155 restaurant recommendation system https github com prathimacode hub ml projectkart tree main restaurant 20recommendation 20system the goal of this project is to make a recommendation system which will recommend the users the best restaurant that they are looking for 156 resume categorizing https github com prathimacode hub ml projectkart tree main resume 20categorizing the goal of this project is to build the model for the resume categorizing using eight machine learning models 157 road lane detection https github com prathimacode hub ml projectkart tree main road 20lane 20detection this project is the model for the road lane detection which will detect the road lane from an image which will be user given 158 salary prediction https github com prathimacode hub ml projectkart tree main salary 20prediction the goal of this project is to predict the salary based on some parameters 159 salary range classification https github com prathimacode hub ml projectkart tree main salary 20range 20classification the goal of this project is to classify salary range from features as company job degree etc 160 sarcasm detection https github com prathimacode hub ml projectkart tree main sarcasm 20detection the goal of this project is to detect sarcasm from news headlines data set using classification algorithms and compare the algorithms to find out which one is better 161 shoulder x ray classification https github com prathimacode hub ml projectkart tree main shoulder 20x ray 20classification the goal of this project is to create a classification model which will classify different images of shoulder x ray and predict or detect the type of the image 162 sign language prediction https github com prathimacode hub ml projectkart tree main sign 20language 20prediction the sign language prediction project helps in identifying the sign language from images provided 163 snapchat filters https github com prathimacode hub ml projectkart tree main snapchat 20filters we will build our own snapchat filters with the help of some libraries using python programming language 164 social network influencer prediction https github com prathimacode hub ml projectkart tree main social 20network 20influencer 20prediction the goal of the challenge is to train a machine learning model which for a pair of individuals predicts the human judgement on who is more influential with high accuracy 165 soil moisture prediction https github com prathimacode hub ml projectkart tree main soil 20moisture 20prediction the aim of this project is to predict the moisture present in soil 166 solar eclipse classification https github com prathimacode hub ml projectkart tree main solar 20eclipse 20classification the aim of this project is to classify among the main types of solar eclipses which are p partial eclipse a annular eclipse t total eclipse h hybrid or annular total eclipse 167 solar radiation prediction https github com prathimacode hub ml projectkart tree main solar 20radiation 20prediction the goal is to predict radiation of sun 168 song genre classification https github com prathimacode hub ml projectkart tree main song 20genre 20classification the goal of this project is to build the song genre classification model using the spotify dataset 169 spam email detection https github com prathimacode hub ml projectkart tree main spam 20email 20detection this project predicts whether the received message is spam or ham 170 speech emotion recognition https github com prathimacode hub ml projectkart tree main speech 20emotion 20recognition the goal is to predict the emotion of human while talking 171 stack overflow questions quality rating prediction https github com prathimacode hub ml projectkart tree main stack 20overflow 20questions 20quality 20rating 20prediction the gaol of this project is to make a prediction model which will predict the quality of the questions from the stack overflow website depending on the various factors 172 star radiation analysis and predcition https github com prathimacode hub ml projectkart tree main star 20radiation 20analysis 20and 20prediction the aim of the project is to analysis and predict the star radiations and perform a detailed visualizations out of the same 173 stars galaxies and quasars classification https github com prathimacode hub ml projectkart tree main stars 2c 20galaxies 20and 20quasars 20classification the goal of this project is to make a perfect classification model according to the data collected on stars galaxies and quasars 174 startup profit prediction https github com prathimacode hub ml projectkart tree main startup 20profit 20prediction the goal is to predict profit of the startups the dataset contains data about 50 startups it has 5 columns r d spend administration marketing spend state profit 175 stock price prediction https github com prathimacode hub ml projectkart tree main stock 20price 20prediction the goal is to predict the price of stocks in future and make calls according to the results algorithms provide 176 stress level prediction https github com prathimacode hub ml projectkart tree main stress 20level 20prediction the goal of this project is to predict stress level from features taken from the survey responses 177 stroke prediction https github com prathimacode hub ml projectkart tree main stroke 20prediction the goal of this project is to predict the rate of stroke of a person 178 student performance prediction https github com prathimacode hub ml projectkart tree main student 20performance 20prediction the goal of this project is to predict final grade of the student from features as study time failures free time absenses health status going out first period grade second period grade etc 179 supermarket sales prediction https github com prathimacode hub ml projectkart tree main supermarket 20sales 20prediction to predict supermarket sales 180 terrorist attack prediction https github com prathimacode hub ml projectkart tree main terrorist 20attack 20prediction analysis of given dataset and prediction of terrorist attack using regression modal 181 test score prediction https github com prathimacode hub ml projectkart tree main test 20score 20prediction the aim of project is to build machine learning algorithms to predict the scores of the students 182 tetris object counter https github com prathimacode hub ml projectkart tree main tetris 20object 20counter the goal of the project is to identify all the tetris objects tetrominoes in the given tetris input image and return the count it is seen that during gameplay the tetris blocks are broken and this program should be able to identify such blocks too 183 text summarization https github com prathimacode hub ml projectkart tree main text 20summarization the goal of this project is to create a model which will summarize the articles given by the users 184 titanic survival prediction https github com prathimacode hub ml projectkart tree main titanic 20survival 20prediction use machine learning to create a model that predicts which passengers survived the titanic shipwreck 185 traffic sign classification https github com prathimacode hub ml projectkart tree main traffic 20sign 20classification the goal of this project is to make human readable traffic sign classification 186 twitch streamer analysis https github com prathimacode hub ml projectkart tree main twitch 20streamer 20analysis the goal is to perform analysis and predict followers gained per stream 187 twitter sentiment analysis https github com prathimacode hub ml projectkart tree main twitter 20sentiment 20analysis this project helps in analyzing the sentiment using the text data posted on twitter to check it by classifying the statements as positive negative or neutral 188 u s weather history visualizations https github com prathimacode hub ml projectkart tree main u s 20weather 20history 20visualizations the goal is to analyse the 12 months data of us weather history and find the conclusion and display them via graphs 189 usa house pricing prediction https github com prathimacode hub ml projectkart tree main usa 20house 20pricing 20prediction the goal of this project is to make a prediction model which will predict the price of the houses at usa depending on the given features 190 uber fare prediction https github com prathimacode hub ml projectkart tree main uber 20fare 20predictions this project helps in predicting the fare to be charged for the uber travelling person 191 vehicle image classification https github com prathimacode hub ml projectkart tree main vehicle 20image 20classification the aim of this project is to classify a vehicle image which type of vehicle it is 192 walmart sales prediction https github com prathimacode hub ml projectkart tree main walmart 20sales 20prediction the main purpose of this project is to predict the future sales at walmart 193 waste classification https github com prathimacode hub ml projectkart tree main waste 20classification the aim of this project is to recognize whether the image of the waste product is a organic waste or a recyclable waste 194 water quality prediction https github com prathimacode hub ml projectkart tree main water 20quality 20prediction this project indicates if water is safe for human consumption where 1 means potable and 0 means not potable 195 web page phishing detection https github com prathimacode hub ml projectkart tree main web 20page 20phishing 20detection many people get scammed by this web page phishing technique detecting them can save people from getting scammed hackers usually blackmail the people to get their personal information back identifying this techniques can save millions 196 wine quality prediction https github com prathimacode hub ml projectkart tree main wine 20quality 20prediction this model predicts the quality of wine based on some features like ph fixed acidity citric acid etc using svm and random forest algorithm 197 world happiness report analysis https github com prathimacode hub ml projectkart tree main world 20happiness 20report 20analysis the aim of the project is to predict happiness scores and rankings and perform a detailed analysis and visualization of the training dataset and create a model 198 world poverty analysis https github com prathimacode hub ml projectkart tree main world 20poverty 20analysis the goal of this project is to analyze the world poverty using dataset 199 yotube video recommendation system https github com prathimacode hub ml projectkart tree main youtube 20video 20recommendation 20system the model will recommend the video titles on proving the search query by user 200 zomato banglore resturants recommendation https github com prathimacode hub ml projectkart tree main zomato 20bangalore 20restaurants 20recommendation 20analysis the aim of this project is analyse a dataset and recomend the user for top restaurants in bangalore 201 zoo animal classification https github com prathimacode hub ml projectkart tree main zoo animal classification the goal of this project is to predict the zoo animal based on some classifications using machine learning model templates to follow feature request https github com prathimacode hub ml projectkart blob main github issue template feature request md bug report https github com prathimacode hub ml projectkart blob main github issue template bug report md pull request https github com prathimacode hub ml projectkart blob main github pull request template md readme https github com prathimacode hub ml projectkart blob main github readme template md note one should follow these templates while creating a new issue or pull request project structure your projects should contain this flow to maintain similarity across all other projects make sure to note these things before you create a pr dataset this folder would have a csv file model this folder would have your project file that is ipynb file be it analysis or prediction other than project file it should also have a readme md using this template https github com prathimacode hub ml projectkart blob main github readme template md and requirements txt file which would be enclosed with all needed add ons and libraries that are included in the project images this folder would have images added if applicable front end this folder would have files related to coding and designing the webpage back end this folder would have files regarding backend creation for the model using flask and django ui ux this folder would have files regarding dashboards forms and webpages for the model include readme md file for front end back end and ui ux in their respective folders and elaborate briefly about how it works by showing step by step procedure using screenshots workflow fork the repository clone your forked repository using terminal or gitbash make changes to the cloned repository add commit and push then in github in your cloned repository find the option to make a pull request print start contributing for ml projectkart things to note make sure you do not copy codes from external sources because that work will not be considered plagiarism is strictly not allowed you can only work on issues that have been assigned to you if you want to contribute the algorithm it s preferrable that you create a new issue before making a pr and link your pr to that issue if you have modified added code work make sure the code compiles before submitting strictly use snake case underscore separated in your file name and push it in correct folder do not update the readme md https github com prathimacode hub ml projectkart blob main readme md open source programs table tr td align center a href https hacktoberfest digitalocean com img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs hacktoberfest 202021 hacktoberfest 2021 jpg width 100px height 100px br sub b hacktoberfest 2021 b sub a td td align center a href https contribute devincept com img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs devincept 20codes 202021 devincept jpg width 100px height 100px br sub b devincept codes 2021 b sub a td td align center a href https letsgrowmore in soc img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs letsgrowmore 20summer 20of 20code 202021 letsgrowmore 20summer 20of 20code jpg width 100px height 100px br sub b letsgrowmore soc 2021 b sub a td tr table leaderboard table tr td align center a href https github com prathimacode hub ml projectkart blob main github dcp scorecard md img src https cdn icons png flaticon com 512 1986 1986941 png width 100px alt br sub b devincept codes 2021 b sub a td td align center a href https github com prathimacode hub ml projectkart blob main github lgmsoc scorecard md img src https cdn icons png flaticon com 512 1986 1986941 png width 100px alt br sub b letsgrowmore soc 2021 b sub a td tr table hall of fame thanks go to these wonderful people contributions of any kind are welcome all contributors list start do not remove or modify this section prettier ignore start markdownlint disable a href https github com prathimacode hub ml projectkart graphs contributors img src https contrib rocks image repo prathimacode hub ml projectkart a markdownlint enable prettier ignore end all contributors list end code of conduct you can find our code of conduct here https github com prathimacode hub ml projectkart blob main code of conduct md license this project follows the mozilla public license 2 0 https github com prathimacode hub ml projectkart blob main license project maintainer table tr td align center a href https github com abhisheks008 img src https avatars githubusercontent com u 68724349 v 4 width 80px alt br sub b abhishek sharma b sub a td tr table project admin table tr td align center a href https github com prathimacode hub prathimacode hub blob main profile 20assets prathima kadari picture png width 80px alt br sub b prathima kadari b sub a td tr table visitor count https profile counter glitch me prathimacode hub count svg stargazers over time stargazers over time https starchart cc prathimacode hub ml projectkart svg https starchart cc prathimacode hub ml projectkart give this project a star github followers https img shields io github followers prathimacode hub svg label follow 20 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aws-iot-fleetwise-edge | reference implementation for aws iot fleetwise information source to quickly get started jump to the edge agent developer guide docs dev guide edge agent dev guide md the android guide tools android app readme md or the raspberry pi tutorial docs rpi tutorial raspberry pi tutorial md aws iot fleetwise is a service that makes it easy for automotive oems fleet operators independent software vendors isvs to collect store organize and monitor data from vehicles at scale the reference implementation for aws iot fleetwise fwe provides c libraries that can be run with simulated vehicle data on certain supported vehicle hardware or that can help you develop an edge agent to run an application on your vehicle that integrates with aws iot fleetwise you can then use aws iot fleetwise s to process the collected data gain insights about the vehicle s health and use the service s visual interface to help diagnose and troubleshoot potential issues with your vehicles furthermore aws iot fleetwise s capability to collect ecu data and store them on cloud databases enables you to utilize different aws services analytics services ml etc to develop novel use cases that augment your existing vehicle functionality in particular aws iot fleetwise can leverage fleet data big data and enable you to develop use cases that create business value for example improve electric vehicle range estimation optimized battery life charging optimized vehicle routing etc aws iot fleetwise can be extended to utilize cloud computing capabilities for use cases such as helping to improve pet child detection driver monitoring system applications predictive diagnostics electric vehicle s battery cells outlier detection etc you can use the included sample c application to learn more about the fwe develop an edge agent for your use case and test interactions before integration important as provided in the aws iot fleetwise service terms https aws amazon com service terms you are solely responsible for your edge agent including ensuring that your edge agent and any updates and modifications to it are deployed and maintained safely and securely in any vehicles aws iot fleetwise architecture aws iot fleetwise is an aws service that enables automakers and fleet operators to collect store organize and monitor data from vehicles automakers need the ability to connect remotely to their fleet of vehicles and collect vehicle ecu sensor data aws iot fleetwise can be used by oem engineers and data scientists to build vehicle models that can be used to build custom data collection schemes these data collection schemes enables the oem to optimize the data collection process by defining what signals to collect how often to collect them and most importantly the trigger conditions events that enable the collection process customers can define the data collection schemes to trigger based on a schedule or on specific conditions such as but not limited to 1 ambient temperature dropping to below 0 degree or 2 vehicle crosses state lines or 3 active diagnostic trouble codes these conditions are sent to the vehicle through a set of documents called data collection schemes in summary your edge agent collects the data of interest according to the data collection schemes and decoding rules as specified by the oem on the aws iot fleetwise console https aws amazon com iot fleetwise the following diagram illustrates a high level architecture of the system img src docs iot fleetwise architecture png fwe receives two documents 1 decoder manifest this document describes how signals are collected from the vehicle and will include details such as but not limited to bus id network name decoding information etc 2 data collection schemes this document describes what signals to collect it also describes the condition logic that defines the enablement of the trigger logic that allows these signals to be collected for example when vehicle speed 100 km hr and driver seatbelt is off and ambient temperature 0 degree c fwe deployment supported platforms the functional flexibility of fwe and its use of dynamic memory allocation means that it cannot reside in the real time safety vehicle ecus fwe must also be connected to the internet and preferably has access to a good portion of vehicle ecu data oems have the flexibility to decide where they can deploy their edge agent binary possible options include if present 1 vehicle gateway such as the nxp s32g https www nxp com products processors and microcontrollers arm processors s32g vehicle network processors s32g2 processors for vehicle networking s32g2 and renesas r car s4 https www renesas com jp en products automotive products automotive system chips socs rtp8a779f0askb0sp2s r car s4 reference boardspider 2 vehicle head unit 3 vehicle s high performance computer 4 telecommunication control unit fwe was built and tested on 64 bit architectures it has been tested on both arm and x86 multicore based machines with a linux kernel version of 5 4 and above the kernel module for iso tp can isotp would need to be installed in addition for kernels below 5 10 fwe was also tested on an ec2 instance with the following details platform ubuntu platform details linux unix server amazonec2 instancetype c4 8xlarge availabilityzone us east 1 architecture x86 64 cpuoptions corecount 18 threadspercore 2 ami name ubuntu focal 20 04 amd64 server 20230112 aws iot fleetwise client server communication fwe depends on the aws sdk for c https github com aws aws sdk cpp to send and receive data from and to aws iot fleetwise server all data sent to the aws iot fleetwise server is sent over an encrypted tls connection https docs aws amazon com iot latest developerguide data encryption html using mqtt which is designed to make it secure by default while in transit fwe uses mqtt quality of service zero qos 0 security see security security md for more information license summary and build dependencies fwe depends on the following open source libraries refer to the corresponding links for more information aws sdk for c v1 11 148 https github com aws aws sdk cpp curl v7 58 0 https github com curl curl openssl v1 1 1 https github com openssl openssl zlib v1 2 11 https github com madler zlib googletest v1 10 0 https github com google googletest google benchmark v1 6 1 https github com google benchmark protobuf v3 21 12 https github com protocolbuffers protobuf boost v1 71 1 https github com boostorg boost jsoncpp v1 9 5 https github com open source parsers jsoncpp snappy v1 1 8 https github com google snappy optional the following dependencies are only required when the option fwe feature greengrassv2 is enabled aws iot device sdk for c v2 v1 29 0 https github com aws aws iot device sdk cpp v2 see license license for more information getting help contact aws support https aws amazon com contact us if you have any technical questions about fwe metrics see metrics docs metrics md for details which edge specific metrics exist and how they can be accessed resources the following documents provide more information about fwe 1 change log changelog md provides a summary of feature enhancements updates and resolved and known issues 1 offboarding and data deletion docs aws iotfleetwiseoffboarding md provides a summary of the steps needed on the client side to offboard from the service 1 edge agent developer guide docs dev guide edge agent dev guide md provides step by step instructions for building and running your edge agent the following documents provide more information about the cloud component of aws iot fleetwise 1 aws iot fleetwise api reference https docs aws amazon com iot fleetwise latest apireference welcome html describes all the api operations for fleetwise | server |
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offline_chatgpt | offline chatgpt using large language model llm here we will see how to run a chatgpt like llm on a local machine without internet this i have tested on macos 13 1 prerequisite python 3 9 7 nodejs https nodejs org en download version 18 0 or later pipenv https realpython com pipenv guide build with python 3 9 7 alpaca model https crfm stanford edu 2023 03 13 alpaca html what is alpaca and llama alpaca is a small ai language model developed by a group of computer scientists at stanford university alpaca is fine tuned on llama meta s large language model which recently leaked online follow the below steps to install and start offline chatgpt 1 here we are using alpaca 7b llm model around 4 2 gb disk space if we want to install the alpaca 13b model then we need to replace 7b with 13b note the larger model needs 8 1 gb of space we can use the below command to install alpaca model npx dalai alpaca install 7b 2 run the below command to start the web server npx dalai serve 3 open the web browser at the below url http localhost 3000 offline chatgpt offline gpt png offline alpaca offline chatgpt gif 4 we can now start using this offline chatgpt like language model on our pc without internet connectivity replace prompt with the query we wanna try and click on go 5 optional we can also install llama models llama models are much larger its 7b model takes up to 31 gb of space we can also replace 7b with 13b 30b and 65b the largest model takes up to 432 gb of space we can use the below command to install llama model npx dalai llama install 7b 6 we can also select the model name and try various options references https github com ggerganov llama cpp https github com tatsu lab stanford alpaca | ai |
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numpy-ml | numpy ml ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in numpy no installation for rapid experimentation to use this code as a starting point for ml prototyping experimentation just clone the repository create a new virtualenv https pypi org project virtualenv and start hacking sh git clone https github com ddbourgin numpy ml git cd numpy ml virtualenv npml source npml bin activate pip3 install r requirements dev txt as a package if you don t plan to modify the source you can also install numpy ml as a python package pip3 install u numpy ml the reinforcement learning agents train on environments defined in the openai gym https github com openai gym to install these alongside numpy ml you can use pip3 install u numpy ml rl documentation for more details on the available models see the project documentation https numpy ml readthedocs io available models details summary click to expand summary 1 gaussian mixture model em training 2 hidden markov model viterbi decoding likelihood computation mle parameter estimation via baum welch forward backward algorithm 3 latent dirichlet allocation topic model standard model with mle parameter estimation via variational em smoothed model with map parameter estimation via mcmc 4 neural networks layers layer wise ops add flatten multiply softmax fully connected dense sparse evolutionary connections lstm elman style rnn max average pooling dot product attention embedding layer restricted boltzmann machine w cd n training 2d deconvolution w padding and stride 2d convolution w padding dilation and stride 1d convolution w padding dilation stride and causality modules bidirectional lstm resnet style residual blocks identity and convolution wavenet style residual blocks with dilated causal convolutions transformer style multi headed scaled dot product attention regularizers dropout normalization batch normalization spatial and temporal layer normalization spatial and temporal optimizers sgd w momentum adagrad rmsprop adam learning rate schedulers constant exponential noam transformer dlib scheduler weight initializers glorot xavier uniform and normal he kaiming uniform and normal standard and truncated normal losses cross entropy squared error bernoulli vae loss wasserstein loss with gradient penalty noise contrastive estimation loss activations relu tanh affine sigmoid leaky relu elu selu gelu exponential hard sigmoid softplus models bernoulli variational autoencoder wasserstein gan with gradient penalty word2vec encoder with skip gram and cbow architectures utilities col2im matlab port im2col matlab port conv1d conv2d deconv2d minibatch 5 tree based models decision trees cart bagging random forests boosting gradient boosted decision trees 6 linear models ridge regression logistic regression ordinary least squares weighted linear regression generalized linear model log logit and identity link gaussian naive bayes classifier bayesian linear regression w conjugate priors unknown mean known variance gaussian prior unknown mean unknown variance normal gamma normal inverse wishart prior 7 n gram sequence models maximum likelihood scores additive lidstone smoothing simple good turing smoothing 8 multi armed bandit models ucb1 linucb epsilon greedy thompson sampling w conjugate priors beta bernoulli sampler linucb 8 reinforcement learning models cross entropy method agent first visit on policy monte carlo agent weighted incremental importance sampling monte carlo agent expected sarsa agent td 0 q learning agent dyna q dyna q with prioritized sweeping 9 nonparameteric models nadaraya watson kernel regression k nearest neighbors classification and regression gaussian process regression 10 matrix factorization regularized alternating least squares non negative matrix factorization 11 preprocessing discrete fourier transform 1d signals discrete cosine transform type ii 1d signals bilinear interpolation 2d signals nearest neighbor interpolation 1d and 2d signals autocorrelation 1d signals signal windowing text tokenization feature hashing feature standardization one hot encoding decoding huffman coding decoding byte pair encoding decoding term frequency inverse document frequency tf idf encoding mfcc encoding 12 utilities similarity kernels distance metrics priority queue ball tree discrete sampler graph processing and generators details contributing am i missing your favorite model is there something that could be cleaner less confusing did i mess something up submit a pr the only requirement is that your models are written with just the python standard library https docs python org 3 library and numpy https www numpy org the scipy library https scipy github io devdocs is also permitted under special circumstances see full contributing guidelines here contributing md | machine-learning neural-networks topic-modeling gaussian-mixture-models hidden-markov-models gradient-boosting bayesian-inference wavenet vae resnet lstm wgan-gp attention reinforcement-learning good-turing-smoothing knn mfcc gaussian-processes word2vec | ai |
Web-Development-with-Angular-and-Bootstrap-Third-Edition | web development with angular and bootstrap third edition a href https www packtpub com web development web development angular and bootstrap third edition utm source github utm medium repository utm campaign 9781788838108 img src https www packtpub com media catalog product cache e4d64343b1bc593f1c5348fe05efa4a6 9 7 9781788838108 original jpeg alt book name height 256px align right a this is the code repository for web development with angular and bootstrap third edition https www packtpub com web development web development angular and bootstrap third edition utm source github utm medium repository utm campaign 9781788838108 published by packt embrace responsive web design and build adaptive angular web applications what is this book about angular and bootstrap have undergone a major overhaul to embrace emerging web technologies that help developers build cutting edge web applications this book is for you if you re ready to enhance your web development skills beyond html and css this book covers the following exciting features develop angular single page applications using an ecosystem of helper tools get familiar with bootstrap s new grid and helper classes embrace typescript and ecmascript to write more maintainable code implement custom directives for bootstrap 4 with the ng2 bootstrap library understand the component oriented structure of angular and its router use the built in http library to work with api endpoints if you feel this book is for you get your copy https www amazon com dp 1788838106 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following styles styles css node modules bootstrap dist css bootstrap min css scripts node modules jquery dist jquery min js node modules bootstrap dist js bootstrap min js following is what you need for this book whether you already know a little about bootstrap or angular or you re a complete beginner this book will enhance your capabilities in both frameworks it will guide you in easily building a fully functional web app working knowledge of html css and javascript is required to get a better understanding of bootstrap and angular with the following software and hardware list you can run all code files present in the book chapter 1 15 software and hardware list chapter software required os required 1 15 angular operating system independent 1 15 nodejs 10 operating system independent 15 wamp mamp operating system independent 1 15 visual studio code operating system independent 1 15 protractor operating system independent 1 15 karma operating system independent errata please note the appendix a reference in the book is a mistake related products getting started with web components packt https www packtpub com web development getting started web components utm source github utm medium repository utm campaign 9781838649234 amazon https www amazon com dp b07sb4zlht flask framework cookbook second edition packt https www packtpub com web development flask framework cookbook second edition utm source github utm medium repository utm campaign 9781789951295 amazon https www amazon com dp b07vhyr9bj get to know the authors sridhar rao chivukula is a technical lead at mindtree ltd and is based out of new york city he brings with him more than a decade of rich hands on experience in all aspects of frontend engineering he has worked with leading companies such as oracle tech mahindra and cognizant technology solutions he has a bachelor s degree in information technology he is the author of the books expert angular and php and script aculo us web 2 0 application interfaces published by packt aki iskandar is an entrepreneur and software architect with over two decades of programming experience he has worked as a consultant for microsoft compuware progressive insurance allstate insurance keybank ernst young and charles schwab to name a few his last full time job before leaving the corporate world in 2011 was at pnc where he served as an enterprise architect on their enterprise architecture team during that time he served as a core member on pnc s architecture review board which was responsible for creating reference architectures reviewing the architectural diagrams for it projects that were in the tens of millions of dollars in size and establishing it governance for the corporation he is the founder of vizcaro com an online motivational service that helps thousands of people and maintains a blog focusing on angular and related technologies other books by the authors php and script aculo us web 2 0 application interfaces https www packtpub com web development php and scriptaculous web 20 application interfaces utm source github utm medium repository utm campaign 9781847194046 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions | front_end |
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NLP_Learning | nlp learning code for cssi course natural language processing part for nltk tool | ai |
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