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plc4x | licensed to the apache software foundation asf under one or more contributor license agreements see the notice file distributed with this work for additional information regarding copyright ownership the asf licenses this file to you 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 https 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 maven central https img shields io maven central v org apache plc4x plc4j api svg https img shields io maven central v org apache plc4x plc4j api svg license https img shields io github license apache plc4x svg https www apache org licenses license 2 0 jenkins build status https ci builds apache org job plc4x job plc4x job develop badge icon https ci builds apache org job plc4x job plc4x job develop last commit https img shields io github last commit apache plc4x svg twitter https img shields io twitter follow apacheplc4x svg label follow style social https twitter com apacheplc4x java platform compatibility https github com apache plc4x actions workflows java platform yml badge svg https github com apache plc4x actions workflows java platform yml go platform compatibility https github com apache plc4x actions workflows go platform yml badge svg https github com apache plc4x actions workflows go platform yml c platform compatibility https github com apache plc4x actions workflows c platform yml badge svg https github com apache plc4x actions workflows c platform yml python platform compatibility https github com apache plc4x actions workflows python platform yml badge svg https github com apache plc4x actions workflows python platform yml h1 align center br a href https plc4x apache org img src https plc4x apache org images apache plc4x logo png alt apache plc4x logo title apache plc4x logo a br h1 h3 align center the industrial iot adapter h3 h4 align center the ultimate goal of plc4x is to create a set of libraries that allow unified access to any type of plc h4 table of contents about plc4x about apache plc4x getting started getting started developers developers community community contributing contributing licensing licensing about apache plc4x apache plc4x is an effort to create a set of libraries for communicating with industrial grade programmable logic controllers plcs in a uniform way we are planning on shipping libraries for usage in 1 java 2 go 3 c not ready for usage 4 python not ready for usage 5 c net not ready for usage abandoned plc4x also integrates with other apache projects such as apache calcite https calcite apache org apache camel https camel apache org apache kafka connect https kafka apache org apache karaf https karaf apache org apache nifi https nifi apache org and brings stand alone java utils like opc ua server enables you to communicate with legacy devices using plc4x with opc ua plc4x server enables you to communicate with a central plc4x server which then communicates with devices via plc4x it also provides java tools for usage inside an application connection cache new implementation of our framework for re using and sharing plc connections connection pool old implementation of our framework for re using and sharing plc connections opm object plc mapping allows binding plc fields to properties in java pojos similar to jpa scraper utility to do scheduled and repeated data collection getting started depending on the programming language the usage will differ therefore please go to the getting started https plc4x apache org users gettingstarted html on the plc4x website to look up the language of choice java note currently the java version which supports building of all parts of apache plc4x is at least java 11 currently with java 19 the apache kafka integration module is excluded from the build as the plugins it requires are incompatible with this version see the plc4j user guide on the website to start using plc4x in your java application https plc4x apache org users getting started plc4j html https plc4x apache org users getting started plc4j html developers environment currently the project is configured to require the following software 1 java 11 jdk for running maven in general as well as compiling the java and scala modules java home configured to point to that 2 git even when working on the source distribution 3 optional for running all tests libpcap npcap for raw socket tests in java or use of passive mode drivers 4 optional for building the website graphviz https www graphviz org for generating the graphs in the documentation warning the code generation uses a utility which requires some additional vm settings when running a build from the root the settings in the mvn jvm config are automatically applied when building only a sub module it is important to set the vm args add exports jdk compiler com sun tools javac api all unnamed add exports jdk compiler com sun tools javac file all unnamed add exports jdk compiler com sun tools javac parser all unnamed add exports jdk compiler com sun tools javac tree all unnamed add exports jdk compiler com sun tools javac util all unnamed in intellij for example set these in the ide settings under preferences build execution deployment build tools maven runner jvm options a more detailed description is available on our website https plc4x apache org developers preparing index html for building plc4c we also need all requirements are retrieved by the build itself for building plc4go we also need all requirements are retrieved by the build itself for building plc4py we also need 1 python 3 7 or higher 2 python pyenv for building plc4net we also need 1 dotnet sdk 6 0 with this setup you will be able to build the java part of plc4x the when doing a full build we automatically run a prerequisite check and fail the build with an explanation if not all requirements are meet building with docker if you don t want to bother setting up the environment on your normal system and you have docker installed you can also build everything in a docker container docker compose up this will build a local docker container able to build all parts of plc4x and will run a maven build of the local directory inside this container the default build will run a local release build so it can also be used to ensure reproducible builds when releasing per default will it store files locally downloaded maven artifacts will go to out repository deployed artifacts will go to out local snapshots dir the reason for this is that otherwise the artifacts would be packaged in with the source release artifact resulting in a 12gb or more zip archive however saving it in the main target directory would make the build delete the local repo every time a mvn clean is run the out directory however is excluded per default from the assembly descriptor and therefore it is not included in the source zim getting started you must have at least java 11 installed on your system and connectivity to maven central for downloading external third party dependencies maven 3 6 is required to build so be sure it s installed and available on your system note when using java 19 currently the apache kafka integration module is excluded from the build as one of the plugins it requires has proven to be incompatible with this version note there is a convenience maven wrapper installed in the repo when used this automatically downloads and installs maven if you want to use this please use mvnw or mvnw instead of the normal mvn command note when running from sources zip the mvnw might not be executable on mac or linux this can easily be fixed by running the following command in the directory chmod x mvnw note if you are working on a windows system please use mvnw cmd instead of mvnw in the following build commands build plc4x java jars and install them in your local maven repository mvnw install you can now construct java applications that use plc4x the plc4x examples are a good place to start and are available inside the plc4j examples directory the go drivers can be built by enabling the with go profile mvnw p with go install the c net implementation is currently in a work in progress state in order to be able to build the c net module you currently need to activate the with dotnet profiles mvnw p with dotnet install the python implementation is currently in a somewhat unclean state and still needs refactoring in order to be able to build the python module you currently need to activate the with sandbox and with python profiles mvnw p with sandbox with python install in order to build everything the following command should work mvnw p with c with dotnet with go with python with sandbox enable all checks install community join the plc4x community by using one of the following channels we ll be glad to help mailing lists subscribe to the following mailing lists apache plc4x developer list dev subscribe plc4x apache org mailto dev subscribe plc4x apache org apache plc4x commits list commits subscribe plc4x apache org mailto commits subscribe plc4x apache org apache plc4x jira notification list issues subscribe plc4x apache org mailto issues subscribe plc4x apache org see also https plc4x apache org mailing lists html https plc4x apache org mailing lists html twitter get the latest plc4x news on twitter https twitter com apacheplc4x https twitter com apacheplc4x contributing there are multiple forms in which you can become involved with the plc4x project these are but are not limited to providing information and insights testing plc4x and providing feedback submitting pull requests filing bug reports active communication on our mailing lists promoting the project articles blog posts talks at conferences documentation we are a very friendly bunch so don t be afraid to step forward if you d like to contribute to plc4x have a look at our contribution guide https plc4x apache org developers contributing html licensing apache plc4x is released under the apache license version 2 0 | iot python java cpp c go net ab ads ethernetip firmata knx modbus opcua s7 siemens bacnet can | server |
lamorel | language models for reinforcement learning lamorel lamorel is a python library designed for people eager to use large language models llms in interactive environments e g rl setups news 2023 07 12 an example examples ppo lora finetuning showing how to use lora https arxiv org abs 2106 09685 through the peft https github com huggingface peft library for lightweight finetuning has been added why lamorel what is the difference between lamorel and rlhf libs first of all lamorel was initially designed to easily use llms in interactive environments for this reason it is not specialised in rl however one can easily implement an rl loop as provided in examples examples a key component of rlhf is that generating a sequence of tokens given a prompt is considered as a full and terminated episode steps are therefore the generation of each token in the generated sequence this method is for instance useful to finetune llms to generate token sequences that maximize a reward function e g learned from interactions with humans however this setup is not suited for interacting with an external environment that requires multiple interactions e g multiple text generations to end an episode this is why we advise users that lie in the rlhf setup and do not want to reimplement the rl loop by themselves to use such libs that already come with rl implementations e g rl4lms https github com allenai rl4lms trl https github com lvwerra trl on the opposite if you want to use your llm in an interactive environment where acting in the environment leads your agent to a new state and possibly a new prompt for your llm that is still part of the same episode lamorel may help you lamorel s key features 1 abstracts the use of llms e g tonekization batches into simple calls python lm server generate contexts this is an examples prompt continue it with lm server score contexts this is an examples prompt continue it with candidates a sentence another sentence 2 provides a method to compute the probability of token sequences e g action commands given a prompt 3 is made for scaling up your experiments by deploying multiple instances of the llm and dispatching the computation thanks to a simple configuration file yaml distributed setup args n rl processes 1 n llm processes 1 4 provides access to open sourced llms from the hugging face s hub https huggingface co models along with model parallelism https huggingface co docs transformers v4 15 0 parallelism to use multiple gpus for an llm instance yaml llm args model type seq2seq model path t5 small pretrained true minibatch size 4 parallelism use gpu true model parallelism size 2 5 allows one to give their own pytorch modules to compute custom operations e g to add new heads on top of the llm 6 allows one to train the llm or part of it thanks to a data parallelism https pytorch org tutorials intermediate ddp tutorial html setup where the user provides its own update method distributed and scalable lamorel relies on a client server s architecture where your rl script acts as the client sending requests to the llm server s in order to match the computation requirements of rl lamorel can deploy multiple llm servers and dispatch the requests on them without any modification in your code distributed scoring docs images distributed architecture gif installation 1 cd lamorel 2 pip install examples we provide a set of examples examples that use lamorel in interactive environments saycan examples saycan a saycan https arxiv org abs 2204 01691 implementation that controls a robot hand in a simulated pickplace environment ppo finetuning examples ppo finetuning a lightweight implementation of the ppo approach introduced in grounding large language models in interactive environments with online reinforcement learning https arxiv org abs 2302 02662 to finetune an llm policy in babyai text https github com flowersteam grounding llms with online rl tree main babyai text ppo lora finetuning examples ppo lora finetuning a lightweight implementation of the ppo approach introduced in grounding large language models in interactive environments with online reinforcement learning https arxiv org abs 2302 02662 but use lora https arxiv org abs 2106 09685 through the peft https github com huggingface peft library for lightweight finetuning in babyai text https github com flowersteam grounding llms with online rl tree main babyai text how to use lamorel lamorel is built of three main components a python api to interact with llms a configuration file to set the llm servers a launcher deploying the multiple llm servers and launching your rl script instantiating the server in your rl script lamorel leverages hydra https hydra cc for its configuration file because of this you need to add the hydra decorator on top of your main function then you must instantiate the caller class from lamorel which will create the object allowing you to interact with the llm servers do not forget to initialize lamorel once imported with lamorel init to initialize the communication with the servers python import hydra from lamorel import caller lamorel init lamorel init hydra main config path config config name config def main config args lm server caller config args lamorel args do whatever you want with your llm lm server close if name main main do not forget to close the connection with servers at the end of your script using the caller once instantiated you can use the different methods of the caller object to send requests to your llms scoring first we provide the score method to compute the probability of a sequence of tokens a candidate given a prompt context lamorel allows to provide multiple candidates for a single context but also to batch this computation for multiple contexts along with their associated candidates using this one can use a classic vectorized rl setup where at each step multiple environments running in parallel return their current state and expect an action python lm server score contexts this is an examples prompt continue it with candidates a sentence another sentence generation lamorel also provides a method for text generation similarly to the score method one can give multiple prompts contexts our generate method can use any keyword argument from transformers api https huggingface co docs transformers main classes text generation in addition of the generated texts it also returns the probability of each generated sequence python lm server generate contexts this is an examples prompt continue it with lm server generate contexts this is an examples prompt continue it with temperature 0 1 max length 25 custom modules while lamorel provides two main uses of llms i e scoring and generating we also allow users to provide their own methods to perform custom operations using llms we expect these custom methods to be pytorch modules https pytorch org docs stable generated torch nn module html additional modules docs images additional modules gif in order to define such a custom operation users must extend our basemodulefunction class for this you must extend two main methods initialize self initialize your custom operations here forward self forward outputs minibatch tokenized contexts kwargs perform your operations here and return the results lamorel will give your custom module to all llm servers and ensure your variables e g weights are the same on each server see the example below where we implement a multi layer perceptron mlp on top of our llm python from lamorel import basemodulefunction class twolayersmlpmodulefn basemodulefunction def init self model type n outputs super init self model type model type self n outputs n outputs def initialize self use this method to initialize your module operations self llm config gives the configuration of the llm e g useful to know the size of representations self device gives you access to the main device e g gpu the llm is using llm hidden size self llm config to dict self llm config attribute map hidden size self mlp torch nn sequential torch nn linear llm hidden size 128 torch nn linear 128 128 torch nn linear 128 self n outputs to self device def forward self forward outputs minibatch tokenized contexts kwargs perform your operations here forward outputs gives access the output of the computations performed by the llm e g representations of each layer minibatch gives access to the input data i e a prompt and multiple candidates given to the llm tokenized context gives access to the prompt used get the last layer s representation from the token right after the prompt if self model type causal adapt to the transformers api differing between encoder decoder and decoder only models model head forward outputs hidden states 0 0 len tokenized contexts input ids 1 else model head forward outputs encoder last hidden state 0 len tokenized contexts input ids 1 give representation to our mlp output self mlp model head return output once implemented you can give your custom module s to the lamorel caller along with a key it will then be possible to use your module either by calling it directly using the custom module fns or by using it in addition of scoring python lm server caller config args lamorel args custom module functions mlp head twolayersmlpmodulefn config args lamorel args llm args model type 2 direct call lm server custom module fns module function keys mlp head contexts this is an examples prompt continue it with scoring lm server score additional module function keys mlp head contexts this is an examples prompt continue it with candidates a sentence another sentence updaters we have seen so far how to use an llm along with possibly custom modules for inference however lamorel also provides tools to update e g train these operations with a baseupdater class which can be extended and perform any update operation our class gives access to the whole computation graph self llm module it can for instance be used to perform operations with gradient by calling self llm module mlp head score with the leaf operations wanted i e scoring and or custom modules or to select weights to train the llm itself self llm module llm model custom modules self llm module module module functions the whole graph self llm module python from lamorel import baseupdater class testupdater baseupdater def perform update self contexts candidates current batch ids kwargs if not hasattr self loss fn self loss fn torch nn crossentropyloss if not hasattr self optimizer you can train 1 only the llm self optimizer torch optim adam self llm module llm model parameters 2 only some custom modules self optimizer torch optim adam self llm module module module functions mlp head parameters 3 everything self optimizer torch optim adam self llm module parameters use the computational graph with gradient 1 only the llm s scoring module output self llm module score contexts contexts require grad true 2 only some custom modules output self llm module mlp head contexts contexts require grad true 3 both output self llm module score mlp head contexts contexts require grad true stack outputs to batch loss computation stacked output torch stack o mlp head for o in output to cpu compute loss with the labels corresponding to the current batch loss self loss fn stacked output kwargs labels current batch ids compute gradients and update graph loss backward self optimizer step self optimizer zero grad return anything you want using a dictionary return loss loss once defined users must give their custom updater to the caller whenever needed users can call their updater with data i e contexts and candidates along with any additional keyword argument e g labels because multiple llm servers can be deployed we also dispatch the updater s computation when one calls the updater with data contexts and candidates are dispatched over the multiple servers where each runs the updater because lamorel does not know a priori what additional keyword arguments are these are copied and sent to each llm as users may need to know how to associate these arguments to the data handled by the current server we provide the current batch ids variable giving the indexes of contexts and by extension candidates that are given to the current llm lamorel is in charge of gathering the gradients of all servers such that self optimizer step produces the same on each server python lm server caller config args lamorel args custom module functions mlp head twolayersmlpmodulefn config args lamorel args llm args model type 2 custom updater testupdater result lm server update contexts this is an examples prompt continue it with candidates a sentence labels torch tensor 0 1 dtype torch float32 losses r loss for r in result one loss returned per llm training docs images training gif initializers additionally one may also provide an initializer object applying any modification on the model e g freezing some weights before it is given to the distributed architecture that synchronizes all llms python from lamorel import basemodelinitializer class custominitializer basemodelinitializer def initialize model self model do whatevever your want here for instance freeze all the llm s weights llm module model modules llm model for param in llm module parameters param requires grad false lm server caller config args lamorel args custom model initializer custominitializer setting up the configuration the configuration of the llm and the client server s architecture is done using a yaml configuration file following hydra https hydra cc s api here is what this file should contain yaml lamorel args arguments for lamorel log level debug debug info error level of logs returned by lamorel allow subgraph use whith gradient false distributed setup args use this section to specify how many llm servers must be deployed n rl processes 1 n llm processes 1 llm servers accelerate args lamorel leverages hugging face accelerate for the distributed setup config file accelerate default config yaml keep this machine rank each machine should launch the script and provide its rank id see section below num machines 2 number of machines used in the experiment llm args specify which llm to use and how model type seq2seq seq2seq causal encoder decoder or decoder only model model path t5 small name if downloaded from the hugging face s hub or absolute path to the model pretrained true true false set this to false if you want to keep the llm s architecture but re initialize its wegihts pre encode inputs true whether encoding contexts should be optimized when using encoder decoder models minibatch size 4 batch size per forward passes adapt this number to your gpu memory parallelism model parallelism use gpu true true false set this to false if you want your llm s to use cpu model parallelism size 1 number of gpus user per llm server synchronize gpus after scoring false only useful for specific gpu optimizations empty cuda cache after scoring false only useful for specific gpu optimizations rl script args arguments for lamorel path absolute path to your rl script provide any additional arguments for your rl script here examples of configuration files are provided in section below launch lamorel comes with a launcher that handles how to launch multiple processes on each machine to launch your experiment on a machine you must use the following command python m lamorel launcher launch config path path config name name rl script args path path lamorel args accelerate args machine rank rank additional arguments to override config warning use absolute paths launch examples several examples of configurations can be found in examples examples single machine and no gpu config local cpu config yaml examples configs local cpu config yaml launch command s shell python m lamorel launcher launch config path absolute path to project examples configs config name local cpu config rl script args path absolute path to project examples example script py single machine and gpu s config local gpu config yaml examples configs local gpu config yaml launch command s rl script shell python m lamorel launcher launch config path absolute path to project examples configs config name local gpu config rl script args path absolute path to project examples example script py lamorel args accelerate args machine rank 0 llm server shell python m lamorel launcher launch config path absolute path to project examples configs config name local gpu config rl script args path absolute path to project examples example script py lamorel args accelerate args machine rank 1 if you do not want your llm process to use all your gpus for instance if you plan to launch multiple llm servers set an appropriate value to model parallelism size in the config slurm cluster we here provide an example with a slurm cluster where our experiment deploys 6 llm servers on 3 machines as the schema below shows the first machine hosts 2 llm servers and the rl script which also has access to gpus the two other machines both host only 2 llm servers multi nodes docs images multi nodes png config multi node slurm cluster config yaml examples configs multi node slurm cluster config yaml launch command s shell sbatch examples slurm job slurm technical details and contributions lamorel relies on pytorch distributed for communications we chose to use the gloo backend to allow both cpu and gpu platforms lamorel launches the rl script n rl processes n llm processes times every time it reaches the lm server caller line lamorel checks whether the current process should be part of the client or the llm servers if it is a client the script returns the caller object that the user can use to send requests and continues otherwise it creates a server lamorel src lamorel server server py that loads the llm and starts listening for the communication between the client and servers we create a process group between the client and one of the servers which is considered as the master this master server listens to requests and dispatches calls using the dispatcher lamorel src lamorel server dispatcher py to all the servers using another process group only shared by llm servers each llm server performs the asked operations on its received data and sends the results to the master llm server the latter gathers results and sends them back to the rl script lamorel is still in its very early phase and we are happy to accept any external contribution to it please follow the contributing md contributing md file | ai |
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myLearn | mylearn make demo sh model conf 1 lasso 2 logistic softmax 3 4 fisher 5 6 1 2 3 4 5 6 1 id3 c4 5 cart 2 rf 3 adaboost 4 gbdt 5 xgboost 6 1 2 k means 3 gmm 4 em lda 5 dbscan 6 1 2 3 1 2 pac 3 vc 4 5 6 | adaboost bayes cart crf dnn id3 gmm hmm knn linear-regression log logistic-regression memm rf svm softmax-regression kmeans-clustering | ai |
Surf-spot-SEI-Project-2 | surf spot sei project 2 database project storing surf details through a crud app full stack development with ruby on rails for backend api with openweather looks at user location for weather input each surf location has a weather feature based on a different api request p align center width 100 img src images surf spot 1 png alt width 30 heigh 50 img src images surf spot 2 png alt width 30 heigh 50 img src images surf spot 3 png alt width 30 heigh 50 p surfer surfing man surfing woman page url https surf fly dev page facing up about a crud application with different levels of access basic access to view content make by users user access to provide reviews and rate locations etc admin access to edit and delete information from the site list of surf locations locations can be added to by users upvoted etc goal is to link to a weather api and have the ability to list locations via distance from the user or another location of choice features in the pipe line get it pipeline rate your board quiver and see the top rated boards swell details at the best locations board suggestions for the day s conditions planning problem solving img src images surf wireframe png alt drawing width 100 heigh 70 p align center width 100 img src images surf starting plan png alt flow width 50 heigh 50 p tech programming language ruby erb ruby gems sinatra puma pg bcyrpt httparty api requests sql databases location pin adapted from https codepen io mahdiarjangi pen kkkzzjz bug bugs to fix surf board page links in top of header not linked yet user cannot input their location yet default to syd australia sob lessons learnt started with css layout in mind so far so good above statement stands correct by project end happy with the improvements in css styling and understanding lots of lessons about working with api data white check mark cool things i would add if i can keep working on this css desing knowing this was an area i was not skilled in prior to the project i made an effort to make it a top priority around responsive design i am happy with the achievements made however would love to continue my knwoeldge in this area more api requests data base merge | server |
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how-to-webdev | how to webdev http boilercamp github io how to webdev a mostly complete introduction into web development it can be difficult to figure out where to start when you want to get into web development how to webdev is a series of accelerated workshops and projects that build on one another with the goal of teaching web development this course will cover html https developer mozilla org en us docs web html css https developer mozilla org en us docs web css javascript https developer mozilla org en us docs web javascript node js https nodejs org en and mongodb https www mongodb com how to webdev was first debuted september 2015 at boilercamp http boilercamp org at purdue university you can checkout all of the slides from the presentation s in both pdf https raw githubusercontent com boilercamp how to webdev master docs assets slides boilercamp pdf and keynote https raw githubusercontent com boilercamp how to webdev master docs assets slides boilercamp key formats written and developed by kirby kohlmorgen http kirby xyz roy fu http royfu me the goal by the end of this tutorial you should have a sound understanding of how to build an application for the web specifically in this exercise you ll be building a personal website and blog you ll start by building a static website with vanilla html and css we ll quickly introduce the power of frameworks by hooking you up with some bootstrap http getbootstrap com we ll add a little client side javascript which will give us a chance to introduce jquery https jquery com once we have a pretty static site set up we ll the introduce the idea of a backend with node js running a webserver with express http expressjs com this provides us with a nice segway into server side rendering and templating handlebars http handlebarsjs com and finally we ll introduce the idea of a database utilizing mongodb you can see an example of the finished web app here http ricksanchez herokuapp com getting it running the current state of this repo https github com boilercamp how to webdev is the final product after completing all of the walkthroughs if you want to get this server up and running so you can check it out you need to do a few things 1 install dependancies you ll first need to install all of the server side dependancies with npm and the client side depedancies with bower bash npm install npm run prepare 2 setup mongodb you ll also need a mongodb instance running somewhere first we need to install mongodb on mac you use homebrew http brew sh to install mongodb bash brew install mongodb you ll then need to make a directory for mongodb to write the database to bash sudo mkdir p data db sudo chown whoami data db finally you ll need to get mongodb running bash mongod 3 connecting to mongodb we need our server to be able to connect to mongodb we specify the address and port to our mongodb instance with a env file go ahead and copy the env sample file bash cp env sample env if you re running mongodb locally on a standard address and port then you don t need to do anything else we want to put some fake data in our mongodb instance so we ll have some nice blog posts to look at bash npm run seed 4 starting the server lastly we just need to start our server bash npm start then go to http localhost 8080 http localhost 8080 to checkout the final project thanks many thanks are due to the entire boilercamp team adam loeb https github com aloeb ben alderfer https github com balderfer fisher adelakin https github com fadelakin kedar vaidya https github com kedarv kurt kroeger https github com kwkroeger shriyash jalukar https github com infinitebattery7 roy fu https github com roystbeef usmann khan https github com usmannk license mit | teaching learn-to-code | front_end |
sfn-client | coverage status https coveralls io repos github agileventures sfn client badge svg branch develop https coveralls io github agileventures sfn client branch develop build status https travis ci org agileventures sfn client svg branch develop https travis ci org agileventures sfn client https dxssrr2j0sq4w cloudfront net 3 2 0 img external zenhub badge png https app zenhub com workspaces sfn client 5cbedb54074dff0857634473 board repos 110595899 main contributors contributor faces a href https github com aonomike img src https avatars3 githubusercontent com u 1543546 s 400 v 4 title aonomike width 80 height 80 a a href https github com dhemmingson img src https avatars3 githubusercontent com u 2614417 s 400 v 4 title dhemmingson width 80 height 80 a a href https github com federicoesparza img src https avatars0 githubusercontent com u 11988089 s 400 v 4 title federicoesparza width 80 height 80 a a href https github com javpet img src https avatars0 githubusercontent com u 9334646 s 460 v 4 title javpet width 80 height 80 a a href https github com jmpainter img src https avatars2 githubusercontent com u 10214686 s 460 v 4 title jmpainter width 80 height 80 a a href https github com daumie img src https avatars0 githubusercontent com u 11542388 s 460 v 4 title daumie width 80 height 80 a a href https github com rachitkansal ntnx img src https avatars1 githubusercontent com u 48485082 s 460 v 4 title rachitkansal ntnx width 80 height 80 a a href https github com mhobesong img src https avatars1 githubusercontent com u 5602093 s 460 v 4 title mhobesong width 80 height 80 a a href https github com arku img src https avatars3 githubusercontent com u 7039523 s 460 v 4 title arku width 80 height 80 a a href https github com mattwr18 img src https avatars3 githubusercontent com u 26943915 s 460 v 4 title mattwr18 width 80 height 80 a about this project sing for needs is a donation platform meant to be a positive means for giving inspired by music performances from artists unknown and famous the artists get to see and choose the various causes to support with their performances while getting a view of all the funds generated the sing for needs project is currently under active developement by a team of volunteers at agileventures https www agileventures org an official uk charity 1170963 dedicated to crowdsourced learning and project development under the umbrella of championer projects the sing for needs backend https github com agileventures sing for needs uses the elixir phoenix https phoenixframework org framework while the frontend uses react https reactjs org mainly to facilitate mentorship for the volunteers to learn these modern technologies this is a guide to help easily get set up and started with the frontend for any voluteer who would like to contribute through code pr reviews mentorship or in any other way getting started this describes how to contribute to sfn client the tools we use to track and coordinate the work that is happening and that needs to happen this also describes the workflow the processes and sequences for getting contributions merged into the project in an organized and coherent way we use zenhub https app zenhub com workspaces sfn client 5cbedb54074dff0857634473 board repos 110595899 to manage our work on features chores and bugfixes we keep our code on github http github com and use git https git scm com for version control recomended ide linting most of our developers use vs code https code visualstudio com and we really recommend it as an ide for this project once you have it installed be sure to set es lint https eslint org as your linter and enable format on save so that your code can be formated as soon as you save please ensure that the eslint you install is the same one in the image below img width 1254 alt screenshot 2019 06 08 13 02 15 src https user images githubusercontent com 11988089 59150726 ea241b00 89ed 11e9 9d5f 7caf8978e375 png search for and select eslint autofixonsave in the settings or add it as a true property in settings json as shown below img width 1280 alt screenshot 2019 06 08 12 56 45 src https user images githubusercontent com 11988089 59150776 a847a480 89ee 11e9 8bf0 161f43aa01e3 png forking the repository each developer will usually work with a fork https help github com articles fork a repo of the main repository on agile ventures https github com agileventures sfn client before starting work on a new feature or bugfix please ensure you have synced your fork to upstream develop https help github com articles syncing a fork node version management hammer and wrench please ensure you have nvm installed in your local machine https github com creationix nvm if you are using osx you can run the command below brew install nvm run the following command to install and switch to the current node version for the project nvm install v10 13 0 to ensure that the correct node version for the project is automatically selected when you cd into the sfn client project s directory please install avn https github com wbyoung avn in your local machine and run the commands below in your terminal npm install g avn avn nvm avn n or yarn global add avn avn nvm avn n then run avn setup unfortunately if you are using vs code s integrated terminal you have to cd and cd back in cd sfn client in mac s terminal it works automatically if you are using fish shell https gist github com idleberg 9c7aaa3abedc58694df5 please use this https medium com joshuacrass nvm on mac for fish users e00af124c540 to install nvm and install avn for fish https github com martinkacmar fish avn install yarn npm install g yarn run yarn to install dependencies yarn setting up with the backend the backend https github com agileventures sing for needs is currently deployed https sing for needs gigalixirapp com graphiql on gigalixir https gigalixir com to enable front end developers ease of setup to get set up with the deployed backend create a env local file in the root of the app you just cloned add the following react app base url http localhost 4002 react app sfn backend https sing for needs gigalixirapp com graphiql skip preflight check true please note that the react app base url variable is meant to be the base rest api endpoint for the application also you can choose to clone the backend https github com agileventures sing for needs and get setup locally starting the development server yarn start set up env local you may need to set up env local with your backend server s base url this can be easily done by coppying the content of your env default file to env local as shown below cp env default env local update the value of the react app base url to correspond to your server url e g react app base url http localhost 4000 running tests jest this codebase uses enzyme javascript testing utility to learn more about the enzyme you can checkout their documentation https airbnb io enzyme start the test by running yarn test then press a to run all test running eslint standard autofix command you can t commit or run the tests if you have lint errors so run yarn lint fix setup project with docker prerequisite ensure you have docker installed install docker https docs docker com install instructions change to the project root directory sfn client create an image with the following command docker build f docker dockerfile t sfn client production run the created image with docker run p 80 80 sfn client production access the application on localhost port 80 http 127 0 0 1 80 using the debugger if tests are failing or you found a bug running the development server you can debug using the inline debug tool https github com agileventures sfn client blob develop how to use debug script md creating an issue you can create an issue by clicking on this link https github com agileventures sfn client issues new choose or by clicking on the new issue button on for github issues https github com agileventures sfn client issues for the sfn client project click on the get started https github com agileventures sfn client issues new assignees labels template issue template md title button to open the issue creation template fill in all the relevant sections provided in the template as you create your issue submit your issue by clicking on submit issue button choosing stories tickets when deciding on an issue to work on look for the help wanted or good first issue tags request to be added as a collaborator in our agileventures org slack chat channel https agileventures slack com messages phoenix one after you re a collaborator you can move the ticket to the in progress column here https waffle io agileventures sfn client to indicate you ve started work on it how to create a feature branch git checkout develop git pull upstream develop after you pulled the latest develop branch make sure you have also the dependencies installed each time by running in the console yarn ensure you have setup agileventures sfn client s upstream develop otherwise you will not have the latest develop changes to confirm this run git remote v you should see a simillar output origin https github com yourgithubusername sfn client git fetch origin https github com yourgithubusername sfn client git push upstream https github com agileventures sfn client git fetch upstream https github com agileventures sfn client git push if not you need to set the remote develop in order to get the latest copy once changes are merged in order to achieve that run git remote add upstream https github com agileventures sfn client git pull upstream develop this depends on the name of your origin counter check before running the above command you will now have the latest copy of develop in your local once this is done you can proceed with naming your branch following the below convention branch naming conventions git checkout b 17 add sfn logo where 17 is the ticket number and add sfn logo is a short description of the purpose of your branch commit messages ensure your commit message clearly communicate the work you have done for example git commit m implement user login pull requests after feature branch work is complete push up to the upstream repo for example git push set upstream upstream 17 add sfn logo for your pull requests ensure you have a proper title describing your task make sure to add a link to the ticket you ve worked on and add any screenshots if necessary in your pull request description please include a sensible description of your code and a tag fixes issue id e g this pr adds a contributing md file and a docs directory fixes 799 which will associate the pull request with the issue in the zenhub board your pull request needs to be reviewed by at least two people in the team for it to be merged in develop branch instructions on how to review a pull request can be found here https github com agileventures sfn client blob develop how to review a develop pr md design designing new features the sing for needs project follows user centered design principles https www interaction design org literature topics user centered design accordingly the platform is built with the different key personas https github com agileventures sfn client blob develop personas md artists donors and the people behind causes in mind imagine as these personas are the key stakeholders of the project if you want to introduce a new feature then your responsibility is to consult each of the personas mentioned above and empathize with their jobs to do major pains and major gains to properly use this knowledge to shape the new feature the personas documentation https github com agileventures sfn client blob develop personas md gives additional guidance how you can immerse yourself in the topic designing components we try to make designing components easier with setting up some base grounds of colors typography and layouts before start designing a component for the page please consult these materials you can find them in the src components styles directory the layout of the pages is following bootstrap s 12 column https getbootstrap com docs 4 0 layout grid grid system but with using css grid https css tricks com snippets css complete guide grid and flexbox https css tricks com snippets css a guide to flexbox together we recommend to have a look at the documentation or just give a shot to grid garden game http cssgridgarden com the structure of the components and spacing is suggested to follow an 8px grid system what does that mean it means every spacing padding and size width height of a component is following a multiple of 8 to give you a head start related to this rule we set up the margins and paddings already to use in the styles utilities scss file as sass variables how do we name things in css scss we use bem stands for block element modifier which is a naming convention standard for css class names it has fairly wide adoption and is immensely useful in writing css that is easier to read understand and scale a bem class name includes up to three parts block the outermost parent element of the component is defined as the block element inside of the component may be one or more children called elements modifier either a block or element may have a variation signified by a modifier if all three are used in a name it would look something like this block element modifier for full reference with examples please consult this amazing article https seesparkbox com foundry bem by example if you see something different in our codebase than the best practises mentioned in the article please open a new issue design reference in 2019 we ve created a simple branding document to establish the basis for the design in terms of colors typography and photographic direction the values for typography and colors will be reflected in the scss variables variable as well before delivering a final ui for screens please consult this little guidance and the stylesheets src styles if all the aspects are right in the designs it s worth checking back because the branding document will evolve in time so changes will occur branding basic document sfn https user images githubusercontent com 9334646 59974463 d27f9180 95ac 11e9 9c4b 9a396ab5b385 png as moving towards with the project the individual screens that are in progress and assets will be documented here wireframes which have been implemented already are under the documentation wireframes finished folder design inspiration https dribbble com shots 3144986 online music streaming service artist page attachments 666587 editing artist page mockup flow artist profile edit https user images githubusercontent com 9334646 56868353 9fc47d00 69f1 11e9 90b1 40418ca7097f png for more mockups that have been used in this project please follow this link documentation wireframes finished | front_end |
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computer-vision-raspberrypi | computer vision on raspberry pi by danh doan introduction this is a series about developing common computer vision projects on raspberry pi board some of them requires the support of movidius neural compute stick to boost the performance openvino toolkit is mainly the development tool that helps optimize the hardware and models to work well with raspi the main purpose of this work is to help developers from all levels to gain insights and resources to working with raspberry pi for computer vision projects every sample project is refactored and organized so that it is easily understandable and approachable if you have any project ideas and issues with those projects feel free to comment it will help improve and enrich the contents thanks in advance with my sincere other computer vision demos link https www youtube com watch v suprnm2eiee list pl9gpyunnkehjsag8rxtrnj046gqj1k9q1 updates 2021 jan 04 publish docker image for opencv link https hub docker com repository docker danhdoan opencv4 slim buster 2019 nov 27 add 012 tflite object detection 2019 nov 15 add openvino models link https drive google com drive folders 11g98fs2 klb4qgiz4yzezfdnd0g2xnrl 2019 nov 05 add 008 pi emotion recognition update installation guide to support iecore 2019 oct 23 add 004 pi head pose estimation 2019 oct 16 add 006 pi face verification 2019 oct 15 add 003 pi face alignment 2019 oct 12 add 005 pi object detection add 002 pi facial landmark detection add 001 pi face detection add 000 show pi camera sample projects test with pi camera module link https github com danhdoan computer vision raspberrypi tree master 000 show pi camera play around with builtin pi camera module face detection with high accuracy link https github com danhdoan computer vision raspberrypi tree master 001 pi face detection develop an accurate and robust face detector with pretrained ssd model trained from wider dataset facial keypoint detection link https github com danhdoan computer vision raspberrypi tree master 002 pi facial landmark detection demo https www youtube com watch v en nsyf8kjm demo https www youtube com watch v wzvgrhrdc1s develop a simple facial keypoints localization that detect 5 main keypoints of human faces center eyes nose tip and mouth corner face alignment link https github com danhdoan computer vision raspberrypi tree master 003 pi face alignment based on 5 keypoints align human faces to support other problem e g face identification face verification headpose estimation link https github com danhdoan computer vision raspberrypi tree master 004 pi head pose estimation demo https www youtube com watch v kn qra3h4oo estimate human head pose in tait bryan angles yaw pit h or roll human detection link https github com danhdoan computer vision raspberrypi tree master 005 pi object detection demo https www youtube com watch v suprnm2eiee develop an object detector especially with ssd model notice human is just an example of objects any object detection model can be converted to work with this sample project face verification link https github com danhdoan computer vision raspberrypi tree master 006 pi face identification verify face identity by face embeddings emotion recognition link https github com danhdoan computer vision raspberrypi tree master 008 pi emotion recognition demo https www youtube com watch v rxcug3i1mkw recognize emotional states of human faces car and license plate detection ongoing tflite object detection link https github com danhdoan computer vision raspberrypi tree master 012 tflite object detection demo https www youtube com watch v ncdyjjntd5w installation follow install md instructions link https github com danhdoan computer vision raspberrypi blob master install md to install essential packages and modules for working with raspberry pi installing openvino as a toolkit to the development usage 1 clone this repository cd mkdir workspace cd workspace git clone https github com danhdoan computer vision raspberrypi 2 download openvino pretrained model mkdir openvino models tflite models you can notice a soft symbol link in any projects that maps to this directory if you want to store it elsewhere beware of re map this symbol link to download a model just go to the official openvino site from intel https download 01 org opencv 2019 open model zoo r1 in this project models from r1 sub dir are used r3 is currently the latest it also works well with sample code just download the fp16 models they can be applied to raspberry pi in my code i usually add a postfix fp16 to clarify this issue thus download a pretrained model and change its name correspondingly 3 run a sample project all sample projects have the same argument parser usage app name py h video video name name show record flip hor flip ver optional arguments h help show this help message and exit video video v video video streamming link or path to video source name name n name name of video source show s whether to show the output visualization record r whether to save the output visualization flip hor fh horizontally flip video frame flip ver fv vertically flip video frame it is implemented in altusi helper py you can customize as you wanted to see argument list of a sample e g object detection just type python3 app object detector py h references openvino model zoo https download 01 org opencv 2019 open model zoo r3 official intel site https docs openvinotoolkit org latest demos readme html https docs openvinotoolkit org latest demos readme html https docs openvinotoolkit org latest models intel index html raspberry pi and openvino installation https www hackster io news getting started with the intel neural compute stick 2 and the raspberry pi 6904ccfe963 | computervision raspberry-pi openvino movidius face-identification face-detection emotion-recognition | ai |
QuestionAnswerNLP | question answer nlp project author contributor debjyoti paul deb cs utah edu unid u0992708 poster of question answer project https github com debjyoti385 questionanswernlp blob master poster qa poster png note in cade machines use following python path usr local stow python amd64 linux26 python 2 7 3 bin python please run first chmod x prepare sh prepare sh prepare module modules require numpy scipy scikit learn beautifulsoup4 requests to install them locally and run use following command prepare sh execution help usr local stow python amd64 linux26 python 2 7 3 bin python qa py h execution without coreference resolution usr local stow python amd64 linux26 python 2 7 3 bin python qa py i input txt o response txt c 0 execution with coreference resolution usr local stow python amd64 linux26 python 2 7 3 bin python qa py i input txt o response txt c 1 note the final f score obtained for developset is 32 68 without coref resolution the final f score obtained for testset1 is 38 64 without coref resolution information it usually takes some 30 seconds to initialize question classifier this script also installs bart anaphora resolution kit and runs a web server with port 8125 tested on lab1 13 eng utah edu in osx macbook developset execution completes in 12 15 minutes | ai |
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chain33 | api reference https camo githubusercontent com 915b7be44ada53c290eb157634330494ebe3e30a 68747470733a2f2f676f646f632e6f72672f6769746875622e636f6d2f676f6c616e672f6764646f3f7374617475732e737667 https godoc org github com 33cn chain33 pipeline status https github com 33cn chain33 actions workflows build yml badge svg https github com 33cn chain33 actions go report card https goreportcard com badge github com 33cn chain33 https goreportcard com report github com 33cn chain33 windows build status https ci appveyor com api projects status github 33cn chain33 svg true branch master passingtext windows 20 20ok failingtext windows 20 20failed pendingtext windows 20 20pending https ci appveyor com project 33cn chain33 codecov https codecov io gh 33cn chain33 branch master graph badge svg https codecov io gh 33cn chain33 join the chat at https gitter im 33cn lobby https badges gitter im 33cn lobby svg https gitter im 33cn lobby utm source badge utm medium badge utm campaign pr badge utm content badge chain33 kiss https chain 33 cn https github com 33cn plugin https github com bityuan bityuan chain33 chain33 https mp weixin qq com s 9g5zfdkji9uzr nfxfeuaa https chain 33 cn document 289 https github com 33cn chain33 issues utf8 e2 9c 93 q label 3a e8 85 be e8 ae af e7 8e 84 e6 ad a6 e5 ae 9e e9 aa 8c e5 ae a4 bug bug 4 l3 l0 1000 l1 3000 l2 10000 l3 20000 building from source go 1 19 shell git clone https github com 33cn chain33 git gopath src github com 33cn chain33 cd gopath src github com 33cn chain33 export goproxy https mirrors aliyun com goproxy make mod makefile shell make test shell chain33 f chain33 toml chain33 master issues fork chain33 fork vipwzw chain33 git clone https github com vipwzw chain33 git gopath src github com 33cn chain33 clone gopath src github com 33cn chain33 go 33cn chain33 git remote add upstream https github com 33cn chain33 git makefile make addupstream 33cn chain33 vipwzw chain33 master make sync git fetch upstream git checkout master git merge upstream master master master upstream master 33cn chain33 git fetch upstream git checkout master git merge upstream master git branch b fixbug ci push vipwzw chain33 git fetch upstream git checkout master git merge upstream master git checkout fixbug ci git merge master git push origin fixbug ci pull request fork chain33 fork vipwzw chain33 git clone https github com vipwzw chain33 git gopath src github com 33cn chain33 clone gopath src github com 33cn chain33 go make addupstream make branch b mydevbranchname push make push b mydevbranchname m m git commit pull requset name libangzhu branch chain33 p2p listenport pr step1 make pull name libangzhu b chain33 p2p listenport commit step2 push make pullpush name libangzhu b chain33 p2p listenport license bsd 3 clause license copyright c 2018 33 cn all rights reserved redistribution and use in source and binary forms with or without modification are permitted provided that the following conditions are met redistributions of source code must retain the above copyright notice this list of conditions and the following disclaimer redistributions in binary form must reproduce the above copyright notice this list of conditions and the following disclaimer in the documentation and or other materials provided with the distribution neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission this software is provided by the copyright holders and contributors as is and any express or implied warranties including but not limited to the implied warranties of merchantability and fitness for a particular purpose are disclaimed in no event shall the copyright holder or contributors be liable for any direct indirect incidental special exemplary or consequential damages including but not limited to procurement of substitute goods or services loss of use data or profits or business interruption however caused and on any theory of liability whether in contract strict liability or tort including negligence or otherwise arising in any way out of the use of this software even if advised of the possibility of such damage | blockchain framework go golang | blockchain |
textokit-core | textokit core build status https travis ci org textocat textokit core svg branch master https travis ci org textocat textokit core textokit is a set of components for natural language processing based on apache uima platform | natural-language-processing uima | ai |
SearchReduce | searchreduce search reduce allows users to upload documents to the s3 bucket service provided by amazon the main intention of the application is to provide users with a quick and relevant search using a machine learning model the application allows users to upload pdf text and word documents to s3 the application uses the services like docker and kubernetes for creating and autoscaling the nodes in order to provide a reliable service the application uses redis caching mechanism to fetch the records faster it also uses mongodb for database operations flask platform for backend operations and react for frontend operations about deployment multi container deployment kubernetes react flask mongo ingress nginx redis it contains react client flask backend redis cache mongo database and nginx ingress you should install ingress nginx with command minikube addons enable ingress after that you can use the following command to start in main folder kubectl apply f k8s | cloud |
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tlapse | tlapse build status https travis ci org typicode tlapse svg branch master https travis ci org typicode tlapse npm https img shields io npm v tlapse svg https www npmjs com package tlapse create a timelapse of your web development a tiny utility that takes periodic screenshots of your site while you develop uses puppeteer https github com googlechrome puppeteer for creating beautiful high resolution screenshots a href https www patreon com typicode img src https c5 patreon com external logo become a patron button 2x png width 160 a usage sh npm install npm run all tlapse save dev json scripts dev run p server tlapse server node server tlapse tlapse localhost 3000 sh npm run dev note npm run all https github com mysticatea npm run all lets you run scripts in parallel and makes sure that both tlapse and server are started options tlapse can be configured to use different screenshot resolution interval etc to view available options run tlapse help articles css tricks front end tools my favorite finds of 2017 https css tricks com front end tools favorite finds 2017 pentacode how to automatically take screenshots of your site with tlapse http www penta code com how to automatically take screenshots of your site with tlapse moongift jp url http www moongift jp 2017 02 tlapse e6 8c 87 e5 ae 9a e3 81 97 e3 81 9furl e3 81 ab e5 ae 9a e6 9c 9f e7 9a 84 e3 81 ab e3 82 a2 e3 82 af e3 82 bb e3 82 b9 e3 81 97 e3 81 a6 e3 82 b9 e3 82 af e3 83 aa e3 83 bc e3 83 b3 license mit typicode cactus https github com typicode patreon https patreon com typicode | screenshot timelapse | front_end |
altschool-cloud-exercises | altschool cloud exercies repo for altschool cloud engineering assignments | cloud |
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Self-Driving-Car-Engines | p align center a href readme img alt logo width 50 src self driving jpeg a p p align center a href 18 lane augmentation offroad img alt mit license src https img shields io badge license mit yellow svg a p self driving cars engine gathers signal processing computer vision machine learning and deep learning for self driving car engines what done 1 signal processing 1d smoothing 2d smoothing convolution 2 signals pass filters 1 signal processing 2 simple straight lane detection 2 simple straight lane detection 3 steering suggestion 3 steering suggestion 4 multi lane detection 4 multi lane detection 5 multi lane angle 5 multi lane angle 6 curve lane detection 5 multi lane angle 7 car detection using sliding hog extreme boosting 5 multi lane angle 8 object detection using tensorflow 8 object detection using tensorflow 9 distance angle for object detection 9 distance angle for object detection 10 distance speed for object detection 10 distance speed for object detection 11 traffic light detection 11 traffic light detection 12 gradient smoothing 12 gradient smoothing 13 lane smoothing 13 lane smoothing 14 dynamic count lane detection 14 dynamic count lane detection 15 road segmentation 15 road segmentation 16 plate detection 16 plate detection 17 image augmentation 17 image augmentation 18 lane augmentation offroad 18 lane augmentation offroad 19 sensor fusion 19 sensor fusion 20 kitti pcl 20 kitti pcl results 1 signal processing 1 signal processing img src 1 signal processing smoothing png width 70 align 2 simple straight lane detection 2 simple straight lane img src 2 simple straight lane simple straight lane detection png width 70 align 3 steering suggestion 3 steering suggestion img src 3 steering suggestion steering suggestion png width 70 align 4 multi lane detection 4 multi lane detection img src 4 multi lane detection multi lane detection png width 70 align 5 multi lane angle 5 multi lane angle img src 5 multi lane angle multi lane angle png width 70 align 6 curve lane detection 6 curve lane detection img src 6 curve lane detection curve lane detection png width 70 align 7 car detection using sliding hog extreme boosting 7 car detection sliding hog xgb img src 7 car detection sliding hog xgb hog xgb png width 70 align 8 object detection using tensorflow 8 object detection tensorflow img src 8 object detection tensorflow object detection tensorflow png width 70 align 9 distance angle for object detection 9 object distance angle img src 9 object distance angle object distance angle png width 70 align 10 distance speed for object detection 9 object distance speed img src 10 object distance speed object distance speed gif width 70 align 11 traffic light detection 11 traffic light detection img src 11 traffic light detection traffic light detection png width 70 align 12 gradient smoothing 12 gradient smoothing img src 12 gradient smoothing gradient smoothing gif width 70 align 13 lane smoothing 13 lane smoothing img src 13 lane smoothing lane smoothing png width 70 align 14 dynamic count lane detection 14 dynamic count lane img src 14 dynamic count lane dynamic count lane png width 70 align 15 road segmentation 15 segmentation vgg16 road segmentation img src 15 segmentation vgg16 png width 70 align mobilenet city segmentation img src 15 segmentation mobilenet png width 70 align 16 plate detection 16 plate detection img src 16 plate detection plate detection jpg width 70 align 17 image augmentation 17 augmentation originally from https github com ujjwalsaxena automold road augmentation library img src 17 augmentation augmentation png width 70 align 18 lane augmentation offroad 18 lane augmentation offroad img src 18 lane augmentation offroad lane augmentation png width 70 align 19 sensor fusion 19 sensor fusion img src 19 sensor fusion output gif width 70 align 20 pykitti pcl 20 kitti pcl img src 20 kitti pcl pykitti png width 70 align | lane-detection plate-detection multi-lane lane-smoothing curve-lane-detection multi-lane-angle driving-cars road-segment | ai |
box-ui-elements | p align center img width 50 alt box elements logo src https repository images githubusercontent com 95743138 c161b500 021b 11ea 8bf9 3aa8776acdec img width 50 alt box developer logo src https raw githubusercontent com box sdks ded98439b27c2e635c45607131c54e1b6075e252 images box dev logo png p project status https img shields io badge status active brightgreen svg http opensource box com badges circleci https circleci com gh box box ui elements tree master svg style shield https circleci com gh box box ui elements tree master styled with prettier https img shields io badge styled with prettier ff69b4 svg https github com prettier prettier mergify status https img shields io endpoint svg url https gh mergify io badges box box ui elements style flat https mergify io semantic release https img shields io badge 20 20 f0 9f 93 a6 f0 9f 9a 80 semantic release e10079 svg https github com semantic release semantic release a href https www npmjs com package box ui elements img alt npm latest version src https img shields io npm v box ui elements latest svg a a href https www npmjs com package box ui elements img alt npm beta version src https img shields io npm v box ui elements beta svg a box ui elements https developer box com docs box ui elements box ui elements are pre built ui components that allow developers to add features of the main box web application into their own applications use box ui elements to navigate through upload preview and select content stored on box box ui elements are available as react components and framework agnostic javascript libraries demo https opensource box com box ui elements please note that the demo page has limited functionality installation yarn add box ui elements or npm install box ui elements to prevent library duplication the ui elements require certain peer dependencies to be installed manually for a list of required peer dependencies see package json package json usage this documentation describes how to use ui elements in a react https facebook github io react application using webpack https webpack js org if instead you require a framework agnostic solution please refer to our developer documentation https developer box com docs box ui elements you can also reference our elements demo app https github com box box ui elements demo and preview demo app https github com box box content preview demo for examples of minimal react applications using contentexplorer and contentpreview respectively common authentication src elements readme md authentication internationalization i18n src elements readme md internationalization elements contentexplorer src elements content explorer readme md contentopenwith src elements content open with readme md contentpicker src elements content picker readme md contentpreview src elements content preview readme md contentsidebar src elements content sidebar readme md contentuploader src elements content uploader readme md these components utilize code splitting see the code splitting code splitting section for more information code splitting code splitting https webpack js org guides code splitting is currently supported for some ui elements in order to use an element with code splitting you need to set it up in webpack stylesheets box ui elements use scss stylesheets https sass lang com guide each of the elements include their corresponding scss stylesheet to render properly once you import an element within your react app the corresponding stylesheet will automatically get included however you will need to setup webpack https github com webpack contrib mini css extract plugin minimal example to handle scss files by using the sass loader css loader this will direct webpack to properly include our scss files in your final css output a sample configuration is shown here https github com box box ui elements demo blob master webpack config js under the rules section browser support desktop chrome firefox safari edge latest 2 versions mobile chrome and safari supported packages react 17 box ui elements currently supports usage in react 17 environments developers implementing ui elements in react 18 may experience unexpected issues contributing our contributing guidelines can be found in contributing md contributing md the development setup instructions can be found in developing md developing md questions if you have any questions please visit our developer forum https community box com t5 box developer forum bd p developerforum or contact us via one of our available support channels https community box com t5 community ct p english copyright and license copyright 2016 present box inc all rights reserved licensed under the box software license agreement v 20170516 you may not use this file except in compliance with the license you may obtain a copy of the license at https developer box com docs box sdk license 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 | box-api box-platform react javascript box-preview box-content-picker box-content-explorer box-content-uploader box-content-preview box-content-open-with | os |
dance-moves-determiner | dance moves prediction we aim to predict dance moves using a combination of random forest and support vector machine svm to get the best possible accuracy train and test data are based on the readings obtained from two sensors gy 521 mpu6050 3 axis acceleration gyroscope 6dof module attached to the dancers wrists the raw features are acceleration x y z and rotation x y z there are a total of 11 dance moves to be classified resolving dependencies 1 create a virtual environment in the directory shell python3 m venv venv 2 activate the virtual environment shell source venv bin activate 3 install all the required dependencies shell pip install r requirements txt generating machine learning models 1 activate the virtual environment 2 resolve all the dependencies 3 run python train random forest py to generate random forest model 4 find the models in sav format in models directory 5 find the training statistics and evaluation results in eval results directory to generate svm model run python train svm py at step 3 instead alternatively run fast generate models sh at step 3 to generate both models at the same time see sample evaluation results here https github com ct15 dance moves prediction tree master sample results eval results note that if you run this without gpu each machine learning model takes around 1 5 hours to generate owing to the hyperparameter tuning involved as such you may want to train the models from a detached session of a terminal multiplexer plotting graphs for all dance moves 1 activate the virtual environment 2 resolve all the dependencies 3 make sure that the code below is commented out in train data py python comment out the code below before running plot py df max min prep flatten df interval df var prep flatten df var interval df concat prep concat df df max min df var df prep append truth df concat number 4 run python plot py 5 find the graphs in png format in plots directory for some reason adding an additional parameter in the dode to skip step 3 makes the code runs 99999x slower see sample plots here https github com ct15 dance moves prediction tree master sample results plots testing final model 1 activate the virtual environment 2 resolve all the dependencies 3 generate machine learning models 3 customise how the models generated are utilised in mlmodel class test model init py file 4 run python test py note that final model can be a combination of both random forest and svm | random-forest support-vector-machine | os |
Alink | font size 7 english readme en us md font alink alink flink pai alink div align center img src https img alicdn com tfs tb1kqu0sqy2gk0jszfgxxc5ofxa 614 554 png height 25 width 25 div alink http alinklab cn manual index html alink http alinklab cn tutorial index html alink https www yuque com pinshu alink guide plugin downloader alink div align center img src https img alicdn com imgextra i2 o1cn01z7sbcr1hg22glisdk 6000000000786 0 tps 1280 781 jpg height 50 width 50 div alink java http alinklab cn tutorial book java html alink python http alinklab cn tutorial book python html https github com alibaba alink tree master tutorial java http alinklab cn tutorial book java 00 reference html python http alinklab cn tutorial book python 00 reference html alink java http alinklab cn tutorial book java 00 code help html alink python http alinklab cn tutorial book python 00 code help html div align center img src https img alicdn com imgextra i2 o1cn01rkhble202moqzvyjw 6000000006792 2 tps 1876 955 png height 100 width 100 div pyalink div align center img src https img alicdn com tfs tb1tmkloal0gk0jszfxxxxwhvxa 2070 1380 png height 60 width 60 div pyalink pyalink alink flink python pyalink alink flink 1 13 pyalink flink flink pyalink flink 1 12 pyalink flink 1 11 pyalink flink 1 10 pyalink flink 1 9 python alink 1 6 1 1 python3 3 6 3 7 3 8 2 java 8 3 pip pip install pyalink pip install pyalink flink 1 12 pip install pyalink flink 1 11 pip install pyalink flink 1 10 pip install pyalink flink 1 9 1 pyalink pyalink flink pyalink pyalink flink pip uninstall pyalink pip uninstall pyalink flink 2 pip https segmentfault com a 1190000006111096 pip whl pip flink 1 13 https alink release oss cn beijing aliyuncs com v1 6 1 pyalink 1 6 1 py3 none any whl md5 a10d57a19c53d206d324273f377a1b13 flink 1 12 https alink release oss cn beijing aliyuncs com v1 6 1 pyalink flink 1 12 1 6 1 py3 none any whl md5 82b2395740fbd960895d16350266ab4d flink 1 11 https alink release oss cn beijing aliyuncs com v1 6 1 pyalink flink 1 11 1 6 1 py3 none any whl md5 5bf901c084b51ebfa13a62489fafc2f2 flink 1 10 https alink release oss cn beijing aliyuncs com v1 6 1 pyalink flink 1 10 1 6 1 py3 none any whl md5 e18c620a3a3423407973b8c3d23a02e0 flink 1 9 https alink release oss cn beijing aliyuncs com v1 6 1 pyalink flink 1 9 1 6 1 py3 none any whl md5 2feaed5f159bb8970400eb3f6eafc7e5 3 python pip pip3 anaconda anaconda jupyter notebook pyalink 1 jupyter jupyter notebook python 3 notebook 2 pyalink from pyalink alink import 3 uselocalenv parallism flinkhome none config none parallism flinkhome flink config flink jvm listening on 4 pyalink python source csvsourcebatchop setschemastr sepal length double sepal width double petal length double petal width double category string setfilepath https alink release oss cn beijing aliyuncs com data files iris csv res source select sepal length sepal width df res collecttodataframe print df pyalink java api setxxx link linkto linkfrom jupyter notebook print collecttodataframe collecttodataframes batchoperator execute streamoperator execute dataframe operator docs pyalink pyalink dataframe md streamoperator docs pyalink pyalink stream operator preview md udf udtf sql docs pyalink pyalink udf md pyflink docs pyalink pyalink pyflink md pyalink docs pyalink pyalink qa md java java string url https alink release oss cn beijing aliyuncs com data files iris csv string schema str sepal length double sepal width double petal length double petal width double category string batchoperator data new csvsourcebatchop setfilepath url setschemastr schema str vectorassembler va new vectorassembler setselectedcols new string sepal length sepal width petal length petal width setoutputcol features kmeans kmeans new kmeans setvectorcol features setk 3 setpredictioncol prediction result setpredictiondetailcol prediction detail setreservedcols category setmaxiter 100 pipeline pipeline new pipeline add va add kmeans pipeline fit data transform data print flink 1 13 maven xml dependency groupid com alibaba alink groupid artifactid alink core flink 1 13 2 11 artifactid version 1 6 1 version dependency dependency groupid org apache flink groupid artifactid flink streaming scala 2 11 artifactid version 1 13 0 version dependency dependency groupid org apache flink groupid artifactid flink table planner 2 11 artifactid version 1 13 0 version dependency dependency groupid org apache flink groupid artifactid flink clients 2 11 artifactid version 1 13 0 version dependency flink 1 12 maven xml dependency groupid com alibaba alink groupid artifactid alink core flink 1 12 2 11 artifactid version 1 6 1 version dependency dependency groupid org apache flink groupid artifactid flink streaming scala 2 11 artifactid version 1 12 1 version dependency dependency groupid org apache flink groupid artifactid flink table planner 2 11 artifactid version 1 12 1 version dependency dependency groupid org apache flink groupid artifactid flink clients 2 11 artifactid version 1 12 1 version dependency flink 1 11 maven xml dependency groupid com alibaba alink groupid artifactid alink core flink 1 11 2 11 artifactid version 1 6 1 version dependency dependency groupid org apache flink groupid artifactid flink streaming scala 2 11 artifactid version 1 11 0 version dependency dependency groupid org apache flink groupid artifactid flink table planner 2 11 artifactid version 1 11 0 version dependency dependency groupid org apache flink groupid artifactid flink clients 2 11 artifactid version 1 11 0 version dependency flink 1 10 maven xml dependency groupid com alibaba alink groupid artifactid alink core flink 1 10 2 11 artifactid version 1 6 1 version dependency dependency groupid org apache flink groupid artifactid flink streaming scala 2 11 artifactid version 1 10 0 version dependency dependency groupid org apache flink groupid artifactid flink table planner 2 11 artifactid version 1 10 0 version dependency flink 1 9 maven xml dependency groupid com alibaba alink groupid artifactid alink core flink 1 9 2 11 artifactid version 1 6 1 version dependency dependency groupid org apache flink groupid artifactid flink streaming scala 2 11 artifactid version 1 9 0 version dependency dependency groupid org apache flink groupid artifactid flink table planner 2 11 artifactid version 1 9 0 version dependency alink 1 flink shell wget https archive apache org dist flink flink 1 13 0 flink 1 13 0 bin scala 2 11 tgz tar xf flink 1 13 0 bin scala 2 11 tgz cd flink 1 13 0 bin start cluster sh 2 alink shell git clone https github com alibaba alink git add scope provided scope in pom xml of alink examples cd alink mvn dmaven test skip true clean package shade shade 3 java shell bin flink run p 1 c com alibaba alink alsexample path to alink examples target alink examples 1 5 snapshot jar bin flink run p 1 c com alibaba alink gbdtexample path to alink examples target alink examples 1 5 snapshot jar bin flink run p 1 c com alibaba alink kmeansexample path to alink examples target alink examples 1 5 snapshot jar docs deploy cluster deploy md | machine-learning flink classification clustering regression graph-algorithms xgboost recommender recommender-system feature-engineering statistics kafka data-mining apriori word2vec flink-ml fm flink-machine-learning graph-embedding | ai |
IPT | ipt information processing technologies | server |
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documentation | 360 qrcode https p2 ssl qhimg com t014eaf6f590f45d97c jpg 360 xchainlab documentation bitcoin txt md bitcoin md qt ui bitcoin qt ui md bitcoin docx bitcoin docx bitcoin md bitcoin md bitcoin pdf bitcoin md bitcoin md bitcoin md bitcoin md ethereum cunzheng md hyperledger fabric hyperledger fabric fabric fabric block data structure md eos eos eos eos md eos eos eos md eos eos eos md eos eos eos md eos eos eos md eos eos eos md eos eos eos md eos eos eos md polkadot polkadot md polkadot md polkadot md polkadot md polkadot md libra libra libra libra md move libra move md nebulas nebulas md nebulas md quorum quorum quorum quorum introduction md quorum quorum quorum md quorum quorum quorum raft md quorum quorum quorum node permission md monoxide monoxide monoxide monoxide md rsa acc rsa rsa acc rsa md rsa rsa acc rsa md pairing rsa acc pairing md pairing rsa acc pairing md cosmos cosmos cosmos cosmos md cosmos cosmos sdk cosmos cosmos cosmos sdk md cosmos cosmos cosmos md cosmos staking cosmos cosmos staking md cosmos delegators cosmos cosmos delegators md cosmos validators cosmos cosmos validators md cosmos photon cosmos cosmos photon md cosmos tendermint cosmos cosmos tendermint md cosmos tendermint cosmos cosmos tendermint md khipu khipu khipu khipu md khipu khipu khipu md khipu md khipu khipu md privacy zec privacy zec md zec privacy zec md zec privacy zec md zec zcash privacy zec zcash md plasma scalability plasma plasma in 10 mins md scalability chain interoperability md making sense of ethereum s layer 2 scaling solutions state channels plasma and truebit https medium com l4 media making sense of ethereums layer 2 scaling solutions state channels plasma and truebit 22cb40dcc2f4 scalability docx consensus consensus basic md 2pc consensus consensus 2pc md paxos consensus consensus paxos md raft consensus consensus raft md consensus consensus byzantine generals md raft http thesecretlivesofdata com raft pow consensus ethereum pow md hyperledger fabric pbft consensus fabric pbft md webassembly vm evm ewasm webassembly md wasm vm evm ewasm wasm md wasm vm evm ewasm wasm md wasm vm evm ewasm wasm md wasm vm evm ewasm wasm md bytecode vm solc bytecode md wasm vm evm ewasm wasm md eos wasm vm evm ewasm eos wasm md wabt vm pub wabt md p2p p2p design testdoc xlsx translate translate readme md ipfs translate ipfs learn to securely share files on the blockchain with ipfs md translate consensus a hitchhiker s guide to consensus algorithms md 200 go https mp weixin qq com s eqqozp4qhlit19paifhhta 200 go https mp weixin qq com s pjbnejv7xmvkstwmkvgadq 200 go https mp weixin qq com s nf7qg1nwafyso6x yvyzxg go pos https mp weixin qq com s et1lxh32bsckpzrwxqg7ww go p2p https mp weixin qq com s phsull1qsxqwrvgyxsim5g | blockchain documentation | blockchain |
datatracker | div align center img src https raw githubusercontent com ietf tools common main assets logos datatracker svg alt ietf datatracker height 125 release https img shields io github release ietf tools datatracker svg style flat maxage 300 https github com ietf tools datatracker releases license https img shields io github license ietf tools datatracker https github com ietf tools datatracker blob main license code coverage https codecov io gh ietf tools datatracker branch feat bs5 graph badge svg token v4dxb0q28c https codecov io gh ietf tools datatracker python version https img shields io badge python 3 9 blue logo python logocolor white prerequisites django version https img shields io badge django 4 x 51be95 logo django logocolor white prerequisites node version https img shields io badge node js 16 x green logo node js logocolor white prerequisites mariadb version https img shields io badge postgres 14 blue logo postgresql logocolor white prerequisites the day to day front end to the ietf database for people who work on ietf standards div production website https datatracker ietf org changelog https github com ietf tools datatracker releases contributing https github com ietf tools github blob main contributing md getting started getting started tl dr the tldr to get going creating a fork creating a fork git cloning tips git cloning tips docker dev environment docker readme md database assets database assets old datatracker branches https github com ietf tools old datatracker branches branches all frontend development frontend development intro intro vite vite vue 3 parcel parcel legacyjquery bootstrap bootstrap serving static files via cdn serving static files via cdn handling of external javascript and css components handling of external javascript and css components handling of internal static files handling of internal static files changes to template files changes to template files deployment deployment running tests running tests python python tests frontend frontend tests diff tool diff tool getting started this project is following the standard git feature workflow development model learn about all the various steps of the development workflow from creating a fork to submitting a pull request in the contributing https github com ietf tools github blob main contributing md guide make sure to read the styleguides https github com ietf tools github blob main contributing md styleguides section to ensure a cohesive code format across the project you can submit bug reports enhancement and new feature requests in the discussions https github com ietf tools datatracker discussions area accepted tickets will be converted to issues creating a fork click the kbd fork kbd button in the top right corner of the repository to create a personal copy that you can work on note that some github actions might be enabled by default in your fork you should disable them by going to settings actions general and selecting disable actions then save git cloning tips as outlined in the contributing https github com ietf tools github blob main contributing md guide you will first want to create a fork of the datatracker project in your personal github account before cloning it windows developers start with wsl2 from the beginning https github com ietf tools github blob main docs windows dev md because of the extensive history of this project cloning the datatracker project locally can take a long time disk space you can speed up the cloning process by limiting the history depth for example replace username with your github username to fetch only up to the 10 latest commits sh git clone depth 10 https github com username datatracker git to fetch only up to a specific date sh git clone shallow since date https github com username datatracker git the tl dr to get going note that you will have to have cloned the datatracker code locally please read the above sections datatracker development is performed using docker containers you will need to be able to run docker and docker compose on your machine to effectively develop it is possible to get a purely native install working but it is very complicated and typically takes a first time datatracker developer a full day of setup where the docker setup completes in a small number of minutes many developers are using vs code https code visualstudio com and taking advantage of vs code s ability to start a project in a set of containers if you are using vs code simply start vs code in your clone and inside vs code choose restart in container if vs code is not available to you in your clone type cd docker run once the containers are started run the tests to make sure your checkout is a good place to start from all tests should pass if any fail ask for help at tools develop inside the app container s shell type sh ietf manage py test settings settings test note that we recently moved the datatracker onto postgresql you may still find older documentation that suggests testing with settings sqlitetest that will no longer work for a more detailed description of getting going see docker readme md docker readme md overview of the datatracker models a beginning of a walkthrough of the datatracker models https notes ietf org iab aid datatracker database overview was prepared for the iab aid workshop docker dev environment in order to simplify and reduce the time required for setup a preconfigured docker environment is available read the docker dev environment docker readme md guide to get started database assets nightly database dumps of the datatracker are available as docker images ghcr io ietf tools datatracker db latest note that to update the database in your dev environment to the latest version you should run the docker cleandb script frontend development intro we now use yarn to manage assets for the datatracker and vite parcel to package them yarn maintains its node packages under the yarn directory the datatracker uses 2 different build systems depending on the use case vite https vitejs dev for vue 3 pages components parcel https parceljs org for legacy pages jquery vite vue 3 pages will gradually be updated to vue 3 components these components are located under the client directory each vue 3 app has its own sub directory for example the agenda app is located under client agenda the datatracker makes use of the django vite plugin to point to either the vite js server or the precompiled production files the django vite dev mode flag found in the ietf settings local py file determines whether the vite js server is used or not in development mode you must start the vite js development server in addition to the usual datatracker server sh yarn dev any changes made to the files under client will automatically trigger a hot reload of the modified components to generate production assets run the build command sh yarn build this will create packages under ietf static dist neue which are then served by the django development server and which must be uploaded to the cdn parcel legacy jquery the datatracker includes these packages from the various javascript and css files in ietf static js and ietf static css respectively bundled using parcel static images are likewise in ietf static images whenever changes are made to the files under ietf static you must re run the build command to package them shell yarn legacy build this will create packages under ietf static dist ietf which are then served by the django development server and which must be uploaded to the cdn bootstrap the new datatracker uses twitter bootstrap for the ui get familiar with https getbootstrap com getting started and use those ui elements css classes etc instead of cooking up your own some ground rules think hard before tweaking the bootstrap css it will make it harder to upgrade to future releases no style tags in the html put css into the morecss block of a template instead css that is used by multiple templates goes into static css ietf css or a new css file javascript that is only used on one template goes into the js block of that template javascript that is used by multiple templates goes into static js ietf js or a new js file avoid css html styling or javascript in the python code serving static files via cdn production mode if resources served over a cdn and or with a high max age don t have different urls for different versions then any component upgrade which is accompanied by a change in template functionality will have a long transition time during which the new pages are served with old components with possible breakage we want to avoid this the intention is that after a release has been checked out but before it is deployed the standard django collectstatic management command will be run resulting in all static files being collected from their working directory location and placed in an appropriate location for serving via cdn this location will have the datatracker release version as part of its url so that after the deployment of a new release the cdn will be forced to fetch the appropriate static files for that release an important part of this is to set up the static root and static url settings appropriately in 6 4 0 the setting is as follows in production mode static url https www ietf org lib dt s version static root cdn root a www www6s lib dt s version the result is that all static files collected via the collectstatic command will be placed in a location served via cdn with the release version being part of the url development mode in development mode static url is set to static and django s staticfiles infrastructure makes the static files available under that local url root unless you set settings serve cdn files locally in dev mode to false it is not necessary to actually populate the static directory by running collectstatic in order for static files to be served when running ietf manage py runserver the runserver command has extra support for finding and serving static files without running collectstatic in order to work backwards from a file served in development mode to the location from which it is served the mapping is as follows development url working copy location localhost 8000 static ietf ietf static ietf localhost 8000 static secr ietf secr static secr handling of external javascript and css components in order to make it easy to keep track of and upgrade external components these are now handled by a tool called yarn via the configuration in package json to add a new package simply run replace package name with the npm module name sh yarn add package name handling of internal static files previous to this release internal static files were located under static mixed together with the external components they are now located under ietf static ietf and ietf secr static secr and will be collected for serving via cdn by the collectstatic command any static files associated with a particular app will be handled the same way which means that all admin static files automatically will be handled correctly too changes to template files in order to make the template files refer to the correct versioned cdn url as given by the static url root all references to static files in the templates have been updated to use the static template tag when referring to static files this will automatically result in both serving static files from the right place in development mode and referring to the correct versioned url in production mode and the simpler static urls in development mode deployment during deployment it is now necessary to run the management command sh ietf manage py collectstatic before activating a new release running tests python tests from a datatracker container run the command sh ietf manage py test settings settings test you can limit the run to specific tests using the pattern argument frontend tests frontend tests are done via playwright there re 2 different type of tests tests that test vue pages components and run natively without any external dependency tests that require a running datatracker instance to test against usually legacy views make sure you have node js 16 x or later installed on your machine run vue tests warning all commands below must be run from the playwright directory unless noted otherwise 1 run once to install dependencies on your system sh npm install npm run install deps 2 run in a separate process from the project root directory sh yarn preview 3 run the tests in of these 3 modes from the playwright directory 3 1 to run the tests headlessly command line mode sh npm test 3 2 to run the tests visually cannot run in docker sh npm run test visual 3 3 to run the tests in debug mode cannot run in docker sh npm run test debug run legacy views tests first you need to start a datatracker instance dev or prod ideally from a docker container exposing the 8000 port warning all commands below must be run from the playwright directory 1 run once to install dependencies on your system sh npm install npm run install deps 2 run the tests headlessly command line mode sh npm run test legacy diff tool to compare 2 different datatracker instances and look for diff read the diff tool instructions dev diff | datatracker ietf database django | front_end |
computer-vision-research-papers | computer vision research papers a list of computer vision research papers that i found interesting i will continue adding more papers whenever i come across something interesting paper on the classical approaches to image based image synthesis is photo clip art http graphics cs cmu edu projects photoclipart imagenet a large scale hierarchical image database website http www image net org paper http www image net org papers imagenet cvpr09 pdf imagenet classification with deep convolutional neural networks alex krizhevsky ilya sutskever geoffrey e hinton nips 2012 http www cs toronto edu fritz absps imagenet pdf interesting paper on learning how to design deep neural network architectures https arxiv org pdf 1611 02167v2 pdf sketch2photo internet image montage project page http cg cs tsinghua edu cn montage main htm paper http cg cs tsinghua edu cn papers siggraphasia 2009 sketch2photo pdf how do humans sketch objects project page http cybertron cg tu berlin de eitz projects classifysketch biing binarized normed gradients for objectness estimation project page http mmcheng net bing paper http mmcheng net mftp papers objectnessbing pdf what makes paris look like paris project page http graphics cs cmu edu projects whatmakesparis deepface closing the gap to human level performance in face verification paper https www cs toronto edu ranzato publications taigman cvpr14 pdf robust video segment proposals with painless occlusion handling project page https web engr oregonstate edu lif segtrack2 occlusion paper https web engr oregonstate edu lif segtrack2 occlusion occlusion paper pdf dynamicfusion reconstruction and tracking of non rigid scenes in real time project page http grail cs washington edu projects dynamicfusion paper http grail cs washington edu projects dynamicfusion papers dynamicfusion pdf deep neural networks are easily fooled high confidence predictions for unrecognizable images project page http www evolvingai org fooling a neural algorithm of artistic style paper https arxiv org abs 1508 06576 understanding deep features with computer generated imagery paper https arxiv org abs 1506 01151 learning visual biases from human imagination paper http web mit edu vondrick imagination paper pdf facenet a unified embedding for face recognition and clustering paper https arxiv org abs 1503 03832 joint embeddings of shapes and images via cnn image purification paper https shapenet cs stanford edu projects jointembedding the sketchy database learning to retrieve badly drawn bunnies project page http sketchy eye gatech edu do deep convolutional nets really need to be deep and convolutional https arxiv org abs 1603 05691 unsupervised learning of visual representations using videos paper http www cs cmu edu xiaolonw papers unsupervised video pdf project page http www cs cmu edu xiaolonw unsupervise html xnor net imagenet classification using binary convolutional neural networks paper https arxiv org abs 1603 05279 deep residual learning for image recognition paper http arxiv org abs 1512 03385 learning aligned cross modal representations from weakly aligned data project page https projects csail mit edu cmplaces visually indicated sounds vis csail mit edu what happens if learning to predict the effect of forces in images http allenai org plato forces yolo9000 better faster stronger https arxiv org abs 1612 08242 visual dialog paper https arxiv org abs 1611 08669 project page http visualdialog org generative adversarial nets paper https arxiv org pdf 1406 2661 pdf unsupervised representation learning with deep convolutional gans paper https arxiv org abs 1511 06434 object contour detection with a fully convolutional encoder decoder network project page https eng ucmerced edu people jyang44 objectcontourdetection html paper https arxiv org pdf 1603 04530 pdf photo realistic style transfer recent work in transferring style between images paper https arxiv org abs 1703 07511 code https github com luanfujun deep photo styletransfer context encoders feature learning by inpainting https people eecs berkeley edu pathak papers cvpr16 pdf https arxiv org abs 1604 07379 generative adversarial text to image synthesis project page https github com reedscot icml2016 paper http arxiv org abs 1605 05396 generative visual manipulation on the natural image manifold project page http people eecs berkeley edu junyanz projects gvm paper https arxiv org pdf 1609 03552v2 pdf improved techniques for training gans paper https arxiv org abs 1606 03498 code https github com openai improved gan conditional image generation with pixelcnn decoders paper https papers nips cc paper 6527 conditional image generation with pixelcnn decoders pdf code https github com anantzoid conditional pixelcnn decoder attribute2image conditional image generation from visual attributes paper https drive google com file d 0b9q4vh7pproodxpvtgz6a1flzw8 view project page https sites google com site attribute2image presentation slides https docs google com presentation d 1qoawy3qmvigrjm2qfmyyn3zgvcxqiemmvnw56dit 4i edit slide id g1d6af2990f 0 95 semantic segmentation using adversarial networks paper https arxiv org abs 1611 08408 netvlad cnn architecture for weakly supervised place recognition paper https arxiv org abs 1511 07247 project page http www di ens fr willow research netvlad image to image translation with conditional adversarial nets project https phillipi github io pix2pix have fun with the demo https affinelayer com pixsrv follow up work cyclegan https github com junyanz cyclegan stackgan text to photo realistic image synthesis with stacked gans paper https arxiv org pdf 1612 03242v1 pdf github https github com hanzhanggit stackgan neural architecture search with reinforcement learning paper https arxiv org abs 1611 01578 openreview discussion https openreview net forum id r1ue8hcxg | computer-vision deep-learning | ai |
SubiScrape | subiscrape a web scraping application which summarizes scraped information using openai s gpt 3 5 turbo simple ui easy to use and efficient ko fi https ko fi com img githubbutton sm svg https ko fi com joelkong subiscrape assets subiscrape png application code technology stack front end nextjs with reactjs typescript back end nodejs tools used tailwindcss mui gpt 3 5 turbo api cheerio readability deployed with vercel generating a summary through scraped information users can input a link which contains relevant information articles reports and the ai model would then be able to generate a summary from the scraped information summary assets summary gif | artificial-intelligence gpt-35-turbo summary webscraper-website | server |
uber-clone-blockchain | this is a next js https nextjs org project bootstrapped with create next app https github com vercel next js tree canary packages create next app getting started first run the development server bash npm run dev or yarn dev open http localhost 3000 http localhost 3000 with your browser to see the result you can start editing the page by modifying pages index js the page auto updates as you edit the file api routes https nextjs org docs api routes introduction can be accessed on http localhost 3000 api hello http localhost 3000 api hello this endpoint can be edited in pages api hello js the pages api directory is mapped to api files in this directory are treated as api routes https nextjs org docs api routes introduction instead of react pages learn more to learn more about next js take a look at the following resources next js documentation https nextjs org docs learn about next js features and api learn next js https nextjs org learn an interactive next js tutorial you can check out the next js github repository https github com vercel next js your feedback and contributions are welcome deploy on vercel the easiest way to deploy your next js app is to use the vercel platform https vercel com new utm medium default template filter next js utm source create next app utm campaign create next app readme from the creators of next js check out our next js deployment documentation https nextjs org docs deployment for more details | blockchain |
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CuRT_v1_MIPS | curt v1 mips porting jserv s mini rtos curt to jz4740 curt jserv jz4740 cpu mips see mips run 09 | embedded-systems mips rtos | os |
LED_Sketchpad | led sketchpad embedded system design final project | os |
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esp-build | esp open rtos build environment docker pulls https img shields io docker pulls bschwind esp open rtos svg https hub docker com r bschwind esp open rtos docker stars https img shields io docker stars bschwind esp open rtos svg https hub docker com r bschwind esp open rtos https images microbadger com badges image bschwind esp open rtos svg https microbadger com images bschwind esp open rtos get your own image badge on microbadger com license https img shields io badge license mit blue svg style flat https github com bschwind esp build blob master license this dockerfile contains the dependencies necessary to create a toolchain for the esp8266 chip it is based on esp open rtos https github com superhouse esp open rtos and allows for easy building and flashing to the esp8266 chip for projects written with esp open rtos note espressif now offers an official freertos based sdk https github com espressif esp8266 rtos sdk which is quite easy to set up and use without docker especially if you place it in your project as a git submodule i would recommend giving it a try as i m not currently maintaining this docker image dependencies docker https www docker com products docker toolbox virtualbox https www virtualbox org wiki downloads if you re not on linux quick setup docker pull bschwind esp open rtos cd to your esp open rtos project without usb flashing support docker run rm it e espbaud 921600 v pwd home esp esp open rtos examples project bschwind esp open rtos bin bash with usb flashing support docker run rm it privileged e espbaud 921600 v dev bus usb dev bus usb v pwd home esp esp open rtos examples project bschwind esp open rtos bin bash either step will put you in an interactive shell inside the container if you have a makefile in your project directory you can immediately run make and your source should get compiled make flash will attempt to flash the code to dev ttyusb0 assuming you re using the makefiles from esp open rtos examples flashing images from the container if you re on docker machine os x or windows you need to forward your usb device within virtualbox this is best managed in the virtualbox gui steps stop your docker virtual machine host if applicable plug in the usb serial device you will use to flash to the esp8266 install virtualbox extensions https www virtualbox org wiki downloads to support usb ctrl f extension on that page os x under virtualbox preferences go to the extensions tab windows same thing click the adds new package button and select the extension pack you downloaded return to the main virtualbox gui right click on your docker vm and select settings select ports usb check the box enable usb controller and select usb 2 0 ehci controller under usb device filters click the usb icon with the green plus sign to add a usb device select your usb serial device in my case it was ftdi ft232r usb uart 0600 click ok until you re back to the main virtualbox gui at this point you can restart your virtual machine with docker machine start your docker vm name run docker as we did in quick setup docker run rm it privileged v dev bus usb dev bus usb v pwd home esp esp open rtos examples project bschwind esp open rtos bin bash note with the v dev bus usb dev bus usb volume the dev bus usb on the lefthand side of the colon refers to docker vm s usb directory not your host machine you likely won t find that path on os x dev ttyusb0 should now be available run make and then make flash on an example project or your own if you re on linux it should be sufficient to share your usb device either as a docker volume or with the device flag however i have not yet tested linux serial debugging picocom https github com npat efault picocom is installed in this image by default invoke it with picocom b 115200 dev ttyusb0 change the baud rate and device path accordingly stop it with ctrl a ctrl x pro tip when running make flash from esp open rtos s makefiles it will print out the flashing command it uses for example esptool py p dev ttyusb0 baud 115200 write flash fs 16m fm qio ff 40m 0x0 bootloader firmware prebuilt rboot bin 0x1000 bootloader firmware prebuilt blank config bin 0x2000 firmware blink timers bin see that baud 115200 you can change it to higher values with the espbaud environment variable which is passed into the docker run command and the esp8266 should honor it in most cases valid values i ve had success with are 230400 460800 921600 with 921600 flashing in under 4 seconds 241 664 bytes this can significantly speed up development time with any flashing baud rate i have noticed more occasional errors with this setup than i have with the arduino ide or other environments i m not sure what the cause is the flash will sometimes get stuck at 99 with a fatal error occurred timed out waiting for packet header i ve found this actually happens less often on 921600 baud but it can still happen if anyone knows what s up with that please let me know | os |
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arm-bare-rtos | nano rtos this is bare rtos smallest possible cortex m thread switcher you can write in c or c originally the scheduler was mostly written in c with just a few helper function written in assembly to perform some sensitive tasks which couldn t be written in c later a c 20 module version was added it does only do the heavy lifting needed to perform thread switching and lets the user to decide on everything else it doesn t even implement any specific scheduling model so in fact you can extend it and have whatever scheduler model either preemptive or cooperative round robin or prioritized rt scheduling resulting in small memory and flash footprint allows you to have the luxury of running threads even in more advanced flash bootloaders it also has barely any dependencies all that this code needs is a cmsis header to grab some structs out of it it should therefore be a quick drop in addition to any project where thread switching is needed usage to actually use nano rtos all you have to do is to use the contents of src kernel src kernel subdirectory there is one c one c file and a bunch of headers cloning this repository as a submodule will totally work too this repository does not have any submodules defined and contains only small dead code in examples subdirectory everything else here is entirely optional there are two versions of the code provided kernel c api h arm arch h kernel h purely c based implementation kernel cpp arm arch h c 20 module based implementation you can use whichever you like they are entirely independent from each other except of arm arch h which contains shared intrinsics aside from that you ll have to provide a thread table variable and one call back method which provides kernel with amount of available entries in the thread table documentation the api of both implementations is well documented by doxygen comments inside the code we have some additional documentation inside doc doc subdirectory example in examples examples subdirectory there is an example of how to use the kernel to create an environment with preemptive multi threading more examples to come | arm cmsis cortex-m rtos | os |
Land-Registration-with-Blockchain | land registration system with blockchain img src https img shields io badge ethereum 20232a style for the badge logo ethereum logocolor white img src https img shields io badge react 20232a style for the badge logo react logocolor 61dafb project description this is an application of land registration system land registry in india as well as in many parts of the world is a very slow and inconvenient process current land registration verification systems include an increasing number of fraud cases and loss of paperwork and court cases due to thousands of land records to maintain the intuition behind building this was to make the process of land registration resilient and decreases the cases of fraud in the process using the system validation of the lands is also possible as immutable transactions are being stored in the public ledger so the land registration system using blockchain is a distributed system that will store all the transactions made during the process of land buying this will also be helpful for buyers sellers and government registrars to transfer the land ownership from seller to new buyer as well as it will accelerate the process of registration tech stack used frontend javascript react framework css metamask chrome extension backend ethereum blockchain truffle suite solidity ganache application features registration page seller buyer can register for an account on the application land inspector dashboard land inspector works as the admin and is already registered he can then verify the sellers buyers and approve land transfer process user profile seller buyer can view their profile via their respective dashboards edit profile seller buyer can edit their profile seller dashboard a brief description of added lands and features to add a new land and approve a land request from a buyer add land seller can add a land after he she is verified by the land inspector approve land request approve a request by buyer to buy a land buyer dashboard a brief description of all lands and features to request a land to land owner of the particular land owned lands details of lands owned by the buyer after buying some lands make payment complete payment transfer to seller after land request is approved view lands complete information of lands along with its images and required documents land ownership transfer transfer of land ownership from seller to buyer via land inspector steps to run the application 1 clone the github repository and cd to the folder 2 open ganache and keep it running in the background 3 make sure you have metamask extension in your browser 4 in the root directory run truffle migrate reset 5 cd to the client folder and run npm install 6 run npm start project demo link https youtu be 6vlaaa8gndc some features of the application landing page buyer registration img src screenshots landing png height 200 img src screenshots registration png height 200 buyer dashboard seller dashboard img src screenshots buyer dashboard png height 200 img src screenshots seller dashboard2 png height 200 add land by seller view all lands details img src screenshots add land png height 200 img src screenshots land gallery png height 200 help faq page verify buyer by land inspector img src screenshots help png height 200 img src screenshots verify buyer png height 200 approve land request by seller payment by buyer img src screenshots approve request png height 200 img src screenshots payment png height 200 verify land transaction by land inspector owned lands buyer img src screenshots verify transaction png height 200 img src screenshots owned lands png height 200 view profile before verification edit profile after verification img src screenshots profile png height 200 width 100 img src screenshots edit profile png height 200 width 80 make sure to star the repository if you find it helpful visitors https visitor badge laobi icu badge page id vrii14 land registration with blockchain a href https github com vrii14 land registration with blockchain stargazers img src https img shields io github stars vrii14 land registration with blockchain color yellow alt stars badge a a href https github com vrii14 land registration with blockchain graphs contributors img alt github contributors src https img shields io github contributors vrii14 land registration with blockchain color 2b9348 a | blockchain |
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ABigSurvey | a survey of surveys nlp ml in this document we survey hundreds of survey papers on natural language processing nlp and machine learning ml we categorize these papers into popular topics and do simple counting for some interesting problems in addition we show the list of the papers with urls 1063 papers new we add a new category of large language models a href large language models large language models a categorization we follow the acl and icml submission guideline of recent years covering a broad range of areas in nlp and ml the categorization is as follows natural language processing a href computational social science and social media computational social science and social media a a href dialogue and interactive systems dialogue and interactive systems a a href generation generation a a href information extraction information extraction a a href information retrieval and text mining information retrieval and text mining a a href interpretability and analysis of models for nlp interpretability and analysis of models for nlp a a href knowledge graph knowledge graph a a href language grounding to vision robotics and beyond language grounding to vision robotics and beyond a a href large language models large language models a a href linguistic theories cognitive modeling and psycholinguistics linguistic theories cognitive modeling and psycholinguistics a a href machine learning for nlp machine learning for nlp a a href machine translation machine translation a a href named entity recognition named entity recognition a a href natural language inference natural language inference a a href natural language processing natural language processing a a href nlp applications nlp applications a a href pre trained models pre trained models a a href prompt prompt a a href question answering question answering a a href reading comprehension reading comprehension a a href recommender systems recommender systems a a href resources and evaluation resources and evaluation a a href semantics semantics a a href sentiment analysis stylistic analysis and argument mining sentiment analysis stylistic analysis and argument mining a a href speech and multimodality speech and multimodality a a href summarization summarization a a href tagging chunking syntax and parsing tagging chunking syntax and parsing a a href text classification text classification a machine learning a href architectures architectures a a href automl automl a a href bayesian methods bayesian methods a a href classification clustering and regression classification clustering and regression a a href computer vision computer vision a a href contrastive learning contrastive learning a a href curriculum learning curriculum learning a a href data augmentation data augmentation a a href deep learning general methods deep learning general methods a a href deep reinforcement learning deep reinforcement learning a a href diffusion models diffusion models a a href federated learning federated learning a a href few shot and zero shot learning few shot and zero shot learning a a href general machine learning general machine learning a a href generative adversarial networks generative adversarial networks a a href graph neural networks graph neural networks a a href interpretability and analysis interpretability and analysis a a href knowledge distillation knowledge distillation a a href meta learning meta learning a a href metric learning metric learning a a href ml and dl applications ml and dl applications a a href model compression and acceleration model compression and acceleration a a href multi label learning multi label learning a a href multi task and multi view learning multi task and multi view learning a a href online learning online learning a a href optimization optimization a a href semi supervised weakly supervised and unsupervised learning semi supervised weakly supervised and unsupervised learning a a href transfer learning transfer learning a a href trustworthy machine learning trustworthy machine learning a to reduce class imbalance we separate some of the hot sub topics from the original categorization of acl and icml submissions e g named entity recognition is a first level area in our categorization because it is the focus of several surveys statistics we show the number of paper in each area in figures 1 2 p align center img src https s2 loli net 2023 05 26 dua43miwf5nflzx png width 70 height 70 p p align center figure 1 of papers in each nlp area p p align center img src https s2 loli net 2023 05 26 z3psluxbzfd6qrb png width 70 height 70 p p align center figure 2 of papers in each ml area p also we plot paper number as a function of publication year see figure 3 p align center img src https s2 loli net 2023 05 26 7tmmcro1lk9n5hf png width 70 height 70 p p align center figure 3 of papers vs publication year p in addition we generate word clouds to show hot topics in these surveys see figures 4 5 p align center img src https s2 loli net 2023 05 26 6rqnckbwsezta3h png width 60 height 60 p p align center figure 4 the word cloud for nlp p p align center img src https s2 loli net 2023 05 26 zln92qyvmglwmue png width 60 height 60 p p align center figure 5 the word cloud for ml p the nlp paper list computational social science and social media content 1 a comprehensive survey on community detection with deep learning arxiv 2021 paper https arxiv org pdf 2105 12584 pdf bib bib natural language processing computational social science and social media su2021a md xing su shan xue fanzhen liu jia wu jian yang chuan zhou wenbin hu c cile paris surya nepal di jin quan z sheng philip s yu 2 a survey of fake news fundamental theories detection methods and opportunities acm comput surv 2021 paper https arxiv org abs 1812 00315 bib bib natural language processing computational social science and social media zhou2021a md xinyi zhou reza zafarani 3 a survey of race racism and anti racism in nlp acl 2021 paper https arxiv org abs 2106 11410 bib bib natural language processing computational social science and social media field2021a md anjalie field su lin blodgett zeerak waseem yulia tsvetkov 4 a survey on computational propaganda detection ijcai 2020 paper https arxiv org pdf 2007 08024 pdf bib bib natural language processing computational social science and social media martino2020a md giovanni da san martino stefano cresci alberto barr n cede o seunghak yu roberto di pietro preslav nakov 5 a survey on trust prediction in online social networks ieee access 2020 paper https ieeexplore ieee org abstract document 9142365 bib bib natural language processing computational social science and social media ghafari2020a md seyed mohssen ghafari amin beheshti aditya joshi c cile paris adnan mahmood shahpar yakhchi mehmet a orgun 6 computational sociolinguistics a survey comput linguistics 2016 paper https arxiv org abs 1508 07544 bib bib natural language processing computational social science and social media nguyen2016computational md dong nguyen a seza dogru z carolyn p ros franciska de jong 7 confronting abusive language online a survey from the ethical and human rights perspective j artif intell res 2021 paper https arxiv org abs 2012 12305 bib bib natural language processing computational social science and social media kiritchenko2021confronting md svetlana kiritchenko isar nejadgholi kathleen c fraser 8 from symbols to embeddings a tale of two representations in computational social science j soc comput 2021 paper https arxiv org pdf 2106 14198 bib bib natural language processing computational social science and social media chen2021from md huimin chen cheng yang xuanming zhang zhiyuan liu maosong sun jianbin jin 9 language technology is power a critical survey of bias in nlp acl 2020 paper https arxiv org abs 2005 14050 bib bib natural language processing computational social science and social media blodgett2020language md su lin blodgett solon barocas hal daum iii hanna m wallach 10 societal biases in language generation progress and challenges acl 2021 paper https arxiv org pdf 2105 04054 pdf bib bib natural language processing computational social science and social media sheng2021societal md emily sheng kai wei chang prem natarajan nanyun peng 11 tackling online abuse a survey of automated abuse detection methods arxiv 2019 paper https arxiv org pdf 1908 06024 pdf bib bib natural language processing computational social science and social media mishra2019tackling md pushkar mishra helen yannakoudakis ekaterina shutova 12 when do word embeddings accurately reflect surveys on our beliefs about people acl 2020 paper https arxiv org abs 2004 12043 bib bib natural language processing computational social science and social media joseph2020when md kenneth joseph jonathan h morgan dialogue and interactive systems content 1 a survey of arabic dialogues understanding for spontaneous dialogues and instant message arxiv 2015 paper https arxiv org abs 1505 03084 bib bib natural language processing dialogue and interactive systems elmadany2015a md abdelrahim a elmadany sherif m abdou mervat gheith 2 a survey of available corpora for building data driven dialogue systems the journal version dialogue discourse 2018 paper https journals uic edu ojs index php dad article view 10733 9501 bib bib natural language processing dialogue and interactive systems serban2018a md iulian vlad serban ryan lowe peter henderson laurent charlin joelle pineau 3 a survey of document grounded dialogue systems dgds arxiv 2020 paper https arxiv org abs 2004 13818 bib bib natural language processing dialogue and interactive systems ma2020a md longxuan ma wei nan zhang mingda li ting liu 4 a survey of intent classification and slot filling datasets for task oriented dialog arxiv 2022 paper https arxiv org pdf 2207 13211 pdf bib bib natural language processing dialogue and interactive systems larson2022a md stefan larson kevin leach 5 a survey of natural language generation techniques with a focus on dialogue systems past present and future directions arxiv 2019 paper https arxiv org abs 1906 00500 bib bib natural language processing dialogue and interactive systems santhanam2019a md sashank santhanam samira shaikh 6 a survey of neural models for the automatic analysis of conversation towards a better integration of the social sciences arxiv 2022 paper https arxiv org pdf 2203 16891 pdf bib bib natural language processing dialogue and interactive systems clavel2022a md chlo clavel matthieu labeau justine cassell 7 a survey on dialog management recent advances and challenges arxiv 2020 paper https arxiv org abs 2005 02233 bib bib natural language processing dialogue and interactive systems dai2020a md yinpei dai huihua yu yixuan jiang chengguang tang yongbin li jian sun 8 a survey on dialogue systems recent advances and new frontiers sigkdd explor 2017 paper https arxiv org abs 1711 01731 bib bib natural language processing dialogue and interactive systems chen2017a md hongshen chen xiaorui liu dawei yin jiliang tang 9 advances in multi turn dialogue comprehension a survey arxiv 2021 paper https arxiv org abs 2103 03125 bib bib natural language processing dialogue and interactive systems zhang2021advances md zhuosheng zhang hai zhao 10 challenges in building intelligent open domain dialog systems acm trans inf syst 2020 paper https arxiv org abs 1905 05709 bib bib natural language processing dialogue and interactive systems huang2020challenges md minlie huang xiaoyan zhu jianfeng gao 11 conversational agents theory and applications arxiv 2022 paper https arxiv org pdf 2202 03164 pdf bib bib natural language processing dialogue and interactive systems wahde2022conversational md mattias wahde marco virgolin 12 conversational machine comprehension a literature review coling 2020 paper https arxiv org abs 2006 00671 bib bib natural language processing dialogue and interactive systems gupta2020conversational md somil gupta bhanu pratap singh rawat hong yu 13 how to evaluate your dialogue models a review of approaches arxiv 2021 paper https arxiv org pdf 2108 01369 pdf bib bib natural language processing dialogue and interactive systems li2021how md xinmeng li wansen wu long qin quanjun yin 14 neural approaches to conversational ai acl 2018 paper https arxiv org pdf 1809 08267 bib bib natural language processing dialogue and interactive systems gao2018neural md jianfeng gao michel galley lihong li 15 neural approaches to conversational ai question answering task oriented dialogues and social chatbots now foundations and trends 2019 paper https ieeexplore ieee org document 8649787 bib bib natural language processing dialogue and interactive systems gao2019neural md jianfeng gao michel galley lihong li 16 pomdp based statistical spoken dialog systems a review proc ieee 2013 paper https ieeexplore ieee org document 6407655 bib bib natural language processing dialogue and interactive systems young2013pomdp based md steve j young milica gasic blaise thomson jason d williams 17 recent advances and challenges in task oriented dialog system arxiv 2020 paper https arxiv org pdf 2003 07490 bib bib natural language processing dialogue and interactive systems zhang2020recent md zheng zhang ryuichi takanobu minlie huang xiaoyan zhu 18 recent advances in deep learning based dialogue systems a systematic survey arxiv 2021 paper https arxiv org pdf 2105 04387 pdf bib bib natural language processing dialogue and interactive systems ni2021recent md jinjie ni tom young vlad pandelea fuzhao xue vinay adiga erik cambria 19 utterance level dialogue understanding an empirical study arxiv 2020 paper https arxiv org abs 2009 13902 bib bib natural language processing dialogue and interactive systems ghosal2020utterance level md deepanway ghosal navonil majumder rada mihalcea soujanya poria generation content 1 a survey of controllable text generation using transformer based pre trained language models arxiv 2022 paper https arxiv org pdf 2201 05337 pdf bib bib natural language processing generation zhang2022a md hanqing zhang haolin song shaoyu li ming zhou dawei song 2 a survey of knowledge enhanced text generation acm comput surv 2022 paper https arxiv org pdf 2010 04389 pdf bib bib natural language processing generation yu2022a md wenhao yu chenguang zhu zaitang li zhiting hu qingyun wang heng ji meng jiang 3 a survey on multi hop question answering and generation arxiv 2022 paper https arxiv org pdf 2204 09140 pdf bib bib natural language processing generation mavi2022a md vaibhav mavi anubhav jangra adam jatowt 4 a survey on retrieval augmented text generation arxiv 2022 paper https arxiv org pdf 2202 01110 pdf bib bib natural language processing generation li2022a md huayang li yixuan su deng cai yan wang lemao liu 5 a survey on text simplification arxiv 2020 paper https arxiv org abs 2008 08612 bib bib natural language processing generation sikka2020a md punardeep sikka vijay mago 6 automatic detection of machine generated text a critical survey coling 2020 paper https arxiv org pdf 2011 01314 pdf bib bib natural language processing generation jawahar2020automatic md ganesh jawahar muhammad abdul mageed laks v s lakshmanan 7 automatic story generation challenges and attempts arxiv 2021 paper https arxiv org abs 2102 12634 bib bib natural language processing generation alabdulkarim2021automatic md amal alabdulkarim siyan li xiangyu peng 8 chatgpt is not all you need a state of the art review of large generative ai models arxiv 2023 paper https arxiv org pdf 2301 04655 pdf bib bib natural language processing generation gozalo brizuela2023chatgpt md roberto gozalo brizuela eduardo c garrido merch n 9 content selection in data to text systems a survey arxiv 2016 paper https arxiv org abs 1610 08375 bib bib natural language processing generation gkatzia2016content md dimitra gkatzia 10 data driven sentence simplification survey and benchmark comput linguistics 2020 paper https www mitpressjournals org doi pdf 10 1162 coli a 00370 bib bib natural language processing generation alva manchego2020data driven md fernando alva manchego carolina scarton lucia specia 11 deep learning for text style transfer a survey comput linguistics 2022 paper https arxiv org pdf 2011 00416 pdf bib bib natural language processing generation jin2022deep md di jin zhijing jin zhiting hu olga vechtomova rada mihalcea 12 evaluation of text generation a survey arxiv 2020 paper https arxiv org abs 2006 14799 bib bib natural language processing generation celikyilmaz2020evaluation md asli celikyilmaz elizabeth clark jianfeng gao 13 human evaluation of creative nlg systems an interdisciplinary survey on recent papers arxiv 2021 paper https arxiv org pdf 2108 00308 pdf bib bib natural language processing generation h m l inen2021human md mika h m l inen khalid al najjar 14 keyphrase generation a multi aspect survey fruct 2019 paper https arxiv org abs 1910 05059 bib bib natural language processing generation ano2019keyphrase md erion ano ondrej bojar 15 neural language generation formulation methods and evaluation arxiv 2020 paper https arxiv org pdf 2007 15780 pdf bib bib natural language processing generation garbacea2020neural md cristina garbacea qiaozhu mei 16 neural text generation past present and beyond arxiv 2018 paper https arxiv org pdf 1803 07133 pdf bib bib natural language processing generation lu2018neural md sidi lu yaoming zhu weinan zhang jun wang yong yu 17 quiz style question generation for news stories www 2021 paper https arxiv org abs 2102 09094 bib bib natural language processing generation lelkes2021quiz style md d m d lelkes vinh q tran cong yu 18 recent advances in neural question generation arxiv 2019 paper https arxiv org abs 1905 08949 bib bib natural language processing generation pan2019recent md liangming pan wenqiang lei tat seng chua min yen kan 19 recent advances in sql query generation a survey arxiv 2020 paper https arxiv org abs 2005 07667 bib bib natural language processing generation kalajdjieski2020recent md jovan kalajdjieski martina toshevska frosina stojanovska 20 survey of hallucination in natural language generation arxiv 2022 paper https arxiv org pdf 2202 03629 pdf bib bib natural language processing generation ji2022survey md ziwei ji nayeon lee rita frieske tiezheng yu dan su yan xu etsuko ishii yejin bang andrea madotto pascale fung 21 survey of the state of the art in natural language generation core tasks applications and evaluation j artif intell res 2018 paper https arxiv org abs 1703 09902 bib bib natural language processing generation gatt2018survey md albert gatt emiel krahmer information extraction content 1 a review on fact extraction and verification acm comput surv 2023 paper http arxiv org abs 2010 03001 bib bib natural language processing information extraction bekoulis2023a md giannis bekoulis christina papagiannopoulou nikos deligiannis 2 a survey of deep learning methods for relation extraction arxiv 2017 paper https arxiv org abs 1705 03645 bib bib natural language processing information extraction kumar2017a md shantanu kumar 3 a survey of event extraction from text ieee access 2019 paper https ieeexplore ieee org document 8918013 bib bib natural language processing information extraction xiang2019a md wei xiang bang wang 4 a survey of event extraction methods from text for decision support systems decis support syst 2016 paper https www sciencedirect com science article abs pii s0167923616300173 bib bib natural language processing information extraction hogenboom2016a md frederik hogenboom flavius frasincar uzay kaymak franciska de jong emiel caron 5 a survey of joint intent detection and slot filling models in natural language understanding arxiv 2021 paper https arxiv org abs 2101 08091 bib bib natural language processing information extraction weld2021a md henry weld xiaoqi huang siqi long josiah poon soyeon caren han 6 a survey of textual event extraction from social networks lpkm 2017 paper http ceur ws org vol 1988 lpkm2017 paper 15 pdf bib bib natural language processing information extraction mejri2017a md mohamed mejri jalel akaichi 7 a survey on deep learning event extraction approaches and applications arxiv 2021 paper https arxiv org pdf 2107 02126 pdf bib bib natural language processing information extraction li2021a md qian li jianxin li jiawei sheng shiyao cui jia wu yiming hei hao peng shu guo lihong wang amin beheshti philip s yu 8 a survey on open information extraction coling 2018 paper https arxiv org abs 1806 05599 bib bib natural language processing information extraction niklaus2018a md christina niklaus matthias cetto andr freitas siegfried handschuh 9 a survey on temporal reasoning for temporal information extraction from text extended abstract ijcai 2020 paper https arxiv org abs 2005 06527 bib bib natural language processing information extraction leeuwenberg2020a md artuur leeuwenberg marie francine moens 10 an overview of event extraction from text derive iswc 2011 paper http ceur ws org vol 779 derive2011 submission 1 pdf bib bib natural language processing information extraction hogenboom2011an md frederik hogenboom flavius frasincar uzay kaymak franciska de jong 11 automatic extraction of causal relations from natural language texts a comprehensive survey arxiv 2016 paper https arxiv org abs 1605 07895 bib bib natural language processing information extraction asghar2016automatic md nabiha asghar 12 complex relation extraction challenges and opportunities arxiv 2020 paper https arxiv org pdf 2012 04821 pdf bib bib natural language processing information extraction jiang2020complex md haiyun jiang qiaoben bao qiao cheng deqing yang li wang yanghua xiao 13 extracting events and their relations from texts a survey on recent research progress and challenges ai open 2020 paper https www sciencedirect com science article pii s266665102100005x pdfft md5 3983861e9ae91ce7b45f0c5533071077 pid 1 s2 0 s266665102100005x main pdf bib bib natural language processing information extraction liu2020extracting md kang liu yubo chen jian liu xinyu zuo jun zhao 14 knowledge extraction in low resource scenarios survey and perspective arxiv 2022 paper https arxiv org pdf 2202 08063 pdf bib bib natural language processing information extraction deng2022knowledge md shumin deng ningyu zhang hui chen feiyu xiong jeff z pan huajun chen 15 more data more relations more context and more openness a review and outlook for relation extraction aacl 2020 paper https arxiv org abs 2004 03186 bib bib natural language processing information extraction han2020more md xu han tianyu gao yankai lin hao peng yaoliang yang chaojun xiao zhiyuan liu peng li jie zhou maosong sun 16 neural relation extraction a survey arxiv 2020 paper https arxiv org abs 2007 04247 bib bib natural language processing information extraction aydar2020neural md mehmet aydar ozge bozal furkan zbay 17 no pattern no recognition a survey about reproducibility and distortion issues of text clustering and topic modeling arxiv 2022 paper https arxiv org pdf 2208 01712 pdf bib bib natural language processing information extraction silva2022no md mar lia costa rosendo silva felipe alves siqueira jo o pedro mantovani tarrega jo o vitor pataca beinotti augusto sousa nunes miguel de mattos gardini vin cius adolfo pereira da silva n dia f lix felipe da silva andr carlos ponce de leon ferreira de carvalho 18 recent neural methods on slot filling and intent classification for task oriented dialogue systems a survey coling 2020 paper https arxiv org abs 2011 00564 bib bib natural language processing information extraction louvan2020recent md samuel louvan bernardo magnini 19 relation extraction a survey arxiv 2017 paper https arxiv org abs 1712 05191 bib bib natural language processing information extraction pawar2017relation md sachin pawar girish k palshikar pushpak bhattacharyya 20 techniques for jointly extracting entities and relations a survey cicling 2019 paper https arxiv org abs 2103 06118 bib bib natural language processing information extraction pawar2019techniques md sachin pawar pushpak bhattacharyya girish k palshikar information retrieval and text mining content 1 a brief survey of text mining classification clustering and extraction techniques arxiv 2017 paper https arxiv org abs 1707 02919 bib bib natural language processing information retrieval and text mining allahyari2017a md mehdi allahyari seyed amin pouriyeh mehdi assefi saied safaei elizabeth d trippe juan b gutierrez krys j kochut 2 a survey of methods to ease the development of highly multilingual text mining applications lang resour evaluation 2012 paper https arxiv org abs 1401 2937 bib bib natural language processing information retrieval and text mining steinberger2012a md ralf steinberger 3 a survey on retrieval augmented text generation arxiv 2022 paper https arxiv org pdf 2202 01110 pdf bib bib natural language processing information retrieval and text mining li2022a md huayang li yixuan su deng cai yan wang lemao liu 4 data mining and information retrieval in the 21st century a bibliographic review comput sci rev 2019 paper https www sciencedirect com science article abs pii s1574013719301297 bib bib natural language processing information retrieval and text mining liu2019data md jiaying liu xiangjie kong xinyu zhou lei wang da zhang ivan lee bo xu feng xia 5 dense text retrieval based on pretrained language models a survey arxiv 2022 paper https arxiv org pdf 2211 14876 pdf bib bib natural language processing information retrieval and text mining zhao2022dense md wayne xin zhao jing liu ruiyang ren ji rong wen 6 neural entity linking a survey of models based on deep learning semantic web 2022 paper https arxiv org abs 2006 00575 bib bib natural language processing information retrieval and text mining sevgili2022neural md zge sevgili artem shelmanov mikhail y 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supervised learning arxiv 2021 paper https arxiv org abs 2103 00550 bib bib machine learning semi supervised weakly supervised and unsupervised learning yang2021a md xiangli yang zixing song irwin king zenglin xu 4 a survey on semi self and unsupervised learning for image classification ieee access 2021 paper https arxiv org abs 2002 08721 bib bib machine learning semi supervised weakly supervised and unsupervised learning schmarje2021a md lars schmarje monty santarossa simon martin schr der reinhard koch 5 a survey on semi supervised learning techniques arxiv 2014 paper https arxiv org abs 1402 4645 bib bib machine learning semi supervised weakly supervised and unsupervised learning prakash2014a md v jothi prakash l m nithya 6 deep learning for weakly supervised object detection and object localization a survey arxiv 2021 paper https arxiv org pdf 2105 12694 pdf bib bib machine learning semi supervised weakly supervised and unsupervised learning shao2021deep md feifei shao long chen jian shao wei ji shaoning xiao lu ye yueting zhuang jun xiao 7 graph based semi supervised learning a comprehensive review arxiv 2021 paper https arxiv org abs 2102 13303 bib bib machine learning semi supervised weakly supervised and unsupervised learning song2021graph based md zixing song xiangli yang zenglin xu irwin king 8 improvability through semi supervised learning a survey of theoretical results arxiv 2019 paper https arxiv org abs 1908 09574 bib bib machine learning semi supervised weakly supervised and unsupervised learning mey2019improvability md alexander mey marco loog 9 learning from positive and unlabeled data a survey mach learn 2020 paper https arxiv org abs 1811 04820 bib bib machine learning semi supervised weakly supervised and unsupervised learning bekker2020learning md jessa bekker jesse davis 10 robust deep semi supervised learning a brief introduction arxiv 2022 paper https arxiv org pdf 2202 05975 pdf bib bib machine learning semi supervised weakly supervised and unsupervised learning guo2022robust md lan zhe guo zhi zhou yu feng li 11 self supervised learning for recommender systems a survey arxiv 2022 paper https arxiv org pdf 2203 15876 pdf bib bib machine learning semi supervised weakly supervised and unsupervised learning yu2022self supervised md junliang yu hongzhi yin xin xia tong chen jundong li zi huang 12 self supervised learning for videos a survey arxiv 2022 paper https arxiv org pdf 2207 00419 pdf bib bib machine learning semi supervised weakly supervised and unsupervised learning schiappa2022self supervised md madeline c schiappa yogesh s rawat mubarak shah 13 unsupervised cross lingual representation learning acl 2019 paper https www aclweb org anthology p19 4007 pdf bib bib machine learning semi supervised weakly supervised and unsupervised learning ruder2019unsupervised md sebastian ruder anders s gaard ivan vulic transfer learning content 1 a comprehensive survey on transfer learning proc ieee 2021 paper https arxiv org abs 1911 02685 bib bib machine learning transfer learning zhuang2021a md fuzhen zhuang zhiyuan qi keyu duan dongbo xi yongchun zhu hengshu zhu hui xiong qing he 2 a survey of unsupervised deep domain adaptation acm trans intell syst technol 2020 paper https arxiv org abs 1812 02849 bib bib machine learning transfer learning wilson2020a md garrett wilson diane j cook 3 a survey on deep transfer learning icann 2018 paper https arxiv org abs 1808 01974 bib bib machine learning transfer learning tan2018a md chuanqi tan fuchun sun tao kong wenchang zhang chao yang chunfang liu 4 a survey on domain adaptation theory learning bounds and theoretical guarantees arxiv 2020 paper https arxiv org abs 2004 11829 bib bib machine learning transfer learning redko2020a md ievgen redko emilie morvant amaury habrard marc sebban youn s bennani 5 a survey on negative transfer ieee caa journal of automatica sinica 2020 paper http arxiv org pdf 2009 00909 pdf bib bib machine learning transfer learning zhang2020a md wen zhang lingfei deng lei zhang dongrui wu 6 a survey on transfer learning ieee trans knowl data eng 2010 paper https ieeexplore ieee org abstract document 5288526 bib bib machine learning transfer learning pan2010a md sinno jialin pan qiang yang 7 a survey on transfer learning in natural language processing arxiv 2020 paper https arxiv org abs 2007 04239 bib bib machine learning transfer learning alyafeai2020a md zaid alyafeai maged saeed alshaibani irfan ahmad 8 evolution of transfer learning in natural language processing arxiv 2019 paper https arxiv org abs 1910 07370 bib bib machine learning transfer learning malte2019evolution md aditya malte pratik ratadiya 9 exploring the limits of transfer learning with a unified text to text transformer j mach learn res 2020 paper https arxiv org pdf 1910 10683 pdf bib bib machine learning transfer learning raffel2020exploring md colin raffel noam shazeer adam roberts katherine lee sharan narang michael matena yanqi zhou wei li peter j liu 10 neural unsupervised domain adaptation in nlp a survey coling 2020 paper https arxiv org abs 2006 00632 bib bib machine learning transfer learning ramponi2020neural md alan ramponi barbara plank 11 source free unsupervised domain adaptation a survey arxiv 2023 paper https arxiv org pdf 2301 00265 pdf bib bib machine learning transfer learning fang2023source free md yuqi fang pew thian yap weili lin hongtu zhu mingxia liu 12 transfer adaptation learning a decade survey arxiv 2019 paper https arxiv org pdf 1903 04687 pdf bib bib machine learning transfer learning zhang2019transfer md lei zhang 13 transfer learning for reinforcement learning domains a survey j mach learn res 2009 paper https dl acm org doi pdf 10 5555 1577069 1755839 bib bib machine learning transfer learning taylor2009transfer md matthew e taylor peter stone 14 transfer learning in deep reinforcement learning a survey arxiv 2020 paper https arxiv org abs 2009 07888 bib bib machine learning transfer learning zhu2020transfer md zhuangdi zhu kaixiang lin jiayu zhou 15 transferability in deep learning a survey arxiv 2022 paper https arxiv org pdf 2201 05867 pdf bib bib machine learning transfer learning jiang2022transferability md junguang jiang yang shu jianmin wang mingsheng long trustworthy machine learning content 1 a comprehensive survey on trustworthy graph neural networks privacy robustness fairness and explainability arxiv 2022 paper https arxiv org pdf 2204 08570 pdf bib bib machine learning trustworthy machine learning dai2022a md enyan dai tianxiang zhao huaisheng zhu junjie xu zhimeng guo hui liu jiliang tang suhang wang 2 a survey of neural trojan attacks and defenses in deep learning arxiv 2022 paper https arxiv org pdf 2202 07183 pdf bib bib machine learning trustworthy machine learning wang2022a md jie wang ghulam mubashar hassan naveed akhtar 3 a survey of privacy attacks in machine learning arxiv 2020 paper https arxiv org pdf 2007 07646 bib bib machine learning trustworthy machine learning rigaki2020a md maria rigaki sebastian garcia 4 a survey of safety and trustworthiness of deep neural networks verification testing adversarial attack and defence and interpretability comput sci rev 2020 paper https www sciencedirect com science article abs pii s1574013719302527 via 3dihub bib bib machine learning trustworthy machine learning huang2020a md xiaowei huang daniel kroening wenjie ruan james sharp youcheng sun emese thamo min wu xinping yi 5 a survey on bias and fairness in machine learning acm comput surv 2022 paper https arxiv org abs 1908 09635 bib bib machine learning trustworthy machine learning mehrabi2022a md ninareh mehrabi fred morstatter nripsuta saxena kristina lerman aram galstyan 6 backdoor learning a survey arxiv 2020 paper https arxiv org abs 2007 08745 bib bib machine learning trustworthy machine learning li2020backdoor md yiming li baoyuan wu yong jiang zhifeng li shu tao xia 7 differential privacy and machine learning a survey and review arxiv 2014 paper https arxiv org abs 1412 7584 bib bib machine learning trustworthy machine learning ji2014differential md zhanglong ji zachary chase lipton charles elkan 8 fairness in machine learning a survey arxiv 2020 paper https arxiv org pdf 2010 04053 pdf bib bib machine learning trustworthy machine learning caton2020fairness md simon caton christian haas 9 local differential privacy and its applications a comprehensive survey arxiv 2020 paper https arxiv org abs 2008 03686 bib bib machine learning trustworthy machine learning yang2020local md mengmeng yang lingjuan lyu jun zhao tianqing zhu kwok yan lam 10 privacy in deep learning a survey arxiv 2020 paper https arxiv org abs 2004 12254 bib bib machine learning trustworthy machine learning mireshghallah2020privacy md fatemehsadat mireshghallah mohammadkazem taram praneeth vepakomma abhishek singh ramesh raskar hadi esmaeilzadeh 11 taxonomy of machine learning safety a survey and primer acm comput surv 2023 paper https arxiv org abs 2106 04823 bib bib machine learning trustworthy machine learning mohseni2023taxonomy md sina mohseni haotao wang chaowei xiao zhiding yu zhangyang wang jay yadawa 12 technology readiness levels for machine learning systems arxiv 2021 paper https arxiv org pdf 2101 03989 pdf bib bib machine learning trustworthy machine learning lavin2021technology md alexander lavin ciar n m gilligan lee alessya visnjic siddha ganju dava newman sujoy ganguly danny lange atilim g nes baydin amit sharma adam gibson yarin gal eric p xing chris mattmann james parr 13 the creation and detection of deepfakes a survey acm comput surv 2022 paper https arxiv org abs 2004 11138 bib bib machine learning trustworthy machine learning mirsky2022the md yisroel mirsky wenke lee 14 toward transparent ai a survey on interpreting the inner structures of deep neural networks arxiv 2022 paper https arxiv org pdf 2207 13243 pdf bib bib machine learning trustworthy machine learning r uker2022toward md tilman r uker anson ho stephen casper dylan hadfield menell 15 trustworthy ai from principles to practices acm comput surv 2023 paper https arxiv org pdf 2110 01167 pdf bib bib machine learning trustworthy machine learning li2023trustworthy md bo li peng qi bo liu shuai di jingen liu jiquan pei jinfeng yi bowen zhou 16 trustworthy graph neural networks aspects methods and trends arxiv 2022 paper https arxiv org pdf 2205 07424 pdf bib bib machine learning trustworthy machine learning zhang2022trustworthy md he zhang bang wu xingliang yuan shirui pan hanghang tong jian pei 17 tutorial safe and reliable machine learning arxiv 2019 paper https arxiv org abs 1904 07204 bib bib machine learning trustworthy machine learning saria2019tutorial md suchi saria adarsh subbaswamy 18 when machine learning meets privacy a survey and outlook acm comput surv 2022 paper https arxiv org pdf 2011 11819 pdf bib bib machine learning trustworthy machine learning liu2022when md bo liu ming ding sina shaham wenny rahayu farhad farokhi zihuai lin 19 wild patterns reloaded a survey of machine learning security against training data poisoning arxiv 2022 paper https arxiv org pdf 2205 01992 pdf bib bib machine learning trustworthy machine learning cin 2022wild md antonio emanuele cin kathrin grosse ambra demontis sebastiano vascon werner zellinger bernhard alois moser alina oprea battista biggio marcello pelillo fabio roli team members the project is maintained by natural language processing lab school of computer science and engineering northeastern university niutrans research please feel free to contact us if you have any questions libei neu at outlook com acknowledge we would like to thank the people who have contributed to this project they are chuanhao lv kaiyan chang ziyang wang shuhan zhou nuo xu bei li yinqiao li quan du xin zeng laohu wang chenglong wang xiaoqian liu xuanjun zhou jingnan zhang yongyu mu zefan zhou yanhong jiang xinyang zhu xingyu liu dong bi ping xu zijian li fengning tian hui liu kai feng yuhao zhang chi hu di yang lei zheng hexuan chen zeyang wang tengbo liu xia meng weiqiao shan tao zhou runzhe cao yingfeng luo binghao wei wandi xu yan zhang yichao wang mengyu ma zihao liu | natural-language-processing machine-learning deep-learning neural-networks paper-list surveys | ai |
DATA-ENGINEERING-PROJECTS--udacity_nanodegree- | udacity data engineer nanodegree classwork projects and home works done through udacity data engineering nano degree 1 data modelling wit postegresql 2 etl in cloud data warehouses 3 data lakes with spark 4 data pipelines with airflow 5 data engineering final capstone project us migration data etl pipeline with spark | cloud |
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aws_architectures | architectures virtual container for all my architectures devops cloud and data engineering solutions | cloud |
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fswd-project-seed | intro to full stack web development project seed setup steps 1 npm install 2 npm run dev db create 3 npm run dev running the tests 1 npm run test db create only once 2 npm test | javascript | front_end |
react-starter-kit | react starter kit a href https github com kriasoft react starter kit sponsor 1 img src https img shields io badge github 23555 svg logo github sponsors height 20 a a href http patreon com koistya img src https img shields io badge dynamic json color 23ff424d label patreon query data attributes patron count suffix 20patrons url https 3a 2f 2fwww patreon com 2fapi 2fcampaigns 2f233228 height 20 a a href https discord gg 2nkenkq img src https img shields io discord 643523529131950086 label chat height 20 a a href https github com kriasoft react starter kit stargazers img src https img shields io github stars kriasoft react starter kit svg style social label star maxage 3600 height 20 a a href https twitter com koistya img src https img shields io twitter follow koistya svg style social label follow maxage 3600 height 20 a the web s most popular jamstack front end template for building web applications with react https react dev features optimized for serverless deployment to cdn edge locations cloudflare workers html page rendering ssr at cdn edge locations all 100 points on lighthouse hot module replacement during local development using react refetch pre configured with css in js styling using emotion js pre configured with code quality tools eslint prettier typescript vitest etc pre configured with vscode code snippets and other vscode settings the ongoing design and development is supported by these wonderful companies a href https reactstarter com s 1 img src https reactstarter com s 1 png height 60 a nbsp nbsp a href https reactstarter com s 2 img src https reactstarter com s 2 png height 60 a nbsp nbsp a href https reactstarter com s 3 img src https reactstarter com s 3 png height 60 a this project was bootstrapped with react starter kit https github com kriasoft react starter kit be sure to join our discord channel https discord com invite 2nkenkq for assistance directory structure github github github configuration including ci cd workflows br vscode vscode vscode settings including code snippets recommended extensions etc br app app web application front end built with react https react dev and material ui https mui com core br edge edge cloudflare workers cdn edge endpoint br env env application settings api keys etc br scripts scripts automation scripts such as yarn deploy br tsconfig base json tsconfig base json the common shared typescript configuration br tsconfig json tsconfig json the root typescript configuration br tech stack react https react dev react router https reactrouter com recoil https recoiljs org emotion https emotion sh material ui https next material ui com firebase authentication https firebase google com docs auth cloudflare workers https workers cloudflare com vite https vitejs dev vitest https vitejs dev typescript https www typescriptlang org eslint https eslint org prettier https prettier io yarn https yarnpkg com with pnp requirements node js https nodejs org v18 with corepack https nodejs org api corepack html corepack enable vs code https code visualstudio com editor with recommended extensions vscode extensions json optionally react developer tools https chrome google com webstore detail react developer tools fmkadmapgofadopljbjfkapdkoienihi hl en and reactime https chrome google com webstore detail reactime cgibknllccemdnfhfpmjhffpjfeidjga hl en browser extensions getting started generate https github com kriasoft react starter kit generate a new project from this template clone it install project dependencies update the environment variables found in env env env and start hacking git clone https github com kriasoft react starter kit git example cd example yarn install yarn start the app will become available at http localhost 5173 http localhost 5173 press q key to exit important ensure that vscode is using the workspace version of typescript https code visualstudio com docs typescript typescript compiling using newer typescript versions and eslint https files tarkus me typescript workspace png scripts yarn start launches the app in development mode on http localhost 5173 http localhost 5173 yarn build compiles and bundles the app for deployment yarn lint validate the code using eslint yarn tsc validate the code using typescript compiler yarn test run unit tests with vitest supertest yarn edge deploy deploys the app to cloudflare how to deploy ensure that all the environment variables for the target deployment environment test prod found in env env env files are up to date if you haven t done it already push any secret values you may need to cf workers environment by running yarn workspace edge wrangler secret put name env 0 finally build and deploy the app by running yarn build yarn deploy env 0 version 0 where env argument is the target deployment area e g yarn deploy env prod how to update yarn set version latest bump yarn to the latest version yarn upgrade interactive update node js modules dependencies yarn dlx yarnpkg sdks vscode update typescript eslint and prettier settings in vscode contributors a href https reactstarter com c 1 img src https reactstarter com c 1 png height 60 a nbsp nbsp a href https reactstarter com c 2 img src https reactstarter com c 2 png height 60 a nbsp nbsp a href https reactstarter com c 3 img src https reactstarter com c 3 png height 60 a nbsp nbsp a href https reactstarter com c 4 img src https reactstarter com c 4 png height 60 a nbsp nbsp a href https reactstarter com c 5 img src https reactstarter com c 5 png height 60 a nbsp nbsp a href https reactstarter com c 6 img src https reactstarter com c 6 png height 60 a nbsp nbsp a href https reactstarter com c 7 img src https reactstarter com c 7 png height 60 a nbsp nbsp a href https reactstarter com c 8 img src https reactstarter com c 8 png height 60 a backers a href https reactstarter com b 1 img src https reactstarter com b 1 png height 60 a nbsp nbsp a href https reactstarter com b 2 img src https reactstarter com b 2 png height 60 a nbsp nbsp a href https reactstarter com b 3 img src https reactstarter com b 3 png height 60 a nbsp nbsp a href https reactstarter com b 4 img src https reactstarter com b 4 png height 60 a nbsp nbsp a href https reactstarter com b 5 img src https reactstarter com b 5 png height 60 a nbsp nbsp a href https reactstarter com b 6 img src https reactstarter com b 6 png height 60 a nbsp nbsp a href https reactstarter com b 7 img src https reactstarter com b 7 png height 60 a nbsp nbsp a href https reactstarter com b 8 img src https reactstarter com b 8 png height 60 a related projects graphql api and relay starter kit https github com kriasoft graphql starter monorepo template pre configured with graphql api react and relay cloudflare workers starter kit https github com kriasoft cloudflare starter kit typescript project template for cloudflare workers node js api starter kit https github com kriasoft node starter kit project template pre configured with node js graphql and postgresql how to contribute anyone and everyone is welcome to contribute github contributing md start by checking out the list of open issues https github com kriasoft react starter kit issues marked help wanted https github com kriasoft react starter kit issues q label help wanted however if you decide to get involved please take a moment to review the guidelines github contributing md license copyright 2014 present kriasoft this source code is licensed under the mit license found in the license https github com kriasoft react starter kit blob main license file sup made with by konstantin tarkus koistya https twitter com koistya blog https medium com koistya and contributors https github com kriasoft react starter kit graphs contributors sup | react javascript graphql boilerplate reactjs nodejs starter-kit typescript template relay cloudflare serverless scaffolding hackathon material-ui firebase vite recoil vitest hono | front_end |
nlp | nlp build status https travis ci org freyskeyd nlp svg https travis ci org freyskeyd nlp coverage status https coveralls io repos freyskeyd nlp badge svg service github https coveralls io github freyskeyd nlp rust nlp implemented algorithm distance x levenshtein explanation https fr wikipedia org wiki distance de levenshtein x jaro jaro winkler explanation https fr wikipedia org wiki distance de jaro winkler phonetics x soundex explanation https en wikipedia org wiki soundex x metaphone explanation https en wikipedia org wiki metaphone x double metaphone explanation https en wikipedia org wiki metaphone double metaphone caverphone explanation https en wikipedia org wiki caverphone beider morse phonetic explanation https en wikipedia org wiki daitch e2 80 93mokotoff soundex beider e2 80 93morse phonetic name matching algorithm k lner phonetik explanation https de wikipedia org wiki k c3 b6lner phonetik nysiis explanation https en wikipedia org wiki new york state identification and intelligence system development workflow use multirust with nightly bash cd to project cargo test license licensed under either of apache license version 2 0 license apache license apache or http www apache org licenses license 2 0 mit license license mit license mit or http opensource org licenses mit at your option contribution unless you explicitly state otherwise any contribution intentionally submitted for inclusion in the work by you as defined in the apache 2 0 license shall be dual licensed as above without any additional terms or conditions | ai |
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WebVoc | gs1 web vocabulary development site the current official version of the gs1 web vocabualary is published at https www gs1 org voc it is an external extension to schema org that allows further details about products and assets to be expressed using linked data technology gs1 product https www gs1 org voc product is semantically equivalent to schema product http schema org product the gs1 web vocabulary also defines subclasses of gs1 product https www gs1 org voc product the gs1 web vocabulary is released under an apache 2 0 licence a further licence statement is provided within the gs1 web vocabulary files this repository is intended to encourage suggestions for improvements to the gs1 web vocabulary please note the license terms apache 2 0 files in this repository are arranged as follows an immutable copy of each ratified version of the gs1 web vocabulary is stored in a directory that gives the version number there is a copy of the current version provided in the root directory so there is a stable uri from which the current version can be obtained in addition to the formally published version on gs1 org in case of any discrepency the formally published version at https www gs1 org voc is authoritative there is a patch file that just contains informally proposed terms a copy of the current file plus the patch is also included each file is made available in both turtle and json ld most terms within the gs1 web vocabulary can usually be classified as one of the following classes classes properties properties code lists code lists defined values within code lists code list values link types link types classes one use of a class rdfs class owl class is to express an entity such as a product organization place postal address another use of a class is to provide a repeatable data structure as a container for a number of interdependent properties for example a gs1 quantitativevalue https www gs1 org voc quantitativevalue or schema quantitativevalue http schema org quantitativevalue express both a numeric value and a code for a unit of measure similarly gs1 certificationdetails https www gs1 org voc certificationdetails is used to group interdependent properties such as gs1 certificationagency https www gs1 org voc certificationagency gs1 certificationstandard https www gs1 org voc certificationstandard and gs1 certificationvalue https www gs1 org voc certificationvalue so that when a product has more than one certification there is no ambiguity about which standard was evaluated by which agency or which value was obtained for each standard some classes are defined as subclasses of other classes for example gs1 foodbeveragetobaccoproduct https www gs1 org voc foodbeveragetobaccoproduct is a subclass of gs1 product https www gs1 org voc product gs1 fruitsvegetables https www gs1 org voc fruitsvegetables is a subclass of gs1 foodbeveragetobaccoproduct https www gs1 org voc foodbeveragetobaccoproduct by defining subclasses we can attach more specialised properties that are relevant to the subclass but not generally relevant to all members of the parent class superclass browse all classes within the gs1 web vocabulary https www gs1 org voc show classes properties each property has an expected value type indicated via rdfs range each property also has an attachment point indicated via rdfs domain a property that could apply to all kinds of products will generally have an rdfs domain of gs1 product https www gs1 org voc product schema product http schema org product other properties that are only really relevant to a more specialised category of products will have an rdfs domain that is a subclass of gs1 product https www gs1 org voc product such as gs1 wearableproduct https www gs1 org voc wearableproduct or even a subclass of a subclass of gs1 product https www gs1 org voc product such as gs1 footwear https www gs1 org voc footwear for example gs1 isseedless https www gs1 org voc isseedless is a property of gs1 fruitsvegetables https www gs1 org voc fruitsvegetables and it probably has little relevance to any other kind of product formally the gs1 web vocabulary expresses that gs1 isseedless https www gs1 org voc isseedless has an rdfs domain of gs1 fruitsvegetables https www gs1 org voc fruitsvegetables browse all properties within the gs1 web vocabulary https www gs1 org voc show properties code lists the gs1 web vocabulary defines a large number of code lists for enumerated values code lists are useful because a globally defined uri can be used for each permitted value each of these uris may have multilingual labels and descriptions in different human languages but we agree to use the global uri to express the value to avoid the need to translate free text values expressed in different human languages when exchanging data globally using such code lists each code list is modelled as a class rdfs class owl class and as a subclass of gs1 typecode https www gs1 org voc typecode the abstract superclass for all gs1 code lists within the gs1 web vocabulary for example gs1 sharpnessofcheesecode https www gs1 org voc sharpnessofcheesecode is a subclass of gs1 typecode https www gs1 org voc typecode browse all code lists within the gs1 web vocabulary https www gs1 org voc show typecodes code list values each defined value within a code list has its own web uri and may have multilingual human labels and descriptions membership of a particular code list is indicated via rdf type for example gs1 sharpnessofcheesecode extra sharp https www gs1 org voc sharpnessofcheesecode extra sharp is an individual code value within the gs1 sharpnessofcheesecode https www gs1 org voc sharpnessofcheesecode code list its rdf type is gs1 sharpnessofcheesecode https www gs1 org voc sharpnessofcheesecode the code list to which it belongs link types the gs1 web vocabulary introduced a group of additional properties that connect a gs1 digital link https www gs1 org standards gs1 digital link uri to a target resource url such as a product information page instruction manual related video electronic patient information leaflet for a pharmaceutical etc each link type property expresses a distinct kind of information resource found at the target resource url in this way a web request to a resolver for gs1 digital link https www gs1 org standards gs1 digital link uri can specify a particular kind of information resource that is desired e g give me the instruction manual the link type is typically expressed as a compact uri expression curie within the uri query string as the value of the special linktype key it is also expressed in the linkset of results returned by a resolver conforming to the gs1 digital link standard https www gs1 org standards gs1 digital link the gs1 web vocabulary defines one abstract property gs1 linktype https www gs1 org voc linktype all specific link type values are defined as a subproperty of gs1 linktype https www gs1 org voc linktype for example gs1 instructions https www gs1 org voc instructions is a subproperty of gs1 linktype https www gs1 org voc linktype gs1 instructions https www gs1 org voc instructions is a typed link that is used to link to instructions related to the product such as assembly instructions usage tips etc browse all link types defined within the gs1 web vocabulary https www gs1 org voc show linktypes | front_end |
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farm-expense-management | farm expense management a mobile application to help farmers manage their expenses features expense addition expense summary comparison expense filters using tags and date default tags related to common farm expenses loan management interest calculation built using flutter https flutter dev how to build we used dart plugin in vscode to build and test the application built by abhineet pandey https github com abhineet99 bhawna https github com bhawnapaliwal mehakjot singh https github com hnysidhu under the guidance of dr puneet goyal https sites google com view goyalpuneet iit ropar www iitrpr ac in acknowledgments google translate bing translate for translations into hindi and punjabi jack doyle gor kirakosyan artur rymarz naveen vignesh for the background on existing such applications | dart flutter android | front_end |
BlockchainParser | blockchain parser a net bitcoin s blockchain parser that reads raw blocks from the blk dat files goals there are lots of articles websites and blogs on the internet that display very interesting analysis about bitcoin usage trends events an all kind of things based on information available in the blockchain however there are few tools that allow us perform those analysis blockchainparser can parse the blockchain to provide an object model for each block with that info we are able to do what we need tested with net yes tested with mono no how to use with nuget no nuget package yet go on the nuget website https for more information example the simplest scenario c public class sillyblockchainprocessor parser private static datetime first1970 new datetime 1970 1 1 private long blocknumber protected override void processblock block block console writeline processing blk 0 mined on 1 blocknumber first1970 addseconds block timestamp the previous piece of code shows how to display the block s timestamp for each parsed block blockchain to sql it is a complete example that shows how to export the whole blockchain into an ms sql server database doing this we can execute queries against the database in order to get all kind of valuable information logo http i imgur com lxd2pc4 png exporting the blockchain can takes several hours in my case it took 25 hours long for a 15gb blockchain the parser is very fast the sql inserts are not please if you find a way to improve the general performance then you can help a lot documentation to understand the internal blockchain data structure take a look at the excellent post how to parse the bitcoin blockchain http codesuppository blogspot com ar 2014 01 how to parse bitcoin blockchain html by john ratcliff s code suppository http codesuppository blogspot com ar blog development blockchainparser is been developed by lucas ontivero http geeks ms blogs lontivero lontivero http twitter com lontivero you are welcome to contribute code you can send code both as a patch or a github pull request build status build status https ci appveyor com api projects status b9hk21kpa6xtk1ey svg true https ci appveyor com project lontivero blockchainparser version 0 0 1 initial version bitcoin tip jar if this code helps you consider helping 1mv8gizxd2mbsz2t4ltfqmzv9zpbta6s2h | blockchain |
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rq-dashboard | introduction rq dashboard is a general purpose lightweight flask https flask palletsprojects com based web front end to monitor your rq http python rq org queues jobs and workers in realtime build pull request https github com parallels rq dashboard actions workflows pr yaml badge svg https github com parallels rq dashboard actions workflows pr yaml publish release https github com parallels rq dashboard actions workflows publish yaml badge svg https github com parallels rq dashboard actions workflows publish yaml python support https img shields io pypi pyversions rq dashboard svg https pypi python org pypi rq dashboard pypi downloads https img shields io pypi dw rq dashboard maturity notes the rq dashboard is currently being developed and is in beta stage how migrate to version 1 0 you can find here https github com parallels rq dashboard wiki how to migrate to 1 0 you can find help in the discussion page in github http https github com parallels rq dashboard or join our discord server https discord gg reuhvmft installing with docker you can also run the dashboard inside of docker copy the docker compose yml file from the root of the repository to docker compose override yml and change the environment variables to your liking run the following command console docker compose up you can also find the official image on cjlapao rq dashboard latest installing from pypi console pip install rq dashboard running the dashboard run the dashboard standalone like this console rq dashboard running on http 127 0 0 1 9181 console rq dashboard help usage rq dashboard options run the rq dashboard flask server all configuration can be set on the command line or through environment variables of the form rq dashboard for example rq dashboard username a subset of the configuration the configuration parameters used by the underlying flask blueprint can also be provided in a python module referenced using config or with a cfg file referenced by the rq dashboard settings environment variable options b bind text ip or hostname on which to bind http server p port integer port on which to bind http server url prefix text url prefix e g for use behind a reverse proxy username text http basic auth username not used if not set password text http basic auth password c config text configuration file python module on search path u redis url text redis url can be specified multiple times default redis 127 0 0 1 6379 poll interval interval integer refresh interval in ms extra path text append specified directories to sys path debug normal enter debug mode v verbose enable verbose logging help show this message and exit integrating the dashboard in your flask app the dashboard can be integrated in to your own flask http flask pocoo org app by accessing the blueprint directly in the normal way e g python from flask import flask import rq dashboard app flask name app config from object rq dashboard default settings rq dashboard web setup rq connection app app register blueprint rq dashboard blueprint url prefix rq app route def hello return hello world if name main app run if you start the flask app on the default port you can access the dashboard at http localhost 5000 rq the cli py main entry point provides a simple working example running on heroku consider using third party project rq dashboard on heroku https github com metabolize rq dashboard on heroku which installs rq dashboard from pypi and wraps in in gunicorn https gunicorn org for deployment to heroku rq dashboard on heroku is maintained indepdently running behind a reverse proxy you can run the dashboard as a systemd service in linux or via a suprevisor script and then use apache or nginx to direct traffic to the dashboard this is for non production functionality apache reverse proxy example proxypass rq http 127 0 0 1 5001 rq proxypassreverse rq http 127 0 0 1 5001 rq systemd service example unit description redis queue dashboard install wantedby multi user target service execstart bin rq dashboard b 127 0 0 1 p 5001 url prefix rq c rq settings dashboard debug v standardoutput file var log redis rq dasbhoard log standarderror file var log redis rq dashboard log user redis dash group redis dash remainafterexit yes type simple permissionsstartonly false privatetmp no debug v are optional they will write stdout to your specified files rq settings dashboard is a python file with settings defined you can use options that are available as environmental variables ex rq dashboard redis password password developing develop in a virtualenv and make sure you have all the necessary build time and run time dependencies with pip install r requirements txt develop in the normal way with python setup py develop stats pypi stats https pypistats org packages rq dashboard github stats https github com parallels rq dashboard graphs traffic | python rq | front_end |
genreXpose | genrexpose v0 1 docs this program allows fast and automatic detection of the genre of audio music files this project is not under active development this document gives a quick overview on how to use this library in your projects for more technical details on what underlying technologies have been used in building this library and how does this library work read this post on my blog genrexpose quick music audio genre recognition 1 installation download the whole project source from github do this by clicking here https github com jazdev genrexpose archive master zip once downloaded extract the source to a directory of your choice the project has the following directory structure your working dir docs readme md genrexpose graphs test ceps py classifier py config cfg tester py utils py license requirements txt the genrexpose directory contains the main code base this directory also contains the config cfg file which is used for the configuraton of the software graphs will contain all the generated graphs the graphs are an excellent indicator of the performance of the algorithm test houses all the tests the function of the other files will be explained in subsequent sections the docs directory contains all the relevant documentation of the software the license contains important copyright references and redistribution terms the requirements txt file lists all the requirements that need to be satisfied in order to run this software to install the required packages use the following command pip install r path to requirements txt in particular this software requires you to have the following numpy pydub ffmpeg scipy scikit learn scikits statsmodels scikits talkbox 2 setup configuration all the configuraton can be done using the config cfg file this file follows a particular syntax for storing the configurations please respect the syntax if you want to avoid bugs this file contains three variables that the user can modify as per his need comments begin with a symbol and run till the end of a line rest everything is supposed to be valid configuration data the variables are genre dir this is directory where the music dataset is located gtzan dataset test dir this is the directory where the test music is located genre list this is a list of the available genre types that you can use modify this list if you want to work with a subset of the available genres set these three variables according to your system before proceeding to the next steps 3 the dataset the dataset used for training the model is the gtzan dataset a brief of the data set this dataset was used for the well known paper in genre classification musical genre classification of audio signals by g tzanetakis and p cook in ieee transactions on audio and speech processing 2002 the files were collected in 2000 2001 from a variety of sources including personal cds radio microphone recordings in order to represent a variety of recording conditions the dataset consists of 1000 audio tracks each 30 seconds long it contains 10 genres each represented by 100 tracks the tracks are all 22050hz mono 16 bit audio files in wav format official web page marsyas info http marsyas info download data sets download size approximately 1 2gb download link download the gtzan genre collection http opihi cs uvic ca sound genres tar gz since the files in the dataset are in the au format which is lossy because of compression they need to be converted in the wav format which is lossless before we proceed further 4 model generation caching note be sure to fully complete step 2 before moving further now the script ceps py has to be run this script analyzes and converts each file in the gtzan dataset in a representation that can be used by the classifier and can be easily cached onto the disk this little step prevents the classifier to convert the dataset each time the system is run the gtzan dataset is used for training the classifier which generates an in memory regression model this process is done by the logisticregression module of the scikit learn library the classifier py script has been provided for this purpose once the model has been generated we can use it to predict genres of other audio files for effecient further use of the generated model it is permanently serialized to the disk and is deserialized when it needs to be used again this simple process improves performance greatly for serialization the joblib module in the sklearn externals package is used as of now the classifier py script must be run before any testing with unknown music can be done once the script is run it will save the generated model at this path saved model model ceps pkl once the model has been sucessfully saved the classification script need not be run again until some newly labelled training data is available 5 testing and live usage note be sure to fully complete step 2 and step 4 before moving further the tester py script is used for the classification of new and unlabelled audio files this script deserializes the previously cached model stored at the path saved model model ceps pkl and uses it for classifying new audio files important note as of now the classifier script must be run at least once before you run the testing script this is needed because the classifier script builds and saves the trained model to the disk 6 interpreting the output when the classifier py script is run it generates and saves the trained model to the disk this process also results in the creation of some graphs which are stored in the graphs directory the genereated graphs tell the performance of the classification process roc curves for each selected genre type a roc receiver operating characteristic http en wikipedia org wiki receiver operating characteristic curve is generated and stored as a png file in the graphs directory the roc curve is created by plotting the fraction of true positives out of the total actual positives true positive rate vs the fraction of false positives out of the total actual negatives false positive rate at various threshold settings some of the sample graphs are shown below alongwith their proper interpretation roc curve of metal genre img style float right src https raw githubusercontent com jazdev genrexpose master genrexpose graphs roc ceps metal png alt roc curve of metal genre roc curve of pop genre img style float right src https raw githubusercontent com jazdev genrexpose master genrexpose graphs roc ceps pop png alt roc curve of pop genre confusion matrix to judge the overall performance a confusion matrix is produced a confusion matrix is a specific table layout that allows visualization of the performance of an algorithm each column of the matrix represents the instances in a predicted class while each row represents the instances in an actual class the confusion matrix with all the genres selected is shown below img style float right src https raw githubusercontent com jazdev genrexpose master genrexpose graphs confusion matrix ceps png alt confusion matrix of the classifier 7 internal details spectrograms proof of concept a spectrogram is a visual representation of the frequency content in a song it shows the intensity of the frequencies on the y axis in the specified time intervals on the x axis that is the darker the color the stronger the frequency is in the particular time window of the song sample spectrograms of a few songs from the gtzan dataset img style float right src https raw githubusercontent com jazdev genrexpose master genrexpose graphs spectrogram genres clean png alt spectrograms it can be clearly seen from the above image that songs belonging to the same genre have similar spectrograms keeping this in mind we can easily design a classifier that can learn to differentiate between the different genres with sufficient accuracy improved performance by using mfcc mfcc mel frequency cepstral coefficients the mel frequency cepstrum mfc encodes the power spectrum of a sound it is calculated as the fourier transform of the logarithm of the signal s spectrum the talkbox scikit scikits talkbox contains an implementation of of mfc that we can directly use the data that we feed into the classifier is stored as ceps which contain 13 coeffecients to uniquely represent an audio file | machine-learning python music gtzan-dataset | ai |
front-end-handbook-2017 | available now front end developer handbook 2019 https frontendmasters com books front end handbook 2019 front end developer handbook 2017 written by cody lindley http codylindley com sponsored by frontend masters https frontendmasters com cover jpg this is a guide that anyone could use to learn about the practice of front end development it broadly outlines and discusses the practice of front end engineering how to learn it and what tools are used when practicing it in 2017 it is specifically written with the intention of being a professional resource for potential and currently practicing front end developers to equip themselves with learning materials and development tools secondarily it can be used by managers ctos instructors and head hunters to gain insights into the practice of front end development the content of the handbook favors web technologies html css dom and javascript and those solutions that are directly built on top of these open technologies the materials referenced and discussed in the book are either best in class or the current offering to a problem the book should not be considered a comprehensive outline of all resources available to a front end developer the value of the book is tied up in a terse focused and timely curation of just enough categorical information so as not to overwhelm anyone on any one particular subject matter the intention is to release an update to the content yearly the handbook is divided into three parts part i the front end practice part one broadly describes the practice of front end engineering part ii learning front end development part two identifies self directed and direct resources for learning to become a front end developer part iii front end development tools part three briefly explains and identifies tools of the trade read online https frontendmasters com books front end handbook 2017 https frontendmasters com books front end handbook 2017 download a pdf epub or mobi file from https www gitbook com book frontendmasters front end handbook 2017 details https www gitbook com book frontendmasters front end handbook 2017 details a rel license href http creativecommons org licenses by nc nd 3 0 img alt creative commons license style border width 0 src https i creativecommons org l by nc nd 3 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by nc nd 3 0 creative commons attribution noncommercial noderivs 3 0 unported license a | html css javascript front-end-development front-end-developer handbook web-development-tools | front_end |
webdev | dart https github com dart lang webdev workflows dart 20ci badge svg https github com dart lang webdev actions query workflow 3a 22dart ci 22 branch 3amaster packages package description version dwds dwds a service that proxies between the chrome debug protocol and the dart vm service protocol pub package https img shields io pub v dwds svg https pub dev packages dwds frontend server client frontend server client client code to start and interact with the frontend server compiler from the dart sdk pub package https img shields io pub v frontend server client svg https pub dev packages frontend server client webdev webdev a cli for dart web development provides an easy and consistent set of features for users and tools to build and deploy web applications with dart pub package https img shields io pub v webdev svg https pub dev packages webdev publishing automation for information about our publishing automation and release process see https github com dart lang ecosystem wiki publishing automation | front_end |
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blockchain-wallet-v4-frontend | license agpl v3 https img shields io badge license agpl 20v3 blue svg https www gnu org licenses agpl 3 0 js standard style https img shields io badge code 20style standard brightgreen svg http standardjs com code style prettier https img shields io badge code style prettier ff69b4 svg style flat square https github com prettier prettier codechecks https raw githubusercontent com codechecks docs master images badges badge default svg sanitize true https codechecks io blockchain com wallet be your own bank at login blockchain com https login blockchain com please contact support https support blockchain com if you have any issues using the wallet about this repo contains the three codebases packages listed below packages blockchain info components packages blockchain info components the shared ui components library blockchain wallet v4 packages blockchain wallet v4 the functional library for handling wallets blockchain wallet v4 frontend packages blockchain wallet v4 frontend the frontend application built with react redux local development 1 ensure node version 14 16 is installed using nvm https github com nvm sh nvm is recommended 2 from the project root run the following command to install dependencies setup sh 4 start the application in development mode yarn start 5 the frontend application will now be accessible via browser at localhost 8080 if you require the application to run locally over https follow the instructions here config ssl ssl md you can disable ssl by setting the disable ssl env param to true with any start command e g disable ssl true yarn start staging windows support to ensure proper support for windows please take the following actions before running the above setup instructions 1 open a powershell window with rights elevated to an administrator 2 run npm install g windows build tools this will install python 2 7 and visual c build tools which are required to compile some native node modules 3 ensure python has been added to your environment variables by opening a cmd prompt and typing python if you get a commandnotfoundexception message add the folder userprofile windows build tools python27 to your environment variables tips useful commands 1 to completely remove all dependencies and artifacts run yarn clean 2 to add remove an npm package run yarn add or yarn remove in the package folder after installing or uninstalling a npm package run yarn in the root folder to re init the project 3 all development specific dependencies should be installed as a dev dependency in the top level package json via yarn i save dev package name 4 all application specific dependencies should be installed in the specific packages package json via yarn i save package name running environments locally the frontend application can be ran locally with different build configurations found in config env the following commands are available yarn start runs the application with the development js configuration file yarn start dev runs the application with the development js configuration file yarn start staging runs the application with the staging js configuration file yarn start prod runs the application with the production js configuration file yarn run prod runs the application mimicking the production environment entirely i e code is bundled and minified hmr is disabled express server is used server js and the production js configuration file is loaded notes developers will need to manually create the development js and staging js files custom application runtimes are possible by modifying the corresponding environment files found in the config env folder useful chrome extensions react developer tools https chrome google com webstore detail react developer tools fmkadmapgofadopljbjfkapdkoienihi hl en inspect the react component tree redux devtools https chrome google com webstore detail redux devtools lmhkpmbekcpmknklioeibfkpmmfibljd hl en view debug redux state changes release process prerequisites to be able to create a release follow these steps starting with obtain a personal access token https github com release it release it github releases github token should be saved as release it token instead in your bash profile or wherever you keep env variables you ll need git changelog to generate the history since the last release npm install g changelog release steps 1 from the tip of the development branch run yarn release 2 answer the questions prompted via cli accepting the defaults for each 3 once completed this will create a new tag which will trigger a builds 4 once builds have finished deploy the images to desired environments 5 test and verify the latest changes in desired environments 6 create pr to merge the head of development into master 7 merge pr to master so that master always reflects what is currently in production code quality yarn vet runs prettier lint js lint css and finally all unit tests linting we follow the rules outlined by the javascript standard style https standardjs com rules html as well as a few react specific rules code linting is handled by eslint https eslint org the following commands are available yarn lint lints all packages yarn lint components lints only blockchain info components packages blockchain info components yarn lint core lints only blockchain wallet v4 packages blockchain wallet v4 yarn lint frontend lints only blockchain wallet v4 frontend packages blockchain wallet v4 frontend yarn lint fix automatically resolves fixable issues via eslint these ide plugins packages assist with complying with these lint rules while developing atom https atom io packages linter js standard vs code https marketplace visualstudio com items itemname chenxsan vscode standardjs webstorm https blog jetbrains com webstorm 2017 04 using javascript standard style prettier we follow all standard rules that are provided by prettier the following commands are available yarn prettier runs prettier against all packages yarn prettier components runs prettier against only blockchain info components packages blockchain info components yarn prettier core runs prettier against only blockchain wallet v4 packages blockchain wallet v4 yarn prettier frontend runs prettier against only blockchain wallet v4 frontend packages blockchain wallet v4 frontend it is recommended to setup a prettier plugin for your ide plugins packages that will automatically prettify your files on save atom https atom io packages prettier atom vs code https marketplace visualstudio com items itemname esbenp prettier vscode webstorm https prettier io docs en webstorm html when installing the plugin for vs code make sure you are on v3 7 0 or lower https github com prettier prettier vscode issues 1085 issuecomment 557027886 unit tests testing is done via jest https facebook github io jest and enzyme http airbnb io enzyme running tests yarn test runs unit tests for all packages yarn test components runs unit tests for only blockchain info components packages blockchain info components yarn test core runs unit tests for only blockchain wallet v4 packages blockchain wallet v4 yarn test frontend runs unit tests for only blockchain wallet v4 frontend packages blockchain wallet v4 frontend note if you see errors that jest cannot resolve package imports you may need to run yarn test before testing specific packages eg yarn test frontend running tests via watch yarn test watch watches and then runs desired tests yarn test components watch watches and then runs desired tests for only blockchain info components packages blockchain info components yarn test core watch watches and then runs desired tests for only blockchain wallet v4 packages blockchain wallet v4 yarn test frontend watch watches and then runs desired tests for only blockchain wallet v4 frontend packages blockchain wallet v4 frontend debugging tests to enable debugging for unit tests via the chrome browser run the following commands yarn test components debug debugs unit tests for only blockchain info components packages blockchain info components yarn test core debug debugs unit tests for only blockchain wallet v4 packages blockchain wallet v4 yarn test frontend debug debugs unit tests for only blockchain wallet v4 frontend packages blockchain wallet v4 frontend after running one of the above commands node will wait for a debugger to attach before starting the tests to attach simply open your browser and go to chrome inspect and click on open dedicated devtools for node which will give you a list of available node instances you can connect to click on the address displayed in the terminal usually localhost 9229 and you will be able to debug tests using chrome s devtools updating snapshot tests we are snapshot testing ui some components here are the commands to update them when necessary yarn test components update updates component snapshots for only blockchain info components packages blockchain info components yarn test frontend update updates component snapshots for only blockchain wallet v4 frontend packages blockchain wallet v4 frontend code coverage to generate code coverage reports via istanbul https istanbul js org the following commands are available yarn coverage generates a coverage report for all packages yarn coverage components generates coverage report for only blockchain info components packages blockchain info components yarn coverage core generates coverage report for only blockchain wallet v4 packages blockchain wallet v4 yarn coverage frontend generates coverage report for only blockchain wallet v4 frontend packages blockchain wallet v4 frontend depending upon which coverage report was ran the results can be found in the following directories coverage index html coverage blockchain info components index html coverage blockchain wallet v4 index html coverage blockchain wallet v4 frontend index html simply open the index html file in your browser to view typescript typescript is supported and should be used when adding new code it s also recommended to replace legacy js with ts when time allows ts coverage we are using codechecks https www codechecks io and typecov https github com codechecks typecov for coverage reporting coverage is automatically analyzed for prs and the following command is available yarn codechecks code bundle analysis reports to visualize and interact with the tree of the production code bundles files yarn analyze once completed a browser will automatically open with the results storybook storybook https github com storybooks storybook is used by the blockchain info components packages blockchain info components and blockchain wallet v4 frontend packages blockchain wallet v4 frontend packages to interactively view develop and test components the following commands are available storybook build wallet builds the static storybook assets for wallet specific components if base components is running locally storybook will put wallet and base components into the same storybook ui storybook build base builds the static storybook assets for base shared components storybook serve wallet builds storybook assets and then serves them locally at localhost 6006 storybook serve base builds storybook assets and then serves them locally at localhost 6007 storybook deploy wallet builds storybook assets and then serves them to github pages https blockchain github io blockchain wallet v4 frontend you will probably need to run cd packages blockchain info components git remote add origin git github com blockchain blockchain wallet v4 frontend git first storybook deploy base builds storybook assets and then serves them to github pages https blockchain github io blockchain wallet v4 frontend you will probably need to run cd packages blockchain info components git remote add origin git github com blockchain blockchain wallet v4 frontend git first if the deploy begins to fail deleting the static build file before redeploy will likely help contribute please review to the wiki https github com blockchain blockchain wallet v4 frontend wiki security security issues can be reported to us in the following venues email security blockchain info bug bounty https hackerone com blockchain | bitcoin ethereum cryptocurrency react redux redux-saga typescript | blockchain |
ai-resources | this list can be found on github and medium https github com memo ai resources https medium com memoakten selection of resources to learn artificial intelligence machine learning statistical inference 23bc56ba655 update april 2017 it s been almost a year since i posted this list of resources and over the year there s been an explosion of articles videos books tutorials etc on the subject even an explosion of lists of resources such as this one it s impossible for me to keep this up to date however the one resource i would like to add is https ml4a github io https github com ml4a led by gene kogan it s specifically aimed at artists and the creative coding community introduction this is a very incomplete and subjective selection of resources to learn about the algorithms and maths of artificial intelligence ai machine learning ml statistical inference si deep learning dl reinforcement learning rl it is aimed at beginners those without computer science background and not knowing anything about these subjects and hopes to take them to quite advanced levels able to read and understand dl papers it is not an exhaustive list and only contains some of the learning materials that i have personally completed so that i can include brief personal comments on them it is also by no means the best path to follow nowadays most moocs have full paths all the way from basic statistics and linear algebra to ml dl but this is the path i took and in a sense it s a partial documentation of my personal journey into dl actually i bounced around all of these back and forth like crazy as someone who has no formal background in computer science but has been programming for many years the language notation and concepts of ml si dl and even cs was completely alien to me and the learning curve was not only steep but vertical treacherous and slippery like ice a lot of the resources below are actually not for dl but more comprehensive ml si dl is mostly just tweaks on top of older techniques so once you have a solid foundation in ml si it makes a lot more sense if you go through the video lectures below including advanced ones you ll be able to pick up current dl developments directly from the published papers if you really want to understand ai ml si dl rl indepth with all the maths you need a good understanding of linear algebra vectors matrices probability and statistics which is more complex than it sounds and calculus mainly multivariate differential calculus which is often simpler than it sounds i ve included lectures for these too you can t cut corners take the time and study as much of the below as you can from the beginning strong foundations are crucial i often started watching one lecture 5 minutes in i realized i didn t understand anything so went back to watch another lecture which covered slightly more fundamental topics 5 minutes in i realized i still didn t understand anything so went back to watch another lecture which covered even more fundamental topics etc until i went back 10 lectures it has been depressing at times like trying to climb vertical treacherous slippery walls of ice without the right tools if you just want to use the algorithms without necessarily understanding or delving into the maths or just want to understand the algorithms at a high conceptual level that s perfectly fine too hopefully my comments below will make it clear what s what tips there s a lot of overlap in the lectures below that s a good thing don t skip things because you ve already read or seen them elsewhere if you re trying to learn and understand something which is potentially quite complicated having different people explain the same thing to you in different ways is very useful and often gives insight or intuition you might not otherwise find if there are sections which you are 100 comfortable with then you could watch those sections at 1 25x 1 5x or even 2 0x speed just to see what s going on and then switch back to 1 0x speed once you encounter new material or interesting new angles on the same material video lectures workshops these are one off video lectures 1 hour or workshops 2 3 hours that give overviews intuition or advanced crash courses you won t learn much about the depths of how things work but will at least probably understand what things are what they mean and what you can do with them these are usually a good intro before you dive into the heavier moocs introductory summaries deep learning by yann lecun and yohua bengio nips 2015 http research microsoft com apps video id 259574 quite up to date overview of what dl is how it works at a very high level and latest developments from the grandmasters yoshua bengio and yann lecun bit of an ad for their respective research labs it s not very technical and doesn t require much maths some slides do get a little technical and in those cases if you are familiar with the usual linear algebra calculus etc you ll get more out of it if you know nothing or very little about ai ml dl this course will probably be useful for you and give you an idea of what dl is if you know ml quite well but not dl then this might be more useful for you the unreasonable effectiveness of deep learning by yann lecun 2014 http videolectures net sahd2014 lecun deep learning famous lecture by yannn lecun godfather of deep convolutional neural networks cnn brief intro to dl why it s awesome and then mainly focuses on cnns very similar to above could probably be skipped if you watch the above video deep learning rnnaissance by juergen schmidhuber nyc ml meetup 2014 https www youtube com watch v 6bomf9zr7n8 an alternate history of dl from jurgen scmidhuber another grandmaster of dl he goes into more detailed history of the algorithms and where they come from and then focuses on recurrent neural networks rnn which his lab made many innovations on including lstm bit of an ad for his own research lab s this video is a bit more advanced than the above ones arguably more interesting too basics of computational reinforcement learning by michael littman rldm 2015 http videolectures net rldm2015 littman computational reinforcement kind of an overview intro to rl some experience with mdps etc would be useful but not essential michael littman is one of the old school stars of rl and a lot of fun advanced crash courses deep learning by ruslan salakhutdinov kdd 2014 http videolectures net kdd2014 salakhutdinov deep learning overview of dl including dbn rbm pgm etc which are not as popular these days very theoretical dense and mathematical maybe not that useful for beginners salakhutdinov is another major player in dl introduction to reinforcement learning with function approximation by rich sutton nips 2015 http research microsoft com apps video id 259577 another intro to rl but more technical and theoretical rich sutton is old school king of rl deep reinforcement learning by david silver rldm 2015 http videolectures net rldm2015 silver reinforcement learning advanced intro to deep rl as used by deepmind on the atari games and alphago quite technical and requires decent understanding of rl td learning and q learning etc see rl courses below david silver is the new school king of rl and superstar of deepmind s alphago which uses deep rl monte carlo inference methods by ian murray nips 2015 http research microsoft com apps video id 259575 good introduction and overview of sampling monte carlo based methods not essential for a lot of dl but good side knowledge to have how to grow a mind statistics structure and abstraction by josh tenenbaum aaai 2012 http videolectures net aaai2012 tenenbaum grow mind completely unrelated to current dl and takes a very different approach bayesian heirarchical models not much success in real world yet but i m still a fan as the questions and problems they re looking at feels a lot more applicable to real world than dl e g one shot learning and transfer learning though deepmind is looking at this with dl as well now two architectures for one shot learning by josh tenenbaum nips 2013 http videolectures net nipsworkshops2013 tenenbaum learning similar to above but slightly more recent optimal and suboptimal control in brain and behavior by nathaniel daw nips 2015 http videolectures net rldm2015 daw brain and behavior quite unrelated to dl looks at human learning combined with research from pyschology and neuroscience through the computational lens of rl requires decent understanding of rl lots more one off video lectures at http videolectures net top computer science artificial intelligence http videolectures net top computer science machine learning massive open online courses mooc these are concentrated long term courses consisting of many video lectures ordered very roughly in the order that i recommend they are watched foundation maths if you want to understand the maths of ml si dl then these are crucial if you don t want to understand the maths but only want to understand the concepts then you could probably skip these and go straight to the introductory ml courses however this is also where some of the fundamental terminology is defined prior conditional expected value derivative vector matrix etc so it helps to at least know what these things mean instead of going through all of these now you could just watch some of the basic lessons first to help you understand the fundamentals and then come back to some of the more advanced lessons if and when you encounter them e g it s quite probable that you ll never encounter a hessian matrix or require eigenvectors or calculate the determinant of a matrix by hand and only if and when you do then you could come back and watch the relevant lessons you can also skip proofs if you re short on time but they do help you understand better try not to be impatient khan is a superhero and will make you understand things you never knew you could khan academy probability statistics https www khanacademy org math probability ml is basically a subdiscipline of applied statistics mashed with computer science so basic understanding of probability statistics is essential you don t need to watch all lessons but at least the first few sections to understand the concepts bear in mind as you watch the more advanced stuff which may not be nessecary for ml they actually help you understand the basic stuff better khan academy linear algebra https www khanacademy org math linear algebra again you probably don t need to watch them all but at least vectors matrics operations on them dot cross product matrix multiplication etc is essential for the most basic understanding of ml maths basis eigenvalues eigenvectors is essential for deeper understanding for some areas but you could scrape by without them at least for now khan academy calculus https www khanacademy org math calculus home precalculus trigonometry vectors matrices are essential if you watched the linear algebra lessons above you may not need this complex numbers sequences and series etc are useful and come up in various advanced areas but not nessecary for basics differential calculus essential esp chain rule if you want to understand maths of ml you could skip proofs and the applications if you re impatient though maxima minima concavity inflection optimization is important integral calculus i don t think integral calculus is integral to dl see what i did there i ve seen it in a few proofs but it s more of a niche thing i think in ml which beginners could skip for now watch at least the first sections to know what it is function approximation series etc do come up in more advanced areas but safe to skip for now multivariate calculus essential if you really want to understand the maths of dl especially partial derivatives of multivariable functions hessians jacobians laplacians etc come up a lot in advanced areas but you could get by and understand basic ml without knowing these i e you could skip these for now and come back later once you encounter them machine learning deep learning short introductory courses these are probably enough combined with some tutorials if you just want to be able to play with and tweak existing ml dl code and algorithms machine learning by andrew ng coursera https www coursera org learn machine learning fantastic introductory course and foundation for ml covers basics of ml from linear and logistic regression to artificial neural networks gives great insight into concepts and techniques with minimal maths requires basic knowledge of linear algebra and differential calculus note doesn t cover specifities of current deep learning e g convolutional neural networks recurrent neural networks etc so is mainly a great foundation for more advanced studies andrew ng was co founder of google brain and now chief scientist at baidu research he is great at giving intuition deep learning by google udacity https www udacity com course deep learning ud730 brief introduction to dl for those who are familiar with ml this is a very short course i think i went through the whole thing in under 2 hours it s almost a reading of the tensorflow tutorials https www tensorflow org versions master tutorials index html it gives a top level summary of basic dl techniques assumes you re comfortable with ml and related concepts so at least andrew ng s coursera or equivalent knowledge is a must don t expect to be a dl wizard after this but at least you might know what a cnn or rnn is if you re going to look at any of the advanced ml courses below watch this dl course after them longer advanced courses these will help you understand what s actually going on perhaps even understand some dl papers i say some dl papers because others are just insanely theoretical and dense cs188 introduction to artificial intelligence by pieter abbeel berkeley some videos have audio issues so below are a bunch of playlists from different years i had to pick and choose from different playlists depending on audio problems https www youtube com channel ucdzuttqj8ytfasqicvslygg spring 2015 https www youtube com channel ucb4 w1v kfwptlxh9jg1 ia spring 2014 https www youtube com channel ucshmld2msyqakbx8ctivb5q fall 2013 https www youtube com user cs188spring2013 spring 2013 this is a fantastic introduction to ai in general not specifically ml and introduces many different fundamental areas of ai and ml spreads the net very wide so if all you re interested in is playing convolutional neural networks to make things like deepdream then 90 of this course won t be relevant the first half is more agent based ai starting with csps decision trees mdps etc and in that respect it is a bit unique compared to the other courses on this list then goes into various different classic ml topics it is an introduction so requires no prior knowledge of ai or ml but it does go into maths so requires decent understanding of the usual probability linear algebra calculus etc doesn t cover dl but a great foundation for a lot of ai and ml especially if you want to get more into agent based ai such as rl and monte carlo tree search mcts cs540 machine learning by nando de freitas ubc 2013 https www youtube com playlist list ple6wd9fr edyj5lbfl8uugjecvvw66f6 this covers many classic ml and si etc from start all the way to neural networks doesn t require prior knowledge of ml so can be considered comprehensive introduction it s way more thorough and detailed than andrew ng s coursera and goes heavy into maths bear in mind it s a post graduate cs course so it s quite advanced again spreads the net quite wide but not as wide as cs188 instead goes deeper into some areas only brief intro to dl but comprehensive foundation in ml and si nando is ace also prof at oxford and works for deepmind cs340 machine learning by nando de freitas ubc 2012 https www youtube com playlist list ple6wd9fr ecf 5ncbnsqmhqorpichfjf similar to above but undergraduate version i haven t actually watched these so i don t know how they differ from cs540 probably bit simpler deep learning by nando de freitas oxford 2015 https www youtube com playlist list ple6wd9fr efw8dtjaupotupcqmov53fu similar to cs540 but more about dl definitely requires more understanding of statistics and multivariate differential calculus and prior knowledge in ml si andrew ng s coursera may be enough but i really recommend nando s cs540 or pieter s cs188 even knowledge of information theory would be useful great guest lectures by alex graves on generative rnns and karol gregor on vaes cs229 machine learning by andrew ng stanford 2008 https www youtube com view play list p a89dcfa6adace599 another very comprehensive introduction to ml si nothing like his coursera way more theoretical and covers lots more topics and much more thorough kind of like a mashup of pieter abbeel s cs188 ai course and nando de freitas s cs540 ml course this course is more detailed in some areas and less detailed in others e g afair goes deeper into mdps and rl than abbeel s cs188 but doesn t cover bayes nets they all provide slightly different perspectives and insights also doesn t cover dl just a really solid comprehensive foundation for ml and si neural networks for machine learning by geoffrey hinton coursera https www coursera org course neuralnets goes deep into some areas of dl and rather advanced hinton is one of the titans of dl and there is a lot of insight in here but i found it a bit all over the place and i wasn t a huge fan of it i e i don t think it s very useful as a linear educational resource and requies prior knowledge of ml si and dl if you first learn these topics elsewhere e g videos above and then come back to this course then you can find great insight otherwise if you dive straight into this you will get lost computational neuroscience by rajesh rao adrienne fairhall coursera https www coursera org course compneuro not directly related to dl but fascinating nevertheless starts quite fun but gets rather heavy especially adrienne s sections rajesh takes things quite slow and re iterates everything but i think adrienne is used to dealing with comp neuroscience postgrad students and flies through the slides expect to pause the video on every slide while you try to digest what s on the screen requires decent understanding of the usual suspects linear algebra differential calculus probability and statistical analysis including things like pca etc i haven t completed but started or skimmed through machine learning for musicians and artists by rebecca fiebrink kadenze https www kadenze com courses machine learning for musicians and artists info aimed at artists and musicians i haven t watched this but knowing rebecca and her work this is bound to ace machine learning by georgia tech charles isbell michael littman udacity https www udacity com course machine learning supervised learning ud675 https www udacity com course machine learning unsupervised learning ud741 https www udacity com course machine learning reinforcement learning ud820 looks like basic introduction to main topics doesn t look too heavy probably requires basic linear algebra etc but not too complex charles isbell and michael littman are really good reinforcement learning by michael littman chris pryby charles isbell udacity https www udacity com course reinforcement learning ud600 did about half of this then got distracted by other things similar to above but focuses on mdps and rl and goes quite thorough i d like to finish it but have other priorties right now reinforcement learning by david silver ucl 2015 http www0 cs ucl ac uk staff d silver web teaching html https www youtube com playlist list pl5x3mdkkajrl42i jhe4n p6e2ol62ofa introduction to mdps and rl looks lighter and briefer than above but perhaps enough for most rl explorations david silver is superstar of deepmind s alphago which uses deep rl probabilistic graphical models by daphne koller stanfod coursera https www coursera org course pgm not actually directly related to dl but probabilistic methods bayes networks etc i e related to josh tenenbaum s talks at the top i started this but stopped after a while as i got busy with other things starts fun but gets quite heavy looks like it s very thorough perhaps too thorough as it seems to be covering a whole range of topics past and present i d like to finish it but have other priorties right now tutorials articles blogs there are so many articles tutorials now especially tutorials on very specific subjects that i can t list them all so below a few main ones that cover broad topics and mainly foundational material many of these require some prior knowledge of ml dl and linear algebra calculus etc blogs unstructured tutorials these are sites which have unstructured tutorials on various different topics http colah github io chris olah s blog lots of great insight on complex topics and concepts http karpathy github io andrei karpathy s blog similar to above http blog otoro net hardmaru s blog great explanations of concepts and example code too http fastml com lots of good examples http blog keras io tutorials on dl as implemented in keras a python based dl framework that sits on top of tensorflow and theano linear tutorials these are linear tutorials that start from beginning to end https www tensorflow org versions master tutorials index html tutorials on dl as implemented in tensorflow google s python based dl framework requires understanding of ml fundamentals linear algebra calculus etc http deeplearning net tutorial contents html tutorials on dl as implemented in theano a python based dl framework requires understanding of ml fundamentals linear algebra calculus etc books deep learning by ian goodfellow yoshua bengio and aaron courville http www deeplearningbook org free online book very recent briefly covers required maths too information theory inference and learning algorithms by david mackay http www inference phy cam ac uk itprnn book html free online book relatively old 1st 1997 current 2005 but classic textbook very statistical and theoretical heavy requires good understanding of multivariate calculus linear algebra etc pattern recognition and machine learning by chris bishop http research microsoft com en us um people cmbishop prml similar to above not online or free though classic text book very theoretical reinforcement learning an introduction by richard s sutton and andrew g barto https webdocs cs ualberta ca sutton book the book html online book on rl the mathematical theory of communication by claude e shannon 1948 1949 http ieeexplore ieee org xpl articledetails jsp reload true arnumber 6773024 http www amazon com mathematical theory communication claude shannon dp 0252725484 classic article book which gives birth to modern information theory i realize i m on a slippery slope by recommending this as it s originally a paper and if i suggest this i d have to suggest a dozen others but i find this book so useful as a foundation and alternative supplementary angle to help understand ml si concepts that i couldn t resist it i actually recommend the book version as opposed to paper because it has an additional article by warren weaver which explains the concepts in plain english before shannon explains them with maths the maths isn t actually that hairy and mainly requires good understanding of basic probability and bayes law other recommendations these are resources which i have not read or watched but come highly recommended by others linear algebra video lectures by gilbert strang mit http ocw mit edu courses mathematics 18 06 linear algebra spring 2010 video lectures machine learning a probabilistic perspective book by kevin patrick murphy https www cs ubc ca murphyk mlbook most cited deep learning papers by terry taewoong um https github com terryum awesome deep learning papers a curated list of the most cited deep learning papers since 2010 i believe that there exist classic deep learning papers which are worth reading regardless of their applications rather than providing overwhelming amount of papers i would like to provide a curated list of the classic deep learning papers which can be considered as must reads in some area critical algorithm studies a reading list by tarleton gillespie and nick seaver https socialmediacollective org reading lists critical algorithm studies not related to the maths algorithms directly but important nevertheless this list is an attempt to collect and categorize a growing critical literature on algorithms as social concerns the work included spans sociology anthropology science and technology studies geography communication media studies and legal studies among others programming community curated resources for learning ai https hackr io tutorials learn artificial intelligence ai learn artificial intelligence ai from the best online artificial intelligence courses tutorials submitted and voted by the programming community notes this is a list of resources that i have personally watched or read accompanied by my brief thoughts on them it doesn t make sense for me to accept pull requests unless there are typos but please do send me suggestions probably via issues for others i have added an other recommendations section where i am including other resources which come highly recommended by others | ai |
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vscode-iot-workbench | azure iot device workbench for visual studio code gitter https img shields io badge chat on 20gitter blue svg https gitter im microsoft vscode iot workbench build status https dev azure com mseng vsiot apis build status vscode iot workbench github branchname master https dev azure com mseng vsiot build latest definitionid 9768 branchname master azure iot device workbench extension https aka ms iot workbench is now part of azure iot tools https marketplace visualstudio com items itemname vsciot vscode azure iot tools extension pack we highly recommend installing azure iot tools https marketplace visualstudio com items itemname vsciot vscode azure iot tools extension pack which makes it easy to discover and interact with azure iot hub that power your iot edge and device applications this extension pack can help you develop and connect your azure iot applications https azure microsoft com en us overview iot to azure with this extension you can interact with an azure iot hub manage connected devices and enable distributed tracing for your azure iot applications develop and debug certifies azure iot devices https catalog azureiotsolutions com alldevices including mxchip iot devkit https aka ms iot devkit esp32 https catalog azureiotsolutions com details title esp32 devkitc source all devices page raspberry pi https www adafruit com category 288 to azure this extension pack makes it easy to code build deploy and debug your iot applications with popular iot development boards develop and deploy artificial intelligence and your custom logic to azure iot edge https azure microsoft com en us services iot edge this extension pack makes it easy to code build deploy and debug your iot edge applications overview the azure iot device workbench is a visual studio code extension that provides an integrated environment to code build deploy and debug your iot device project with multiple azure services supported if you would like to develop device using iot plug and play sup preview refresh sup https docs microsoft com azure iot pnp and the digital twin definition language dtdl https aka ms dtdl you could refer to the dtdl extension https marketplace visualstudio com items itemname vsciot vscode vscode dtdl to get intellisense completion and syntax validation of dtdl get started develop on generic device currently the following device platforms and languages are supported in azure iot device workbench embedded linux sup public preview sup for embedded linux devices the device workbench uses container to simplify the cross compiling tool chain setup and configuration which means all cross compiling work happens in the container languages supported c c devices supported cortex a series https developer arm com ip products processors cortex a devices e g raspberry pi nxp i mx6 that are running embedded linux such as debian ubuntu or yocto linux follow the setup guide docs embedded linux embedded linux setup md to setup the prerequisite including docker runtime here are a set of tutorials to help you get started get started connect raspberry pi to azure iot hub docs embedded linux embedded linux get started md configure an existing cmake project using containerized toolchain docs embedded linux embedded linux configure project md develop iot plug and play device using containerized toolchain docs embedded linux embedded linux pnp md customize your device toolchain docs embedded linux embedded linux customization md arduino currently device workbench supports mxchip iot devkit and esp32 devkits using arduino for generic arduino device we recommend you use arduino extension in vs code https marketplace visualstudio com items itemname vsciot vscode vscode arduino languages supported arduino c c devices supported mxchip iot devkit https aka ms iot devkit esp32 https www espressif com en products hardware esp wroom 32 overview the device workbench relies on arduino ide as a dependency to develop on the above devices if you have installed device workbench prior to arduino ide you may need to restart vs code to make it find the arduino ide installation path correctly mxchip iot devkit follow the setup guide https docs microsoft com azure iot hub iot hub arduino iot devkit az3166 get started prepare the development environment to setup the environment including the arduino extension here are a set of tutorials to help you get started get started connect devkit to azure iot hub https docs microsoft com azure iot hub iot hub arduino iot devkit az3166 get started use azure iot hub device provisioning service auto provisioning device with iot hub https docs microsoft com azure iot dps how to connect mxchip iot devkit connect to the remote monitoring solution accelerator https docs microsoft com azure iot accelerators iot accelerators arduino iot devkit az3166 devkit remote monitoring v2 translate voice message with azure cognitive services https docs microsoft com en us samples azure samples mxchip iot devkit translator sample send messages to an mqtt server using eclipse paho apis https docs microsoft com en us samples azure samples mxchip iot devkit mqtt client sample devkit state https docs microsoft com en us samples azure samples mxchip iot devkit state sample devkit ota https docs microsoft com en us samples azure samples mxchip iot devkit firmware ota sample esp32 follow the setup guide docs esp32 esp32 setup md to setup esp32 device including the arduino extension here are a set of tutorials to help you get started get started connect esp32 device to azure iot hub https docs microsoft com en us samples azure samples esp32 iot devkit get started sample esp32 state https docs microsoft com en us samples azure samples esp32 iot devkit state sample please take the survey https www surveymonkey com r c7ny7kj to let us know extra device platforms and languages you want to see support in device workbench develop device using iot plug and play this section is deprecated for azure iot device workbench 0 16 0 or above if you are using azure iot device workbench extention before 0 16 0 this extension provides an integrated environment to author iot plug and play sup public preview sup device capability models dcm and interfaces publish to model repositories and generate skeleton c code to implement the device application learn how to get started with iot plug and play sup public preview sup and use the device workbench extension to build an iot plug and play device what is iot plug and play https docs microsoft com azure iot pnp overview iot plug and play and the digital twin definition language dtdl https aka ms dtdl that enables it quickstart use a device capability model to create an iot plug and play device https docs microsoft com azure iot pnp quickstart create pnp device build an iot plug and play preview device that s ready for certification https docs microsoft com azure iot pnp tutorial build device certification use azure iot device workbench extension in visual studio code https docs microsoft com azure iot pnp howto develop with vs vscode connect an mxchip iot devkit device to your azure iot central application via iot plug and play https docs microsoft com azure iot central howto connect devkit pnp commands generic device development command description azure iot device workbench create project create new iot device workbench projects azure iot device workbench open examples load existing examples of iot device workbench project azure iot device workbench provision azure services provision azure services for current project azure iot device workbench deploy to azure deploy the code of the azure services azure iot device workbench compile device code compile device code azure iot device workbench upload device code compile and upload device code azure iot device workbench configure device settings manage the settings on the device azure iot device workbench set workbench path set the default path for azure iot device workbench azure iot device workbench help get help for azure iot device workbench iot plug and play these commands are deprecated for azure iot device workbench 0 16 0 or above command description iot plug and play create capability model create new iot plug and play device capability model file iot plug and play create interface create new iot plug and play interface file iot plug and play generate device code stub generate skeleton device code and project based on given device capability model file iot plug and play open model repository open public or company model repository view to manage device model files iot plug and play submit files to model repository submit files to model repository iot plug and play sign out model repository sign out the company model repository documentation faq https github com microsoft vscode iot workbench wiki faq mxchip iot devkit https aka ms iot devkit privacy statement the microsft enterprise and developer privacy statement https www microsoft com privacystatement enterprisedev default aspx describes the privacy statement of this software contributing there are a couple of ways you can contribute to this repo ideas feature requests and bugs we are open to all ideas and we want to get rid of bugs use the issues section to either report a new issue provide your ideas or contribute to existing threads documentation found a typo or strangely worded sentences submit a pr code contribute bug fixes features or design changes clone the repository locally and open in vs code install tslint for visual studio code https marketplace visualstudio com items itemname eg2 tslint open the terminal press code ctrl 96 code and run npm install to build press f1 and type in tasks run build task debug press f5 to start debugging the extension run gts check and gts fix to follow typescript style guide example contribute examples for the supported devices create a git repo to host the code of your example project write a tutorial to describe how to run the example submit a new issue https github com microsoft vscode iot workbench issues new and provide the following information item description name name of the example to be displayed in example gallery description a short statement to describe the example location url of the github repo image url of the example image size 640 370 shown in the gallery if not provided the default image will be used tutorial url of tutorial that describes how to run the example difficulty difficulty of the example easy medium or difficult code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information please see the code of conduct faq https opensource microsoft com codeofconduct faq howadopt or contact opencode microsoft com with any additional questions or comments contact us if you would like to help to build the best iot experience with azure iot device workbench you can reach us directly at gitter https gitter im microsoft vscode iot workbench telemetry vs code collects usage data and sends it to microsoft to help improve our products and services read our privacy statement https go microsoft com fwlink linkid 528096 clcid 0x409 to learn more if you don t wish to send usage data to microsoft you can set the telemetry enabletelemetry setting to false learn more in our faq https code visualstudio com docs supporting faq how to disable telemetry reporting | server |
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asm9260t | asm9260t arm9 asm9260t rtos tcp ip gui qq 298708237 asm9260t iar ewarm 6 40 5 asm9260t ucos2 92 timer0 0 1ms tick usrentry c void userentryinit void void userentryloop void userentryinit userentryloop frmentry c main c sdram 0x20008000 mmu h ld icf sysloader demo with rtos bin spi flash sdram 0x20008000 j link 500ms gpio9 4 led 1hz led led c icoll c switch ucos ii svc demo with rtos hw cfg ld icf asm9260t qq 298708237 qq txt 2016 03 01 emwin http v youku com v show id xnzmzmdm2ndgw html asm9260t lcd lcd 480 272 2016 03 01 sysloader qspi bootloader sysloader 4kb asm9260t spi sysloader spi flash crc32 sdram asm9260t pdf sysloader qspi doc | os |
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design-system | thoughtbot design system tbds a design system for thoughtbot websites purpose the purpose of the thoughtbot design system is to learn about design systems so that we establish our skillset in the space and provide services to our clients support our internal and external websites and set a minimum standard of design accessibility maintainability and quality applications using tbds hub hub thoughtbot com thoughtbot com thoughtbot search thoughtbot search tbot tbot vellum vellum design thoughtbot com design thoughtbot com hub https hub thoughtbot com thoughtbot com https thoughtbot com thoughtbot search https search thoughtbot com tbot https tbot io vellum https vellum thoughtbot com design thoughtbot com https design thoughtbot com installation for ruby on rails tbds is available through npm and requires rails 5 1 yarn is used to support heroku deployment 1 make sure you have the yarn yarn package manager installed https yarnpkg com en docs install 1 install the design system package and save it as a dependency yarn add thoughtbot design system 1 import the system in your sass manifest import thoughtbot design system src index yarn https yarnpkg com en alternatively you can assign an alias to the package for more terse usage 1 install the design system package with an alias yarn add tbds npm thoughtbot design system 1 import the system using the alias import tbds src index to import assets using the rails asset pipeline 1 add node modules to the asset path ruby assets rb rails application config assets paths rails root join node modules 1 use the assets in rb and erb erb image tag thoughtbot design system src logo horizontal svg title thoughtbot deploy ruby on rails app with tbds to heroku 1 add webpacker to your gemfile and install gem webpacker require false bundle install 1 add the node js buildpack nodejs buildpack to your heroku app note you ll need to order the buildpacks to have node js first followed by ruby nodejs buildpack https elements heroku com buildpacks heroku heroku buildpack nodejs compiled css each version of tbds starting with v0 7 0 is available as a minified css file through the unpkg cdn unpkg while this method is not recommended for production usage it can be useful for adding tbds as an external stylesheet to codepen s or static sites to quickly prototype ideas https unpkg com thoughtbot design system 0 7 1 dist tbds css unpkg https unpkg com browser support tbds supports latest versions of chrome firefox and safari edge 15 contributing to learn about contributing to the project please have a look at the contributing guide contributing contributing md license thoughtbot design system is copyright c 2020 thoughtbot inc it is free software and may be redistributed under the terms specified in the license file license license md about thoughtbot https thoughtbot com brand assets 93 44 svg thoughtbot design system is maintained and funded by thoughtbot inc the names and logos for thoughtbot are trademarks of thoughtbot inc we love open source software see our other projects community or hire us hire to help build your product community https thoughtbot com community utm source github hire https thoughtbot com hire us utm source github | design-system sass css | os |
nlp00 | natural language processing con python il corso pratico | ai |
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Complete-DE-Project | data engineering zoomcamp 2023 cohort this repository contains my solutions for the exercises and projects in the data engineering zoomcamp https github com datatalksclub data engineering zoomcamp by datatalksclub https github com datatalksclub course outline the course covers the following topics and contains code and examples for these google cloud cloud storage and bigquery terraform shell scripting docker and containerization python specifically prefect a python based etl elt tool sql dbt in the course you create cloud infrastructure using terraform cover the principles of docker and containerization create python based etl pipelines using prefect interact with the data using google cloud storage and google bigquery create models in dbt and create reports from the data prerequisites i created a dev container that includes all required dependencies for the course this includes python 3 9 pandas sqlalchemy pyspark pyarrow polars prefect and all required python dependencies confluent kafka scikit learn snowpark ipykernel google cloud sdk azure cli github cli gitlens github pull requests dbt core dbt postgres dbt bigquery dbt extensions for vs code snowflake for vs code ms sql server for vs code terraform jupyter notebooks for vs code docker spark jdk version 11 xml tools yaml tools oh my posh https github com jandedobbeleer oh my posh powershell themes acknowledgments the data engineering zoomcamp was created by datatalksclub https github com datatalksclub and is an amazing resource for anyone looking to learn more about data engineering as a practicing data engineer i personally found the course to be great and learned a lot thank you to the instructors and contributors who created the course materials and provided guidance throughout the program | cloud |
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BISS3306 | readme md 1 chapter01 readme md 2 chapter02 readme md 3 chapter03 readme md 4 chapter04 readme md 5 chapter05 readme md 6 chapter06 readme md 7 chapter07 readme md 8 chapter08 readme md 9 chapter09 readme md 10 chapter10 readme md 2556 html5 css3 javascript 2550 9 2553 2551 web programming dreamweaver cs3 php ajax 1 2552 php my sql for web programing 2556 php 2552 1 2554 2556 php ajax jquery 1 2555 joomla 2 5 2557 php mysql jquery 1 2550 2551 css 2 2555 3 2556 html5 2557 mobile web joomla basic workshop 2554 javascript programming guide 2551 insight php 7 2550 php 5 2555 2556 s commerce 5 1 2551 2557 2559 10gb host 2561 web hosting 10gb host https 10gb host com hosting php appserv 2561 appserv https www appserv org download barksdale k and turner s 2011 html and javascript basics 4th edition course technology cengage learning connolly r and hoar r 2015 fundamentals of web development global edition england pearson education limited creigh b 2016 practical guide to web development understand full web development lifecycle kindle edition dotsiam 2561 domain name hostgator https www dotsiam com elmasri r and navathe s b 2011 fundamentals of database systems 6th edition pearson education inc facebook page 2561 facebook page https www facebook com pages create flanagan d 2011 javascript the definitive guide 6th edition united states of america o reilly media inc forbes a 2015 the joy of programming php abeginners guide to programming interactive web applications with php and mysql 3rd edition createspace independent publishing platform forta b 2012 sql in 10 minutes sams tech yourself 4th edition sams publishing godaddy 2561 domain name godaddy https th godaddy com google forms 2561 google forms https docs google com forms google sites 2561 google sites https www facebook com pages create hostgator 2561 web hosting hostgator https www hostgator com jackson j c 2007 web technologies a computer science perspective new jersey pearson prentice hall joomla 2561 joomla https downloads joomla org makewebeasy 2561 makewebeasy https www makewebeasy com nath k and iswary r 2015 what comes after web 3 0 web 4 0 and the future international conference on computing and communication systems i3cs 15 notepad plus plus 2561 notepad https notepad plus plus org download v7 5 4 html o learn timoth j o learn linda i and o learn daniel a 2015 computing essentials pallis g zeinalipour yazti d and dikaiakos m d 2011 online social networks status and trends new directions in web data management 331 1 213 234 pollock p 2013 web hosting for dummies new jersey john wiley sons inc rainer jr r kelly prince brad and cegielski casey 2015 introduction to information systems 5th edition international john wiley sons ramakrishnan r and gehrke j 2009 database management systems 3rd edition the mcgraw hill companies robbins j 2012 learning web design a beginner s guide to html css javascript and web graphics 4th edition canada o reilly media vaswani v 2008 php a beginner s guide 1st edition new york mcgraw hill education w3schools 2561 php https www w3schools com php 2561 html https www w3schools com html 2561 sql https www w3schools com sql 2561 javascript https www w3schools com js 2561 xml https www w3schools com xml 2561 css https www w3schools com css wix 2561 wix https www wix com wong c 2000 http pocket reference o reilly associates 1st edition united states of america www chandra ac th 2561 http www chandra ac th xammp 2561 xammp https www apachefriends org index html | front_end |
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bits | bits better information technology systems | server |
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front-end-architecture | front end architecture raise a banner take up the torch it s time to make front end architecture matter this is meant as a public space for people to contribute and show their support for the discipline of front end architecture feel free to create pull requests to add content open issues to discuss ideas or get clarification as sections grow we ll eventually break them into their own files definition front end architecture is a collection of tools and processes that aims to improve the quality of our front end code while creating a more efficient and sustainable workflow a front end developer s audience is the website user a front end architect s audience is the developer themselves working components of front end architecture code html5 wai aria css sass code standards and organization object oriented approach how do objects break down and get put together js frameworks organization performance optimization techniques asset delivery front end ops documentation onboarding docs styleguide pattern library architecture diagrams code flow tool chain testing performance testing visual regression unit testing end to end testing process git workflow dependency management npm bundler bower build systems grunt gulp deploy process continuous integration travis ci jenkins resources books front end architecture a modern blueprint for scalable and sustainable design systems http fea pub general articles micahgodbolt com https micahgodbolt com styleguide generator roundup http welchcanavan com styleguide roundup klamping s fea outline https github com klamping front end architecture outline what is front end architecture by elyse holladay http www elyseholladay com posts 2014 10 16 front end architect frontend architecture as a forethought by gideon kreitzer http stacksage com frontend architecture as a forethought duties skills knowledge of a software architect by carnegie mellon university https www sei cmu edu architecture research previousresearch duties cfm oo and code standard links idiomatic css https github com necolas idiomatic css idiomatic sass https github com anthonyshort idiomatic sass used and abused css inheritance and our misuse of the cascade http phase2technology com blog used and abused css inheritance and our misuse of the cascade css guidelines http cssguidelin es by harry roberts web performance metrics http nparashuram com perfslides testing tools performance testing browser perf https github com axemclion browser perf latency benchmark http google github io latency benchmark lighthouse https developers google com web tools lighthouse yslow http yslow org visual regression wraith https github com bbc news wraith phantomcss https github com huddle phantomcss diffux https github com diffux diffux huxley https github com facebook huxley gemini https github com bem gemini screener https screener io unit testing jasmine http jasmine github io jest https facebook github io jest mocha http visionmedia github io mocha documentation tools hologram https github com trulia hologram sassdoc https github com sassdoc sassdoc automation tools selenium http www seleniumhq org | front_end |
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front-end-test-wordpress | front end test home wordpress aqui temos um projeto para voc nos mostrar todo o seu conhecimento para desenvolver um site como funciona fa a um fork do projeto implemente o layout que est dentro da pasta design vale usar todo o seu conhecimento em html5 e css3 este projeto precisa ter apenas uma p gina utilize suas habilidades com o wordpress para tornar o conte do deste projeto o mais din mico poss vel mande um pull request com o conte do do seu tema wordpress at a data estipulada precisamos receber seu banco e pasta uploads o projeto precisa ser responsivo a compreens o do layout e do que precisa ser feito faz parte do teste se voc ainda n o passou pela fase de entrevista cadastre seu curr culo aqui http www mktvirtual com br carreira estamos torcendo por voc supreenda nos | front_end |
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hc-sr04 | this is a zephyr rtos driver for the hc sr04 ultrasonic ranging module https www sparkfun com products 15569 it conforms to the sensor subsystem api https developer nordicsemi com nrf connect sdk doc latest zephyr reference peripherals sensor html and was built from the v1 4 0 tag of the nrf connect sdk ncs https github com nrfconnect sdk nrf about the sensor the hc sr04 ranging module is an inexpensive sensor for measuring distance in a variety of applications it is relatively easy to use 1 set the trig pin high for at least 10us 1 wait for the echo pin to go high and then measure how long it remains high 1 convert the measured time to meters using the speed of sound although the hc sr04 is a 5v device its trig pin works fine when driven at 3v and it s trivial to use a couple of resistors as a voltage divider on the echo pin the following configuration works fine with an nrf52840 dk https www nordicsemi com software and tools development kits nrf52840 dk p align center img src https user images githubusercontent com 6494431 98627864 10787500 22ca 11eb 9a64 a5d4383ecc3b png width 256 p the trig top 11us and echo bottom 9 3ms pulses look like this when the sensor is pointed at a ceiling that is 1 5m away p align center img src https user images githubusercontent com 6494431 98500542 a1374e00 2201 11eb 9783 fd52ad7a6a71 png width 768 p if the sensor can t get a valid measurement because the target is too close or too far away then the echo pulse is 128 6ms long followed by a second 6us pulse about 145us later this error pulse can t be truncated so when it occurs it effectively reduces the sensor s 40hz working rate perhaps the biggest consideration when using these devices is that performing measurements using multiple hc sr04 devices simultaneously can cause erroneous results because the individual sensors can t differentiate their own echo pulses from the pulses produced by the other devices about the driver the driver assumes and uses a mutex to enforce that only one hc sr04 will be actively measuring at any given time there are two variants of the driver hc sr04 uses a pin change interrupt to measure by calling k cycle get 32 in rising edge and falling edge interrupts pro should work reasonably well on most platforms pro uses the standard gpio driver con any latency when servicing the pin change interrupt will affect the measurement hc sr04 nrfx uses gpiote timer ppi and egu peripherals to measure using a hardware timer and then triggers an interrupt after completion pro only one cpu interrupt per measurement pro measurement is not affected if interrupt is delayed con uses nrf52 specific hardware peripherals con uses gpiote driver but not the gpiote interrupt so it s not entirely compatible with standard gpio driver using the hc sr04 variant this is an example dt entry in the project s local overlay e g nrf52840dk nrf52840 overlay when using hc sr04 us0 hc sr04 compatible elecfreaks hc sr04 label hc sr04 0 trig gpios gpio0 26 gpio active high echo gpios gpio0 27 gpio active high status okay then add the following to prj conf config sensor y config gpio y config hc sr04 y using the hc sr04 nrfx variant the hc sr04 nrfx version is similar but uses nrfx style pin numbers instead us0 nrfx hc sr04 nrfx compatible elecfreaks hc sr04 nrfx label hc sr04 nrfx 0 trig pin 26 echo pin 27 status okay device tree definitions for socs like the nrf52840 don t always contain egu instances they can be added to the project s overlay by using the memory addresses from the product specification https infocenter nordicsemi com index jsp topic 2fps nrf52840 2fmemory html cp 4 0 0 3 1 3 anchor topic soc egu0 egu 40014000 compatible nordic nrf egu reg 0x40014000 0x1000 interrupts 20 2 status okay egu1 egu 40015000 compatible nordic nrf egu reg 0x40015000 0x1000 interrupts 21 2 status okay egu2 egu 40016000 compatible nordic nrf egu reg 0x40016000 0x1000 interrupts 22 2 status okay egu3 egu 40017000 compatible nordic nrf egu reg 0x40017000 0x1000 interrupts 23 2 status okay egu4 egu 40018000 compatible nordic nrf egu reg 0x40018000 0x1000 interrupts 24 2 status okay egu5 egu 40019000 compatible nordic nrf egu reg 0x40019000 0x1000 interrupts 25 2 status okay then add the following to prj conf config sensor y config gpio n config hc sr04 nrfx y the hc sr04 nrfx kconfig allows the user to select which timer and egu instances to use note the project will compile normally if config gpio is enabled but unexpected side effects will happen if the native gpio driver is used to configure pin change interrupts the nrfx gpiote driver should be used instead | driver nordicsemi ncs zephyr sensor hc-sr04 nrfx | os |
friend-list | friend list a non trivial yet simple front end programming challenge featuring solutions in react https facebook github io react redux http redux js org redux saga https github com yelouafi redux saga cycle js http cycle js org motorcycle js https github com motorcyclejs core and snabbdom https github com paldepind snabbdom check out the elm solution here https github com derekcuevas friend list elm alt tag friendlist gif the problem create an app with a dynamic and search able list of data that keeps a search input text query in sync with the url via a query parameter at all times assume the data will be fetched from some api and the api will perform the actual search the query should be a simple string and kept in sync with the url via a query parameter q ex localhost 3000 q batman this problem is harder than it first appears actions must be managed in the correct order and if not can result in infinite loops and other undesirable behavior the spec hit the api once and only once per query change when the query updates update the url and fetch results from the api when the url updates update the query and fetch results from the api the browser s back forward buttons should keep the app state query results in sync with the url this is a gotcha if not thought about carefully bonus features handle the concurrent actions issue see the redux saga solution redux saga solution the cyclejs solution cyclejs solution the motorcyclejs solution motorcyclejs solution and the better observable solution better observable solution if the user changes the query input while there is still a pending request from a previous query change the current pending request should be cancelled and a new request should be made thanks yelouafi https github com yelouafi debounce the fetching of results by 100ms log any state changing action with the newly changed state add loading and or error states see the redux meta reducer friend list https github com derekcuevas redux meta reducer tree master examples friend list example solutions solutions are in their own subdirectories above check out the readme files in each of the subdirectories for example specific details many have similar structures identical store state hitting the same mock api the difference being when and where the apps read router state and when and where the apps dispatch actions to run first clone the repo sh git clone https github com derekcuevas friend list git then cd into an example npm install and npm start to get going sh cd friend list imperative solution or the others npm install npm start contributors redux saga solution redux saga solution yelouafi https github com yelouafi cyclejs solution cyclejs solution justinwoo https github com justinwoo motorcyclejs solution motorcyclejs solution cyclejs snabbdom solution cyclejs snabbdom solution tylors https github com tylors have a better implementation please make an issue or send in a pull request | programming-challenges javascript | front_end |
compv | compv computer vision open source stuff | ai |
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BlockChain | blockchain demonstration a very simple blockchain implementation intended to illustrate the concept marty anstey https marty anstey ca file formats all values are stored as little endian and the hash used is sha256 isam index this file is simply a quick way to access any block in the chain it s intended to be used to fetch the offset and length of any block without walking the entire chain which is particularly useful when appending new blocks to the blockchain header type size description uint32 4 record count records type size description uint32 4 offset uint32 4 length blockchain the blockchain is simply a concatenated collection of blocks the hash from the previous block is stored in the current block forming a cryptographically verifiable chain and hardening the preceding blocks against tampering type size description uint32 4 magic uint8 1 block format uint32 4 timestamp uint8 32 32 previous hash uint32 4 data length data arbitrary data example tool output for the test blockchain dumpindex 0 ofs 0 len 195 1 ofs 195 len 332 2 ofs 527 len 97 3 ofs 624 len 451 4 ofs 1075 len 117 walkchain height 1 magic d5e8a97f version 1 timestamp 1440021658 22 00 58 08 19 2015 prevhash 0000000000000000000000000000000000000000000000000000000000000000 blockhash 87988ac16e72dd2b6878c83e03ef99264d7f6e6955df83ac955ac7e7e6f1185e datalen 150 data aug 19 2015 bitcoin price falls 14 following bitfinex flash crash http www coindesk com bitcoin price falls 14 following bitfinex flash crash height 2 magic d5e8a97f version 1 timestamp 1440021687 22 01 27 08 19 2015 prevhash 87988ac16e72dd2b6878c83e03ef99264d7f6e6955df83ac955ac7e7e6f1185e blockhash 5314b241d82e60d35786ee3a876c883cf6c623ec8f81c443d306ed2cbc808d80 datalen 287 data he had come a long way to this blue lawn and his dream must have seemed so close that he could hardly fail to grasp it he did not know that it was already behind him somewhere back in that vast obscurity beyond the city where the dark fields of the republic rolled on under the night height 3 magic d5e8a97f version 1 timestamp 1440024279 22 44 39 08 19 2015 prevhash 5314b241d82e60d35786ee3a876c883cf6c623ec8f81c443d306ed2cbc808d80 blockhash 25eb4659f629659f3074d3f485b736307f24968490eef359cfe2d364d9ab7048 datalen 52 data how a bitcoin transaction works www bit ly 1hwnbc4 height 4 magic d5e8a97f version 1 timestamp 1440024364 22 46 04 08 19 2015 prevhash 25eb4659f629659f3074d3f485b736307f24968490eef359cfe2d364d9ab7048 blockhash ae0bfb7a9ee6ddfc8a8443320e59b22421971391504a70399eb0a1ff8fe9a60f datalen 406 data https en bitcoin it wiki genesis block a genesis block is the first block of a block chain modern versions of bitcoin assign it block number 0 though older versions gave it number 1 the genesis block is almost always hardcoded into the software it is a special case in that it does not reference a previous block and for bitcoin and almost all of its derivatives it produces an unspendable subsidy height 5 magic d5e8a97f version 1 timestamp 1440024695 22 51 35 08 19 2015 prevhash ae0bfb7a9ee6ddfc8a8443320e59b22421971391504a70399eb0a1ff8fe9a60f blockhash 25940c512f3795f460dedd9e359e6c2cd7ac3f8269531d4aa1a7be2fd74fad69 datalen 72 data rvhbtvbmrtogquxjq0ugu0vorfmgqk9cicqyljk1ifvtrcbbvcaymjo0osaxos84lziwmtu https pastebin com raw php i dpwg7xvy | blockchain |
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company-box | company box melpa http melpa org packages company box badge svg http melpa org company box a company front end with icons company box company box png differences with the built in front end differents colors for differents backends icons associated to functions variables and their backends display candidate s documentation support quickhelp string not limited by the current window size buffer s text properties it s better than you might think this package requires emacs 26 also not compatible with emacs in a tty installation el with use package use package company box hook company mode company box mode or require company box add hook company mode hook company box mode to customize m x customize group ret company box ret backends colors see the docstring of the variable company box backends colors c h v company box backends colors themes you can select different themes with company box icons alist icons see the variable company box icons functions for now there are customs icons for 4 backends only company lsp company elisp company yasnippet and company php you can customize their icons with the variables company box icons lsp company box icons elisp company box icons yasnippet and company box icons acphp notes by default images are used to display icons you can also use font icons https github com sebastiencs company box wiki icons with images you can t change icons colors | emacs completion front-end company | front_end |
oreilly-hands-on-transformers | oreilly logo images oreilly png hands on nlp with transformers this repository contains code for the o reilly live online training for hands on nlp with transformers https learning oreilly com live events hands on nlp with transformers 0636920063159 0636920063158 this training will provide an introduction to the novel transformer architecture which is currently considered state of the art for modern nlp tasks we will take a deep dive into what makes the transformer unique in its ability to process natural language including attention and encoder decoder architectures we will see several examples of how people and companies are using transformers to solve a wide variety of nlp tasks including conversation holding image captioning reading comprehension and more this training will feature several code driven examples of transformer derived architectures including bert gpt t5 and the vision transformer each of our case studies will be inspired by real use cases and will lean on transfer learning to expedite our process while using actionable metrics to drive results notebooks classification with bert notebooks bert clf ipynb classification with xlnet notebooks xlnet clf ipynb off the shelf nlp with t5 notebooks t5 ipynb generating latex with gpt2 notebooks latex gpt2 ipynb image captioning with vision transformers notebooks image captioning vision transformer ipynb 3rd part transformer models notebooks other transformers ipynb instructor sinan ozdemir is currently the director of data science at directly managing the ai and machine learning models that power the company s intelligent customer support platform sinan is a former lecturer of data science at johns hopkins university and the author of multiple textbooks on data science and machine learning additionally he is the founder of the recently acquired kylie ai an enterprise grade conversational ai platform with rpa capabilities he holds a master s degree in pure mathematics from johns hopkins university and is based in san francisco ca | gpt gpt-2 bert deep-learning huggingface machine-learning natural-language-processing nlp pytorch vision-transformer xlnet | ai |
introduction-to-it | introduction to information technology this course aims to help learners locate the position of information technology and its purpose and utilization in context of god s work of building his kingdom through giving christian or non christian it engineers with inspiration for inventing new technology this course especially do this by introducing historical development and advancement of open source developer community instruction learners are advised to watch video read the week s textbook and then think and write answers to the questions of the week s test textbook this course uses writings from eric raymond as textbook week 1 a brief history of hackerdom http www catb org esr writings cathedral bazaar hacker history week 2 3 the cathedral and the bazaar http www catb org esr writings cathedral bazaar cathedral bazaar week 4 5 homesteading the noosphere http www catb org esr writings cathedral bazaar homesteading week 6 7 the magic cauldron http www catb org esr writings cathedral bazaar magic cauldron week 8 9 revenge of the hackers http www catb org esr writings cathedral bazaar hacker revenge lesson week 1 revolution os gnu linux foss video http img youtube com vi 4vw62kqkj5a 0 jpg https www youtube com watch v 4vw62kqkj5a week 2 the code story of linux documentary video http img youtube com vi xmm0hsmotfi 0 jpg http www youtube com watch v xmm0hsmotfi week 3 project code rush the beginnings of netscape mozilla documentary video http img youtube com vi 4q7ftjhvz7y 0 jpg http www youtube com watch v 4q7ftjhvz7y week 4 how open source projects survive poisonous people and you can too video http img youtube com vi q52kfl8zvom 0 jpg http www youtube com watch v q52kfl8zvom week 5 corporate open source anti patterns video http img youtube com vi pm8p4ociy3g 0 jpg http www youtube com watch v pm8p4ociy3g week 6 achieving massive open source project adoption video http img youtube com vi 5fpwaugne 4 0 jpg http www youtube com watch v 5fpwaugne 4 week 7 overcoming barriers to open source adoption in the public sector video http img youtube com vi 5hx4yoet dc 0 jpg http www youtube com watch v 5hx4yoet dc week 8 open source adoption in the enterprise video http img youtube com vi dte3m4wswf4 0 jpg http www youtube com watch v dte3m4wswf4 week 9 see why microsoft loves linux and open source video http img youtube com vi hjugbcb0wsq 0 jpg http www youtube com watch v hjugbcb0wsq more to read bye bye ballmer hello open source microsoft s upcoming options http www infoworld com article 2612373 open source software bye bye ballmer hello open source microsoft s upcoming options html by simon phipps open government https github com oreillymedia open government by o reilly media open source guides https opensource guide by github discussion ask hn why don t more open source projects monetize https news ycombinator com item id 14446516 by hacker news | christian opensource | server |
LLM-catalog | large language model catalog majority of the large language models and not only summarized in a table from the original transformer to chatgpt and beyond the list is long and still may not be exhaustive if you think any other model is worth adding or you notice any incorrect information let me know model year paper model type objective short info parameters training corpora 2015 dai le https arxiv org abs 1511 01432 google autoregressive or autoencoder rnn lstm idea of pre training domain specific language models to be later fine tuned imdb dbpedia 20 newsgroups transformer 2017 vaswani et al https arxiv org abs 1706 03762 google seq2seq for machine translation original transformer architecture up to 213m wmt 2014 translation dataset ulmfit 2018 howard ruder https arxiv org abs 1801 06146 fast ai autoregressive rnn awd lstm idea of pre training general domain language models to be later fine tuned wikitext 103 elmo 2018 peters et al https arxiv org abs 1802 05365 allen institute for ai bidirectional rnn lm lstm embeddings from lm added as input to other task specific models 94m 1b word lm benchmark gpt 2018 https openai com blog language unsupervised radford et al https s3 us west 2 amazonaws com openai assets research covers language unsupervised language understanding paper pdf openai autoregressive first llm using the transformer model decoder only 117m bookscorpus bert weights https huggingface co bert base uncased 2018 https ai googleblog com 2018 11 open sourcing bert state of art pre html devlin et al https arxiv org abs 1810 04805 google masked lm next sentence prediction idea of masked language modeling bidirectional encoder 110m 340m bookscorpus wikipedia transformer xl 2019 dai et al https arxiv org abs 1901 02860 cmu google autoregressive learning dependency beyond fixed length context processing segments up to 0 8b wikitext 103 1b word lm benchmark xlm 2019 lample conneau https arxiv org abs 1901 07291 facebook autoregressive or masked lm cross lingual language models 570m wikipedia multiun opus gpt 2 weights https huggingface co gpt2 2019 https openai com blog better language models radford et al https d4mucfpksywv cloudfront net better language models language models pdf openai autoregressive first model to surpass 1b parameters up to 1 5b webtext openai internal 40gb ernie 2019 zhang et al https arxiv org abs 1905 07129 tsinghua university masked lm denoising autoencoder text encoder knowledge graph 114m wikipedia wikidata xlnet weights https huggingface co xlnet base cased 2019 yang et al https arxiv org abs 1906 08237 cmu google permutation lm idea of permutation language modeling 340m bookscorpus wikipedia giga5 clueweb commoncrawl roberta weights https huggingface co roberta base 2019 liu et al https arxiv org abs 1907 11692 facebook masked lm modifications to bert after ablation study 355m bookscorpus wikipedia cc news openwebtext stories 160 gb megatron lm 2019 shoeybi et al https arxiv org abs 1909 08053 nvidia autoregressive or mlm even larger multi billion parameter models based on gpt bert 8 3b wikipedia cc stories realnews openwebtext albert weights https huggingface co albert base v2 2019 lan et al https arxiv org abs 1909 11942 google masked lm sentence order prediction reduced params by embedding decomposition cross layer param sharing up to 235m same as bert distilbert weights https huggingface co distilbert base uncased 2019 sanh et al https arxiv org abs 1910 01108 hugging face masked lm next sentence prediction obtained from bert via knowledge distillation teacher student 66m same as bert t5 br weights https huggingface co t5 base 2019 raffel et al https arxiv org abs 1910 10683 google seq2seq encoder decoder pre trained with unsupervised denoising objective fine tuned with multi task objective tasks formulated as text to text up to 11b c4 colossal clean crawled corpus 750gb stage 1 supervised datasets stage 2 bart weights https huggingface co facebook bart base 2019 lewis et al https arxiv org abs 1910 13461 facebook seq2seq pre trained as a denoising autoencoder to restore the corrupted input bert 10 same as roberta https github com facebookresearch fairseq issues 3550 xlm roberta weights https huggingface co xlm roberta base 2019 conneau et al https arxiv org abs 1911 02116 facebook masked lm multi lingual model pre trained on texts in 100 languages 550m commoncrawl in 100 languages meena 2020 adiwardana et al https arxiv org abs 2001 09977 google seq2seq for dialogue multi turn chatbot trained to minimize perplexity of the next token 2 6b public domain social media conversations turing nlg 2020 https www microsoft com en us research blog turing nlg a 17 billion parameter language model by microsoft only blogpost microsoft autoregressive a language model scaled up to 17b parameters 17b same type of data that megatron lm models were trained on electra weights https huggingface co google electra base discriminator 2020 clark et al https arxiv org abs 2003 10555 stanford google replaced token detection gan like pre training generator corrupts the input discriminator detects corrupted tokens same as bert same as bert for largest model same as xlnet gpt 3 br api https beta openai com docs model index for researchers 2020 brown et al https arxiv org abs 2005 14165 openai autoregressive very similar to gpt 2 but larger 175b params largest at that time 175b commoncrawl extended webtext books wikipedia deberta weights https huggingface co microsoft deberta base 2020 he et al https arxiv org abs 2006 03654 microsoft masked lm bert with disentangled attention word content and position separated enhanced mask decoder up to 1 5b wikipedia bookscorpus openwebtext stories mt5 br weights https huggingface co google mt5 base 2020 xue et al https arxiv org abs 2010 11934 google seq2seq multilingual t5 for 101 languages up to 11b commoncrawl in 101 languages mc4 switch transformer 2021 fedus et al https arxiv org abs 2101 03961 google seq2seq mixture of experts sparsely activated model moe parameters part of the model to be used depend on the input data 1 6t moe same as in t5 and mt5 glm br weights https huggingface co baai glm 10b 2021 du et al https arxiv org abs 2103 10360 tsinghua university autoregressive blank infilling idea of autoregressive blank infilling up to 10b same as bert gpt neo weights https huggingface co eleutherai gpt neo 2 7b 2021 https www eleuther ai research projects gpt neo br eleutherai autoregressive replication of the gpt 3 architecture with much less parameters 2 7b the pile gpt j weights https huggingface co eleutherai gpt j 6b 2021 eleutherai autoregressive replication of the gpt 3 architecture with much less parameters seems very similar to gpt neo 6b the pile jurassic 1 api https www ai21 com studio 2021 lieber et al https uploads ssl webflow com 60fd4503684b466578c0d307 61138924626a6981ee09caf6 jurassic tech paper pdf ai21 labs autoregressive gpt 3 like with optimized depth to width ratio shallower but wider and larger vocabulary 178b attempt to replicate gpt 3 data using publicly available data flan 2021 wei et al https arxiv org abs 2109 01652 google autoregressive 137b lamda pt model fine tuned on instructions 137b a mixture of 62 nlu and nlg tasks see paper for details t0 br weights https huggingface co bigscience t0 2021 sanh et al https arxiv org abs 2110 08207 hugging face seq2seq t5 model fine tuned on a large mixture of supervised tasks with a unified prompt format 11b p3 https huggingface co datasets bigscience p3 br public pool of prompts megatron turing nlg 2021 https developer nvidia com blog using deepspeed and megatron to train megatron turing nlg 530b the worlds largest and most powerful generative language model smith et al https arxiv org abs 2201 11990 microsoft nvidia autoregressive largest model at that time 3x larger than gpt 3 530b a subset of the pile commoncrawl realnews cc stories retro 2022 borgeaud et al https arxiv org abs 2112 04426 deepmind seq2seq retrieval input is split into chunks for each chunk nearest neighbor entries are retrieved from db to improve modeling up to 7b multilingual massivetext see gopher paper glam 2022 du et al https arxiv org abs 2112 06905 google autoregressive mixture of experts another moe model this time autoregressive with over a trillion parameters 1 3t moe a mixture of webpages conversations forums books news gopher 2022 rae et al https arxiv org abs 2112 11446 deepmind autoregressive a family of language models up to 280b plus analysis of effect of model scaling up to 280b massivetext massiveweb c4 books news wiki github lamda 2022 thoppilan et al https arxiv org abs 2201 08239 google autoregressive for dialogue pre trained on public dialogues and web documents fine tuned for safety and factual correctness knowledge retrieval from external tools 137b publicly available dialogues and web documents details in paper st moe 2022 zoph et al https arxiv org abs 2202 08906 google seq2seq mixture of experts stable training of a large scale sparse mixture of experts language model 269b moe mix of c4 corpus and dataset used for glam instructgpt api https beta openai com docs model index for researchers 2022 ouyang et al https arxiv org abs 2203 02155 openai autoregressive gpt 3 model trained to follow instructions using reinforcement learning with human feedback rlhf 175b human demonstrations of desired model behavior for prompts manually written collected via openai api chinchilla 2022 hoffmann et al https arxiv org abs 2203 15556 deepmind autoregressive compute optimal training 4x smaller than gopher but trained on 4x more data beats larger models on many downstream tasks 70b massivetext a different subset distribution than in gopher palm 2022 chowdhery et al https arxiv org abs 2204 02311 google autoregressive largest model to date efficiently trained using google pathways system 540b based on datasets used in glam and lamda anthropic assistant 2022 bai et al https arxiv org abs 2204 05862 anthropic autoregressive for dialogue dialogue agent based on a language model trained with rlhf to be helpful and harmless up to 52b the pile gpt neox weights https huggingface co eleutherai gpt neox 20b 2022 https www eleuther ai research projects gpt neox black et al https arxiv org abs 2204 06745 eleutherai autoregressive largest publicly available dense autoregressive model at that time 20b the pile opt weights https huggingface co facebook opt 66b 2022 zhang et al https arxiv org abs 2205 01068 meta autoregressive a family of language models up to 175b that apart from the largest one have publicly available weights up to 175b dataset from roberta the pile reddit yalm weights https huggingface co yandex yalm 100b 2022 only repository https github com yandex yalm 100b yandex autoregressive bilingual gpt like model for english and russian 100b the pile a large collection of russian texts atlas 2022 izacard et al https arxiv org abs 2208 03299 meta seq2seq retrieval t5 language model retrieval from a corpus of documents joint pretraining up to 11b wikipedia commoncrawl sparrow 2022 glaese et al https arxiv org abs 2209 14375 deepmind autoregressive for dialogue dialogue agent based on chinchilla lm trained with rlhf to be helpful and harmless able to retrieve information from external source 70b dialogue data collected by interaction with human annotators glm 130b weights https github com thudm glm 130b 2022 zeng et al https arxiv org abs 2210 02414 tsinghua university autoregressive blank infilling open bilingual 130b model for english and chinese 130b the pile lambada flan t5 weights https huggingface co google flan t5 base flan palm 2022 chung et al https arxiv org abs 2210 11416 google seq2seq autoregressive t5 and palm models fine tuned with instructions flan t5 weights released in several sizes up to 540b a mixture of 1836 finetuning tasks from 4 sources details in paper bloom weights https huggingface co bigscience bloom 2022 le scao et al https arxiv org abs 2211 05100 bigscience autoregressive a 176b parameter model resulting from the bigscience collaboration trained for 3 5 months in the first half of the year 176b roots dataset mix of natural and programming languages bloomz weights https huggingface co bigscience bloomz 2022 muennighof et al https arxiv org abs 2211 01786 bigscience autoregressive bloom finetuned on instructions 176b xp3 https github com bigscience workshop xmtf data galactica weights https huggingface co facebook galactica 6 7b 2022 taylor et al https arxiv org abs 2211 09085 meta autoregressive a model trained on a corpus of scientific knowledge performing strongly in knowledge intensive scientific tasks up to 120b papers textbooks encyclopedias code knowledge bases etc chatgpt api https chat openai com 2022 https openai com blog chatgpt only blogpost for now openai autoregressive for dialogue a model trained in a similar way as instructgpt using rlhf in a dialogue chat framework human demonstrations of desired model behavior for prompts see instructgpt | large-language-models llm natural-language-processing nlp | ai |
skewer-mode | skewer live web development with emacs provides live interaction with javascript css and html in a web browser expressions are sent on the fly from an editing buffer to be evaluated in the browser just like emacs does with an inferior lisp process in lisp modes watch the demo video on youtube http youtu be 4tytgyzujqm webm http nullprogram s3 amazonaws com skewer demo webm skewer is available from melpa melpa which will install the dependencies for you this package and its dependencies are pure elisp meaning setup is a breeze the whole thing is highly portable and it works with many browsers dependencies simple httpd simple httpd available on melpa js2 mode js2 mode available on elpa skewer requires emacs 24 3 or later usage quick version if skewer was installed from melpa skip to step 3 1 put this repository directory in your load path 2 load skewer mode el 3 m x run skewer to attach a browser to emacs 4 from a js2 mode buffer with skewer mode minor mode enabled send forms to the browser to evaluate the function skewer setup can be used to configure all of mode hooks previously this was the default this can also be done manually like so el add hook js2 mode hook skewer mode add hook css mode hook skewer css mode add hook html mode hook skewer html mode the keybindings for evaluating expressions in the browser are just like the lisp modes these are provided by the minor mode skewer mode kbd c x c e kbd evaluate the form before the point and display the result in the minibuffer if given a prefix argument insert the result into the current buffer kbd c m x kbd evaluate the top level form around the point kbd c c c k kbd load the current buffer kbd c c c z kbd select the repl buffer the result of the expression is echoed in the minibuffer additionally css mode and html mode get similar sets of bindings for modifying the css rules and html on the current page css kbd c x c e kbd load the declaration at the point kbd c m x kbd load the entire rule around the point kbd c c c k kbd load the current buffer as a stylesheet html kbd c m x kbd load the html tag immediately around the point note run skewer uses browse url to launch the browser this may require further setup depending on your operating system and personal preferences multiple browsers and browser tabs can be attached to emacs at once javascript forms are sent to all attached clients simultaneously and each will echo back the result individually use list skewer clients to see a list of all currently attached clients sometimes skewer s long polls from the browser will timeout after a number of hours of inactivity if you find the browser disconnected from emacs for any reason use the browser s console to call skewer to reconnect this avoids a page reload which would lose any fragile browser state you might care about manual version to skewer your own document rather than the provided blank one 1 load the dependencies 2 load skewer mode el 3 start the http server httpd start 4 include http localhost 8080 skewer as a script see example html and check your httpd port 5 visit the document from your browser skewer fully supports cors so the document need not be hosted by emacs itself a greasemonkey userscript is provided skewer everything for injecting skewer into any arbitrary page you re visiting without needing to modify the page on the host more information below don t copy skewer js anywhere or use it directly emacs hosts this script itself manipulating it in memory before it reaches the browser always access it through the servlet on the emacs webserver as skewer browser support skewer is known to work properly with firefox chrome safari opera and ie8 except for css and html skewer will work in ie7 when document queryselector and json are polyfilled if you find any other javascript supported browser that doesn t work with skewer please report it repl a repl into the browser can be created with m x skewer repl or kbd c c c z kbd this should work like a console within the browser messages can be logged to this repl with skewer log like console log results of expressions evaluated in the repl are printed more verbosely than in the minibuffer when possible this may help in debugging skewering with cors skewer supports cross origin resource sharing cors cors this means you can skewer a document hosted from any server without needing any special changes on that server except for including skewer as a script in that document if you don t control the server from which you want to skewer pages such that you can t add the skewer s script the provided greasemonkey userscript user js can be used to inject it into any page you visit note that this userscript will assume you re running the skewer server at http localhost 8080 simple httpd s default port if this isn t true you need to edit the top of the userscript the script isn t actually injected until you switch the toggle in the top right corner the red green triangle alternatively the following bookmarklet will load skewer on demand js javascript function var d document var s d createelement script s src http localhost 8080 skewer d body appendchild s with a browser plugin like custom javascript for websites https chrome google com webstore detail custom javascript for web poakhlngfciodnhlhhgnaaelnpjljija hl en you can use the bookmarklet to auto skewer specific domains saving you a mouse click on each reload bower also provided are some functions for loading libraries from the bower infrastructure on the fly this is accessed with skewer bower load for example i often find it useful to load jquery when skewering a page that doesn t have jquery installed note to use this bower does not need to be installed only git it s just the bower infrastructure being used unfortunately this infrastructure is a mess right now many packages are in some sort of broken state missing dependencies missing metadata broken metadata or an invalid repository url some of this is due to under specification of the metadata by the bower project motivation i wanted something like swank js swank js but without all the painful setup having already written an emacs web server i was halfway there it took relatively little code to accomplish i also didn t want to rely a browser specific feature like mozrepl or webkit s remote debugger kite kite the name refers to the idea that emacs is skewering the browser from server side simple httpd https github com skeeto emacs http server js2 mode https github com mooz js2 mode melpa https melpa org swank js https github com swank js swank js cors http en wikipedia org wiki cross origin resource sharing kite https github com jscheid kite | front_end |
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edu-design-system | education design system test ci https github com chanzuckerberg edu design system actions workflows test yml badge svg release https github com chanzuckerberg edu design system actions workflows release yml badge svg https github com chanzuckerberg edu design system actions workflows release yml codecov https codecov io gh chanzuckerberg edu design system branch main graph badge svg https codecov io gh chanzuckerberg edu design system education design system eds is a repository of presentational https medium com dan abramov smart and dumb components 7ca2f9a7c7d0 components used to build react based products for chan zuckerberg initiative https chanzuckerberg com education installation first install the package bash via npm npm install save chanzuckerberg eds or if using yarn yarn add chanzuckerberg eds app setup import the eds stylesheet and tokens somewhere in your app root e g an init ts or app ts file js import chanzuckerberg eds index css optionally import eds font faces import chanzuckerberg eds fonts css we also surface an eds font size base property to set your base rem font size eg css html font size var eds font size base resets the default pixel to rem ratio tailwind setup the eds tailwind theme provides many eds tokens tokens and some screen sizes import the tailwind config into the app s tailwind config and supply the content https tailwindcss com docs content configuration property for use applying all of the eds tokens to tailwind to take all of what eds provides base colors and extended utility classes for named tokens use the following js const edsconfig require chanzuckerberg eds tailwind config module exports content app ts tsx jsx js theme edsconfig theme this will replace the default color tokens that come with tailwind https tailwindcss com docs customizing colors with those defined by eds note this might cause regressions in your project if you have been using the default colors from tailwind applying the eds tailwind extensions piecemeal if you want a gentler transition to using eds tailwind config you can instead import just the extended values js const edsconfig require chanzuckerberg eds tailwind config module exports content app ts tsx jsx js theme extend edsconfig theme extend this will add in the utility classes for properties like background color bg border border and text color text these match the styles and variables defined in figma designs you can spread each of the sub objects in theme as desired e g fontsize fontweight etc refer to the tokens tailwind section tokens for usage guidelines tokens https chanzuckerberg github io edu design system path docs documentation guidelines tokens docs css variable setup eds also provides the tokens used in the internal styles to use in any custom component recipes and designs if using vscode you can set up the ide to expose the token values and perform autocomplete 1 install the css var complete https marketplace visualstudio com items itemname phoenisx cssvar vscode extension 2 add the following setting to your user or workspace settings file jsonc rest of the settings here cssvar files node modules chanzuckerberg eds lib index css 3 restart vscode theming setup refer to the eds token and theme tools in the tokens documentation https chanzuckerberg github io edu design system path docs documentation theming docs to learn about the optional tooling setup usage import any of the components from the top level package js import components by name at the top of your file import heading from chanzuckerberg eds and then use them in your react components jsx heading variant neutral strong size h2 coffee heading eds provides a suite https chanzuckerberg github io edu design system of components for use and documentation for available props and overrides development this project is under active development see contributing md docs contributing md for more information on how to contribute to eds also read our guidelines https chanzuckerberg github io edu design system path story documentation guidelines code guidelines page for additional information instead if you want to report an issue you can open an issue https github com chanzuckerberg edu design system issues this project is governed under the contributor covenant https www contributor covenant org code of conduct reporting security issues see our security readme https github com chanzuckerberg edu design system blob main security md faq more information and support please review our education design system site sso required paper https eds czi design 0843bc428 p 581284 education design system | design-system | os |
Douyu-danmu-spark | douyu danmu spark version 3 0 fin version introduction br compared to the first version of douyu danmu https github com kaygoym data mining tree master data mining app douyutv in this repository the analysis of douyu tv s danmu is based on spark instead of mysql pymysql br environment br python 3 6 br module jieba wordcloud br spyder br spark pyspark br windows10 64bit br how to use scrapy in anaconda prompt cmd print python spark danmu scrapy py and then input the room id to activate the scrapy process br or use the exe app in the link below analyze after the live broadcast show stop the scrapy process in anaconda prompt cmd print python spark danmu analyze py and then input the room id to activate the analyze process br result the results include hot words the histogram of level the top5 badges and so on just as shown in 687423 03 07 2018 jpg https github com kaygoym douyu danmu spark blob master reports 687423 03 07 2018 jpg br and br 156277 01 21 2018 jpg https github com kaygoym douyu danmu spark blob master reports 156277 01 21 2018 jpg br two examples br tips the psd files are the templets that i use to make the daily reports br like nvliu66 and yjjimpaopao like 156277 and 687423 br app baidu cloud link http t cn r8mzkgv pwd h5ed br further work monthly or yearly report by applying kmeans https github com kaygoym douyu danmu spark tree master kmeans to help host improve the live br https github com kaygoym douyu danmu spark blob master kmeans month report2 jpg br | spark big-data data-mining danmu douyutv scrapy-spider anaconda python3 python data-analysis barrage douyu-danmu douyu douyulive | server |
tree-of-thoughts | multi modality agorabanner png https discord gg qutxnk2nmf tree of thoughts banner treeofthoughts png discord https img shields io discord 999382051935506503 twitter https img shields io twitter url style social url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts https twitter com intent tweet text check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 20https github com kyegomez tree of thoughts linkedin https img shields io badge share linkedin blue style social logo linkedin https www linkedin com sharing share offsite url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts facebook https img shields io badge share facebook blue style social logo facebook https www facebook com sharer sharer php u https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts reddit https img shields io badge share reddit orange style social logo reddit https www reddit com submit url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts title check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 21 hacker news https img shields io badge share hacker 20news orange style social logo y combinator https news ycombinator com submitlink u https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts t check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 21 pinterest https img shields io badge share pinterest red style social logo pinterest https pinterest com pin create button url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts media https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts 2fraw 2fmain 2ftree of thoughts jpeg description check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 21 whatsapp https img shields io badge share whatsapp green style social logo whatsapp https api whatsapp com send text check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 21 20https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts paper link https arxiv org pdf 2305 10601 pdf author s implementation https github com princeton nlp tree of thought llm introduction tree of thoughts tot is a powerful and flexible algorithm that significantly advances model reasoning by up to 70 this plug and play version allows you to connect your own models and experience superintelligence updates langchain tot montecarlo a search best first search coming soon iterative depth search any search algorithms you like open an issue basic prompts no complex implementations just pass in one of these prompts to your model head over to prompts txt three experts with exceptional logical thinking skills are collaboratively answering a question using the tree of thoughts method each expert will share their thought process in detail taking into account the previous thoughts of others and admitting any errors they will iteratively refine and expand upon each other s ideas giving credit where it s due the process continues until a conclusive answer is found organize the entire response in a markdown table format the question is getting started method 1 clone this repository bash git clone https github com kyegomez tree of thoughts cd tree of thoughts python3 m pip install r requirements txt cd tree of thoughts set openai key in an environment file 1 create a file called env 2 get your openai key and input it inside the env file as openai api key sk your key then go to montecarlo example py and fill in your api key for much improved performance provide custom prompt shots in the generate thoughts and generate states in the examples folder we have other examples for hugging face transformers hugging face pipelines method 2 alternatively you can use pip to install tree of thoughts bash pip install tree of thoughts create a python script e g example py and import the necessary classes python import os from tree of thoughts import openailanguagemodel montecarlotreeofthoughts api model gpt 3 5 turbo model openailanguagemodel api key api key api model api model initialize the montecarlotreeofthoughts class with the model tree of thoughts montecarlotreeofthoughts model to reproduce the same results from the tree of thoughts paper or even better craft a one shot chain of thought prompt for your task below initial prompt input 2 8 8 14 possible next steps 2 8 10 left 8 10 14 8 2 4 left 4 8 14 14 2 16 left 8 8 16 2 8 16 left 8 14 16 8 2 6 left 6 8 14 14 8 6 left 2 6 8 14 2 7 left 7 8 8 14 2 12 left 8 8 12 input use 4 numbers and basic arithmetic operations to obtain 24 in 1 equation possible next steps num thoughts 1 max steps 3 max states 4 pruning threshold 0 5 solution tree of thoughts solve initial prompt initial prompt num thoughts num thoughts max steps max steps max states max states pruning threshold pruning threshold print f solution solution or integrate your own custom language model python class customlanguagemodel abstractlanguagemodel def init self model self model model def generate thoughts self state k implement the thought generation logic using self model pass def evaluate states self states implement state evaluation logic using self model pass run the example script features general problem solving framework for language models supports both breadth first search bfs and depth first search dfs algorithms easy integration with popular language models like openai and hugging face extensible and adaptable to different problem properties and resource constraints algorithmic pseudocode 1 define the thought decomposition based on the problem properties 2 create a thought generator function g p s k with two strategies a sample i i d thoughts from a cot prompt b propose thoughts sequentially using a propose prompt 3 create a state evaluator function v p s with two strategies a value each state independently b vote across states 4 choose a search algorithm bfs or dfs based on the tree structure 5 implement the chosen search algorithm 6 execute the chosen search algorithm with the input problem thought generator state evaluator and other required parameters usage examples openai api to use tree of thoughts with openai s api create a custom model class that inherits from abstractlanguagemodel and implements the required methods using openai s api then create an instance of the treeofthoughts class with the custom model and the desired search algorithm bfs or dfs hugging face transformers to run hugging face transformers with tree of thoughts bash git clone https github com kyegomez tree of thoughts cd tree of thoughts python3 huggingfaceexample py python from tree of thoughts import hugginglanguagemodel model name gpt2 model tokenizer your tokenizer huggingface model hugginglanguagemodel model name model tokenizer python class hugginglanguagemodel abstractlanguagemodel def init self model name self model automodelforcausallm from pretrained model name self tokenizer autotokenizer from pretrained model name def generate thoughts self state k state text join state prompt f given the current state of reasoning state text generate k coherent thoughts to achieve the reasoning process inputs self tokenizer prompt return tensors pt outputs self model generate inputs num return sequences k thoughts self tokenizer decode output skip special tokens true for output in outputs return thoughts def evaluate states self states initial prompt state values for state in states state text join state prompt f given the current state of reasoning state text pessimistically evaluate its value as a float between 0 and 1 based on its potential to achieve initial prompt inputs self tokenizer prompt return tensors pt outputs self model generate inputs num return sequences 1 value text self tokenizer decode outputs 0 skip special tokens true try value float value text except valueerror value 0 assign a default value if the conversion fails state values state value return state values contributing this algorithm is still in its infancy but its potential remains unimaginable let s advance the reasoning of ai together under this banner share with your network you can easily share this repository by clicking on the following buttons twitter https img shields io twitter url style social url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts https twitter com intent tweet text check 20out 20this 20amazing 20project 20on 20improving 20ai 20reasoning 20 20tree 20of 20thoughts 20https github com kyegomez tree of thoughts linkedin https img shields io badge share linkedin blue style social logo linkedin https www linkedin com sharing share offsite url https 3a 2f 2fgithub com 2fkyegomez 2ftree of thoughts for instagram while it doesn t directly support sharing web links you can share the screenshot of our project and the link in your caption or bio you can download the project screenshot by clicking the image below tree of thoughts https github com kyegomez tree of thoughts raw main tree of thoughts jpeg https github com kyegomez tree of thoughts raw main tree of thoughts jpeg we greatly appreciate any help in spreading the word about our project thank you for your support roadmap resilient prompting teach model how to think rather than what to think add pruning threshold management for precise bad state cutoff evaluating each thought as soon as it s generated then evaluating a chain of thoughts or the state of thoughts by averaging out the values of each thought evaluation add traversal method which will encapsulate the run of either dfs or bfs under the hood so that the issue of different args is solved from ivanzhovannik add delay between generating solutions and generating values dynamic and adaptive parameters like max steps num thoughts max states and value threshold that shift depending on the complexity of the user objective add rejected reasoning metadata thought state reasoning on state into generate solutions and more feel free to suggest any other ideas this algorithm is very young and its potential is limitless chain of thought hub evaluation tests documentation search algorithms in tree of thoughts the tree of thoughts library supports a variety of search algorithms that can be employed for different problem solving contexts here s a brief overview of each search algorithm along with their primary benefits and use cases 1 breadth first search bfs bfs explores all the nodes at the present depth before going on to the nodes at the next depth level it is an excellent choice when the depth of the tree is relatively small and solutions are spread out evenly benefits it guarantees to find the shallowest goal i e the solution with fewer steps it is a simple and straightforward algorithm for traversing trees or graphs use cases ideal for problems where the depth of the tree graph is not very large useful when the goal is close to the root 2 depth first search dfs dfs explores as far as possible along each branch before backing up it is suitable when the tree depth is significant and solutions are located deep in the tree benefits it uses less memory compared to bfs as it needs to store only a single path from the root to a leaf node along with remaining unexplored sibling nodes for each node on the path it can explore deeper solutions that are not accessible with bfs use cases it is often used in simulations due to its more aggressive deeper search ideal for searching through a big search space 3 best first search best first search uses an evaluation function to decide which adjacent node is most promising and then explores it is suitable for problems where we have some heuristic information about the distance from the current state to the goal benefits it can provide a more efficient solution by using heuristics it does not explore unnecessary paths thus saving resources use cases suitable for a large dataset where the goal node s location is unknown ideal for problems where some heuristic can guide the search to the goal 4 a search a search finds the least cost path from the given initial node to one goal node out of one or more possible goals it uses a best first search and finds the least cost path to a goal benefits it is complete optimal optimally efficient and uses heuristics to guide itself a balances between bfs and dfs and avoids expanding paths that are already expensive use cases widely used in pathfinding and graph traversal the process of plotting an efficiently directed path between multiple points suitable for games mapping apps and routing paths for vehicles where we need an optimal solution 5 monte carlo tree search mcts mcts uses random sampling of the search space and uses the results to guide the search it is best when the search space is vast and cannot be completely traversed benefits it can provide good solutions for extremely complex problems with a large search space where traditional methods fail it uses statistical analysis of the results for decision making which can handle the uncertainty and variability in the problem use cases suitable for perfect information games which are games where players have complete knowledge of all events and states also useful in real time video games and other domains where the decision making time is limited input problem str the initial problem statement or prompt for which the tree of thoughts algorithm will generate a solution num thoughts int default 5 the number of thoughts to generate at each state a higher value of k will result in more thoughts being generated potentially leading to a more diverse set of solutions however increasing k may also increase the computational complexity and time required to find a solution max steps int default 3 the maximum depth of the search tree a higher value of t allows the algorithm to explore deeper states potentially leading to better solutions however increasing t may also increase the computational complexity and time required to find a solution max states int default 5 the branching factor of the search tree which determines the maximum number of child nodes for each parent node a higher value of b allows the algorithm to explore more states potentially leading to better solutions however increasing b may also increase the computational complexity and time required to find a solution value threshold float default 0 5 the value threshold for pruning states states with a value below this threshold will be discarded reducing the search space a higher value of vth will result in a more aggressive pruning strategy potentially speeding up the search process however setting vth too high may cause the algorithm to discard promising states leading to suboptimal solutions langchaintot class langchaintot is the main class you ll interact with it acts as a wrapper for the large language model and allows you to set a problem description add thoughts and check the validity of these thoughts using a specified checker initialization you initialize a langchaintot object with an optional problem description and a checker class python problem description 3 2 1 3 1 3 4 1 this is a 4x4 sudoku puzzle the represents a cell to be filled the character separates rows at each step replace one or more with digits 1 4 there must be no duplicate digits in any row column or 2x2 subgrid keep the known digits from previous valid thoughts in place each thought can be a partial or the final solution strip langchain tot langchaintot problem description problem description checker class lambda my checker if you want to change the problem description or checker class later you can use the set problem description and set checker class methods adding thoughts once you have your langchaintot object you can add thoughts to it a thought is a string representing a possible solution or step towards a solution you can add thoughts using the add thought method python langchain tot add thought 3 2 1 3 1 3 4 1 checking thoughts once you ve added one or more thoughts you can check their validity using the check thoughts method python print langchain tot check thoughts this method will return a thoughtvalidity value representing whether the latest thought is a final valid solution valid final an intermediate valid step valid intermediate or invalid invalid mychecker class mychecker is a class for creating custom checkers it inherits from totchecker and must implement the evaluate method initialization you initialize a mychecker object with a validation function python my checker mychecker validate fn lambda p t validate sudoku p t sudoku solution custom validation function the validation function is a callable that takes a problem description and a tuple of thoughts and returns a thoughtvalidity value it defines how to check the validity of thoughts for a specific problem in this code we re using the validate sudoku function as our validation function this function takes a problem description a tuple of thoughts and a solution string and returns the validity of the last thought based on whether it matches the solution and whether it s a possible step towards the solution the validate sudoku function is specific to 4x4 sudoku puzzles and you ll need to create your own validation function for different types of problems acknowledgements thanks to shunyu yao princeton university dian yu google deepmind jeffrey zhao google deepmind izhak shafran google deepmind thomas l griffiths princeton university yuan cao google deepmind karthik narasimha princeton university for sharing this amazing work with the world and thanks to phil wang or lucidrains for inspiring me to devote myself to open source ai research | artificial-intelligence chatgpt gpt4 multimodal prompt-engineering deep-learning prompt prompt-learning prompt-tuning | ai |
SubsidieAanvragenSampleDB | subsidieaanvragensampledb dit is een gesampelde database voor het practicum van de minor data engineering om de database te maken doe je het volgende 1 maak een azure sql database met de naam subsidie aanvragen 2 maak in een tool als microsoft sql server management studio verbinding met deze database 3 run het script create tables subsidie aanvragen sql let op het database schema moet je handmatig met een apart statement uitvoeren 4 run het script insert referentiedata subsidie aanvragen sql 5 run het script insert aanvragers subsidie aanvragen sql 6 run het script insert aanvragen subsidie aanvragen sql 7 je kunt het script tellingen subsidie aanvragen sql gebruiken om te kijken of de database gevuld is 8 volg de instructies in het script create readonlyuser sql om een readonlyuser te maken | server |
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IoT | iot the internet of things smart home home automation based on openwrt arduino only server s code is updated arduino s code is on the way based on wifi shield this is just is a specific requirement from a first party not a pervasive templete for anybody i ll try my best to make it reusable so api is on the way git is just a storage so far not really only for openwrt server this can be used to any linux server based on jquery amazeui php and so force not finished maybe usque ad this august hardware items contains 220v light curtain screen tap water projector camara changable led luminance and color sound access control gas tv air conditioner todolist 1 chinese wiki branch and doc 2 nodejs transfer 3 android client 4 frontend design 5 after all items finished i will add the docs in this subject and make it pervasive so please look forward to | server |
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KnownSpys | knownspys contains xcode 11 swift 5 friendly version of knownspys project for ios app development design patterns for mobile architecture https www linkedin com learning ios app development design patterns for mobile architecture course in linkedin master databinding end branches contains the end project with data binding mvp starter branch contains the starter project | front_end |
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CRTO | powershell the lynx team x hackerzat 88 88 88 88 d 88 88 88 88 dppyba adppyyba adppyba 88 d8 adppyba 8b dppyba 888888888 adppyyba mm88mmm 88p 8a y8 a8 88 a8 a8p 88 88p y8 a8p y8 88 88 88 adppppp88 8b 8888 8pp 88 d8p adppppp88 88 88 88 88 88 8a aa 88 yba 8b aa 88 d8 88 88 88 88 88 8bbdp y8 ybbd8 88 y8a ybbd8 88 888888888 8bbdp y8 y888 lynxes are back and just wanna have fun again crto repo s objective to gather all the info that we d found useful and interesting for the crto we also collect material from other resources websites courses blogs git repos books etc before continue we are still working on this repo as we go on with our crto journey this means we ll add or remove parts without giving notice furthermore this is not intended to be a comprehensive repo cross reference and information gathering are your friends toc official zero point security references official zero point security references blog blog about the rto course and exam github repos github repos persistence persistence privilege escalation privilege escalation uncategorized stuff uncategorized stuff official zero point security references course page red team ops adversary simulation red team operations https training zeropointsecurity co uk courses red team ops zero point security training faq https training zeropointsecurity co uk pages frequently asked questions exam booking and info https training zeropointsecurity co uk pages red team ops exam blog about the rto course and exam operate like you mean it red team ops crto course review https casvancooten com posts 2021 07 operate like you mean it red team ops crto course review zeropointsecurity certified red team operator crto course a comprehensive review https heartburn dev zeropointsecurity certified red team operator crto course a comprehensive review zero point security s red team ops crto review https www bencteux fr posts crto my crto course and exam review https gustavshen medium com my crto course and exam review 433f967f712e github repos enumeration wikis https github com theonlykernel enumeration wiki sharpup https github com ghostpack sharpup sharpersist https github com mandiant sharpersist persistence mitre att ck ta003 https attack mitre org tactics ta0003 youtube all about dll hijacking my favorite persistence method https www youtube com watch v 3erosg wnpe by ippsec blog windows persistence commands http pwnwiki io persistence windows index md from pwnwiki io http pwnwiki io privilege escalation mitre att ck ta004 https attack mitre org tactics ta0004 uncategorized stuff genereal useful resources powershell basics https www darkoperator com powershellbasics pentest tips and tricks https jivoi github io 2015 07 01 pentest tips and tricks big list with tips tricks and cheat sheets https guif re red team operations vs penetration testing https www mitnicksecurity com blog red team operations vs penetration testing microsoft defender antivirus how to manage microsoft defender antivirus with powershell on windows 10 https www windowscentral com how manage microsoft defender antivirus powershell windows 10 active directory blog posts attacking active directory 0 to 0 9 https zer1t0 gitlab io posts attacking ad s 09 stealthbits attack catalog https attack stealthbits com youtube red teaming adventures in active directory https www youtube com watch v do2czu7090a cheast sheets cheat sheet attack active directory https github com drak3hft7 cheat sheet active directory active directory kill chain attack defense https github com infosecn1nja ad attack defense active directory exploitation cheat sheet https github com s1ckb0y1337 active directory exploitation cheat sheet windows active directory exploitation cheat sheet and command reference https casvancooten com posts 2020 11 windows active directory exploitation cheat sheet and command reference extra labs vulnerable ad local lab https github com wazehell vulnerable ad active directory labs exams review https github com ryan412 adlabsreview | redteam crto hacking offensivesecurity zeropointsecurity | os |
Awesome-Large-Language-Models-for-Science | awesome large language models for science llm4science awesome https awesome re badge svg https awesome re p align center img src https github com zyzisastudyreallyhardguy awesome large language models for science assets 75228223 aa635e90 ec77 46f4 90dc 94a9f148a753 width 400 p a curated list of science focused large language model resources llm4science keep updating bulb bulb bulb please let us know if papers are missing highlight high brightness title venue date d m y code note large language models for scientific synthesis inference and explanation https arxiv org abs 2310 07984 arxiv 12 10 2023 github https github com zyzisastudyreallyhardguy llm4sd automatic scientific discovery general science title venue date d m y code note large language models for scientific synthesis inference and explanation https arxiv org abs 2310 07984 arxiv 12 10 2023 github https github com zyzisastudyreallyhardguy llm4sd automatic scientific discovery emergent autonomous scientific research capabilities of large language models https arxiv org abs 2304 05332 arxiv 11 04 2023 automatic scientific experiments scieval a multi level large language model evaluation benchmark for scientific research https arxiv org abs 2308 13149 arxiv 25 08 2023 scibench evaluating college level scientific problem solving abilities of large language models https arxiv org abs 2307 10635 arxiv 20 07 2023 sci cot leveraging large language models for enhanced knowledge distillation in small models for scientific qa https arxiv org abs 2308 04679 arxiv 09 08 2023 scitune aligning large language models with scientific multimodal instructions https arxiv org abs 2307 01139 arxiv 03 07 2023 science in the age of large language models https www nature com articles s42254 023 00581 4 nature review physics 26 04 2023 towards the ultimate brain exploring scientific discovery with chatgpt ai https onlinelibrary wiley com doi full 10 1002 aaai 12113 ai magazine 26 08 2023 pretrained large language models title venue date d m y code note galactica a large language model for science https arxiv org abs 2211 09085 arxiv 16 11 2022 github https github com paperswithcode galai scibert a pretrained language model for scientific text https arxiv org abs 1903 10676 arxiv 26 03 2019 github https github com allenai scibert biology chemistry molecules title venue date d m y code note codonbert large language models for mrna design and optimization https www biorxiv org content 10 1101 2023 09 09 556981v1 full biorxiv 12 09 2023 chatgpt gpt 4 and other large language models the next revolution for clinical microbiology https academic oup com cid advance article doi 10 1093 cid ciad407 7217675 login false clinical infectious diseases 03 07 2023 large language model for molecular chemistry https www nature com articles s43588 023 00399 1 nature computational science 23 01 2023 do large language models understand chemistry a conversation with chatgpt https pubs acs org doi 10 1021 acs jcim 3c00285 journal of chemical information and modeling 03 2023 empowering molecule discovery for molecule caption translation with large language models a chatgpt perspective https arxiv org abs 2306 06615 arxiv 11 06 2023 is gpt 3 all you need for low data discovery in chemistry https chemrxiv org engage chemrxiv article details 63eb5a669da0bc6b33e97a35 chemrxiv 14 02 2023 github https github com kjappelbaum gptchem translation between molecules and natural language https arxiv org abs 2204 11817 emnlp 25 04 2022 github https github com blender nlp molt5 llms can generate robotic scripts from goal oriented instructions in biological laboratory automation https arxiv org abs 2304 10267 arxiv 18 04 2023 14 examples of how llms can transform materials science and chemistry a reflection on a large language model hackathon digital discovery 2 5 1233 1250 https pubs rsc org en content articlehtml 2023 dd d3dd00113j digital discovery 12 06 2023 git mol a multi modal large language model for molecular science with graph image and text https arxiv org pdf 2308 06911 pdf arxiv 14 08 2023 comparative performance evaluation of large language models for extracting molecular interactions and pathway knowledge https arxiv org abs 2307 08813 arxiv 17 07 2023 automated extraction of molecular interactions and pathway knowledge using large language model galactica opportunities and challenges https aclanthology org 2023 bionlp 1 22 acl 2023 from artificially real to real leveraging pseudo data from large language models for low resource molecule discovery https arxiv org abs 2309 05203 arxiv 11 09 2023 exploring the potential of gpt 4 in biomedical engineering the dawn of a new era https link springer com article 10 1007 s10439 023 03221 1 annals of biomedical engineering 28 04 2023 a gpt 4 reticular chemist for guiding mof discovery https onlinelibrary wiley com doi abs 10 1002 anie 202311983 angewandte chemie international edition 05 10 2023 is gpt 3 all you need for machine learning for chemistry https openreview net forum id dgpgtez6g neurips 2022 workshop ai4mat 22 11 2022 the future of chemistry is language https www nature com articles s41570 023 00502 0 nature reviews chemistry 19 05 2023 what indeed can gpt models do in chemistry a comprehensive benchmark on eight tasks https arxiv org abs 2305 18365 arxiv 27 05 2023 chemcrow augmenting large language models with chemistry tools https arxiv org abs 2304 05376 arxiv 11 04 2023 prompt engineering of gpt 4 for chemical research what can cannot be done https chemrxiv org engage chemrxiv article details 647d305dbe16ad5c577b6627 chemrxiv 05 06 2023 fine tuning gpt 3 for machine learning electronic and functional properties of organic molecules https chemrxiv org engage chemrxiv article details 64e4dba0dd1a73847f4dc904 chemrxiv 23 08 2023 medicine radiology clinical science title venue date d m y code note large language models in medicine https www nature com articles s41591 023 02448 8 nature medicine review the shaky foundations of large language models and foundation models for electronic health records https www nature com articles s41746 023 00879 8 npj digital medicine large language models encode clinical knowledge https www nature com articles s41586 023 06291 2 nature 12 07 2023 evaluating large language models for radiology natural language processing https arxiv org abs 2307 13693 arxiv 25 07 2023 artificial intelligence in sports medicine could gpt 4 make human doctors obsolete https link springer com article 10 1007 s10439 023 03213 1 annals of biomedical engineering 25 04 2023 revolutionizing radiology with gpt based models current applications future possibilities and limitations of chatgpt https www sciencedirect com science article pii s221156842300027x casa token eaws5fhvvhqaaaaa opiqmi4asnafmj5kae1sxlrud8qvpi0db1utquiklxb87b87vyqznivxssw 95gauumcnwskdg diagnostic and interventional imaging june 2023 gpt 4 in radiology improvements in advanced reasoning https pubs rsna org doi abs 10 1148 radiol 230987 journalcode radiology radiology 16 05 2023 physics title venue date d m y code note physnlu a language resource for evaluating natural language understanding and explanation coherence in physics https arxiv org abs 2201 04275 arxiv 02 06 2023 evaluating large language models on a highly specialized topic radiation oncology physics https arxiv org abs 2304 01938 arxiv 01 04 2023 advances in apparent conceptual physics reasoning in gpt 4 https arxiv org abs 2303 17012 arxiv 29 03 2023 using large language model to solve and explain physics word problems approaching human level https arxiv org pdf 2309 08182 pdf arxiv 20 09 2023 melm a generative pretrained language modeling framework that solves forward and inverse mechanics problems https www sciencedirect com science article pii s0022509623002582 journal of the mechanics and physics of solids 23 09 2023 perspective large language models in applied mechanics https asmedigitalcollection asme org appliedmechanics article abstract 90 10 101008 1164084 perspective large language models in applied redirectedfrom fulltext jouranl of applied mechanics 17 07 2023 evaluating large language models on a highly specialized topic radiation oncology physics https arxiv org abs 2304 01938 arxiv 01 04 2023 material science title venue date d m y code note large language models as master key unlocking the secrets of materials science with gpt https arxiv org abs 2304 02213 arxiv 05 04 2023 14 examples of how llms can transform materials science and chemistry a reflection on a large language model hackathon https pubs rsc org en content articlehtml 2023 dd d3dd00113j digital discovery 12 06 2023 accelerated materials language processing enabled by gpt https arxiv org abs 2308 09354 arxiv 18 08 2023 benefits limits and risks of gpt 4 as an ai chatbot for medicine https www nejm org doi full 10 1056 nejmsr2214184 the new england journal of medicine 30 03 2023 bioinspiredllm conversational large language model for the mechanics of biological and bio inspired materials https arxiv org ftp arxiv papers 2309 2309 08788 pdf arxiv 15 09 2023 matscibert a materials domain language model for text mining and information extraction https www nature com articles s41524 022 00784 w computational materials 03 05 2022 material transformers deep learning language models for generative materials design https iopscience iop org article 10 1088 2632 2153 acadcd meta machine learning science and technology 05 01 2021 math title venue date d m y code note large language model for science a study on p vs np https arxiv org abs 2309 05689 arxiv 11 09 2023 can gpt 4 formulate and test a novel hypothesis yes and no https www techrxiv org articles preprint can gpt 4 formulate and test a novel hypothesis yes and no 22517278 text in 20three 20other 20attempts 2c 20gpt to 20advance 20in 20the 20future techrxiv 07 04 2023 plant science title venue date d m y code note one hundred important questions facing plant science derived using a large language model https www cell com trends plant science fulltext s1360 1385 23 00199 1 trends in plant science 2023 elsevier | ai |
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chainlink | br p align center a href https chain link target blank img src https raw githubusercontent com smartcontractkit chainlink develop docs logo chainlink blue svg width 225 alt chainlink logo a p br github tag latest semver https img shields io github v tag smartcontractkit chainlink style flat square https hub docker com r smartcontract chainlink tags github license https img shields io github license smartcontractkit chainlink style flat square https github com smartcontractkit chainlink blob master license github workflow changelog https img shields io github workflow status smartcontractkit chainlink changelog style flat square label github actions https github com smartcontractkit chainlink actions query workflow 3achangelog github contributors https img shields io github contributors anon smartcontractkit chainlink style flat square https github com smartcontractkit chainlink graphs contributors github commit activity https img shields io github commit activity y smartcontractkit chainlink style flat square https github com smartcontractkit chainlink commits master official documentation https img shields io static v1 label docs message latest color blue https docs chain link chainlink https chain link expands the capabilities of smart contracts by enabling access to real world data and off chain computation while maintaining the security and reliability guarantees inherent to blockchain technology this repo contains the chainlink core node and contracts the core node is the bundled binary available to be run by node operators participating in a decentralized oracle network https link smartcontract com whitepaper all major release versions have pre built docker images available for download from the chainlink dockerhub https hub docker com r smartcontract chainlink tags if you are interested in contributing please see our contribution guidelines docs contributing md if you are here to report a bug or request a feature please check currently open issues https github com smartcontractkit chainlink issues for more information about how to get started with chainlink check our official documentation https docs chain link resources for solidity developers can be found in the chainlink hardhat box https github com smartcontractkit hardhat starter kit community chainlink has an active and ever growing community discord https discordapp com invite ask4zew is the primary communication channel used for day to day communication answering development questions and aggregating chainlink related content take a look at the community docs docs community md for more information regarding chainlink social accounts news and networking build chainlink 1 install go 1 21 1 https golang org doc install and add your gopath s bin directory to your path https golang org doc code html gopath example path for macos export path gopath bin path export gopath users user go 2 install nodejs v16 https nodejs org en download package manager pnpm via npm https pnpm io installation using npm it might be easier long term to use nvm https nodejs org en download package manager nvm to switch between node versions for different projects for example assuming node version was set to a valid version of nodejs you could run nvm install node version nvm use node version 3 install postgres 11 x and 15 x https wiki postgresql org wiki detailed installation guides you should configure postgres https www postgresql org docs 12 ssl tcp html to use ssl connection or for testing you can set sslmode disable in your postgres query string 4 ensure you have python 3 installed this is required by solc select https github com crytic solc select which is needed to compile solidity contracts 5 download chainlink git clone https github com smartcontractkit chainlink cd chainlink 6 build and install chainlink make install 7 run the node chainlink help for the latest information on setting up a development environment see the development setup guide https github com smartcontractkit chainlink wiki development setup guide apple silicon arm64 native builds on the apple silicon should work out of the box but the docker image requires more consideration bash docker build t chainlink develop latest f core chainlink dockerfile ethereum execution client requirements in order to run the chainlink node you must have access to a running ethereum node with an open websocket connection any ethereum based network will work once you ve configured https github com smartcontractkit chainlink configure the chain id ethereum node versions currently tested and supported officially supported parity openethereum https github com openethereum openethereum note parity is deprecated and support for this client may be removed in future geth https github com ethereum go ethereum releases supported but broken these clients are supported by chainlink but have bugs that prevent chainlink from working reliably on these execution clients nethermind https github com nethermindeth nethermind blocking issues https github com nethermindeth nethermind issues 4384 besu https github com hyperledger besu blocking issues https github com hyperledger besu issues 4212 https github com hyperledger besu issues 4192 https github com hyperledger besu issues 4114 erigon https github com ledgerwatch erigon blocking issues https github com ledgerwatch erigon discussions 4946 https github com ledgerwatch erigon issues 4030 issuecomment 1113964017 we cannot recommend specific version numbers for ethereum nodes since the software is being continually updated but you should usually try to run the latest version available running a local chainlink node note by default chainlink will run in tls mode for local development you can disable this by using a dev build using make chainlink dev and setting the toml fields toml webserver securecookies false tls httpsport 0 insecure devwebserver true alternatively you can generate self signed certificates using tools bin self signed certs or manually https github com smartcontractkit chainlink wiki creating self signed certificates to start your chainlink node simply run bash chainlink node start by default this will start on port 6688 you should be able to access the ui at http localhost 6688 http localhost 6688 chainlink provides a remote cli client as well as a ui once your node has started you can open a new terminal window to use the cli you will need to log in to authorize the client first bash chainlink admin login you can also set admin credentials file path to credentials file in future if you like to avoid having to login again now you can view your current jobs with bash chainlink jobs list to find out more about the chainlink cli you can always run chainlink help check out the doc https docs chain link pages on jobs https docs chain link docs jobs to learn more about how to create jobs configuration node configuration is managed by a combination of environment variables and direct setting via api ui cli check the official documentation https docs chain link docs configuration variables for more information on how to configure your node external adapters external adapters are what make chainlink easily extensible providing simple integration of custom computations and specialized apis a chainlink node communicates with external adapters via a simple rest api for more information on creating and using external adapters please see our external adapters page https docs chain link docs external adapters development running tests 1 install pnpm via npm https pnpm io installation using npm 2 install gencodec https github com fjl gencodec and jq https stedolan github io jq download to be able to run go generate and make abigen 3 install mockery make mockery using the make command will install the correct version 4 build contracts bash pushd contracts pnpm i pnpm compile native popd 4 generate and compile static assets bash go generate 5 prepare your development environment bash export cl database url postgresql 127 0 0 1 5432 chainlink test sslmode disable note other environment variables should not be set for all tests to pass 6 drop create test database and run migrations make testdb if you do end up modifying the migrations for the database you will need to rerun 7 run tests bash go test notes the parallel flag can be used to limit cpu usage for running tests in the background parallel 4 the default is gomaxprocs the p flag can be used to limit the number of packages tested concurrently if they are interferring with one another p 1 the short flag skips tests which depend on the database for quickly spot checking simpler tests in around one minute race detector as of go 1 1 the runtime includes a data race detector enabled with the race flag this is used in ci via the tools bin go core race tests script if the action detects a race the artifact on the summary page will include race files with detailed stack traces it will not issue false positives so take its warnings seriously for local targeted race detection you can run bash gorace log path pwd race go test race core path to pkg count 10 gorace log path pwd race go test race core path to pkg count 100 run testfoobar sub test https go dev doc articles race detector fuzz tests as of go 1 18 fuzz tests func fuzzxxx testing f are included as part of the normal test suite so existing cases are executed with go test additionally you can run active fuzzing to search for new cases bash go test pkg path run xxx fuzz fuzztestname https go dev doc fuzz go modules this repository contains three go modules mermaid flowchart rl github com smartcontractkit chainlink v2 github com smartcontractkit chainlink integration tests github com smartcontractkit chainlink v2 github com smartcontractkit chainlink core scripts github com smartcontractkit chainlink v2 the integration tests and core scripts modules import the root module using a relative replace in their go mod files so dependency changes in the root go mod often require changes in those modules as well after making a change go mod tidy can be run on all three modules using make gomodtidy solidity inside the contracts directory 1 install dependencies bash pnpm i 2 run tests bash pnpm test note chainlink is currently in the process of migrating to foundry and contains both foundry and hardhat tests in some versions more information can be found here chainlink foundry documentation https github com smartcontractkit chainlink blob develop contracts foundry md any t sol files associated with foundry tests contained within the src directories will be ignored by hardhat code generation go generate is used to generate mocks in this project mocks are generated with mockery https github com vektra mockery and live in core internal mocks nix a shell nix https nixos wiki wiki development environment with nix shell is provided for use with the nix package manager https nixos org with optional flakes https nixos wiki wiki flakes support it defines a declarative reproducible development environment flakes version use deterministic frozen flake lock dependencies while non flakes shell will use your channel s packages versions to use it 1 install nix package manager https nixos org download html in your system optionally enable flakes support https nixos wiki wiki flakes enable flakes 2 run nix shell you will be put in shell containing all the dependencies to use the flakes version run nix develop instead of nix shell optionally nix develop command shell will make use of your current shell instead of the default bash you can use direnv to enable it automatically when cd ing into the folder for that enable nix direnv https github com nix community nix direnv and use nix or use flake on it 3 create a local postgres database sh mkdir p pgdata cd pgdata initdb pg ctl l postgres log o unix socket directories pwd start createdb chainlink test h localhost createuser superuser password chainlink h localhost then type a test password e g chainlink and set it in shell nix cl database url 4 when re entering project you can restart postgres cd pgdata pg ctl l postgres log o unix socket directories pwd start now you can run tests or compile code as usual 5 when you re done stop it cd pgdata pg ctl o unix socket directories pwd stop tips for more tips on how to build and test chainlink see our development tips page https github com smartcontractkit chainlink wiki development tips contributing contributions are welcome to chainlink s source code please check out our contributing guidelines docs contributing md for more details thank you | chainlink ethereum blockchain golang solidity oracle | blockchain |
css | p align center img width 300px alt src docs src readme png p h1 align center primer css h1 p align center the css implementation of github s primer design system p p align center a aria label npm package href https www npmjs com package primer css img alt src https img shields io npm v primer css svg a a aria label build status href https github com primer css actions workflows ci yml img alt src https github com primer css actions workflows ci yml badge svg a a aria label contributors graph href https github com primer css graphs contributors img alt src https img shields io github contributors primer css svg a a aria label last commit href https github com primer css commits main img alt src https img shields io github last commit primer css svg a a aria label license href https github com primer css blob main license img src https img shields io github license primer css svg alt a p documentation warning the documentation of this repo is not maintained anymore please raise any documentation specific pull requests in primer style design https github com primer design our documentation site lives at primer style css https primer style css you ll be able to find detailed documentation on getting started all of the components our theme our principles and more install this repository is distributed with npm after installing npm install npm you can install primer css with this command sh npm install save primer css usage the included source files are written in sass using scss syntax after installing install with npm you can add your project s node modules directory to your sass include paths https github com sass node sass includepaths aka load paths http technology customink com blog 2014 10 09 understanding and using sass load paths in ruby then import it like this scss import primer css index scss you can import individual primer modules directly from the primer css package scss import primer css core index scss import primer css product index scss import primer css marketing index scss development see develop md develop md for development docs releasing for github staff you can find docs about our release process in releasing md releasing md license mit license copy github https github com install npm https docs npmjs com getting started installing node npm https www npmjs com primer https primer style sass http sass lang com | sass ui-components framework design-system design-systems primer css primer-css meta styleguide | os |
Course-Design-of-Embedded-System-Application | course design of embedded system application course design of embedded system application | os |
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explorer | blockchain unified explorer tldr try this https explorer bitquery io https explorer bitquery io bitquery explorer is built by bitquery io https bitquery io using bitquery widgets https github com bitquery widgets as user interface components the backend is supplied by the graphql interface vision we are building blockchain explorer in most extent all blockchain explorers are similar they all shows blocks transactions operations coin transfers and other technical information of the blockchain state almost all of them are specific for the blockchain protocol in rare cases explorer covers blockchain networks with different protocols why are we building just one more blockchain explorer we believe that it will give access to much larger amount of valuable information in the manner that was not possible before there are many layers of data in a blockchain as network block transaction transfer operation address and others depending on protocol technically giving access to all of them seems sufficient but it is not to see the full picture you need to look on information gathered from several of these layers in combination analytical capabilities are the key to our approach we use multi dimensional olap analytical database to display most of the information in explorer this gives us capability to aggregate data by dimensions as time block address token and others aggregation helps to build graphs diagrams and present data in the way that allows to see the bigger picture rather than just numbers explorer does not show the data just in the form of database records or tables there are more powerfull ways of presenting information as graphs diagrams plots maps histograms the way how we have to present information depends on typical ways of working with this kind of information and very dependent on the problem we want to solve for example transfers of the address can be presented as the list table graph by time period graph tree and in many other ways there is no single right way to make this presentation as for different tasks you may need a special way to work with this data that s why flexibility and adoption to a particular task is another key feature of bitquery explorer explorer is implemented as client side web application based on javascript it is built from independent parts widgets or components see bitquery widgets https github com bitquery widgets for details the parts are independent they can be placed on other web pages and will work the same way as in explorer widgets can be composed to build generic or specialized dashboards customize and personalize explorer pages in extreme they allow to build completely specialized explorer which will be ideal to solve some particular task we are following the following principles to execute the project explorer is for users not for the blockchains we are flexible about the information retrieval and presentation as it dependent on tasks and problems to solve explorer is modular as particular tasks may require specialized view of data dashboards or dedicated tools many blockchains are very similar in protocol or concept there is no reason to explore them differently new protocols appear every day extending the explorer should be easy as well as integrating this changes into ui community is open to extendm enhance explorer or build their open all presentation components are open sourced features supports multiple blockchains the full list available on the search button and counting unified extensible user interface you can use bitquery widgets https github com bitquery widgets with arbitrary queries to data thus making custom interfaces and explorers analytical view of data shows many types of data aggregations slices and relations which typical blockchain explorers do not free to use extend deploy and customize according to mit license https github com bitquery explorer blob master license quick start with docker and docker compose open env example docker compose env example file based on this file create env file in docker compose directory populate fields lc compose nameserver lc compose ipaddr lc compose secret key base lc compose explorer api key and lc compose bitquery ide api lc compose ipaddr should be your local ip use ipconfig command in terminal to get it if you don t have other values ask for them in development chat in slack or your team lead buddy in docker compose folder execute docker compose up command via terminal if you on windows make sure docker service for windows is running known issues if you see unable to load application keyerror key not found data error when puma starts it means one of your api keys either corrupted or not provided installation explorer is a typical ruby on rails https rubyonrails org project there is a minimum server side logic used mostly this is a set of pages with javascript loading bitquery widgets https github com bitquery widgets requirements are ruby v 2 6 3 rails v 6 0 1 refer to the rails installation guide https guides rubyonrails org how to set up environment and run the project no database or backend is required this is pure set of front end ui deployment for server deployment we use capistrano https github com capistrano capistrano to pre configure the server the following may be needed ruby installation sudo apt get install ruby all dev git gcc zlib1g dev make mysql client default libmysqlclient dev g puma libssl1 0 dev see https github com postmodern ruby install readme wget o ruby install 0 7 0 tar gz https github com postmodern ruby install archive v0 7 0 tar gz tar xzvf ruby install 0 7 0 tar gz cd ruby install 0 7 0 sudo make install sudo ruby install system ruby 2 6 3 sudo gem install bundler re login ruby version ruby 2 6 3p62 2019 04 16 revision 67580 x86 64 linux capistrano installation sudo adduser deploy sudo passwd l deploy sudo mkdir var www sudo mkdir var www explorer sudo chown r deploy deploy var www explorer nodejs installation curl sl https deb nodesource com setup 12 x sudo e bash sudo apt get install y nodejs curl ss https dl yarnpkg com debian pubkey gpg sudo apt key add echo deb https dl yarnpkg com debian stable main sudo tee etc apt sources list d yarn list sudo apt get update sudo apt get install yarn build widgets yarn upgrade widgets yarn install check files on some cases you need to downgrade the openssl usage in nodejs by export node options openssl legacy provider bundle exec rails assets precompile otherwise server will respond with error like error error 0308010c digital envelope routines unsupported run capistrano setup local name bitquery in your etc hosts file before running cap production deploy puma run as service look scripts explorer service scripts explorer service for details replace put your secret here with the secret for service sudo vim etc systemd system explorer service sudo systemctl enable explorer license you can check out the full license here https github com bitquery explorer blob master license this project is licensed under the terms of the mit license you are encouraged to build your ownb explorers data analytical web sites embed part of this project on your web site and more | blockchain explorer analytics ethereum bitcoin | blockchain |
trice | tricegirls png docs ref tricegirl 167x222 png trice tr ace i ds c e mbedded github io trice https rokath github io trice github workflow status https img shields io github workflow status rokath trice goreleaser github issues https img shields io github issues rokath trice github all releases https img shields io github downloads rokath trice total github code size in bytes https img shields io github languages code size rokath trice github watchers https img shields io github watchers rokath trice label watch github release latest by date https img shields io github v release rokath trice github commits since latest release https img shields io github commits since rokath trice latest go report card https goreportcard com badge github com rokath trice https goreportcard com report github com rokath trice prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com test https github com shogo82148 actions goveralls workflows test badge svg branch main https coveralls io github rokath trice coverage status https coveralls io repos github rokath trice badge svg branch master https coveralls io github rokath trice branch master log in a trice s g https www screentogif com docs ref life0 gif even inside interrupts in less than 1 s about replace printf or log in c code for getting speed docs tricespeed md to be usable also inside interrupts space docs tricespace md to reduce needed flash memory size features docs tracewithtrice md tricefeatures delighting the developers heart u main idea u logging strings not into an embedded device to display them later on a pc but keep usage comfortable and simple docs triceuserguide md 2 get started trice consists of 2 parts 1 c code trice macros generating tiny super fast embedded device real time trace log code 2 tool trice for managing and visualization written in go https golang org and therefore usable on all platforms go supports you can also use your own environment to receive the trice packages exchange the carried ids with the format string and print out trice user guide docs triceuserguide md possible use cases using trice not only for printf debugging but also as logging technique is possible and gives the advantage to have very short messages no strings for transmission but keep in mind that the file til json test testdata til json is the key to read all output if your devices in the field for 10 or more years optionally add til json test testdata til json as a compressed resource to your target image one possibility is using srecord http srecord sourceforge net download html or simply provide a download link you can see trice also as a kind of data compression what could be interesting for iot https en wikipedia org wiki internet of things things especially nb iot https en wikipedia org wiki narrowband iot where you have very low data rates storing trice messages in flash memory https en wikipedia org wiki flash memory for later log analysis saves memory because a typical trice occupies only 4 bytes independently of the format string length plus optional values also it is possible to encrypt the trice transfer packets to get a reasonable protection for many cases this way you can deliver firmware images with encrypted trice output only readable with the appropriate key and til json test mdk arm stm32f030r8 til json xtea https en m wikipedia org wiki xtea is implemented as one option you can even translate the til json test mdk arm stm32f030r8 til json file in different languages so changing a language is just changing the til json test mdk arm stm32f030r8 til json file without touching the target binary with trice it is easy to do timing analysis on distributed embedded systems host and target timestamps are supported how it approximately works uart example this slightly simplified view https github com jgraph drawio is explained here docs tracewithtrice md 4 how it works the main idea trice docs ref tricecobsblockdiagram svg data transfer implemented uart https en wikipedia org wiki universal asynchronous receiver transmitter connectable to virtual uart over usb rtt https www segger com products debug probes j link technology about real time transfer over j link third party segger com readme md and rtt over st link third party gost readme md a small separate microcontroller is always usable as interfaces bridge to gpio https circuitcellar com cc blog a trace tool for embedded systems i c https en wikipedia org wiki i c2 b2c spi https en wikipedia org wiki serial peripheral interface can https en wikipedia org wiki can bus lin https en wikipedia org wiki local interconnect network with a chip from ftdi check for example adafruit ft232h breakout https learn adafruit com adafruit ft232h breakout gpio i2c and spi are easy accessable display server option start trice ds inside a console option third party alacritty third party alacritty locally or on a remote pc and connect with several trice tool instances like with trice log p com15 ds for example documentation https interrupt memfault com blog trice https interrupt memfault com blog trice trice user guide docs triceuserguide md check the docs docs folder no need to read all this stuff it is just for help and reference support yes please or simply star it cloning the repo b git clone https github com rokath trice git similar projects baical net http baical net up7 html c call stack logger function instrumentation https dev to taugustyn call stack logger function instrumentation as a way to trace programs flow of execution 419a a way to trace programs flow of execution debugging with dynamic printf breakpoints https mcuoneclipse com 2022 02 09 debugging with dynamic printf breakpoints eclipse ide option defmt https github com knurling rs defmt rust diagnostic log and trace https github com covesa dlt daemon autosar elog https github com martinribelotta elog embedded logger with minimal footprint and memory usage j link system view https www segger com products development tools systemview technology what is systemview segger logging with symbols the embedonomicon https docs rust embedded org embedonomicon logging html memfault compact log library https docs memfault com docs mcu compact logs host decoding metal serial library https github com metal ci test tree master doc metal serial md minimal structured logging for autonomous vehikles https youtu be fyji4z6jd4w c closed source talk nanolog https github com platformlab nanolog linux c percepio tracealyzer https percepio com tracealyzer visual trace diagnostics pigweed trace tokenized https pigweed dev pw trace tokenized qpspy https www state machine com qtools qpspy html c c serial studio https github com serial studio serial studio data visualisation traces https github com yotamr traces api tracing framework for linux c c applications zepyr dictionary based logging https docs zephyrproject org 3 1 0 services logging index html dictionary based logging debugging using vs code and clang for a trice instrumented project in direct out mode over segger rtt see folder examples examples for more details x examples animation gif b a c k u p call stack logger function instrumentation https sii pl blog call stack logger function instrumentation as a way to trace programs flow of execution a way to trace programs flow of execution trice macros for c c code real fast 12 cpu clocks per short trice possible with a 48mhz clock this is 250ns light travels about 80 meters in that time trice in your code reduces the needed flash memory because the instrumentation code is very small can be less 200 bytes flash and about 100 bytes ram and no printf library code nor log strings are inside the embedded device anymore attention 4 in release v0 41 0 now the trice macro works additionally to use it simply use it like printf no need for parameter count and bit width the internal used parameter bit width is 32 bit but you can use also trice8 trice16 trice32 trice64 0 to 12 parameters possible extendable no strings supported s use trice s than many usage options inside pkg src tricecheck c visible needs better tests and updated documentation attention 3 in release v0 39 0 now encryption works again to implement it well and open for future the additional cobs package descriptor is now 4 bytes long that means the trice tool version 0 39 0 does not work with older target code please update your target code or stay with an older release probably the cobs encoding will not change in the next time anymore attention 2 in release v0 38 0 now target timestamps possible to implement it well and open for future an additional cobs package descriptor byte was added that means the trice tool version 0 38 0 does not work with older target code please update your target code or stay with an older release attention the trice technique changed heavily between release 0 33 0 and 0 34 0 the flex and esc encodings are replaced by a cobs https en wikipedia org wiki consistent overhead byte stuffing encoding which will be the default now the stuff works already well but is not in its final state and is not documented vet it lacks also automated tests the internal speed goes to its limit 6 clocks per trice on m0 possible by using a double buffer instead of a fifo also porting is easier now the documentation is outdated but gets updated soon but first the tests if you have a project with flex or esc encoding please update the target code or stay with version 0 33 0 because of the very short execution time of a trice you could add to the scheduler c trice16i tim tick 5u clock trice8i sig task u u n previoustaskid nextaskid the execution of this code block produces totally 8 log bytes to visualize the output on pc what looks similar to this for 3 task switches alt docs ref taskswitchtimesexample png first are the pc reception timestamps and after the port info are the used trice ids just for easy location inside the source code see the diferences between the blue ticks in this 3 lines these are 28 or 36 processor clocks only the code producing this is alt docs ref taskswitchtimesexamplecode png the same is possible for interrupt timing analysis mixed case trice macros are short docs triceencodings md flex short sub encoding trices and the letter i at the end says i nside critical section flex encoding trice16 tim myfunc d n systick before and after a function call lets you easy measure the function execution time as graphical visualization you could use a tool similar to https github com sqshq sampler https github com sqshq sampler target timestamps trice has intentionally no target timestamps for performance reasons also it is not foreseeable which time base is needed in which application on the pc you can display the reception timestamps because several trice statements can form a single log line a generally added timestamp would cause difficulties with that this could be handled but adds complexity not worth the effort but you can add own timestamps as parameters for exact embedded time measurements having several devices with trice timestamps network timing measurement is possible target timestamp examples lets say you have a 16 bit systick called systickval16 and 16 bit timestamps are fine for you simply add trice16 time 5u systickval16 everywhere you need exact time or use trice16 time 5u my values are d d d n systickval16 my0 my1 my2 same with a 32 bit systick called systickval32 simply add trice32 time 9u systickval32 everywhere you need exact time or use trice32 time 9u my values are d d d n systickval32 my0 my1 my2 this is a slightly simplified view https github com jgraph drawio trice docs ref trice4blockdiagram svg when the program flow passes the line trice16 id 12345 msg d kelvin n k the id 12345 and the 16 bit temperature value are transferred as one combined 32 bit value into the tricefifo what goes really fast different encodings are possible the program flow is nearly undisturbed so trice macros are usable also inside interrupts or in the scheduler for visualization a background service is needed in the simplest case it is just an uart triggered interrupt for tricefifo reading or you can use rtt docs triceoverrtt md so the whole target instrumentation are the trice macros the trice fifo and the uart isr during runtime the pc trice tool receives the trice as a 4 byte package 0x30 0x39 0x00 0x0e from the uart port the 0x30 0x39 is the id 12345 and a map lookup delivers the format string msg d kelvin n and also the format information trice16 1 now the trice tool is able to execute printf msg d kelvin n 0x000e and the full log information is displayed in the msg color only the parameter count and size affect encoding size but not the format string length trice pc tool manages trice macro ids inside a c or c source tree and extracts the strings in an id string list during target device compile time displays trice macros like printf output in real time during target device runtime the received ids and parameters are printed out can receive trices on several pcs and display them on a remote display server written in go https github com golang go simply usage no installer needs to be in path structured logging right now only event logging is implemented according to the design aim keep embedded device code small and fast there is no structuring code inside the target device but you can add channel information to the trice log strings c trice32 id 12345 verbose bla bla these can be understood as tags too but only one tag per trice right now look into linetransformeransi go internal emitter linetransformeransi go for options or extensions also you can at compile time disable trice code generation on file level with define trice off before including trice h because one trice consists typically only of 4 to 8 bytes docs triceencodings md flex encoding there is usually no need to dynamically switch trices on and off inside the embedded device this can be done on the display side inside the trice tool with the command line switches ban or pick for example pick err wrn disables all output despite error and warning messages switching trices on and off inside the target increases the overhead and demands some kind of command interface if needed always an if is usable the trice tool can also perform further tasks like json encoding with additional log information and transferring this information to some webserver in the future search counters github search hit counter https img shields io github search rokath trice trace github search hit counter https img shields io github search rokath trice instrumentation github search hit counter https img shields io github search rokath trice embedded github search hit counter https img shields io github search rokath trice logging github search hit counter https img shields io github search rokath trice real time github search hit counter https img shields io github search rokath trice debugging github search hit counter https img shields io github search rokath trice monitoring github search hit counter https img shields io github search rokath trice terminal github search hit counter https img shields io github search rokath trice cli github search hit counter https img shields io github search rokath trice diagnostics github search hit counter https img shields io github search rokath trice tool github search hit counter https img shields io github search rokath trice data recording github search hit counter https img shields io github search rokath trice rtos github search hit counter https img shields io github search rokath trice multi language support github search hit counter https img shields io github search rokath trice compression github search hit counter https img shields io github search rokath trice timing analysis github search hit counter https img shields io github search rokath trice time measurement github search hit counter https img shields io github search rokath trice golang github search hit counter https img shields io github search rokath trice printf github search hit counter https img shields io github search rokath trice encryption github search hit counter https img shields io github search rokath trice serial github search hit counter https img shields io github search rokath trice c | trace instrumentation embedded logging real-time debugging monitoring diagnostics tool data-recording rtos multi-language-support compression timing-analysis time-measurement golang printf encryption serial c | os |
llm4code.github.io | website for the llm4code workshop 2024 the website is built with jekyll https jekyllrb com and the theme is modified from https github com jekyll minima | ai |
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Agriculture-Vision | agriculture vision agriculture vision dataset challenge and workshop cvpr 2020 a joint effort with many great collaborators to bring agriculture and computer vision ai communities together to benefit humanity update the 2nd agriculture vision prize challenge has started https twitter com humphrey shi status 1382803774243287043 s 20 and new deadline is june 5 2021 total prize 20 000 content papers papers dataset dataset agriculture vision challenge dataset agriculture vision challenge dataset download download evaluation evaluation evaluation metric evaluation metric submission submission challenge challenge teams and methods teams and methods challenge leaderboard challenge leaderboard workshop workshop schedule schedule paper track paper track accepted papers accepted papers accepted posters accepted posters papers cvpr paper on agriculture vision br arxiv https arxiv org abs 2001 01306 cvpr 2020 open access https openaccess thecvf com content cvpr 2020 html chiu agriculture vision a large aerial image database for agricultural pattern analysis cvpr 2020 paper html bibtex article chiu2020agriculture title agriculture vision a large aerial image database for agricultural pattern analysis author chiu mang tik and xu xingqian and wei yunchao and huang zilong and schwing alexander and brunner robert and khachatrian hrant and karapetyan hovnatan and dozier ivan and rose greg and others journal arxiv preprint arxiv 2001 01306 year 2020 inproceedings chiu 2020 cvpr author chiu mang tik and xu xingqian and wei yunchao and huang zilong and schwing alexander g and brunner robert and khachatrian hrant and karapetyan hovnatan and dozier ivan and rose greg and wilson david and tudor adrian and hovakimyan naira and huang thomas s and shi honghui title agriculture vision a large aerial image database for agricultural pattern analysis booktitle proceedings of the ieee cvf conference on computer vision and pattern recognition cvpr month june year 2020 cvpr workshop paper on the 1st agriculture vision challenge challenge dataset methods and results br arxiv https arxiv org abs 2004 09754 cvpr 2020 open access https openaccess thecvf com content cvprw 2020 html w5 chiu the 1st agriculture vision challenge methods and results cvprw 2020 paper html bibtex article chiu20201st title the 1st agriculture vision challenge methods and results author chiu mang tik and xu xingqian and wang kai and hobbs jennifer and hovakimyan naira and huang thomas s and shi honghui and others journal arxiv preprint arxiv 2004 09754 year 2020 inproceedings chiu 2020 cvpr workshops author chiu mang tik and xu xingqian and wang kai and hobbs jennifer and hovakimyan naira and huang thomas s and shi honghui title the 1st agriculture vision challenge methods and results booktitle proceedings of the ieee cvf conference on computer vision and pattern recognition cvpr workshops month june year 2020 dataset agriculture vision challenge dataset img src images cs gif width 300 title cloud shadow img src images dp gif width 300 title double plant img src images ps gif width 300 title planter skip img src images sw gif width 300 title standing water img src images ww gif width 300 title waterway img src images wc gif width 300 title weed cluster the dataset used in this challenge is a subset of the agriculture vision dataset https arxiv org abs 2001 01306 the challenge dataset contains 21 061 aerial farmland images captured throughout 2019 across the us each image consists of four 512x512 color channels which are rgb and near infra red nir each image also has a boundary map and a mask the boundary map indicates the region of the farmland and the mask indicates valid pixels in the image regions outside of either the boundary map or the mask are not evaluated this dataset contains six types of annotations cloud shadow double plant planter skip standing water waterway and weed cluster these types of field anomalies have great impacts on the potential yield of farmlands therefore it is extremely important to accurately locate them in the agriculture vision dataset these six patterns are stored separately as binary masks due to potential overlaps between patterns users are free to decide how to use these annotations each field image has a file name in the format of field id x1 y1 x2 y2 jpg png each field id uniquely identifies the farmland that the image is cropped from and x1 y1 x2 y2 is a 4 tuple indicating the position in which the image is cropped please refer to our paper for more details regarding how we construct the dataset download the challenge dataset contains images boundaries and masks for train val and test set it also contains labels for the train and val set only the 2021 new dataset tar gz file is around 20 gb please visit this page https www agriculture vision com agriculture vision 2021 dataset 2021 to get access by downloading or using the dataset user signifies its agreement to agriculture vision workshop terms and conditions https drive google com file d 1tijpmjcr8mlf1pyu 6u6zy5jnngbn9xf view usp sharing evaluation evaluation metric we use mean intersection over union miou as our main quantitative evaluation metric which is one of the most commonly used measures in semantic segmentation datasets the miou is computed as img src images metric png where c is the number of annotation types c 7 in our dataset with 6 patterns background p sub c sub and t sub c sub are the predicted mask and ground truth mask of class c respectively since our annotations may overlap we modify the canonical miou metric to accommodate this property for pixels with multiple labels a prediction of either label will be counted as a correct pixel classification for that label and a prediction that does not contain any ground truth labels will be counted as an incorrect classification for all ground truth labels concretely we construct the confusion matrix m sup c c sup with the following rules for each prediction x and label set y 1 if x y then m sub y y sub m sub y y sub 1 for each y in y br 2 otherwise m sub x y sub m sub x y sub 1 for each y in y br the miou is finally computed by true positive prediction target true positive averaged across all classes submission update the 1st agriculture vision prize challenge has ended on april 20 2020 10 00 am pdt the final results as of the end date of the challenge can be found in these links png https github com shi labs agriculture vision raw master codalab challenge results png csv https raw githubusercontent com shi labs agriculture vision master codalab challenge results csv the codalab evaluation server will remain open to encourage further research in agriculture vision no registration is required registration we are now hosting our evaluation competition on codalab the competition page can be found here https competitions codalab org competitions 23732 secret key dba10d3a a676 4c44 9acf b45dc92c5fcf each participating team is required to register for the challenge to register your team fill out the registration form here then register on the competition page make sure your codalab account email matches one of the member emails in the registration form each team can only register once codalab submission all registered teams can evaluate their results on codalab and publish their results on the leaderboard the submission file should be a compressed zip file that contains all prediction images all prediction images should be in png format and the file names and image sizes should match the input images exactly the prediction images will be converted to a 2d numpy array with the following code numpy array pil image open field id x1 y1 x2 y2 png in the loaded numpy array only 0 6 integer labels are allowed and they represent the annotations in the following way 0 background br 1 cloud shadow br 2 double plant br 3 planter skip br 4 standing water br 5 waterway br 6 weed cluster br this label order will be strictly followed during evaluation all teams can have 2 submissions per day and 20 submissions in total challenge we hosted the 1st agriculture vision prize challenge with the following terms b model size will be limited below 150m parameters in total b b the miou derived from the results folder in the final submission should match the miou on the leaderboard b b predictions in results in the final submission can be reproduced with the resources in code and models b b the training process of the method can be reproduced and the retrained model should have a similar performance b b the test set is off limits in training b b for fairness teams need to specify what public datasets are used for training pre training their models in their challenge report pdf results that are generated from models using private datasets and results without such details will be excluded from prize evaluation results using private datasets can still be included in the report b b the prize award will be granted to the top 3 teams on the prize challenge leaderboard closed on 4 20 2020 that provide a valid final submission teams and methods please refer to our workshop summary paper https arxiv org abs 2004 09754 for more details regarding notable methods selected teams methods team hyunseong hyunseong park junhee kim sungho kim agency for defense development south korea method residual densenet with expert network for semantic segmentation team scg vision qinghui liu michael c kampffmeyer robert jenssen arnt b salberg norwegian computing center uit the arctic university of norway method multi view self constructing graph convolutional networks with adaptive class weighting loss for semantic segmentation https arxiv org abs 2004 10327 team agr alexandre barbosa rodrigo trevisan university of illinois at urbana champaign method techniques for improving aerial agricultural image semantic segmentation team tju bingchen zhao shaozuo yu siwei yang yin wang tongji university method reducing the feature divergence of rgb and near infrared images using switchable normalization http info zhaobc me files cvprw 2020 agrivis pdf team haossr hao sheng xiao chen jingyi su ram rajagopal andrew ng stanford university chegg inc method effective data fusion with generalized vegetation index evidence from land cover segmentation in agriculture team cnupr th2l van thong huynh soo hyung kim in seop na chonnam national university chosun university method multi spectra attention in aerial agricultural segmentation team teamtiger ujjwal baid shubham innani prasad dutande bhakti baheti sanjay talbar sggs institute of engineering and technology method eff pn a novel deep learning approach for anomaly pattern segmentation in aerial agricultural images challenge leaderboard team miou background cloud shadow double plant planter skip standing water waterway weed cluster hyunseong 63 9 80 6 56 0 57 9 57 5 75 0 63 7 56 9 seungjae 62 2 79 3 44 4 60 4 65 9 76 9 55 4 53 2 yjl912 2 61 5 80 1 53 7 46 1 48 6 76 8 71 5 53 6 ddcm 60 8 80 5 51 0 58 6 49 8 72 0 59 8 53 8 rodrigotrevisan 60 5 80 2 43 8 57 5 51 6 75 3 66 2 49 2 sydu 59 5 81 3 41 6 50 3 43 4 73 2 71 7 55 2 agri 59 2 78 2 55 8 42 9 42 0 77 5 64 7 53 2 tennant 57 4 79 9 36 6 54 8 41 4 69 8 66 9 52 0 celery03 0 55 4 79 1 38 9 43 3 41 2 73 0 61 5 50 5 stevenwudi 55 0 77 4 42 0 54 4 20 1 69 5 67 7 53 8 paii 55 0 79 9 38 6 47 6 26 2 74 6 62 1 55 7 agrichallenge1 2 54 6 80 9 50 9 39 3 29 2 73 4 57 8 50 5 hui 54 0 80 2 41 6 46 4 20 8 72 8 64 8 51 4 shenchen61 6 53 7 79 4 36 7 56 3 21 6 67 0 61 8 52 8 ntu 53 6 79 8 41 4 49 4 13 5 73 3 61 8 56 0 tpys 53 0 81 1 50 5 37 1 25 9 67 4 58 7 50 1 simple 52 7 80 2 40 0 45 2 24 6 70 9 57 6 50 4 ursus 52 3 78 9 36 3 37 8 34 4 69 3 57 1 52 3 liepieshov 52 1 77 2 40 2 46 0 16 0 71 3 62 9 51 1 lunhao 49 4 79 5 40 4 38 8 10 5 69 4 58 3 49 1 tetelias mipt 49 2 80 4 37 8 34 8 04 6 70 6 62 5 53 8 dataloader 48 9 79 1 42 0 35 8 09 1 68 7 56 7 51 3 hakjin 46 4 78 6 32 0 38 3 01 8 66 2 58 0 49 9 jianyutang 44 6 78 1 37 9 31 8 15 4 47 3 54 8 46 9 haossr 43 9 79 2 21 4 28 1 02 7 67 5 56 4 52 3 rpartsey 41 5 72 5 21 6 36 2 09 1 59 7 40 7 50 6 baidujjwal 40 8 75 2 26 1 40 1 09 9 48 0 37 1 49 5 chaturlal 40 7 77 7 23 0 20 4 05 0 55 0 51 0 52 9 sciforce 40 2 80 5 29 6 24 4 0 0 41 2 55 9 50 0 mustafaa 40 1 76 5 34 4 25 6 11 1 46 0 36 5 50 3 haotianyan 36 8 77 1 21 9 25 1 13 7 57 5 24 3 37 9 gro 36 3 76 4 37 5 08 4 0 0 60 3 29 7 41 8 oscmansan 35 5 71 6 29 6 03 0 0 0 52 4 46 2 45 9 thorstenc 33 6 72 3 22 3 10 0 02 0 40 8 40 1 47 8 zhwang 33 5 76 5 32 4 12 9 0 0 57 2 15 9 39 9 fayzur2 0 22 1 65 4 21 8 02 2 00 2 23 3 13 4 28 7 gaslen 2 21 5 71 0 03 3 17 9 00 8 10 2 06 9 40 1 dvkhandelwal 16 3 71 5 0 0 0 0 0 0 42 6 0 0 0 0 ajeetsinghiitd 10 3 56 9 00 2 00 4 0 0 0 0 00 1 14 5 workshop schedule sunday jun 14 pdt 08 30 08 40 opening remarks and welcome http cvpr20 com events w57 opening remarks humphrey shi university of oregon naira hovakimyan uiuc 08 40 09 00 invited talk 1 scaling spatio temporal analytics a case in agricultural insights http cvpr20 com event w57 invited talk 1 talk video here https www youtube com watch v vhsflxlzmd4 list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 2 t 0s sharath pankanti ibm t j watson research center 09 00 09 20 invited talk 2 multi modality remote sensing in high throughput phenotyping opportunities for machine learning http cvpr20 com event w57 invited talk 2 talk video here https www youtube com watch v muzzlo doni list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 3 t 0s melba m crawford edward j delp purdue 09 20 09 40 invited talk 3 bridging application and research is agriculture just another application http cvpr20 com event w57 invited talk 3 talk video here https www youtube com watch v vwy0esw f4w list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 4 t 0s jennifer hobbs intelinair inc 09 40 10 00 invited talk 4 improving visual and speech recognition on out domain data http cvpr20 com event w57 invited talk 4 talk video here https www youtube com watch v 2kovfm8ye1o list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 5 t 0s liangliang cao google inc and umass 10 00 10 20 invited talk 5 learning to anticipate http cvpr20 com event w57 invited talk 5 talk video here https www youtube com watch v wrldo8pqj1e list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 6 t 8s alex schwing uiuc 10 20 11 10 poster session 1 http cvpr20 com w57 poster session 1 11 10 11 20 oral 1 mscg net with adaptive class weighting loss for semantic segmentation http cvpr20 com w57 oral presentation 1 talk video here https youtu be rpln kneyyo br 11 20 11 30 oral 2 finding berries segmentation and counting of cranberries using point supervision and shape priors http cvpr20 com w57 oral presentation 2 talk video here https youtu be vzwvbdbrvvk br 11 30 11 40 oral 3 visual 3d reconstruction and dynamic simulation of fruit trees for robotic manipulation http cvpr20 com w57 oral presentation 3 talk video here https youtu be lllcolf vsw br 11 40 11 50 oral 4 cross regional oil palm tree detection http cvpr20 com w57 oral presentation 4 talk video here https youtu be udcfznura14 br 11 50 11 00 oral 5 effective data fusion with generalized vegetation index http cvpr20 com w57 oral presentation 5 talk video here https youtu be 8pdwvr3qovi br 11 00 12 10 oral 6 weakly supervised learning guided by activation mapping applied to a novel citrus pest benchmark http cvpr20 com w57 oral presentation 6 talk video here https youtu be haqpohzlzdo br 12 10 12 20 oral 7 fine grained recognition in high throughput phenotyping http cvpr20 com w57 oral presentation 7 talk video here https youtu be hbhrfrmwzww br 12 20 12 30 oral 8 climate adaptation reliably predicting from imbalanced satellite data http cvpr20 com w57 oral presentation 8 talk video here https youtu be 47rcdg0gejk br 12 30 13 30 lunch break http cvpr20 com events w57 opening remarks 13 30 13 50 invited talk 6 towards understanding agricultural systems at scale using machine learning http cvpr20 com event w57 invited talk 6 talk video here https www youtube com watch v eptoqwbitmi list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 7 t 0s stefano ermon stanford 13 50 14 10 invited talk 7 interactive object segmentation live http cvpr20 com events w57 opening remarks talk video here https www youtube com watch v iwfcbl yadm list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 8 t 2s yunchao wei university of technology sydney 14 10 14 30 invited talk 8 project farmbeats aerial mapping foragricultural farms and beyond live http cvpr20 com events w57 opening remarks talk video here https youtu be 7xgx2ydr5q ranveer chandra sudipta sinha microsoft 14 30 14 50 invited talk 9 enabling the african farmer live http cvpr20 com events w57 opening remarks talk video here https www youtube com watch v xjh lrhk4bk list plptqk8rjz9hwykozoeh08mfuxxsijoggu index 10 t 0s munther dahleh mit 14 50 15 10 invited talk 10 live http cvpr20 com events w57 opening remarks zsolt kira georgia tech 15 10 15 55 poster session 2 http cvpr20 com w57 poster session 2 15 55 16 10 agriculture vision prize challenge results summary http cvpr20 com event w57 challenge summary talk video here https drive google com drive folders 1lbz15ut58r a rcflar0bsu2mlujusqe usp sharing 16 10 16 20 challenge presentation 1 residual densenet with expert network for semantic segmentation http cvpr20 com event w57 challenge presentation 1 16 20 16 30 challenge presentation 2 mscg net models for the 1st agriculture vision challenge http cvpr20 com event w57 challenge presentation 2 16 30 17 20 panel discussion challenge opportunities for computer vision in agriculture http cvpr20 com event w57 panel discussion video here https youtu be 3afvcmw92va host br naira hovakimyan uiuc panelists br sharath pankanti ibm t j watson research center liangliang cao google research ranveer chandra microsoft azure global sudipta sinha microsoft munther dahleh mit ed delp purdue melba crawford purdue jennifer hobbs intelinair inc yunchao wei university of technology sydney alexander schwing uiuc zsolt kira georgia tech david wilson intelinair inc jim yuan google x humphrey shi u of oregon 17 20 17 30 closing remarks http cvpr20 com event w57 closing remarks humphrey shi university of oregon naira hovakimyan uiuc paper track we accept papers related to agriculture vision with a rigorous peer review process with program committee members from multiple research communities including computer vision machine learning image processing remote sensing agriculture etc all accepted paper will be published in ieee cvpr 2020 workshop proceedings several top accepted papers will be given oral presentation opportunity at our workshop the rest accepted papers will be given poster presentation slots during the workshop topic description the workshop will be open to the broader community addressing various technical and application aspects of challenges and opportunities in computer vision research for agriculture we aim to provide a venue to both show current relevant efforts in interdisciplinary areas between computer vision and agriculture and to encourage further research and conversations within the computer vision community to tackle impactful agriculture vision problems we will solicit papers from the following wide range of topics computer vision research for agricultural imagery in general resources and benchmarks for agricultural imagery based pattern analysis farmland pattern classification and segmentation from lshr aerial imagery efficient data sampling methods for effective training on agricultural imageries effective data fusion of multi spectral image data such as rgb and near infrared rgb nir robust and stable pattern recognition on sparse and imbalanced annotations other novel vision applications in agriculture accepted papers multi view self constructing graph convolutional networks with adaptive class weighting loss for semantic segmentation https arxiv org abs 2004 10327 oral br qinghui liu michael c kampffmeyer robert jenssen arnt b salberg norwegian computing center uit the arctic university of norway reducing the feature divergence of rgb and near infrared images using switchable normalization http info zhaobc me files cvprw 2020 agrivis pdf poster br siwei yang shaozuo yu bingchen zhao yin wang equal contribution tongji university finding berries segmentation and counting of cranberries using point supervision and shape priors https arxiv org abs 2004 08501 oral br peri akiva kristin dana peter oudemans michael mars rutgers university leaf spot attention network for apple leaf disease identification poster br hee jin yu chang hwan son kunsan national university visual 3d reconstruction and dynamic simulation of fruit trees for robotic manipulation oral br francisco j yandun abhisesh silwal george kantor carnegie mellon university cross regional oil palm tree detection oral br wenzhao wu juepeng zheng haohuan fu weijia li le yu tsinghua university the chinese university of hong kong multi stream cnn for spatial resource allocation a crop management application poster br alexandre barbosa thiago marinho nicolas martin naira hovakimyan university of illinois at urbana champaign effective data fusion with generalized vegetation index evidence from land cover segmentation in agriculture oral br hao sheng xiao chen jingyi su ram rajagopal andrew ng stanford university deep transfer learning for plant center localization poster br enyu cai sriram baireddy changye yang melba crawford edward delp purdue university segmentation and detection from organised 3d point clouds a case study in broccoli head detection poster br justin le lou dec hector a montes tom duckett grzegorz cielniak university of lincoln deep learning based corn kernel classification poster br henry velesaca ra l mira patricia l suarez christian larrea angel sappa espol computer vision center spain improving in field cassava whitefly pest surveillance with machine learning poster br jeremy tusubira solomon nsumba flavia ninsiima benjamin akera guy acellam joyce nakatumba nabende ernest mwebaze john a quinn tonny oyana makerere university google weakly supervised learning guided by activation mapping applied to a novel citrus pest benchmark https arxiv org abs 2004 11252 oral br edson riberto bollis helio pedrini sandra avila unicamp fine grained recognition in high throughput phenotyping oral br beichen lyu stuart smith keith cherkauer purdue university a novel technique combining image processing plant development properties and the hungarian algorithm to improve leaf detection in maize https arxiv org abs 2005 09022 poster br nazifa khan ian mcquillan mark eramian oliver a s lyon university of saskatchewan queen s university farm parcel delineation using spatio temporal convolutional networks https arxiv org abs 2004 05471 poster br han lin aung burak uzkent marshall burke david lobell stefano ermon stanford university climate adaptation reliably predicting from imbalanced satellite data https arxiv org abs 2004 12344 oral br ruchit rawal prabhu pradhan nsit max planck institute for intelligent systems accepted posters scoring root necrosis in cassava using semantic segmentation br joyce nakatumba nabende jeremy tusubira benjamin akera ernest mwebaze makerere university google active learning sampling strategies for weed segmentation br andrei polzounov anshul tibrewal ben cline olgert denas chris padwick jim ostrowski blue river technology convolutional neural networks for image based corn kernel detection and counting https arxiv org abs 2003 12025 br saeed khaki hieu pham ye han andy kuhl wade kent lizhi wang iowa state university syngenta eff pn a novel deep learning approach for anomaly pattern segmentation in aerial agricultural images br shubham innani prasad dutande ujjwal r baid bhakti vilas baheti sanjay talbar sggsiet nanded deep unsupervised deblurring approach for improving crops disease classification br javed ahmad fahad shamshad junaid maqbool ali ahmed information technology university ndvi vegetation index estimation through an unsupervised deep learning based approach br patricia l suarez angel sappa boris vintimilla espol polytechnic university plants disease detection in low data regime br shruti jadon brown university rhode island hospital semi supervised crop detection in uav orthophotographs br jana wieme sam leroux simon cool ruben van de vijver wouter h maes jan pieters ghent university counting corn stands using drone images from cloud to edge a deep learning based object detection approach br zhiyi li kshitiz dhakal song li virginia tech | ai |
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Cloud-DevOps-ND---C4--Microservices-at-Scale-using-AWS-Kubernetes---Supporting-Material-and-Projec | cloud devops nd c4 microservices at scale using aws kubernetes supporting material and project starter this repository is associated with cloud devops nd course 04 microservices at scale using aws kubernetes in here you ll find 1 supporting material used in the video demonstration in the course 1 starting code for a project in which you can containerize and deploy a machine learning srevice using kubernetes a dependencies a 1 python download and install the python https www python org downloads a 2 docker desktop you would require you to install docker desktop to create containers for individual microservices refer the following links for instructions macos https docs docker com docker for mac install windows 10 64 bit pro enterprise or education https docs docker com docker for windows install windows 10 64 bit home https docs docker com toolbox toolbox install windows you can find installation instructions for other operating systems at https docs docker com install a 3 kubernetes you would need to install any one tool for creating a kubernetes cluster kubeone minikube kubectl on top of docker desktop 1 install and set up kubectl https kubernetes io docs tasks tools install kubectl directly on top of docker desktop for windows macos 2 install minikube https kubernetes io docs tasks tools install minikube for linux macos a 4 aws account to access aws lambda you ll need an aws account https aws amazon com free all free tier all free tier sort by item additionalfields sortrank all free tier sort order asc to get started with aws lambda https aws amazon com lambda which is a serverless computing platform on cloud a 5 an account with circle ci you may sign up on circleci com https circleci com signup with your github credentials b the overarching diagram overview https camo githubusercontent com bb29cd924f9eb66730bbf7b0ed069a6ae03d2f1a 68747470733a2f2f757365722d696d616765732e67697468756275736572636f6e74656e742e636f6d2f35383739322f35353335343438332d62616537616638302d353437612d313165392d393930392d6135363231323531303635622e706e67 c tutorials c 1 aws lambda serverless making change https github com udacity devops microservices tree master lambda functions make change tutorial create and deploy a serverless lambda function that responds to an input request this example creates the correct amount of change to make up a value in us dollars wikipedia query https github com udacity devops microservices tree master lambda functions wikipedia query deploy a lambda function that responds to an input wikipedia page query this example returns the first sentence of a specific wikipedia page upon being queried d project instructions operationalize a machine learning microservice api https github com udacity devops microservices tree master project ml microservice kubernetes deploy a containerized machine learning application using kubernetes to run any project code you ll have to set up a virtual environment with the project dependencies all of the following instructions are to be completed via a terminal command line prompt e create and activate an environment e 1 git and version control these instructions also assume you have git installed for working with github from a terminal window but if you do not you can download that first from this github installation page https www atlassian com git tutorials install git now you re ready to create your local environment 1 if you haven t already done so clone the project repository and navigate to the main project folder bash git clone https github com udacity devops microservices git cd devops microservices project ml microservice kubernetes 2 create and activate a new environment named devops with python 3 if prompted to proceed with the install proceed y n type y bash python3 m venv devops source devops bin activate at this point your command line should look something like devops user project ml microservice kubernetes user the devops indicates that your environment has been activated and you can proceed with further package installations 3 installing dependencies via project makefile many of the project dependencies are listed in the file requirements txt these can be installed using pip commands in the provided makefile while in your project directory type the following command to install these dependencies bash make install now most of the devops libraries are available to you there are a couple of other libraries that we ll be using which can be downloaded as specified below e 2 other libraries while you still have your devops environment activated you will still need to install docker hadolint kubernetes minikube https kubernetes io docs tasks tools install minikube if you want to run kubernetes locally e 3 docker you will need to use docker to build and upload a containerized application if you already have this installed and created a docker account you may skip this step 1 you ll need to create a free docker account https hub docker com signup where you ll choose a unique username and link your email to a docker account your username is your unique docker id 2 to install the latest version of docker choose the community edition ce for your operating system on docker s installation site https docs docker com v17 12 install it is also recommended that you install the latest stable release 3 after installation you can verify that you ve successfully installed docker by printing its version in your terminal docker version e 4 run lint checks this project also must pass two lint checks hadolint checks the dockerfile for errors and pylint checks the app py source code for errors 1 install hadolint following the instructions on hadolint s page https github com hadolint hadolint for mac bash brew install hadolint for windows bash scoop install hadolint 2 in your terminal type make lint to run lint checks on the project code if you haven t changed any code all requirements should be satisfied and you should see a printed statement that rates your code and prints out any additional comments bash your code has been rated at 10 00 10 that s about it when working with kubernetes you may need to install some other libraries but these instructions will set you up with an environment that can build and deploy docker containers | cloud |
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iotdb | internet of things nmap fingerprints a repository of nmap scans for various iot devices the following command was run to perform the scan nmap n pn ss pt 0 65535 v a ox name ip where name is a descriptive name for the device ex wink hub and ip is the ip address the device has on your local network the above command should complete within an hour but it doesn t check for any udp services that the device may be running to perform a complete scan of the device that checks both tcp and udp please run the following command nmap n pn ssu pt 0 65535 u 0 65535 v a ox name ip if you re on an ipv6 network also try running the following command to get a fingerprint of its ipv6 capabilities nmap 6 n pn ssu pt 0 65535 u 0 65535 v a ox name ip to have your nmap scan results added to this repository either submit a pull request or send your xml file to support shodan io | server |
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ContextDET | h2 align center width 100 contextual object detection with multimodal large language models h2 div div align center a href https yuhangzang github io target blank yuhang zang a emsp a href https weivision github io target blank wei li a emsp a href https www linkedin com in han jun 581849193 target blank jun han a emsp a href https kaiyangzhou github io target blank kaiyang zhou a emsp br a href https www mmlab ntu com person ccloy index html target blank chen change loy a emsp div div div align center s lab nanyang technological university div p align center a href https arxiv org abs 2305 18279 target blank img src http img shields io badge cs cv arxiv 3a2305 18279 b31b1b svg a a href https www mmlab ntu com project contextdet index html target blank img src https img shields io badge project page f0 9f 93 9a lightblue a a href https huggingface co spaces yuhangzang contextdet demo img src https img shields io badge f0 9f a4 97 20hugging 20face spaces blue a p currently we only offer the a href https huggingface co spaces yuhangzang contextdet demo hugging face demo code a the code dataset and training scripts will be made available once this paper is accepted contextual object detection recent multimodal large language models mllms are remarkable in vision language tasks such as image captioning and question answering but lack the essential perception ability i i e i object detection in this work we address this limitation by introducing a novel research problem of strong contextual object detection strong understanding visible objects within different human ai interactive contexts three representative scenarios are investigated including the language cloze test visual captioning and question answering div style text align center img src asset benchmark png width 100 height 100 div comparison with related works task language input output s remark object detection box class label pre defined class labels open vocabulary object detection optional class names for clip box class label pre defined class labels referring expression comprehension complete referring expression box that expression refers to b contextual cloze test b ours b incomplete b expression object names are masked box b name b to complete the mask b name b could be most valid english word image captioning language caption b contextual captioning b ours language caption b box b visual question answering language question language answer b contextual qa b ours language question language question b box b method we present contextdet a novel i generate then detect i framework specialized for contextual object detection contextdet is end to end and consists of three key architectural components 1 a visual encoder that extracts high level image representations and computes visual tokens 2 a pre trained llm that decodes multimodal contextual tokens with a task related multimodal prefix and 3 a visual decoder that predicts matching scores and bounding boxes for conditional queries linked to contextual object words the new strong generate then detect strong framework enables us to detect object words within human vocabulary div style text align center img src asset framework png width 100 height 100 div qualitative examples div style text align center img src asset background png width 100 height 100 div try demo you can try our demo on a href https huggingface co spaces yuhangzang contextdet demo huggingface spaces a to avoid waiting in the queue and speed up your inference consider a href https huggingface co spaces yuhangzang contextdet demo duplicate true duplicating the space a and use gpu resources if you want to try the demo on your own computer with gpu follow these steps 1 install the required python packages bash pip install r requirements txt 2 download the checkpoint file from the following a href https drive google com file d 1ko qpvhahpmi7asrkalnsakj2myhmqfg view usp share link url a and save it in your local directory 3 now you re ready to run the demo execute the following command bash python app py you are expected to see the following web page div style text align center img src asset demo png width 100 height 100 div citation we would be grateful if you consider citing our work if you find it useful bibtex article zang2023contextual author zang yuhang and li wei and han jun and zhou kaiyang and loy chen change title contextual object detection with multimodal large language models journal arxiv preprint arxiv 2305 18279 year 2023 liscense this project is licensed under a rel license href https github com yuhangzang contextdet blob master license s lab license 1 0 a redistribution and use for non commercial purposes should follow this license acknowledgement we acknowledge the use of the following public code in this project sup 1 sup detr https github com facebookresearch detr sup 2 sup deformable detr https github com fundamentalvision deformable detr sup 3 sup deta https github com jozhang97 deta sup 4 sup ov detr https github com yuhangzang ov detr sup 5 sup blip2 https github com salesforce lavis tree main projects blip2 contact if you have any questions please feel free to contact yuhang zang b zang0012 at ntu edu sg b | large-language-model object-detection | ai |
Algorithmic-Trading | algorithmic trading this machine learning algorithm was built using python 3 and scikit learn with a decision tree classifier the program gathers stock data using the google finance api and pandas the data is illustrated using matplotlib screenshot algo jpg the red lines illustrate the stock price movements when we are not holding the stock while the green lines show these movements when we are holding the stock the blue lines illustrate cash levels over time where we start with 100 so in this case we can also interpret this as the percentage return on the stock the expected cash value is the return we would have received if we simply held onto the stock for the entire period the performance is the ratio between the cash value over the expected cash value and is expressed as a percentage below is a screenshot of the algorithm s results on a large sample of stocks where changes in price are generated randomly screenshot results jpg overall this algorithm predicts whether the stock price will rise or fall with approximately a 75 accuracy this is far better than the 50 produced when randomly guessing additionally the performance value of 204 indicates that applying this algorithm returns 204 of the amount that would have been returned if we simply held on to the stock for the entire time period this illustrates that it is more profitable to apply this algorithm than to simply invest in a stock for a long duration furthermore this applies to both cases where the stock price rises or falls in the long run since this performance figure was generated by a large sample | python scikit-learn pandas matplotlib stock google-finance decision-tree data-mining machine-learning algorithmic-trading data-science scipy | ai |
clinspacy | readme md is generated from readme rmd please edit that file clinspacy badges start lifecycle stable https img shields io badge lifecycle stable brightgreen svg https lifecycle r lib org articles stages html stable badges end the goal of clinspacy is to perform biomedical named entity recognition unified medical language system umls concept mapping and negation detection using the python spacy scispacy and medspacy packages installation you can install the cran version of clinspacy with install packages clinspacy you can install the github version of clinspacy with remotes install github ml4lhs clinspacy install opts no multiarch how to load clinspacy r library clinspacy initiating clinspacy note the very first time you run clinspacy init or clinspacy after installing the package you may receive an error stating that spacy was unable to be imported because it was not found restarting your r session should resolve the issue initiating clinspacy is optional if you do not initiate the package using clinspacy init it will be automatically initiated without the umls linker the umls linker takes up 12 gb of ram so if you would like to use the linker you can initiate clinspacy with the linker the linker can still be added on later by reinitiating with the use linker argument set to true r clinspacy init this is optional the default functionality is to initiatie clinspacy without the umls linker named entity recognition without the umls linker the clinspacy function can take a single string a character vector or a data frame it can output either a data frame or a file name a single character string as input r clinspacy this patient has diabetes and ckd stage 3 but no htn 0 100 clinspacy id entity lemma is family is historical is hypothetical is negated is uncertain section category 1 1 patient patient false false false false false na 2 1 diabetes diabetes false false false false false na 3 1 ckd stage 3 ckd stage 3 false false false false false na 4 1 htn htn false false false true false na clinspacy history he presents with chest pain pmh htn medications this patient with diabetes is taking omeprazole aspirin and lisinopril 10 mg but is not taking albuterol anymore as his asthma has resolved allergies penicillin verbose false clinspacy id entity lemma is family is historical is hypothetical is negated is uncertain 1 1 chest pain chest pain false true false false false 2 1 pmh pmh false false false false false 3 1 htn htn false false false false false 4 1 patient patient false false false false false 5 1 diabetes diabetes false false false false false 6 1 omeprazole omeprazole false false false false false 7 1 aspirin aspirin false false false false false 8 1 lisinopril lisinopril false false false false false 9 1 albuterol albuterol false false false true false 10 1 asthma asthma false false false true false 11 1 penicillin penicillin false false false false false section category 1 na 2 past medical history 3 past medical history 4 medications 5 medications 6 medications 7 medications 8 medications 9 medications 10 medications 11 allergies a character vector as input r clinspacy c this pt has ckd and htn pt only has ckd but no htn verbose false clinspacy id entity lemma is family is historical is hypothetical is negated is uncertain section category 1 1 ckd ckd false false false false false na 2 1 htn htn false false false false false na 3 2 pt pt false false false false false na 4 2 ckd ckd false false false false false na 5 2 htn htn false false false true false na a data frame as input r data frame text c this pt has ckd and htn diabetes is present stringsasfactors false clinspacy df col text verbose false clinspacy id entity lemma is family is historical is hypothetical is negated is uncertain section category 1 1 ckd ckd false false false false false na 2 1 htn htn false false false false false na 3 2 diabetes diabetes false false false false false na saving the output to file the output file can then be piped into bind clinspacy or bind clinspacy embeddings this saves a lot of time because you can try different strategies of subsetting in both of these functions without needing to re process the original data r if dir exists rappdirs user data dir clinspacy dir create rappdirs user data dir clinspacy recursive true mtsamples dataset mtsamples mtsamples 1 5 note id description medical specialty 1 1 a 23 year old white female presents with complaint of allergies allergy immunology 2 2 consult for laparoscopic gastric bypass bariatrics 3 3 consult for laparoscopic gastric bypass bariatrics 4 4 2 d m mode doppler cardiovascular pulmonary 5 5 2 d echocardiogram cardiovascular pulmonary sample name 1 allergic rhinitis 2 laparoscopic gastric bypass consult 2 3 laparoscopic gastric bypass consult 1 4 2 d echocardiogram 1 5 2 d echocardiogram 2 transcription 1 subjective this 23 year old white female presents with complaint of allergies she used to have allergies when she lived in seattle but she thinks they are worse here in the past she has tried claritin and zyrtec both worked for short time but then seemed to lose effectiveness she has used allegra also she used that last summer and she began using it again two weeks ago it does not appear to be working very well she has used over the counter sprays but no prescription nasal sprays she does have asthma but doest not require daily medication for this and does not think it is flaring up medications her only medication currently is ortho tri cyclen and the allegra allergies she has no known medicine allergies objective vitals weight was 130 pounds and blood pressure 124 78 heent her throat was mildly erythematous without exudate nasal mucosa was erythematous and swollen only clear drainage was seen tms were clear neck supple without adenopathy lungs clear assessment allergic rhinitis plan 1 she will try zyrtec instead of allegra again another option will be to use loratadine she does not think she has prescription coverage so that might be cheaper 2 samples of nasonex two sprays in each nostril given for three weeks a prescription was written as well 2 past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating and lifting objects off the floor he exercises three times a week at home and does cardio he has difficulty walking two blocks or five flights of stairs difficulty with snoring he has muscle and joint pains including knee pain back pain foot and ankle pain and swelling he has gastroesophageal reflux disease past surgical history includes reconstructive surgery on his right hand 13 years ago social history he is currently single he has about ten drinks a year he had smoked significantly up until several months ago he now smokes less than three cigarettes a day family history heart disease in both grandfathers grandmother with stroke and a grandmother with diabetes denies obesity and hypertension in other family members current medications none allergies he is allergic to penicillin miscellaneous eating history he has been going to support groups for seven months with lynn holmberg in greenwich and he is from eastchester new york and he feels that we are the appropriate program he had a poor experience with the greenwich program eating history he is not an emotional eater does not like sweets he likes big portions and carbohydrates he likes chicken and not steak he currently weighs 312 pounds ideal body weight would be 170 pounds he is 142 pounds overweight if he lost 60 of his excess body weight that would be 84 pounds and he should weigh about 228 review of systems negative for head neck heart lungs gi gu orthopedic and skin specifically denies chest pain heart attack coronary artery disease congestive heart failure arrhythmia atrial fibrillation pacemaker high cholesterol pulmonary embolism high blood pressure cva venous insufficiency thrombophlebitis asthma shortness of breath copd emphysema sleep apnea diabetes leg and foot swelling osteoarthritis rheumatoid arthritis hiatal hernia peptic ulcer disease gallstones infected gallbladder pancreatitis fatty liver hepatitis hemorrhoids rectal bleeding polyps incontinence of stool urinary stress incontinence or cancer denies cellulitis pseudotumor cerebri meningitis or encephalitis physical examination he is alert and oriented x 3 cranial nerves ii xii are intact afebrile vital signs are stable 3 history of present illness i have seen abc today he is a very pleasant gentleman who is 42 years old 344 pounds he is 5 9 he has a bmi of 51 he has been overweight for ten years since the age of 33 at his highest he was 358 pounds at his lowest 260 he is pursuing surgical attempts of weight loss to feel good get healthy and begin to exercise again he wants to be able to exercise and play volleyball physically he is sluggish he gets tired quickly he does not go out often when he loses weight he always regains it and he gains back more than he lost his biggest weight loss is 25 pounds and it was three months before he gained it back he did six months of not drinking alcohol and not taking in many calories he has been on multiple commercial weight loss programs including slim fast for one month one year ago and atkin s diet for one month two years ago past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating difficulty walking high cholesterol and high blood pressure he has asthma and difficulty walking two blocks or going eight to ten steps he has sleep apnea and snoring he is a diabetic on medication he has joint pain knee pain back pain foot and ankle pain leg and foot swelling he has hemorrhoids past surgical history includes orthopedic or knee surgery social history he is currently single he drinks alcohol ten to twelve drinks a week but does not drink five days a week and then will binge drink he smokes one and a half pack a day for 15 years but he has recently stopped smoking for the past two weeks family history obesity heart disease and diabetes family history is negative for hypertension and stroke current medications include diovan crestor and tricor miscellaneous eating history he says a couple of friends of his have had heart attacks and have had died he used to drink everyday but stopped two years ago he now only drinks on weekends he is on his second week of chantix which is a medication to come off smoking completely eating he eats bad food he is single he eats things like bacon eggs and cheese cheeseburgers fast food eats four times a day seven in the morning at noon 9 p m and 2 a m he currently weighs 344 pounds and 5 9 his ideal body weight is 160 pounds he is 184 pounds overweight if he lost 70 of his excess body weight that would be 129 pounds and that would get him down to 215 review of systems negative for head neck heart lungs gi gu orthopedic or skin he also is positive for gout he denies chest pain heart attack coronary artery disease congestive heart failure arrhythmia atrial fibrillation pacemaker pulmonary embolism or cva he denies venous insufficiency or thrombophlebitis denies shortness of breath copd or emphysema denies thyroid problems hip pain osteoarthritis rheumatoid arthritis gerd hiatal hernia peptic ulcer disease gallstones infected gallbladder pancreatitis fatty liver hepatitis rectal bleeding polyps incontinence of stool urinary stress incontinence or cancer he denies cellulitis pseudotumor cerebri meningitis or encephalitis physical examination he is alert and oriented x 3 cranial nerves ii xii are intact neck is soft and supple lungs he has positive wheezing bilaterally heart is regular rhythm and rate his abdomen is soft extremities he has 1 pitting edema impression plan i have explained to him the risks and potential complications of laparoscopic gastric bypass in detail and these include bleeding infection deep venous thrombosis pulmonary embolism leakage from the gastrojejuno anastomosis jejunojejuno anastomosis and possible bowel obstruction among other potential complications he understands he wants to proceed with workup and evaluation for laparoscopic roux en y gastric bypass he will need to get a letter of approval from dr xyz he will need to see a nutritionist and mental health worker he will need an upper endoscopy by either dr xyz he will need to go to dr xyz as he previously had a sleep study we will need another sleep study he will need h pylori testing thyroid function tests lfts glycosylated hemoglobin and fasting blood sugar after this is performed we will submit him for insurance approval 4 2 d m mode 1 left atrial enlargement with left atrial diameter of 4 7 cm 2 normal size right and left ventricle 3 normal lv systolic function with left ventricular ejection fraction of 51 4 normal lv diastolic function 5 no pericardial effusion 6 normal morphology of aortic valve mitral valve tricuspid valve and pulmonary valve 7 pa systolic pressure is 36 mmhg doppler 1 mild mitral and tricuspid regurgitation 2 trace aortic and pulmonary regurgitation 5 1 the left ventricular cavity size and wall thickness appear normal the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70 to 75 there is near cavity obliteration seen there also appears to be increased left ventricular outflow tract gradient at the mid cavity level consistent with hyperdynamic left ventricular systolic function there is abnormal left ventricular relaxation pattern seen as well as elevated left atrial pressures seen by doppler examination 2 the left atrium appears mildly dilated 3 the right atrium and right ventricle appear normal 4 the aortic root appears normal 5 the aortic valve appears calcified with mild aortic valve stenosis calculated aortic valve area is 1 3 cm square with a maximum instantaneous gradient of 34 and a mean gradient of 19 mm 6 there is mitral annular calcification extending to leaflets and supportive structures with thickening of mitral valve leaflets with mild mitral regurgitation 7 the tricuspid valve appears normal with trace tricuspid regurgitation with moderate pulmonary artery hypertension estimated pulmonary artery systolic pressure is 49 mmhg estimated right atrial pressure of 10 mmhg 8 the pulmonary valve appears normal with trace pulmonary insufficiency 9 there is no pericardial effusion or intracardiac mass seen 10 there is a color doppler suggestive of a patent foramen ovale with lipomatous hypertrophy of the interatrial septum 11 the study was somewhat technically limited and hence subtle abnormalities could be missed from the study keywords 1 allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic 2 bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkin s diet weight watcher s body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diets overweight lost 3 bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypass heart pounds weight 4 cardiovascular pulmonary 2 d m mode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic function tricuspid tricuspid valve normal lv 5 cardiovascular pulmonary 2 d doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation pattern stenosis systolic function tricuspid valve ventricular ventricular cavity wall motion pulmonary artery clinspacy output file mtsamples 1 5 1 2 clinspacy df col description verbose false output file file path rappdirs user data dir clinspacy output csv overwrite true clinspacy output file 1 c users kdpsingh appdata local clinspacy clinspacy output csv binding named entities to a data frame without the umls linker negated concepts as identified by the medspacy cycontext flag are ignored by default and do not count towards the frequencies however you can now change the subsetting criteria note that you now need to re provide the original dataset to the bind clinspacy function r mtsamples 1 5 1 2 clinspacy df col description verbose false bind clinspacy mtsamples 1 5 1 2 clinspacy id note id description 2 d 2 d m mode allergy 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 0 0 0 3 3 3 consult for laparoscopic gastric bypass 0 0 0 4 4 4 2 d m mode doppler 0 1 0 5 5 5 2 d echocardiogram 1 0 0 complaint consult doppler echocardiogram laparoscopic gastric bypass white female 1 1 0 0 0 0 1 2 0 1 0 0 1 0 3 0 1 0 0 1 0 4 0 0 1 0 0 0 5 0 0 0 1 0 0 we can also store the intermediate result so that bind clinspacy does not need to re process the text r clinspacy output data mtsamples 1 5 1 2 clinspacy df col description verbose false clinspacy output data bind clinspacy mtsamples 1 5 1 2 clinspacy id note id description 2 d 2 d m mode allergy 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 0 0 0 3 3 3 consult for laparoscopic gastric bypass 0 0 0 4 4 4 2 d m mode doppler 0 1 0 5 5 5 2 d echocardiogram 1 0 0 complaint consult doppler echocardiogram laparoscopic gastric bypass white female 1 1 0 0 0 0 1 2 0 1 0 0 1 0 3 0 1 0 0 1 0 4 0 0 1 0 0 0 5 0 0 0 1 0 0 clinspacy output data bind clinspacy mtsamples 1 5 1 2 cs col entity clinspacy id note id description 2 d 2 d m mode consult doppler 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 0 0 2 2 2 consult for laparoscopic gastric bypass 0 0 1 0 3 3 3 consult for laparoscopic gastric bypass 0 0 1 0 4 4 4 2 d m mode doppler 0 1 0 1 5 5 5 2 d echocardiogram 1 0 0 0 echocardiogram allergies complaint laparoscopic gastric bypass white female 1 0 1 1 0 1 2 0 0 0 1 0 3 0 0 0 1 0 4 0 0 0 0 0 5 1 0 0 0 0 clinspacy output data bind clinspacy mtsamples 1 5 1 2 subset is uncertain false is negated false clinspacy id note id description 2 d 2 d m mode allergy 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 0 0 0 3 3 3 consult for laparoscopic gastric bypass 0 0 0 4 4 4 2 d m mode doppler 0 1 0 5 5 5 2 d echocardiogram 1 0 0 complaint consult doppler echocardiogram laparoscopic gastric bypass white female 1 1 0 0 0 0 1 2 0 1 0 0 1 0 3 0 1 0 0 1 0 4 0 0 1 0 0 0 5 0 0 0 1 0 0 we can also re use the output file we had created earlier and pipe this directly into bind clinspacy r clinspacy output file 1 c users kdpsingh appdata local clinspacy clinspacy output csv clinspacy output file bind clinspacy mtsamples 1 5 1 2 clinspacy id note id description 2 d 2 d m mode allergy 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 0 0 0 3 3 3 consult for laparoscopic gastric bypass 0 0 0 4 4 4 2 d m mode doppler 0 1 0 5 5 5 2 d echocardiogram 1 0 0 complaint consult doppler echocardiogram laparoscopic gastric bypass white female 1 1 0 0 0 0 1 2 0 1 0 0 1 0 3 0 1 0 0 1 0 4 0 0 1 0 0 0 5 0 0 0 1 0 0 clinspacy output file bind clinspacy mtsamples 1 5 1 2 cs col entity clinspacy id note id description 2 d 2 d m mode consult doppler 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 0 0 2 2 2 consult for laparoscopic gastric bypass 0 0 1 0 3 3 3 consult for laparoscopic gastric bypass 0 0 1 0 4 4 4 2 d m mode doppler 0 1 0 1 5 5 5 2 d echocardiogram 1 0 0 0 echocardiogram allergies complaint laparoscopic gastric bypass white female 1 0 1 1 0 1 2 0 0 0 1 0 3 0 0 0 1 0 4 0 0 0 0 0 5 1 0 0 0 0 clinspacy output file bind clinspacy mtsamples 1 5 1 2 subset is uncertain false is negated false clinspacy id note id description 2 d 2 d m mode allergy 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 0 0 0 3 3 3 consult for laparoscopic gastric bypass 0 0 0 4 4 4 2 d m mode doppler 0 1 0 5 5 5 2 d echocardiogram 1 0 0 complaint consult doppler echocardiogram laparoscopic gastric bypass white female 1 1 0 0 0 0 1 2 0 1 0 0 1 0 3 0 1 0 0 1 0 4 0 0 1 0 0 0 5 0 0 0 1 0 0 binding entity embeddings to a data frame without the umls linker with the umls linker disabled 200 dimensional entity embeddings can be extracted from the scispacy python package for this to work you must set return scispacy embeddings to true when running clinspacy it s also a good idea to write the output directly to file because the embeddings can be quite large r clinspacy output file mtsamples 1 5 1 2 clinspacy df col description return scispacy embeddings true verbose false output file file path rappdirs user data dir clinspacy output csv overwrite true clinspacy output file bind clinspacy embeddings mtsamples 1 5 1 2 clinspacy id note id description emb 001 emb 002 1 1 1 a 23 year old white female presents with complaint of allergies 0 1959790 0 28813400 2 2 2 consult for laparoscopic gastric bypass 0 1115363 0 01725144 3 3 3 consult for laparoscopic gastric bypass 0 1115363 0 01725144 4 4 4 2 d m mode doppler 0 3077586 0 25928350 emb 003 emb 004 emb 005 emb 006 emb 007 emb 008 emb 009 emb 010 emb 011 emb 012 1 0 09685702 0 20641684 0 1554238 0 01624470 0 027011001 0 05331314 0 1006668 0 3682853 0 0581439 0 29079599 2 0 13519235 0 05496463 0 1488807 0 19577999 0 052658666 0 10433200 0 0763495 0 1199215 0 1860092 0 05465447 3 0 13519235 0 05496463 0 1488807 0 19577999 0 052658666 0 10433200 0 0763495 0 1199215 0 1860092 0 05465447 4 0 37220851 0 06021732 0 0386426 0 07756314 0 002676249 0 22511028 0 3279995 0 2274373 0 1656060 0 30020200 emb 013 emb 014 emb 015 emb 016 emb 017 emb 018 emb 019 emb 020 emb 021 emb 022 1 0 1611375 0 1118952 0 03922822 0 06888010 0 1862742 0 1454458 0 04115367 0 049065500 0 39795328 0 05879098 2 0 1267057 0 2041533 0 01984984 0 01107489 0 1080266 0 1128684 0 23062316 0 005933613 0 06126638 0 05048515 3 0 1267057 0 2041533 0 01984984 0 01107489 0 1080266 0 1128684 0 23062316 0 005933613 0 06126638 0 05048515 4 0 5237787 0 1472114 0 02312062 0 11272645 0 3415540 0 2255931 0 02385290 0 074861225 0 12910485 0 02176433 emb 023 emb 024 emb 025 emb 026 emb 027 emb 028 emb 029 emb 030 emb 031 emb 032 1 0 05246135 0 19981400 0 03346085 0 1395520 0 01792375 0 06969561 0 04942485 0 06613978 0 08035761 0 12418544 2 0 12351524 0 02489970 0 26744565 0 3418240 0 12783451 0 38420413 0 20168215 0 06550949 0 26997083 0 07201438 3 0 12351524 0 02489970 0 26744565 0 3418240 0 12783451 0 38420413 0 20168215 0 06550949 0 26997083 0 07201438 4 0 21616454 0 08218845 0 33230226 0 2420833 0 08455360 0 22111987 0 57962301 0 32054099 0 26178523 0 46501200 emb 033 emb 034 emb 035 emb 036 emb 037 emb 038 emb 039 emb 040 emb 041 1 0 11839510 0 04266573 0 04319873 0 06394462 0 02425202 0 2158322 0 1064802 0 005398401 0 01459978 2 0 13039007 0 13608095 0 10342984 0 03349850 0 06359592 0 2497478 0 1312915 0 068015995 0 12897950 3 0 13039007 0 13608095 0 10342984 0 03349850 0 06359592 0 2497478 0 1312915 0 068015995 0 12897950 4 0 05091595 0 22430425 0 07319695 0 19518739 0 21279503 0 1980325 0 3900315 0 214830723 0 03985715 emb 042 emb 043 emb 044 emb 045 emb 046 emb 047 emb 048 emb 049 emb 050 emb 051 1 0 03936125 0 216860471 0 01146569 0 01707370 0 08789315 0 48977432 0 11840488 0 24063642 0 23959090 0 1258371 2 0 20849532 0 001854315 0 02034700 0 04105476 0 26218344 0 05762917 0 08367021 0 01368977 0 02369371 0 1266086 3 0 20849532 0 001854315 0 02034700 0 04105476 0 26218344 0 05762917 0 08367021 0 01368977 0 02369371 0 1266086 4 0 32672650 0 067201529 0 43131340 0 10445137 0 36873272 0 39958726 0 03923560 0 06519943 0 12042060 0 1947917 emb 052 emb 053 emb 054 emb 055 emb 056 emb 057 emb 058 emb 059 emb 060 1 0 0001312072 0 15632193 0 2063196 0 02019964 0 002069766 0 14390510 0 112056380 0 12671516 0 3076788 2 0 1197809521 0 04324770 0 2046735 0 21317951 0 029707700 0 04107177 0 003977332 0 03327019 0 1377243 3 0 1197809521 0 04324770 0 2046735 0 21317951 0 029707700 0 04107177 0 003977332 0 03327019 0 1377243 4 0 5587487221 0 02909975 0 1112386 0 29085600 0 051582206 0 03322158 0 090760550 0 01738100 0 4675597 emb 061 emb 062 emb 063 emb 064 emb 065 emb 066 emb 067 emb 068 emb 069 emb 070 1 0 01722672 0 04037631 0 14633203 0 072336150 0 04734538 0 2444712 0 005439494 0 07232769 0 1972760 0 007281476 2 0 18907296 0 26335296 0 01884718 0 009265006 0 16859459 0 2767420 0 048937336 0 35522249 0 1164578 0 345116988 3 0 18907296 0 26335296 0 01884718 0 009265006 0 16859459 0 2767420 0 048937336 0 35522249 0 1164578 0 345116988 4 0 29520441 0 62886798 0 14435785 0 002738898 0 03027805 0 4466182 0 080596073 0 29857932 0 2307856 0 032678135 emb 071 emb 072 emb 073 emb 074 emb 075 emb 076 emb 077 emb 078 emb 079 emb 080 1 0 03698583 0 07433472 0 0170116 0 15559705 0 0142159 0 03095377 0 14973202 0 07275485 0 1265165 0 0756736 2 0 03482347 0 09575927 0 1530600 0 08885341 0 1138750 0 24408367 0 01405296 0 00684475 0 1356777 0 1306460 3 0 03482347 0 09575927 0 1530600 0 08885341 0 1138750 0 24408367 0 01405296 0 00684475 0 1356777 0 1306460 4 0 02464749 0 05315572 0 2278580 0 05121428 0 3368990 0 12042545 0 05976460 0 20906300 0 3898960 0 2403080 emb 081 emb 082 emb 083 emb 084 emb 085 emb 086 emb 087 emb 088 emb 089 emb 090 1 0 1064746 0 04138183 0 1262948 0 07008250 0 0581785 0 08323197 0 1252120 0 10060352 0 01839051 0 24945817 2 0 2395754 0 24276201 0 1975068 0 03769429 0 2019527 0 09356334 0 2311737 0 01929579 0 18456985 0 16967812 3 0 2395754 0 24276201 0 1975068 0 03769429 0 2019527 0 09356334 0 2311737 0 01929579 0 18456985 0 16967812 4 0 2094990 0 43718034 0 2580445 0 36398449 0 1863167 0 38763523 0 1124806 0 25680842 0 21670937 0 02249805 emb 091 emb 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111 emb 112 emb 113 emb 114 emb 115 emb 116 emb 117 emb 118 1 0 03355397 0 06281934 0 09064088 0 1812218 0 08294683 0 09746995 0 16949679 0 001256246 0 09206300 2 0 28191616 0 04745353 0 04532966 0 1529041 0 04579017 0 02364063 0 31116034 0 160783665 0 07702465 3 0 28191616 0 04745353 0 04532966 0 1529041 0 04579017 0 02364063 0 31116034 0 160783665 0 07702465 4 0 07525725 0 19109026 0 09757482 0 3430861 0 07392349 0 34514988 0 05409198 0 021575954 0 24660901 emb 119 emb 120 emb 121 emb 122 emb 123 emb 124 emb 125 emb 126 emb 127 emb 128 1 0 27094193 0 1914412 0 10522338 0 01736773 0 1658078 0 24409867 0 20621473 0 35578349 0 19991713 0 1075110 2 0 02175729 0 1156647 0 01362599 0 20085029 0 3362202 0 03874875 0 02545092 0 21585878 0 04820869 0 1341518 3 0 02175729 0 1156647 0 01362599 0 20085029 0 3362202 0 03874875 0 02545092 0 21585878 0 04820869 0 1341518 4 0 25714830 0 3096262 0 14711675 0 09584628 0 2465328 0 02228437 0 05287175 0 04758008 0 13082074 0 4366458 emb 129 emb 130 emb 131 emb 132 emb 133 emb 134 emb 135 emb 136 emb 137 emb 138 1 0 050961102 0 08590268 0 07344585 0 11005830 0 2082962 0 03440777 0 15951183 0 04417117 0 1002716 0 07090355 2 0 084913827 0 21485816 0 26201880 0 04661880 0 1594945 0 24577541 0 04687785 0 02120483 0 2707188 0 05038439 3 0 084913827 0 21485816 0 26201880 0 04661880 0 1594945 0 24577541 0 04687785 0 02120483 0 2707188 0 05038439 4 0 002557264 0 30628723 0 24981013 0 01674807 0 3169997 0 12056302 0 09506032 0 01222125 0 4409042 0 23120450 emb 139 emb 140 emb 141 emb 142 emb 143 emb 144 emb 145 emb 146 emb 147 1 0 09013366 0 004567102 0 04074124 0 09970398 0 07412403 0 08118367 0 04151318 0 01023637 0 02712608 2 0 21531074 0 214246295 0 12730155 0 04358483 0 04084410 0 08556246 0 37193301 0 23297635 0 16786779 3 0 21531074 0 214246295 0 12730155 0 04358483 0 04084410 0 08556246 0 37193301 0 23297635 0 16786779 4 0 01691840 0 127434801 0 19368662 0 02984041 0 14155845 0 15326020 0 02936405 0 05187999 0 06006772 emb 148 emb 149 emb 150 emb 151 emb 152 emb 153 emb 154 emb 155 emb 156 emb 157 1 0 1120797 0 07420963 0 2022959 0 02539130 0 1542052 0 09878749 0 11210436 0 190853971 0 2355878 0 1032905 2 0 1552295 0 13361997 0 4047717 0 07385027 0 2168649 0 08279617 0 02853568 0 007983398 0 2673024 0 3518553 3 0 1552295 0 13361997 0 4047717 0 07385027 0 2168649 0 08279617 0 02853568 0 007983398 0 2673024 0 3518553 4 0 0758267 0 04905358 0 0133047 0 25728051 0 2761333 0 10433040 0 02122432 0 066375951 0 3625118 0 2547615 emb 158 emb 159 emb 160 emb 161 emb 162 emb 163 emb 164 emb 165 emb 166 emb 167 1 0 21532827 0 09456767 0 1445503 0 33522494 0 15268593 0 001686232 0 2152747 0 10312133 0 1135696 0 02624894 2 0 07097678 0 08358909 0 1986835 0 29901644 0 01896982 0 052200415 0 1262764 0 10607937 0 0321700 0 25643115 3 0 07097678 0 08358909 0 1986835 0 29901644 0 01896982 0 052200415 0 1262764 0 10607937 0 0321700 0 25643115 4 0 13501658 0 28645951 0 1917117 0 01892012 0 02507000 0 031375002 0 2519416 0 08888888 0 3796148 0 25476800 emb 168 emb 169 emb 170 emb 171 emb 172 emb 173 emb 174 emb 175 emb 176 emb 177 1 0 1098730 0 09047928 0 12684340 0 0694985 0 11949543 0 2164041 0 29396720 0 16588253 0 1348005 0 1148055 2 0 1073976 0 26462262 0 03679075 0 2173935 0 07656907 0 1012526 0 02410151 0 02048860 0 1179298 0 2362113 3 0 1073976 0 26462262 0 03679075 0 2173935 0 07656907 0 1012526 0 02410151 0 02048860 0 1179298 0 2362113 4 0 1437821 0 15589955 0 23368900 0 1311810 0 52442150 0 0487657 0 25153150 0 02299049 0 1953604 0 1572996 emb 178 emb 179 emb 180 emb 181 emb 182 emb 183 emb 184 emb 185 emb 186 emb 187 1 0 08968537 0 05097483 0 09355133 0 008875800 0 1106400 0 1088511 0 02326688 0 17733055 0 07351807 0 0222525 2 0 30876314 0 22625668 0 07487945 0 008851715 0 1024263 0 2249113 0 06455390 0 07631866 0 01623236 0 1098196 3 0 30876314 0 22625668 0 07487945 0 008851715 0 1024263 0 2249113 0 06455390 0 07631866 0 01623236 0 1098196 4 0 29195935 0 05653973 0 12341889 0 312314242 0 1885454 0 2873893 0 02149600 0 16462975 0 14877875 0 2350687 emb 188 emb 189 emb 190 emb 191 emb 192 emb 193 emb 194 emb 195 emb 196 1 0 12066887 0 179350998 0 01909462 0 13228424 0 024832169 0 05002003 0 20531311 0 00853500 0 0639337 2 0 04689731 0 033685058 0 16270872 0 05825762 0 069446986 0 05563271 0 17479033 0 13635058 0 1291080 3 0 04689731 0 033685058 0 16270872 0 05825762 0 069446986 0 05563271 0 17479033 0 13635058 0 1291080 4 0 36260483 0 004200405 0 20571376 0 09558415 0 006550124 0 30820300 0 01686265 0 05414012 0 1694009 emb 197 emb 198 emb 199 emb 200 1 0 29886368 0 01618892 0 08192083 0 37027851 2 0 09743453 0 09941812 0 05773153 0 09702638 3 0 09743453 0 09941812 0 05773153 0 09702638 4 0 13313706 0 15822850 0 14830773 0 34555282 reached max getoption max print omitted 1 rows adding the umls linker the umls linker can be turned on and off even if clinspacy init has already been called the first time you turn it on it takes a while because the linker needs to be loaded into memory on subsequent removal and addition this occurs much more quickly because the linker is only removed added to the pipeline and does not need to be reloaded into memory r clinspacy init use linker true named entity recognition with the umls linker by turning on the umls linker you can restrict the results by semantic type in general restricting the result in clinspacy is not a good idea because you can always subset the results later within bind clinspacy and bind clinspacy embeddings r clinspacy this patient has diabetes and ckd stage 3 but no htn 0 100 clinspacy id cui entity lemma semantic type definition is family 1 1 c0030705 patient patient patient or disabled group patients false 2 1 c1550655 patient patient body substance specimen type patient false 3 1 c1578483 patient patient idea or concept report source patient false 4 1 c1578484 patient patient idea or concept relationship modifier patient false 5 1 c1705908 patient patient organism veterinary patient false 6 1 c0011847 diabetes diabetes disease or syndrome diabetes false 7 1 c0011849 diabetes diabetes disease or syndrome diabetes mellitus false 8 1 c2316787 ckd stage 3 ckd stage 3 disease or syndrome chronic kidney disease stage 3 false 9 1 c0020538 htn htn disease or syndrome hypertensive disease false is historical is hypothetical is negated is uncertain section category 1 false false false false na 2 false false false false na 3 false false false false na 4 false false false false na 5 false false false false na 6 false false false false na 7 false false false false na 8 false false false false na 9 false false true false na clinspacy this patient with diabetes is taking omeprazole aspirin and lisinopril 10 mg but is not taking albuterol anymore as his asthma has resolved semantic types pharmacologic substance 0 100 clinspacy id cui entity lemma semantic type definition is family is historical 1 1 c0028978 omeprazole omeprazole pharmacologic substance omeprazole false false 2 1 c0004057 aspirin aspirin pharmacologic substance aspirin false false 3 1 c0065374 lisinopril lisinopril pharmacologic substance lisinopril false false 4 1 c0001927 albuterol albuterol pharmacologic substance albuterol false false is hypothetical is negated is uncertain section category 1 false false false na 2 false false false na 3 false false false na 4 false true false na clinspacy this patient with diabetes is taking omeprazole aspirin and lisinopril 10 mg but is not taking albuterol anymore as his asthma has resolved semantic types disease or syndrome 0 100 clinspacy id cui entity lemma semantic type definition is family is historical is hypothetical 1 1 c0011847 diabetes diabetes disease or syndrome diabetes false false false 2 1 c0011849 diabetes diabetes disease or syndrome diabetes mellitus false false false 3 1 c0004096 asthma asthma disease or syndrome asthma false false false is negated is uncertain section category 1 false false na 2 false false na 3 true false na binding umls concept unique identifiers to a data frame with the umls linker this function binds columns containing concept unique identifiers with which scispacy has 99 confidence of being present with values containing frequencies negated concepts as identified by the medspacy cycontext is negated flag are ignored and do not count towards the frequencies however this behavior can be changed using the subset argument note that by turning on the umls linker you can restrict the results by semantic type r clinspacy output file mtsamples 1 5 1 2 clinspacy df col description return scispacy embeddings true only so we can retrieve these below verbose false output file file path rappdirs user data dir clinspacy output csv overwrite true clinspacy output file bind clinspacy mtsamples 1 5 1 2 clinspacy id note id description c0009818 c0013516 c0020517 1 1 1 a 23 year old white female presents with complaint of allergies 0 0 1 2 2 2 consult for laparoscopic gastric bypass 1 0 0 3 3 3 consult for laparoscopic gastric bypass 1 0 0 4 4 4 2 d m mode doppler 0 0 0 5 5 5 2 d echocardiogram 0 1 0 c0277786 c0554756 c1705052 c2243117 c3864418 c4039248 1 1 0 0 0 1 0 2 0 0 0 0 0 1 3 0 0 0 0 0 1 4 0 1 0 0 0 0 5 0 0 1 1 0 0 clinspacy output file bind clinspacy mtsamples 1 5 1 2 subset is negated false semantic type diagnostic procedure clinspacy id note id description c0013516 c0554756 1 1 1 a 23 year old white female presents with complaint of allergies na na 2 2 2 consult for laparoscopic gastric bypass na na 3 3 3 consult for laparoscopic gastric bypass na na 4 4 4 2 d m mode doppler 0 1 5 5 5 2 d echocardiogram 1 0 binding concept embeddings to a data frame with the umls linker the default embeddings are from the scispacy r package if you want to use the cui2vec embeddings only available with the linker enabled you ned to set the type arguement to cui2vec up to 500 dimensional embeddings can be returned note that by turning on the umls linker you can restrict the results by semantic type with either type of embedding scispacy embeddings with the umls linker with the umls linker enabled you can restrict by semantic type when obtaining scispacy embeddings note the mean embeddings may be slightly different than if the linker was disabled because entities may be captured twice as entities may map to multiple concepts thus if you do not need to restrict by semantic type the recommended setting is to turn the umls linker off by re running clinspacy init use linker false note that use linker false is the default in clinspacy init r clinspacy output file bind clinspacy embeddings mtsamples 1 5 1 2 clinspacy id note id description emb 001 emb 002 1 1 1 a 23 year old white female presents with complaint of allergies 0 3611527 0 30379833 2 2 2 consult for laparoscopic gastric bypass 0 1115363 0 01725144 3 3 3 consult for laparoscopic gastric bypass 0 1115363 0 01725144 4 4 4 2 d m mode doppler 0 4044230 0 21798199 emb 003 emb 004 emb 005 emb 006 emb 007 emb 008 emb 009 emb 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157 emb 158 1 na na na na na na na na na na na na na na emb 159 emb 160 emb 161 emb 162 emb 163 emb 164 emb 165 emb 166 emb 167 emb 168 emb 169 emb 170 emb 171 emb 172 1 na na na na na na na na na na na na na na emb 173 emb 174 emb 175 emb 176 emb 177 emb 178 emb 179 emb 180 emb 181 emb 182 emb 183 emb 184 emb 185 emb 186 1 na na na na na na na na na na na na na na emb 187 emb 188 emb 189 emb 190 emb 191 emb 192 emb 193 emb 194 emb 195 emb 196 emb 197 emb 198 emb 199 emb 200 1 na na na na na na na na na na na na na na emb 201 emb 202 emb 203 emb 204 emb 205 emb 206 emb 207 emb 208 emb 209 emb 210 emb 211 emb 212 emb 213 emb 214 1 na na na na na na na na na na na na na na emb 215 emb 216 emb 217 emb 218 emb 219 emb 220 emb 221 emb 222 emb 223 emb 224 emb 225 emb 226 emb 227 emb 228 1 na na na na na na na na na na na na na na emb 229 emb 230 emb 231 emb 232 emb 233 emb 234 emb 235 emb 236 emb 237 emb 238 emb 239 emb 240 emb 241 emb 242 1 na na na na na na na na na na na na na na emb 243 emb 244 emb 245 emb 246 emb 247 emb 248 emb 249 emb 250 emb 251 emb 252 emb 253 emb 254 emb 255 emb 256 1 na na na na na na na na na na na na na na emb 257 emb 258 emb 259 emb 260 emb 261 emb 262 emb 263 emb 264 emb 265 emb 266 emb 267 emb 268 emb 269 emb 270 1 na na na na na na na na na na na na na na emb 271 emb 272 emb 273 emb 274 emb 275 emb 276 emb 277 emb 278 emb 279 emb 280 emb 281 emb 282 emb 283 emb 284 1 na na na na na na na na na na na na na na emb 285 emb 286 emb 287 emb 288 emb 289 emb 290 emb 291 emb 292 emb 293 emb 294 emb 295 emb 296 emb 297 emb 298 1 na na na na na na na na na na na na na na emb 299 emb 300 emb 301 emb 302 emb 303 emb 304 emb 305 emb 306 emb 307 emb 308 emb 309 emb 310 emb 311 emb 312 1 na na na na na na na na na na na na na na emb 313 emb 314 emb 315 emb 316 emb 317 emb 318 emb 319 emb 320 emb 321 emb 322 emb 323 emb 324 emb 325 emb 326 1 na na na na na na na na na na na na na na emb 327 emb 328 emb 329 emb 330 emb 331 emb 332 emb 333 emb 334 emb 335 emb 336 emb 337 emb 338 emb 339 emb 340 1 na na na na na na na na na na na na na na emb 341 emb 342 emb 343 emb 344 emb 345 emb 346 emb 347 emb 348 emb 349 emb 350 emb 351 emb 352 emb 353 emb 354 1 na na na na na na na na na na na na na na emb 355 emb 356 emb 357 emb 358 emb 359 emb 360 emb 361 emb 362 emb 363 emb 364 emb 365 emb 366 emb 367 emb 368 1 na na na na na na na na na na na na na na emb 369 emb 370 emb 371 emb 372 emb 373 emb 374 emb 375 emb 376 emb 377 emb 378 emb 379 emb 380 emb 381 emb 382 1 na na na na na na na na na na na na na na emb 383 emb 384 emb 385 emb 386 emb 387 emb 388 emb 389 emb 390 emb 391 emb 392 emb 393 emb 394 emb 395 emb 396 1 na na na na na na na na na na na na na na emb 397 emb 398 emb 399 emb 400 emb 401 emb 402 emb 403 emb 404 emb 405 emb 406 emb 407 emb 408 emb 409 emb 410 1 na na na na na na na na na na na na na na emb 411 emb 412 emb 413 emb 414 emb 415 emb 416 emb 417 emb 418 emb 419 emb 420 emb 421 emb 422 emb 423 emb 424 1 na na na na na na na na na na na na na na emb 425 emb 426 emb 427 emb 428 emb 429 emb 430 emb 431 emb 432 emb 433 emb 434 emb 435 emb 436 emb 437 emb 438 1 na na na na na na na na na na na na na na emb 439 emb 440 emb 441 emb 442 emb 443 emb 444 emb 445 emb 446 emb 447 emb 448 emb 449 emb 450 emb 451 emb 452 1 na na na na na na na na na na na na na na emb 453 emb 454 emb 455 emb 456 emb 457 emb 458 emb 459 emb 460 emb 461 emb 462 emb 463 emb 464 emb 465 emb 466 1 na na na na na na na na na na na na na na emb 467 emb 468 emb 469 emb 470 emb 471 emb 472 emb 473 emb 474 emb 475 emb 476 emb 477 emb 478 emb 479 emb 480 1 na na na na na na na na na na na na na na emb 481 emb 482 emb 483 emb 484 emb 485 emb 486 emb 487 emb 488 emb 489 emb 490 emb 491 emb 492 emb 493 emb 494 1 na na na na na na na na na na na na na na emb 495 emb 496 emb 497 emb 498 emb 499 emb 500 1 na na na na na na reached max getoption max print omitted 4 rows umls cui definitions r cui2vec definitions dataset cui2vec definitions head cui2vec definitions cui semantic type definition 1 c0000005 amino acid peptide or protein 131 i macroaggregated albumin 2 c0000005 pharmacologic substance 131 i macroaggregated albumin 3 c0000005 indicator reagent or diagnostic aid 131 i macroaggregated albumin 4 c0000039 organic chemical 1 2 dipalmitoylphosphatidylcholine 5 c0000039 pharmacologic substance 1 2 dipalmitoylphosphatidylcholine 6 c0000052 amino acid peptide or protein 1 4 alpha glucan branching enzyme | ai |
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microk8s | img src docs images microk8s logo rgb 2022 png width 400px https img shields io badge kubernetes 1 28 326de6 svg img src docs images certified kubernetes color 222x300 png align right width 200px the smallest fastest kubernetes single package fully conformant lightweight kubernetes that works on 42 flavours of linux https snapcraft io microk8s perfect for developer workstations iot edge ci cd canonical might have assembled the easiest way to provision a single node kubernetes cluster kelsey hightower https twitter com kelseyhightower status 1120834594138406912 why microk8s small developers want the smallest k8s for laptop and workstation development microk8s provides a standalone k8s compatible with azure aks amazon eks google gke when you run it on ubuntu simple minimize administration and operations with a single package install that has no moving parts for simplicity and certainty all dependencies and batteries included secure updates are available for all security issues and can be applied immediately or scheduled to suit your maintenance cycle current microk8s tracks upstream and releases beta rc and final bits the same day as upstream k8s you can track latest k8s or stick to any release version from 1 10 onwards comprehensive microk8s includes a curated collection of manifests for common k8s capabilities and services service mesh istio linkerd serverless knative monitoring fluentd prometheus grafana metrics ingress dns dashboard clustering automatic updates to the latest kubernetes version gpgpu bindings for ai ml drop us a line at microk8s in the wild docs community md if you are doing something fun with microk8s quickstart install microk8s with snap install microk8s classic microk8s includes a microk8s kubectl command sudo microk8s kubectl get nodes sudo microk8s kubectl get services to use microk8s with your existing kubectl sudo microk8s kubectl config view raw home kube config user access without sudo the microk8s user group is created during the snap installation users in that group are granted access to microk8s commands to add a user to that group sudo usermod a g microk8s username kubernetes add ons microk8s installs a barebones upstream kubernetes additional services like dns and the kubernetes dashboard can be enabled using the microk8s enable command sudo microk8s enable dns sudo microk8s enable dashboard use microk8s status to see a list of enabled and available addons you can find the addon manifests and or scripts under snap actions with snap pointing by default to snap microk8s current documentation the official docs https microk8s io docs are maintained in the kubernetes upstream discourse take a look at the build instructions docs build md if you want to contribute to microk8s a href https snapcraft io microk8s title get it from the snap store img src https snapcraft io static images badges en snap store white svg alt get it from the snap store width 200 a a href https github com canonical microk8s graphs contributors img src https contrib rocks image repo canonical microk8s a | kubernetes snap iot cicd developer-workstations k8s hacktoberfest | server |
servez | servez img src servez jpg width 512px a simple web server for local web development screenshow servez gif download click here https github com greggman servez releases latest choose the dmg for mac the exe for windows or the appimage for linux what servez is an stand alone app that runs a simple web server with a gui to start stop and choose a folder to serve i ve worked with many people often students who are not comfortable with command lines and certainly not comfortable setting up a big server like apache servez provides them with an easy way to get started without having to install multiple dependencies nor having to integrate things with their system no adding to paths no downloading 3 different pieces of software just run and start https servez has an option to serve https servez uses a self signed certificate which means your browser will complain that the certificate is not valid you can click advanced on the warning page and then proceed to lt localhost gt to load the page the point of https support in servez is to make it easy to access https only apis like webxr etc clicking through a warning once in a while is a small tradeoff for automating https support command line arguments if you want an actual command line version then go here https github com greggman servez cli otherwise these are the command line arguments to this app version of servez note you must include an extra by itself before your arguments servez exe port 1234 c path to serve bad servez exe port 1234 c path to serve good on windows the default path is c users username appdata local programs servez servez exe on macos the default path is applications servez app contents macos servez usage servez options path to serve help prints the command line arguments p or port sets the port as in port 1234 ssl use https see above no dirs don t show directory listings for folders no cors don t supply cors headers local only allow access from local machine no index don t serve index html for folders development setup install node js you can download it here https nodejs org clone this repo git clone https github com greggman servez git change to the project s folder cd servez install dependencies npm install organization main js runs the browser process src index html the main window src index js js for the main window src style css css for the main window running npm start will launch with devtools open setting the environment variable servez echo to true will make issue many logging commands in main js into the log in the app building npm run dist will build a file for distribution in the dist folder for the current platform note on macos you ll need an apple developer account then build like this bash appleid your apple id appleidpass see below password npm run dist for your apple id instead your apple id example appleid myaccount gmail com or whatever your apple id is for see below password the first step is go to https appleid apple com https appleid apple com and generate an app specific password then add that password to your keychain with an id of your choosing security add generic password a your apple id w app specific password s key id example security add generic password a myaccount gmail com w thea pppa sswo rddd s foobar from then on to build appleid myaccount gmail com appleidpass keychain foobar npm run dist during the build it will ask you to give it access to foobar | server webserver | front_end |
awesome-azure-iot | awesome azure iot awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg build status https travis ci org formulahendry awesome azure iot svg branch master https travis ci org formulahendry awesome azure iot a curated list of awesome azure internet of things projects and resources please visit https github com azure iot for the official list hardware hardware operating system operating system iot clouds iot clouds get started with iot hub get started with iot hub framework framework sdk sdk libraries and tools libraries and tools resources resources hardware azure iot developer kit https microsoft github io azure iot developer kit the microsoft azure mxchip iot developer kit a k a devkit can be used to develop and prototype iot solutions leveraging microsoft azure services it includes an arduino compatible board with rich peripherals and sensors an open source board package and a growing projects catalog azure certified for iot device catalog https catalog azureiotsuite com certified for iot devices tailored to your needs microsoft azure iot starter kits https catalog azureiotsuite com kits start innovating today with kits that include development boards that are azure certified for iot sensors and actuators simple user friendly tutorials help you seamlessly connect your devices to the cloud with microsoft azure iot operating system windows 10 iot core https developer microsoft com en us windows iot windows 10 iot is a family of windows 10 editions targeted towards a wide range of intelligent devices from small industrial gateways to larger more complex devices like point of sales terminals and atms iot clouds azure iot hub https azure microsoft com en us services iot hub connect monitor and manage billions of iot assets azure iot hub is a fully managed service that enables reliable and secure bidirectional communications between millions of iot devices and a solution back end azure iot edge https docs microsoft com en us azure iot edge azure iot edge moves cloud analytics and custom business logic to devices so that your organization can focus on business insights instead of data management azure event hubs https azure microsoft com en us services event hubs cloud scale telemetry ingestion from websites apps and any streams of data azure stream analytics https azure microsoft com en us services stream analytics real time data stream processing from millions of iot devices microsoft iot central https www microsoft com en us iot central a fully managed saas offering for customers and partners that enables powerful iot scenarios without requiring cloud solution expertise azure time series insights https azure microsoft com en us services time series insights a fully managed analytics storage and visualization service that makes it simple to explore and analyze billions of iot events simultaneously get started with iot hub simulated device https docs microsoft com en us azure iot hub iot hub node node getstarted connect your simulated device to your iot hub using node js raspberry pi 3 https docs microsoft com en us azure iot hub iot hub raspberry pi kit node get started connect your raspberry pi 3 device to your iot hub using node js adafruit feather m0 wifi https docs microsoft com en us azure iot hub iot hub adafruit feather m0 wifi kit arduino get started get started with your arduino board adafruit feather m0 wifi adafruit feather huzzah esp8266 https docs microsoft com en us azure iot hub iot hub arduino huzzah esp8266 get started get started with your arduino board adafruit feather huzzah esp8266 sparkfun esp8266 thing dev https docs microsoft com en us azure iot hub iot hub sparkfun esp8266 thing dev get started get started with your arduino board sparkfun esp8266 thing dev intel edison https docs microsoft com en us azure iot hub iot hub intel edison kit c get started connect your intel edison device to your iot hub using c mxchip iot developer kit https microsoft github io azure iot developer kit docs projects connect iot hub get started with your mxchip iot developer kit framework azure iot protocol gateway https github com azure azure iot protocol gateway azure iot protocol gateway is a framework for protocol adaptation that enables bi directional communication with azure iot hub it is a pass through component that bridges traffic between connected iot devices and iot hub sdk azure iot device sdk https docs microsoft com en us azure iot hub iot hub devguide sdks azure iot device sdk the microsoft azure iot device sdks contain code that facilitates building devices and applications that connect to and are managed by azure iot hub services azure iot service sdk https docs microsoft com en us azure iot hub iot hub devguide sdks azure iot service sdk the azure iot service sdks contain code to facilitate building applications that interact directly with iot hub to manage devices and security azure iot edge sdk https github com azure iot edge azure iot edge moves cloud analytics and custom business logic to devices so that your organization can focus on business insights instead of data management libraries and tools azure iot edge https marketplace visualstudio com items itemname vsciot vscode azure iot edge develop deploy debug and manage your iot edge solution azure iot toolkit https marketplace visualstudio com items itemname vsciot vscode azure iot toolkit interact with azure iot hub iot device management iot hub code snippets platformio for visual studio code https marketplace visualstudio com items itemname formulahendry platformio platformio is an open source ecosystem for iot development it supports 350 embedded boards 20 development platforms and 10 frameworks azure iot web client https azure iot github io a web based client tool for azure iot hub to send and monitor device to cloud messages azure cli https docs microsoft com en us cli azure iot view azure cli latest commands to connect monitor and control millions of iot assets iot hub rest api https docs microsoft com en us rest api iothub the rest apis for iot hub offer programmatic access to the device and messaging services as well as the resource provder in iot hub iot hub explorer https github com azure iothub explorer a cli tool to manage device identities in your iot hub registry send and receive messages and files from your devices and monitor your iot hub operations device explorer https github com azure azure iot sdk csharp tree master tools deviceexplorer this tool enables you to perform operations such as manage the devices registered to an iot hub view device to cloud messages sent to an iot hub and send cloud to device messages from an iot hub note this tool only runs on windows iot hub diagnostics tool https github com azure iothub diagnostics this tool is provided to help diagnose issues with a device connecting to azure iot hubs resources azure iot developer center https azure microsoft com en us develop iot get started with azure iot suite and iot hub and learn how easy it is to connect your iot devices to microsoft azure azure iot hub code samples https azure microsoft com en us resources samples service iot hub learn to interact with azure iot hub through code azure iot hub updates https azure microsoft com en us updates product iot hub service updates for azure iot hub azure iot hub limits https docs microsoft com en us azure azure subscription service limits iot hub limits list the limits associated with the different service tiers s1 s2 s3 f1 azure iot suite https azure microsoft com en us suites iot suite get started quickly with microsoft azure iot suite use preconfigured solutions and accelerate the development of your internet of things iot solution internet of things partners https www microsoft com en us internet of things find a partner connect with a partner to unleash the powerful potential and business value of the internet of things iot whether you re looking for a complete iot solution or building your own using certified devices engage with an azure iot partner to tailor a solution for the needs of your business azure blog for internet of things https azure microsoft com en us blog topics internet of things the official microsoft azure blog for topics about internet of things iot developer blog https blogs msdn microsoft com iotdev msdn blog about tooling and experience for iot developer | azure-iot windows-iot azure iot iothub awesome awesome-list | server |
CDAC-INTELLIGENT-DISTRIBUTED-CONTROL-SYSTEM-FOR-BACKHOE | cdac intelligent distributed control system for backhoe the objective of the project is to demonstrate the smart control system for the backhoe by the help of can and rtos so as to control the functioning of the different arms of the backhoe sir we tested the python codes required for the input output for servomotor adc and potentiometer sir while we tried for clock configuration settings on stm 32 cubemx for different components that specially required for project we did not understand it properly checking for the beaglebone to beaglebone code was performed and the result was a success | os |
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swiss-engineering-cloud-demo | swiss engineering cloud demo zsh j swiss engineering cloud demo heroku create git push heroku master open link show website convert to pro dyno change servers show metrics logs delete dyno | swiss-engineering se-zh project ruby performance heroku demo | cloud |
thoughttreasure | thoughttreasure thoughttreasure commonsense knowledge base and architecture for natural language processing http www amazon com gp product 1478171650 functionality comprehensive natural language processing commonsense platform question answering chatterbot story understanding classified ad parsing simulation of a 2 dimensional virtual world identification of entities in text information extraction from ascii tables concordance generation shell command interpreter web api java based client api python based client api knowledge base flat files applications commonsense reasoning question answering information extraction interactive fiction source code language ansi c lines of code including comments 75 000 knowledge base and lexicon upper ontology domain lower ontologies clothing food music and so on concepts words and phrases defined using concise language english words and phrases 35 000 french words and phrases 20 000 closed class lexical items not in wordnet adverbs and adverbials prepositions determiners pronouns conjunctions interjections temporal relations and so on concepts 25 000 assertions 50 000 including hierarchical links scripts 100 with events and roles linguistic features english and french lexicon ontology verb nominalization argument structure name dictionary with grouping of related names text agents part of speech tagging identification of names places products dates phone numbers email headers english and french syntactic parser base component filters transformations english and french semantic parser intension extension relative clauses appositives genitives tense aspect anaphoric parser deixis determiners pronouns c command understanding agents converting surfacy parse into detailed understanding steering planning agents contexts emotions goals question answering asking clarifying questions appointments sleep grids by analogy english and french generator learning of new words using derivational rules learning of new inflections by algorithmic analogical morphology simulation features space represented by 2 dimensional grids connected by wormholes prototypical grids house apartment restaurant theater street subway planning agents for simulating human behavior graspers containers ptrans atrans mtrans interpersonal relations planning agents for simulating device behavior telephone television procedures database assertion retrieval of assertions contexts anagram finder palindrome finder inverted dictionary generator english french faux ami finder intension resolver find objects matching a description subgoaling 2 dimensional occupancy array path planner grid operations distance subspace etc intergrid path planner trip planner clothing color matching operations for parts and wholes of objects operations for nested space room floor building city planet operations for large space planetary distance polity containment theorem prover temporal projection abduction relation learner corporate successions assertion learner language translation dictionary generator free association generator corpus analysis tools multi language multi stream discourse channel chatterbot main loop typing simulator with errors html utilities report generation | ai |
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cherry | cherry logo https github com windsooon cherry blob master imgs text png raw true image https travis ci org windsooon cherry svg branch master https travis ci org windsooon cherry image https img shields io pypi v cherry svg https pypi python org pypi cherry image https img shields io pypi l cherry svg https pypi python org pypi cherry image https img shields io pypi pyversions cherry svg https pypi python org pypi cherry cherry text classification in 5 minutes no machine learning knowledge needed cherry windson download https pypi python org pypi cherry source https github com windsooon cherry keywords machine learning text classification document feature feature requirements requirements install install built in model built in model quickstart quick start example example api api performance performance search search display display feature easy to use fast and simple even though you had never learned about machine learning you can use cherry to train your text classification model in 5 minutes with over 80 accuracy cherry also provides extra features for users who want to improve their model easy to debug and optimize cherry provide performence performance and display display api to help you debug and improve your model requirements python above 3 6 installation install using pip pip install cherry cherry use nltk for text tokenizer pip install nltk after install nltk you need to download punkt for tokenizer import nltk nltk download punkt built in model cherry has three built in text classification models newsgroups review and email the 20 newsgroups dataset http qwone com jason 20newsgroups these datasets contain 11 315 news they were organized into 20 different newsgroups each corresponding to one of the below topic alt atheism comp graphics comp os ms windows misc comp sys ibm pc hardware comp sys mac hardware comp windows x misc forsale rec autos rec motorcycles rec sport baseball rec sport hockey sci crypt sci electronics sci med sci space soc religion christian talk politics guns talk politics mideast talk politics misc talk religion misc comics graphic book review https sites google com eng ucsd edu ucsdbookgraph home these datasets contain 108 463 reviews from the goodreads book review website every book review also has rating from 0 point to 5 points sms spam collection http www dt fee unicamp br tiago smsspamcollection these datasets contain 5 578 sms messages manually extracted from the grumbletext web site and randomly chosen ham messages of the nus sms corpus nsc quick start use built in model in the comics graphic book review https sites google com eng ucsd edu ucsdbookgraph home datasets each review has a corresponding rating from 1 to 5 for example if you want to predict the rating based on this book review this is an extremely entertaining and often insightful collection by nobel physicist richard feynman drawn from slices of his life experiences some might believe that the telling of a physicist s life would be droll fare for anyone other than a fellow scientist but in this instance nothing could be further from the truth train the model train the model in your python environment python3 cherry train review this line of code will 1 download review datasets from remote server user in china may need use vpn 2 train datasets using default settings countvectorizer https scikit learn org stable modules generated sklearn feature extraction text countvectorizer html and multinomialnb https scikit learn org stable modules generated sklearn naive bayes multinomialnb html you only need to train the model once and subsequent classification tasks do not need to be retrained classify the review you can use classify to predict the rating now res cherry classify review text this is an extremely entertaining and often insightful collection by nobel physicist richard feynman drawn from slices of his life experiences some might believe that the telling of a physicist s life would be droll fare for anyone other than a fellow scientist but in this instance nothing could be further from the truth the return res is a classify object has two built in method get probability will return an array contains the probability of each category the order of the return array depend on category name in this case would be 0 1 2 3 4 we can see that there is 99 63 9 96313288e 01 this review is rated 4 point the probability of this review had been rating as 4 points is 99 6 res get probability array 6 99908424e 11 2 48677319e 11 6 17978214e 06 3 39472694e 03 9 96313288e 01 2 85805135e 04 another method get word list return a list that contains words that cherry use for classifying res get word list 2 physicist 2 life 1 truth 1 telling 1 slices 1 scientist 1 richard 1 nobel 1 instance 1 insightful 1 feynman 1 fellow 1 fare 1 extremely 1 experiences 1 entertaining 1 droll 1 drawn 1 collection 1 believe some of the words in the review didin t show up here there are two reasons for this 1 the training data didn t contain that word for instance the word backend and engineer never show up in training data so the model don t know how to classify these words 2 the word is a stop word https en wikipedia org wiki stop words how it works in the cherry folder you can find a new folder named datasets the five folders inside correspond to 1 to 5 points respectively cherry uses the word frequency inside different folders to determine which word belongs to which score when performing a classification task cherry will calculate the probability of all words in the review to determine which category it belongs to use your own dataset create a folder your model name under datasets in project path like this project path datasets your model name category1 file 1 file 2 category2 file 10 file 11 train you dataset by default encoding will be utf 8 you only need to run train at the first time cherry train your model name encoding your encoding classify text text can be a list of text too res cherry classify your model name text text to be classified example let s build an email classifier from sketch cherry will use this model to predict an email is spam or not project setup mkdir tutorial cd tutorial create a virtual environment to isolate our package dependencies locally python3 m venv env source env bin activate on windows use env scripts activate install cherry and nltk pip install cherry pip install nltk import nltk nltk download punkt create a new folder for email dataset mkdir p datasets email tutorial prepare dataset 1 download the datasets from sms spam collection v 1 http www dt fee unicamp br tiago smsspamcollection then unzip it and put it inside tutorial datasets email tutorial folder now you got a file named smsspamcollection txt which contains lots of emails 2 create a folder name ham and spam inside email tutorial dir 3 create a script email py in the same folder using code below to extract the email content and group them by category every file would only contain text import os import json ham counter 0 spam counter 0 with open smsspamcollection txt r as f for line in f readlines if line startswith ham ham counter 1 with open os path join ham str ham counter w as nf text line split ham 1 nf write text strip else spam counter 1 with open os path join spam str spam counter w as nf text line split spam 1 nf write text strip 4 now your folder structure should look like this tutorial dataset email tutorial email py smsspamcollection txt ham spam 5 run python email py 6 delete smsspamcollection txt and email py 7 back to the path of tutorial like cd path to tutorial 6 train the email model import cherry cherry train email tutorial encoding latin1 7 inside email tutorial folder you can find clf pkz ve pkz email tutorial pkz which cherry will use them for classify later res cherry classify email tutorial thank you for your interest in cherry we wanted to let you know we received your application for backend engineer and we are delighted that you would consider joining our team 99 9 is a ham email res get probability array 9 99985571e 01 1 44288379e 05 res get word list 1 wanted 1 thank 1 team 1 received 1 let 1 joining 1 consider 1 application 8 if you want to know good your model did you can use performance which will use k fold cross validation by default k equals to 10 res cherry performance email tutorial encoding latin1 output files res get score the report will be save in report files you can find the precision recall and f1 score precision recall f1 score support 0 0 99 1 00 0 99 485 1 0 97 0 95 0 96 73 accuracy 0 99 558 macro avg 0 98 0 97 0 98 558 weighted avg 0 99 0 99 0 99 558 if you want to know which text had been clasiify wrong res cherry performance email tutorial encoding latin1 res get score text dhoni have luck to win some big title so we will win has been classified as 1 should be 0 text back 2 work 2morro half term over can u c me 2nite 4 some sexy passion b4 i have 2 go back chat now 09099726481 luv dena calls 1 minmobsmorelkpobox177hp51fl has been classified as 0 should be 1 text latest news police station toilet stolen cops have nothing to go on has been classified as 0 should be 1 9 to display the graph you can use res display email tutorial encoding latin1 img https github com windsooon cherry blob master imgs display png raw true 10 if you want to improve your model you can use search method parameters clf alpha 0 1 0 5 1 clf fit prior true false cherry search email tutorial parameters api def train model language english preprocessing none categories none encoding utf 8 vectorizer none vectorizer method count clf none clf method mnb x data none y data none model string the name of the model you can use build in models email review and newsgroups or pass the folder name of your dataset language string the language of the training dataset cherry supports english and chinese preprocessing function the function will be called once for every input data before training categories list specify the training directory for instance alt atheism comp graphics comp os ms windows misc encoding string the encoding of the dataset vectorizer sklearn object feature extraction function use to convert the data into vertcor by default is countvectorizer you can pass different feature extraction function https scikit learn org stable modules classes html module sklearn feature extraction text from sklearn for some long texts you can use tfidfvectorizer if you need to save memory you can use hashingvectorizer get word list function wouldn t work at this case vectorizer method string cherry supports shortcut to set up feature extraction function when vectorizer is none count corresponds to countvectorizer tokenizer tokenizer stop words get stop words model tfidf corresponds to tfidfvectorizer and hashing corresponds to hashingvectorizer clf sklearn object classify function by default is multinomialnb you can pass classify function https scikit learn org stable auto examples classification plot classifier comparison html from sklearn clf method string cherry supports shortcut to set up classify function when clf is none mnb corresponds to multinomialnb alpha 0 1 sgd corresponds to sgdclassifier randomforest corresponds to randomforestclassifier adaboost corresponds to adaboostclassifier x data numpy array training text data if x data and y data is none cherry will try to find the text files data in model y data numpy array correspond labels data if x data and y data is none cherry will try to find the text files data in model def classify model text model string the name of the model you can use build in models email review and newsgroups or pass the folder name of your dataset text list string the text to be classify def performance model language english preprocessing none categories none encoding utf 8 vectorizer none vectorizer method count clf none clf method mnb x data none y data none n splits 10 output stdout just as same as train api n splits integer number of folds must be at least 2 output stdout or files stdout will print the scores to standerd output and files will store the scores into a local file named report def search model parameters language english preprocessing none categories none encoding utf 8 vectorizer none vectorizer method count clf none clf method mnb x data none y data none method randomizedsearchcv cv 3 n jobs 1 todo def display model language english preprocessing none categories none encoding utf 8 vectorizer none vectorizer method count clf none clf method mnb x data none y data none just as same as train api tests python runtests py | ai |
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DB1-Agile | web app agile calendar web app for project management that using agile scrum methodology how to run 1 enable virtual enviroment with venv scripts activate 2 set debug mode with env flask env development on powershell set flask env development on cmd 3 run with flask run update db structure 1 open a python shell inside the project folder 2 import the update function with from backend database import init db 3 run the update function with init db made by paolo zanotti https github com zanottipaolo federico de duro https github com jfkmdd matteo soldini https github com matteosoldini | database web flask scrum-agile | server |
altinn-design-system | design system for altinn designsystem altinn studio https designsystem altinn studio this library is used for components that are unique for altinn we have moved reusable core components that can be used in multiple products and situations to our common design system https digdir github io designsystem path story introduksjon page we will continue to develop reusable components in the common design system https digdir github io designsystem path story introduksjon page how to install to add the design system to your project navigate to the directory where your package json file is located and run one of the following commands npm npm install altinn altinn design system yarn yarn add altinn altinn design system prerequisites as of version 0 27 this design system is no longer compatible with the old design system https github com altinn designsystem this is because the old design system sets the rem unit to 10px while the default is 16px and this is also the unit expected by the figma tokens in older versions these tokens are converted to match the rem unit of the old design system linking if you plan on making any contributions to the design system and develop features test changes with a more rapid feedback loop you ll most likely want to link your project to a local clone of altinn design system instead if using npm link or yarn link works for your project that ll be the easiest solution most likely however you ll probably want to create local packages for altinn design system and refer to those in your project package json first in your local altinn design system 1 run yarn immutable if you haven t already 2 run yarn build 3 run yarn pack then in your project 1 update the version number for altinn altinn design system to instead point to a path for the package tgz file generated in the steps above for example altinn altinn design system altinn design system package tgz 2 run yarn install or npm install depending on your project 3 build and or run your project tip whenever you re build and re pack in altinn design system the output filename stays the same package tgz this might cause caching issues where your project uses an older package tgz to force using a newer package rename it to something else after running yarn pack for example package2 tgz be sure to run your package manager after every time you pack the design system library getting started node and corepack we are using the latest lts release of node but minimum version 16 9 0 since we are using corepack https nodejs org api corepack html to enable corepack execute corepack enable from the terminal start storybook execute yarn start to start storybook it should open a browser automatically when it is ready if you prefer to not automatically open a browser you can execute yarn start no open tests yarn test to run unit tests yarn lint to run lint checks lint checks and auto fixes will be run automatically on commit adding new components new components can be added by executing yarn add component componentname the name of the component should be written using pascalcase this will generate all important files for you in the correct location and also update the index file for exporting the component the generated code includes some todo statements that you should fix adding new dependencies when adding new dependencies you should also add that dependency to the external array in rollup config mjs this is done to avoid having the dependency being part of the bundle styling styling should primarily be done in css files using css variables the css files should end with module css so unique classnames will be generated this ensures we will not run into naming collision issues with classnames we are using figma as our design tool and we are extracting tokens directly from figma that can be used in code these tokens are defined in the figma design tokens repository https github com altinn figma design tokens new components should ideally be using design tokens from there to define their layout before work is started on the component you should discuss with the ux group first because they need to define the tokens for the components classname naming conventions using bem naming convention http getbem com naming gives a pretty clear view of what parts are the root and what parts are the children and is preferred this also helps you think about when a component grows too big and should be split into smaller isolated parts good to know how are the figma design tokens connected to this repo the design tokens live in a separate repository https github com altinn figma design tokens which is used in figma with a plugin the ux group will set use these tokens in their design and in the end it will be synced to the tokens json in the figma design tokens repository these tokens are publised to npm as a package altinn figma design tokens during this process the tokens json is transformed to tokens esm js and tokens css files so the values can be used directly from js or as css variables figma tokens usage diagam docs figma tokens diagram svg when using these tokens in this project we are also transforming the values a bit to stay compatible with our old design system the long term goal is to get rid of the dependency to the old design system so we no longer have to do this transformation for more information about this see the below explaination rem values code style we use eslint https eslint org and prettier https prettier io and automatically set up git hooks to enforce these on commits to avoid confusion it is recommended to set this up your ide visual studio code install the eslint extension from the marketplace https marketplace visualstudio com items itemname dbaeumer vscode eslint webstorm and intellij idea configure your ide to run eslint fix on save prettier will also reformat your code when doing this it is also recommended to set up prettier as the default formatter https www jetbrains com help webstorm prettier html ws prettier default formatter creating a new release go to github actions and select the release pipeline run the workflow and select the appropriate version major minor or patch we use semver https semver org spec release notes currently release notes is semi automatic after the release is done go to the releases page and edit the release that was just created click the generate release notes button to get release notes and update the release | design-system react storybook | os |
SystemDesign | trade off sql nosql do you gather requirement do you define the right limits of the system what the system can cannot do do you realize trade offs do you think big enough to have the right level of abstraction and keep it simple do you have basic knowledge such as avoid single point of failure cap theorem scaling fault tolerant consistent hasing can you think clearly do you have a good working method do you loop back on requirement to test if your design work well do you call out how the system can and can t envolve do you have domain expertise qps qps availability consistency systemdesignbasics estimations md cap systemdesignbasics cap md uuid lbs systemdesignbasics lbs md id systemdesignbasics globaluuid md systemdesignbasics massive data processing md systemdesignbasics message queue md write through cache aside sql nosql file system systemdesignbasics cache md sql nosql systemdesignbasics sqlvsnosql md single point of failure load balance master slave consistent hashing systemdesignbasics consistent hashing md sharding partitioning systemdesignbasics sharding md top k systemdesignquestions topk md typeahead systemdesignquestions typeahead md youtube netflix systemdesignquestions videoplatform md systemdesignquestions webcrawl md systemdesignquestions chat md facebook twitter systemdesignquestions newsfeed md yelp systemdesignquestions placesuggestionpoi md uber lyft systemdesignquestions rideshare md google facebook status search systemdesignquestions search md systemdesignquestions clickcount md systemdesignquestions distributedfilesystem md systemdesignquestions eventscheduler md | os |
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windows-iotcore-docs | this repository has been archived we re no longer updating this content regularly check the microsoft product lifecycle for information about how this product service technology or api is supported microsoft open source code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments how to contribute to windows 10 iotcore documentation legal notices microsoft and any contributors grant you a license to the microsoft documentation and other content in this repository under the creative commons attribution 4 0 international public license https creativecommons org licenses by 4 0 legalcode see the license license file and grant you a license to any code in the repository under the mit license https opensource org licenses mit see the license code license code file microsoft windows microsoft azure and or other microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of microsoft in the united states and or other countries the licenses for this project do not grant you rights to use any microsoft names logos or trademarks microsoft s general trademark guidelines can be found at http go microsoft com fwlink linkid 254653 privacy information can be found at https privacy microsoft com en us microsoft and any contributors reserve all others rights whether under their respective copyrights patents or trademarks whether by implication estoppel or otherwise contributing this is the repository for published windows for iot documentation https learn microsoft com windows iot core if you would like to see new coverage or have feedback please consider contributing contributing md you can edit the existing content add new content or simply create new issues https github com microsoftdocs windows iotcore docs issues we ll take a look at your suggestions and will work together to incorporate them into the docs to edit content just click edit on the article you want to make changes to gif on how to edit docs windows iotcore media edit doc gif you can also clone or download the repo to make changes gif on how to clone or download repo windows iotcore media download repo gif you will also need to add a reviewer or reviews to your pull requests to get them approved adding reviewers to your pull request windows iotcore media reviewers gif conventions when adding a page you must add an entry for it in toc md windows iotcore toc md for it to appear a folder can contain more folders or readme md s folder directory names are dash separated e g f12 tools and lowercase they are used in urls for articles on microsoft learn don t use underscores or pascalcase or camelcase other text elements these other text elements have styling available unordered lists have regular bullets you can also nest bullets bullets lists should have more than one entry pretty standard 1 ordered lists 2 use regular ol western style numbering 3 should be used only when a list truly has order horizontal rules are available we suggest using them sparingly to reduce clutter do not combine horizontal rules with heading tags some already used line styles for visual hierarchy also do not combine notes see below in the middle of numbered lists this messes with the numbering order displaying code you can use inline code markdown syntax with the backticks or you can display blocks of code like so css body background fff tables you can use headers on tables left aligned unless a 456 text value more text 0 00 notes use notes sparingly they are blocks designed to highlight don t miss it information we have four different versions of notes currently styled note warning tip important for multi line blockquote notes use a in front of each line of the notes as seen in the example below images images should be stored in a media folder and referenced with a relative path note patterns media notes png code of conduct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server |
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conFusion-Ionic | confusion ionic | server |
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NLP-Interpretation | nlp interpretation a paper list for interpreting neural networks in natural language processing review arxiv 16 the mythos of model interpretability paper https arxiv org pdf 1606 03490 pdf book https dl acm org doi pdf 10 1145 3236386 3241340 arxiv 17 towards a rigorous science of interpretable machine learning paper https arxiv org pdf 1702 08608 pdf tacl 19 analysis methods in neural language processing a survey paper https www aclweb org anthology q19 1004 pdf appendix https boknilev github io nlp analysis methods this survey reviews methods of analyzing understanding and interpreting neural network models in nlp existing works can be categorized as the following 6 categories i probing task methods for exploiting what kind of linguistic information is captured in neural networks ii visualization iii the compilation of challenge sets or test suites for fine grained evaluation iv the generation and use of adversarial examples to probe weaknesses of neural networks v explaining model predictions vi other methods acl 20 tutorial interpretability and analysis in neural nlp slides https sebastiangehrmann com assets files acl 2020 interpretability tutorial pdf emnlp 20 tutorial interpreting predictions of nlp models slides https github com eric wallace interpretability tutorial emnlp2020 blob master tutorial slides pdf zju paper https nesa zju edu cn download e6 a8 a1 e5 9e 8b e5 8f af e8 a7 a3 e9 87 8a e6 80 a7 e5 85 b3 e9 94 ae e6 8a 80 e6 9c af e3 80 81 e5 ba 94 e7 94 a8 e5 8f 8a e5 85 b6 e5 ae 89 e5 85 a8 e6 80 a7 e7 a0 94 e7 a9 b6 e7 bb bc e8 bf b0 pdf workshop emnlp 18 blackboxnlp 2018 analyzing and interpreting neural networks for nlp workshop https www aclweb org anthology events emnlp 2018 w18 54 acl 19 blackboxnlp 2019 analyzing and interpreting neural networks for nlp workshop https www aclweb org anthology events acl 2019 w19 48 paper probing task method arxiv 21 probing classifiers promises shortcomings and alternatives paper https arxiv org pdf 2102 12452 pdf acl 16 does string based neural mt learn source syntax paper https www aclweb org anthology d16 1159 pdf this paper exploited i does the encoder learn syntactic information about the source sentence ii what kind of syntactic information is learned and how much they predicted syntactic labels and extracted syntactic trees using encoder intermediate representations to examine whether encoder capture syntactic information experimental results showed that i encoder captures both global and local syntactic information of the source sentence and different information tends to be stored at different layers ii much syntactic information is learned while various types of syntactic information are still missing emnlp 16 analyzing linguistic knowledge in sequential model of sentence paper https www aclweb org anthology d16 1079 pdf iclr 17 fine grained analysis of sentence embeddings using auxiliary prediction tasks paper https openreview net pdf id bjh6ztuxl acl 17 what do neural machine translation models learn about morphology paper https www aclweb org anthology p17 1080 pdf eacl 17 how grammatical is character level neural machine translation assessing mt quality with contrastive translation pairs paper https www aclweb org anthology e17 2060 pdf ijcnlp 17 evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks paper https www aclweb org anthology i17 1001 pdf machine translation 17 the representational geometry of word meanings acquired by neural machine translation models paper https link springer com article 10 1007 2fs10590 017 9194 2 acl 18 what you can cram into a single vector probing sentence embeddings for linguistic properties paper https www aclweb org anthology p18 1198 pdf acl 18 lstms can learn syntax sensitive dependencies well but modeling structure makes them better paper https www aclweb org anthology p18 1132 pdf acl 18 are bleu and meaning representation in opposition paper https www aclweb org anthology p18 1126 pdf acl 18 deep rnns encode soft hierarchical syntax paper https www aclweb org anthology p18 2003 pdf emnlp 18 dissecting contextual word embeddings architecture and representation paper https arxiv org pdf 1808 08949 pdf emnlp 18 workshop an analysis of encoder representations in transformer based machine translation paper https www aclweb org anthology w18 5431 pdf emnlp 18 workshop under the hood using diagnostic classifiers to investigate and improve how language models track agreement information paper https www aclweb org anthology w18 5426 pdf iclr 19 what do you learn from context probing for sentence structure in contextualized word representations paper https arxiv org pdf 1905 06316 pdf naacl 19 a structural probe for finding syntax in word representations paper https www aclweb org anthology n19 1419 pdf acl 19 bert rediscovers the classical nlp pipeline paper https www aclweb org anthology p19 1452 pdf acl 19 what does bert learn about the structure of language paper https www aclweb org anthology p19 1356 pdf acl 19 assessing the ability of self attention networks to learn word order paper https www aclweb org anthology p19 1354 pdf acl 19 workshop open sesame getting inside bert s linguistic knowledge paper https www aclweb org anthology w19 4825 pdf cikm 19 how does bert answer questions a layer wise analysis of transformer representations paper https arxiv org pdf 1909 04925 pdf emnlp 19 designing and interpreting probes with control tasks paper https www aclweb org anthology d19 1275 pdf emnlp 19 encoders help you disambiguate word sensesin neural machine translation paper https www aclweb org anthology d19 1149 pdf emnlp 19 revealing the dark secrets of bert paper https www aclweb org anthology d19 1445 pdf ranlp 19 understanding neural machine translation by simplification the case of encoder free models paper https www aclweb org anthology r19 1136 pdf arxiv 20 what s so special about bert s layers a closer look at the nlp pipeline in monolingual and multilingual models paper https arxiv org pdf 2004 06499 pdf arxiv 20 telling bert s full story from local attention to global aggregation paper https arxiv org pdf 2004 05916 pdf acl 20 interpretability analysis for named entity recognition to understand system predictions and how they can improve paper https arxiv org pdf 2004 04564 pdf acl 20 probing linguistic features of sentence level representations in neural relation extraction paper https arxiv org pdf 2004 08134 pdf acl 20 what is learned in visually grounded neural syntax acquisition paper https arxiv org pdf 2005 01678 pdf arxiv 20 probing text models for common ground with visual representations paper https arxiv org pdf 2005 00619 pdf acl 20 spying on your neighbors fine grained probing of contextual embeddings for information about surrounding words paper https arxiv org pdf 2005 01810 pdf acl 20 a tale of a probe and a parser paper https arxiv org pdf 2005 01641 pdf arxiv 20 can bert reason logically equivalent probes for evaluating the inference capabilities of language models paper https arxiv org pdf 2005 00782 pdf acl 20 probing the probing paradigm does probing accuracy entail task relevance paper https arxiv org pdf 2005 00719 pdf acl 20 intermediate task transfer learning with pretrained models for natural language understanding when and why does it work paper https arxiv org pdf 2005 00628 pdf arxiv 20 assessing the bilingual knowledge learned by neural machine translation models paper https arxiv org pdf 2004 13270 pdf arxiv 20 quantifying the contextualization of word representationswith semantic class probing paper https arxiv org pdf 2004 12198 pdf visualization method iclr 16 visualizing and understanding recurrent networks paper https arxiv org pdf 1506 02078 pdf naacl 16 visualizing and understanding neural models in nlp paper https www aclweb org anthology n16 1082 pdf arxiv 16 representation of linguistic form and function in recurrent neural networks paper https arxiv org pdf 1602 08952 pdf acl 17 visualizing and understanding neural machine translation paper https www aclweb org anthology p17 1106 pdf ijcnlp 17 what does attention in neural machine translationpay attention to paper https www aclweb org anthology i17 1004 pdf ijcai 18 visualisation and diagnostic classi ers reveal how recurrent and recursive neural networks process hierarchical structure paper https www ijcai org proceedings 2018 0796 pdf ieee vis 18 seq2seq vis a visual debugging tool for sequence to sequence models paper https arxiv org pdf 1804 09299 pdf emnlp 18 workshop extracting syntactic trees from transformer encoder self attentions paper https www aclweb org anthology w18 5444 pdf acl 19 workshop from balustrades to pierre vinken looking for syntax in transformer self attention paper https www aclweb org anthology w19 4827 pdf acl 19 workshop what does bert look at an analysis of bert s attention paper https www aclweb org anthology w19 4828 pdf arxiv 20 amnesic probing behavioral explanation with amnesic counterfactuals paper https arxiv org pdf 2006 00995 pdf attribution icml 17 understanding black box predictions via influence functions paper https arxiv org pdf 1703 04730 pdf icml 17 axiomatic attribution for deep networks paper https arxiv org pdf 1703 01365 pdf emnlp 17 a causal framework for explaining the predictions of black box sequence to sequence models paper https www aclweb org anthology d17 1042 pdf nips 18 sanity checks for saliency maps paper https arxiv org pdf 1810 03292 pdf emnlp 19 towards understanding neural machine translation with word importance paper https arxiv org pdf 1909 00326 pdf i word importance estimation aims to indentify most important words to translation performance in original input ii the paper proposed a gradient based method which outperforms erasion based method and attention based method iii additional analysis shows that words of certain syntactic categories have higher importance while the categories vary across language arxiv 20 aligning faithful interpretations with their social attribution paper https arxiv org pdf 2006 01067 pdf arxiv 20 causalm causal model explanation through counterfactual language models paper https arxiv org pdf 2005 13407 pdf attention interpretability wmt 18 an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation paper https www aclweb org anthology w18 6304 pdf sigir 19 workshop do transformer attention heads provide transparency in abstractive summarization paper https arxiv org pdf 1907 00570 pdf acl 19 analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned paper https arxiv org pdf 1905 09418 pdf naacl 19 attention is not explanation paper https www aclweb org anthology n19 1357 pdf arxiv 19 attention interpretability across nlp tasks paper https arxiv org pdf 1909 11218 pdf acl 19 is attention interpretable paper https www aclweb org anthology p19 1282 pdf emnlp 19 attention is not not explanation paper https www aclweb org anthology d19 1002 pdf acl 20 towards transparent and explainable attention models paper https arxiv org pdf 2004 14243 pdf representation similarity analysis iclr 16 convergent learning do different neural networks learn the same representations paper https arxiv org pdf 1511 07543v3 pdf nips 17 svcca singular vector canonical correlation analysis for deep learning dynamics and interpretability paper https arxiv org pdf 1706 05806 pdf nips 18 insights on representational similarity in neuralnetworks with canonical correlation paper https papers nips cc paper 7815 insights on representational similarity in neural networks with canonical correlation pdf icml 19 similarity of neural network representations revisited paper https arxiv org pdf 1905 00414 pdf naacl 19 understanding learning dynamics of language models with svcca paper https www aclweb org anthology n19 1329 pdf emnlp 19 the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives paper https www aclweb org anthology d19 1448 pdf acl 20 similarity analysis of contextual word representation models paper https arxiv org pdf 2005 01172 pdf explanation evaluating naacl 18 comparing automatic and human evaluation of local explanations for text classification paper https www aclweb org anthology n18 1097 pdf acl 19 workshop evaluating recurrent neural network explanations paper https www aclweb org anthology w19 4813 pdf acl 20 evaluating explainable ai which algorithmic explanations help users predict model behavior paper https arxiv org pdf 2005 01831 pdf acl 20 evaluating explanation methods for neural machine translation paper https arxiv org pdf 2005 01672 pdf acl 20 towards faithfully interpretable nlp systems how should we define and evaluate faithfulness paper https arxiv org pdf 2004 03685 pdf information theory based interpretation hujj 99 the information bottleneck method paper https www cs huji ac il labs learning papers allerton pdf itw 15 deep learning and the information bottleneck principle paper https arxiv org pdf 1503 02406 pdf arxiv 17 opening the black box of deep neural networks via information paper https arxiv org pdf 1703 00810 pdf acl 20 information theoretic probing for linguistic structure paper https arxiv org pdf 2004 03061 pdf arxiv 20 information theoretic probing with minimum description length paper https arxiv org pdf 2003 12298 pdf blog https lena voita github io posts mdl probes html acl 20 it s easier to translateout ofenglish thanintoit measuring neural translation difficulty by cross mutual information paper https arxiv org pdf 2005 02354 pdf natural language explanations acl 20 expbert representation engineering with natural language explanations paper https arxiv org pdf 2005 01932 pdf optimization and generalization of deep learning iclr 17 understanding deep learning requires rethinking generalization paper https arxiv org pdf 1611 03530 pdf arxiv 18 reconciling modern machine learning practice and the bias variance trade off paper https arxiv org pdf 1812 11118 pdf iclr 19 the lottery ticket hypothesis finding sparse trainable neural networks paper https arxiv org pdf 1803 03635 pdf arxiv 20 when bert plays the lottery all tickets are winning paper https arxiv org pdf 2005 00561 pdf individual neurons analysis aaai 19 what is one grain of sand in the desert analyzing individual neurons in deep nlp models paper https arxiv org pdf 1812 09355 pdf iclr 19 how important is a neuron paper https arxiv org pdf 1805 12233 pdf iclr 19 identifying and controlling important neurons in neural machine translation paper https arxiv org pdf 1811 01157 pdf difficulty of interpreting blog the problem of faithfulness in neural network nlp interpretations blog https medium com alonjacovi the problem of faithfulness in neural network nlp interpretations ee98d7027cbd arxiv 17 the un reliability of saliency methods paper https arxiv org pdf 1711 00867 pdf icml 18 workshop on the robustness of interpretability methods paper https arxiv org pdf 1806 08049 pdf emnlp 18 pathologies of neural models make interpretations difficult paper https arxiv org pdf 1804 07781 pdf nips 18 towards robust interpretability with self explaining neural networks paper https arxiv org pdf 1806 07538 aaai 19 interpretation of neural networks is fragile paper https arxiv org pdf 1710 10547 pdf adversarial examples for probing the weaknesses of neural networks iclr 19 adversarial examples are not bugs they are features paper https arxiv org pdf 1905 02175 pdf arxiv 19 what does bert learn from multiple choice reading comprehension datasets paper https arxiv org pdf 1910 12391 pdf local interpretable model agnostic explanations lime naacl 16 why should i trust you explaining the predictions of any classifier paper https www aclweb org anthology n16 3020 pdf interpreting with nearest neighbors emnlp 18 workshop interpreting neural networks with nearest neighbors paper https www aclweb org anthology w18 5416 pdf other acl 16 workshop explaining predictions of non linear classifiers in nlp paper https arxiv org pdf 1606 07298 eamt 18 an analysis of source context dependency in neural machine translation paper https rua ua es dspace bitstream 10045 76048 1 eamt2018 proceedings 21 pdf emnlp 18 interpreting recurrent and attention based neural models a case study on natural language inference paper https www aclweb org anthology d18 1537 pdf neurips 19 are sixteen heads really better than one paper https arxiv org pdf 1905 10650 pdf acl 19 interpretable neural predictions with differentiable binary variables paper https www aclweb org anthology p19 1284 pdf arxiv 20 lost in embedding space explaining cross lingual task performance with eigenvalue divergence paper https arxiv org pdf 2001 11136 pdf this paper proposed a language similarity distance measure called evd based on isomorphism between monolingual embedding spaces which shows strong correlation with performance of bli and downstream cross lingual tasks machine translation cross lingual dependency parsing and pos tagging additional analysis shows that isomorphism based language distances and typologically driven language distances capture different aspects of language characters the combination of evd and typologically driven language distances has stronger correlation with cross lingual task performance arxiv 20 toward interpretability of dual encoder models for dialogue response suggestions paper https arxiv org pdf 2003 04998 pdf arxiv 20 a primer in bertology what we know about how bert works paper https arxiv org pdf 2002 12327 pdf this paper is an overview of studies about what bert learned and how bert works i bert embedding bert s contextualized embeddings form distinct and clear clusters corresponding to word senses which confirms that the basic distributional hypothesis holds for these representations later bert layers produce more context specific representations ii what knowledge does bert have bert encodes syntactic knowledge semantic knowledge and world knowledge partially but the encoding of that knowledge does not indicate that it actually relies on that knowledge iii localizing linguistic knowledge most self attention heads do not directly encode any non trivial linguistic information some bert heads seem to specialize in certain types of syntactic relations lower layers have the most linear word order information syntactic information is the most prominent in the middle bert layers the final layers of bert are the most task specific semantics is spread across the entire model iv training bert lots of improvements of pre training objectives model architecture choices and fine tuning have been made see more details in the paper v how big should bert be bert is over parametrized and can be compressed vi multilingual bert mbert seems to naturally learn high quality cross lingual word alignments and can retrieve parallel sentences mbert is simply trained on a multilingual corpus with no language ids but it encodes language identities and adding the ids in pre training was not beneficial vii limitations and directions for further research a linguistic pattern is not observed by our probing classifier does not guarantee that it is not there and the observation of a pattern does not tell us how it is used a more complex probe might be able to recover more information but it becomes less clear whether this is due to the probed model or the probing classifier bert does not form good representations for numbers and fails to generalize away from training data bert performs bad at verbal reasoning and can not reason with its world knowledge acl 20 generating fact checking explanations paper https arxiv org pdf 2004 05773 pdf arxiv 20 do sequence to sequence vaes learn global features of sentences paper https arxiv org pdf 2004 07683 pdf acl 20 influence paths for characterizing subject verb number agreement in lstm language models paper https arxiv org pdf 2005 01190 pdf acl 20 how does selective mechanism improve self attention networks paper https arxiv org pdf 2005 00979 pdf acl 20 contextualizing hate speech classifiers with post hoc explanation paper https arxiv org pdf 2005 02439 pdf arxiv 20 what if i ask you to explain explaining the effects of perturbations in procedural text paper https arxiv org pdf 2005 01526 pdf arxiv 20 teaching machine comprehension with compositional explanations paper https arxiv org pdf 2005 00806 pdf acl 20 obtaining faithful interpretations from compositional neural networks paper https arxiv org pdf 2005 00724 pdf acl 20 esprit explaining solutions to physical reasoning tasks paper https arxiv org pdf 2005 00730 pdf iclr 20 understanding and improving information transfer in multi task learning paper https arxiv org pdf 2005 00944 pdf arxiv 20 causal mediation analysis for interpreting neural nlp the case of gender bias paper https arxiv org pdf 2004 12265 pdf weak related papers acl 20 what are the goals of distributional semantics paper https arxiv org pdf 2005 02982 pdf acl 20 generalized entropy regularization or there s nothing special about label smoothing paper https arxiv org pdf 2005 00820 pdf | natural-language-processing neural-networks interpretability | ai |
core-of-the-web | core of the web it contains information about web technologies a href live demo a code chillout https user images githubusercontent com 72499839 105611480 b36b6600 5dc6 11eb 8659 8ca5fb26cdb5 png license mit license copyright c 2021 enes dev permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | server |
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sg-orbit | p align center img alt sharegate orbit src https raw githubusercontent com gsoft inc sg orbit master assets orbit full svg sanitize true width 480 p p align center orbit the design system for sharegate p p align center a href https circleci com gh gsoft inc sg orbit tree master img alt build src https img shields io circleci build github gsoft inc sg orbit master a p p align center a href https lerna js org img alt lerna src https img shields io badge maintained 20with lerna cc00ff svg a a href https yarnpkg com img alt yarn src https img shields io badge dependencies 20managed 20by yarn blue a p orbit is a design system developed by sharegate to help create the best experience for our customers and drive consistency between all our web apps documentation website netlify status https api netlify com api v1 badges 65b52a34 8224 4783 bed2 64ffd05d36af deploy status https app netlify com sites sg orbit deploys the documentation website contains information about installation the orbit foundation and orbit components https orbit sharegate design storybook website netlify status https api netlify com api v1 badges 4b420380 aed1 4dc6 b002 6efe7b413025 deploy status https app netlify com sites sg storybook deploys the storybook website contains stories for orbit custom components https sg storybook netlify com maintainers view the contributors documentation contributing md license copyright 2023 gsoft inc this code is licensed under the apache license version 2 0 you may obtain a copy of this license at https github com gsoft inc gsoft license blob master license | react design-system components storybook | os |
aws-nlp-workshop | aws nlp workshop in this workshop you will explore the aws services needed to enhance your a voice of the customer application with natural langage processing techniques the application architecture uses amazon comprehend https aws amazon com comprehend amazon sagemaker https aws amazon com sagemaker aws lambda https aws amazon com lambda amazon api gateway https aws amazon com api gateway amazon s3 https aws amazon com s3 amazon dynamodb https aws amazon com dynamodb and amazon ecr https aws amazon com ecr amazon comprehend provides natural language processing service needed to predict the sentiment from the feedback entered by users amazon sagemaker is used to orchestrate the machine learning process needed to predict gender of the user from name s3 hosts static web resources including html css javascript and image files which are loaded in the user s browser javascript executed in the browser sends and receives data from a public backend api built using lambda and api gateway dynamodb provides a persistence layer where data can be stored by the api s lambda function ecr is used to host the machine learning training code finally python binding for keras machine learning framework is used to create the model needed for gender prediction prerequisites aws account in order to complete this workshop you ll need an aws account with access to create aws iam s3 dynamodb lambda api gateway comprehend and sagemaker the code and instructions in this workshop assume only one student is using a given aws account at a time if you try sharing an account with another student you ll run into naming conflicts for certain resources you can work around these by appending a unique suffix to the resources that fail to create due to conflicts but the instructions do not provide details on the changes required to make this work all of the resources you will launch as part of this workshop are eligible for the aws free tier if your account is less than 12 months old see the aws free tier page https aws amazon com free for more details browser we recommend you use the latest version of chrome to complete this workshop modules this workshop is broken up into multiple modules you must complete each module before proceeding to the next the first module has a slidedeck to understand the context then second module explores the use of amazon comprehend the next model helps you build a tensorflow model in sagemaker and in the last module we build the complete voice of the customer application using a cloudformation template 1 nlp workshop slides presentation aws nlp workshop pptx 15 mins 2 creating a voc application framework 1 vocframework 15 mins 3 using amazon comprehend to add sentiment analysis 2 sentimentanalysis 30 mins 4 create your own gender classifier 3 genderclassification 60 mins 5 create a summarizer coming soon cleanup after you have completed the workshop you can delete all of the resources using the following steps 1 delete all cloudformation stacks created in all modules 2 delete the sagemaker deployment instance hoting the endpoint 3 delete the sagemaker notebook instance | ai |
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DSS | div align center img src assets sketch png alt drawing width 400 div div align center h1 align center dss deep sensor systems h1 h3 align center from training and deployment of vits to development of real time cross platform mobile apps h3 license https img shields io badge license mit green svg https opensource org licenses mit github downloads https img shields io github downloads andreped dss total label github 20downloads logo github https github com andreped dss releases doi https zenodo org badge doi 10 5281 zenodo 7568040 svg https doi org 10 5281 zenodo 7568040 codecov https codecov io gh andreped dss branch main graph badge svg token nf2gkxxyxe https codecov io gh andreped dss dss was developed by sintef medical image analysis with aim to integrate ais into sensor systems div this project serves as a demonstration on how to do it and does not claim to be a generic framework below there are described some of the key features of this project but to see what else is possible please see the wiki https github com andreped dss wiki continuous integration https github com andreped dss continuous integration build type status test training ci https github com andreped dss workflows test 20training badge svg test flutter ci https github com andreped dss workflows test 20flutter badge svg build apk ci https github com andreped dss workflows build 20apk badge svg how to your train own model https github com andreped dss how to train your own model details summary setup https github com andreped dss setup summary when using this framework it is a good idea to setup a virtual environment virtualenv ppython3 venv clear source venv bin activate pip install r requirements txt the following dependencies will be installed pandas 1 5 3 tensorflow 2 12 0 tensorflow addons 0 19 0 tensorflow datasets 4 8 3 tested with python 3 7 9 on win10 macos and ubuntu linux operating systems also tested with python 3 10 4 on github codespaces note that to activate the virtual environment on windows instead run venv scripts activate details details summary usage https github com andreped dss usage summary to train a model simply run python main py the script supports multiple arguments to see supported arguments run python main py h details details open summary training history https github com andreped dss training history summary to visualize training history use tensorboard with example tensorboard logdir output logs gesture classifier arch rnn example of training history for a recurrent neural network rnn can be seen underneath img src assets rnn training curve png the figure shows macro averaged f1 score for each step during training with black curve for training and blue curve for validation sets best model reached a macro averaged f1 score of 99 66 on the validation set across all 20 classes disclaimer this model was only trained for testing purposes the input features were stratified on sample level and not patient level and thus validation performance will likely not represent true performance on new data however having a trained model enables us to test it in a mobile app details details summary available datasets https github com andreped dss available datasets summary smartwatch gestures https github com andreped dss smartwatch gestures the current data used to train the ai model is the smartwatch gestures dataset which is available in tensorflow datasets https www tensorflow org datasets catalog smartwatch gestures the dataset has the following structure featuresdict attempt tf uint8 features sequence accel x tf float64 accel y tf float64 accel z tf float64 time event tf uint64 time millis tf uint64 time nanos tf uint64 gesture classlabel shape dtype tf int64 num classes 20 participant tf uint8 details how to test the model in a mobile app https github com andreped dss how to test the model in a mobile app details summary converting model to tf lite https github com andreped dss converting model to tf lite summary in order to be able to use the trained model in a mobile app it is necessary to convert the model to a compatible format tensorflow lite is an inference engine tailored for mobile devices to convert the model to tf lite simply run this command python dss keras2tflite py m path to pretrained saved model o path to save converted model tflite details details open summary model integration and testing in app https github com andreped dss model integration and testing in app summary a simple mobile app was developed in flutter which demonstrates the ai in action using the accelerometer data from the mobile phone in real time the data can also be stored and deleted locally p align center width 100 img src sw app assets homescreen jpg width 18 height 20 img src sw app assets prediction jpg width 18 height 20 img src sw app assets chartwithfps jpg width 18 height 20 img src sw app assets recording jpg width 18 height 20 img src sw app assets database jpg width 18 height 20 p to use the app you need an android phone and have developer mode enabled see here https developer android com studio debug dev options for how to enable it then simply download the apk from here https github com andreped dss releases double click to install and use the app as you normally would info on how the mobile app was developed and how to make your own app can be found in the wiki https github com andreped dss wiki getting started with mobile development details acknowledgements https github com andreped dss acknowledgements the training framework was mainly developed using keras https github com keras team keras with tensorflow https github com tensorflow tensorflow backend the mobile app was developed using flutter https github com flutter flutter which is a framework developed by google for the app the following open packages were used either mit bsd 2 or bsd 3 licensed flutter sensors https pub dev packages flutter sensors tflite flutter https pub dev packages tflite flutter wakelock https pub dev packages wakelock sqflite https pub dev packages sqflite intl https pub dev packages intl csv https pub dev packages csv path provider https pub dev packages path provider how to cite https github com andreped dss how to cite if you found this project useful please consider citing it in your research article software andre pedersen 2023 7701510 author andr pedersen and ute spiske and javier p rez de frutos title andreped dss v0 2 0 month mar year 2023 publisher zenodo version v0 2 0 doi 10 5281 zenodo 7701510 url https doi org 10 5281 zenodo 7701510 | android mobile sensor tensorboard tensorflow tf2 tflite flutter ios dss cnn data-visualization internet-of-things iot real-time rnn vision-transformer vit deep-learning recording | front_end |
python-machine-learning-book | python machine learning book code repository google group https img shields io badge google 20group lightgrey svg https groups google com forum forum python machine learning reader discussion board important note 09 21 2017 this github repository contains the code examples of the 1st edition of python machine learning book if you are looking for the code examples of the 2nd edition please refer to this https github com rasbt python machine learning book 2nd edition whats new in the second edition from the first edition repository instead what you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning from theory to the actual code that you can directly put into action this is not yet just another this is how scikit learn works book i aim to explain all the underlying concepts tell you everything you need to know in terms of best practices and caveats and we will put those concepts into action mainly using numpy scikit learn and theano you are not sure if this book is for you please checkout the excerpts from the foreword docs foreword ro pdf and preface docs preface sr pdf or take a look at the faq faq section for further information images pymle cover double small jpg https www amazon com python machine learning sebastian raschka dp 1783555130 ref sr 1 1 ie utf8 qid 1470882464 sr 8 1 keywords python machine learning 1st edition published september 23rd 2015 br paperback 454 pages br publisher packt publishing br language english br isbn 10 1783555130 br isbn 13 978 1783555130 br kindle asin b00ysilnl0 br br images crbadgenotablebook jpg http www computingreviews com recommend bestof notableitems cfm bestyear 2016 br german isbn 13 978 3958454224 br japanese isbn 13 978 4844380603 br italian isbn 13 978 8850333974 br chinese traditional isbn 13 978 9864341405 br chinese mainland isbn 13 978 7111558804 br korean isbn 13 979 1187497035 br russian isbn 13 978 5970604090 br table of contents and code notebooks simply click on the ipynb nbviewer links next to the chapter headlines to view the code examples currently the internal document links are only supported by the nbviewer version please note that these are just the code examples accompanying the book which i uploaded for your convenience be aware that these notebooks may not be useful without the formulae and descriptive text excerpts from the foreword docs foreword ro pdf and preface docs preface sr pdf instructions for setting up python and the jupiter notebook code ch01 readme md br 1 machine learning giving computers the ability to learn from data dir code ch01 ipynb code ch01 ch01 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch01 ch01 ipynb 2 training machine learning algorithms for classification dir code ch02 ipynb code ch02 ch02 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch02 ch02 ipynb 3 a tour of machine learning classifiers using scikit learn dir code ch03 ipynb code ch03 ch03 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch03 ch03 ipynb 4 building good training sets data pre processing dir code ch04 ipynb code ch04 ch04 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch04 ch04 ipynb 5 compressing data via dimensionality reduction dir code ch05 ipynb code ch05 ch05 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch05 ch05 ipynb 6 learning best practices for model evaluation and hyperparameter optimization dir code ch06 ipynb code ch06 ch06 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch06 ch06 ipynb 7 combining different models for ensemble learning dir code ch07 ipynb code ch07 ch07 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch07 ch07 ipynb 8 applying machine learning to sentiment analysis dir code ch08 ipynb code ch08 ch08 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch08 ch08 ipynb 9 embedding a machine learning model into a web application dir code ch09 ipynb code ch09 ch09 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch09 ch09 ipynb 10 predicting continuous target variables with regression analysis dir code ch10 ipynb code ch10 ch10 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch10 ch10 ipynb 11 working with unlabeled data clustering analysis dir code ch11 ipynb code ch11 ch11 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch11 ch11 ipynb 12 training artificial neural networks for image recognition dir code ch12 ipynb code ch12 ch12 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch12 ch12 ipynb 13 parallelizing neural network training via theano dir code ch13 ipynb code ch13 ch13 ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code ch13 ch13 ipynb br equation reference a href https github com rasbt python machine learning book tree master docs equations img src images equation ref logo png width 200 height 200 a pdf docs equations pymle equations pdf tex docs equations pymle equations tex slides for teaching a big thanks to dmitriy dligach dmitriydligach for sharing his slides from his machine learning course that is currently offered at loyola university chicago http www luc edu cs https github com dmitriydligach pymlslides https github com dmitriydligach pymlslides additional math and numpy resources some readers were asking about math and numpy primers since they were not included due to length limitations however i recently put together such resources for another book but i made these chapters freely available online in hope that they also serve as helpful background material for this book algebra basics pdf https sebastianraschka com pdf books dlb appendix b algebra pdf epub https sebastianraschka com pdf books dlb appendix b algebra epub a calculus and differentiation primer pdf https sebastianraschka com pdf books dlb appendix d calculus pdf epub https sebastianraschka com pdf books dlb appendix d calculus epub introduction to numpy pdf https sebastianraschka com pdf books dlb appendix f numpy intro pdf epub https sebastianraschka com pdf books dlb appendix f numpy intro epub code notebook https github com rasbt deep learning book blob master code appendix f numpy intro appendix f numpy intro ipynb citing this book you are very welcome to re use the code snippets or other contents from this book in scientific publications and other works in this case i would appreciate citations to the original source bibtex book raschka2015python author raschka sebastian title python machine learning publisher packt publishing year 2015 address birmingham uk isbn 1783555130 mla raschka sebastian python machine learning birmingham uk packt publishing 2015 print feedback reviews docs feedback md short review snippets docs feedback md images pymle amzn png https www amazon com python machine learning sebastian raschka dp 1783555130 ref sr 1 1 ie utf8 qid 1472342570 sr 8 1 keywords sebastian raschka sebastian raschka s new book python machine learning has just been released i got a chance to read a review copy and it s just as i expected really great it s well organized super easy to follow and it not only offers a good foundation for smart non experts practitioners will get some ideas and learn new tricks here as well lon riesberg at data elixir http dataelixir com issues 55 start superb job thus far for me it seems to have hit the right balance of theory and practice math and code brian thomas http sebastianraschka com blog 2015 writing pymle html comment 2295668894 i ve read virtually every machine learning title based around scikit learn and this is hands down the best one out there jason wolosonovich https www linkedin com pulse python machine learning sebastian raschka review jason wolosonovich trk prof post the best book i ve seen to come out of packt publishing this is a very well written introduction to machine learning with python as others have noted a perfect mixture of theory and application josh d https www amazon com gp customer reviews r27wb1gwtngir2 ref cm cr getr d rvw ttl ie utf8 asin 1783555130 a book with a blend of qualities that is hard to come by combines the needed mathematics to control the theory with the applied coding in python also great to see it doesn t waste paper in giving a primer on python as many other books do just to appeal to the greater audience you can tell it s been written by knowledgeable writers and not just diy geeks amazon customer https www amazon com gp customer reviews rzwy4tf66z6v0 ref cm cr getr d rvw ttl ie utf8 asin 1783555130 sebastian raschka created an amazing machine learning tutorial which combines theory with practice the book explains machine learning from a theoretical perspective and has tons of coded examples to show how you would actually use the machine learning technique it can be read by a beginner or advanced programmer william p ross 7 must read python books http williampross com 7 must read python books longer reviews if you need help to decide whether this book is for you check out some of the longer reviews linked below if you wrote a review please let me know and i d be happy to add it to the list python machine learning review http www bcs org content conwebdoc 55586 by patrick hill at the chartered institute for it book review python machine learning by sebastian raschka http whatpixel com python machine learning book review by alex turner at whatpixel links ebook and paperback at amazon com http www amazon com python machine learning sebastian raschka dp 1783555130 ref sr 1 2 ie utf8 qid 1437754343 sr 8 2 keywords python machine learning essentials amazon co uk http www amazon co uk python machine learning sebastian raschka dp 1783555130 amazon de http www amazon de s ref nb sb noss 2 mk de de m url search alias 3daps field keywords python machine learning ebook and paperback https www packtpub com big data and business intelligence python machine learning from packt the publisher at other book stores google books https books google com books id govocwaaqbaj source gbs slider cls metadata 7 mylibrary o reilly http shop oreilly com product 9781783555130 do safari https www safaribooksonline com library view python machine learning 9781783555130 barnes noble http www barnesandnoble com w python machine learning essentials sebastian raschka 1121999969 ean 9781783555130 apple ibooks https itunes apple com us book python machine learning id1028207310 mt 11 social platforms goodreads https www goodreads com book show 25545994 python machine learning translations italian translation https www amazon it learning costruire algoritmi generare conoscenza dp 8850333978 via apogeo german translation https www amazon de machine learning python mitp professional dp 3958454224 via mitp verlag japanese translation http www amazon co jp gp product 4844380605 via impress top gear chinese translation traditional chinese https taiwan kinokuniya com bw 9789864341405 chinese translation simple chinese https book douban com subject 27000110 korean translation http www kyobobook co kr product detailviewkor laf mallgb kor ejkgb kor barcode 9791187497035 via kyobo polish translation https www amazon de python uczenie maszynowe sebastian raschka dp 8328336138 ref sr 1 11 ie utf8 qid 1513601461 sr 8 11 keywords sebastian raschka via helion literature references further reading resources docs references md errata docs errata md bonus notebooks not in the book logistic regression implementation dir code bonus ipynb code bonus logistic regression ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus logistic regression ipynb a basic pipeline and grid search setup dir code bonus ipynb code bonus svm iris pipeline and gridsearch ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus svm iris pipeline and gridsearch ipynb an extended nested cross validation example dir code bonus ipynb code bonus nested cross validation ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus nested cross validation ipynb a simple barebones flask webapp template view directory code bonus flask webapp ex01 download as zip file https github com rasbt python machine learning book raw master code bonus flask webapp ex01 flask webapp ex01 zip reading handwritten digits from mnist into numpy arrays github ipynb code bonus reading mnist ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus reading mnist ipynb scikit learn model persistence using json github ipynb code bonus scikit model to json ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus scikit model to json ipynb multinomial logistic regression softmax regression github ipynb code bonus softmax regression ipynb nbviewer http nbviewer ipython org github rasbt python machine learning book blob master code bonus softmax regression ipynb hr related content not in the book model evaluation model selection and algorithm selection in machine learning part i http sebastianraschka com blog 2016 model evaluation selection part1 html model evaluation model selection and algorithm selection in machine learning part ii http sebastianraschka com blog 2016 model evaluation selection part2 html model evaluation model selection and algorithm selection in machine learning part iii http sebastianraschka com blog 2016 model evaluation selection part3 html scipy 2016 we had such a great time at scipy 2016 http scipy2016 scipy org ehome index php eventid 146062 tabid 332930 in austin it was a real pleasure to meet and chat with so many readers of my book thanks so much for all the nice words and feedback and in case you missed it andreas mueller and i gave an introduction to machine learning with scikit learn if you are interested the video recordings of part i https www youtube com watch v ob1rey6ix o index 91 list plyx7xa2ny5gf37zyzmw6oqgfrpjb1jcy6 and part ii https www youtube com watch v cte8fycpylk list plyx7xa2ny5gf37zyzmw6oqgfrpjb1jcy6 index 90 are now online images scipy2016 jpg https www youtube com watch v ob1rey6ix o index 91 list plyx7xa2ny5gf37zyzmw6oqgfrpjb1jcy6 pydata chicago 2016 i attempted the rather challenging task of introducing scikit learn machine learning in just 90 minutes at pydata chicago 2016 the slides and tutorial material are available at learning scikit learn an introduction to machine learning in python https github com rasbt pydata chicago2016 ml tutorial note i have set up a separate library mlxtend http rasbt github io mlxtend containing additional implementations of machine learning and general data science algorithms i also added implementations from this book for example the decision region plot the artificial neural network and sequential feature selection algorithms with additional functionality images mlxtend logo png http rasbt github io mlxtend br hr translations images pymle cover it jpg https www amazon it learning costruire algoritmi generare conoscenza dp 8850333978 images pymle cover de jpg https www amazon de machine learning python mitp professional dp 3958454224 images pymle cover jp jpg http www amazon co jp gp product 4844380605 images pymle cover cn jpg https taiwan kinokuniya com bw 9789864341405 images pymle cover cn mainland jpg https book douban com subject 27000110 images pymle cover kr jpg http www kyobobook co kr product detailviewkor laf ejkgb kor mallgb kor barcode 9791187497035 orderclick lea kc images pymle cover ru jpg http www ozon ru context detail id 140152222 images pymle cover pl jpg https www amazon de python uczenie maszynowe sebastian raschka dp 8328336138 ref sr 1 11 ie utf8 qid 1513601461 sr 8 11 keywords sebastian raschka hr dear readers first of all i want to thank all of you for the great support i am really happy about all the great feedback you sent me so far and i am glad that the book has been so useful to a broad audience over the last couple of months i received hundreds of emails and i tried to answer as many as possible in the available time i have to make them useful to other readers as well i collected many of my answers in the faq section below in addition some of you asked me about a platform for readers to discuss the contents of the book i hope that this would provide an opportunity for you to discuss and share your knowledge with other readers google groups discussion board https groups google com forum forum python machine learning reader discussion board and i will try my best to answer questions myself if time allows the only thing to do with good advice is to pass it on it is never of any use to oneself oscar wilde examples and applications by readers once again i have to say big thanks for all the nice feedback about the book i ve received many emails from readers who put the concepts and examples from this book out into the real world and make good use of them in their projects in this section i am starting to gather some of these great applications and i d be more than happy to add your project to this list just shoot me a quick mail 40 scripts on optical character recognition https github com rrlyman pythonmachinelearingexamples by richard lyman https github com rrlyman code experiments https github com jeremyn python machine learning book by jeremy nation https github com jeremyn what i learned implementing a classifier from scratch in python http www jeannicholashould com by jean nicholas hould http www jeannicholashould com faq general questions what are machine learning and data science faq datascience ml md why do you and other people sometimes implement machine learning algorithms from scratch faq implementing from scratch md what learning path discipline in data science i should focus on faq data science career md at what point should one start contributing to open source faq open source md how important do you think having a mentor is to the learning process faq mentor md where are the best online communities centered around data science machine learning or python faq ml python communities md how would you explain machine learning to a software engineer faq ml to a programmer md how would your curriculum for a machine learning beginner look like faq ml curriculum md what is the definition of data science faq definition data science md how do data scientists perform model selection is it different from kaggle faq model selection in datascience md questions about the machine learning field how are artificial intelligence and machine learning related faq ai and ml md what are some real world examples of applications of machine learning in the field faq ml examples md what are the different fields of study in data mining faq datamining overview md what are differences in research nature between the two fields machine learning data mining faq datamining vs ml md how do i know if the problem is solvable through machine learning faq ml solvable md what are the origins of machine learning faq ml origins md how was classification as a learning machine developed faq classifier history md which machine learning algorithms can be considered as among the best faq best ml algo md what are the broad categories of classifiers faq classifier categories md what is the difference between a classifier and a model faq difference classifier model md what is the difference between a parametric learning algorithm and a nonparametric learning algorithm faq parametric vs nonparametric md what is the difference between a cost function and a loss function in machine learning faq cost vs loss md questions about ml concepts and statistics cost functions and optimization fitting a model via closed form equations vs gradient descent vs stochastic gradient descent vs mini batch learning what is the difference faq closed form vs gd md how do you derive the gradient descent rule for linear regression and adaline faq linear gradient derivative md regression analysis what is the difference between pearson r and simple linear regression faq pearson r vs linear regr md tree models how does the random forest model work how is it different from bagging and boosting in ensemble models faq bagging boosting rf md what are the disadvantages of using classic decision tree algorithm for a large dataset faq decision tree disadvantages md why are implementations of decision tree algorithms usually binary and what are the advantages of the different impurity metrics faq decision tree binary md why are we growing decision trees via entropy instead of the classification error faq decisiontree error vs entropy md when can a random forest perform terribly faq random forest perform terribly md model evaluation what is overfitting faq overfitting md how can i avoid overfitting faq avoid overfitting md is it always better to have the largest possible number of folds when performing cross validation faq number of kfolds md when training an svm classifier is it better to have a large or small number of support vectors faq num support vectors md how do i evaluate a model faq evaluate a model md what is the best validation metric for multi class classification faq multiclass metric md what factors should i consider when choosing a predictive model technique faq choosing technique md what are the best toy datasets to help visualize and understand classifier behavior faq clf behavior data md how do i select svm kernels faq select svm kernels md interlude comparing and computing performance metrics in cross validation imbalanced class problems and 3 different ways to compute the f1 score faq computing the f1 score md logistic regression what is softmax regression and how is it related to logistic regression faq softmax regression md why is logistic regression considered a linear model faq logistic regression linear md what is the probabilistic interpretation of regularized logistic regression faq probablistic logistic regression md does regularization in logistic regression always results in better fit and better generalization faq regularized logistic regression performance md what is the major difference between naive bayes and logistic regression faq naive bayes vs logistic regression md what exactly is the softmax and the multinomial logistic loss in the context of machine learning faq softmax md what is the relation between loigistic regression and neural networks and when to use which faq logisticregr neuralnet md logistic regression why sigmoid function faq logistic why sigmoid md is there an analytical solution to logistic regression similar to the normal equation for linear regression faq logistic analytical md neural networks and deep learning what is the difference between deep learning and usual machine learning faq difference deep and normal learning md can you give a visual explanation for the back propagation algorithm for neural networks faq visual backpropagation md why did it take so long for deep networks to be invented faq inventing deeplearning md what are some good books papers for learning deep learning faq deep learning resources md why are there so many deep learning libraries faq many deeplearning libs md why do some people hate neural networks deep learning faq deeplearning criticism md how can i know if deep learning works better for a specific problem than svm or random forest faq deeplearn vs svm randomforest md what is wrong when my neural network s error increases faq neuralnet error md how do i debug an artificial neural network algorithm faq nnet debugging checklist md what is the difference between a perceptron adaline and neural network model faq diff perceptron adaline neuralnet md what is the basic idea behind the dropout technique faq dropout md other algorithms for supervised learning why is nearest neighbor a lazy algorithm faq lazy knn md unsupervised learning what are some of the issues with clustering faq issues with clustering md semi supervised learning what are the advantages of semi supervised learning over supervised and unsupervised learning faq semi vs supervised md ensemble methods is combining classifiers with stacking better than selecting the best one faq logistic boosting md preprocessing feature selection and extraction why do we need to re use training parameters to transform test data faq scale training test md what are the different dimensionality reduction methods in machine learning faq dimensionality reduction md what is the difference between lda and pca for dimensionality reduction faq lda vs pca md when should i apply data normalization standardization faq when to standardize md does mean centering or feature scaling affect a principal component analysis faq pca scaling md how do you attack a machine learning problem with a large number of features faq large num features md what are some common approaches for dealing with missing data faq missing data md what is the difference between filter wrapper and embedded methods for feature selection faq feature sele categories md should data preparation pre processing step be considered one part of feature engineering why or why not faq dataprep vs dataengin md is a bag of words feature representation for text classification considered as a sparse matrix faq bag of words sparsity md naive bayes why is the naive bayes classifier naive faq naive naive bayes md what is the decision boundary for naive bayes faq naive bayes boundary md can i use naive bayes classifiers for mixed variable types faq naive bayes vartypes md is it possible to mix different variable types in naive bayes for example binary and continues features naive bayes vartypes md other what is euclidean distance in terms of machine learning faq euclidean distance md when should one use median as opposed to the mean or average faq median vs mean md programming languages and libraries for data science and machine learning is r used extensively today in data science faq r in datascience md what is the main difference between tensorflow and scikit learn faq tensorflow vs scikitlearn md br questions about the book can i use paragraphs and images from the book in presentations or my blog faq copyright md how is this different from other machine learning books faq different md which version of python was used in the code examples faq py2py3 md which technologies and libraries are being used faq technologies md which book version format would you recommend faq version md why did you choose python for machine learning faq why python md why do you use so many leading and trailing underscores in the code examples faq underscore convention md what is the purpose of the return self idioms in your code examples faq return self idiom md are there any prerequisites and recommended pre readings faq prerequisites md how can i apply svm to categorical data faq svm for categorical md contact i am happy to answer questions just write me an email mailto mail sebastianraschka com or consider asking the question on the google groups email list https groups google com forum forum python machine learning book if you are interested in keeping in touch i have quite a lively twitter stream rasbt https twitter com rasbt all about data science and machine learning i also maintain a blog http sebastianraschka com articles html where i post all of the things i am particularly excited about | machine-learning machine-learning-algorithms logistic-regression data-science data-mining python scikit-learn neural-network | ai |
frend.co | about frend a collection of accessible modern front end components frend components are modest and dependency free they are built with web standards as a priority and aim to avoid assumptions about tooling or environment care has been taken to make sure each component is compliant keyboard navigable and properly interpreted by assistive technologies the purpose of frend is to offer ready to use components for projects these also provide specifics on how they ve utilised aria attributes and event bindings based on global recommendations in order to make them more predictable and usable for all the goal is to work on these components collaboratively this allows us to share our implementation experiences approaches to supporting different environments and any bugs we ve come across along the way standards appropriate semantic elements are at the core of each frend component this ensures that a useable baseline for the content or interaction is in place before we introduce enhancements using css or javascript enhancements are based upon available features so components won t necessarily function the same in every browser and that s okay css css included with each component is purely functional we include the relevant selectors in our stylesheets but what you do within them is entirely up to you we favour a bem syntax when defining selectors however these are all configurable in the component options if you d like to use a different convention styling hooks are added to the component when it s initialised so we have the ability to apply styles to their static state before we style them in their fully functioning state javascript javascript is written using es2015 module syntax modules export themselves so they re available to import into your existing project where and when they are needed if this doesn t suit your workflow we also transpile modules to es5 and bundle them using umd so they can be compiled using module loaders or directly in the browser via a script tag you can find these files in the dist directory of each component a simple api is exposed for each component instance which usually contains init and destroy methods more information about invoking components and any additional methods can be found on each of their respective pages the javascript written to enhance these components has been purposefully left with as little abstraction as possible while we bundle scripts into ready to use plugins it s also important that the source files can remain a clear reference for those interested in stepping through each of the methods we hope this helps other developers in uncovering the steps involved in making the components more accessible testing our thanks go to browserstack https www browserstack com for providing us with an open source account platform frend is powered by jekyll http jekyllrb com and served directly from the frend frend co github repository https github com frend frend co thanks to github pages https pages github com if you re cloning the repo running the site locally will first require installation of jekyll gems gem install jekyll gem install jekyll redirect from jekyll serve automated builds for component script files are handled by running gulp s watch task as you develop npm install gulp watch contributing please refer to our contributing guidelines https github com frend frend co blob gh pages contributing md feedback if you have feedback or suggestions on how to make frend better please email us mailto hello frend co or tweet us http www twitter com ffffrend | accessible front-end components | front_end |
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