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CH32V307_lwIP-1.41_FreeRTOS | ch32v307 lwip 1 41 freertos lwip1 41 freertos ch32v307 netconn api 10mphy tcpip mcu lwip freertos tcp echo sever mounriver | os |
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cardano-ledger | h1 align center cardano ledger h1 p align center a href https input output hk github io cardano engineering handbook img alt ceh src https img shields io badge policy cardano 20engineering 20handbook informational style for the badge a a href https github com input output hk cardano ledger actions workflows haskell yml img alt github workflow status master src https img shields io github actions workflow status input output hk cardano ledger haskell yml branch master style for the badge a a href https input output hk github io cardano ledger img alt haddock master src https img shields io badge documentation haddock yellow style for the badge a p this repository contains the formal specifications executable models and implementations of the cardano ledger the documents are built in our ci and can be readily accessed using the following links era design documents formal specification cddl byron chain spec https github com input output hk cardano ledger releases latest download byron blockchain pdf specification of the blockchain layer ledger spec https github com input output hk cardano ledger releases latest download byron ledger pdf a formal specification of the cardano ledger cddl https github com input output hk cardano ledger tree master eras byron cddl spec byron cddl pdf https github com input output hk cardano ledger releases latest download byron binary pdf shelley design https github com input output hk cardano ledger releases latest download shelley delegation pdf design specification for delegation and incentives in cardano spec https github com input output hk cardano ledger releases latest download shelley ledger pdf a formal specification of the cardano ledger cddl https github com input output hk cardano ledger tree master eras shelley test suite cddl files allegra mary multi currency https eprint iacr org 2020 895 multi currency ledgers utxoma https iohk io en research library papers utxoma utxo with multi asset support utxoma utxo with multi asset support spec https github com input output hk cardano ledger releases latest download mary ledger pdf a formal specification of the cardano ledger with a native multi asset implementation cddl https github com input output hk cardano ledger tree master eras shelley ma test suite cddl files alonzo eutxo https iohk io en research library papers the extended utxo model the extended utxo model spec https github com input output hk cardano ledger releases latest download alonzo ledger pdf a formal specification of the cardano ledger integrating plutus core cddl https github com input output hk cardano ledger tree master eras alonzo test suite cddl files babbage batch verification https iohk io en research library papers on uc secure range extension and batch verification for ecvrf on uc secure range extension and batch verification for ecvrf cip 31 https github com cardano foundation cips pull 159 reference inputs cip 32 https github com cardano foundation cips pull 160 inline datums cip 33 https github com cardano foundation cips pull 161 reference scripts spec https github com input output hk cardano ledger releases latest download babbage ledger pdf formal specification of the cardano ledger for the babbage era cddl https github com input output hk cardano ledger tree master eras babbage test suite cddl files conway cip 1694 https github com cardano foundation cips tree master cip 1694 spec wip https github com input output hk formal ledger specifications cddl https github com input output hk cardano ledger tree master eras conway test suite cddl files other documents non integer calculations specification https github com input output hk cardano ledger releases latest download non integer calculations pdf details on the parts of the shelley specification that use real numbers stake pool ranking specification https github com input output hk cardano ledger releases latest download pool ranking pdf details for a robust stake pool ranking mechanism explanation of the small step semantics framework https github com input output hk cardano ledger releases latest download small step semantics pdf a guide to the notation and style used by our ledger rules in addition there is a formalization of the ledger specification in isabelle hol which can be found here https github com input output hk fm ledger formalization some user documentation is published on read the docs https cardano ledger readthedocs io en latest haddock code documentation of the latest master branch is available here https input output hk github io cardano ledger repository structure the directory structure of this repository is as follows byron eras byron ledger eras byron ledger formal spec eras byron ledger formal spec executable spec eras byron ledger executable spec implementation eras byron ledger impl chain eras byron chain formal spec eras byron chain formal spec executable spec eras byron chain executable spec cddl eras byron cddl spec shelley eras shelley design spec eras shelley design spec formal spec eras shelley formal spec implementation eras shelley impl tests eras shelley test suite cddl eras shelley test suite cddl files timelocks and multi assets eras shelley ma formal spec eras shelley ma formal spec implementation eras shelley ma impl tests eras shelley ma test suite smart contracts eras alonzo formal spec eras alonzo formal spec implementation eras alonzo impl tests eras alonzo test suite libraries libs building it is recommended to use nix https nixos org nix download html for building everything in this repository haskell files can be built with cabal https www haskell org cabal inside of a nix shell nix cache when using nix it is recommended that you setup the cache so that it can reuse built artifacts reducing the compilation times dramatically if you are using nixos https nixos org add the snippet below to your etc nixos configuration nix nix settings experimental features nix command flakes substituters https cache nixos org https cache iog io trusted public keys hydra iohk io f ea s dfdn 3y g fdgsq a5newhjgzdjvkngv0 eq if you are using the nix package manager next to another operating system put the following in etc nix nix conf experimental features nix command flakes substituters https cache iog io https cache nixos org trusted public keys hydra iohk io f ea s dfdn 3y g fdgsq a5newhjgzdjvkngv0 eq cache nixos org 1 6nchdd59x431o0gwypbmraurkbj16zpmqfgspcdshjy building the latex documents and executable specifications when using nix the documents and haskell code can be readily built by running shell nix build specs the latex documents will be placed inside a directory named result e g shell result byron ledger pdf result shelley delegation pdf result non integer calculations pdf result small step semantics pdf result shelley ledger pdf result byron blockchain pdf building individual latex documents change to the latex directory where the latex document is e g eras shelley formal spec for the ledger specification corresponding to the shelley release or eras byron ledger formal spec for the ledger specification corresponding to the byron release then build the latex document by running shell cd mylatexdir nix develop command make for a continuous compilation of the latex file run shell cd mylatexdir nix develop command make watch testing before running tests with cabal test the cardano mainnet mirror environment variable should be set to the path to mainnet epochs https github com input output hk cardano mainnet mirror tree master epochs on your local storage note that this is only needed for the byron tests submitting an issue issues can be filed in the github issue tracker https github com input output hk cardano ledger issues however note that this is pre release software so we will not usually be providing support contributing see contributing https github com input output hk cardano ledger blob master contributing md | cryptocurrency blockchain cardano ada | blockchain 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Front-End-Monitoring | p align center img width 100px align center src http n sinaimg cn sinacn w576h342 20180219 8a9b fyrswmu2740442 jpg p h1 align center h1 repo star https github com ricardocao biker front end monitoring blob master basicknowledge md bad js https github com ricardocao biker front end monitoring blob master badjs sourcedoce explain md bad js https github com ricardocao biker front end monitoring blob master badjs sourcecode explain js | front_end |
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FastBee | https oscimg oschina net oscnet up 09e0fd4e7966a3049aa39e7ab2a99dc5586 png 0 wumei smart fastbee https fastbee cn 1 fastbee 2 spring boot vue emqx h5 uniapp mysql tdengine redis esp32 esp8266 img src https oscimg oschina net oscnet up 004a50200a81efff7530d2cf963052b4e8c png ota emq mqtt sdk wifi mqtt aes ntp ap tcp modbus netty mqtt tdengine https oscimg oschina net oscnet up a9a7fdaf40208becd26c2485783bc0f86e6 png https fastbee cn doc pages hardware https fastbee cn doc pages hardware air724 https fastbee cn doc pages hardware https fastbee cn doc pages hardware https fastbee cn doc pages hardware https oscimg oschina net oscnet up ad98a81677e5e68d660866770e3266ca4cf png https oscimg oschina net oscnet up 68caf860d0659444e73f42717a03d2fdf72 png https oscimg oschina net oscnet up cf690f6058042dccb567ff382ea9432ebab png https oscimg oschina net oscnet up c4a7971510127324d6566dd6ea95d571483 jpg https oscimg oschina net oscnet up 4ce09be3599e3ff7ed91fe182572abd258b jpg spring boot mybatis spring security jwt mysql redis tdengine emqx netty idea web es6 vue vuex vue router vue cli axios element ui visual studio code android ios h5 uniapp uview https www uviewui com uchart https www ucharts cn hbuilder esp idf arduino freertos python lua visual studio code arduino nbsp nbsp nbsp nbsp spring boot br nbsp nbsp nbsp nbsp vue br nbsp nbsp nbsp nbsp docker docker br nbsp nbsp nbsp nbsp sdk sdk br agpl3 https fastbee cn doc pages sponsor nbsp https fastbee cn doc pages sponsor https fastbee cn doc pages sponsor nbsp https fastbee cn doc pages sponsor 1 qq x1f680 946029159 x1f680 1073236354 2 ruoyi vue mqtt emqx4 0 https iot fastbee cn https fastbee cn doc http doc ruoyi vip ruoyi vue emqx4 0 https www emqx io docs zh v4 0 ucharts https www ucharts cn 3 https gitee com iot xiaoyi crazydull https gitee com crazydull ybzx https github com ybzx cqadu https gitee com iot adu https gitee com sunalong xxmfl https gitee com xxmfl 3715687 qq com https fastbee cn sxh https gitee com sixiaohu redamancy zxp https gitee com redamancy zxp lee https gitee com yueming188 lemontree https gitee com fishhunterplus tang https gitee com mexiaotang tang https gitee com mexiaotang kun https gitee com l kun kun 4 guanshubiao https gitee com guanshubiao https gitee com zhuangpengli kami0314 https github com kami0314 https oscimg oschina net oscnet up 972dea7b54eca705dcc8bf2fe0680b12c09 png https oscimg oschina net oscnet up 6d89f1558797a9becf07c20f92c1407a13a png table tr td img src https oscimg oschina net oscnet up 19ef5b528bb044253848722118b694bef47 png td td img src https oscimg oschina net oscnet up 8b4c24353bc2340b8362438b87ceac27a9d png td tr tr td img src https oscimg oschina net oscnet up a0c864679be6c4f9bb5547244aeb19657b4 png td td img src https oscimg oschina net oscnet up 9cc3bc5abe8dd95cb3924e5f7b6864a0342 png td tr tr td img src https oscimg oschina net oscnet up ec8c6251884d81f234487d3e25d459ce302 png td td img src https oscimg oschina net oscnet up e5e7ef5027723051e97d6861d4296c08da5 png td tr tr td img src https oscimg oschina net oscnet up 3ae8cef86db794ff37d9186e83b12b88958 png td td img src https oscimg oschina net oscnet up e20900a12e3781467d05163665ca04645fa png td tr tr td img src https oscimg oschina net oscnet up 5c755895fc1a7ba02d487b75cf493ffb0df png td td img src https oscimg oschina net oscnet up 4e279e657c6f8b6af2d58fa215ab7fae02d jpg td tr table | iot esp8266 smarthome java | server |
Embedded-IoT-Project | embedded iot project pcb | embedded-systems iot arm-linux mcu qt | server |
setup | in chinese a href readme cn md a en espa ol haga clic en a href readme es md este enlace a para acceder a la versi n en espa ol de la configuraci n em portugu s clique a href readme pt md neste link a para acessar a vers o em portugu s da configura o en fran ais clique sur a href readme fr md ce lien a pour acc der la version fran aise du setup in english setup guides for le wagon https www lewagon com web development course please choose your operating system os table tr td a href macos md img src images apple logo png alt macos a td td a href windows md img src images windows logo png alt windows a td td a href ubuntu md img src images linux logo png alt ubuntu a td tr table | setup ruby | front_end |
DIYgm-iOS | diygm ios ios app for diy geiger muller radiation detector project at umich radiological health engineering lab apis create a keys plist in the diygm ios folder add an entry with the key being googleapi and value being your google cloud platform api key color scale the saturation of each marker is based on how high its measured count rate is compared to a specified high value this high value was set to 100 meaning a value of 0 is the least saturated and a value of 100 and above is the most saturated to change the high value modify the value of the highvalue variable in setcountrate the diygm raspberry pi files needed to connect the diygm to this app can be found at https github com experimex diygm rpi | os |
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obvia | obvia obvia is an mit licensed open source javascript framework created to make the development of single page applications as easy and obvious as possible it is designed with the objective of being easy to work with separating code from data separating structural components layout from functional components and allowing embedded state and history management obvia consists of ui components application and applets general use libraries and code generation utilities browser compatibility obvia supports all browsers that are es5 compliant ie8 and below are not supported documentation to check out the documents and live examples please visit obviajs com questions for questions and support please use the community chat the issue list of this repository is exclusively for bug reports and feature requests issues before reporting an issue please read the issue reporting guidelines issues that do not follow the guidelines may get closed want to help if you would like to contribute some code or improve the documentation please read our contribution guidelines then check out the current issues your work is always appreciated libraries this is a list of all the open source libraries used in the obvia framework yaml js tokenize js micro requirejs deep clone get font awesome icon from mime debounce decorator css hasstylerule coroutine md5 message digest algorithm roadmap this is a basic roadmap with some instructions to keep in mind when working with obvia state management hot reload the applet app should handle url hash changes via an external dependency the existing logic is to be wrapped in a default provider security should be provided by external dependencies contribution thank you to everyone who has contributed to obvia if you would like to financially support obvia s development please consider making a donation on our paypal license mit obviajs com https obviajs com issue reporting guidelines https github com obviajs obvia blob master github contributing md issue reporting guidelines contribution guidelines https github com obviajs obvia blob master github contributing md paypal https paypal me obviajs mit https opensource org licenses mit chat https discord gg byd5a2q yaml js https github com jeremyfa yaml js blob develop dist yaml js tokenize js https gist github com bonsaiden 1810887 micro requirejs https github com dsheiko micro requirejs deep clone https github com mbrowne simpleoo js blob master simpleoo js get font awesome icon from mime https gist github com colemanw 9c9a12aae16a4bfe2678de86b661d922 gistcomment 2632557 debounce decorator https medium freecodecamp org here are a few function decorators you can write from scratch 488549fe8f86 css hasstylerule https stackoverflow com a 46589334 coroutine https github com antivanov coroutine js md5 message digest algorithm http www webtoolkit info | javascript framework web-development js | front_end |
learning_backend_development | learning backend development in this repo i am learning backend development with typescript nodejs exprerss mongodb so that i can develop complete full stack flutter app with my own backend | server |
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iclcv | iclcv this is a git version of the svn repostitory found at https opensource cit ec de svn icl trunk icl is a novel c computer vision library developed in the neuroinformatics group of the university of bielefeld and in citec it unifies both performance and user friendliness icl provides a large set of simple to use classes and functions to facilitate development of complex computer vision applications for the documentation and more info please look at the authors website http www iclcv org | ai |
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GwtMobile | gwt mobile gwt mobile is a cross platform mobile development tool using google web toolkit technology it provides a set of ui widgets optimized for mobile devices a orm module to persist objects to the browser database and a wrapper to access phonegap functions from gwt gwt mobile ui gwt mobile ui http github com dennisjzh gwtmobile ui is a mobile ui widget system for gwt application gwt mobile persistence gwt mobile persistence http github com dennisjzh gwtmobile persistence is a gwt wrapper of the javascript object relational mapper library persistence js it provides client side object persistence capability to gwt applications a feature similar to what hibernate provides on the server side gwt mobile phonegap gwt mobile phonegap http github com dennisjzh gwtmobile phonegap is a gwt wrapper of the phonegap javascript library phonegap is a cross platform development framework that provides core mobile device features to web based mobile apps gwtmobile phonegap enables gwt mobile applications to use phonegap functions apps that use gwt mobile fantasy predictor http www touchonmobile com manage all you fantasy football teams with the help of the fantasy predictor android market http market android com details id com touchonmobile fantasypredictor itunes app store http itunes apple com us app fantasy predictor id405605997 gwt mobile phonegap a phonegap showcase app demonstrate all phonegap functions android market http market android com details id com gwtmobile phonegap itunes app store http itunes apple com us app gwt mobile phonegap showcase id419032500 mt 8 ls 1 gwt mobile google group have questions post it on the gwt mobile google group http groups google com group gwtmobile | front_end |
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ODSC_East_2017_PythonNLP | learning from text introduction to natural language processing with python odsc east may 3 2017 michelle l gill ph d senior data scientist metis software installation instructions tested on mac os x 10 12 and ubuntu 14 04 1 download the anaconda distribution for python 3 6 python 2 7 will not work from this link https www continuum io downloads and configure your environment as described at the end of the installation process this will install the following necessary libraries jupyter notebook numpy scipy pandas scikit learn matplotlib and seaborn 2 with the above anaconda environment activated install the following additional libraries using the commands listed console conda install y c anaconda gensim nltk conda install y c conda forge textblob pip install pyldavis packages can also installed with pip the conda installation does not work 3 download the corpora associated with nltk using the following command from a terminal console python m nltk downloader d home nltk data all this will create a folder nltk data in your home directory that is large 4 gb when expanded 4 download google s pre trained word2vec files from this link https drive google com file d 0b7xkcwpi5kdynlnuttlss21pqmm edit note that this file is also somewhat large 1 5 gb this file can be downloaded to a preferred location and left there 5 clone this github repo note that the materials associated with this workshop are being updated so this step should be performed or updated the evening before the workshop | ai |
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guardrails | guardrails ai div align center discord https badgen net badge icon discord icon discord label https discord gg jsey3mx98b twitter https badgen net badge icon twitter icon twitter label https twitter com guardrails ai guardrails is an open source python package for specifying structure and type validating and correcting the outputs of large language models llms docs https docs guardrailsai com div note guardrails is an alpha release so expect sharp edges and bugs what is guardrails guardrails is a python package that lets a user add structure type and quality guarantees to the outputs of large language models llms guardrails does pydantic style validation of llm outputs including semantic validation such as checking for bias in generated text checking for bugs in generated code etc takes corrective actions e g reasking llm when validation fails enforces structure and type guarantees e g json under the hood guardrails provides a file format rail for enforcing a specification on an llm output and a lightweight wrapper around llm api calls to implement this spec 1 rail r eliable ai markup l anguage files for specifying structure and type information validators and corrective actions over llm outputs 2 gd guard wraps around llm api calls to structure validate and correct the outputs mermaid graph lr a create rail spec b initialize guard from spec b c wrap llm api call with guard check out the getting started https docs guardrailsai com guardrails ai getting started guide to learn how to use guardrails rail spec at the heart of guardrails is the rail spec rail is intended to be a language agnostic human readable format for specifying structure and type information validators and corrective actions over llm outputs rail is a flavor of xml that lets users specify 1 the expected structure and types of the llm output e g json 2 the quality criteria for the output to be considered valid e g generated text should be bias free generated code should be bug free 3 and corrective actions to be taken if the output is invalid e g reask the llm filter out the invalid output etc to learn more about the rail spec and the design decisions behind it check out the docs https docs guardrailsai com defining guards rail to learn how to write your own rail spec check out this link https docs guardrailsai com api reference rail installation python pip install guardrails ai roadmap adding more examples new use cases and domains adding integrations with langchain gpt index minichain manifest expanding validators offering more compilers from rail llm prompt e g rail typescript informative logging improving reasking logic a guardrails js implementation vscode extension for rail files next version of rail format add more llm providers getting started let s go through an example where we ask an llm to explain what a bank run is in a tweet and generate urls to relevant news articles we ll generate a rail spec for this and then use guardrails to enforce it you can see more examples in the docs creating a rail spec we create a rail spec to describe the expected structure and types of the llm output the quality criteria for the output to be considered valid and corrective actions to be taken if the output is invalid using rail we request the llm to generate an object with two fields explanation and follow up url for the explanation field ensure the max length of the generated string should be between 200 and 280 characters if the explanation is not of valid length reask the llm for the follow up url field the url should be reachable if the url is not reachable we will filter it out of the response xml rail version 0 1 output object name bank run format length 2 string name explanation description a paragraph about what a bank run is format length 200 280 on fail length reask url name follow up url description a web url where i can read more about bank runs format valid url on fail valid url filter object output prompt explain what a bank run is in a tweet gr xml prefix prompt output schema gr json suffix prompt v2 wo none prompt rail we specify our quality criteria generated length url reachability in the format fields of the rail spec below we reask if explanation is not valid and filter the follow up url if it is not valid using guardrails to enforce the rail spec next we ll use the rail spec to create a guard object the guard object will wrap the llm api call and enforce the rail spec on its output python import guardrails as gd guard gd guard from rail f name the guard object compiles the rail specification and adds it to the prompt right now this is a passthrough operation more compilers are planned to find the best way to express the spec in a prompt here s what the prompt looks like after the rail spec is compiled and added to it xml explain what a bank run is in a tweet given below is xml that describes the information to extract from this document and the tags to extract it into output object name bank run format length 2 string name explanation description a paragraph about what a bank run is format length 200 280 on fail length reask url name follow up url description a web url where i can read more about bank runs required true format valid url on fail valid url filter object output only return a valid json object no other text is necessary the json must conform to the xml format including any types and format requests e g requests for lists objects and specific types be correct and concise json output call the guard object with the llm api call as the first argument and add any additional arguments to the llm api call as the remaining arguments python import openai wrap the openai api call with the guard object raw llm output validated output guard openai completion create engine text davinci 003 max tokens 1024 temperature 0 3 print validated output python bank run explanation a bank run is when a large number of people withdraw their deposits from a bank due to concerns about its solvency this can cause a financial crisis if the bank is unable to meet the demand for withdrawals follow up url https www investopedia com terms b bankrun asp contributing get started by checking out github issues and of course using guardrails to familiarize yourself with the project guardrails is still actively under development and any support is gladly welcomed feel free to open an issue or reach out if you would like to add to the project | ai foundation-model gpt-3 llm openai | ai |
Embedded | embedded project files for ece4534 embedded system design the repo project new board is the master and the other one is slave | os |
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Metrics | note the current releases of this toolbox are a beta release to test working with haskell s python s and r s code repositories build status https travis ci org benhamner metrics png metrics provides implementations of various supervised machine learning evaluation metrics in the following languages python https github com benhamner metrics tree master python easy install ml metrics r https github com benhamner metrics tree master r install packages metrics from the r prompt haskell https github com benhamner metrics tree master haskell cabal install metrics matlab octave https github com benhamner metrics tree master matlab clone the repo run setup from the matlab command line for more detailed installation instructions see the readme for each implementation evaluation metrics table tr td evaluation metric td td python td td r td td haskell td td matlab octave td tr tr td absolute error ae td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td average precision at k apk ap k td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td area under the roc auc td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td classification error ce td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td f1 score f1 td td td td 10003 td td td td td tr tr td gini td td td td td td td td 10003 td tr tr td levenshtein td td 10003 td td td td 10003 td td 10003 td tr tr td log loss ll td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td mean log loss logloss td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td mean absolute error mae td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td mean average precision at k mapk map k td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td mean quadratic weighted kappa td td 10003 td td 10003 td td td td 10003 td tr tr td mean squared error mse td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td mean squared log error msle td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td normalized gini td td td td td td td td 10003 td tr tr td quadratic weighted kappa td td 10003 td td 10003 td td td td 10003 td tr tr td relative absolute error rae td td td td 10003 td td td td td tr tr td root mean squared error rmse td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td relative squared error rse td td td td 10003 td td td td td tr tr td root relative squared error rrse td td td 10003 td td td td td td tr tr td root mean squared log error rmsle td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td squared error se td td 10003 td td 10003 td td 10003 td td 10003 td tr tr td squared log error sle td td 10003 td td 10003 td td 10003 td td 10003 td tr table to implement f1 score multiclass log loss lift average precision for binary classification precision recall break even point cross entropy true pos false pos true neg false neg rates precision recall sensitivity specificity mutual information higher level transformations to handle groupby reduce weight individual samples or groups properties metrics can have nonexhaustive and to be added in the future min or max optimize through minimization or maximization binary classification scores predicted class labels scores predicted ranking most likely to least likely for being in one class scores predicted probabilities multiclass classification scores predicted class labels scores predicted probabilities regression discrete rater comparison confusion matrix | ai |
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Digi_Coder | digi coder information technology my portfolio website | server |
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iot-traffic-monitor | iot traffic monitor below is the architecture diagram for iot traffic monitor application read the article at infoq https www infoq com articles traffic data monitoring iot kafka and spark streaming iot traffic monitor architecture https github com baghelamit iot traffic monitor blob master iot architecture png traffic monitor application uses following tools and technologies jdk 1 8 maven 3 3 9 zookeeper 3 4 8 kafka 2 10 0 10 0 0 cassandra 2 2 6 spark 1 6 2 pre built for hadoop 2 6 spring boot 1 3 5 jquery js bootstrap js sockjs js stomp js chart js iot traffic monitor is a maven aggregator project it includes following three projects iot kafka producer iot spark processor iot spring boot dashboard for building these projects it requires following tools please refer readme md files of individual projects for more details jdk 1 8 maven 3 3 9 use below command to build all projects sh mvn package | java kafka spark cassandra spring-boot | server |
nancy | nancy sinatra s little daughter frank and nancy by classic film scans http farm6 staticflickr com 5212 5386187897 e3155cec68 jpg description minimal ruby microframework for web development inspired in sinatra http www sinatrarb com and cuba https github com soveran cuba requires ruby 2 7 or greater installation install the gem gem install nancy or add it to your gemfile gem nancy usage here s a simple application ruby hello rb require nancy require nancy render class hello nancy base use rack session cookie secret env secret token for sessions include nancy render get do hello world end get hello do response redirect end get hello name do hello params name end post hello do hello params name end get files root path do from params root download params path end get template do message hello world render views hello erb end post login do user user find params username halt 401 unauthorized unless user authenticate params password session authenticated true render views layout erb render views welcome erb end before protected do halt 401 unauthorized unless session authenticated end get protected do protected area end after extension do params case params extension when json response content type application json else response content type text html end end get users id json do user user find params id halt 404 unless user userserializer new user to json end map resque do run resque server end map nancy do run anothernancyapp new end end to run it you can create a config ru file ruby config ru require hello run hello new you can now run rackup and enjoy what you have just created check examples folder for a detailed example features very fast sinatra like routes support for get post put patch delete options head template rendering and caching through tilt or erb from stdlib set basic filters callbacks with the before after methods include middlewares with the use method mount rack apps with the map method sessions through rack session halt execution at any point using ruby s throw catch mechanism thread safe version history 0 6 0 unreleased now requires ruby 2 7 version contains fixes for modern ruby and rack 0 5 0 unreleased switches the routing backend to sinatra mustermann supporting wildcard routes optional parameters etc 0 4 0 unreleased added support for basic before after filters added nancy basicrender for rendering of templates using erb from stdlib refactored full code to set proper accessors for all methods removed nancy base call only instances can be used to run apps removed redirect helper use instead response redirect uri nancy render is not loaded automatically nancy render needs to be required removed tilt dependency to use nancy render add it manually to app nancy base halt can t be used with a rack response object anymore 0 3 0 octuber 3 2012 removed unneccesary thread accessors use simple instance getters instead refactored nancy base halt 0 2 0 april 12 2012 set path info to when is blank fixed session method raise error when is used but rack session isn t present added support for head and options http verbs refactored base use to use a rack builder internally added base map to redirect requests to rack sub apps 0 1 0 april 4 2012 created a new github page http guilleiguaran github com nancy for the project added env accessor this add support for shield https github com cyx shield added support for templates caching using tilt cache moved render method from nancy base to nancy render module refactored nancy base to evaluate code blocks at instance level fixed passing of render options to tilt thanks to lporras https github com lporras 0 0 1 march 20 2012 initial release contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am added some feature 4 push to the branch git push origin my new feature 5 create new pull request copyright copyright c 2012 2014 guillermo iguaran see license for further details | front_end |
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text-preprocessing | text preprocessing for natural language processing build https github com berknology text preprocessing workflows build badge svg release https github com berknology text preprocessing workflows release badge svg pypi https img shields io pypi v text preprocessing svg a python package for text preprocessing task in natural language processing usage to use this text preprocessing package first install it using pip bash pip install text preprocessing then import the package in your python script and call appropriate functions python from text preprocessing import preprocess text from text preprocessing import to lower remove email remove url remove punctuation lemmatize word preprocess text using default preprocess functions in the pipeline text to process helllo i am john doe my email is john doe email com visit our website www johndoe com preprocessed text preprocess text text to process print preprocessed text output hello email visit website preprocess text using custom preprocess functions in the pipeline preprocess functions to lower remove email remove url remove punctuation lemmatize word preprocessed text preprocess text text to process preprocess functions print preprocessed text output helllo i am john doe my email is visit our website if you have a lot of data to preprocess and would like to run text preprocessig in a parallel manner in pyspark on databricks please use the following udf function python from text preprocessing import preprocess text from pyspark sql functions import udf from pyspark sql types import stringtype from pyspark sql import dataframe as sparkdataframe def preprocess text spark df sparkdataframe target column str preprocessed column name str preprocessed text sparkdataframe preprocess text in a column of a pyspark dataframe by leveraging pyspark udf to preprocess text in parallel preprocess text udf preprocess text stringtype new df df withcolumn preprocessed column name preprocess text df target column return new df features feature function convert to lower case to lower convert to upper case to upper keep only alphabetic and numerical characters keep alpha numeric check and correct spellings check spelling expand contractions expand contraction remove urls remove url remove names remove name remove emails remove email remove phone numbers remove phone number remove ssns remove ssn remove credit card numbers remove credit card number remove numbers remove number remove bullets and numbering remove itemized bullet and numbering remove special characters remove special character remove punctuations remove punctuation remove extra whitespace remove whitespace normalize unicode e g caf cafe normalize unicode remove stop words remove stopword tokenize words tokenize word tokenize sentences tokenize sentence substitute custom words e g vs versus substitute token stem words stem word lemmatize words lemmatize word preprocess text through a sequence of preprocessing functions preprocess text | natural-language-processing text-preprocessing python machine-learning | ai |
BuildmLearn-Toolkit-Android | preview the app https img shields io badge preview appetize io green svg https appetize io app k56b095hpt7ppmhmmhbg2ayj20 build status https travis ci org buildmlearn buildmlearn toolkit android svg https travis ci org buildmlearn buildmlearn toolkit android codacy badge https api codacy com project badge grade 05c83f4ecad84cc0a2e57d7ea39df41f https www codacy com app anupam buildmlearn toolkit android utm source github com amp utm medium referral amp utm content buildmlearn buildmlearn toolkit android amp utm campaign badge grade buildmlearn toolkit android this repository contains the android version of the buildmlearn toolkit https github com buildmlearn buildmlearn toolkit buildmlearn toolkit app is an easy to use android app that helps the users make another mobile apps without any knowledge of android application development the toolkit helps creating mobile application with various functionality and allows teachers to input their custom content targeted at teachers this toolkit helps them make learning fun and engaging through mobile apps toolkit mobile templates https github com buildmlearn toolkit mobile templates contains the template applications of the toolkit application screenshots img src https cloud githubusercontent com assets 13872065 20721460 d83d3c9a b688 11e6 891d 1f28092de314 jpeg width 250 height 400 img src https cloud githubusercontent com assets 13872065 20721466 d8c038fc b688 11e6 9b9e 1394c6dedc74 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20721462 d87f6d7c b688 11e6 82e1 f3faa596d5d0 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20721464 d8b36672 b688 11e6 897c 8a0c849dbeb0 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20722851 3542f4de b68e 11e6 8079 aaa35d5b9943 gif width 250 height 400 img src https cloud githubusercontent com assets 13872065 20722869 427b28ce b68e 11e6 9885 48147e87b531 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20722870 42803256 b68e 11e6 966b 2ea24db13a60 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20722871 428db1e2 b68e 11e6 97cf 5adab6ff0a63 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20724136 10f7366c b693 11e6 8c53 a69707f2f9c8 jpeg width 250 height 400 img src https cloud githubusercontent com assets 13872065 20724137 1133c4e2 b693 11e6 9d83 61dc7676f426 jpeg width 200 height 400 img src https cloud githubusercontent com assets 13872065 20724129 0cd258a0 b693 11e6 90f3 66e88aeec244 gif width 200 height 400 img src https cloud githubusercontent com assets 13872065 20724248 83706d30 b693 11e6 8c17 acd711ec11f7 gif width 200 height 400 development setup 1 go to the project repo and click the fork button 2 clone your forked repository git clone git github com your name buildmlearn toolkit android git 3 move to android project folder cd source code 4 open the project with android studio glosarry folders description source code android project files ui design contains ui mockups and wireframes x source code app src main java org buildmlearn toolkit x activity contains various activities x adapters contains various adapters x fragment contains various fragment x simulator contains simulator activity x templates contains various template activities x model contains keystoredetails savedproject templateinfos tutorial etc x utilities contains various utilities including signerthread x views contains text view font support for old backed sdks x infotemplate contains simulator s code for info template x learnspelling contains simulator s code for learnspelling template x flashcardtemplate contains simulator s code for flashcard template x quiztemplate contains simulator s code for quiz template x videocollectiontemplate contains simulator s code for flashcard template x comprehensiontemplate contains simulator s code for comprehension template x dictationtemplate contains simulator s code for dictation template x matchtemplate contains simulator s code for match template x adapter contains simulator s adapter for template x data contains simulator s sqlitedatabase code for template x fragment contains simulator s fragment for template for complete documentation please visit http buildmlearn github io buildmlearn toolkit android how to build all dependencies are defined in source code app build gradle import the project in android studio or use gradle in command line gradlew assemblerelease the result apk file will be placed in source code app build outputs apk contribution policy all contributions should be done in bug fixes branch prs must pass build check on travis ci license for use and distribution all the code in this repository unless specified otherwise is governed by the bsd 3 clause license quoted below copyright c 2012 buildmlearn contributors listed at http buildmlearn org people 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 buildmlearn 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 maintainers the project is maintained by pankaj nathani croozeus https github com croozeus anupam das opticod https github com opticod | front_end |
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Cars-Info-App | cars info app car information app is an android app that displays information about each item by clicking on it this app is developed with flutter technology | server |
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ML-Notebooks | machine learning notebooks this repo contains machine learning notebooks for different tasks and applications the notebooks are meant to be minimal easily reusable and extendable you are free to use them for educational and research purposes this repo supports codespaces spin up a new instance by clicking on the green code button followed by the configure and create codespace option make sure to select the dev container config provided with this repo this setups an environment with all the dependencies installed and ready to go once the codespace is fully running you can install all the libraries you will need to run the notebooks under the notebooks folder open up a terminal and simply run conda create name myenv file spec file txt to install all the python libraries including pytorch activate your environment conda activate myenv you might need to run conda init zsh or whatever shell you are using and then close reopen terminal finally you can try out if everything is working by opening a notebook such as notebooks bow ipynb getting started table class tg tr th class tg yw4l b name b th th class tg yw4l b description b th th class tg yw4l b notebook b th tr tr td class tg yw4l introduction to computational graphs td td class tg yw4l a basic tutorial to learn about computational graphs td td class tg yw4l a href https colab research google com drive 1eg1af36wa0eaanandahrsbc3j04487sh usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l pytorch hello world td td class tg yw4l build a simple neural network and train it td td class tg yw4l a href https colab research google com drive 1ac0k9 aa46c77xeeytamafsofmh1bl9l usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l a gentle introduction to pytorch td td class tg yw4l a detailed explanation introducing pytorch concepts td td class tg yw4l a href https colab research google com drive 1k7ks1eras w4rzw ukebag8myrzme1es usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l counterfactual explanations td td class tg yw4l a basic tutorial to learn about counterfactual explanations for explainable ai td td class tg yw4l a href https colab research google com drive 1mtsrjqki3vsh9mvpfntj5njxcchvl8b6 usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l linear regression from scratch td td class tg yw4l an implementation of linear regression from scratch using stochastic gradient descent td td class tg yw4l a href https colab research google com drive 1br0hm79ortvnxupvgkv5t4o4aiggxfwk usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l logistic regression from scratch td td class tg yw4l an implementation of logistic regression from scratch td td class tg yw4l a href https colab research google com drive 1iboj0kngkothy7sgvavqa1aherot5mra usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l concise logistic regression td td class tg yw4l concise implementation of logistic regression model for binary image classification td td class tg yw4l a href https colab research google com drive 14hnfjvhdq9w7fgb8p6pd6 i7f3djtrg9 usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l first neural network image classifier td td class tg yw4l build a minimal image classifier using mnist td td class tg yw4l a href https colab research google com drive 1i94k n97z5r1kwv9vly9iiknyxf3tfvu usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l neural network from scratch td td class tg yw4l an implementation of simple neural network from scratch td td class tg yw4l a href https colab research google com drive 1ybcezmuhhjuiwoiwqbqwmaagrrznpp e usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l introduction to gnns td td class tg yw4l introduction to graph neural networks applies basic gcn to cora dataset for node classification td td class tg yw4l a href https colab research google com drive 1d0jldwgnbtjbvqofe8lo 1wrqtvevzx9 usp sharing img src https colab research google com assets colab badge svg width a td tr table nlp table class tg tr th class tg yw4l b name b th th class tg yw4l b description b th th class tg yw4l b notebook b th tr tr td class tg yw4l bag of words text classifier td td class tg yw4l build a simple bag of words text classifier td td class tg yw4l a href https colab research google com drive 19sudts9mnihx0tego26 biy2xc0n6dbc usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l continuous bag of words cbow text classifier td td class tg yw4l build a continuous bag of words text classifier td td class tg yw4l a href https colab research google com drive 1lqs67 mbcspikzx6y9wn7cup96utwzp2 usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l deep continuous bag of words deep cbow text classifier td td class tg yw4l build a deep continuous bag of words text classifier td td class tg yw4l a href https colab research google com drive 18yz qvmqyiyzt1blihsjrkqzxh8zjh8x usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l text data augmentation td td class tg yw4l an introduction to the most commonly used data augmentation techniques for text and their implementation td td class tg yw4l a href https colab research google com drive 1kylarevvf7vvy9bxjbjal et4wyksi s usp sharing img src https colab research google com assets colab badge svg width a td tr tr td class tg yw4l emotion classification with fine tuned bert td td class tg yw4l emotion classification using fine tuned bert model td td class tg yw4l a href https colab research google com drive 1nwce6b9pxikhv2hvbqf1ozkigkxmti1x usp sharing img src https colab research google com assets colab badge svg width a td tr table transformers table class tg tr th class tg yw4l b name b th th class tg yw4l b description b th th class tg yw4l b notebook b th tr tr td class tg yw4l text classification using transformer td td class tg yw4l an implementation of attention mechanism and positional embeddings on a text classification task td td class tg yw4l a href https colab research google com drive 1jc kco3xyhdmfyssicuimcc38 wflyh usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 text classification attention target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l neural machine translation using transformer td td class tg yw4l an implementation of transformer to translate human readable dates in any format to yyyy mm dd format td td class tg yw4l a href https colab research google com drive 1y6jhwmmgu52mu9vyronhf6ik760qx35h usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 neural machine translation attention target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l feature tokenizer transformer td td class tg yw4l an implementation of feature tokenizer transformer on a classification task td td class tg yw4l a href https colab research google com drive 1tdpifazctvpjzch1foygpywmefrbbzlh usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 feature tokenizer transformer target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l named entity recognition using transformer td td class tg yw4l an implementation of transformer to perform token classification and identify species in pubmed abstracts td td class tg yw4l a href https colab research google com drive 12adzquovfmrhlvi92dfavpttkgbcgwcf usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 named entity recognition attention target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l extractive question answering using transformer td td class tg yw4l an implementation of transformer to perform extractive question answering td td class tg yw4l a href https colab research google com drive 1eq3pkritgtzmtww wlzume2j4nkqysdp usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 question answering attention target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr table computer vision table class tg tr th class tg yw4l b name b th th class tg yw4l b description b th th class tg yw4l b notebook b th tr tr td class tg yw4l siamese network td td class tg yw4l an implementation of siamese network for finding image similarity td td class tg yw4l a href https colab research google com drive 1sn7bdkvvi8 ng37gvfynw8ocf8kzy91o usp sharing img src https colab research google com assets colab badge svg width a br a href https kaggle com code ritvik1909 siamese network target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l variational auto encoder td td class tg yw4l an implementation of variational auto encoder to generate augmentations for mnist handwritten digits td td class tg yw4l a href https colab research google com drive 13na qarsmwd2jplxjg oappdmvozizll usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 variational auto encoder target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l object detection using sliding window and image pyramid td td class tg yw4l a basic object detection implementation using sliding window and image pyramid on top of an image classifier td td class tg yw4l a href https colab research google com drive 1ciln6g7pubhehq tfk4yb4x lgypur8m usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 object detection sliding window target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l object detection using selective search td td class tg yw4l a basic object detection implementation using selective search on top of an image classifier td td class tg yw4l a href https colab research google com drive 1c7zo5jrmrcetmxn 8jruszrexxjrbn37 usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 object detection selective search target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr table generative adversarial network table class tg tr th class tg yw4l b name b th th class tg yw4l b description b th th class tg yw4l b notebook b th tr tr td class tg yw4l deep convolutional gan td td class tg yw4l an implementation of deep convolutional gan to generate mnist digits td td class tg yw4l a href https colab research google com drive 1ss9fchvi0zaus1pjl6rxlgehm glzr6s usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 deep convolutional gan target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l wasserstein gan with gradient penalty td td class tg yw4l an implementation of wasserstein gan with gradient penalty to generate mnist digits td td class tg yw4l a href https colab research google com drive 1x7adlxkv3pyev2asmzrl8pii2zsy dvs usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 wasserstein gan with gradient penalty target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr tr td class tg yw4l conditional gan td td class tg yw4l an implementation of conditional gan to generate mnist digits td td class tg yw4l a href https colab research google com drive 1z74cpeu76e6a62gxkxij8u8xyvjf21 k usp sharing img src https colab research google com assets colab badge svg width a br a href https www kaggle com code ritvik1909 conditional gan target blank img align left alt kaggle title open in kaggle src https kaggle com static images open in kaggle svg a td tr table if you find any bugs or have any questions regarding these notebooks please open an issue we will address it as soon as we can reach out on twitter https twitter com omarsar0 if you have any questions please cite the following if you use the code examples in your research misc saravia2022ml title ml notebooks author saravia elvis and rastogi ritvik journal https github com dair ai ml notebooks year 2022 | deep-learning machine-learning python pytorch ai | ai |
CADOMonitoringWebsite-ClientFrontend | frontend this is the frontend used by schools where they have to fill in all of the required information for the monitoring done by cado development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class guard interface enum module build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build running unit tests run ng test to execute the unit tests via karma https karma runner github io running end to end tests run ng e2e to execute the end to end tests via protractor http www protractortest org further help to get more help on the angular cli use ng help or go check out the angular cli readme https github com angular angular cli blob master readme md | angular azure frontend webapp | server |
prompt-eng-test-drafts | prompt eng test drafts drafts of prompt engineering lessons to test in saturn cloud | cloud |
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Embedded-Systems | embedded systems upon completion of this course students will understand the fundamental principles specification process and formal methodology in the design implementation and testing of embedded systems | os |
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unit | unit 8px grid based design system | os |
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mist | mist browser deprecated github all releases https img shields io github downloads ethereum mist total svg http www somsubhra com github release stats username ethereum repository mist build status develop branch https travis ci org ethereum mist svg branch develop https travis ci org ethereum mist build status https ci appveyor com api projects status bcfm3v0y2ovq9xob svg true https ci appveyor com project ethereum mist join the chat at https gitter im ethereum mist https badges gitter im join 20chat svg https gitter im ethereum mist code triagers badge https www codetriage com ethereum mist badges users svg https www codetriage com ethereum mist mist and ethereum wallet have been deprecated see the announcement https medium com avsa sunsetting mist da21c8e943d2 and view the migration guide https medium com omgwtfmarc mist migration patterns 6bcf066ac383 the mist browser is the tool of choice to browse and use apps for the mist api see mistapi md mistapi md this repository is also the electron host for the meteor based wallet dapp https github com ethereum meteor dapp wallet help and troubleshooting in order to get help regarding mist or ethereum wallet 1 please check the mist troubleshooting guide https github com ethereum mist wiki 1 go to our gitter channel https gitter im ethereum mist to connect with the community for instant help 1 search for similar issues https github com ethereum mist issues q is 3aopen is 3aissue label 3a 22type 3a canonical 22 and potential help 1 or create a new issue https github com ethereum mist issues and provide as much information as you can to recreate your problem how to contribute contributions via pull requests are welcome you can see where to help looking for issues with the enhancement https github com ethereum mist issues q is 3aopen is 3aissue label 3a 22type 3a enhancement 22 or bug https github com ethereum mist issues q is 3aopen is 3aissue label 3a 22type 3a bug 22 labels we can help guide you towards the solution you can also help by responding to issues https github com ethereum mist issues q is 3aissue is 3aopen label 3a 22status 3a triage 22 sign up on codetriage https www codetriage com ethereum mist and it ll send you gentle notifications with a configurable frequency it is a nice way to help while learning installation if you want to install the app from a pre built version on the release page https github com ethereum mist releases you can simply run the executable after download for updating simply download the new version and copy it over the old one keep a backup of the old one if you want to be sure linux zip installs in order to install from zip files please install libgconf2 4 first bash apt get install libgconf2 4 config folder the data folder for mist depends on your operating system windows appdata mist macos library application support mist linux config mist development for development a meteor server assists with live reload and css injection once a mist version is released the meteor frontend part is bundled using the meteor build client npm package to create pure static files dependencies to run mist in development you need node js https nodejs org v7 x use the preferred installation method for your os meteor https www meteor com install javascript app framework yarn https yarnpkg com package manager install the latter ones via bash curl https install meteor com sh curl o l https yarnpkg com install sh bash initialization now you re ready to initialize mist for development bash git clone https github com ethereum mist git cd mist git submodule update init recursive yarn run mist for development we start the interface with a meteor server for auto reload etc start the interface in a separate terminal window bash yarn dev meteor in the original window you can then start mist with bash cd mist yarn dev electron note client binaries e g geth https github com ethereum go ethereum specified in clientbinaries json https github com ethereum mist blob master clientbinaries json will be checked during every startup and downloaded if out of date binaries are stored in the config folder config folder note use help to display available options e g loglevel debug or trace for verbose output run the wallet start the wallet app for development in a separate terminal window bash yarn dev meteor in another terminal bash cd my path meteor dapp wallet app meteor port 3050 in the original window you can then start mist using wallet mode bash cd mist yarn dev electron mode wallet connect your own node this is useful if you are already running your own node or would like to connect with a private or development network bash yarn dev electron rpc path to geth ipc passing options to geth you can pass command line options directly to geth by prefixing them with node in the command line invocation bash yarn dev electron mode mist node rpcport 19343 node networkid 2 the rpc mist option is a special case if you set this to an ipc socket file path then the ipcpath option automatically gets set i e bash yarn dev electron rpc path to geth ipc is the same as doing bash yarn dev electron rpc my geth ipc node ipcpath path to geth ipc creating a local private net if you would like to quickly set up a local private network on your computer run bash geth dev look for the ipc path in the resulting geth output then start mist with bash yarn dev electron rpc path to geth ipc deployment our build system relies on gulp http gulpjs com and electron builder https github com electron userland electron builder dependencies cross platform builds require additional dependencies https www electron build multi platform build needed by electron builder please follow their instructions for up to date dependency information generate packages to generate the binaries for mist run bash yarn build mist to generate the ethereum wallet bash yarn build wallet the generated binaries will be under dist mist release or dist wallet release starting from 0 11 0 both ethereum wallet and mist ships with a meteor dapp wallet instance https github com ethereum meteor dapp wallet options platform to build binaries for specific platforms default all available use the following flags bash yarn build mist mac mac yarn build mist linux linux yarn build mist win windows skiptasks when building a binary you can optionally skip some tasks generally for testing purposes bash yarn build mist mac skiptasks build interface release dist checksums prints the sha 256 checksums of the distributables it expects installer zip files to be in the generated folders e g dist mist release bash yarn task checksums wallet tasks found in gulpfile js and gulptasks any other gulp task can be run using yarn task bash yarn task clean dist testing tests run using spectron https github com electron spectron a webdriver io runner built for electron first make sure to build mist with bash yarn build mist then run the tests bash yarn test unit once yarn test e2e note integration tests are not yet supported on windows | ethereum browser meteor web3 geth electron blockchain | blockchain |
pytorch_mgie | guiding instruction based image editing via multimodal large language models the code will be released soon after the internal review img src mgie png width 70 | ai |
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S3863026TrentWilson | s3863026trentwilson introduction to information technology assessment01 | server |
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firebug | firebug web development evolved getfirebug com https getfirebug com on november 14 with the launch of firefox quantum aka 57 support for old school extensions will stop in firefox that means firebug will no longer work for many developers however the work continues in the great firefox devtools you can try firefox devtools by updating your release browser https www mozilla org en us firefox new utm source github utm campaign switch or downloading developer edition https www mozilla org en us firefox developer utm source github utm campaign switch read more here https hacks mozilla org 2017 10 saying goodbye to firebug download https addons mozilla org en us firefox addon firebug license firebug is free and open source software distributed under the bsd license https github com firebug firebug blob master extension license txt source repository structure see more https getfirebug com wiki index php source information about firebug repository structure extension firebug extension directory tests firebug automated test files and test harness trace firebug tracing console build firebug xpi in order to build firebug xpi package run following in your command line you need apache ant http ant apache org cd firebug extension ant the xpi file should be located within release directory run firebug from source the extension directory directly contains firebug extension files and so you can run firebug directly from it this is the recommended way how to quickly test your code changes and provide a patch 1 locate your firefox profile folder http kb mozillazine org profile folder 2 open extensions folder create if it doesn t exist 3 create a new text file and put the full path to your development folder inside e g c firebug extension or firebug extension windows users should retain the os slash direction and everyone should remember to include a closing slash and remove any trailing spaces 4 save the file with firebug id as it s name firebug software joehewitt com hacking on firebug see detailed instructions http www softwareishard com blog firebug hacking on firebug about how to provide a patch to firebug source further resources home https getfirebug com https getfirebug com blog https blog getfirebug com https blog getfirebug com twitter https twitter com firebugnews https twitter com firebugnews discussion group https groups google com forum fromgroups forum firebug https groups google com forum fromgroups forum firebug wiki https getfirebug com wiki https getfirebug com wiki index php main page report an issue http code google com p fbug issues list http code google com p fbug issues list firebug extensions https getfirebug com extensions https getfirebug com wiki index php firebug extensions | front_end |
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Blockchain-Capstone | udacity blockchain capstone the capstone will build upon the knowledge you have gained in the course in order to build a decentralized housing product project resources remix solidity ide https remix ethereum org visual studio code https code visualstudio com truffle framework https truffleframework com ganache one click blockchain https truffleframework com ganache open zeppelin https openzeppelin org interactive zero knowledge 3 colorability demonstration http web mit edu ezyang public graph svg html docker https docs docker com install zokrates https github com zokrates zokrates | blockchain |
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nlp_lss_2023 | natural language processing for law and social science course materials for spring 2023 eth course natural language processing for law and social science instructed by prof elliott ash elliottash com | ai |
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react-mindee-js | check react mindee documentation https react mindee js netlify app for docs guides api and more introduction react mindee is a very opinionated javascript library that will help you build interactive canvas for computer vision detection use cases there are many powerful javascript frameworks and tools that can help you make an interactive canvas but almost all of them are low level like konva https konvajs org is a 2d canvas framework it is good it is powerful but you may need to write a lot of code this library was made for building frontend interfaces on top of mindee https mindee com document parsing apis and more generally on top of any computer vision detection apis npm https img shields io npm v react mindee js svg https www npmjs com package react mindee js v 1 3 0 tests https github com mindee react mindee js actions workflows cypress workflow yml badge svg branch new version https github com mindee react mindee js actions workflows cypress workflow yml ezgif com video to gif 12 https user images githubusercontent com 41388086 87852820 92045b80 c905 11ea 808e 5a971de2b29f gif features support for image and pdf files interactive shapes with events binding extensible styling api controllable state props and modular architecture zoom in and out feature out of the box magnified zoomed view api compatibility the react sdk is compatible with react 16 8 0 installation and dependencies the easiest way to use react select is to install it from npm and build it into your app with webpack bash npm install save react mindee js or using yarn yarn add react mindee js usage you only need an image and a list of shapes to get started jsx import react from react import annotationviewer from react mindee js import dummyimage from path to your file jpg const dummyshapes id 1 coordinates 0 479 0 172 0 611 0 172 0 611 0 196 0 479 0 196 id 2 coordinates 0 394 0 068 0 477 0 068 0 477 0 087 0 394 0 087 const data image dummyimage shapes dummyshapes function app return annotationviewer data data props data include 3 properties image file to draw in the canvas shapes which expect a list of shapes and orientation of the provided image default 0 onshapeclick return the shape object after a click event onshapemouseenter return the shape object after a mouse enter event onshapemouseleave return the shape object after a mouse leave event onshapemultiselect return the selected shapes using ctrl mouse click move options object of properties to customize default configs id unique id if not provided it will be automatically generated style style object to change container css properties classname apply a classname to the control browser support react mindee supports all recent browsers and works where react works however you may need check the ssr docs ssr section contribute to this repo feel free to use github to submit issues pull requests or general feedback you can also visit our website https mindee com or drop us an email mailto contact mindee com please read our contributing section https github com publicmindee react mindee js blob master contributing md before contributing license mit mindee https mindee com | ocr computer-vision sdk react reactjs javascript | ai |
covid-19-tracker-MAD-mini-project | covid 19 tracker mad mini project mobile application and development mini project for tracking covid cases all over the world | hacktoberfest hacktoberfest-accepted | front_end |
website-modus.trimble.com | modus style guide the modus design system was developed as a common open source platform for all of trimble s web applications setup you will need node https nodejs org en download git https git scm com downloads visual studio code https code visualstudio com download install recommended vs code extensions cmd ctrl shift p show recommended extensions installation bash npm install start dev server bash npm run serve build prod version bash npm run build testing and audits resource link google lighthouse https googlechrome github io lighthouse viewer psiurl https modus trimble com opengraph test https www opengraph xyz url https 3a 2f 2fmodus trimble com page speed test https pagespeed web dev report utm source psi utm medium redirect url https modus trimble com website grader https website grader com results modus trimble com seo social more https www seoptimer com modus trimble com security https observatory mozilla org analyze modus trimble com security headers https securityheaders com q modus trimble com followredirects on ssl labs https www ssllabs com ssltest analyze d modus trimble com dead link checker https www deadlinkchecker com website dead link checker asp | website styleguide | os |
paack-ui | paack ui the paack ui package provides ready to use components using elm ui based on the design sketches elaborated for paack s apps the components api strikes to be strict that means it pragmatically stops the developer from performing undesired options combinations thus producing unexpected behaviors or non planned visuals artifacts the purpose of using elm ui is mostly to keep components accessible the usage of this library is mostly internal but we are open to suggestions and feedback example here s an example of this package in action elm module pages login view exposing container import element exposing element fill maximum minimum shrink import ui button as button import ui document as nav import ui renderconfig as renderconfig exposing renderconfig import ui text as text import ui textfield as textfield and others container appconfig model nav page msg container appconfig model viewbody appconfig model nav bodysingle nav page pagetitle appconfig locale model loginform locale renderconfig model element msg loginform terms as locale rendercfg model element column element centery element centerx element spacingxy 8 24 element padding 32 textfield username setemail ui textfield terms pages login username model email textfield withplaceholder jon paack co textfield setlabelvisible true textfield withwidth textfield widthfull textfield renderelement rendercfg textfield currentpassword setpassword ui textfield terms pages login password model password textfield withplaceholder textfield setlabelvisible true textfield withwidth textfield widthfull textfield withonenterpressed getcredentials textfield renderelement rendercfg ui textfield button fromlabel terms pages login login ui button button cmd getcredentials button primary button renderelement rendercfg logintitle locale renderconfig element msg logintitle terms rendercfg terms pages login description string split n text multiline text heading5 ui text text withcolor palette color toneprimary brightnessmiddle text renderelement rendercfg element el element paddingxy 0 20 element width fill showcase this module s repository includes a showcase that itself is not an example of the module s usage but demonstrates the components and their appearance a published version of the showcase is available at paackeng github io paack ui https paackeng github io paack ui assets an external repository holds static assets used within this api for including these assets we recommend using parcel first add paack ui assets using npm install secondly add import paack ui assets js paacksvgiconsprite js to index js also you have to import or provide the fira sans font family with the following weights 400 500 600 and 700 here s an example for you html link href https fonts googleapis com css2 family fira sans wght 400 500 600 700 display swap rel stylesheet where to begin see the ui document module documentation http package elm lang org packages paackeng paack ui latest ui document for the top level component there is a showcase demo available run npm showcase and navigate to the pointed link in this demo you can see all different components in actions with some code samples | os |
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blockchain | blockchain a bitcoin blockchain parser in python | blockchain |
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python-workshop | this repository holds the archived content for unidata s python training workshops it has been superseded by unidata s python training https github com unidata python training repository | python jupyter notebooks atmospheric-science meteorology weather | server |
Fundamentals-of-Database-Engineering | fundamentals of database engineering learn acid indexing partitioning sharding concurrency control replication db engines best practices and more acid properties acid acid md | acid concurrency-control database database-engine indexing partitioning replication sharding | server |
EmulationStation | emulationstation a cross platform graphical front end for emulators with controller navigation project website http emulationstation org raspberry pi users a cool guy named petrockblog made a script which automatically installs many emulators and es it also includes options for configuring your rpi and setting it up to boot directly into es you can find it here https github com petrockblog retropie setup download download a pre compiled version at emulationstation org http emulationstation org download i found a bug i have a problem first try to check the issue list https github com aloshi emulationstation issues state open for some entries that might match your problem make sure to check closed issues too if you re running emulationstation on a on raspberry pi and have problems with config file changes not taking effect content missing after editing etc check if your sd card is corrupted see issues 78 https github com aloshi emulationstation issues 78 and 107 https github com aloshi emulationstation issues 107 you can do this with free tools like h2testw http www heise de download h2testw html or f3 http oss digirati com br f3 try to update to the latest version of emulationstation using git you might need to delete your es input cfg and es settings cfg after that to reset them to default values bash cd youremulationstationdirectory git pull cmake make if your problem still isn t gone the best way to report a bug is to post an issue on github try to post the simplest steps possible to reproduce the bug include files you think might be related except for roms of course if you haven t re run es since the crash the log file emulationstation es log txt is also helpful building emulationstation uses some c 11 code which means you ll need to use at least g 4 7 on linux or vs2010 on windows to compile emulationstation has a few dependencies for building you ll need cmake sdl2 boost system filesystem datetime locale freeimage freetype eigen3 and curl you also should probably install the fonts droid package which contains fallback fonts for chinese japanese korean characters but es will still work fine without it this package is only used at run time on debian ubuntu all of this be easily installed with apt get bash sudo apt get install libsdl2 dev libboost system dev libboost filesystem dev libboost date time dev libboost locale dev libfreeimage dev libfreetype6 dev libeigen3 dev libcurl4 openssl dev libasound2 dev libgl1 mesa dev build essential cmake fonts droid then generate and build the makefile with cmake bash cd youremulationstationdirectory cmake make on the raspberry pi complete raspberry pi build instructions at emulationstation org http emulationstation org gettingstarted html install rpi standalone on windows boost http www boost org users download you ll need to compile yourself or get the pre compiled binaries eigen3 http eigen tuxfamily org index php title main page header only library freeimage http downloads sourceforge net freeimage freeimage3154win32 zip freetype2 http download savannah gnu org releases freetype freetype 2 4 9 tar bz2 you ll need to compile sdl2 http www libsdl org release sdl2 devel 2 0 3 vc zip curl http curl haxx se download html you ll need to compile or get the pre compiled dll version remember to copy necessary dlls into the same folder as the executable probably freeimage dll freetype6 dll sdl2 dll libcurl dll and zlib1 dll exact list depends on if you built your libraries in static mode or not cmake http www cmake org cmake resources software html this is used for generating the visual studio project if you don t know how to use cmake here are some hints run cmake gui and point it at your emulationstation folder point the build directory somewhere i use emulationstation build click configure choose visual studio year project fill in red fields as they appear and keep clicking configure you may need to check advanced then click generate configuring emulationstation es systems cfg when first run an example systems configuration file will be created at emulationstation es systems cfg is home on linux and homepath on windows this example has some comments explaining how to write the configuration file see the writing an es systems cfg section for more information keep in mind you ll have to set up your emulator separately from emulationstation emulationstation es input cfg when you first start emulationstation you will be prompted to configure an input device the process is thus 1 hold a button on the device you want to configure this includes the keyboard 2 press the buttons as they appear in the list some inputs can be skipped by holding any button down for a few seconds e g page up page down 3 you can review your mappings by pressing up and down making any changes by pressing a 4 choose save to save this device and close the input configuration screen the new configuration will be added to the emulationstation es input cfg file both new and old devices can be re configured at any time by pressing the start button and choosing configure input from here you may unplug the device you used to open the menu and plug in a new one if necessary new devices will be appended to the existing input configuration file so your old devices will remain configured if your controller stops working you can delete the emulationstation es input cfg file to make the input configuration screen re appear on next run you can use help or h to view a list of command line options briefly outlined here resolution width height try and force a particular resolution gamelist only only display games defined in a gamelist xml file ignore gamelist do not parse any gamelist xml files draw framerate draw the framerate no exit do not display exit in the es menu debug show the console window on windows do slightly more logging windowed run es in a window works best in conjunction with resolution w h vsync 1 on or 0 off turn vsync on or off default is on scrape run the interactive command line metadata scraper as long as es hasn t frozen you can always press f4 to close the application writing an es systems cfg complete configuration instructions at emulationstation org http emulationstation org gettingstarted html config the es systems cfg file contains the system configuration data for emulationstation written in xml this tells emulationstation what systems you have what platform they correspond to for scraping and where the games are located es will check two places for an es systems cfg file in the following order stopping after it finds one that works emulationstation es systems cfg etc emulationstation es systems cfg the order emulationstation displays systems reflects the order you define them in note a system must have at least one game present in its path directory or es will ignore it if no valid systems are found es will report an error and quit here s an example es systems cfg xml this is the emulationstation systems configuration file all systems must be contained within the systemlist tag systemlist here s an example system to get you started system a short name used internally name snes name a pretty name displayed in the menus and such this one is optional fullname super nintendo entertainment system fullname the path to start searching for roms in will be expanded to home or homepath depending on platform all subdirectories and non recursive links will be included path roms snes path a list of extensions to search for delimited by any of the whitespace characters r n t you must include the period at the start of the extension it s also case sensitive extension smc sfc smc sfc extension the shell command executed when a game is selected a few special tags are replaced if found in a command like rom see below command snesemulator rom command this example would run the bash command snesemulator home user roms snes super mario world sfc the platform s to use when scraping you can see the full list of accepted platforms in src platformids cpp it s case sensitive but everything is lowercase this tag is optional you can use multiple platforms too delimited with any of the whitespace characters r n t eg genesis megadrive platform snes platform the theme to load from the current theme set see themes md for more information this tag is optional if not set it will use the value of name theme snes theme system systemlist the following tags are replaced by es in launch commands rom replaced with absolute path to the selected rom with most bash special characters escaped with a backslash basename replaced with the base name of the path to the selected rom for example a path of foo bar rom this tag would be bar this tag is useful for setting up advancemame rom raw replaced with the unescaped absolute path to the selected rom if your emulator is picky about paths you might want to use this instead of rom but enclosed in quotes see systems md systems md for some live examples in emulationstation gamelist xml the gamelist xml file for a system defines metadata for games such as a name image like a screenshot or box art description release date and rating if at least one game in a system has an image specified es will use the detailed view for that system which displays metadata alongside the game list you can use es s scraping http en wikipedia org wiki web scraping tools to avoid creating a gamelist xml by hand there are two ways to run the scraper if you want to scrape multiple games press start to open the menu and choose the scraper option adjust your settings and press scrape now if you just want to scrape one game find the game on the game list in es and press select choose edit this game s metadata and then press the scrape button at the bottom of the metadata editor you can also edit metadata within es by using the metadata editor just find the game you wish to edit on the gamelist press select and choose edit this game s metadata a command line version of the scraper is also provided just run emulationstation with scrape currently broken the switch ignore gamelist can be used to ignore the gamelist and force es to use the non detailed view if you re writing a tool to generate or parse gamelist xml files you should check out gamelists md gamelists md for more detailed documentation themes by default emulationstation looks pretty ugly you can fix that if you want to know more about making your own themes or editing existing ones read themes md themes md i ve put some themes up for download on my emulationstation webpage http aloshi com emulationstation themes if you re using retropie you should already have a nice set of themes automatically installed alec aloshi lofquist http www aloshi com http www emulationstation org | front_end |
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app_rtos | app rtos master branch rtos on tm4c123gh6pm controller | os |
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Learn-to-Reason | democratizing reasoning ability code for paper https arxiv org pdf 2310 13332 pdf democratizing reasoning ability tailored learning from large language model accepted by emnlp 2023 poster figures poster png requirements 1 python 3 8 16 2 pytorch 1 13 1 3 transformers 4 28 1 4 accelerate 0 18 0 5 datasets 2 10 1 6 deepspeed 0 9 1 training run the following command to train the student lm gpt j 6b on the gsm8k dataset for the initial round of learning we have released the collected rationales in the github release page for research use if your device supports bf16 please replace fp16 to bf16 for a more stable training bash python run trainer py device 0 1 2 3 4 5 6 7 task gsm8k load eleutherai gpt j 6b save best 0 epoch 10 teacher data localdataset gsm8k gsm8k train round1 json fewshot yes train bsz 2 fp16 yes contrastive yes cl pos path localdataset gsm8k gsm8k train round1 json cl neg path localdataset gsm8k gsm8k student train round0 neg4 json cl ratio 0 5 merge losses yes do eval yes weight decay 0 01 lr 7e 6 deepspeed ds stage3 config json evaluation bash python infer py task gsm8k test on test device seq s 0 7 fewshot rationale checkpoint checkpoints student train saved ckpt path for the next round take the exam on the train set first to collect the self made mistakes bash python infer student wrong py task gsm8k rationale fewshot batch 4 test on train note round1 device seq s 0 7 checkpoint checkpoints student train saved ckpt path use the prompt template mentioned in the paper to collect the teacher s feedback with these mistakes from chatgpt replace the teacher data cl pos path cl neg path and update checkpoint path to ckpt of round1 in the above training command to start the next round of training please refer to args py for detailed usage citation if you find our paper or codes useful please kindly cite misc wang2023democratizing title democratizing reasoning ability tailored learning from large language model author zhaoyang wang and shaohan huang and yuxuan liu and jiahai wang and minghui song and zihan zhang and haizhen huang and furu wei and weiwei deng and feng sun and qi zhang year 2023 eprint 2310 13332 archiveprefix arxiv primaryclass cs cl | ai |
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comp3040-50 | embedded systems design comp3040 50 this is a design lab that i took course page http www eng auburn edu nelsovp courses elec3040 3050 | os |
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ESP32-FreeRTOS-BEM280-MQ5-0.96OLED-AliyunIoT | esp32 esp32 arduino freertos java nodered dashboard esp32 bme280 mq 2 0 96 oled led image circuit jpg image framework png wifi esp32 api esp32 http arduinojson mqtt blinker bme280 mq 2 oled oled https pan xunlei com s vn yxmz8jsmzedzqgzchdagca1 6rx8 app | os |
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dl-uncertainty | uncertainty in deep learning some code tensorflow based on the paper a kendall y gal what uncertainties do we need in bayesian deep learning for computer vision nips 2017 arxiv https arxiv org abs 1703 04977 disclaimer this is not the official repo it is just based on my understanding of the paper any feedback is welcome i am training a simple autoencoder regression to reconstruct mnist digits getting mnist download mnist download sh rescale and save in a python dictionary possibly resize python prepro py license this repository is released under the mit license | deep-learning bayesian uncertainty computer-vision | ai |
AltSchool-of-Cloud-Engineering-3rd-Semester-Docker-Assignment-Submission | p align center a href https laravel com target blank img src https raw githubusercontent com laravel art master logo lockup 5 20svg 2 20cmyk 1 20full 20color laravel logolockup cmyk red svg width 400 alt laravel logo a p p align center a href https github com laravel framework actions img src https github com laravel framework workflows tests badge svg alt build status a a href https packagist org packages laravel framework img src https img shields io packagist dt laravel framework alt total downloads a a href https packagist org packages laravel framework img src https img shields io packagist v laravel framework alt latest stable version a a href https packagist org packages laravel framework img src https img shields io packagist l laravel framework alt license a p about laravel laravel is a web application framework with expressive elegant syntax we believe development must be an enjoyable and creative experience to be truly fulfilling laravel takes the pain out of development by easing common tasks used in many web projects such as simple fast routing engine https laravel com docs routing powerful dependency injection container https laravel com docs container multiple back ends for session https laravel com docs session and cache https laravel com docs cache storage expressive intuitive database orm https laravel com docs eloquent database agnostic schema migrations https laravel com docs migrations robust background job processing https laravel com docs queues real time event broadcasting https laravel com docs broadcasting laravel is accessible powerful and provides tools required for large robust applications learning laravel laravel has the most extensive and thorough documentation https laravel com docs and video tutorial library of all modern web application frameworks making it a breeze to get started with the framework you may also try the laravel bootcamp https bootcamp laravel com where you will be guided through building a modern laravel application from scratch if you don t feel like reading laracasts https laracasts com can help laracasts contains over 2000 video tutorials on a range of topics including laravel modern php unit testing and javascript boost your skills by digging into our comprehensive video library laravel sponsors we would like to extend our thanks to the following sponsors for funding laravel development if you are interested in becoming a sponsor please visit the laravel patreon page https patreon com taylorotwell premium partners vehikl https vehikl com tighten co https tighten co kirschbaum development group https kirschbaumdevelopment com 64 robots https 64robots com cubet techno labs https cubettech com cyber duck https cyber duck co uk many https www many co uk webdock fast vps hosting https www webdock io en devsquad https devsquad com curotec https www curotec com services technologies laravel op gg https op gg webreinvent https webreinvent com utm source laravel utm medium github utm campaign patreon sponsors lendio https lendio com contributing thank you for considering contributing to the laravel framework the contribution guide can be found in the laravel documentation https laravel com docs contributions code of conduct in order to ensure that the laravel community is welcoming to all please review and abide by the code of conduct https laravel com docs contributions code of conduct security vulnerabilities if you discover a security vulnerability within laravel please send an e mail to taylor otwell via taylor laravel com mailto taylor laravel com all security vulnerabilities will be promptly addressed license the laravel framework is open sourced software licensed under the mit license https opensource org licenses mit | cloud |
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Lifestyle-Ecommerce-Website | lifestyle ecommerce website simple front end design for ecommerce website implemented in html css bootstrap visit link to preview https tarandeep97 github io lifestyle ecommerce website screenshots screenshot 100 https user images githubusercontent com 28994081 47411649 15bc0300 d787 11e8 81fc 55d51d1c2b32 png screenshot 97 https user images githubusercontent com 28994081 47411712 40a65700 d787 11e8 9ff9 6abd4400d2b5 png screenshot 98 https user images githubusercontent com 28994081 47411758 6895ba80 d787 11e8 83fe 8bcbf6a9f335 png | html5 bootstrap3 jquery-plugin css3 static-site | front_end |
3_Project_Boost | background this is the complete unity c developer 3d http gdev tv cu2github the long awaited sequel to the complete unity c developer 2d http gdev tv cudgithub one of the most successful e learning courses on the internet completely re worked from scratch with brand new projects our latest teaching techniques you will benefit from the fact we have already taught over 350 000 students game development many shipping commercial games as a result you re welcome to download fork or do whatever else legal with all the files the real value is in our huge high quality online tutorials that accompany this repo you can check out the course here complete unity c developer 3d http gdev tv cu2github project pre requisites in order to start this section you should have either finished the previous section of this course https github com completeunitydeveloper2 2 terminal hacker or be able to complete it s learning outcomes project 3 project boost this is a game inspired by the 1986 classic thrust the goal is to battle gravity and avoid obstacles to skillfully pilot your ship from a launch pad to a landing pad a simple concept with a huge array of gameplay opportunities this project s learning outcomes basic particle effects local version control create c scripts add unity components use coordinate systems origins and anchor points create and maniplulate unity prefabs do basic level design ref pb cu2 how to build compile this is a unity project if you re familiar with source control then clone this repo otherwise download the contents and navigate to assets levels then open any unity file this branch is the course branch each commit corresponds to a lecture in the course the current state is our latest progress video list curriculum here are the video lectures that comprise this section they are typically about 5 15 minutes long and each commit contains the exact changes made to the project by the instructor in that video 1 welcome to section 3 1 this game is based on the old classic thrust 2 we ll be using unity s physics engine 3 rick will be teaching design 2 project boost game design 1 this game is based on the old classic thrust 2 we ll be using unity s physics engine 3 rick will be teaching design 3 onion design 1 common game design challenges that we need to resolve 2 what is onion design and how are we using it to inform priorities for developing our game 4 introducing version control 1 about version control and why we care 2 how version control helps with game development 3 setting up git and sourcetree with unity 5 add unity gitignore easily 1 what a gitignore file is 2 unity s library folder is a cache 3 how to easily add a unity gitignore 6 the origin of our world 1 which axis is right forward and up in unity 2 everything should be prefabbed 3 how to create a material 4 setting our world origin 7 placeholder art from primitives 1 guidelines for setting up compound game objects 2 using primitive shapes to create placeholder art 3 creating our first rocket ship 8 basic input binding 1 how to read direct from keys 2 testing key logic with the console 3 preparing to launch our ship 9 physics and rigidbodies 1 how to access a rigid body in unity 2017 2 using addrelativeforce 3 adjusting mass to get our ship hovering 9b coordinate system handedness 1 some things in 2d and 3d have handedness 2 this is important when making computer games and drugs 3 how to use your hands to predict rotations 10 using time deltatime 1 using a play mode tint 2 how to make things frame rate independent 3 using time deltatime to predict frame time 4 getting our ship rotating in space 10b instructor hangout 3 1 1 why git rather than unity collab jason 2 clarifying the handedness rule finger order 3 struggling sourcetree on mac forum frank 4 how to re centre pivot point on rocket rory 5 adding box collider to odd shaped rocket andy 6 adding prefix to q a question and comments 7 mad how disease and that 1000y old text 11 adding a touch of audio 1 there s an audio listener on the main camera 2 an audio source component makes sounds 3 how to create and attach an audio clip 4 making sounds when the rocket thrutsts 12 resolving movement bugs 1 a minor code refactor 2 update our ship prefab 3 create new gameplay platforms 4 use rigid body constraints 5 using rigidbody freezerotation true 6 adding some drag to our ship 13 using serializefield vs public 1 multiply a vector by a float to change length 2 serializefield vs public to expose to inspector 3 creating design levers 4 tweaking our rocket movement 14 tagging game objects as friendly 1 the pros and cons of using tags in unity 2 how to use oncollisionenter 3 differentiating between collisions 15 basic level design 1 tweak our camera to suit our design intention 2 design a simple game moment to form the basis of our level 3 add a backdrop 16 design levers and tuning 1 what design levers do we currently have at our disposal 2 some examples of extreme tuning 17 making a second level 1 improving the look of our current level 2 creating a new scene to create a new level 18 prefabs in detail 1 understanding what happens when a new prefab is created 2 exploring when an instance gets changed if a prefab is updated 3 adding a landing pad prefab 19 level loading scene management 1 how to add scenes to the build order 2 about the build index vs the scene name 3 why we need using unityengine scenemanagement 4 using scenemanager loadscene 20 invoke as a coroutine warm up 1 how to fix scene getting dark on level load 2 creating an enum for our player state 3 using unity s invoke to delay load 20b instructor hangout 3 2 1 abrupt sound stopping issue thanks gregory 1 care of differences in debug mode thanks jeff 1 side effect in freezerotation code reviews jeff 1 well done morgaine for 1st screen recording 1 curtis robert re too slick for neophytes 1 default values serializefield mitchell 1 frame rate fixedupdate straesso 1 tip about solid background manuel 1 loving the levels on forum resources 1 keep engaging even if it s all clear 21 playing multiple audio clips 1 you don t need a default audio clip on a source 2 use serializefield audioclip clipname to expose a clip 3 use audiosource playoneshot clipname to play 4 how to handle multiple audio clips 22 introducing particle effects 1 what a particle effect is 2 how we designate which effect to play 3 using particlesystem play to trigger effect 23 moving platform pattern 1 using disallowmultiplecomponent attribute 2 using range 0 1 attribute 3 a pattern for moving platforms 24 mathf sin for movement cycles 1 how to initialise a vector3 2 using mathf sin for oscillation 3 getting your offsets right 4 making thrust frame rate independent 25 protecting against nan 1 use mathf epsilon for floats 2 tidy code 3 remember our discord chat server 26 organise your assets 1 create new folders within our assets directory 2 create new layout to help visualise all our assets 3 manage our hierarchy by using empty game objects 4 discover and address prefab linking issues 27 light your scene 1 understand all of the lights currently impacting your scene 2 add point light and spotlight to your scene 3 add light to your player object 28 nested prefab joy 1 challenge understand how nested prefabs work using our rocket ship 2 how prefabs are copied as game objects when nested under a prefab 3 solution is to instantiate which we will cover in the next section 29 make game moments 1 recap of all the design levers we now have at our disposal 2 examples of a number of game moments and level layouts 3 level flow options 4 challenge to capture your game moment using screen capture software and share 30 debug keys builds 1 what debug keys are 2 why they are useful 3 setup debug keys to ignore collisions and immediately load next level 4 using debug isdebugbuild to keep debug keys out of final player build 30b instructor hangout 3 3 1 when will we teach mobile inputs ken 2 important to be good at math adam cam 3 one script versus many scripts 4 what to do next with the project 31 looping through levels 1 use scenemanager getactivescene buildindex to get current scene in unity 2017 2 scenemanager scenecountinbuildsettings to count scenes in build settings 3 why we can t yet record total levels won 32 sharing with teaser video 1 more details about build order 2 sharing with a teaser video 3 using obs to record your teaser 33 spit polish note there were some off screen changes by rick before this video 1 this is an open ended video where we apply bugfixes and improvements 34 section 3 wrap up 1 great work in this section 2 we covered a lot of great c unity and game design territory this section 3 let s push on to the next section of the course | unity game-development csharp | os |
imbackend | imbackend license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 gitter https badges gitter im join chat svg https gitter im imbackend lobby utm source share link utm medium link utm campaign share link | baas backend go golang acl filesystem | server |
deepspeed_xglm | deepspeed xglm a deepspeed project with large language model training | ai |
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wonder-blocks | img width 32 src static logo svg wonder blocks release https github com khan wonder blocks actions workflows release yml badge svg codecov https codecov io gh khan wonder blocks branch main graph badge svg https codecov io gh khan wonder blocks the khan academy wonder blocks design system this is work in progress and a lot of things are still in motion more information https khan github io wonder blocks getting started prerequisites node js v16 x https nodejs org download release v16 16 0 yarn https yarnpkg com lang en docs install installation to install wonder blocks you need to run the following commands yarn install installs project dependencies yarn start runs the docs in dev mode now you can open http localhost 6061 to view the docs this page will automatically update as you make changes to components contributing note for external contributors we are not accepting external contributions at this time we are working to make this happen in the future and we will let you know when this is possible for a more detailed explanation about how to develop wonder blocks components please refer to the internal documentation https khanacademy atlassian net wiki spaces frontend pages 100827261 developing wonder blocks please note before contributing ensure that any design changes you are wanting to make are reflected in the wonder blocks project in figma https www figma com file vbvu3h2bpbhh80niq101mhhe wonder blocks thanks a href https www chromaticqa com img src https cdn images 1 medium com letterbox 147 36 50 50 1 ohhjtjindobxiuyhdy2gfa png source logoavatar d7276495b101 37816ec27d7a width 120 a thanks to chromatic https www chromaticqa com for providing the visual testing platform that helps us catch unexpected changes on time | design-system ui-components react | os |
IILFM | intensity image based lidar fiducial marker system 2489 graph https github com york sdcnlab iilfm assets 58899542 6d58d12c 9939 474a a99b 3bb1c2de8ae6 this work has been accepted by the ieee robotics and automation letters https ieeexplore ieee org document 9774900 br br youtube link to the introduction video https www youtube com watch v aybqhaewblm br bilibili link to the introduction video https www bilibili com video bv1s34y147um br br for fiducial marker detection in the point cloud captured by a lidar in motion please refer to our new work occlusion resistant lidar fiducial marker detection https github com york sdcnlab marker detection general br background extensive research has been carried out on the visual fiducial marker vfm systems however no single study utilizes these systems to their fullest potential in lidar applications in this work we develop an intensity image based lidar fiducial marker iilfm system that fills the above mentioned gap the proposed system only requires an unstructured point cloud with intensity as the input and it outputs the detected markers information and the 6 dof pose that describes the transmission from the world coordinate system to the lidar coordinate system the use of the iiflm system is as convenient as the conventional vfm systems with no restrictions on marker placement and shape different vfm systems such as apriltag 3 https github com aprilrobotics apriltag aruco https docs opencv org 3 4 d5 dae tutorial aruco detection html and cctag https cctag readthedocs io en latest can be easily embedded into the system hence the proposed system inherits the functionality of the vfm systems such as the coding and decoding methods br img width 400 height 400 src https user images githubusercontent com 58899542 151822834 e7758e70 849f 483d b2fd df93b1fe0aa5 png br marker detection demos one and two markers detection demo1 https user images githubusercontent com 58899542 151841293 f2f4f2d0 f5ba 427e b5e7 ff6106e4a8d0 gif apriltag grid 35 markers detection br img width 480 height 320 src https user images githubusercontent com 58899542 152581823 ca10f8db 8d3e 4025 91e9 eb111489b911 jpeg br demo2 https user images githubusercontent com 58899542 152580373 71096105 8b6a 47ba a852 767922dcf39a gif demo3 https user images githubusercontent com 58899542 152580126 5306eb2e 7899 494a a7bd bb0f43427daa gif lidar pose estimation demo demo4 https user images githubusercontent com 58899542 152581365 ff25f9c3 3fd2 4a1d 9525 2383717266b3 gif other applications the proposed system shows potential in augmented reality slam multisensor calbartion etc here an augumented reality demo using the proposed system is presented the teapot point cloud is transmitted to the location of the marker in the lidar point cloud based on the pose provided by the iilfm system br demo5 https user images githubusercontent com 58899542 152583787 add4a9f2 59c6 4e15 a112 f1d2ad10324e gif br in this repository we only release the version of which the embedded system is apriltag 3 https github com aprilrobotics apriltag the versions with aruco https docs opencv org 3 4 d5 dae tutorial aruco detection html cctag https cctag readthedocs io en latest detectors are coming soon it is a very straightforward process to replace the embedded visual fiducial marker system hence following the method introduced in our scripts you may add any visual marker detector as you like requirements ubuntu 20 04 br other versions of the ubuntu system could work if the following libraries are installed correctly br ros noetic br ubuntu install of ros noetic http wiki ros org noetic installation ubuntu br lower ros versions could work yet you might have to deal with the conflicts of opencv4 and opencv3 pcl br sudo apt update br sudo apt install libpcl dev br opencv br sudo apt update br sudo apt install libopencv dev python3 opencv br catkin br sudo apt update br sudo apt install catkin br yaml cpp br sudo apt update br sudo apt get install libyaml cpp dev br boost br sudo apt update br sudo apt get install libboost all dev commands git clone https github com york sdcnlab iilfm git br cd iilfm br catkin build br br modify the yorktag launch in iilfm src yorkapriltag launch according to your lidar model e g rostopic angular resolutions and so on and the employed tag family then modify the config yaml in iilfm src yorkapriltag resources based on your setup define the locations of the vertices with respect to the world coordinate system otherwise the outputted pose is meaningless afterward run br source devel setup bash br roslaunch yorkapriltag yorktag launch br open a new terminal in iilfm src yorkapriltag resources and run br rosbag play l bagname bag br br to view the 6 dof pose open a new terminal and run br rostopic echo iilfm pose br br to view the point could of the detected 3d fiducials in rviz open a new terminal and run rviz in rviz change the fixed frame to livox frame add by topic iilfm features pointcloud2 br br by default the settings in yorktag launch are corresponding to livox mid 40 if you just want to try our system and see how it works there is no need to modify yorktag launch and config yaml you may simply run br source devel setup bash br roslaunch yorkapriltag yorktag launch br then in iilfm src yorkapriltag resources open a new terminal and run br rosbag play l bagname bag br experimental result due to the page limitation we removed this huge table from our manuscript submitted to ra l and replaced it with a histogram considering that some readers might be interested in the ground truth we present the table here please refer to our paper to see the detailed experimental setup table1 https user images githubusercontent com 58899542 162066700 d5afbac9 aa5e 49b9 a4d6 648e8bb8956c png citation if you find this work helpful for your research please cite our paper https ieeexplore ieee org document 9774900 article 9774900 author liu yibo and schofield hunter and shan jinjun journal ieee robotics and automation letters title intensity image based lidar fiducial marker system year 2022 volume 7 number 3 pages 6542 6549 doi 10 1109 lra 2022 3174971 | lidar ros fiducial-markers slam lidar-calibration lidar-camera-calibration augmented-reality robotics point-cloud apriltag aruco cctag aruco-marker lidartag | os |
dog1 | dog1 quadruped using servos and computer vision for navigation video image alt text here media dog1 jpg https www youtube com watch v fhqwazvzqhe 3d model 3d model media 3dmodel jpg parts raspberry pi 4 pololu micro maestro 18ch servo controller intel realsense t265 tracking camera intel realsense d435 depth camera 12 x rds3235 robot servo 35kg 180 degrees https www aliexpress com item 32556521374 html | ai |
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ML-paper-notes | ml papers this repo contains notes and short summaries of some ml related papers i come across organized by subjects and the summaries are in the form of pdfs self supervised contrastive learning self supervised relational reasoning for representation learning 2020 paper https arxiv org abs 2006 05849 notes notes 103 ssl relation reasoning pdf big self supervised models are strong semi supervised learners 2020 paper https arxiv org abs 2006 10029 notes notes 95 big self supervised models pdf debiased contrastive learning 2020 paper https arxiv org abs 2007 00224 notes notes 97 debiased contrastive learning pdf selfie self supervised pretraining for image embedding 2019 paper https arxiv org abs 1906 02940 notes notes 76 selfie pretraining for img embeddings pdf self supervised representation learning by rotation feature decoupling 2019 paper http openaccess thecvf com content cvpr 2019 papers feng self supervised representation learning by rotation feature decoupling cvpr 2019 paper pdf notes notes 73 ssl by rotation decoupling pdf revisiting self supervised visual representation learning 2019 paper https arxiv org abs 1901 09005 notes notes 72 revisiting ssl pdf aet vs aed unsupervised representation learning by auto encoding transformations 2019 paper https arxiv org abs 1901 04596 notes notes 74 aft vs aed pdf boosting self supervised learning via knowledge transfer 2018 paper https arxiv org abs 1805 00385 notes notes 67 boosting self super via trsf learning pdf self supervised feature learning by learning to spot artifacts 2018 paper https arxiv org abs 1806 05024 notes notes 69 ssl by learn to spot artifacts pdf unsupervised representation learning by predicting image rotations 2018 paper https arxiv org abs 1803 07728 notes notes 68 unsup img rep learn by rot predic pdf cross pixel optical flow similarity for self supervised learning 2018 paper https arxiv org abs 1807 05636 notes notes 75 cross pixel optical flow pdf multi task self supervised visual learning 2017 paper https arxiv org abs 1708 07860 notes notes 64 multi task self supervised pdf split brain autoencoders unsupervised learning by cross channel prediction 2017 paper https arxiv org abs 1611 09842 notes notes 65 split brain autoencoders pdf colorization as a proxy task for visual understanding 2017 paper https arxiv org abs 1703 04044 notes notes 66 colorization as a proxy for viz under pdf unsupervised learning of visual representations by solving jigsaw puzzles 2017 paper https arxiv org abs 1603 09246 notes notes 63 solving jigsaw puzzles pdf unsupervised visual representation learning by context prediction 2016 paper https arxiv org abs 1505 05192 notes notes 62 unsupervised learning with context prediction pdf colorful image colorization 2016 paper https richzhang github io colorization notes notes 59 colorful colorization pdf learning visual groups from co occurrences in space and time 2015 paper https arxiv org abs 1511 06811 notes notes 61 visual groups from co occurrences pdf discriminative unsupervised feature learning with exemplar convolutional neural networks 2015 paper https arxiv org abs 1406 6909 notes notes 60 exemplar cnns pdf semi supervised learning negative sampling in semi supervised learning 2020 paper https arxiv org abs 1911 05166 notes notes 94 nagative sampling ssl pdf time consistent self supervision for semi supervised learning 2020 paper https proceedings icml cc static paper files icml 2020 5578 paper pdf notes notes 93 time consistent ssl pdf dual student breaking the limits of the teacher in semi supervised learning 2019 paper https arxiv org abs 1909 01804 notes notes 79 dual student pdf s4l self supervised semi supervised learning 2019 paper https arxiv org abs 1905 03670 notes notes 83 s4l pdf semi supervised learning by augmented distribution alignment 2019 paper https arxiv org abs 1905 08171 notes notes 80 ssl aug dist align pdf mixmatch a holistic approach tosemi supervised learning 2019 paper https arxiv org abs 1905 02249 notes notes 45 mixmatch pdf unsupervised data augmentation 2019 paper https arxiv org abs 1904 12848 notes notes 39 unsupervised data aug pdf interpolation consistency training for semi supervised learning 2019 paper https arxiv org abs 1903 03825 notes notes 44 interpolation consistency tranining pdf deep co training for semi supervised image recognition 2018 paper https arxiv org abs 1803 05984 notes notes 46 deep co training img rec pdf unifying semi supervised and robust learning by mixup 2019 paper https openreview net forum id r1gp1jrn 4 notes notes 42 mixmixup pdf realistic evaluation of deep semi supervised learning algorithms 2018 paper https arxiv org abs 1804 09170 notes notes 37 realistic eval of deep ss pdf semi supervised sequence modeling with cross view training 2018 paper https arxiv org abs 1809 08370 notes notes 38 cross view semi supervised pdf virtual adversarial training 2017 paper https arxiv org abs 1704 03976 notes notes 40 virtual adversarial training pdf mean teachers are better role models 2017 paper https arxiv org abs 1703 01780 notes notes 56 mean teachers pdf temporal ensembling for semi supervised learning 2017 paper https arxiv org abs 1610 02242 notes notes 55 temporal ensambling pdf semi supervised learning with ladder networks 2015 paper https arxiv org abs 1507 02672 notes notes 33 ladder nets pdf video understanding multiscale vision transformers 2021 paper https arxiv org abs 2104 11227 notes notes multiscale vision transformers pdf vivit a video vision transformer 2021 paper https arxiv org abs 2103 15691 notes notes vivit a video vision transformer pdf space time mixing attention for video transformer 2021 paper https arxiv org abs 2106 05968 notes notes space time mixing attention for video transformer pdf is space time attention all you need for video understanding 2021 paper https arxiv org abs 2102 05095 notes notes is space time attention all you need for video understanding pdf an image is worth 16x16 words what is a video worth 2021 paper https arxiv org abs 2103 13915 notes notes an image is worth 16x16 words what is a video worth pdf temporal query networks for fine grained video understanding 2021 paper https arxiv org abs 2104 09496 notes notes temporal query networks for fine grained video understanding pdf x3d expanding architectures for efficient video recognition 2020 paper https arxiv org abs 2004 04730 notes notes x3d expanding architectures for efficient video recognition pdf temporal pyramid network for action recognition 2020 paper https arxiv org abs 2004 03548 notes notes temporal pyramid network for action recognition pdf stm spatiotemporal and motion encoding for action recognition 2019 paper https arxiv org abs 1908 02486 notes notes stm spatiotemporal and motion encoding for action recognition pdf video classification with channel separated convolutional networks 2019 paper https arxiv org abs 1904 02811 notes notes video classification with channel separated convolutional networks pdf video modeling with correlation networks 2019 paper https arxiv org abs 1906 03349 notes notes video modeling with correlation networks pdf videos as space time region graphs 2018 paper https arxiv org abs 1806 01810 notes notes videos as space time region graphs pdf slowfast networks for video recognition 2018 paper https arxiv org abs 1812 03982 notes notes slowfast networks for video recognition pdf tsm temporal shift module for efficient video understanding 2018 paper https arxiv org abs 1811 08383 notes notes tsm temporal shift module for efficient video understanding pdf timeception for complex action recognition 2018 paper https arxiv org abs 1812 01289 notes notes timeception for complex action recognition pdf non local neural networks 2017 paper https arxiv org abs 1711 07971 notes notes non local neural networks pdf temporal segment networks for action recognition in videos 2017 paper https arxiv org abs 1705 02953 notes notes temporal segment networks for action recognition in videos pdf quo vadis action recognition a new model and the kinetics dataset 2017 paper https arxiv org abs 1705 07750 notes notes quo vadis action recognition a new model and the kinetics dataset pdf a closer look at spatiotemporal convolutions for action recognition 2017 paper https arxiv org abs 1711 11248 notes notes a closer look at spatiotemporal convolutions for action recognition pdf actionvlad learning spatio temporal aggregation for action classification 2017 paper https arxiv org abs 1704 02895 notes notes actionvlad learning spatio temporal aggregation for action classification pdf spatiotemporal residual networks for video action recognition 2016 paper https arxiv org abs 1611 02155 notes notes spatiotemporal residual networks for video action recognition pdf deep temporal linear encoding networks 2016 paper https arxiv org abs 1611 06678 notes notes deep temporal linear encoding networks pdf temporal convolutional networks for action segmentation and detection 2016 paper https arxiv org abs 1611 05267 notes notes temporal convolutional networks for action segmentation and detection pdf learning spatiotemporal features with 3d convolutional network 2014 paper https arxiv org abs 1412 0767 notes notes learning spatiotemporal features with 3d convolutional network pdf domain adaptation domain out of distribution generalization rethinking distributional matching based domain adaptation 2020 paper https arxiv org abs 2006 13352 notes notes 98 rethinking distributional matching pdf transferability vs discriminability batch spectral penalization 2019 paper http proceedings mlr press v97 chen19i html notes notes 91 batch spectral normalization pdf on learning invariant representations for domain adaptation 2019 paper https arxiv org abs 1901 09453 notes notes 90 on learning invariant repr pdf universal domain adaptation 2019 paper http openaccess thecvf com content cvpr 2019 papers you universal domain adaptation cvpr 2019 paper pdf notes notes 87 universal da pdf transferable adversarial training 2019 paper http proceedings mlr press v97 liu19b liu19b pdf notes notes 86 tdt pdf multi adversarial domain adaptation 2018 paper https arxiv org abs 1809 02176 notes notes 92 multi adversarial domain adaptation pdf conditional adversarial domain adaptation 2018 paper https arxiv org abs 1705 10667 notes notes 85 cdan pdf learning adversarially fair and transferable representations 2018 paper https arxiv org abs 1802 06309 notes notes 88 learning adv fair and tsf repres pdf what is the effect of importance weighting in deep learning 2018 paper https arxiv org abs 1812 03372 notes notes 89 effect of importance weighting pdf explainability towards interpreting and mitigating shortcut learning behavior of nlu models 2021 paper https arxiv org abs 2103 06922 notes notes towards interpreting and mitigating shortcut learning behavior of nlu models pdf transformer interpretability beyond attention visualization 2020 paper https arxiv org abs 2012 09838 notes notes transformer interpretability beyond attention visualization pdf what shapes feature representations exploring datasets architectures and training 2020 paper https arxiv org abs 2006 12433 notes notes what shapes feature representations exploring datasets architectures and training pdf attention based dropout layer for weakly supervised object localization 2019 paper http openaccess thecvf com content cvpr 2019 papers choe attention based dropout layer for weakly supervised object localization cvpr 2019 paper pdf notes notes 58 attention based dropout pdf attention is not explanation 2019 paper https arxiv org abs 1902 10186 notes notes attention is not explanation pdf smoothgrad removing noise by adding noise 2017 paper https arxiv org abs 1706 03825 notes notes smoothgrad removing noise by adding noise pdf axiomatic attribution for deep networks 2017 paper https arxiv org abs 1703 01365 notes notes axiomatic attribution for deep networks pdf attention branch network learning of attention mechanism for visual explanation 2019 paper https arxiv org abs 1812 10025 notes notes 57 attention branch netwrok pdf paying more attention to attention improving the performance of cnns via attention transfer 2016 paper https arxiv org abs 1612 03928 notes notes 71 attention transfer pdf natural language processing nlp pre train prompt and predict a systematic survey of prompting methods in natural language processing 2021 paper https arxiv org abs 2107 13586 notes notes pre train prompt and predict a systematic survey of prompting methods in natural language processing pdf unsupervised data augmentation with naive augmentation and without unlabeled data 2020 paper https arxiv org abs 2010 11966 notes notes 102 uda with naive aug pdf supervised contrastive learning for pre trained language model fine tuning 2021 paper https arxiv org abs 2011 01403 notes notes 104 supervised const for fine tuning pdf bert and pals projected attention layers for efficient adaptation in multi task learning 2020 paper https arxiv org abs 1902 02671 notes notes 99 bert and pals pdf freelb enhanced adversarial training for natural language understanding 2020 paper https arxiv org abs 1909 11764 notes notes 101 freelb pdf mixtext linguistically informed interpolation for semi supervised text classification 2020 paper https arxiv org abs 2004 12239 notes notes 100 mixtext pdf generative modeling generative pretraining from pixels 2020 paper https cdn openai com papers generative pretraining from pixels v2 pdf notes notes 96 generative pretraining from pixels pdf consistency regularization for generative adversarial networks 2020 paper https arxiv org abs 1910 12027 notes notes 84 cr gans pdf unsupervised learning invariant information clustering for unsupervised image classification and segmentation 2019 paper https arxiv org abs 1807 06653 notes notes 78 iic pdf deep clustering for unsupervised learning of visual feature 2018 paper https arxiv org abs 1807 05520 notes notes 70 deep clustering for un visual features pdf semantic segmentation deeplabv3 encoder decoder with atrous separable convolution 2018 paper https arxiv org abs 1802 02611 notes notes 26 deeplabv3 pdf large kernel matter improve semantic segmentation by global convolutional network 2017 paper https arxiv org abs 1703 02719 notes notes 28 large kernel maters pdf understanding convolution for semantic segmentation 2018 paper https arxiv org abs 1702 08502 notes notes 29 understanding conv for sem seg pdf rethinking atrous convolution for semantic image segmentation 2017 paper https arxiv org abs 1706 05587 notes notes 25 deeplab v3 pdf refinenet multi path refinement networks for high resolution semantic segmentation 2017 paper https arxiv org abs 1611 06612 notes notes 31 refinenet pdf pyramid scene parsing network 2017 paper http jiaya me papers pspnet cvpr17 pdf notes notes 22 pspnet pdf segnet a deep convolutionalencoder decoder architecture for imagesegmentation 2016 paper https arxiv org pdf 1511 00561 notes notes 21 segnet pdf enet a deep neural network architecture for real time semantic segmentation 2016 paper https arxiv org abs 1606 02147 notes notes 27 enet pdf attention to scale scale aware semantic image segmentation 2016 paper https arxiv org abs 1511 03339 notes notes 30 atttention to scale pdf deeplab semantic image segmentation with dcnn atrous convs and crfs 2016 paper https arxiv org abs 1606 00915 notes notes 23 deeplab v2 pdf u net convolutional networks for biomedical image segmentation 2015 paper https arxiv org abs 1505 04597 notes notes 20 unet pdf fully convolutional networks for semantic segmentation 2015 paper https people eecs berkeley edu jonlong long shelhamer fcn pdf notes notes 19 fcn pdf hypercolumns for object segmentation and fine grained localization 2015 paper http home bharathh info pubs pdfs bharathcvpr2015 pdf notes notes 24 hypercolumns pdf weakly and semi supervised semantic segmentation box driven class wise region masking and filling rate guided loss 2019 paper http arxiv org abs 1904 11693 notes notes 54 boxe driven weakly segmentation pdf ficklenet weakly and semi supervised semantic segmentation using stochastic inference 2019 paper https arxiv org abs 1902 10421 notes notes 49 ficklenet pdf weakly supervised semantic segmentation network with deep seeded region growing 2018 paper http openaccess thecvf com content cvpr 2018 papers huang weakly supervised semantic segmentation cvpr 2018 paper pdf notes notes 53 deep seeded region growing pdf learning pixel level semantic affinity with image level supervision 2018 paper https arxiv org abs 1803 10464 notes notes 81 affinity for ws segmentation pdf object region mining with adversarial erasing 2018 paper https arxiv org abs 1703 08448 notes notes 51 object region manning for sem seg pdf revisiting dilated convolution a simple approach for weakly and semi supervised segmentation 2018 paper https arxiv org abs 1805 04574 notes notes 52 dilates convolution semi super segmentation pdf tell me where to look guided attention inference network 2018 paper https arxiv org abs 1802 10171 notes notes 50 tell me where to look pdf semi supervised semantic segmentation using generative adversarial network 2017 paper https arxiv org abs 1703 09695 notes notes 82 ss segmentation gans pdf decoupled deep neural network for semi supervised semantic segmentation 2015 paper https arxiv org abs 1506 04924 notes notes 47 decoupled nn for segmentation pdf weakly and semi supervised learning of a dcnn for semantic image segmentation 2015 paper https arxiv org abs 1502 02734 notes notes 48 weakly and ss for segmentation pdf information retrieval vse improving visual semantic embeddings with hard negatives 2018 paper https arxiv org abs 1707 05612 notes notes 77 vse pdf graph neural network pixels to graphs by associative embedding 2017 paper https arxiv org abs 1706 07365 notes notes 36 pixels to graphs pdf associative embedding end to end learning forjoint detection and grouping 2017 paper https arxiv org abs 1611 05424 notes notes 35 associative emb pdf interaction networks for learning about objects relations and physics 2016 paper https arxiv org abs 1612 00222 notes notes 18 interaction nets pdf deepwalk online learning of social representation 2014 paper http www perozzi net publications 14 kdd deepwalk pdf notes notes deep walk pdf the graph neural network model 2009 paper https persagen com files misc scarselli2009graph pdf notes notes graph neural nets pdf regularization manifold mixup better representations by interpolating hidden states 2018 paper https arxiv org abs 1806 05236 notes notes 43 manifold mixup pdf deep learning methods models autoaugment 2018 paper https arxiv org abs 1805 09501 notes notes 41 autoaugment pdf stacked hourgloass 2017 paper http ismir2018 ircam fr doc pdfs 138 paper pdf notes notes 34 stacked hourglass pdf document analysis and segmentation dhsegment a generic deep learning approach for document segmentation 2018 paper https arxiv org abs 1804 10371 notes notes dhsegement pdf learning to extract semantic structure from documents using multimodal fully convolutional neural networks 2017 paper https arxiv org abs 1706 02337 notes notes learning to extract pdf page segmentation for historical handwritten document images using conditional random fields 2016 paper https www researchgate net publication 312486501 page segmentation for historical handwritten document images using conditional random fields notes notes seg with crfs pdf icdar 2015 competition on text line detection in historical documents 2015 paper http ieeexplore ieee org abstract document 7333945 notes notes icdar2015 pdf handwritten text line segmentation using fully convolutional network 2017 paper https ieeexplore ieee org document 8270267 notes notes handwritten text seg fcn pdf deep neural networks for large vocabulary handwritten text recognition 2015 paper https tel archives ouvertes fr tel 01249405 document notes notes andwriten text recognition pdf page segmentation of historical document images with convolutional autoencoders 2015 paper https ieeexplore ieee org abstract document 7333914 notes notes segmentation with cae pdf | machine-learning computer-vision natural-language-processing summary deep-learning nlp | ai |
MachineLearning | machinelearning deeplearning tutorials dive into keras https github com wepe machinelearning tree master deeplearning 20tutorials dive into keras keras cnn cnn http blog csdn net u012162613 article details 45581421 gist https gist github com wepe a05ad572dca002046de443061909ff7a keras usage https github com wepe machinelearning tree master deeplearning 20tutorials keras usage keras mnist cnn 30 http blog csdn net u012162613 article details 45397033 facerecognition cnn olivettifaces https github com wepe machinelearning demo tree master deeplearning 20tutorials facerecognition cnn olivettifaces cnn demo olivettifaces cnn lenet5 python theano numpy pil demo http blog csdn net u012162613 article details 43277187 cnn lenet https github com wepe machinelearning demo tree master deeplearning 20tutorials cnn lenet cnn lenet mnist deeplearning net python theano cnn http blog csdn net u012162613 article details 43225445 mlp https github com wepe machinelearning demo tree master deeplearning 20tutorials mlp mnist deeplearning net python theano mlp http blog csdn net u012162613 article details 43221829 softmax sgd or logistic sgd https github com wepe machinelearning demo tree master deeplearning 20tutorials softmax sgd or 20logistic sgd softmax mnist python theano deeplearning net python theano softmax http blog csdn net u012162613 article details 43157801 pca python numpy pca pca http blog csdn net u012162613 article details 42177327 knn python numpy k mnist http blog csdn net u012162613 article details 41768407 logistic regression c eigen logistic https github com wepe machinelearning tree master logistic 20regression use cpp and eigen python numpy logistic http blog csdn net u012162613 article details 41844495 manifoldlearning dimensionalityreduction datavisualizing https github com wepe machinelearning tree master manifoldlearning dimensionalityreduction datavisualizing matplotlib 2 3 svm libsvm liblinear usage https github com wepe machinelearning tree master svm libsvm 20liblinear usage libsvm liblinear http blog csdn net u012162613 article details 45206813 svm by smo svm svm by smo smo svm svm by qp svm svm by qp qp svm gmm gmm k means em gmm python http blog csdn net gugugujiawei article details 45583051 decisiontree python numpy matplotlib id3 c4 5 c4 5 cart https github com wepe machinelearning tree master decisiontree kmeans kmeans kmeans numpy matplotlib http blog csdn net u012162613 article details 47811235 naivebayes python numpy http blog csdn net u012162613 article details 48323777 ridge and kernel ridge ridge kernel ridge kernel ridge kernel ridge py contributor wepon https github com wepe gogary https github com enjoyhot locky https github com junlulocky | ai |
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govuk-design-system-architecture | gov uk design system architecture we use this repository as a forum to discuss and record technical decisions that affect the products supported by the gov uk design system team including gov uk design system gov uk frontend gov uk prototype kit this repository is open to enable involvement from the wider community and as a reference for other teams within gds and wider government there are 2 types of documentation proposals proposals often referred to as requests for comments which allow us and the wider community to suggest and refine ideas decision records decision records which document decisions that have been made along with any context code of conduct this project follows the alphagov github organisation code of conduct coc coc https github com alphagov code of conduct | govuk-design-system portfolio govuk-design-system-team | os |
Kobuki_Hawk | kobuki hawk embedded system design kobuki robot final project roomba style robot project br includes drift feedback based control br obstacle avoidance br hill climb br hill climb ramp straightner br br contibuters joshua biggins br max fielding br gunjeet singh | os |
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XamarinCommunityToolkit | img src https raw githubusercontent com dotnet foundation swag master logo dotnetfoundation v4 svg alt net foundation width 100 https dotnetfoundation org img src assets xamarincommunitytoolkit 128x128 png width 64 xamarin community toolkit the xamarin community toolkit is a collection of common elements for mobile development with xamarin forms that people tend to replicate across multiple apps it simplifies and demonstrates common developer tasks when building apps with xamarin forms support timeline more information on the support timeline for the xamarin community toolkit can be found on our wiki https github com xamarin xamarincommunitytoolkit wiki faq support timeline build status if you like to live dangerously you can use our nightly https pkgs dev azure com xamarin public packaging xamarincommunitytoolkitnightly nuget v3 index json feed to try out the latest and greatest build server type platform status azure devops build windows mac build status https dev azure com xamarin public apis build status xamarin communitytoolkit xamarin xamarincommunitytoolkit 20 public branchname main https dev azure com xamarin public build definitionid 55 a summary sample app browsing the sample app samples is the best place to start exploring what s available today installation the toolkit is available via nuget and should be installed into all of your projects shared and individual platforms nuget official releases nuget https img shields io nuget vpre xamarin communitytoolkit svg label nuget https www nuget org packages xamarin communitytoolkit nuget nightly releases nuget nightly https img shields io badge nuget nightly yellow https pkgs dev azure com xamarin public packaging xamarincommunitytoolkitnightly nuget v3 index json browse with the nuget manager in your ide to install them or run this command install package xamarin communitytoolkit note that the c markup extensions are in a separate package install that with install package xamarin communitytoolkit markup getting started after installation start using the features you re after if you re using xaml you can add this namespace to your root node to get access to all the goodness the toolkit has to offer without having to add all kinds of namespaces seperately xmlns xct http xamarin com schemas 2020 toolkit i e xaml contentpage xmlns http xamarin com schemas 2014 forms xmlns x http schemas microsoft com winfx 2009 xaml xmlns xct http xamarin com schemas 2020 toolkit xct avatarview the rest of your page here contentpage documentation the documentation is still under construction but we ve published the most important things over at microsoft docs https docs microsoft com xamarin community toolkit if you want to contribute some of the missing bits you can do this over at the official docs repo https github com microsoftdocs xamarin communitytoolkit as contributions are very much welcomed contributions welcome if you have one or more of these common pieces of code that you are always replicating across apps don t hesitate to contribute we aim to be the first nuget package you install when creating a new xamarin forms app please have a look at our contribution guide contributing md before you get started as well as some information on the wiki https github com xamarin xamarincommunitytoolkit wiki contributing to xamarincommunitytoolkit also take note of the code of conduct https dotnetfoundation org code of conduct we adhere to project structure to structure our project we have adopted a range of namespaces you can find them below with a short description of what you will find where for simply consuming the toolkit in xaml you can use our simplified namespace see the getting started getting started section above namespace description xamarin communitytoolkit core core objects that do not fit other namespaces and are used by multiple other components xamarin communitytoolkit behaviors extended behaviors for the xamarin forms components xamarin communitytoolkit converters xaml converters used to converted your data binding data into something your xaml understands xamarin communitytoolkit effects effects to apply light weight renderer changes to the xamarin forms renderers xamarin communitytoolkit extensions xaml markup extensions to make your xaml even more functional xamarin communitytoolkit objectmodel things that have to do with your models and objects probably handy for your mvvm needs xamarin communitytoolkit ui views controls such as tabview etc xamarin communitytoolkit sampleapp sample app where you can find all of the above for reference learn how to use them and see how it all looks xamarin communitytoolkit unittests this is where our unit tests live please keep growing them xamarin communitytoolkit markup everthing that has to do with writing your ui in c code also known as c markup extensions code of conduct as a part of the net foundation we have adopted the net foundation code of conduct https dotnetfoundation org code of conduct please familiarize yourself with that before participating with this repository thanks net foundation this project is supported by the net foundation https dotnetfoundation org | xamarin-forms behaviors effects converters uwp android ios xamarin-community-toolkit hacktoberfest | front_end |
stm32-rtc-scheduler | stm32 rtc scheduler this project demonstrates how to implement an rtc based scheduler for ultra low power applications where recurring tasks are needed to be executed with long periods increasing number of applications including iot applications use real time operating systems rtos due to the ever increasing complexity however achieving truly ultra low power consumption while utilizing an rtos is not trivial this example uses freertos to show how to combine the rtc scheduler with an rtos while maintaining ultra low power dissipation when the device is in low power mode the rtc scheduler is demonstrated on the stm32l496 discovery board 1 references please refer to https akospasztor github io stm32 rtc scheduler for complete documentation of the source code build status https dev azure com akospasztor stm32 rtc scheduler apis build status akospasztor stm32 rtc scheduler branchname master table of contents scheduler operation scheduler operation example application example application source code organization source code organization compile and build compile and build references references scheduler operation the rtc scheduler uses the real time clock peripheral to implement an efficient scheduling mechanism it has a configurable alarm feature that can be configured to generate an interrupt on a specific date with sub second precision furthermore the rtc has the advantage of being able to run even in ultra low power consumption modes of the microcontroller the rtc scheduler can be configured to schedule several recurring jobs the phrase job is used throughout this demonstration for tasks that are scheduled by the rtc scheduler and the expression task is reserved for the actual tasks of the rtos upon launching the scheduler it checks all jobs and selects the job that needs to be executed the earliest then it configures an rtc alarm with the required date and time when the application needs to execute this job if nothing else is to be done the application can put the microcontroller into an ultra low power mode the rtc peripheral generates an alarm interrupt thus waking up the microcontroller from the low power mode when the earliest job is due for execution after wakeup the scheduler checks all the jobs and marks those which are due for execution i e their remaining time until next execution is zero then the scheduler adjusts the remaining time of all jobs and schedules the next job that needs to be executed by configuring and setting the rtc alarm to the next wakeup time afterwards the application processes the jobs that are pending for execution if the application is done with all its operation and has nothing else to do until the next wakeup it can put the microcontroller again into an ultra low power mode and wait for the next rtc alarm interrupt example application the example application utilizes freertos as its real time operating system and it has two demo tasks the first task blinks the ld3 led on the discovery board twice with a 0 5 second period the second task turns on the ld2 led for 1 second leaves it on then turns it off after executing their operations each task waits and blocks for a so called task notification 2 references to be unblocked again the goal of this demonstration is to unblock the first task and execute it with a period of 5 seconds and unblock the second task and execute it with a period of 10 seconds during the time when nothing else is to be done i e no task is to be executed by the rtos the microcontroller is put into the stop2 ultra low power mode where it consumes less than 3 ua with the rtc enabled and running stm32 rtc scheduler docs img stm32 rtc scheduler svg figure 1 stm32 rtc scheduler detailed description after startup the application configures all the required peripherals including the system clocks gpios and the rtc peripheral then it configures the scheduler with two jobs 1 a job with 5 second period with a callback function that unblocks the first rtos task that blinks the ld3 led twice 2 a job with 10 second period with a callback function that unblocks the second rtos task that turns on the ld2 led for one second then the application configures the two rtos tasks and launches the rtos right after the rtos starts the rtos startup hook is immediately called once by the kernel in this hook the rtc scheduler is started by processing it the first time and the rtc alarm is configured to generate an interrupt for the job that needs to be executed the earliest the rtos idle task is run by the rtos kernel if nothing else is to be done every time the idle task runs it checks what is the expected idle time of the rtos kernel if this expected idle time is greater than 1 second furthermore the delayed task list of the rtos is empty the application puts the microcontroller into the stop2 ultra low power mode by pausing the rtos tick the systick deinitializing all peripherals except the rtc resetting the system clock to 4 mhz clocked directly by the msi clock and finally executing the wfi instruction note in order to perform the above checks in the idle task the rtos kernel had to be extended by two additional functions these additions can be found in the freertos tasks c additions h file which is automatically included at the end of the tasks c file of freertos for more information please refer to the tasks c file when the rtc alarm interrupt arrives the application resumes its operation by reconfiguring the microcontroller clocks and peripherals and enabling the rtos tick within the rtc interrupt context the rtc scheduler is immediately processed thus the rtc alarm is configured to generate the next wakeup interrupt when the next job is due and flags the jobs that need to be executed the application executes the pending jobs that are flagged for execution within the interrupt service routine by calling the schedulerexecutependingjobs function this requires that the callbacks of all jobs are written to be executed from an interrupt context meaning that they do not block furthermore they only call interrupt safe freertos api functions depending on the application requirements the execution of the pending jobs can be performed from task context as well this can be useful if the job callbacks are required to block or they need to call freertos api functions that are not interrupt safe in this case the execution of the schedulerexecutependingjobs function can be deferred to an rtos task this method is called deferred interrupt processing 3 references after executing the callbacks of the pending jobs the respective freertos tasks are unblocked and run if there is no more operation left and no task is ready to be run the idle task of the rtos puts the microcontroller again into stop2 mode and waits for the next rtc alarm interrupt source code organization repository docs drivers cmsis stm32l4xx hal driver include lib freertos projects ewarm gcc mdk arm python source tests the docs folder contains the generated documentation of the rtc scheduler source code and other documentation related static files the drivers folder contains the cmsis cortex microcontroller software interface standard as well as the hal hardware abstraction layer drivers from st the rtc scheduler source code and corresponding header files can be found in the source and include folders respectively the lib folder contains the source code of freertos the projects folder contains compiler and sdk specific files organized in subfolders for iar keil and gcc for arm toolchains the python folder contains helper scripts and the test folder contains tests for the project compile and build the project can be compiled and built out of the box with iar ewarm keil mdk arm and gnu arm embedded toolchain the iar ewarm and keil uvision projects are already configured with the required parameters and options in order to compile and build the application with a single click iar ewarm 1 open the project eww workspace file with iar 2 configure the debugger within the project options 3 build the project and download to the target keil uvision 1 open the stm32 rtc scheduler uvprojx project file with uvision 2 configure the debugger within the project options 3 build the project and download to the target gnu arm embedded toolchain the gcc subfolder contains the compiler specific files a makefile and a sconscript file to easily compile and build the project with the gnu arm embedded toolchain prerequisites gnu arm embedded toolchain tested version 8 2019 q3 update at least one of the followings gnu make for windows see make for windows http gnuwin32 sourceforge net packages make htm python with pip build with make steps to compile and build with gnu make 1 if the gnu arm embedded toolchain has not been added to path edit the custompath variable in the makefile so that it points to the bin folder of the installed gnu arm embedded toolchain 2 open up your favorite terminal and navigate to the gcc subfolder where the makefile is located 3 type make and hit enter 4 the build subfolder should contain the binary elf and hex output files named stm32 rtc scheduler bin stm32 rtc scheduler elf and stm32 rtc scheduler hex respectively build with scons this project currently supports two build configurations debug default and release follow these steps to compile and build the project with scons please note that the recommended usage is within a virtualenv 1 install the requirements pip install r requirements txt 2 if the bin folder of the gnu arm embedded toolchain does not exist in the path it can be specified in the sconstruct file 3 to build the project with the default debug configuration execute scons j8 4 to build all build configurations at once execute scons all j8 5 to list all supported arguments execute scons help 6 the build subfolder should contain the generated outputs organized in subfolders with the names of the build configurations references 1 discovery kit with stm32l496ag mcu https www st com en evaluation tools 32l496gdiscovery html 2 freertos task notifications https www freertos org rtos task notifications html 3 deferred interrupt processing https www freertos org deferred interrupt processing html | microcontroller firmware rtc rtos freertos ultra-low-power iot scheduler stm32 stm32l4 stm32l496 example demo | os |
Kanban-Board | kanban board description implementation of a kanban board system using a rigorous agile development process with the requirements analysis design implementation test deployment release management disciplines the system architecture and design follow n tier and mvvm patterns technologies c net wpf xaml sqlite nuget package log4net nunit moq design patterns uml github agile | server |
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aucs-hybrid-dev | hybrid mobile app development code repository vincent mary school of science and technology assumption university | front_end |
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design-system | design system design system for jb web components and corresponding react components architecture micro front end with storybook testbed usage clone the project with submodules all components are put in project with submodules git git clone recurse submodules j8 https github com javadbat design system git in case you have already cloned the project use command git submodule update init recursive install packages bash npm i build packages in project root directory bash build all modules npm run build build only a named module npm run build jb input serve testbeds bash npm run serve or npm start if you are using node17 or later please type it before build cmd env node options openssl legacy provider add a new submodule command git submodule add f https github com user repo git web component web component name git submodule add f https github com user repo git react component react component name | os |
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querying-with-sql | querying with sql a mini project using example datasets to demonstrate data engineering data modeling and data analysis data engineering i designed the table schemas prior to loading the data files into a newly created database for all the 6 csv files i defined data columns data types primary keys and foreign keys data analysis once the database is complete i answered some sample questions using various query syntax bonus introducing python using pandas and sqlalchemy packages from python to import sql database in order to create the below visualizations from the data histogram img ex1 png bar plot img ex2 png | server |
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Pipelined-Applications-in-an-FPGA | pipelined multiplier independent simulation of a pipelined multiplier schematic on intel quartus prime for an altera max 10 fpga main file pipemult vhd | os |
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luos_engine | a href https luos io img src https uploads ssl webflow com 601a78a2b5d030260a40b7ad 603e0cc45afbb50963aa85f2 gif 20noir 20rect gif alt luos logo title luos align right height 100 a https github com luos io luos engine actions workflows build yml badge svg https img shields io github license luos io luos engine https github com luos io luos engine blob master license https img shields io badge luos documentation 34a3b4 https www luos io docs http certified luos io https www luos io platformio registry https badges registry platformio org packages luos library luos engine svg https registry platformio org libraries luos luos engine https img shields io discord 902486791658041364 label discord logo discord style social http bit ly joinluosdiscord https img shields io reddit subreddit subscribers luos style social https www reddit com r luos version 2 9 2 luos technology the most for the developer luos provides a simple way to think your hardware products as a group of independant features you can easily manage and share your hardware products features with your team external developers or with the community luos is an open source lightweight library that can be used on any mcu leading to free and fast multi electronic boards products development choosing luos to design a product will help you to develop debug validate monitor and manage it from the cloud the most for the community most of the embedded developments are made from scratch by using luos you will be able to capitalize on the development you your company or the luos community already did the re usability of features encapsulated in luos services will fasten the time your products reach the market and reassure the robustness and the universality of your applications join the luos discord server http discord gg luos join the luos subreddit https www reddit com r luos good practices with luos luos proposes organized and effective development practices guaranteeing development flexibility and evolutivity of your hardware product from the idea to the maintenance of the industrialized product fleet let s do this try on your own with the get started https www luos io tutorials get started consult the full documentation https www luos io docs disclaimer using platformio this library compilation send some anonymous information to luos allowing to improve luos engine experience to disable the telemetry please add dnotelemetry as a compilation flag on your platformio ini file | embedded-systems luos communication-protocol embedded microservice cicd real-time realtime edge freertos arduino raspberry-pi ros micro-ros iot platformio cyber-physical-systems digital-twins cyberphysical-systems digitaltwins | os |
HiRTOS | hirtos this repository hosts hirtos a high integrity rtos written in spark ada motivation an rtos is a safety critical component of any bare metal embedded software system yet most rtoses are written in c which is an unsafe language it would be safer to use an rtos written in a safer language such as ada or even better spark ada however integrating ada code components in bare metal embedded firmware written in other languages typically c is not easy in a portable manner as the available baremetal gnat cross compiler from adacore requires the availability of an ada runtime for the target micrcontroller or embedded platform and such baremetal ada runtimes are available only for a very limited number of platforms hirtos solves this problem by being implemented on onto pf a minimal platform independent ada runtime also hirtos code itself has been written on top of a porting layer that provides a platform agnostic interface to hirtos so to port hirtos to a new target platform all what it is needed is to implement the porting layer for the new target platform formal specification a formal specification of hirtos can be found here doc hirtos pdf building and running the multi core multi threaded sample ada application prerequisites install the alr https alire ada dev docs ada package manager and meta build tool install the latest gnat arm elf cross compiler by running alr toolchain select install the arm fixed virtual platform fvp simulator https developer arm com downloads arm ecosystem models for armv8 r scroll down to armv8 r aem fvp build the hello world sample application for armv8 r cd sample apps fvp armv8r aarch32 hello alr build run the hello world sample application on the arm fvp simulator note cluster0 gicv3 sre el2 enable rao 1 and cluster0 gicv3 cpuintf mmap access level 2 are needed to enable access to the gic cpu interface s system registers bp refcounter non arch start at default 1 enables the system counter that drives the generic timer counter arm fvp install dir models linux64 gcc 9 3 fvp baser aemv8r c bp pl011 uart0 uart enable 1 c bp pl011 uart0 baud rate 460800 c cluster0 gicv3 sre el2 enable rao 1 c cluster0 gicv3 cpuintf mmap access level 2 c bp refcounter non arch start at default 1 application bin fvp armv8r aarch32 hello once the arm fvp simulator starts an xterm for the uart output from each cpu core would be displayed an arm fvp run would look like this doc hirtos sample app running png | os |
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COMP304 | comp 304 | front_end |
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insight-api | insight api build status https img shields io travis axerunners insight api master svg branch master https travis ci org axerunners insight api npm version https img shields io npm v axerunners insight api svg https npmjs org package axerunners insight api api stability https img shields io badge stability stable green svg https nodejs org api documentation html documentation stability index an axe blockchain rest and websocket api service this is a backend only service if you re looking for the web frontend application take a look at https github com axerunners insight ui table of content install install prerequisites prerequisites query rate limit query rate limit usage usage api http endpoints api http endpoints block block block index block index raw block raw block block summaries block summaries transaction transaction address address address properties address properties unspent outputs unspent outputs unspent outputs for multiple addresses unspent outputs for multiple addresses instantsend transactions instantsend transactions transactions by block transactions by block transactions by address transactions by address transactions for multiple addresses transactions for multiple addresses transaction broadcasting transaction broadcasting sporks list sporks list proposals informations proposals informations proposals count proposals count budget proposal list budget proposal list budget triggers list budget triggers list budget proposal detail budget proposal detail proposal check proposal check proposal deserialization proposal deserialization proposal current votes proposal current votes governance budget governance budget masternodes list masternodes list historic blockchain data sync status historic blockchain data sync status live network p2p data sync status live network p2p data sync status status of the bitcoin network status of the bitcoin network utility methods utility methods web socket api web socket api example usage example usage notes on upgrading from v0 3 notes on upgrading from v03 notes on upgrading from v0 2 notes on upgrading from v02 resources resources license license install bash npm install g axerunners axecore node axecore node create mynode cd mynode axecore node install axerunners insight api axecore node start to also start the service the api endpoints will be available by default at http localhost 3001 insight api prerequisites axecore node axe 4 x https github com axerunners axecore node note you can use an existing axe data directory however txindex addressindex timestampindex and spentindex need to be enabled in axe conf as well as a few other additional fields query rate limit to protect the server insight api has a built in query rate limiter it can be configurable in axecore node json with json servicesconfig insight api ratelimiteroptions whitelist ffff 127 0 0 1 with all the configuration options available https github com axerunners insight api blob master lib ratelimiter js l10 17 or disabled entirely with json servicesconfig insight api disableratelimiter true usage follow the install instructions above and bash axecore node start this will start the insight api listening on default port 3001 api http endpoints block insight api block hash insight api block 0000000006e7b38e8ab2d351239019c01de9a148b5baef58cfe52dfd9917cedc block index get block hash by height insight api block index height insight api block index 0 this would return blockhash 00000bafbc94add76cb75e2ec92894837288a481e5c005f6563d91623bf8bc2c which is the hash of the testnet genesis block 0 height raw block insight api rawblock blockhash this would return rawblock blockhexstring block summaries get block summaries by date insight api blocks limit 3 blockdate 2017 04 22 example response blocks height 188928 size 312 hash 00000000ee9a976cf459240c2add1147137ca6126b7906fa13ce3d80b5cadcc7 time 1492905418 txlength 1 poolinfo poolname btcc pool url https pool btcc com length 3 pagination next 2017 04 23 prev 2017 04 21 currentts 1492905599 current 2017 04 22 istoday false more true morets 1492905600 transaction insight api tx txid insight api tx ebdca263fe1c75c8609ce8fe3d82a320a0b3ca840f4df995883f5dab1b9ff8d9 insight api rawtx rawid insight api rawtx ebdca263fe1c75c8609ce8fe3d82a320a0b3ca840f4df995883f5dab1b9ff8d9 address insight api addr addr notxlist 1 from to insight api addr ybi3gej7ea1myseylr7ums3rmuljh5avsw notxlist 1 insight api addr ypv7h2i8v3djjfsh4l3x91jsjszjdbsjja from 1000 to 2000 insight api addrs addrs notxlist 1 from to insight api addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx insight api addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx from 1000 to 2000 address properties insight api addr addr balance insight api addr addr totalreceived insight api addr addr totalsent insight api addr addr unconfirmedbalance insight api addrs addrs balance insight api addrs addrs totalreceived insight api addrs addrs totalsent insight api addrs addrs unconfirmedbalance the response contains the value in satoshis unspent outputs insight api addr addr utxo sample return address ygwnqge5f15ygopbs2kpryms4tcffqbpsz txid 05d70bc1c4cf1c3afefc3250480d733b5666b19cb1f629901ded82cb2d6263d1 vout 0 scriptpubkey 76a914e22dc8acf5bb5624f4beef22fb2238f8479e183f88ac amount 0 01194595 satoshis 1194595 height 142204 confirmations 124317 unspent outputs for multiple addresses get method insight api addrs addrs utxo insight api addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx utxo post method insight api addrs utxo post params addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx instantsend transactions if a transaction lock has been observed by insight api a txlock value of true will be included in the transaction object sample output txid b7ef92d1dce458276f1189e06bf532eff78f9c504101d3d4c0dfdcd9ebbf3879 version 1 locktime 133366 vin vout blockhash 0000001ab9a138339fe4505a299525ace8cda3b9bcb258a2e5d93ed7a320bf21 blockheight 133367 confirmations 37 time 1483985187 blocktime 1483985187 valueout 8 998 size 226 valuein 8 999 fees 0 001 txlock true transactions by block insight api txs block hash insight api txs block 000000000814dd7cf470bd835334ea6624ebf0291ea857a5ab37c65592726375 transactions by address insight api txs address addr insight api txs address ywffdp9nlujy1kjczfhrubmujttkttiymv transactions for multiple addresses get method insight api addrs addrs txs from to insight api addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx txs from 0 to 20 post method insight api addrs txs post params addrs ygwnqge5f15ygopbs2kpryms4tcffqbpsz ygw5yctvkx3hreke4l8qdqqtnnoaipktsx from optional 0 to optional 20 noasm optional 1 will omit script asm from results noscriptsig optional 1 will omit the scriptsig from all inputs nospent option 1 will omit spent information per output sample output totalitems 100 from 0 to 20 items txid 3e81723d069b12983b2ef694c9782d32fca26cc978de744acbc32c3d3496e915 version 1 locktime 0 vin object vout object blockhash 00000000011a135e5277f5493c52c66829792392632b8b65429cf07ad3c47a6c confirmations 109367 time 1393659685 blocktime 1393659685 valueout 0 3453 size 225 firstseents undefined valuein 0 3454 fees 0 0001 txlock false note if pagination params are not specified the result is an array of transactions transaction broadcasting standard transaction post method insight api tx send post params rawtx signed transaction as hex string eg rawtx 01000000017b1eabe0209b1fe794124575ef807057c77ada2138ae4fa8d6c4de0398a14f3f00000000494830450221008949f0cb400094ad2b5eb399d59d01c14d73d8fe6e96df1a7150deb388ab8935022079656090d7f6bac4c9a94e0aad311a4268e082a725f8aeae0573fb12ff866a5f01ffffffff01f0ca052a010000001976a914cbc20a7664f2f69e5355aa427045bc15e7c6c77288ac00000000 post response txid txid eg txid c7736a0a0046d5a8cc61c8c3c2821d4d7517f5de2bc66a966011aaa79965ffba instantsend transaction conditions every inputs should have 6 confirmations fee are 0 001 per input transaction value should be below spork 5 instantsend max value see spork route post method insight api tx sendix post params rawtx signed transaction as hex string post response txid txid sporks list get method insight api sporks sample output sporks spork 2 instantsend enabled 0 spork 3 instantsend block filtering 0 spork 5 instantsend max value 2000 spork 8 masternode payment enforcement 0 spork 9 superblocks enabled 0 spork 10 masternode pay updated nodes 0 spork 12 reconsider blocks 0 spork 13 old superblock flag 4070908800 spork 14 require sentinel flag 4070908800 proposals informations get method insight api gobject info sample output result governanceminquorum 1 masternodewatchdogmaxseconds 7200 proposalfee 5 superblockcycle 24 lastsuperblock 79800 nextsuperblock 79824 maxgovobjdatasize 16384 error null id 68537 proposals count get method insight api gobject count sample output result governance objects 47 proposals 7 triggers 40 watchdogs 0 0 other 0 erased 0 votes 1883 error null id 47025 budget proposal list get method insight api gobject list proposal or insight api gobject list sample output hash b6af3e70c686f660541a77bc035df2e5e46841020699ce3ec8fad786f7d1aa35 dataobject end epoch 1513555200 name flare03 payment address yviyok3nwfh5gxro7e4deykzzhbjdnqaqg payment amount 5 start epoch 1482105600 type 1 url https www axe org absoluteyescount 40 yescount 40 nocount 0 abstaincount 0 budget triggers list get method insight api gobject list trigger sample output hash fa2a7505c52438b2ca3d14def1c2cdcb59d7ccca417920182f04fcb9be968f00 dataobject type 2 absoluteyescount 53 yescount 53 nocount 0 abstaincount 0 budget proposal detail get method insight api gobject get hash insight api gobject get b6af3e70c686f660541a77bc035df2e5e46841020699ce3ec8fad786f7d1aa35 sample output hash b6af3e70c686f660541a77bc035df2e5e46841020699ce3ec8fad786f7d1aa35 collateralhash 24a71d8f221659717560365d2914bc7a00f82ffb8f8c68e7fffce5f35aa23b90 datahex 5b5b2270726f706f73616c222c7b22656e645f65706f6368223a313531333535353230302c226e616d65223a22666c6172653033222c227061796d656e745f61646472657373223a22795669796f4b334e776648354758526f3765344445596b7a7a68426a444e51615147222c227061796d656e745f616d6f756e74223a352c2273746172745f65706f6368223a313438323130353630302c2274797065223a312c2275726c223a2268747470733a2f2f64617368646f742e696f2f702f666c6172653033227d5d5d dataobject end epoch 1513555200 name flare03 payment address yviyok3nwfh5gxro7e4deykzzhbjdnqaqg payment amount 5 start epoch 1482105600 type 1 url https www axe org creationtime 1482223714 fundingresult absoluteyescount 40 yescount 40 nocount 0 abstaincount 0 validresult absoluteyescount 74 yescount 74 nocount 0 abstaincount 0 deleteresult absoluteyescount 0 yescount 0 nocount 0 abstaincount 0 endorsedresult absoluteyescount 0 yescount 0 nocount 0 abstaincount 0 proposal check get method insight api gobject check hexdata insight api gobject check 5b5b2270726f706f736 sample output object status ok proposal deserialization get method insight api gobject deserialize hexdata insight api gobject deserialize 5b5b2270726f706f736 sample output result proposal end epoch 1519848619 name ghijklmnopqrstuvwxyz01234567891519097947 payment address yik5hagvagjh1ozkjcdfvcf22bwbnbsyzb payment amount 10 start epoch 1519097947 type 1 url https www axecentral org p test proposal 1519097947 error null id 78637 proposal current votes get method insight api gobject votes current hash insight api gobject votes current fbda8cdc1f48917f53b7d63fbce81c85d6dedd3d0e476e979926dfd154b84034 sample output result proposal end epoch 1519848619 name ghijklmnopqrstuvwxyz01234567891519097947 payment address yik5hagvagjh1ozkjcdfvcf22bwbnbsyzb payment amount 10 start epoch 1519097947 type 1 url https www axecentral org p test proposal 1519097947 error null id 78637 governance budget get method insight api governance budget blockindex insight api governance budget 79872 sample output result 60 00 error null id 75619 submit proposal post method insight api gobject submit example input parenthash abc revision 1 time 10009 datahex abc feetxid abc sample output result 60 00 error null id 75619 masternodes list insight api masternodes list validate masternode insight api masternodes validate payee insight api masternodes validate yrualkppeyptgxdnn2l5yggktasjydypao sample valid output valid true vin e3a6b7878a7e9413898bb379b323c521676f9d460db17ec3bf42d9ac0c9a432f 1 status enabled rank 1 ip 217 182 229 146 19937 protocol 70208 payee yrualkppeyptgxdnn2l5yggktasjydypao activeseconds 158149 lastseen 1507810068 historic blockchain data sync status insight api sync live network p2p data sync status insight api peer status of the bitcoin network insight api status q xxx where xxx can be getinfo getdifficulty getbestblockhash getlastblockhash utility methods insight api utils estimatefee nbblocks 2 web socket api the web socket api is served using socket io http socket io the following are the events published by insight tx new transaction received from network txlock boolean is set true if a matching txlock event has been observed this event is published in the inv room data will be a app models transaction object sample output txid 00c1b1acb310b87085c7deaaeba478cef5dc9519fab87a4d943ecbb39bd5b053 txlock false processed false txlock instantsend transaction received from network this event is published alongside the tx event when a transaction lock event occurs data will be a app models transaction object sample output txid 00c1b1acb310b87085c7deaaeba478cef5dc9519fab87a4d943ecbb39bd5b053 processed false block new block received from network this event is published in the inv room data will be a app models block object sample output hash 000000004a3d187c430cd6a5e988aca3b19e1f1d1727a50dead6c8ac26899b96 time 1389789343 bitcoinaddress new transaction concerning bitcoinaddress received from network this event is published in the bitcoinaddress room status every 1 increment on the sync task this event will be triggered this event is published in the sync room sample output blockstosync 164141 syncedblocks 475 uptoexisting true scanningbackward true isendgenesis true end 000000000933ea01ad0ee984209779baaec3ced90fa3f408719526f8d77f4943 isstartgenesis false start 000000009f929800556a8f3cfdbe57c187f2f679e351b12f7011bfc276c41b6d example usage the following html page connects to the socket io insight api and listens for new transactions html html body script src http insight server port socket io socket io js script script eventtolistento tx room inv var socket io http insight server port socket on connect function join the room socket emit subscribe room socket on eventtolistento function data if data txlock console log new instantsend transaction received data txid else console log new transaction received data txid script body html notes on upgrading from v0 3 the unspent outputs format now has satoshis and height address mo9ncxismeaoxwqcv5ewuyncbmccqn4rvs txid d5f8a96faccf79d4c087fa217627bb1120e83f8ea1a7d84b1de4277ead9bbac1 vout 0 scriptpubkey 76a91453c0307d6851aa0ce7825ba883c6bd9ad242b48688ac amount 0 000006 satoshis 600 confirmations 0 ts 1461349425 address mo9ncxismeaoxwqcv5ewuyncbmccqn4rvs txid bc9df3b92120feaee4edc80963d8ed59d6a78ea0defef3ec3cb374f2015bfc6e vout 1 scriptpubkey 76a91453c0307d6851aa0ce7825ba883c6bd9ad242b48688ac amount 0 12345678 satoshis 12345678 confirmations 1 height 300001 the timestamp property will only be set for unconfirmed transactions and height can be used for determining block order the confirmationsfromcache is nolonger set or necessary confirmation count is only cached for the time between blocks there is a new get endpoint or raw blocks at rawblock blockhash response format rawblock blockhexstring there are a few changes to the get endpoint for addr address the list of txids in an address summary does not include orphaned transactions the txids will be sorted in block order the list of txids will be limited at 1000 txids there are two new query options from and to for pagination of the txids e g addr address from 1000 to 2000 some additional general notes the transaction history for an address will be sorted in block order the response for the sync endpoint does not include startts and endts as the sync is no longer relevant as indexes are built in axed the endpoint for peer is no longer relevant connection to axed is via zmq tx endpoint results will now include block height and spenttx related fields will be set to null if unspent block endpoint results does not include confirmations and will include poolinfo notes on upgrading from v0 2 some of the fields and methods are not supported the tx txid endpoint json response will not include the following fields on the vin object doublespenttxid double spends are not currently tracked isconfirmed confirmation of the previous output confirmations confirmations of the previous output unconfirmedinput the tx txid endpoint json response will not include the following fields on the vout object spentts the status q gettxoutsetinfo method has also been removed due to the query being very slow and locking axed plug in support for insight api is also no longer available as well as the endpoints email retrieve rates code caching support has not yet been added in the v0 3 upgrade resources medium how to setup a axe instant send transaction using insight api the comprehensive way https medium com obusco setup instant send transaction the comprehensive way a80a8a0572e contributing feel free to dive in open an issue https github com axerunners insight api issues new or submit prs license mit license copy axe core group inc | blockchain rest-api websocket-api | blockchain |
awesome-computer-vision | awesome computer vision this is an attempt to create a curated list of computer vision resources university courses and moocs coursera image and video processing from mars to hollywood with a stop at the hospital https www coursera org course images coursera fundamentals of digital image and video processing https www coursera org course digital mit advances in computer vision http 6 869 csail mit edu fa13 schedule html stanford vision lab http vision stanford edu teaching html brown introduction to computer vision http cs brown edu courses cs143 books feature extraction and image processing http users ecs soton ac uk msn book by mark s nixon and alberto s aguado computer vision algorithms and applications http szeliski org book by richard szeliski research papers real time detection of lane markers in urban streets http www vision caltech edu malaa publications aly08realtime pdf lane detection and tracking using b snake http www1 i2r a star edu sg ywang papers ivc lane 20detection 20and 20tracking 20using 20bsnake pdf | ai |
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quant | quant build status https travis ci com ntap quant svg branch master https travis ci com github ntap quant total alerts https img shields io lgtm alerts g ntap quant svg logo lgtm logowidth 18 https lgtm com projects g ntap quant alerts coverity badge https scan coverity com projects 13161 badge svg https scan coverity com projects ntap quant language grade c c https img shields io lgtm grade cpp g ntap quant svg logo lgtm logowidth 18 https lgtm com projects g ntap quant context cpp codacy badge https app codacy com project badge grade b01870db4e774aa2b17fc0955cf374b3 https www codacy com manual larseggert quant utm source github com amp utm medium referral amp utm content ntap quant amp utm campaign badge grade quant is a bsd licensed c11 implementation of the emerging ietf quic https quicwg github io standard for a new transport protocol over udp intending to support the new http 3 standard and other application protocols quant uses the warpcore https github com ntap warpcore zero copy userspace udp ip stack which in addition to running on on top of the standard socket api has support for the netmap http info iet unipi it luigi netmap fast packet i o framework as well as the particle https github com particle iot device os and riot http riot os org iot stacks quant hence supports traditional posix platforms linux macos freebsd etc as well as embedded systems the quant repository is on github https github com ntap quant note quant implements the quic transport layer but does not implement an http 3 binding note quant is a research effort and not meant for production use prerequisites quant uses picotls https github com h2o picotls for its tls 1 3 https datatracker ietf org doc draft ietf tls tls13 implementation and klib https github com attractivechaos klib and timeout http 25thandclement com william projects timeout c html for some data structures and functions these dependencies will be built automatically the example http 0 9 client and server use http parser https github com nodejs http parser so you need to install some dependencies on the mac the easiest way is via homebrew http brew sh so install that first then do brew install cmake http parser pkg config on debian based linux systems do apt install libssl dev libhttp parser dev libbsd dev pkgconf g on darwin you must also install the xcode command line tools first xcode select install quant uses the cmake https cmake org build system and doxygen http www doxygen nl to generate the documentation if doxygen is available th documentation can be locally built vi the doc target building to do an out of source build of quant best practice with cmake do the following to build with make as a generator git submodule update init recursive mkdir debug cd debug cmake make the default build per above is without optimizations and with extensive debug logging enabled in order to build an optimized build do this git submodule update init recursive mkdir release cd release cmake dcmake build type release make i really recommend ninja https ninja build org over make building for riot or particle please see readme md in the riot and particle subdirectories docker container instead of building quant for yourself you can also obtain a pre built docker container https cloud docker com u ntap repository docker ntap quant for example docker pull ntap quant latest should download the latest build on the master branch the docker container by default exposes a quic server on port 4433 that can serve index html and possibly other resources to map the udp port run the docker container with docker run p4433 4433 udp ntap quant testing and interop the libquant library will be in lib there are client and server examples in bin they explain their usage when called with a h argument the current interop status of quant against other stacks https github com quicwg base drafts wiki implementations is captured in this spreadsheet https docs google com spreadsheets d 1d0tw89vooascs3iy9rgc0ueswgawe6xylk0l4jtvtvg edit gid 1510984897 development and contributing at the moment development happens in master and branches numbered according to the ietf internet drafts https quicwg github io they implement serve as archives i m happy to merge contributions that fix bugs https github com ntap quant issues q is 3aopen is 3aissue label 3abug or add features https github com ntap quant issues q is 3aopen is 3aissue label 3aenhancement please send pull requests contributions to the underlying warpcore https github com ntap warpcore stack are also very welcome copyright copyright c 2016 2022 netapp inc all rights reserved redistribution and use in source and binary forms with or without modification are permitted provided that the following conditions are met 1 redistributions of source code must retain the above copyright notice this list of conditions and the following disclaimer 2 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 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 acknowledgment this software has received past funding from the european union s horizon 2020 research and innovation program 2014 2018 under grant agreement 644866 ssiclops https ssiclops eu the european commission is not responsible for any use that may be made of this software example client c example server c | quic ietf riot-os posix netmap netapp-public | server |
libretiny | libretiny small formerly libretuya small div align center markdown github workflow status https img shields io github actions workflow status libretiny eu libretiny push master yml label docs logo markdown https docs libretiny eu github last commit https img shields io github last commit libretiny eu libretiny logo github code style clang format https img shields io badge code 20style clang format purple svg clang format code style black https img shields io badge code 20style black 000000 svg https github com psf black discord https img shields io discord 967863521511608370 color 235865f2 label discord logo discord logocolor white https discord gg sygcb9xwtf platformio registry https badges registry platformio org packages kuba2k2 platform libretiny svg https registry platformio org platforms kuba2k2 libretiny rtl8710bn https img shields io badge rtl8710bn blue bk7231 https img shields io badge bk7231 blue div platformio development platform for bk7231 and rtl8710 iot chips the main goal of this project is to provide a usable build environment for iot developers while also providing vendor sdks as platformio cores the project focuses on developing working arduino compatible cores for supported families the cores are inspired by espressif s official core for esp32 which should make it easier to port run existing esp apps on less common unsupported iot modules there s an esphome port https docs libretiny eu docs projects esphome based on libretiny which supports bk7231 and rtl8710b chips note this project is work in progress div align center markdown getting started https docs libretiny eu docs getting started div license see license license project is licensed under mit license parts of the code may come from third parties vendor sdks or other open source projects most of these files are marked with appropriate copyright author license notices 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 | esp32 iot platformio platformio-arduino platformio-platform rtl8710 tuya amebaiot arduino-platform tuya-iot rtl8710bn arduino bk7231n bk7231t esphome hacktoberfest | server |
Practical-Machine-Learning-With-Python | practical machine learning with python machine learning tutorials in python 1 part 1 theory https savan77 github io 2017 04 19 ml part1 code https savan77 github io lab machine learning part1 what is machine learning and types of machine learning linear regression gradient descent logistic regression overfitting and underfitting regularization cross validation 2 part 2 theory and code https savan77 github io machine learning part2 html naive bayes support vector machines decision tree random forest and boosting algorithms preprocessing and feature extraction techniques 3 part 3 theory and code https savan77 github io machine learning part3 k nearest neighbors algorithm k means clustering principal component analysis neural networks 4 part 4 ipynb https github com savan77 practical machine learning with python blob master part 20 204 titanic survival ipynb project 1 5 part 5 convolutional neural networks recurrent neural networks 6 part 6 autoencoder denoising autoencoder restricted boltzmann machine deep belief network 7 part 7 generative adversarial networks variational autoencoder 8 part 8 project 2 | machine-learning scikit-learn python tensorflow machine-learning-tutorials | ai |
tensil | tensil build status https badge buildkite com 11c53cfb0931de5a89dfece9634fe3a5f5cefc9525e1162e1a svg branch main https buildkite com tensil tensil tensil toolchain flow flow https s3 us west 1 amazonaws com downloads tensil ai doc flow png tutorials for in depth end to end instructions check our tutorials learn how to combine tensil and tf lite to run yolo on ultra96 https www tensil ai docs tutorials yolo ultra96v2 learn tensil with resnet and pynq z1 https www tensil ai docs tutorials resnet20 pynqz1 learn tensil with resnet and ultra96 https www tensil ai docs tutorials resnet20 ultra96v2 learn how to build speech controlled robot with tensil and arty a7 part i https www tensil ai docs tutorials speech robot part1 learn how to build speech controlled robot with tensil and arty a7 part ii https www tensil ai docs tutorials speech robot part2 getting tensil to run resnet at 300 fps on zcu104 https www tensil ai docs tutorials resnet20 zcu104 documentation for reference documentation see our website https www tensil ai docs setup 1 pull and run tensil docker container https hub docker com r tensilai tensil see below 2 download and install xilinx vitis or vivado https www xilinx com support download html 3 download and install xilinx pynq http www pynq io board html for your fpga development platform 4 copy tensil pynq driver drivers tcu pynq to home xilinx tcu pynq on your fpga development platform pull and run docker container docker pull tensilai tensil docker run u id u user id g user v pwd work w work it tensilai tensil bash compile ai ml model compile ai ml model resnet20 v2 cifar for specific tcu architecture and fpga development platform pynq z1 https digilent com shop pynq z1 python productivity for zynq 7000 arm fpga soc in this example from onnx tensil compile a demo arch pynqz1 tarch m demo models resnet20v2 cifar onnx o identity 0 s true from frozen tensorflow graph tensil compile a demo arch pynqz1 tarch m demo models resnet20v2 cifar pb o identity s true other ml frameworks are supported by converting to onnx tensorflow and tflite https github com onnx tensorflow onnx blob master readme md pytorch https pytorch org docs stable onnx html other https onnx ai supported tools html run bit accurate tensil emulator tensil emulate m resnet20v2 cifar onnx pynqz1 tmodel i demo models data resnet input 1x32x32x8 csv make verilog rtl make verilog rtl for specific tcu architecture and fpga development platform pynq z1 https digilent com shop pynq z1 python productivity for zynq 7000 arm fpga soc in this example tensil rtl a demo arch pynqz1 tarch s true create vivado design create vivado design for specific fpga development platform we include detailed steps in our pynq z1 tutorial https www tensil ai docs tutorials resnet20 pynqz1 if you get stuck we can help please reach out to us at contact tensil ai mailto contact tensil ai or in discord https discord gg tsw34h3pxr pynq z1 design https s3 us west 1 amazonaws com downloads tensil ai doc pynqz1 design png run ai ml model on fpga use pynq and jupyter notebooks to run ai ml model on fpga see in notebooks resnet on pynq https s3 us west 1 amazonaws com downloads tensil ai doc resnet20 on pynq png for maintainers additional setup steps 1 download and install openjdk 11 from azul https www azul com downloads version java 11 lts package jdk 2 download and install verilator https verilator org guide latest install html 3 download test models wget https github com tensil ai tensil models archive main tar gz tar xf main tar gz mv tensil models main models rm main tar gz run rtl tool from source code mill rtl run a arch pynqz1 tarch s true run compiler from source code mill compiler run a arch pynqz1 tarch m models resnet20v2 cifar onnx o identity 0 s true run emulator from source code mill emulator run m resnet20v2 cifar onnx pynqz1 tmodel i models data resnet input 1x32x32x8 csv run full test suite mill test l org scalatest tags slow to run a single rtl test in for example the accumulator module and also output a vcd file do mill rtl test testonly tensil tcu accumulatorspec dwritevcd true z should accumulate values view vcd files to view the latest vcd file generated scripts gtkwave display latest vcd py to view a specific vcd file scripts gtkwave display vcd sh vcd file build and push aws ecs image for web compiler tensil explorer docker build f docker web dockerfile t tensil web compiler aws ecr get login password docker login username aws password stdin account id dkr ecr region amazonaws com docker tag tensil web compiler account id dkr ecr region amazonaws com tf2rtl web compiler docker push account id dkr ecr region amazonaws com tf2rtl web compiler get help say hello and ask a question on our discord https discord gg tsw34h3pxr email us at support tensil ai mailto support tensil ai | machine-learning silicon fpga artificial-intelligence asic hdl scala | ai |
explorer | explorer blockchain explorer for iris hub which includes explorer backend and explorer frontend explorer backend run with docker you can run application with docker xslt cd github com irisnet explorer backend docker build t explorer backend env db addr mongo url eg 192 168 150 7 27017 db database mongo database name eg sync iris db user mongo user eg iris db password mongo password eg irispassword faucet url faucet url eg http dev faucet irisplorer io chain id chain id eg rainbow dev detail github com irisnet explorer backend readme md docker run docker run p 8080 8080 e env variables explorer backend latest explorer frontend config vue config js xslt module exports devserver proxy api target http localhost 8080 backend address port run with docker you can run application with docker xslt cd github com irisnet explorer frontend docker build t explorer frontend docker run docker run p 8080 8080 e env variables explorer frontend latest | irishub explorer irisplorer | blockchain |
tttlm | test time training on nearest neighbors ttt nn the repository contains code to 1 train the embedding model 2 build nearest neighbor index on the pile 3 run distributed servers on top of the index 4 query the servers 5 evaluate ttt nn on the pile 6 run baselines necessary files to evaluate ttt nn you ultimately need the following directory structure indexes roberta large 00 jsonl index 01 jsonl index 29 jsonl index models roberta large pile lr2e 5 bs16 8gpu checkpoint 1700000 pile train 00 jsonl 01 jsonl 29 jsonl val jsonl test jsonl servers addresses txt download the dataset here https the eye eu public ai pile and place the files in the pile subdirectory train or download the embedding model download the embedding model you can download the pretrained embedding model from huggingface https huggingface co socialfoundations roberta large pile lr2e 5 bs16 8gpu 1700000 place the files in the directory models roberta large pile lr2e 5 bs16 8gpu checkpoint 1700000 alternatively train the embedding model to train the embedding model yourself see code trainer lm py this is a standard huggingface training setup this code was used to produce the model checkpoint checkpoint 1700000 in the models directory the model trained for approximately one month on 8 a100 gpus making one pass over the data make sure to have the checkpoint models roberta large pile lr2e 5 bs16 8gpu checkpoint 1700000 before you proceed build or download the index download the index you can download the index files here https edmond mpdl mpg de dataset xhtml persistentid doi 10 17617 3 ejqgak the download size is approximately 800gb place the files in the directory indexes roberta large alternatively build the index to build the index yourself use the code in code build database py to build an index on top of the pile dataset this is a time consuming operation specify data file to build index for given data file python3 code build database py data file pile train 00 jsonl output dir indexes roberta large make sure you have all index files in indexes roberta large before you proceed run the pile server the following command will launch a server with 6 replicas each serving one split of the data this will append 6 ip addresses and ports to the file specified as address path python3 code pile server py address path servers addresses txt data file pile train 00 jsonl num servers 6 to serve from all pile data files start one server for each data file we recommend starting 30 servers with 6 replicas each resulting in 180 instances running make sure servers are up and running before launching evaluation use the pile client use code pile client py to query the server specify address path to indicate which servers to query the client will query all servers it finds under the address path and query each the client then builds a local nearest neighbors structure to find the nearest neighbors among all the retrieved results the client code can be used as a standalone client but will also be called from the evaluation code run test time training with nearest neighbors to evaluate on gptneo with default parameters python3 code eval tttlm py address path servers addresses txt results dir results to evaluate on gpt2 python3 code eval tttlm py model gpt2 large tokenizer gpt2 large embedding model checkpoint models roberta large pile lr2e 5 bs16 8gpu checkpoint 1700000 max length 1024 stride 1024 learning rate 2e 5 address path servers addresses txt results dir results replace gpt2 with gpt2 large to evaluate on gpt2large use code process results py to merge results for distributed evaluation with multiple processes aggregate statistics and make plots the evaluation code uses modified parts of the lm evaluation harness https github com eleutherai lm evaluation harness package by eleuther ai the folder lm eval contains the modified parts also lm eval interpolation for the interpolation baseline as well as the unmodified parts to ensure compatibility run baselines see code baseline context py for in context baseline also code process results context py see code baseline interpolation py for interpolation baseline also code process results interpolation py run code eval tttlm py with option dynamic eval for dynamic evaluation baseline | ai |
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CodeSample | lillian cordelia gwendolyn 01 30 2022 this is the code of my final project for an embedded systems design course i took the class was cse121 l it was the most time crunchy course i ever took and i learned a lot this project is for an oscilloscope as mentioned in my resume s projects section this is only here for the purposes of providing sample code for job applications you are not permitted to copy this code with the intent of committing academic dishonesty additionally note that a noticeable portion of this code is taken from samples provided by the course this sample code is cited with a slide number or video or etc and is mostly boilerplate all code produced by me is located within main cm4 c some portions of other included code may have been modified by me for varying purposes but the main code produced by me is located within that file again to restate this code is only public for the purposes of showing off embedded c code i have produced that is not locked under nda this is not a resource for those currently taking the course i produced this within do not copy or steal this code for the purposes of submitting to any academic institution | os |
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humans-of-starterhacks | humans of starterhacks humans of starterhacks homepage screenshots homepage png hello these are the project instructions for starterhacks 2020 https starterhacks ca web development 101 workshop we re building a website called humans of starterhacks where you can keep track of the people you meet during the hackathon demo the website https mabuyo github io humans of starterhacks add gif technologies html css bootstrap https getbootstrap com material icons https material io resources icons javascript handlebars https handlebarsjs com airtable https airtable com database api first we use html css and bootstrap create a simple static website then we augment it with some javascript for interactivity we use handlebars as a templating language finally we use airtable as our database and api to save and update our data this project should give you a good starting point to hack together your starterhacks project in a weekend pre workshop setup 1 download a text editor like vs code https code visualstudio com atom https atom io or sublime https www sublimetext com i ll be using vs code 1 download prettier https prettier io docs en editors html for your text editor so that it can auto format your code on save you don t need to worry about fixing indentations on nesting how to use these files this project is split up into steps each step has its own folder for the code for each step and instructions if you get lost during the workshop code alongs you can take a look at the relevant folder feel free to also use the code as a jumping off point for your own project links bootstrap https getbootstrap com docs 4 4 getting started introduction for a front end component library undraw co https undraw co for illustrations css cool css cool for css styles bootstrap starter templates https startbootstrap com templates for quick page layouts css tricks flexbox https css tricks com snippets css a guide to flexbox as a cheat sheet for how flexbox works flex zombies game https mastery games p flexbox zombies as a fun way to learn flexbox bootstrap card component https getbootstrap com docs 4 4 components card avataaars https getavataaars com for an avatar representation badge bootstrap component https getbootstrap com docs 4 4 components badge material icons https material io resources icons bootstrap grid cards layout https getbootstrap com docs 4 4 components card grid cards bootstrap utilities spacing https getbootstrap com docs 4 4 utilities spacing robohash org https robohash org for placeholder avatars handlebars https handlebarsjs com installation script src https cdn jsdelivr net npm handlebars latest dist handlebars js script | front_end |
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tensorflow-lite-mobile-development | apress source code this repository accompanies tensorflow lite for mobile development https rd springer com video 10 1007 isbn by faisal zaman apress 2020 comment cover cover image isbn jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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ML_Finance_Codes | ml finance codes this repository is the official repository for the latest version of the python source code accompanying the textbook machine learning in finance from theory to practice book by matthew dixon igor halperin and paul bilokon please refer to setup md for instructions for installing a virtual environment for the notebooks the virtual environment ensures that the python package dependencies are consistent with those used by the authors see readme md in each chapter folder for individual details about the notebooks in each chapter version 1 0 the current version has been tested on mac os windows 10 and linux see setup md for further details note that this repository is is constantly undergoing revisions and the reader should refer to the official github repo https github com mfrdixon ml finance codes to ensure that they have the latest version of the source code please create a gist https help github com en github writing on github creating gists to raise any queries regarding the source code mit license copyright 2020 dixon halperin and bilokon 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 | ai |
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CheesyVision | cheesyvision this project can be used to signal your robot during autonomous mode using computer vision and the webcam on your driver station laptop this is very similar to the kinect but requires no extra hardware or usb ports to run the program simply install the dependencies listed below if using linux or mac os x you can get these packages via apt or homebrew then double click the cheesyvision py icon to launch the program if you want it to communicate with your robot edit the source code so that it tries to connect to your teams crio requirements 1 python 2 7 installers found at https www python org download 32 bit https www python org ftp python 2 7 6 python 2 7 6 msi 64 bit https www python org ftp python 2 7 6 python 2 7 6 amd64 msi 2 numpy installers found at http www lfd uci edu gohlke pythonlibs numpy 32 bit https www dropbox com s aeusld7lffrawgy numpy mkl 1 8 1 win32 py2 7 exe 64 bit https www dropbox com s qzezri6zlteozqe numpy mkl 1 8 1 win amd64 py2 7 exe 3 opencv installers found at http www lfd uci edu gohlke pythonlibs opencv 32 bit https www dropbox com s lb3kkrht0g63nkc opencv python 2 4 8 1 win32 py2 7 exe 64 bit https www dropbox com s t6qs8yde326grf4 opencv python 2 4 8 1 win amd64 py2 7 exe | ai |
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PROJECT-UDACITY- | project udacity my submission to project specification engineering full stack apps in the cloud greetings i present you my project this github repository and this link has been submitted for review this is my link to my running elastic beanstalk deployment app http imagefiltredapp env eba vcqrbxfu us east 1 elasticbeanstalk com filteredimage image url https upload wikimedia org wikipedia commons b bd golden tabby and white kitten n01 jpg a screenshot of the elastic beanstalk application dashboard is included in a deployment screenshot directory thank you | cloud |
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Blog-MERN | blog mern backend the process starts with understanding backend theory and developing an application using express key aspects include setting up a modular approach for nodejs creating an authentication system and integrating json web tokens jwt connecting to a mongodb database creating a user model and managing user authentication are also crucial steps other important features involve handling errors securing user data and implementing crud functionality for articles the backend segment concludes with image upload handling using multer and a final refactoring of the backend code frontend the frontend development begins with examining the project structure and integrating react router and redux toolkit configuring axios fixing cors errors and creating asynchronous actions in redux toolkit are essential steps fetching and displaying articles and tags from the backend as well as implementing user authentication features are also important the process continues with the creation of various forms including registration article creation and editing lastly image uploading sending articles to the backend rendering articles using react markdown and implementing article deletion and editing features are covered | axios axios-react bcrypt jwt jwt-auth jwt-authentication mogoose mongodb multer nodejs react react-markdown react-router react-router-v6 reactjs redux redux-thunk redux-toolkit simple-editor | server |
simple_machine | simple machine state machine design framework a simple state machine design framework for embedded systems sm directory contains core algorithm exm directory contains an example machine you can create your own makefile or simply run the shell command to see the application result gcc exe main c exm sm o main main topmost initial s entry s2 entry s2 init s21 entry s211 entry s21 g s211 exit s21 exit s2 exit s1 entry s1 init s11 entry s1 i s1 a s11 exit s1 exit s1 entry s1 init s11 entry s1 d s11 exit s1 exit s init s1 entry s11 entry s11 d s11 exit s1 init s11 entry s1 c s11 exit s1 exit s2 entry s2 init s21 entry s211 entry s e s211 exit s21 exit s2 exit s1 entry s11 entry s e s11 exit s1 exit s1 entry s11 entry s11 g s11 exit s1 exit s2 entry s21 entry s211 entry s2 i s i i assumed gcc toolchain was added to path you can find the uml statechart representation of the implemented machine at the link https i hizliresim com kmprjj png the machine is cited from the miro samek s book practical uml statechart in c and c page 88 notice if you use this tiny framework in your projects please make sure your machine runs as you expected in the manner of handling all the types of events otherwise please feedback fully working example for stm32f4 discovery can be found on https yadi sk d wujtvhn2hvrf3g | os |
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baran | baran v https badgen net rubygems v baran dt https badgen net rubygems dt baran license https badgen net github license moekidev baran text splitter for large language model datasets to avoid token constraints and improve the accuracy of vector search in the large language model it is necessary to divide the document this gem supports splitting the text in the specified manner installation install the gem and add to the application s gemfile by executing bundle add baran if bundler is not being used to manage dependencies install the gem by executing gem install baran usage default paramters chunk size 1024 characters chunk overlap 64 characters character text splitter splitting by the specified character ruby splitter baran charactertextsplitter new chunk size 1024 chunk overlap 64 separator n n splitter chunks text metadata cursor 0 text metadata recursive character text splitter splitting by the specified characters recursively ruby splitter baran recursivecharactertextsplitter new separators n n n splitter chunks text metadata cursor 0 text metadata markdown text splitter splitting by the markdown descriptions ruby splitter baran markdownsplitter new splitter chunks markdown metadata cursor 0 text metadata split with the following priority ruby n h1 n h2 n h3 n h4 n h5 n h6 n n code block n n n n horizontal rule n n n n horizontal rule n n n n horizontal rule n n new line n new line space empty development after checking out the repo run bin setup to install dependencies then run bundle exec rake to run the tests you can also run bin console for an interactive prompt that will allow you to experiment contributing bug reports and pull requests are welcome on github at https github com moekidev baran this project is intended to be a safe welcoming space for collaboration and contributors are expected to adhere to the code of conduct https github com moekidev baran blob main code of conduct md license the gem is available as open source under the terms of the mit license https opensource org licenses mit code of conduct everyone interacting in the baran project s codebases issue trackers chat rooms and mailing lists is expected to follow the code of conduct https github com moekidev baran blob main code of conduct md | ai llm markdown ruby gem | ai |
slapo | copyright amazon com inc or its affiliates all rights reserved spdx license identifier apache 2 0 slapo a schedule language for large model training documentation https awslabs github io slapo github https img shields io github license awslabs slapo ci lass pass https img shields io endpoint url https gist githubusercontent com aire meta bot 4b5f48daff54fb5d1a862c1649632648 raw awslabs slapo ci badge last pass json slapo is a schedule language for progressive optimization of large deep learning model training large deep learning models demonstrate dominating model accuracy on a range of tasks in nlp and cv but it is hard to train the model efficiently while preserving the usability slapo aims to address this tension through separation of concerns slapo decouples model execution from definition enabling developers to use a set of schedule primitives to convert a pytorch model for common model training optimizations without directly changing the model itself slapo highlights the following features rocket progressive optimization slapo incorporates a trace by need approach that only traces a desired module to be a static graph for compiler based aggressive optimizations building construction structure preserving scheduling slapo preserves the module hierarchy when constructing the schedule so developers can easily locate the module and apply scheduling which also facilitates the users to debug any performance and convergence issue gear auto tuning slapo provides a programming interface that allows developers to specify a set of tuneable knobs to form an efficient tuning space which can then be explored by slapo auto tuner to realize the optimal configuration getting started installation there are two approaches to install slapo 1 install from pypi bash pip3 install slapo 2 install from source bash git clone https github com awslabs slapo git slapo cd slapo pip3 install e dev in addition you can optionally install huggingface transformers https github com huggingface transformers v4 28 1 to retrieve models also slapo currently supports the following frameworks so you can run the scheduled models on these frameworks if needed megatron lm https github com nvidia megatron lm 3 0 2 deepspeed https github com microsoft deepspeed 0 7 7 usage please see the examples examples folder for more details documentations will be released soon python import slapo load a pytorch model from huggingface hub torchvision etc from transformers import bertlmheadmodel autoconfig config autoconfig from pretrained bert large uncased bert bertlmheadmodel config create a default schedule sch slapo create schedule bert apply primitives to optimize the model please refer to examples bert schedule py for details sch bert encoder layer 0 primitve build an optimized model opt model slapo build sch run the optimized model inputs outputs opt model inputs supported primitives to maximally reduce the risk introduced by tracers and compilers we leverage progressive optimization to gradually apply primitives to a part of the model we classify the primitives into two categories the first type of primitives does not require tracing and can be directly applied to modules and parameters the second type of primitives requires a static graph and thus needs to apply the trace primitive first we provide the following primitives for dynamic graph optimizations feature primitive module replacement s op replace new module tensor parallelism s op shard param name axis synchronization s op sync mode fwd pre fwd post bwd post sync op or fn kwargs checkpointing s op checkpoint fork random number generator s op fork rng annotate parameters s op annotate param name key value and the following primitives for static graph optimizations feature primitive module tracing s trace leaves flatten pattern matching s find regex or pattern fn operator fusion s op fuse compiler subgraph layer decomposition s op decompose partial module replacement s op replace new module subgraph partial gradient checkpointing s op checkpoint subgraph pipeline parallelism s op cut pipeline stage you can look for all supported primitvies with the following api python import slapo print slapo list primitives you could also check the description of each primitive on the fly python import slapo help slapo list primitives name only false shard auto tuning we also provide a light weight interface for auto tuning so the developers can 1 construct a polyhedral search space using our apis and 2 leverage slapo auto tuner to automatically search for the best training configuration bash cd benchmark single device the following script will trigger the tuning jobs for all the models python3 tune single device py single node python3 tune single node py benchmarking we provide scripts to reproduce our results on a single aws ec2 p3 16xlarge node with 8 v100 gpus please refer to benchmark benchmark readme md for more details publication if you use slapo in your project please consult authors for citation license slapo is released under the apache 2 0 license license | ai |
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framer-bridge-starter-kit | update framer bridge will be deprecated in favor of es modules you can read more here https www framer com developers framer bridge starter kit framer bridge is a suite of tools that allows you to automatically publish and distribute components to designers with framer https framer com and the framer store https store framer com import in production components built by your engineers it s an automatic and continually synced workflow one that is customizable to your existing development workflow this repository links together folder backed framer projects https www framer com support using framer x folder backed projects with the framer cli https www npmjs com package framer cli and github actions https github com framer publishaction circleci https circleci com integrations github travis ci https travis ci com for an easy package publication flow getting started important this project uses shared colors a feature only available in framer x26 or higher cloning 1 fork this repository https help github com en articles fork a repo 1 clone the forked repository https help github com en articles cloning a repository locally 1 run yarn to install dependencies inside the repository directory you will find two key folders design system design system contains example production components typically this design system is consumed by multiple projects design system framerfx design system framerfx a folder backed project https framer gitbook io teams integrations folder projects that imports the components from design system design system and optionally adds interface properties https www framer com api property controls to use in framer this is the project that gets published to the framer store https store framer com editing from here you can continue modifying the existing design system framerfx design system framerfx file if you edit any of the components in design system design system they will automatically get updated in framer too if you want to import your own design system you can replace both files with your own make sure that your framer project is folder backed https framer gitbook io teams integrations folder projects publishing 1 from the terminal run sh npx framer cli authenticate your framer account email 1 if the package has not been previously published to the store publish the package for the first time by running sh env framer token token npx framer cli publish package name framerfx new display name using github actions if you have access to the github actions beta https github com features actions you can use this repository to automate the deployment of your framer package to the store without needing any external services 1 modify the args property in the build and publish actions inside github workflows publish yml github workflows publish yml with the path of your framer package eg yaml on push branches master name build and publish jobs publish runs on ubuntu latest steps uses actions checkout master name build uses framer bridge master env framer token secrets framer token with args build design system framerfx name publish uses framer bridge master env framer token secrets framer token with args publish design system framerfx yes 1 in github navigate to the forked repository and under your repository name click settings then click secrets in the left sidebar and add the framer token secret 1 push a commit to the master branch and watch as the github actions pick up the commit build the package publish it to the framer store https store framer com using ci as an example of integrating framer cli with an external ci service there is a small circleci configuration https circleci com docs 2 0 configuration reference and travis ci configuration https docs travis ci com user tutorial to get started with travis ci included in this repository that publishes the given package to the framer store https store framer com every time a commit is made to the master branch to integrate with circleci 1 connect your repository with circleci https circleci com integrations github 1 add the framer token environment variable in the ci project settings https circleci com docs 2 0 env vars setting an environment variable in a project 1 update the circleci config yml circleci config yml with your project path e g yml javascript node circleci 2 0 configuration file check https circleci com docs 2 0 language javascript for more details version 2 jobs publish docker image circleci node 10 working directory repo steps checkout run yarn run npx framer cli publish your project path framerfx yes workflows version 2 publish jobs build publish filters branches only master 1 publish a brand new version of your package to the framer store https store framer com by pushing a commit on the master branch to integrate with travis ci 1 connect your repository with travis ci https docs travis ci com user tutorial to get started with travis ci 1 add the framer token environment variable in the ci project settings https docs travis ci com user environment variables 1 update the travis yml travis yml with your project path e g yml language node js node js 10 15 3 jobs include stage build name build if branch master script yarn npx framer cli build your project path framerfx stage publish name publish if branch master script yarn npx framer cli publish your project path framerfx yes 1 publish a brand new version of your package to the framer store https store framer com by pushing a commit on the master branch | os |
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USLM | uslm unified speech language model a href https 0nutation github io speechtokenizer github io img src https img shields io badge project page green a a href https arxiv org abs 2308 16692 img src https img shields io badge paper arxiv red a introduction build upon speechtokenizer https github com zhangxinfd speechtokenizer uslm consists of autoregressive and non autoregressive models it can hierarchically model information in speech the autoregressive ar model captures the content information by modeling tokens from the first rvq quantizer the non autoregressive nar model complements paralinguistic information for the ar model by generating tokens from the subsequent quantizers conditioned on the first layer tokens br p align center img src images overview png width 95 br overview p installation to get up and running quickly just follow the steps below pytorch pip install torch 1 13 1 torchaudio 0 13 1 extra index url https download pytorch org whl cu116 pip install torchmetrics 0 11 1 fbank pip install librosa 0 8 1 phonemizer pypinyin apt get install espeak ng osx brew install espeak pip install phonemizer 3 2 1 pypinyin 0 48 0 lhotse update to newest version https github com lhotse speech lhotse pull 956 https github com lhotse speech lhotse pull 960 pip uninstall lhotse pip install git https github com lhotse speech lhotse k2 find the right version in https huggingface co csukuangfj k2 pip install https huggingface co csukuangfj k2 resolve main cuda k2 1 23 4 dev20230224 cuda11 6 torch1 13 1 cp310 cp310 linux x86 64 whl icefall git clone https github com k2 fsa icefall cd icefall pip install r requirements txt export pythonpath pwd icefall pythonpath echo export pythonpath pwd icefall pythonpath zshrc echo export pythonpath pwd icefall pythonpath bashrc cd source zshrc speechtokenizer pip install u speechtokenizer uslm git clone https github com 0nutation uslm cd uslm pip install e uslm models this version of uslm is trained on the libritts dataset so the performance is not optimal due to data limitations model dataset discription uslm libri https huggingface co fnlp uslm tree main uslm libritts libritts uslm trained on libritts dataset zero shot tts using uslm download pre trained speechtokenizer models bash st dir ckpt speechtokenizer mkdir p st dir cd st dir wget https huggingface co fnlp speechtokenizer resolve main speechtokenizer hubert avg speechtokenizer pt wget https huggingface co fnlp speechtokenizer resolve main speechtokenizer hubert avg config json cd download pre trained uslm models bash uslm dir ckpt uslm mkdir p uslm dir cd uslm dir wget https huggingface co fnlp uslm resolve main uslm libritts uslm pt wget https huggingface co fnlp uslm resolve main uslm libritts unique text tokens k2symbols cd inference bash out dir output mkdir p out dir python3 bin infer py output dir out dir model name uslm norm first true add prenet false share embedding true norm first true add prenet false audio extractor speechtokenizer speechtokenizer dir st dir checkpoint uslm dir uslm pt text tokens uslm dir unique text tokens k2symbols text prompts mr soames was a tall spare man of a nervous and excitable temperament audio prompts prompts 1580 141083 000002 000002 wav text begin with the fundamental steps of the process this will give you a solid foundation to build upon and boost your confidence or you can directly run inference sh bash bash inference sh acknowledge vall e https github com lifeiteng vall e the codebase we build upon citation if you use this code or result in your paper please cite our work as tex misc zhang2023speechtokenizer title speechtokenizer unified speech tokenizer for speech language models author xin zhang and dong zhang and shimin li and yaqian zhou and xipeng qiu year 2023 eprint 2308 16692 archiveprefix arxiv primaryclass cs cl | ai |
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PiZeroRTOS | pizerortos because why not this repo starts out as a pi zero bare metal project and it could very well end up as a viable rtos implementation with a brand as yet to be determined various bare metal developers and hackers out there have made great resources on raspberry pi 2 3 4 arm7 and beyond to aarch64 but have appeared to abandon the pi zero and its arm1176jzf s processor well perhaps i m too stupid to know better but i submit that this is a worthy target now that qemu sufficiently supports the pi zero as an emulated device this is a work in progress project but what i put up is a working example of starting the pi zero on qemu emulator i hope it proves to be a worthy foundation to greater things for you what to anticipate from this work a really bare bones os that is not much more than a task scheduler ability for a task to take ownership of a hardware device directly there is no expectation of a device driver that is brokered by the os a method for tasks to inter communicate preferably by hardware supported means a functioning frame buffer with hardware supported opengl uart i2c and spi device examples interrupt handling dma even if only used between spi and frame buffer what it is now uart working bare metal booting attempt to save the pi 3 4 examples that are copied but then transposed to pi zero where only pi zero is actively tested working build examples built on a macos environment augmented by many gcc et al tools some key requirements https www qemu org download and build from github for the latest pi zero support many distros don t include that xcode xcode select install command line tools https github com armmbed homebrew formulae most usefull links https azeria labs com writing arm assembly part 1 https github com umanovskis baremetal arm https pnx9 github io thehive debugging linux kernel html https github com dwelch67 raspberrypi https www raspberrypi org documentation hardware raspberrypi readme md https github com s matyukevich raspberry pi os i m seeing interest in this subject so i leave it here but also add this use of jtag on raspberry pi with the pi as the target physical connection and config https sysprogs com visualkernel tutorials raspberry jtagsetup basic commands to get started https yeah nah nz embedded pi jtag u boot a command to add to your cfg file to aide in rebooting you want to load from the jtag connection into memory directly and then do a software reboot proc pi reboot targets rspi arm halt mww 0x20100024 0x5a000001 mww 0x2010001c 0x5a000020 update the project embox https github com embox embox is hands down everything this project aspires to be and more regardless this still has value as a tool for foundational understanding also i d add for gui and direct opengl rendering to framebuffer etc the already embox tested project nuklear https github com immediate mode ui nuklear is ideal | os |
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vanillaHTML | vanillahtml vanilla html is a elegantly simple boilerplate for web development projects and websites demo https vanillahtml com vanillahtml https vanillahtml com img promotional github banner jpg getting started clone the git repository or download and install the zip file from https vanillahtml com vanillahtml zip start by editing the following files components header html pages home html pages if you add new pages update js routes js pages are loaded via the page query parameter so https vanillahtml com page terms will load the terms html page this is setup in routes js components web components can be built as single html files css at the top html in the middle and javascript at the bottom include all javascript between script tags at the bottom of the file don t use inline javascript see the examples in components these can be loaded via the utils loadmodule components header html header 1st parameter is the path to the file 2nd is the dom id to inject the code this allows for modular and 3rd party components to be added with ease tips and tricks there is a small library of css classes to make life easier i e text center will add the text align center css style check core css for details https github com jamesbachini vanillahtml blob master html core core css tracking and analytics snippets can be placed in components pixels html this will be loaded 3 seconds after the main page to meet page load time requirements don t modify any files in the core folder so that it s easy to update in future there is site specific css in css style css and js in js script js resources https vanillahtml com https unsplash com https undraw co https coolors co https jamesbachini com https nodejs org en https denocode com https developers google com speed pagespeed insights https search google com search console welcome contribute if anyone would like to add code to the project build templates or make improvements please do so via a pull request i m keen to keep it lightweight but so anything that s not core should be setup as a 3rd party component to do integrate babel or another compiler as a optional build process templates components 3rd party directory docs | front_end |
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Roy | roy img src https colab research google com assets colab badge svg https colab research google com github josefalbers roy blob main quickstart ipynb doi https zenodo org badge 699801819 svg https zenodo org badge latestdoi 699801819 roy is a lightweight alternative to autogen for developing advanced multi agent systems using language models it aims to simplify and democratize the development of emergent collective intelligence features model agnostic use any llm no external apis required defaults to a 4 bit quantized wizard coder python model for efficiency modular and composable roy decomposes agent interactions into reusable building blocks templating retrieving generating executing transparent and customizable every method has a clear purpose easily swap out components or add new capabilities quickstart sh git clone https github com josefalbers roy cd roy pip install r requirements txt pip install u transformers optimum accelerate auto gptq extra index url https huggingface github io autogptq index whl cu118 rapid benchmarking roy provides a simple way to evaluate and iterate on your model architecture this allows you to easily swap out components such as language models prompt formats agent architectures etc benchmark on different tasks like arithmetic python coding etc default is openai s humaneval quantify effect of each component s configuration on the agent s overall areas of strengths and weaknesses python from roy util import piecewise human eval comparing different language models akes around 30 minutes each on a free google colab runtime piecewise human eval 0 lm id thebloke wizardcoder python 7b v1 0 gptq pass 1 0 6341463414634146 piecewise human eval 0 lm id thebloke tora code 7b v1 0 gptq pass 1 0 5609756097560976 piecewise human eval 0 lm id thebloke arithmo mistral 7b gptq pass 1 0 5121951219512195 testing a custom agent architecture piecewise human eval 0 fx your custom roy agent constrained beam search use templates to structure conversations control output length format etc python from roy import roy roys roy roy s what date is today which big tech stock has the largest year to date gain this year how much is the gain roy generate roy format s constrain output structure length include exclude certain tokens etc roy generate s n python n generate a python code block roy generate s n python n javascript n generate python or javascript codes roy generate s n python 100 n generate a code block of size less than 100 tokens retrieval augmented generation enhance generation with relevant knowledge python s create a text to image generator r roy retrieve s n topk 3 src huggingface roy generate s for s in r auto feedback agents collaborate in tight loops to iteratively refine outputs to specification python iterative multiturn chat style s create a secure and unique secret code word with a python script that involves multiple steps to ensure the highest level of confidentiality and protection n for i in range 2 c roy generate s prohibitions input s roy execute c another way to implement auto feedback user request compare the year to date gain for meta and tesla ai response roy generate user request n python yfinance n for i in range 2 shell execution roy execute ai response if modulenotfounderror in shell execution roy execute roy generate roy format f write a shell command to address the error encountered while running this python code n n shell execution elif error in shell execution ai response roy generate roy format f modify the code to address the error encountered n n shell execution else break multi agent flexible primitives to build ecosystems of agents python roys roys autofeedback roys create agents coder i execute generate i roys start requests i create a mobile application that can track the health of elderly people living alone in rural areas retrieval augmented generation roys create agents retriever r retrieve i generator o generate r roys start requests i create a deutsch to english translator providing a custom tool to one of the agents using lambda roys create agents coder c generate i proxy c custom execute c tools custom lambda x f modify the code to address the error encountered n n x if error in x else none roys start requests i compare the year to date gain for meta and tesla another way to create a custom tool for agents def custom switch self c py str modify the code to address the error encountered n n sh str write a shell command to address the error encountered while running this python code n n x self execute c if modulenotfounderror in x self execute self generate sh str x elif error in x self dict cache i py str x else return success n n x roys create agents coder c generate i proxy protocol c tools protocol custom switch roys start requests i compare the year to date gain for meta and tesla emergent multi agent dynamics roy aims to facilitate the emergence of complex adaptive multi agent systems it draws inspiration from biological and ai concepts to enable decentralized coordination and continual learning survival of the fittest periodically evaluate and selectively retain high performing agents based on accuracy speed etc agents adapt through peer interactions mixture of experts designate agent expertise dynamically assemble specialist teams and route tasks to optimal experts continuously refine and augment experts these mechanisms facilitate the emergence of capable adaptive and efficient agent collectives get involved roy is under active development we welcome contributions feel free to open issues and prs support the project if you found this project helpful or interesting and want to support more of these experiments feel free to buy me a coffee a href https www buymeacoffee com albersj66a target blank img src https cdn buymeacoffee com buttons default orange png alt buy me a coffee height 25 width 100 a | agent agentgpt autogpt baby-agi chat chatbot code-generation code-generator gpt langchain llm llm-agent multi-agent nlp prompt-engineering quantization retrieval-augmented-generation vector-index wizardcoder autogen | ai |
MiniChain | img src https user images githubusercontent com 35882 227030644 f70e55e8 68a3 48d3 afa3 54c4de8fc210 png width 100 a tiny library for coding with large language models check out the minichain zoo https srush minichain hf space to get a sense of how it works coding code math demo py https github com srush minichain blob main examples math demo py annotate python functions that call language models python prompt openai template file math pmpt tpl def math prompt model question prompt to call gpt with a jinja template return model dict question question prompt python template import math n code def python model code prompt to call python interpreter code n join code strip split n 1 1 return model dict code code def math demo question chain them together return python math prompt question chains space https srush minichain hf space minichain builds a graph think like pytorch of all the calls you make for debugging and error handling img src https user images githubusercontent com 35882 226965531 78df7927 988d 45a7 9faa 077359876730 png width 50 python show math demo examples what is the sum of the powers of 3 3 i that are smaller than 100 what is the sum of the 10 first positive integers subprompts math prompt python out type markdown queue launch template math pmpt tpl https github com srush minichain blob main examples math pmpt tpl prompts are separated from code question a robe takes 2 bolts of blue fiber and half that much white fiber how many bolts in total does it take code 2 2 2 question question code installation bash pip install minichain export openai api key sk examples this library allows us to implement several popular approaches in a few lines of code retrieval augmented qa https srush github io minichain examples qa chat with memory https srush github io minichain examples chatgpt information extraction https srush github io minichain examples ner interleaved code pal https srush github io minichain examples pal gao et al 2022 https arxiv org pdf 2211 10435 pdf search augmentation self ask https srush github io minichain examples selfask press et al 2022 https ofir io self ask pdf chain of thought https srush github io minichain examples bash wei et al 2022 https arxiv org abs 2201 11903 it supports the current backends openai completions embeddings hugging face google search python manifest ml ai21 cohere together bash why mini chain there are several very popular libraries for prompt chaining notably langchain https langchain readthedocs io en latest promptify https github com promptslab promptify and gptindex https gpt index readthedocs io en latest reference prompts html these library are useful but they are extremely large and complex minichain aims to implement the core prompt chaining functionality in a tiny digestable library tutorial mini chain is based on annotating functions as prompts image https user images githubusercontent com 35882 221280012 d58c186d 4da2 4cb6 96af 4c4d9069943f png python prompt openai def color prompt model input return model f answer yes if this is a color input answer prompt functions act like python functions except they are lazy to access the result you need to call run python if color prompt blue run yes print it s a color alternatively you can chain prompts together prompts are lazy so if you want to manipulate them you need to add transform to your function for example python transform def said yes input return input yes image https user images githubusercontent com 35882 221281771 3770be96 02ce 4866 a6f8 c458c9a11c6f png python prompt openai def adjective prompt model input return model f give an adjective to describe input answer python adjective adjective prompt rainbow if said yes color prompt adjective run print it s a color we also include an argument template file which assumes model uses template from the jinja https jinja palletsprojects com en 3 1 x templates language this allows us to separate prompt text from the python code python prompt openai template file math pmpt tpl def math prompt model question return model dict question question visualization minichain has a built in prompt visualization system using gradio if you construct a function that calls a prompt chain you can visualize it by calling show and launch this can be done directly in a notebook as well python show math demo examples what is the sum of the powers of 3 3 i that are smaller than 100 what is the sum of the 10 first positive integers subprompts math prompt python out type markdown queue launch memory minichain does not build in an explicit stateful memory class we recommend implementing it as a queue image https user images githubusercontent com 35882 221622653 7b13783e 0439 4d59 8f57 b98b82ab83c0 png here is a class you might find useful to keep track of responses python dataclass class state memory list tuple str str human input str def push self response str state memory self memory if len self memory memory limit else self memory 1 return state memory self human input response see the full chat example it keeps track of the last two responses that it has seen tools and agents minichain does not provide agents or tools if you want that functionality you can use the tool num argument of model which allows you to select from multiple different possible backends it s easy to add new backends of your own see the gradioexample python prompt python bash def math prompt model input lang return model input tool num 0 if lang python else 1 documents and embeddings minichain does not manage documents and embeddings we recommend using the hugging face datasets https huggingface co docs datasets index library with built in faiss indexing image https user images githubusercontent com 35882 221387303 e3dd8456 a0f0 4a70 a1bb 657fe2240862 png here is the implementation python load and index a dataset olympics datasets load from disk olympics data olympics add faiss index embeddings prompt openaiembed def get neighbors model inp k embedding model inp res olympics get nearest examples embeddings np array embedding k return res examples content this creates a k nearest neighbors knn prompt that looks up the 3 closest documents based on embeddings of the question asked see the full retrieval augemented qa https srush github io minichain examples qa example we recommend creating these embeddings offline using the batch map functionality of the datasets library python def embed x emb openai embedding create input x content engine embedding model return embeddings np array emb data i embedding for i in range len emb data x dataset map embed batch size batch size batched true x save to disk olympics data there are other ways to do this such as sqllite https github com asg017 sqlite vss or weaviate https weaviate io typed prompts minichain can automatically generate a prompt header for you that aims to ensure the output follows a given typed specification for example if you run the following code minichain will produce prompt that returns a list of player objects python class stattype enum points 1 rebounds 2 assists 3 dataclass class stat value int stat stattype dataclass class player player str stats list stat prompt openai template file stats pmpt tpl parser json def stats model passage out model dict passage passage typ type to prompt player return player j for j in out specifically it will provide your template with a string typ that you can use for this example the string will be of the following form you are a highly intelligent and accurate information extraction system you take passage as input and your task is to find parts of the passage to answer questions you need to output a list of json encoded values you need to classify in to the following types for key color red green blue only select from the above list or other you need to classify in to the following types for key object string you need to classify in to the following types for key explanation string color color object object explanation explanation make sure every output is exactly seen in the document find as many as you can this will then be converted to an object automatically for you | ai |
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CSC440GroupProject | csc440groupproject group project for our applied software engineering class at eku this project allows for editing of grades in a relational database | server |
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kos-avr | kevin s rtos kos kevin cuzner this is a preemptive task based real time operating system designed to consume as few resources as possible while increasing the effective utilization of systems using avr microcontrollers the motivation for this project stems from my own desire to write something like this there are many other options available which are likely better written and easier to use but i m going to have more fun doing it this way goals 1 small codebase that is easy enough to read 2 small memory footprint operation tasks the basic operational unit is a task a task is created from a pointer to the function to invoke to begin the task and a pointer to the top of the memory to use as a stack the absolute minimum stack size is 35 bytes but this will likely run into problems since most programs use part of the stack as storage for variables and such tasks should each contain a loop which never exits the operating system is optimized for the case where tasks always exist the order in which tasks are created sets their priority the last task created will have the highest priority preemption as a preempting operating system tasks can be suspended at any time at the point where a task is suspended the next program counter status register and 32 registers are pushed onto the current stack in general preemption will occur in response to an interrupt this means that if interrupts are disabled the task won t be interrupted since no two tasks can have the same priority there is no time slicing or round robin support a task will run until it blocks or is interrupted scheduling dispatching the scheduler is first run with a call to kos run when it is invoked it will follow the list of tasks in priority order and find the highest priority task that is ready it will then invoke the dispatcher on this task the dispatcher works by storing the context of the current task on the stack overwriting the stack pointer with the next task s stack pointer popping all the previously stored register values and then executing a ret or reti if interrupts are enabled to jump back into executing the next now current task blocking tasks are blocked by calling functions which change the state of the current task to blocked in general they also store a pointer to some data which can be used to determine how to unblock the task examples of blocking functions are kos semaphore pend kos queue pend when these functions are executed there is a chance that the current task will have its state changed to blocked the scheduler will be called and the first ready task which won t be the current task since it was just blocked will be dispatched un blocking a task is unblocked simply by changing its status back to ready examples of functions which do this are kos semaphore post kos queue post these functions all traverse the task list mark a single task as ready if needed and then call the scheduler if the current task is higher priority than the task that was unblocked then no context switch occurs however if a higher priority task is unblocked then the current task is suspended and the higher priority task is resumed conveniences any rtos is almost useless if it just consists of tasks and a dispatcher it needs something to allow the tasks to switch kos provides the following semaphores queues note there is no full checking these are implemented as circular buffers and will overwrite un dequeued data | rtos avr-microcontroller | os |
cs4084example | cs4084example example project for cs4084 mobile application development short description of the app getting started what do i need to do to get the app running on my device any special steps if i want to run the app from source code in android studio pointers to e g firebase project more information technical design design md navigation structure structure md | front_end |
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flask-ml-azure-serverless | flask ml azure serverless deploy flask machine learning application on azure app services continuous delivery https user images githubusercontent com 58792 85061538 f7352780 b174 11ea 8001 b0561c5bad73 jpg if you run into problems build the container using docker commands in the makefile rebuild the model using a later version of sklearn and update requirements txt with your version of sklearn to run it locally follow these steps on python 3 8 there are issues on later version of python 1 create virtual environment and source bash python3 m venv flask ml azure source flask ml azure bin activate 2 run make install 3 run python app py 4 in a separate shell run make prediction sh to run it in azure pipelines 1 refer to azure official documentation guide here throughout https docs microsoft com en us azure devops pipelines ecosystems python webapp view azure devops 2 launch azure shell 1 launch azure shell https user images githubusercontent com 58792 89555246 cc169e00 d7dd 11ea 8164 88caa1b8beba png 3 create github repo with azure pipelines enabled could be a fork of this repo 2 create github repo https user images githubusercontent com 58792 89555912 a3db6f00 d7de 11ea 9d2f 5ac030b43ec9 png 4 clone the repo into azure cloud shell note you make need to follow this youtube video guide on how to setup ssh keys and configure cloudshell environment https www youtube com watch v 3vtbafpjqus 5 create virtual environment and source bash python3 m venv flask ml azure source flask ml azure bin activate 2 run make install 3 create an app service and initially deploy your app in cloud shell az webapp up n your appservice 3 flask ml service https user images githubusercontent com 58792 89557009 2e709e00 d7e0 11ea 9b31 9090c8067a10 png 4 verify deployed application works by browsing to deployed url https your appservice azurewebsites net you will see this output 4 deployed app https user images githubusercontent com 58792 89557343 a8088c00 d7e0 11ea 891c 4d88333b8097 png 5 verify machine learning predictions work change the line in make predict azure app sh to match the deployed prediction x post https yourappname azurewebsites net port predict 5 successful prediction https user images githubusercontent com 58792 89557573 02a1e800 d7e1 11ea 8318 1c628e13dae7 png 6 create an azure devops project and connect to azure as official documentation describes https docs microsoft com en us azure devops pipelines ecosystems python webapp view azure devops 6 devops https user images githubusercontent com 58792 89558313 097d2a80 d7e2 11ea 8b65 df052b300331 png 7 connect to azure resource manager 7 service connection https user images githubusercontent com 58792 89558869 d0918580 d7e2 11ea 8ffe 52cfaf95fe16 png 8 configure connection to previously deployed resource group 8 azure pipelines setup https user images githubusercontent com 58792 89560149 988b4200 d7e4 11ea 9e25 3554ac2bd8fd png 9 create new python pipeline with github integration 9 newpipeline https user images githubusercontent com 58792 89560429 f750bb80 d7e4 11ea 9f85 241d65d25c55 png 10 github integration https user images githubusercontent com 58792 89560627 5282ae00 d7e5 11ea 8b0b bdecfff0e4d3 png this process will create a yaml file that looks roughly like the yaml output shown below refer to the official azure pipeline yaml documentation for more information about it https docs microsoft com en us azure devops pipelines ecosystems python webapp view azure devops yaml pipeline explained python to linux web app on azure build your python project and deploy it to azure as a linux web app change python version to one thats appropriate for your application https docs microsoft com azure devops pipelines languages python trigger master variables azure resource manager connection created during pipeline creation azureserviceconnectionid youridhere web app name webappname flask ml service agent vm image name vmimagename ubuntu latest environment name environmentname flask ml service project root folder point to the folder containing manage py file projectroot system defaultworkingdirectory python version 3 7 pythonversion 3 7 stages stage build displayname build stage jobs job buildjob pool vmimage vmimagename steps task usepythonversion 0 inputs versionspec pythonversion displayname use python pythonversion script python m venv antenv source antenv bin activate python m pip install upgrade pip pip install setup pip install r requirements txt workingdirectory projectroot displayname install requirements task archivefiles 2 displayname archive files inputs rootfolderorfile projectroot includerootfolder false archivetype zip archivefile build artifactstagingdirectory build buildid zip replaceexistingarchive true upload build artifactstagingdirectory build buildid zip displayname upload package artifact drop stage deploy displayname deploy web app dependson build condition succeeded jobs deployment deploymentjob pool vmimage vmimagename environment environmentname strategy runonce deploy steps task usepythonversion 0 inputs versionspec pythonversion displayname use python version task azurewebapp 1 displayname deploy azure web app flask ml service inputs azuresubscription azureserviceconnectionid appname webappname package pipeline workspace drop build buildid zip 10 verify continuous delivery of azure pipelines by changing app py you can watch this youtube walkthrough of this process https www youtube com watch v 3kf9dltyvzu 11 add a lint step this gates your code against syntax failure script python m venv antenv source antenv bin activate make install make lint workingdirectory projectroot displayname run lint tests you can watch this youtube walkthrough of this process https www youtube com watch v titoattfaoc cloud computing for data analysis book https leanpub com cloud4data this book is being written just in time with a weekly release schedule cloud4data books https d2sofvawe08yqg cloudfront net cloud4data hero2x 1578933644 | udacity northwestern oreilly-books northwestern-434 | ai |
Data2AITextbook | data2aitextbook goal automatically convert unstructured data into a high quality textbook format optimized for fine tuning large language models llms about inspired by textbooks are all you need https arxiv org pdf 2306 11644 pdf which produced phi 1 https huggingface co microsoft phi 1 llm trained with textbook quality data principles flexible any form of unstructured data e g speeches blogs code existing texbooks etc grounded trusts your data over model s pre existing knowledge and doesn t make up new data unless explicitly asked efficient highest density of learning per training token leverage best practices undestands of language model training enhanced increase capabilities of trained models versus simply training on the raw input text phases this project is broken into 2 phases ingestion generation extract learning objectives generate high quality 1 lessons and 2 exercises per knowledge type and cognitive process knowledge augmentation e g paraphrase inverse etc training curriculum learning to optimally train a new language model training should get progressively harder and leverage knowledge learned earlier in the curriculum 1 ingestion generation input unstructured text data output training test dataset with rich meta data e g dependencies relationships knowledge type cognitive process etc combining the best practices from teaching humans and training language models inspiration for educating humans bloom s taxonomy for learning objectives learn more about bloom s taxonomy in https www celt iastate edu instructional strategies effective teaching practices revised blooms taxonomy and https cft vanderbilt edu guides sub pages blooms taxonomy curricium learning encoding specificity principle language models prompting inference chain of thought https arxiv org abs 2201 11903 graph of thoughts https arxiv org abs 2308 09687 self consistency https arxiv org abs 2203 11171 training datasets less is more for alignment lima https browse arxiv org pdf 2305 11206 pdf tinystories textbooks are all you need physics of language models part 3 1 and 3 2 and many many more learning objectives bloom s taxonomy breaks down learning objectives based on knowledge types and cognitive processes the combination of each can be treated different to maximize the output generated bloom s taxonomy table github images blooms taxonomy table png exercises for each knowledge type and cognitive process to be taught there are multiple lessons and exercises to consider generating each of these exercises should have an optimzed prompting pipeline to generate which leverages powerful techniques such as chain of thought or graph of thoughts and self consistency etc the following is work in progress remember understand apply analyze evaluate create factual multiple choice br true false br flashcards br labeling br listing summary writing br paraphrasing br explanation matching br identification categorization ranking listing newly synthesized conceptual flashcards concepts br matching explanation br interpretation problem solving br case studies compare and contrast br categorization critical review br assessment designing br planning procedural labeling steps br multiple choice next step summary writing process br explanation how to demonstration br simulation flowcharting br error analysis assessment procedures br recommendation programming br designing new process metacognitive listing strategies paraphrasing strategies role playing strategies investigation br debate self assessment br judgment planning br storyboarding 2 training curriculum input training test dataset output trained language model using curriculum learning by grouping ordering training dataset based on meta data all of the dataset will be consumed however the next chapter of data will only be unlocked once the model in training passes a sufficient of exercises from test dataset mermaid graph lr subgraph chapter1 1 foundational knowledge subgraph knowledge types1 knowledge types factual1 factual end subgraph cognitiveprocesses1 cognitive processes remember1 remember understand1 understand end end subgraph chapter2 2 basic concepts and procedures subgraph knowledge types2 knowledge types conceptual2 conceptual procedural2 procedural end subgraph cognitiveprocesses2 cognitive processes understand2 understand apply2 apply end end subgraph chapter3 3 interconnecting concepts subgraph knowledge types3 knowledge types conceptual3 conceptual procedural3 procedural end subgraph cognitiveprocesses3 cognitive processes analyze3 analyze end end subgraph chapter4 4 practical application subgraph knowledge types4 knowledge types procedural4 procedural metacognitive4 metacognitive end subgraph cognitiveprocesses4 cognitive processes apply4 apply evaluate4 evaluate end end subgraph chapter5 5 critical examination subgraph knowledge types5 knowledge types conceptual5 conceptual metacognitive5 metacognitive end subgraph cognitiveprocesses5 cognitive processes analyze5 analyze evaluate5 evaluate end end subgraph chapter6 6 building new knowledge subgraph knowledge types6 knowledge types metacognitive6 metacognitive conceptual6 conceptual end subgraph cognitiveprocesses6 cognitive processes create6 create end end subgraph chapter7 7 integration and reflection subgraph knowledge types7 knowledge types factual7 factual conceptual7 conceptual procedural7 procedural metacognitive7 metacognitive end subgraph cognitiveprocesses7 cognitive processes remember7 remember understand7 understand apply7 apply analyze7 analyze evaluate7 evaluate create7 create end end model trained language model edges chapter1 pass tests chapter2 chapter2 pass tests chapter3 chapter3 pass tests chapter4 chapter4 pass tests chapter5 chapter5 pass tests chapter6 chapter6 pass tests chapter7 chapter7 pass tests model datasets models check out my huggingface profile for a list of datasets models i ve created https huggingface co glavin001 many will be created for the expertise by ai https github com glavin001 expertise by ai project where you can learn how to train custom models with your own data | ai llm question-generation | ai |
Lightning-Fast-Mobile-App-Development-with-Galio | lightning fast mobile app development with galio a href https www packtpub com product lightning fast mobile app development with galio 9781801073165 img src https static packt cdn com products 9781801073165 cover smaller alt lightning fast mobile app development with galio height 256px align right a this is the code repository for lightning fast mobile app development with galio https www packtpub com product lightning fast mobile app development with galio 9781801073165 published by packt build stylish cross platform mobile apps with galio and react native what is this book about galio is a free open source react native framework that enables beginner level programmers to quickly build cross platform mobile apps by leveraging its beautifully designed ready made components this book helps you to learn about react native app development while building impressive out of the box apps with galio this book covers the following exciting features explore galio and learn how to build beautiful and functional apps familiarize yourself with the galio ecosystem discover how to use npm and understand why galio is needed get to grips with the basics of constructing a basic but attractive ui for an app find out how you can utilize galio s ready made components use galio to drive the process of quickly building cross platform mobile apps build three practical and exciting apps with react native and galio if you feel this book is for you get your copy https www amazon com dp 1801073163 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following const mostplayedgame return block style styles container image style styles image source uri https via placeholder com 300 text size 15 color 707070 most played game league of legends text block following is what you need for this book this book is for developers who are looking to learn new skills or build personal mobile apps anyone trying to change their job as well as beginners and intermediate web developers will also find this book useful a basic understanding of css html and javascript is needed to get the most out of this book with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 12 homebrew macos only 1 12 chocolatey windows only 1 12 text editor windows and macos both 1 12 android studio windows and macos both 1 12 xcode macos only we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781801073165 colorimages pdf related products other books you may enjoy react and react native packt https www packtpub com product react and react native 9781786465658 amazon https www amazon com dp 1839211148 react cross platform application development with react native packt https www packtpub com product react cross platform application development with react native 9781789136081 amazon https www amazon com dp 1789136083 get to know the author alin gheorghe is a web developer based in bucharest romania after he created his first ios game at the age of 15 he fell in love with the world of mobile applications he has worked with a wide range of different technologies while also developing his love of music and photography he truly believes coding is just another form of art which requires the developer to think outside the box download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781801073165 https packt link free ebook 9781801073165 a p | front_end |
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Stress-Check-Project | stress check project my final year cloud computing group project repository transfer from my old college account final grade achieved 2 1 this project contains a stress recorder webapp that uses nextjs nodejs and mongodb that was hosted on a aws server | cloud |
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ES-SW-Design-Boot-Camp | es sw design boot camp capstone project for the embedded systems software design boot camp fan project | os |
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cs571 | cs571 natural language processing this is a graduate level course focusing on topics in natural language processing nlp such as embedding encoding text classification sequence tagging semantic parsing coreference resolution and machine comprehension advanced topics in deep learning with various neural network models in applications to nlp are also discussed syllabus docs syllabus md schedule docs schedule md paper presentations 2021 presentations paper presentations 2021 md 2019 presentations paper presentations 2019 md final projects 2021 projects final projects 2021 md 2019 projects final projects 2019 md 2017 projects final projects 2017 md 2015 projects final projects 2015 md | ai |
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