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DingdongBook | https github com dinghow dingdongbook raw master img c2674995e13445f38fb61cfac48c500b png the do t c dinghow midori 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 5 1 5 1 10 1 1 windows mac os chrome ie 8 1 1 1 1 1 2 2 1 2 1 1 2 1 2 https github com dinghow dingdongbook raw master img a596231e3ae7c21cf32825cc8e2831a4 png 2 1 https github com dinghow dingdongbook raw master img 5db58fe11aab3c51a261d9fc129c600d png 2 2 https github com dinghow dingdongbook raw master img fdbbbe0ccdea39c84ebc9b7c4ae0724e png 2 3 2 2 2 2 1 2 2 2 https github com dinghow dingdongbook raw master img 2b48234499fa5ed9e466ca2d18974b69 png 2 4 2 3 2 3 1 2 3 2 https github com dinghow dingdongbook raw master img 340d8a80fa137944993e1cc237d8aad4 png 2 5 https github com dinghow dingdongbook raw master img b47ab78e49f713824a9b0f3a4cd13925 png 2 6 3 3 1 3 1 1 3 1 2 https github com dinghow dingdongbook raw master img d82fea74c104f96dba1f459cf660a881 png 3 1 https github com dinghow dingdongbook raw master img c56e8be5a922b813baa999548c54cab6 png 3 2 3 2 3 2 1 3 2 2 https github com dinghow dingdongbook raw master img 64aa433663f80174b8df1b1284f5cab5 png 3 3 https github com dinghow dingdongbook raw master img 94c408da43e41c4a768264b8209c4ead png 3 4 https github com dinghow dingdongbook raw master img f193b5083cda1ddadbde0e8bd9c38898 png 3 5 3 3 3 3 1 3 3 2 https github com dinghow dingdongbook raw master img bc7af2120ca3e5eee9555923ff6c481b png 3 6 4 4 1 4 1 1 4 1 2 https github com dinghow dingdongbook raw master img 062a433fb1bebc38b4542a2b3cf50daf png 4 1 4 2 4 2 1 4 2 2 https github com dinghow dingdongbook raw master img e48c0e8b224e61e6f034b4a41eb8dc01 png 4 2 https github com dinghow dingdongbook raw master img 6099f81d8b83715afbfcdd2c3b112f02 png 4 3 https github com dinghow dingdongbook raw master img c07a5fb0fb5bd31b03b3c9d8f11dd866 png 4 4 https github com dinghow dingdongbook raw master img a3bae222721d24593ad27ca5f2c749ce png 4 5 4 3 4 3 1 4 3 2 5 5 1 5 1 1 5 1 2 https github com dinghow dingdongbook raw master img 14c9f36f4184091aa3ab9416d64ac03a png 5 1 https github com dinghow dingdongbook raw master img 3c5aa1ddbe2b2e7d7f392250f1738562 png 5 2 5 2 5 2 1 isbn 5 2 2 https github com dinghow dingdongbook raw master img 6d4f07c70b134124a954f6cf66542d25 png 5 3 https github com dinghow dingdongbook raw master img f6a8428b2d615a7044bf8621b86840d1 png 5 4 https github com dinghow dingdongbook raw master img a6d0103b338f8350e9038557f0cc3bb0 png 5 5 6 6 1 https github com dinghow dingdongbook raw master img 9316cb8c150ebbc86edb5d976836f723 png 6 2 e r https github com dinghow dingdongbook raw master img 9126e4bbc91bcfbcfb4b41a02e575fe0 png 6 3 e r https github com dinghow dingdongbook raw master img 3477b0f0e962006661e1e04e0db6bc27 png 6 4 e r https github com dinghow dingdongbook raw master img c0c316f811a1c6a41b0fa662f55e430d png 6 5 e r https github com dinghow dingdongbook raw master img f4d82e62732b02d8f81c4fb68a76fdc3 png 6 6 e r https github com dinghow dingdongbook raw master img 7d3c7affebf49c809cbe7a47783b49e4 png 7 7 1 user 7 1 user id number id pk name varchar 20 authority varchar 20 account varchar 20 password varchar 20 md5 gender varchar 10 email varchar 40 7 2 address 7 2 address id number 38 id pk name varchar 20 phone varchar 20 country varchar 20 province varchar 20 city varchar 20 district varchar 20 post code number 10 user id number id location varchar 100 7 3 live 7 3 live user id number id pk fk user id address id number 38 id pk fk address id 7 4 book 7 4 book id number 38 id pk isbn varchar 20 isbn name varchar 30 price number image varchar 40 category varchar 20 publisher varchar 100 publish time varchar 20 abstract varchar 400 7 5 author 7 5 author id number id pk name varchar 20 7 6 write 7 6 write author id number id pk fk author id book id number id pk fk book id 7 7 category 7 7 category id number id pk name varchar 20 7 8 belong 7 8 belong book id number id pk fk book id category id number id pk fk category id 7 9 orders 7 9 orders id number id pk user id number id fk user id address id number id fk address id quantity number price number remark varchar 100 time start varchar 20 time get varchar 20 status varchar 20 post cost number 7 10 order include 7 10 order include order id number id pk fk order id book id number id pk fk book id quantity number price number 7 11 favorite 7 11 favorite user id number id pk fk user id book id number id pk fk book id 7 12 cart 7 12 cart user id number id pk fk user id quantity number time start varchar 20 post cost number total price number 7 13 cart include 7 13 cart include user id number id pk fk user id book id number id pk fk book id quantity number total price number 7 14 comments 7 14 comments id number id pk user id number id fk user id book id number id fk book id title varchar 100 content varchar 2000 time varchar 20 score number total like varchar 20 total dislike varchar 20 total number 38 7 15 comment feedback 7 15 comment feedback user id number id pk fk user id comment id number id pk fk comment id attitude varchar 20 time varchar 20 8 a 9 b 9 1 entities ef dbset get set c public virtual dbset address address get set public virtual dbset author author get set public virtual dbset book book get set public virtual dbset cart cart get set public virtual dbset cart include cart include get set public virtual dbset category category get set public virtual dbset comment feedback comment feedback get set public virtual dbset comments comments get set public virtual dbset order include order include get set public virtual dbset orders orders get set public virtual dbset users users get set public virtual dbset write write get set public virtual dbset cartlist cartlist get set public virtual dbset purchase purchase get set public virtual dbset zuozhe zuozhe get set 9 2 models get set get set 9 2 1 book c public decimal id get set public string isbn get set public string name get set public nullable decimal price get set public string image get set public string category get set public string publisher get set public string publishtime get set public string abstract get set book system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection cart include cart include get set cart include system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection comments comments get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection order include order include get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection write write get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection category category1 get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection users users get set 9 2 2 users c public decimal id get set public string name get set public string gender get set public string email get set public string password get set public string account get set public string authority get set users system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection address address get set address public virtual cart cart get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection cart include cart include get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection comment feedback comment feedback get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection comments comments get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection orders orders get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection book book get set system diagnostics codeanalysis suppressmessage microsoft usage ca2227 collectionpropertiesshouldbereadonly public virtual icollection address address1 get set 9 2 3 cart c public decimal user id get set public nullable decimal quantity get set public string time start get set public nullable decimal post cost get set public nullable decimal total price get set cart 9 2 4 cart include c public decimal user id get set public decimal book id get set public nullable decimal quantity get set public nullable decimal total price get set cart included 9 3 models 9 3 1 cartlist cart include book c public decimal user id get set public decimal book id get set public string name get set public nullable decimal price get set public string image get set public nullable decimal quantity get set public nullable decimal total price get set 9 3 2 zuozhe author wrtie book c public string author name get set public decimal book id get set public decimal author id get set 9 3 3 purchase order include book c public decimal order id get set public string name get set public nullable decimal price get set public string image get set public nullable decimal quantity get set public nullable decimal total price get set public string abstract get set public string publisher get set public string author name get set public decimal id get set public decimal author id get set 9 4 controllers controllers razor viewbag htmlhelpers session ajax 9 4 1 bookscontroller c public actionresult index int id id public actionresult addcart cartsender cs httppost model public int directaddcart int bookid 0 1 id 9 4 2 favoritecontroller c public actionresult index int id id public actionresult addfav int id id public int directaddfav int bookid 0 1 id public void deletefav int bookids id 9 4 3 cartscontroller c public actionresult index int id id public void removebook int bookid id public void addfav int bookid id public void editamount int bookid int bookamount id public actionresult addaddress address address httppost model 9 4 4 orderscontroller c public actionresult index int id id public void statuschange int orderid id public void deleteorder int orderid id public void setcomment int bookid int grade string content id public int getorder int bookids session id public actionresult process cart int id id public void check int addrid id public actionresult order complete 9 4 5 admincontroller c public actionresult index string search book string search user public actionresult login public actionresult login loginsender msg httppost model public void deleteuser int id id public void deletebook int id id public void adjustbook int id string name string isbn string writer string category string publisher string time string image string intro | asp asp-net-mvc bookmark | server |
hedwig | hedwig posten and bring design system github release https img shields io github release bring hedwig svg style flat square https github com bring hedwig releases github issues https img shields io github issues bring hedwig svg style flat square https github com bring hedwig issues tl dr npm install npm run dev documentation hedwig documentation https hedwig posten no disclaimers we will not support changes and updates on older major releases of hedwig users of the hedwig repo are responsible for staying up to date on changes made in code and guidelines following the guidelines which also include best practices and correct brand implementation contributing this repo is public https github com bring hedwig issues 6 so please consider this when contributing found a bug create a new issue https github com bring hedwig issues if you want to fix this issue yourself do so and then follow the development giuidelines below need to change or add something new first of all let the community know on the internal hedwig slack channel what you need and how you plan to solve it there might be other teams that also would benefit from your solution either way there will probably be one of two outcomes 1 adding something new if you are adding new functionality to hediwg please follow the below development giuidelines below 2 just do it locally if the thing you re changing or adding is specific to your project and not all sites using hedwig use a separate css file to add or override functionality using your own classes and components development guidelines it is recommended to always develop the components within the documentation the docs will live reload all components should do one thing and do that thing well all css should be written to adhere to the bem methology all js should be classless and use data attributes to attach functionality new functionality should be reviewed by at least one other person before going into hedwig to add a new feature create a new feature branch git checkout b branchname develop the new feature template css and js files can be found in the templates folder document and test the new feature create a pull request and ask for a review to modify an existing feature follow the same process but also remember the versioning system if there is a breaking change or if this might impact existing sites using hedwig update the version number see below for details running hedwig locally clone this repo npm install to install dependencies npm run dev start local development server open http localhost 3000 http localhost 3000 tip in order to test changes on a locally running application which uses hedwig replace the dependencies with the following dev server urls after running hedwig locally http localhost 3001 posten css http localhost 3001 main js staging the staging environment is setup in heroku as a separate app all the assets are copied to docs folder and referred from there in this branch pushing a new change will automatically deploy the app in heroku setup staging task need to be run whenever the staging branch is reset it copies the scripts procfile to the root folder commit and push the file in staging branch only master branch shouldn t have a procfile versioning hedwig uses semantic versioning http semver org to make sure once a site starts using it the css file won t suddenly change and break the site the version number is located in package json and will be appended to the css and js files example bring 1 0 3 css when a breaking change is added a new major version is required hosting production css javascript and assets are served through a cdn using jsdelivr https www jsdelivr com icons functional icons we use fontawesome https fontawesome com icons d gallery p 2 s regular solid for functional icons not all fontaweseome icons are available in hedwig you will find available icons here https github com bring hedwig tree master src shared components icon functional if you need to use an icon from fontawesome that is not allready added in hedwig you need to add it and open a pull request font awesome icons works like this you need to add an environment variable with your authtoken from font awesome for posten and bring developers contact hedwig contributors otherwise you can get a licence https fontawesome com pricing use functional icons like described in the docs https hedwig posten no to add the authtoken to your environment in bashrc add the following line export npm token authtoken the token needs to be replaced by a real token service icons hedwig use svg sprite for special icons and logos these icons must only be used in conjunction with its service or service name svg sprite icons works like this place icons in the assets icons folder run npm run svg to generate svg sprite npm run build or npm run dev will both also produce the svg sprite we use inline svg s for icons https github com bring hedwig issues 9 linting linting our project is import to keep a holistic code base it is recommended to use a linting plugin for your editor while developing javascript uses eslint eslint org with the airbnb config https github com airbnb javascript css uses stylelint https github com stylelint stylelint with the standard config hhttps github com stylelint stylelint config standard build scripts the scripts folder contains a set of custom scripts that helps compile the docs list of npm scripts dev starts the watcher and starts the node server in development mode faq q i have a question who do i ask a use the hedwig slack channel maintainers the hedwig team is resposible for maintaining the hedwig repo contact us with any questions or feedback either in the hedwig slack channel or hedwig posten no dependencies development helpers postcss https github com postcss postcss cssnano https github com ben eb cssnano postcss cli https github com postcss postcss cli postcss cssnext https github com moox postcss cssnext postcss extend https github com travco postcss extend for transpiling css sanitize css https github com jonathantneal sanitize css to make css sane a lightweight version of normalize stylelint https github com stylelint stylelint stylelint config standard https github com stylelint stylelint config standard for litning css rollup https rollupjs org rollup plugin babel https github com rollup rollup plugin babel rollup plugin commonjs https github com rollup rollup plugin commonjs rollup plugin multi entry https github com rollup rollup plugin multi entry rollup plugin node resolve https github com rollup rollup plugin node resolve rollup watch https github com rollup rollup watch for transpiling javascript babel https babeljs io babel preset es2015 https www npmjs com package babel preset es2015 babel cli https babeljs io docs usage cli babel jest https github com babel babel jest to use es6 syntax jest https facebook github io jest for javascript unit testing lodash https lodash com docs for javascript utility functions rest is vanilla javascript eslint http eslint org eslint plugin import https www npmjs com package eslint plugin import eslint config airbnb https www npmjs com package eslint config airbnb eslint plugin jsx a11y https www npmjs com package eslint plugin jsx a11y for linting javascript express https expressjs com live reload https www npmjs com package livereload node watch https www npmjs com package node watch for developemnt s3 https www npmjs com package s3 aws sdk https aws amazon com sdk for node js dotenv https www npmjs com package dotenv for deployment concurrently https www npmjs com package concurrently to run multiple scripts catalog https github com interactivethings catalog for documentation svgo https github com svg svgo svg2sprite https github com mrmlnc svg2sprite for svg s included in bundle zenscroll https github com zengabor zenscroll tiny smooth scroll helper licenses source code is licensed under bsd 2 clause license txt all icons and images are licensed under creative commons attribution noderivatives 4 0 license icons images txt | os |
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awesome-privacy-on-blockchains | awesome privacy on blockchains awesome https awesome re badge svg https awesome re this is a combination of papers and articles that cover various aspects of blockchain privacy high level articles privacy in cryptocurrencies an overview https medium com yi sun privacy in cryptocurrencies d4b268157f6c privacy in cryptocurrencies mixing based approaches https medium com yi sun privacy in cryptocurrencies mixing based approaches ce08d0040c88 zeth nightfall the differences in approach https medium com clearmatics zeth nightfall the differences in approach 62ce33472683 blockchain research newsletter zether and zexe https blockchainresearch substack com p blockchain research newsletter 2 in depth overview of privacy in bitcoin https en bitcoin it wiki privacy privacy and cryptocurrency part 1 how private is bitcoin https medium com human rights foundation hrf privacy and cryptocurrency part i how private is bitcoin e3a4071f8fff privacy and cryptocurrency part 2 bitcoin wallets https medium com human rights foundation hrf privacy and cryptocurrency part ii bitcoin wallets 2f68099b055f privacy and cryptocurrency part 3 should you use a privacy coin https medium com human rights foundation hrf privacy and cryptocurrency part iii should you use a privacy coin 22dc71732a2f zama private smart contracts using homomorphic encryption https www zama ai post private smart contract using homomorphic encryption ethcc 2022 talks and lectures alessandro chiesa on zerocash at cesc2017 https www youtube com watch v 84vbj7 i9ci source post page bitcoin and anonymity https www youtube com watch v glyqy e5lmm satoshi has no clothes failures in on chain privacy https www youtube com watch v 9s3ebskda3o the state of privacy in cryptocurrencies https www youtube com watch v ajglhauv8qm layer 1 increasing anonymity in bitcoin https download wpsoftware net bitcoin wizardry horasyuanmouton owas pdf extending the anonymity of zcash https arxiv org pdf 1902 07337 pdf zkay specifying and enforcing data privacy in smart contracts https files sri inf ethz ch website papers ccs19 zkay pdf zeestar private smart contracts by homomorphic encryption and zero knowledge proofs https files sri inf ethz ch website papers sp22 zeestar pdf layer 2 mixers zeth https arxiv org pdf 1904 00905 pdf mobius https eprint iacr org 2017 881 pdf mixeth https eprint iacr org 2019 341 pdf sharelock https eprint iacr org 2019 563 coinjoin https en bitcoin it wiki coinjoin joinmarket https en bitcoin it wiki joinmarket mixcoin anonymity for bitcoin with accountable mixes https eprint iacr org 2014 077 pdf coinparty https www martinhenze de wp content papercite data pdf zmh 16 pdf tumblebit https eprint iacr org 2016 575 many out of many proofs with applications to anonymous zether https eprint iacr org 2020 293 pdf confidential transactions confidential transactions https people xiph org greg confidential values txt zether https crypto stanford edu buenz papers zether pdf aztec protocol https github com aztecprotocol aztec blob master aztec pdf nightfall https github com eyblockchain nightfall blob master doc whitepaper nightfall v1 pdf source post page payment state channels bolt https acmccs github io papers p473 greena pdf blindly signed contracts anonymous on blockchain and off blockchain bitcoin transactions https eprint iacr org 2016 056 anonymous multi hop locks for blockchain scalability and interoperability https eprint iacr org 2018 472 pdf plasma zexe on plasma morevp https devpost com software zexe on ethereum mpc platforms for privacy preserving smart contracts enigma https enigma co enigma full pdf alternate blockchain designs blockchains with private smart contracts zexe https eprint iacr org 2018 962 pdf one time zero sum ring signature https github com cfromknecht ozcoin blob master whitepaper zerosum pdf hawk https eprint iacr org 2015 675 pdf arbitrum https offchainlabs com arbitrum usenix pdf zkvm https github com stellar slingshot blob main zkvm readme md ekiden https arxiv org abs 1804 05141 using zk snarks zerocash http zerocash project org media pdf zerocash extended 20140518 pdf zerocoin http zerocoin org media pdf zerocoinoakland pdf accountable privacy for decentralized anonymous payments https eprint iacr org 2016 061 pdf using other cryptographic primitives quisquis https eprint iacr org 2018 990 pdf mimblewimble https download wpsoftware net bitcoin wizardry mimblewimble pdf cryptonote https download wpsoftware net bitcoin wizardry cryptonote whitepaper pdf lelantus https lelantus io lelantus pdf one out of many proofs http discovery ucl ac uk 1502142 1 groth 764 pdf zkledger https eprint iacr org 2018 241 pdf cryptonote https eprint iacr org 2019 021 pdf ringct https eprint iacr org 2015 1098 pdf privacy preserving pos protocols ouroborous crypsinous https eprint iacr org 2018 1132 pdf proof of stake protocols for privacy aware blockchains https eprint iacr org 2018 1105 pdf privacy preserving light client designs zlite https eprint iacr org 2018 1024 pdf neutrino https github com lightninglabs neutrino bite https www usenix org system files sec19fall matetic prepub pdf economics of privacy blockchains rational zero economic secuirty of zerocoin for everlasting security https www ifca ai fc14 bitcoin papers bitcoin14 submission 12 pdf the price of anonymity empirical evidence from a market for bitcoin anonymization https academic oup com cybersecurity article 3 2 127 4057584 incentivizing privacy in cryptocurrencies https arxiv org pdf 1901 02695 pdf enhancements bulletproofs https eprint iacr org 2017 1066 pdf instantiations proof of concepts mixeth repo https github com seresistvanandras mixeth miximus https github com barrywhitehat miximus ringtoken https github com sontol ringtoken laundromat https github com blackyblack laundromat tornado mixer https tornado cash wallets wasabi wallet https wasabiwallet io samourai wallet https samouraiwallet com cashshuffle https cashshuffle com network layer privacy for blockchains dandelion https arxiv org pdf 1701 04439 pdf dandelion https arxiv org pdf 1805 11060 pdf a flexible network approach to privacy of blockchain transactions https arxiv org pdf 1807 11338 pdf analyses on anonymity and privacy in blockchains anonymity properties of the bitcoin p2p network https arxiv org pdf 1703 08761 pdf a survey on security and privacy issues of bitcoin https arxiv org pdf 1706 00916 pdf an empirical analysis of traceability of the monero blockchain https arxiv org pdf 1704 04299 pdf obfuscation in bitcoin tecniques and politics https arxiv org pdf 1706 05432 pdf anonymous alone measuring bitcoin s second generation anonymization techniques https informationsecurity uibk ac at pdfs mb2017 anonymousalone pdf an empirical analysis of anonymity in zcash https www usenix org system files conference usenixsecurity18 sec18 kappos pdf a fistful of bitcoins https cseweb ucsd edu smeiklejohn files imc13 pdf privacy enhancing overlays in bitcoin https fc15 ifca ai preproceedings bitcoin paper 5 pdf map z exposing the zcash network in times of transition https arxiv org pdf 1907 09755 pdf de anonymization techniques tracing transactions across cryptocurrency ledgers https arxiv org pdf 1810 12786 pdf floodxmr low cost transaction flooding attack with monero s bulletproof protocol https eprint iacr org 2019 455 pdf privacy coins monero https www getmonero org zero to monero https ww getmonero org library zero to monero 1 0 0 pdf mastering monero https github com monerobook monerobook zcash https z cash zcash protocol specification https github com zcash zips blob master protocol protocol pdf dash https www dash org grin beam grin https grin tech org beam https www beam mw people to follow aaron kumavis https twitter com kumavis anna kaplan https twitter com kaplannie barrywhitehat https twitter com barrywhitehat charles guillemet https twitter com p3b7 chelsea h komlo https twitter com chelseakomlo daniel j bernstein https twitter com hashbreaker david wong https twitter com cryptodavidw deidre connolly https twitter com durumcrustulum harry halpin https twitter com harryhalpin ian miers https twitter com secparam izaak meckler https twitter com izmeckler john adler https twitter com jadler0 justin ehrenhofer https twitter com jehrenhofer kobi gurkan https twitter com kobigurk madars virza https twitter com madarsv matthew green https twitter com matthew d green mikerah https twitter com badcryptobitch morten dahl https twitter com mortendahlcs pascal paillier https twitter com pascal paillier phil daian https twitter com phildaian rand hindi https twitter com randhindi sean bowe https twitter com ebfull tux https twitter com tux wei jie https twitter com catallacticised zooko https twitter com zooko related lists zero knowledge started pack https ethresear ch t zero knowledge proofs starter pack 4519 2 zero knowledge papers zkp science awesome zero knowledge proofs https github com matter labs awesome zero knowledge proofs awesome zk https github com ventali awesome zk | awesome-list privacy blockchain cryptocurrency p2p anonymity confidential bitcoin zcash monero ethereum grin | blockchain |
procedural-advml | procedural noise uaps this repository contains sample code and an interactive jupyter notebook for the papers procedural noise adversarial examples for black box attacks on deep convolutional networks https dl acm org citation cfm id 3345660 ccs 19 sensitivity of deep convolutional networks to gabor noise https openreview net forum id hjx08nsnne icml 19 workshop in this work we show that universal adversarial perturbations uaps can be generated with procedural noise functions without any knowledge of the target model procedural noise functions are fast and lightweight methods for generating textures in computer graphics this enables low cost black box attacks on deep convolutional networks for computer vision tasks we encourage you to explore our python notebooks and make your own adversarial examples 1 intro bopt ipynb shows how bayesian optimization can find better parameters for the procedural noise functions 2 intro gabor ipynb gives a brief introduction to gabor noise slider docs intro png 3 slider gabor ipynb slider perlin visualize and interactively play with the parameters to see how it affects model predictions slider docs slider png see our paper https dl acm org citation cfm id 3345660 for more details procedural noise adversarial examples for black box attacks on deep convolutional networks kenneth t co luis mu oz gonz lez emil c lupu ccs 2019 acknowledgments img src docs dataspartan jpeg align right width 25 learn more about the resilient information systems security riss http rissgroup org group at imperial college london kenneth co is partially supported by dataspartan http dataspartan co uk if you find this project useful in your research please consider citing inproceedings co2019procedural author co kenneth t and mu n oz gonz a lez luis and de maupeou sixte and lupu emil c title procedural noise adversarial examples for black box attacks on deep convolutional networks booktitle proceedings of the 2019 acm sigsac conference on computer and communications security series ccs 19 year 2019 isbn 978 1 4503 6747 9 location london united kingdom pages 275 289 numpages 15 url http doi acm org 10 1145 3319535 3345660 doi 10 1145 3319535 3345660 acmid 3345660 publisher acm address new york ny usa keywords adversarial machine learning bayesian optimization black box attacks deep neural networks procedural noise universal adversarial perturbations this project is licensed under the mit license see the license md license md file for details | deep-convolutional-networks procedural-noise-functions black-box-attacks gabor-noise noise perlin-noise adversarial-attacks adversarial-machine-learning adversarial-examples universal-adversarial-perturbations procedural-noise keras papers | ai |
Embedded-systems-project | embedded systems project ee50226 embedded systems design | os |
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Employee-Database | employee database utilized data engineering and data analysis to build a sql database of employees of a corporation called pewlett hackard from the 1980s and 1990s there are six csv files holding the data of employees the sql tables were designed and the data in the csvs were successfully imported into a sql database data engineering inspected the csvs and sketched out an erd of the tables employee erd https user images githubusercontent com 119978382 222480968 89546f2c 2299 405a a618 b1da8f131d92 png used the information from erd to create a table schema for each of the six csv files and specify data types primary keys foreign keys and other constraints imported each csv file into the corresponding sql table and make sure to import the data in the same order that the tables were created data analysis list the employee number last name first name sex and salary of each employee list the first name last name and hire date for the employees who were hired in 1986 list the manager of each department along with their department number department name employee number last name and first name list the department number for each employee along with that employee s employee number last name first name and department name list first name last name and sex of each employee whose first name is hercules and whose last name begins with the letter b list each employee in the sales department including their employee number last name and first name list each employee in the sales and development departments including their employee number last name first name and department name list the frequency counts in descending order of all the employee last names that is how many employees share each last name data visualization historgram visualization of the most common salary ranges commonsalary https user images githubusercontent com 119978382 222600913 eccf7ff9 fb48 4994 93e8 e899efa53f85 png bar chart for average salary by title average salary by title https user images githubusercontent com 119978382 222600920 f3fed6d6 ecc2 45d9 8124 2fe37fc9f2c3 png | server |
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VTU-2022-Android-MAD-Labs | vtu 2022 android mad labs this repo contains all the code implementation from victor s portal youtube channel for the sixth vi semester vtu mobile application development course 18csmp68 keep coming back as this reo will be constantly updated | front_end |
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ipynb_playground | my ipython notebook playground best viewed on nbviewer http nbviewer ipython org github julienr ipynb playground contents dumbcoin bitcoin dumbcoin dumbcoin ipynb bitcoin dumbcoin dumbcoin ipynb a bitcoin like educational blockchain implementation perceptron perceptron perceptron ipynb perceptron perceptron ipynb a single unit perceptron implementation in python with a nice video of the training if you run the notebook locally perceptron perceptron theano ipynb perceptron perceptron theano ipynb perceptron but with theano this time keras deep learning keras convmnist keras cnn mnist ipynb keras convmnist keras cnn mnist ipynb keras cnn example on mnist with weights and convolutions visualization keras convmnist keras conv autoencoder mnist ipynb keras convmnist keras conv autoencoder mnist ipynb convolutional autoencoder on mnist keras learning cosine ipynb keras learning cosine ipynb learning a cosine with a simple nn using keras strava api gps sport tracking service strava strava get activities ipynb strava strava get activities ipynb getting your activities from strava strava analytics ipynb strava analytics ipynb loading them and doing some basics analytics misc ml some notes about machine learning misc ml curse dimensionality ipynb misc ml curse dimensionality ipynb plots about the curse of dimensionality | perceptron bitcoin blockchain python | blockchain |
eliza | eliza example of eliza assets imgs example shot png about this is an implementation of eliza built in pure javascript i wanted to build it on a mern stack but my starting point is in just js you can find a more indepth description below eliza is an early natural language processing computer program created from 1964 to 1966 1 at the mit artificial intelligence laboratory by joseph weizenbaum created to demonstrate the superficiality of communication between man and machine eliza simulated conversation by using a pattern matching and substitution methodology that gave users an illusion of understanding on the part of the program but had no built in framework for contextualizing events the most famous script doctor simulated a rogerian psychotherapist and used rules dictated in the script to respond with non directional questions to user inputs as such eliza was one of the first chatterbots but was also regarded as one of the first programs capable of passing the turing test taken from wiki https en wikipedia org wiki eliza running you have two options option 1 it s hosted on github pages at http keithweaver github io eliza option 2 just open the index html file on your local machine so this one index html why javascript i decided to build out my implementation of eliza in javascript because it can be run on most computers using most browsers there s no installation time and i include the dependencies in the index html i can also create a github pages demo forever file structure index html readme md assets css imgs js actions js all ui related functions and actions notifications js allows me to create dynamic notifications for informing user demo js runs a few example inputs eliza js the actual eliza implementation explanation the program has been built with two major steps start and send new message the start function runs once the page is loaded it begins by creating a list of keywords based on a pre determined list of responses and a pre determined list of similar words an example of a response var responses sorry weight 1 responses please don t apologize apologies are not necessary apologies are not required an example of a similar word var synonyms sorry apologise the idea is instead of repeating exact responses for multipe words like sorry and apologise or youre and you re it uses the list of synonyms object to find responses in advance of any messages after all potential keywords are added with their weight the keyword list is sorted from on highest weighed to lowest the weight better determines relevance towards particular subjects like family or computer it next adds the first message to begin the conversation hello how are you feeling today the program using jquery to determine when the window loads and when there is a new input a user simply has to type a new message into the textbox and presses enter this eliminates the need for continuous looping when a user sends a new message to eliza the program verifies the textbox is not blank and adds to the ui before beginnig to analyze the string for a response function sendelizanewmessage newmessage add to ui chathistory push iseliza false content newmessage displaychat clearsendtextbox if conversationover makes it lower case newmessage processinput newmessage find the response based on the input check the analyze docs var response analyze newmessage else var response our conversation has ended refresh the page to start again shows the reply from eliza to make it seem more real takes a random short time to response reloads the chat settimeout function usedresponses push response chathistory push iseliza true content response displaychat determineresponsetime the purpose of the analyze function is to find an appropriate response based on the input string the weight of the keywords the past chat messages a degree of randomness and the engagement from the response the program looks for a wildcard in the keyword or just the keyword and starts with the most heavily weighted and working down if a term is found and has a wildcard it begins determining whether or not it matches the rules associated with that wildcard an example is i am 1 3 happy which is similar to i am happy but a user may say something like i am extremely happy the program checks to see that keywords i am and happy exists and are in the proper order the program also verifies the wild card rules in this example there can be 1 to 3 words between am and happy if there are more or less it does not count as a found keyword the implementation helps managed a variety of sentences like i am extremely happy works but i am sad because i am not happy does not when the program recognizes the valid keywords with a wildcard it replaces it with a keyword with a response and continues to the selectresponse function an example i am extremely happy finds a response with i am happy if a term is found without a wildcard and that term does not represent goodbye it continues to find a response the selectresponse uses all attributes mentioned before to determine the optimal and most realistic response it first gathers a list of possible responses for that keyword the program then looks for a response containing a wild card subject or a response that has not been used before if it matches either it adds a duplicate to the list of responses to increase the odds of them occurring the use of a wild card makes it a more engaging conversation being able to reply with the context and content from the known information in the example below it takes the response have you ever fantasized about while you were awake and the program knows to replace the with the incoming content example of a wild card dream is the keyword user i had a dream last night about my dog eating my homework eliza have you ever fantasized about your dog eating your homework while you were awake another way selectresponse increase the realness to eliza is by storing past responses and minimizing the amount of repeated content a therapist may repeat their questions but commonly they ask a variety of questions the last step for selectresponse is a degree of randomness to continuously change the questions coming from eliza function selectresponse word var potentialresponses if word in responses easily find responses by using key value pairing potentialresponses responses word else need to find the related responses potentialresponses findresponsesforsimilarword word var newresponses var originalresponsessize potentialresponses responses length for var i 0 i originalresponsessize i newresponses push potentialresponses responses i if has wild card adds another if potentialresponses responses i indexof 1 newresponses push potentialresponses responses i if the response hasnt been used if usedresponses contains potentialresponses responses i newresponses push potentialresponses responses i return newresponses math floor math random newresponses length looking back at the analyze function the program now has a response this response could potentially have a wild card if no wild card is found the function simply just returns the response from eliza if no response is found there are a list of responses that encourage the user to continue provide more information however if a wild card is found it begins to insert the contents into the response the first step is to take the content to the right of the keyword in the incoming message the second step is to split the response in two around the and excludes it the third step is to paste the pieces together in the proper order there is an example included in the comments of the code during the pasting portion we modified the contents of the user input with the replacewords function in the replacewords function we change the perspective of the content to eliza s point of view as compared to the users as you can see below we change my to be your i to be you etc this is so the reply does not seem like eliza is simply returning the input function analyze newmessage var found false var response check all for var i 0 i keywords length i var word keywords i word if endchatterms contains newmessage conversationover true newmessage goodbye check for a wild card in the keyword if yes then its a keyword with an adjective like i am 1 3 happy also checks if the newmessage contains all parts in proper order and following the rules if word indexof 1 containskeywordwithwildcard newmessage word found response selectresponse findbasickeywordfromkeywordwithwildcard word found true else if newmessage indexof word 1 newmessage length word length newmessage indexof word 1 newmessage indexof word 1 found check to see if the keyword is in the sentence ex input is hi or this and more or hi doc this picks a response response selectresponse word check for wild card found true if found response indexof 1 wild card exists so sub in the phrase ex i had a dream about my dog dream is the keyword right of the keyword in the incoming message var remaininginput newmessage substring newmessage indexof word word length 1 newmessage length remaininginput about my dog right of the wildcard in the response var rightofwildcardinresponse response substring response indexof 1 right of wildcard in response while you were awake start of the response to the wildcard var startofresponsetowildcard response substring 0 response indexof start of response to wildcard have you ever fantasized the start of the remaininginput to the end minus the one character var startofinputminusonecharacter remaininginput substring 0 remaininginput length 1 start of input minus one character about my do this is remaining of the remaining input regular expression replaces things that are not either a z or a z var remainingofinputonright remaininginput substring remaininginput length 1 remaininginput length replace a za z remaining of input on right g response startofresponsetowildcard replacewords startofinputminusonecharacter remainingofinputonright rightofwildcardinresponse response have you ever fantasized about my dog while you were awake changes the words and fixes the tenses ex i had a dream about my dog have you ever fantasized about your dog while you were awake but it only should work on the input not the response so you apply it to the inner parts if found break if found response responses notfound responses math floor math random responses notfound responses length return response replaces the context of words flipping the voice from the user to eliza it s split into an array so it doesn t replace back and forth ex me i i me function replacewords input var wordsforreplacement wordsforreplacement i you wordsforreplacement you i wordsforreplacement me you wordsforreplacement my your wordsforreplacement am are wordsforreplacement are am wordsforreplacement was were wordsforreplacement i d you would wordsforreplacement i ve you have wordsforreplacement i ll you will wordsforreplacement you ve i have wordsforreplacement you ll i will wordsforreplacement your my wordsforreplacement yours mine wordsforreplacement me you added in after testing wordsforreplacement always had always have var inputsplit input split was having an overrite issue var newsplit for var i 0 i inputsplit length i var currentinputword inputsplit i if currentinputword in wordsforreplacement var replacementword wordsforreplacement currentinputword newsplit i replacementword i had a dream about my dog else newsplit i currentinputword var updatedmessage for var i 0 i newsplit length i var word newsplit i if updatedmessage updatedmessage updatedmessage word return updatedmessage checks to see if the keyword exists properly so i am happy is in proper order also checks to against runs so only so many words are between am and happy so i am extremely happy but i am sad because i am happy would fail function containskeywordwithwildcard input keywordswithwildcardstr var responsewildcardobj getresponsewildcardinfo keywordswithwildcardstr var numberofwordsinwildcard 0 var foundkeywords 0 var inputarray input split for var i 0 i inputarray length i var currentword inputarray i if the word is not a keyword add it and we are in the wildcard if foundkeywords responsewildcardobj minnumwords foundkeywords responsewildcardobj maxnumwords responsewildcardobj keywords contains currentword numberofwordsinwildcard if responsewildcardobj keywords length 0 currentword responsewildcardobj keywords 0 so first this would be i for i am happy responsewildcardobj keywords remove currentword foundkeywords doesnot have all keywords i am sad i am happy i am extremely happy sad should stop here cause its not valid if responsewildcardobj keywords length 0 console log not the correct keyword return false if numberofwordsinwildcard responsewildcardobj minnumwords numberofwordsinwildcard responsewildcardobj maxnumwords console log does not follow wildcard rules return false return true after the program updates the chat with the new response it waits for the next input | javascript eliza chatbot js conversation ai queensu | ai |
PEWLETT-HACKARD---SQL | pewlett hackard sql backgound as a new data engineer at pewlett hackard the first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files which are used in this project the tasks in this project involve designing tables to hold data in csvs importing csvs into a sql database and answering questions about the data these activities relates to analytics engineering performing data modeling data engineering and data analysis data modeling inspected the 6 csv files and sketched out an entity relationship diagram of the tables see erd png image or erd code txt code data engineering created a table schema for each of the six csv files created tables in correct order to handle foreign keys appropriately imported each csv file into the corresponding sql table in the same order that the tables were created and accounted for the headers when importing to avoid errors specified data types primary keys foreign keys and other constraints saved schema in a file called schema sql which can be run once to create and populate all base tables in the database data analysis wrote queries to select the following information from the database 1 select the employee number last name first name sex and salary of all employees ordered by employee number 2 select the first name last name and hiring date for all employees hired in 1986 3 select the department number department name employee number last name and first name of all managers of each department 4 select the employee number last name first name and department of each employee ordered by employee number 5 select first name last name birth date and sex of all employees whose first name is hercules and last name begins with a b 6 select employee number last name first name and department name of all employees in the sales department ordered by employee number 7 select employee number last name first name and department name of all employees in the sales or the development department ordered by employee number 8 count the number of employees grouped by last name saved the queries in a file called queries sql | data-engineering data-analysis employee-database csvs sql-database | server |
crfsuite | labelling sequential data in natural language processing this repository contains an r package which wraps the crfsuite c c library https github com chokkan crfsuite allowing the following fit a conditional random field model 1st order linear chain markov use the model to get predictions alongside the model on new data the focus of the implementation is in the area of natural language processing where this r package allows you to easily build and apply models for named entity recognition text chunking part of speech tagging intent recognition or classification of any category you have in mind for users unfamiliar with conditional random field crf models you can read this excellent tutorial https homepages inf ed ac uk csutton publications crftut fnt pdf installation the package is on cran so just install it with the command install packages crfsuite for installing the development version of this package devtools install github bnosac crfsuite build vignettes true model building and tagging for detailed documentation on how to build your own crf tagger for doing ner chunking look to the vignette r library crfsuite vignette crfsuite nlp package crfsuite short example r library crfsuite get example training data enrich with token and part of speech 2 words before after each token x ner download modeldata conll2002 nl x crf cbind attributes x terms c token pos by c doc id sentence id from 2 to 2 ngram max 3 sep split in train test set crf train subset x data ned train crf test subset x data testa build the crf model attributes grep token pos colnames x value true model crf y crf train label x crf train attributes group crf train doc id method lbfgs options list max iterations 25 feature minfreq 5 c1 0 c2 1 model use the model to score on existing tokenised data scores predict model newdata crf test attributes group crf test doc id table scores label b loc b misc b org b per i loc i misc i org i per o 261 211 182 693 24 205 209 605 35297 build custom crfsuite models the package itself does not contain any models to do ner or chunking it s a package which facilitates creating your own crf model for doing named entity recognition or chunking on your own data with your own categories in order to facilitate creating training data of your own text a shiny app is made available in this r package which allows you to easily tag your own chunks of text using your own categories more details about how to launch the app which data is needed for building a model how to start to build and use your model read the vignette in detail vignette crfsuite nlp package crfsuite vignettes app screenshot png support in text mining need support in text mining contact bnosac http www bnosac be | crf crfsuite conditional-random-fields nlp ner chunking data-science natural-language-processing r r-package intent-classification | ai |
IGRP-Framework | asl 2 0 https img shields io hexpm l plug svg https github com nosicode cv igrp java template eclipse blob master readme md img src docs root images logo igrpweb 2 png http www igrp cv loudspeaker about igrp web is a platform developed by the operational nucleus for the information society nosi to create web applications based on business steps processes automatic code generation and incorporation of the once only principle written in java the igrp web core is extremely lightweight and based on simple concepts it s open source and distributed under the apache 2 0 license allows you to build unlimited number of e government applications by yourself ensuring sustainability with an affordable and scalable solution on your own data key why use igrp web igrpweb allows to independently create an unlimited number of applications automatically generate 80 of the code so the customer can focus the other 20 on customizing the application to the organization s needs control your data and applications running tests work with different databases you can choose the database you prefer oracle sqlserver h2 postgresql mysql general sharing between applications integrate and interoperate with all native applications as well as third party applications use of sql tools crud dao generator domain management document type management open source released under the apache 2 0 license so you can customize every aspect of the system to match your needs muscle studio igrp studio is the environment used to develop applications within igrpweb it includes the following components page builder interface generator bpmn designer process flow generator report designer report generator application builder application management computer supported operating systems windows x64 macos linux docker please note that igrp framework may work on other operating systems but these are not tested nor officially supported at this time beginner get start there s available a free demo cloud on how to create and run your own igrp apps on cloud https cloud nosi cv igrp user demo nosi cv password demo computer start igrp project prerequisites before running the maven command to generate the project ensure that your environment meets the following requirements 1 java 17 make sure you have java development kit jdk version 17 or higher installed on your machine 2 compatible database you need a local database available for the project to run correctly ensure that is installed and configured with either the default credentials or the required connection information database minimum recommended mysql 5 7 8 8 0 mariadb 10 3 10 6 postgresql 11 0 14 0 h2 2 0 204 2 1 10 oracle 11g 12c generating the project we can choose to generate the project using the igrp archetype horizon archetype execute the following maven command and type 1 bash mvn archetype generate dfilter igrp maven will download the necessary dependencies and prompt you for some information to configure the generated project follow the instructions that appear in the terminal and provide the required details to customize the project once the generation process is complete you will have a project based on the igrp archetype horizon ready to be developed and executed please note that depending on the information provided during project generation you may need to configure the postgresql database connection details in the project s configuration file to ensure the proper functioning of the application or you can insert the data using this command with the arguments bash mvn archetype generate dfilter igrp ddbuser user1 ddbpassword mypassword ddbjdbcurl jdbc postgresql localhost 5432 my igrp db now you are all set to start development with the generated project happy coding running the project to start the application locally you can use the following maven command bash mvn package tomee run this command will package the application and deploy it to an apache tomee server which will run the web application on your local machine once the server is up and running you can access the application using the following link in your web browser http 127 0 0 1 8080 artifactid replace artifactid with the actual artifact id of your generated project after accessing the link you should be able to interact with the web application in your browser keep the terminal with the running tomee server open as long as you want the application to be available locally if you wish to stop the server you can use ctrl c in the terminal to terminate it please note that the specific steps and commands may vary slightly depending on the configuration of the generated project and the environment in which you are running it always refer to the project s documentation or readme for any project specific instructions customizable properties of the archetype when generating the project using the igrp archetype horizon archetype you have the option to customize certain properties below are the properties that you can modify along with their default values property description default value dbjdbcurl database jdbc url jdbc postgresql localhost 5432 db igrp dbuser database username postgres dbpassword database password password dbjdbcdriver database jdbc driver org postgresql driver to customize these properties during project generation you will be prompted to provide new values for each of these properties by default the values mentioned above will be used unless you specify different values during the project generation process these customizable properties allow you to set up the project to connect to your specific postgresql database instance with the appropriate credentials and configuration make sure to enter the correct values according to your local database setup to ensure a successful connection after generating the project if you need to modify these properties later on you can find and update them in the project s configuration file or relevant configuration classes depending on how the archetype structures the generated project ballot box with check contributing to igrpweb follow contributing guidelines contributing md for more details on how to contribute in the community and how to is the process of submitting pull requests bugs fixing patches remember pull requests bug fixes and new applications development are always encouraged family community support for general help using igrp framework please refer to the official igrp framework documentation https docs igrp cv for additional help you can use one of these channels to ask a question github https https github com nosicode cv igrp framework bug reports contributions feedback section https igrpweb canny io roadmap feature requests youtube channel https www youtube com playlist list pl4wrkydha5czrna6eclqvl9q2ui3878vs learn from video tutorials memo license the project is licensed under the apache 2 0 license see the license license file for more details | front_end |
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CEM_Community | computer engineering major community homepage introduction of our team selfie name position grade major img height 180 src https github com psleon24 cem community assets 59058869 1c4a75a2 fa44 4bde ba6f 1b9b6868de0b yeong min ko director dba 3 computer egineering img height 180 src https github com psleon24 cem community assets 59058869 86cca87e 09b1 40af b82a e63509253dd7 hyun soo choi front end developer 3 computer egineering img height 180 src https github com psleon24 cem community assets 59058869 b5e13f86 dbd8 4b1a 8395 fad3cd06ed0f han gyul choi back end developer ui 3 computer egineering tech stack div h3 front end back end h3 img src https img shields io badge html e34f26 style for the badge logo html5 logocolor white img src https img shields io badge css 1572b6 style for the badge logo css3 logocolor white img src https img shields io badge javascript f7df1e style for the badge logo javascript logocolor black img src https img shields io badge bootstrap 7952b3 style for the badge logo bootstrap logocolor white img src https img shields io badge node js 339933 style for the badge logo node js logocolor white img src https img shields io badge express 000000 style for the badge logo express logocolor white img src https img shields io badge microsoft 20sql 20server cc2927 style for the badge logo microsoft 20sql 20server logocolor white div 1 requirements analysis 1 1 objective setting our goal is activating communication between same major s students through our project 1 2 project scope to be added 1 3 schedule setting pre date status goal 10 17 o project planning idea discussion selection of stack to use all member 10 24 extract main scope 10 31 collect resources related the project 11 07 system design component modeling and detail design all member develop front end 11 14 database design by 2 3 1 3 e r diagram and prepare the mid term presentation 11 21 mid term presentation develop front end logical design scheme design 11 28 develop back end front end maintenance system testing 12 05 develop back end front end maintenance system testing 12 12 final check and prepare final term presentation 12 19 final term presentation finish the project pre 2 design 2 1 system design 2 1 1 component modeling 2 1 2 detail design 2 2 ui ux design 2 3 database design 2 3 1 conceptual design 1 extract entities and attributes 2 extract relationship 3 e r diagram 2 3 2 logical design scheme design 2 3 3 physical design using sql server 3 implementation to be added 4 testing to be added 5 maintanance to be added meeting records 2023 10 13 we have decided a project subject to develop a community homepage about our major because there is little communication although they are same major at our college work records for each members yeong min ko to be added hyun soo choi to be added han gyul choi to be added references 1 to be added 2 to be added 3 to be added p align center img height 200 src https github com psleon24 cem community assets 59058869 b612dd9b e2d9 4887 afe0 dde9406a011b p | server |
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harbour-space-bcn-23 | cs213 mobile development harbour space these materials are part of the mobile development course for harbour space university | front_end |
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online-continual-learning | online continual learning aaai aser jpg official repository of online class incremental continual learning with adversarial shapley value https arxiv org abs 2009 00093 aaai 2021 supervised contrastive replay revisiting the nearest class mean classifier in online class incremental continual learning https openaccess thecvf com content cvpr2021w clvision html mai supervised contrastive replay revisiting the nearest class mean classifier in cvprw 2021 paper html cvpr2021 workshop online continual learning in image classification an empirical survey https arxiv org pdf 2101 10423 pdf neurocomputing official version https authors elsevier com a 1e1yv3inukgu7j requirements https img shields io badge python 3 7 green svg https img shields io badge torch 1 5 1 blue svg https img shields io badge torchvision 0 6 1 blue svg https img shields io badge pyyaml 5 3 1 blue svg https img shields io badge scikit learn 0 23 0 blue svg create a virtual enviroment sh virtualenv online cl activating a virtual environment sh source online cl bin activate installing packages sh pip install r requirements txt datasets online class incremental split cifar10 split cifar100 core50 nc split mini imagenet online domain incremental nonstationary miniimagenet noise occlusion blur core50 ni data preparation cifar10 cifar100 will be downloaded during the first run core50 download source fetch data setup sh mini imagenet download from https www kaggle com whitemoon miniimagenet download and place it in datasets mini imagenet nonstationary miniimagenet will be generated on the fly algorithms aser adversarial shapley value experience replay aaai 2021 paper https arxiv org abs 2009 00093 ewc efficient and online version of elastic weight consolidation ewc eccv 2018 paper http arxiv export lb library cornell edu abs 1801 10112 icarl incremental classifier and representation learning cvpr 2017 paper https arxiv org abs 1611 07725 lwf learning without forgetting eccv 2016 paper https link springer com chapter 10 1007 978 3 319 46493 0 37 agem averaged gradient episodic memory iclr 2019 paper https openreview net forum id hkf2 sc5fx er experience replay icml workshop 2019 paper https arxiv org abs 1902 10486 mir maximally interfered retrieval neurips 2019 paper https proceedings neurips cc paper 2019 hash 15825aee15eb335cc13f9b559f166ee8 abstract html gss gradient based sample selection neurips 2019 paper https arxiv org pdf 1903 08671 pdf gdumb greedy sampler and dumb learner eccv 2020 paper https www robots ox ac uk tvg publications 2020 gdumb pdf cn dpm continual neural dirichlet process mixture iclr 2020 paper https openreview net forum id sjxsojstpr scr supervised contrastive replay cvpr workshop 2021 paper https arxiv org abs 2103 13885 tricks label trick paper https arxiv org pdf 1803 10123 pdf cross entropy with knowledge distillation paper https arxiv org abs 1807 09536 multiple iterations paper https proceedings neurips cc paper 2019 hash 15825aee15eb335cc13f9b559f166ee8 abstract html nearest class mean classifier paper https arxiv org abs 2004 00440 separated softmax paper https arxiv org abs 2003 13947 review trick paper https arxiv org abs 2007 05683 run commands detailed descriptions of options can be found in general main py general main py sample commands to run algorithms on split cifar100 shell er python general main py data cifar100 cl type nc agent er retrieve random update random mem size 5000 mir python general main py data cifar100 cl type nc agent er retrieve mir update random mem size 5000 gss python general main py data cifar100 cl type nc agent er retrieve random update gss eps mem batch 10 gss mem strength 20 mem size 5000 lwf python general main py data cifar100 cl type nc agent lwf icarl python general main py data cifar100 cl type nc agent icarl retrieve random update random mem size 5000 ewc python general main py data cifar100 cl type nc agent ewc fisher update after 50 alpha 0 9 lambda 100 gdumb python general main py data cifar100 cl type nc agent gdumb mem size 1000 mem epoch 30 minlr 0 0005 clip 10 agem python general main py data cifar100 cl type nc agent agem retrieve random update random mem size 5000 cn dpm python general main py data cifar100 cl type nc agent cndpm stm capacity 1000 classifier chill 0 01 log alpha 300 aser python general main py data cifar100 cl type nc agent er update aser retrieve aser mem size 5000 aser type asvm n smp cls 1 5 k 3 scr python general main py data cifar100 cl type nc agent scr retrieve random update random mem size 5000 head mlp temp 0 07 eps mem batch 100 sample command to add a trick to memory based methods shell python general main py review trick true data cifar100 cl type nc agent er retrieve mir update random mem size 5000 sample commands to run hyper parameters tuning shell python main tune py general config general 1 yml data config data cifar100 cifar100 nc yml default config agent mir mir 1k yml tune config agent mir mir tune yml there are four config files controling the experiment general config controls variables that are not changed during the experiment data config controls variables related to the dataset default method config controls variables for a specific method that are not changed during the experiment method tuning config controls variables that are used for tuning during the experiment repo structure description agents files for different algorithms base py abstract class for algorithms agem py file for a gem cndpm py file for cn dpm ewc pp py file for ewc exp replay py file for er mir and gss gdumb py file for gdumb icarl py file for icarl lwf py file for lwf scr py file for scr continuum files for create the data stream objects dataset scripts files for processing each specific dataset dataset base py abstract class for dataset cifar10 py file for cifar10 cifar100 py file for cifar100 core50 py file for core50 mini imagenet py file for mini imagenet openloris py file for openloris continuum py data utils py non stationary py models files for backbone models ndpm files for models of cn dpm pretrained py files for pre trained models resnet py files for resnet utils files for utilities buffer files related to buffer aser retrieve py file for aser retrieval aser update py file for aser update aser utils py file for utilities for aser buffer py abstract class for buffer buffer utils py general utilities for all the buffer files gss greedy update py file for gss update mir retrieve py file for mir retrieval random retrieve py file for random retrieval reservoir update py file for random update global vars py global variables for cn dpm io py code related to load and store csv or yarml kd manager py file for knowledge distillation name match py match name strings to objects setup elements py set up and initialize basic elements utils py file for general utilities config config files for hyper parameters tuning agent config files related to agents data config files related to dataset general yml general yml fixed variables not tuned global yml paths to store results duplicate results the hyperparameters used in the aser and scr papers can be found in the folder config cvpr to duplicate the papers results citation if you use this paper code in your research please consider citing us supervised contrastive replay revisiting the nearest class mean classifier in online class incremental continual learning accepted at cvpr2021 workshop https arxiv org abs 2103 13885 inproceedings mai2021supervised title supervised contrastive replay revisiting the nearest class mean classifier in online class incremental continual learning author mai zheda and li ruiwen and kim hyunwoo and sanner scott booktitle proceedings of the ieee cvf conference on computer vision and pattern recognition pages 3589 3599 year 2021 online continual learning in image classification an empirical survey published in neurocomputing official version https authors elsevier com a 1e1yv3inukgu7j br preprint on arxiv here https arxiv org pdf 2101 10423 pdf article mai202228 title online continual learning in image classification an empirical survey journal neurocomputing volume 469 pages 28 51 year 2022 issn 0925 2312 doi https doi org 10 1016 j neucom 2021 10 021 url https www sciencedirect com science article pii s0925231221014995 author zheda mai and ruiwen li and jihwan jeong and david quispe and hyunwoo kim and scott sanner online class incremental continual learning with adversarial shapley value accepted at aaai2021 https arxiv org abs 2009 00093 inproceedings shim2021online title online class incremental continual learning with adversarial shapley value author shim dongsub and mai zheda and jeong jihwan and sanner scott and kim hyunwoo and jang jongseong booktitle proceedings of the aaai conference on artificial intelligence volume 35 number 11 pages 9630 9638 year 2021 contact contribution zheda mai https zheda mai github io corresponding author zheda mai mail utoronto ca ruiwen li https www linkedin com in ruiwen li 4a272b55 dongsub shim https github com dongsubshim acknowledgments mir https github com optimass maximally interfered retrieval gss https github com rahafaljundi gradient based sample selection cn dpm https github com soochan lee cn dpm gdumb https github com drimpossible gdumb agem https github com facebookresearch agem note the pytorch implementation of aser in this repository is more efficient than the original tensorflow implementation and has better performance the results of the aser paper can be reproduced in the original tensorflow implementation repository https github com raptormai aser | continual-learning computer-vision deep-learning convolutional-neural-networks lifelong-learning incremental-learning catastrophic-forgetting online-continual-learning incremental-continual-learning class-incremental-learning | ai |
protocol | ternity protocol this document defines the ternity protocol it replaces the outdated whitepaper from 2017 security threat model https github com aeternity aetmodel blob master threatmodel md whitepapers 2017 whitepaper https blockchainlab com pdf 91ternity blockchain whitepaper pdf the original whitepaper https github com aeternity whitepaper 2020 whitepaper https github com keypair white paper blob master aeternity whitepaper pdf draft that reflects the current state https github com aeternity white paper | blockchain |
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Web-Development-Bootcamp | web development bootcamp it contains all the projects which taught at saksham ngo | javascript html css web-development | front_end |
llm-agents | exploring the intersection of large language models and agent based modeling via prompt engineering the final frontier for simulation is the accurate representation of complex real world social systems while agent based modeling abm seeks to study the behavior and interactions of agents within a larger system it is unable to faithfully capture the full complexity of human driven behavior large language models llms like chatgpt have emerged as a solution to this bottleneck by enabling researchers to explore human driven interactions in previously unimaginable ways our research investigates simulations of human interactions using llms through prompt engineering as inspired by park et al 2023 https arxiv org pdf 2304 03442 pdf we present two simulations of believable proxies of human behavior a two agent negotiation and a six agent murder mystery game | ai |
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HumanActivityRecognition | human activity recognition this repository was coded by myself and ateexd https github com ateexd i small presented at the a href https saiconference com conferences ficc2018 future of information and communication conference ficc 2018 a singapore on april 6 2018 small i this repository contains code to our research work publication design optimization of activity recognition system on an embedded platform https link springer com chapter 10 1007 978 3 030 03402 3 46 which primarily aims to design an activity recognition engine that is optimized over computational complexity power consumed and cost without compromising on the efficiency optimization of computational complexity was carried out by reducing the number of features to be extracted a set of seven simple time domain features were extracted to classify an activity the power consumption of the overall system was brought down by reducing the number of sensors used the system was built with a single accelerometer in use which is used to get the data to compute and classify an activity thus bringing down the number of sensors to 1 also brings down the points of failure in the system the system was deployed on raspberry pi zero 5 which reduced the cost of the system the selection of minimal number of sensors to contributes to this | activity-recognition optimization python accelerometer | os |
awesome-federated-learning | awesome federated learning awesome https awesome re badge svg https awesome re a list of resources releated to federated learning and privacy in machine learning related awesome lists tushar semwal awesome federated computing https github com tushar semwal awesome federated computing papers introduction survey towards efficient synchronous federated training a survey on system optimization strategies https ieeexplore ieee org document 9780218 the internet of federated things ioft https ieeexplore ieee org document 9611259 advances and open problems in federated learning https arxiv org pdf 1912 04977 pdf federated machine learning concept and applications https arxiv org pdf 1902 04885 federated learning systems vision hype and reality for data privacy and protection https arxiv org abs 1907 09693 demystifying parallel and distributed deep learning an in depth concurrency analysis https arxiv org abs 1802 09941 edgeai a visionfor deep learning in iot era https arxiv org abs 1910 10356 machine learning systems for highly distributed and rapidly growing data https arxiv org abs 1910 08663 no peek a survey of private distributed deep learning https arxiv org pdf 1812 03288 federated learning in mobile edge networks a comprehensive survey https arxiv org abs 1909 11875 privacy and security federated learning with formal differential privacy guarantees https ai googleblog com 2022 02 federated learning with formal html applying differential privacy to large scale image classification https ai googleblog com 2022 02 applying differential privacy to large html towards causal federated learning for enhanced robustness and privacy https arxiv org pdf 2104 06557 pdf iclr dpml 2021 fedaux leveraging unlabeled auxiliary data in federated learning https arxiv org abs 2102 02514 openfl an open source framework for federated learning https arxiv org abs 2105 06413 a bayesian federated learning framework with multivariate gaussian product https arxiv org abs 2102 01936 communication efficient learning of deep networks from decentralized data https arxiv org pdf 1602 05629 pdf practical secure aggregation for federated learning on user held data https arxiv org abs 1611 04482 practical secure aggregation for privacy preserving machine learning https storage googleapis com pub tools public publication data pdf ae87385258d90b9e48377ed49d83d467b45d5776 pdf a hybrid approach to privacy preserving federated learning https arxiv org abs 1812 03224 analyzing federated learning through an adversarial lens https arxiv org pdf 1811 12470 how to backdoor federated learning https arxiv org abs 1807 00459 comprehensive privacy analysis of deep learning stand alone and federated learning under passive and active white box inference attack https arxiv org abs 1812 00910 beyond inferring class representatives user level privacy leakage from federated learning https arxiv org pdf 1812 00535 exploiting unintended feature leakage in collaborative learning https arxiv org abs 1805 04049 analyzing federated learning through an adversarial lens https arxiv org abs 1811 12470 deep models under the gan information leakage from collaborative deep learning https arxiv org abs 1702 07464 protection against reconstruction and its applications in private federated learning https arxiv org pdf 1812 00984 boosting privately privacy preserving federated extreme boosting for mobile crowdsensing https arxiv org abs 1907 10218 differentially private data generative models https arxiv org pdf 1812 02274 differentially private federated learning a client level perspective https arxiv org abs 1712 07557 privacy preserving collaborative deep learning with unreliable participants https arxiv org abs 1812 10113 scalable private learning with pate https arxiv org abs 1802 08908 reducing leakage in distributed deep learning for sensitive health data https www media mit edu publications reducing leakage in distributed deep learning for sensitive health data accepted to iclr 2019 workshop on ai for social good 2019 deep leakage from gradients http papers nips cc paper 9617 deep leakage from gradients pdf gradient leaks understanding and controlling deanonymization in federated learning https arxiv org abs 1805 05838 system and application pisces efficient federated learning via guided asynchronous training https dl acm org doi abs 10 1145 3542929 3563463 record and reward federated learning contributions with blockchain https mblocklab com recordandreward pdf flower a friendly federated learning framework https arxiv org pdf 2007 14390 pdf learning private neural language modeling with attentive aggregation https arxiv org pdf 1812 07108 dynamic sampling and selective masking for communication efficient federated learning https arxiv org abs 2003 09603 decentralized knowledge acquisition for mobile internet applications https link springer com article 10 1007 s11280 019 00775 w a generic framework for privacy preserving deep learning https arxiv org pdf 1811 04017 pdf federated learning of n gram language models https arxiv org pdf 1910 03432 pdf towards federated learning at scale system design https arxiv org pdf 1902 01046 pdf federated learning for keyword spotting https arxiv org abs 1810 05512 federated learning in distributed medical databases meta analysis of large scale subcortical brain data https arxiv org abs 1810 08553 federated collaborative filtering for privacy preserving personalized recommendation system https arxiv org pdf 1901 09888 confederated machine learning on horizontally and vertically separated medical data for large scale health system intelligence https arxiv org abs 1910 02109 privacy preserving deep learning computation for geo distributed medical big data platform http www cs ucf edu mohaisen doc dsn19b pdf institutionally distributed deep learning networks https arxiv org abs 1709 05929 multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation https arxiv org abs 1810 04304 split learning for health distributed deep learning without sharing raw patient data https www media mit edu publications split learning for health distributed deep learning without sharing raw patient data continuous delivery for machine learning https martinfowler com articles cd4ml html evolvingintelligentsystemswithoutbias ease ml ci ease ml meter towards data management for statistical generialization http ease ml visionair using federated learning to estimate air quality using the tensorflow api for java https blog tensorflow org 2020 02 visionair using federated learning to estimate airquality tensorflow api java html federated optimization in heterogeneous networks https arxiv org abs 1812 06127 un org fedprof optimizing federated learning with dynamic data profiling https arxiv org abs 2102 01733 fedbn federated learning on non iid features via local batch normalization https arxiv org abs 2102 07623 a scalable approach for partially local federated learning https ai googleblog com 2021 12 a scalable approach for partially local html m 1 federated visual classification with real world data distribution https arxiv org abs 2003 08082 measuring the effects of non identical data distribution for federated visual classification https arxiv org abs 1909 06335 leaf a benchmark for federated settings https arxiv org abs 1812 01097 on the convergence of fedavg on non iid data https arxiv org abs 1907 02189 privacy preserving federated brain tumour segmentation https arxiv org pdf 1910 00962 pdf expertmatcher automating ml model selection for users in resource constrained countries https www media mit edu publications expertmatcher detailed comparison of communication efficiency of split learning and federated learning https www media mit edu publications detailed comparison of communication efficiency of split learning and federated learning 1 split learning distributed and collaborative learning https aiforsocialgood github io iclr2019 accepted track1 pdfs 31 aisg iclr2019 pdf asynchronous federated optimization https arxiv org pdf 1903 03934 robust and communication efficient federated learning from non iid data https arxiv org pdf 1903 02891 one shot federated learning https arxiv org pdf 1902 11175 high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions https arxiv org pdf 1902 08999 agnostic federated learning https arxiv org pdf 1902 00146 c2 a0 peer to peer federated learning on graphs https arxiv org pdf 1901 11173 secureboost a lossless federated learning framework https arxiv org pdf 1901 08755 federated reinforcement learning https arxiv org pdf 1901 08277 lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems https arxiv org pdf 1901 06455 federated learning via over the air computation https arxiv org pdf 1812 11750 broadband analog aggregation for low latency federated edge learning extended version https arxiv org pdf 1812 11494 multi objective evolutionary federated learning https arxiv org pdf 1812 07478 efficient training management for mobile crowd machine learning a deep reinforcement learning approach https arxiv org pdf 1812 03633 a hybrid approach to privacy preserving federated learning https arxiv org pdf 1812 03224 applied federated learning improving google keyboard query suggestions https arxiv org pdf 1812 02903 differentially private data generative models https arxiv org pdf 1812 02274 protection against reconstruction and its applications in private federated learning https arxiv org pdf 1812 00984 split learning for health distributed deep learning without sharing raw patient data https arxiv org pdf 1812 00564 beyond inferring class representatives user level privacy leakage from federated learning https arxiv org pdf 1812 00535 loadaboost loss based adaboost federated machine learning on medical data https arxiv org pdf 1811 12629 communication efficient on device machine learning federated distillation and augmentation under non iid private data https arxiv org pdf 1811 11479 biscotti a ledger for private and secure peer to peer machine learning https arxiv org pdf 1811 09904 dancing in the dark private multi party machine learning in an untrusted setting https arxiv org pdf 1811 09712 federated learning approach for mobile packet classification https arxiv org abs 1907 13113 collaborative learning on the edges a case study on connected vehicles https www usenix org conference hotedge19 presentation lu federated learning for time series forecasting using hybrid model http www diva portal se smash get diva2 1334629 fulltext01 pdf federated learning challenges methods and future directions https arxiv org pdf 1908 07873 pdf federated learning with matched averaging https openreview net forum id bkluqlsfds code openfl an open source framework for federated learning https github com intel openfl flower https flower dev pysyft https github com openmined pysyft tensorflow federated https www tensorflow org federated crypten https github com facebookresearch crypten fate https fate fedai org dvc https dvc org leaf https leaf cmu edu federated inaturalist landmarkds https github com google research google research tree master federated vision datasets fedml a research library and benchmark for federated machine learning https github com fedml ai fedml xaynet open source framework for federated learning in rust https xaynet webflow io envisedge https github com nimbleedge envisedge use cases mit csail harvard medical tsinghua university s academy of arts and design https arxiv org ftp arxiv papers 1903 1903 09296 pdf https venturebeat com 2019 03 25 federated learning technique predicts hospital stay and patient mortality microsoft research university of chinese academy of sciences beijing china https arxiv org pdf 1907 09173 pdf boston university massachusetts general hospital https www ncbi nlm nih gov pubmed 29500022 google https ai googleblog com 2017 04 federated learning collaborative html https www statnews com 2019 09 10 google mayo clinic partnership patient data tencent webank https www digfingroup com webank clustar nvidia king s college london american college of radiology mgh and bwh center for clinical data science and ucla health etc https venturebeat com 2019 10 13 nvidia uses federated learning to create medical imaging ai https blogs nvidia com blog 2019 12 01 clara federated learning company integrate ai https integrate ai integratefl a saas platform for federated learning https integrate ai integratefl adap https adap com en snips https snips ai https www theverge com 2019 11 21 20975607 sonos buys snips ai voice assistant privacy privacy ai https privacy ai openmined https www openmined org arkhn https arkhn org en scaleout https scaleoutsystems com melloddy https www melloddy eu datafleets https www datafleets com xayn ag https www xayn com nimbleedge https www nimbleedge ai | federated-learning medical-data privacy deep-learning | ai |
joes-cafe | this is a next js https nextjs org project bootstrapped with create next app https github com vercel next js tree canary packages create next app getting started first do the command bash npm i second generate prisma database bash npx prisma migrate dev name init third run the development server bash npm run dev open http localhost 3000 http localhost 3000 with your browser to see the result fourth head over to table tableid | server |
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titanium-erp-protheus | titanium erp protheus description mobile development using appcelerator titanium http www appcelerator com making integration with erp modulo sigamnt protheus totvs http www totvs com this integration uses mvc webservice developed in advpl http tdn totvs com display tec advpl language community driven i encourage everyone to send pull requests keeping this app with new features author vitor emanuel batista email vitorebatista gmail com modules dk napp drawer https github com viezel nappdrawer widgets this app use the following widgets nl fokkezb loading https github com fokkezb nl fokkezb loading nl fokkezb drawer https github com fokkezb nl fokkezb drawer license copyright c 2013 2014 vitor emanuel batista permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | titanium titanium-alloy protheus erp mobile android ios | front_end |
SmartHomeSystem | smarthomesystem smart home system including embedded software hardware design and parts and layout undergrad capstone design project reference | os |
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Embedded_System_Design | embedded system design | os |
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youtrack_cdo | youtrack cloud dev ops templates useful for cloud engineering teams using youtrack to manage their requests br youtrack isn t really a good choice as ticketing system it is a bug tracking tool but some are using it as such br because of that i ve developed a couple of workflows to help us making yt a bit more useful br note this modules have been developed and used on youtrack 2018 2 build 43208 i m not entirely sure they would work on any other version but since the syntax didn t change that much you can probably adapt them for your version if you do that please share it with me modules cdo deployments this is a template to force a description if a field deployment type is chosen note that you need to create the field on yt before using this module cdo restricted assignee this module restrict the change of the assignee field to the team itself prevent people to randomly assign the ticket skipping our traige cdo send to slack this is forwarding any ticket new or changed to slack cdo send to slack new comment same as send to slack this forward new comments instead | cloud |
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frontend-grading-engine | udacity feedback chrome extension immediate visual feedback about any website s html css and javascript not sure what this is try the walkthrough http labs udacity com udacity feedback extension just want to install visit the chrome web store https chrome google com webstore detail udacity front end feedbac melpgahbngpgnbhhccnopmlmpbmdaeoi installing from source 1 clone this repo 2 npm install install dependencies 3 gulp watch or just gulp build the grading engine 4 load in chrome https developer chrome com extensions getstarted unpacked open the extensions window chrome extensions check developer mode click load unpacked extension select ext more on development how udacity feedback works loading tests on sites you own add the following meta tag html meta name udacity grader content relative path to tests json there are two optional attributes libraries and unit tests libraries is always optional and unit tests is only necessary for js quizzes more on js tests here js tests on sites you don t own click on the udacity browser action choose load tests and navigate to a json api json typical structure is an array of suite name code tests description definition nodes collector reporter flags example javascript name learning udacity feedback code this can be an encouraging message tests description test 1 has correct bg color definition nodes test1 cssproperty backgroundcolor equals rgb 204 204 255 name more dacity feedback code some message tests description test 2 says hello world definition nodes test2 get innerhtml hassubstring hello world description test 3 has two columns definition nodes test4 get count equals 2 description test 4 has been dispatched definition waitforevent ud test exists true flags norepeat true note that the feedback json must be an array of objects name the name of the suite the word test or tests gets appended when this name shows up in the widget as a heading for its child tests code a message to display when all tests in the suite pass why is it called a code it s sometimes the code i make students copy and paste into a quiz on the udacity site to prove they finished the quiz tests an array of test objects description shows up in the test widget try to keep titles short as long titles don t wrap well in the current version definition an object with collector and reporter properties more on this below flags optional flags to alter the way a test is run the most common is norepeat which ensures that a test runs only once rather than repeatedly how to write a definition think about this sentence as you write tests i want the nodes of selector to have some property that compares to some value 1 start with nodes most tests against the dom need some nodes to examine this is the start of a collector javascript definition nodes selector exceptions collecting a user agent string device pixel ratio or in conjunction with waitforevent 2 decide what value you want to collect and test css javascript definition nodes anything cssproperty backgroundcolor the cssproperty can be the camelcase version of any css property https developer mozilla org en us docs web css css properties reference cssproperty takes advantage of window getcomputedstyle colors will be returned in the form of rgb 255 255 255 note the spaces all width margin etc measurements are returned as pixel values not percentages attribute javascript definition nodes input attribute for any attribute works absolute position javascript definition nodes left nav absoluteposition side side must be one of top left bottom or right currently the position returned is relative to the viewport count innerhtml childposition uastring user agent string dpr device pixel ratio javascript definition nodes box get count these tests count innerhtml childpositions uastring dpr use get and they are the only tests that use get remember the asterix from earlier about the necessity of nodes device pixel ratios and user agent strings are exceptions you can get uastring or get dpr without nodes child position i haven t seen anything about children a question you might be asking yourself let me answer it children javascript definition nodes flex container children div absoluteposition side children is a deep children selector in this example it was used to select all the divs inside a flex container now reporters will run tests against all of the child divs not the parent flex container 3 decide how you want to grade the values you just collected this is a reporter equals javascript definition nodes flex get count equals 4 or javascript definition nodes input billing address attribute for equals billing address or javascript definition nodes inline cssproperty display equals inline inline block set the equals value to a string a number or an array of strings and numbers this test looks for a strict equality match of either the value or one of the values in the array in the first example the test passes when the count of nodes returned by the selector equals four in the second the for attribute of input class billing address must be set to billing address in the third example the test passes if display is either inline or inline block if you want to compare strings and would prefer to use regex try hassubstring exists javascript definition nodes input billing address attribute for exists true in this example rather than looking for a specific for i m just checking to see that it exists at all the value doesn t matter if exists false then the test will only pass if the attribute does not exist comparison javascript definition nodes flex get count islessthan 4 islessthan and isgreaterthan share identical behavior javascript definition nodes flex get count isinrange lower 4 upper 10 set an upper and a lower value for isinrange substrings javascript definition nodes text get innerhtml hassubstring a z w run regex tests against strings with hassubstring if one or more match groups are returned the test passes note you must escape s eg whello will break but whello will work you can pass an array to hassubstring if you want to match one regex out of many javascript definition nodes text get innerhtml hassubstring a z w a z w there is an alternate syntax with optional configs for hassubstring javascript definition nodes text get innerhtml hassubstring expected a z w another minvalues 1 maxvalues 2 this test checks that either one or both of the expected values are found if you set hassubstring to an object with an array of expected regexes you can optionally use minvalues and maxvalues to determine how many of the expected values need to match in order for the test to pass unless otherwise specified only one value from the expected array will need to be matched in order for the test to pass utility properties not limit javascript definition nodes text get innerhtml not true hassubstring a z w switch behavior with not a failing test will now pass and vice versa javascript definition nodes title cssproperty margintop limit 1 equals 10 currently the values supported by limit are any number 1 all default and some remember by default every node collected by nodes or children must pass the test specified to change that use limit if it is any number 0 numbercorrect limit values must pass in order for the test to pass if more than one value passes the test fails in the case of some one or more values must pass in order for the test to pass flags javascript definition nodes small cssproperty borderleft equals 10 flags norepeat true options here currently include norepeat and alwaysrun by default a test runs every 1000ms until it either passes or encounters an error if norepeat is set the test only runs once when the widget loads and does not rerun every 1000ms if alwaysrun the test continues to run even after it passes a name js tests a javascript tests javascript definition waitforevent custom event exists true for security reasons you can only run javascript tests against pages that you control you can trigger tests by dispatching custom events from inside your application or from a script linked in the unit tests attribute of the meta tag set waitforevent to a custom event as soon as the custom event is detected the test passes example of a custom event javascript window dispatchevent new customevent ud test detail passed i like to use the jsgrader library https github com udacity js grader for writing js tests because it supports grading checkpoints logic to say stop grading if this test fails you can use it too by setting libraries jsgrader in the meta tag test states and debugging wrong answer right answer error images wrong right error png green tests with have passed red tests with have failed and yellow tests with have some kind of error if there is an error run udacityfegradingengine debug from the console to see why the yellow tests are erring you can find an options page in chrome extensions use the options page to see and modify the list of domains on which the extension will run how udacity feedback works at the core of udacity feedback is the grading engine the grading engine performs two tasks collecting information from the dom and reporting on it each test creates its own instance of the grading engine which queries the dom once a second unless otherwise specified overview of the source code ta teaching assistant the ta orchestrates the dom querying and comparison logic of the grading engine there is a collection aspect src js tacollectors js and a reporting aspect src js tareporters js collectors pull info from the dom reporters are responsible for the logic of evaluating the information the ta executes tests as a series of async methods pulled from a queue gradebook every ta has an instance of a gradebook which determines the pass fail state of a test some tests have multiple parts eg examining every element of some class to ensure that all have a blue background each element is a part of the test the gradebook compares the parts to the comparison functions as set by the ta and decides if the test has passed or failed target a target represents a single piece of information pulled from the dom almost every target has an associated element and some value targets may include child targets tests that result in multiple pieces of information create a tree of targets sometimes called a bullseye in comments suite and activetest an individual test ie one line in the widget is an instance of an activetest activetests are organized into suites each suite comes with its own name which is displayed above its set of tests in the widget registrar this file contains the logic for creating new tests when the feedback is turned on and removing tests when the feedback is turned off the test widget and everything inside of it were built as custom elements with html imports development workflow 1 run gulp watch from 2 make changes 3 open sample index html to run regression testing 4 if you re adding a new feature add new passing and failing tests to sample tests json and modify sample index html if necessary update this readme to reflect changes include examples 5 submit a pull request did you read this far you re awesome archival note this repository is deprecated therefore we are going to archive it however learners will be able to fork it to their personal github account but cannot submit prs to this repository if you have any issues or suggestions to make feel free to utilize the https knowledge udacity com forum to seek help on content specific issues submit a support ticket along with the link to your forked repository if learners are blocked for other reasons here are the links for the retail consumers https udacity zendesk com hc en us requests new and enterprise learners https udacityenterprise zendesk com hc en us requests new ticket form id 360000279131 | front_end |
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mock-api | mock api node js http nodejs org node js nvm https github com creationix nvm node js nodemon https github com remy nodemon node js mock api https github com caolvchong mock api json api koajs http koajs com context koa ok mock api json ok ok mock api mock api 1 node js node v0 11 7 nvm node 2 mock api npm install g mock api 1 json 2 3 json 4 5 api text get users id id id name level restful json json user json json method get url users id response id 1 name tom level 3 method get post put delete url response json json method get url users id response id number this params id name tom level 3 response key number string value javascript this params key get this query key post this body key this koa koajs delay status error json method get url users id response id 1 name tom level 3 delay 3000 status 400 error message 0 1 0 js js var store module exports method get url users response function helper return helper pagination store limit this query limit offset this query offset helper pagination json data pagination limit offset total jsoncopy json nodejs bash mock api serve path to restful path to restful restful http localhost 10086 users 1 mock api bash mock api serve path to restful p port http localhost 10086 api d bash mock api serve path to restful d 2000 s bash mock api serve path to restful s 400 s serve static bash mock api serve path to restful s path to static bash mock api serve h | front_end |
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ml-interview | this repos is depreciated check out the latest nailing machine learning concepts https github com jayinai nail ml concept this repository covers how to prepare for machine learning interviews mainly in the format of questions answers asides from machine learning knowledge other crucial aspects include explain your resume explain your resume sql sql go directly to machine learning machine learning explain your resume your resume should specify interesting ml projects you got involved in the past and quantitatively show your contribution consider the following comparison trained a machine learning system vs trained a deep vision system squeezenet that has 1 30 model size 1 3 training time 1 5 inference time and 2x faster convergence compared with traditional convnet e g resnet we all can tell which one is gonna catch interviewer s eyeballs and better show case your ability in the interview be sure to explain what you ve done well spend some time going over your resume before the interview sql although you don t have to be a sql expert for most machine learning positions the interviews might ask you some sql related questions so it helps to refresh your memory beforehand some good sql resources are w3schools sql https www w3schools com sql sqlzoo http sqlzoo net machine learning first it s always a good idea to review chapter 5 http www deeplearningbook org contents ml html of the deep learning book which covers machine learning basics linear regression linear regression logistic regression logistic regression knn knn svm svm naive bayes decision tree decision tree bagging bagging random forest random forest boosting boosting stacking stacking clustering mlp mlp cnn cnn rnn and lstm rnn and lstm word2vec word2vec generative vs discriminative generative vs discriminative paramteric vs nonparametric paramteric vs nonparametric linear regression how to learn the parameter minimize the cost function how to minimize cost function gradient descent regularization l1 lasso can shrink certain coef to zero thus performing feature selection l2 ridge shrink all coef with the same proportion almost always outperforms l1 combined elastic net assumes linear relationship between features and the label can add polynomial and interaction features to add non linearity lr http scikit learn org stable images sphx glr plot cv predict thumb png back to top machine learning logistic regression generalized linear model glm for classification problems apply the sigmoid function to the output of linear models squeezing the target to range 0 1 threshold to make prediction if the output 5 prediction 1 otherwise prediction 0 a special case of softmax function which deals with multi class problem back to top machine learning knn given a data point we compute the k nearest data points neighbors using certain distance metric e g euclidean metric for classification we take the majority label of neighbors for regression we take the mean of the label values note for knn technically we don t need to train a model we simply compute during inference time this can be computationally expensive since each of the test example need to be compared with every training example to see how close they are there are approximation methods can have faster inference time by partitioning the training data into regions note when k equals 1 or other small number the model is prone to overfitting high variance while when k equals number of data points or other large number the model is prone to underfitting high bias knn https cambridgecoding files wordpress com 2016 03 training data only 99 1 png w 610 back to top machine learning svm can perform linear nonlinear or outlier detection unsupervised large margin classifier not only have a decision boundary but want the boundary to be as far from the closest training point as possible the closest training examples are called the support vectors since they are the points based on which the decision boundary is drawn svms are sensitive to feature scaling svm https qph ec quoracdn net main qimg 675fedee717331e478ecfcc40e2e4d38 back to top machine learning decision tree non parametric supervised learning algorithms given the training data a decision tree algorithm divides the feature space into regions for inference we first see which region does the test data point fall in and take the mean label values regression or the majority label value classification construction top down chooses a variable to split the data such that the target variables within each region are as homogeneous as possible two common metrics gini impurity or information gain won t matter much in practice advantage simply to understand interpret mirrors human decision making disadvantage can overfit easily and generalize poorly if we don t limit the depth of the tree can be non robust a small change in the training data can lead to a totally different tree instability sensitive to training set rotation due to its orthogonal decision boundaries decision tree http www fizyka umk pl wduch ref kdd tut d tree iris gif back to top machine learning bagging to address overfitting we can use an ensemble method called bagging bootstrap aggregating which reduces the variance of the meta learning algorithm bagging can be applied to decision tree or other algorithms here is a great illustration http scikit learn org stable auto examples ensemble plot bias variance html sphx glr auto examples ensemble plot bias variance py of a single estimator vs bagging bagging http scikit learn org stable images sphx glr plot bias variance 001 png bagging is when samlping is performed with replacement when sampling is performed without replacement it s called pasting bagging is popular due to its boost for performance but also due to that individual learners can be trained in parallel and scale well ensemble methods work best when the learners are as independent from one another as possible voting soft voting predict probability and average over all individual learners often works better than hard voting out of bag instances 37 can act validation set for bagging back to top machine learning random forest random forest improves bagging further by adding some randomness in random forest only a subset of features are selected at random to construct a tree while often not subsample instances the benefit is that random forest decorrelates the trees for example suppose we have a dataset there is one very predicative feature and a couple of moderately predicative features in bagging trees most of the trees will use this very predicative feature in the top split and therefore making most of the trees look similar and highly correlated averaging many highly correlated results won t lead to a large reduction in variance compared with uncorrelated results in random forest for each split we only consider a subset of the features and therefore reduce the variance even further by introducing more uncorrelated trees i wrote a notebook notebooks bag rf var ipynb to illustrate this point in practice tuning random forest entails having a large number of trees the more the better but always consider computation constraint also min samples leaf the minimum number of samples at the leaf node to control the tree size and overfitting always cv the parameters feature importance in a decision tree important features are likely to appear closer to the root of the tree we can get a feature s importance for random forest by computing the averaging depth at which it appears across all trees in the forest back to top machine learning boosting how it works boosting builds on weak learners and in an iterative fashion in each iteration a new learner is added while all existing learners are kept unchanged all learners are weighted based on their performance e g accuracy and after a weak learner is added the data are re weighted examples that are misclassified gain more weights while examples that are correctly classified lose weights thus future weak learners focus more on examples that previous weak learners misclassified difference from random forest rf rf grows trees in parallel while boosting is sequential rf reduces variance while boosting reduces errors by reducing bias xgboost extreme gradient boosting xgboost uses a more regularized model formalization to control overfitting which gives it better performance back to top machine learning stacking instead of using trivial functions such as hard voting to aggregate the predictions from individual learners train a model to perform this aggregation first split the training set into two subsets the first subset is used to train the learners in the first layer next the first layer learners are used to make predictions meta features on the second subset and those predictions are used to train another models to obtain the weigts of different learners in the second layer we can train multiple models in the second layer but this entails subsetting the original dataset into 3 parts stacking http www kdnuggets com wp content uploads backward propagation stacker models jpg back to top machine learning mlp a feedforward neural network where we have multiple layers in each layer we can have multiple neurons and each of the neuron in the next layer is a linear nonlinear combination of the all the neurons in the previous layer in order to train the network we back propagate the errors layer by layer in theory mlp can approximate any functions mlp http neuroph sourceforge net tutorials images mlp jpg back to top machine learning cnn the conv layer is the building block of a convolutional network the conv layer consists of a set of learnable filters such as 5 5 3 width height depth during the forward pass we slide or more precisely convolve the filter across the input and compute the dot product learning again happens when the network back propagate the error layer by layer initial layers capture low level features such as angle and edges while later layers learn a combination of the low level features and in the previous layers and can therefore represent higher level feature such as shape and object parts cnn http www kdnuggets com wp content uploads dnn layers jpg back to top machine learning rnn and lstm rnn is another paradigm of neural network where we have difference layers of cells and each cell only take as input the cell from the previous layer but also the previous cell within the same layer this gives rnn the power to model sequence rnn http karpathy github io assets rnn diags jpeg this seems great but in practice rnn barely works due to exploding vanishing gradient which is cause by a series of multiplication of the same matrix to solve this we can use a variation of rnn called long short term memory lstm which is capable of learning long term dependencies the math behind lstm can be pretty complicated but intuitively lstm introduce input gate output gate forget gate memory cell internal state lstm resembles human memory it forgets old stuff old internal state forget gate and learns from new input input node input gate lstm http deeplearning net tutorial images lstm memorycell png back to top machine learning word2vec shallow two layer neural networks that are trained to construct linguistic context of words takes as input a large corpus and produce a vector space typically of several hundred dimension and each word in the corpus is assigned a vector in the space the key idea is context words that occur often in the same context should have same opposite meanings two flavors continuous bag of words cbow the model predicts the current word given a window of surrounding context words skip gram predicts the surrounding context words using the current word word2vec https deeplearning4j org img countries capitals png back to top machine learning generative vs discriminative discriminative algorithms model p y x w that is given the dataset and learned parameter what is the probability of y belonging to a specific class a discriminative algorithm doesn t care about how the data was generated it simply categorizes a given example generative algorithms try to model p x y that is the distribution of features given that it belongs to a certain class a generative algorithm models how the data was generated given a training set an algorithm like logistic regression or the perceptron algorithm basically tries to find a straight line that is a decision boundary that separates the elephants and dogs then to classify a new animal as either an elephant or a dog it checks on which side of the decision boundary it falls and makes its prediction accordingly here s a different approach first looking at elephants we can build a model of what elephants look like then looking at dogs we can build a separate model of what dogs look like finally to classify a new animal we can match the new animal against the elephant model and match it against the dog model to see whether the new animal looks more like the elephants or more like the dogs we had seen in the training set back to top machine learning paramteric vs nonparametric a learning model that summarizes data with a set of parameters of fixed size independent of the number of training examples is called a parametric model a model where the number of parameters is not determined prior to training nonparametric does not mean that they have no parameters on the contrary nonparametric models can become more and more complex with an increasing amount of data back to top machine learning | machine-learning interview-preparation | ai |
porosity | porosity alternatives as you can see porosity is now unmaintained we recommend jeb decompiler for smart contracts https www pnfsoftware com blog ethereum smart contract decompiler by nicolas failliere as an alternative why is it unmaintained after some initial research on ethereum smart contracts and ethereum virtual machine i came to the conclusion that the foundation of ethereum were not strong enough to be a sustainable long term and that spending time on it was a waste of time we will surely see alternative languages that will take over ethereum for the smart contract platforms the most shocking part is that decades of work on secure languages and secure virtual machines has been done prior the existance of ethereum and other dlt languages platforms and it had been totally ignored from the beginning we also recommend you to read msuiche blogpost from december 2017 on the future of smart contract languages https blog comae io smart contract languages development to follow 992e30774b39 getting started platform status windows build status https comae visualstudio com apis public build definitions 13b58962 60a0 48ed 879d f56575385e2e 4 badge https comae visualstudio com apis public build definitions 13b58962 60a0 48ed 879d f56575385e2e 4 badge linux supported mac os x supported overview ethereum is gaining a significant popularity in the blockchain community mainly due to fact that it is design in a way that enables developers to write decentralized applications dapps and smart contract using blockchain technology ethereum blockchain is a consensus based globally executed virtual machine also referred as ethereum virtual machine evm by implemented its own micro kernel supporting a handful number of instructions its own stack memory and storage this enables the radical new concept of distributed applications contracts live on the blockchain in an ethereum specific binary format evm bytecode however contracts are typically written in some high level language such as solidity and then compiled into byte code to be uploaded on the blockchain solidity is a contract oriented high level language whose syntax is similar to that of javascript this new paradigm of applications opens the door to many possibilities and opportunities blockchain is often referred as secure by design but now that blockchains can embed applications this raise multiple questions regarding architecture design attack vectors and patch deployments as we reverse engineers know having access to source code is often a luxury hence the need for an open source tool like porosity decompiler for evm bytecode into readable solidity syntax contracts to enable static and dynamic analysis of compiled contracts but also vulnerability discovery getting started first you can either compile your own ethereum contract or analyze public contract from etherscan https etherscan io address 0x8d12a197cb00d4747a1fe03395095ce2a5cc6819 code more vulnerable sol contract sendbalance mapping address uint userbalances bool withdrawn false function getbalance address u constant returns uint return userbalances u function addtobalance userbalances msg sender msg value function withdrawbalance if msg sender call gas 0x1111 value userbalances msg sender throw userbalances msg sender 0 solc abi o output vulnerable sol solc bin o output vulnerable sol solc bin runtime o output vulnerable sol abi get content output sendbalance abi bin get content output sendbalance bin binruntime get content output sendbalance bin runtime echo abi constant false inputs name withdrawbalance outputs type function constant false inputs name addtobalance outputs type function constant true inputs name u type address name ge tbalance outputs name type uint256 type function echo bin 60606040526000600160006101000a81548160ff021916908302179055506101bb8061002b6000396000f360606040526000357c0100000000000000000000000000000000000000000000000000000000900480635fd8c7101461004f578063c0e317fb1461005e578063f8b2cb4f1461006d5761004d56 5b005b61005c6004805050610099565b005b61006b600480505061013e565b005b610083600480803590602001909190505061017d565b6040518082815260200191505060405180910390f35b3373ffffffffffffffffffffffffffffffffffffffff16611111600060005060003373ffffffffffffffff ffffffffffffffffffffffff16815260200190815260200160002060005054604051809050600060405180830381858888f19350505050151561010657610002565b6000600060005060003373ffffffffffffffffffffffffffffffffffffffff168152602001908152602001600020600050819055505b 565b34600060005060003373ffffffffffffffffffffffffffffffffffffffff1681526020019081526020016000206000828282505401925050819055505b565b6000600060005060008373ffffffffffffffffffffffffffffffffffffffff1681526020019081526020016000206000505490506101b6 565b91905056 echo binruntime 60606040526000357c0100000000000000000000000000000000000000000000000000000000900480635fd8c7101461004f578063c0e317fb1461005e578063f8b2cb4f1461006d5761004d565b005b61005c6004805050610099565b005b61006b600480505061013e565b005b61008360048080359060 2001909190505061017d565b6040518082815260200191505060405180910390f35b3373ffffffffffffffffffffffffffffffffffffffff16611111600060005060003373ffffffffffffffffffffffffffffffffffffffff16815260200190815260200160002060005054604051809050600060405180 830381858888f19350505050151561010657610002565b6000600060005060003373ffffffffffffffffffffffffffffffffffffffff168152602001908152602001600020600050819055505b565b34600060005060003373ffffffffffffffffffffffffffffffffffffffff1681526020019081526020 016000206000828282505401925050819055505b565b6000600060005060008373ffffffffffffffffffffffffffffffffffffffff1681526020019081526020016000206000505490506101b6565b91905056 using porosity list functions you can get the list of all the functions from the dispatch routine using the list option porosity code code abi abi list verbose 0 porosity v0 1 https www comae io matt suiche comae technologies support comae io the ethereum bytecode commandline decompiler decompiles the given ethereum input bytecode and outputs the solidity code attempting to parse abi definition success hash 0x0a19b14a trade 1 references hash 0x0b927666 order 1 references hash 0x19774d43 orderfills 1 references hash 0x278b8c0e cancelorder 1 references hash 0x2e1a7d4d withdraw 1 references hash 0x338b5dea deposittoken 1 references hash 0x46be96c3 amountfilled 1 references hash 0x508493bc tokens 1 references hash 0x54d03b5c changefeemake 1 references hash 0x57786394 feemake 1 references hash 0x5e1d7ae4 changefeerebate 1 references hash 0x65e17c9d feeaccount 1 references hash 0x6c86888b testtrade 1 references hash 0x71ffcb16 changefeeaccount 1 references hash 0x731c2f81 feerebate 1 references hash 0x8823a9c0 changefeetake 1 references hash 0x8f283970 changeadmin 1 references hash 0x9e281a98 withdrawtoken 1 references hash 0xbb5f4629 orders 1 references hash 0xc281309e feetake 1 references hash 0xd0e30db0 deposit 1 references hash 0xe8f6bc2e changeaccountlevelsaddr 1 references hash 0xf3412942 accountlevelsaddr 1 references hash 0xf7888aec balanceof 1 references hash 0xf851a440 admin 1 references hash 0xfb6e155f availablevolume 1 references disassemble using the disassm option you will be able to display the assembly code porosity abi abi code code disassm details summary click here to view b disassembly b summary porosity v0 1 https www comae io matt suiche comae technologies support comae io the ethereum bytecode commandline decompiler decompiles the given ethereum input bytecode and outputs the solidity code attempting to parse abi definition success contract setabi name withdrawbalance contract setabi signature 0x5fd8c710 contract setabi name addtobalance contract setabi signature 0xc0e317fb contract setabi name getbalance address contract setabi signature 0xf8b2cb4f total byte code size 0x1bb 443 loc 00000000 0x00000000 60 60 push1 60 0x00000002 60 40 push1 40 0x00000004 52 mstore 0x00000005 60 00 push1 00 0x00000007 35 calldataload 0x00000008 7c 00 00 00 00 push29 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 01 0x00000026 90 swap1 0x00000027 04 div 0x00000028 80 dup1 0x00000029 63 10 c7 d8 5f push4 10 c7 d8 5f 0x0000002e 14 eq 0x0000002f 61 4f 00 push2 4f 00 0x00000032 57 jumpi loc 00000033 0x00000033 80 dup1 0x00000034 63 fb 17 e3 c0 push4 fb 17 e3 c0 0x00000039 14 eq 0x0000003a 61 5e 00 push2 5e 00 0x0000003d 57 jumpi loc 0000003e 0x0000003e 80 dup1 0x0000003f 63 4f cb b2 f8 push4 4f cb b2 f8 0x00000044 14 eq 0x00000045 61 6d 00 push2 6d 00 0x00000048 57 jumpi loc 00000049 0x00000049 61 4d 00 push2 4d 00 0x0000004c 56 jump loc 0000004d 0x0000004d 5b jumpdest 0x0000004e 00 stop withdrawbalance 0x0000004f 5b jumpdest 0x00000050 61 5c 00 push2 5c 00 0x00000053 60 04 push1 04 0x00000055 80 dup1 0x00000056 50 pop 0x00000057 50 pop 0x00000058 61 99 00 push2 99 00 0x0000005b 56 jump loc 0000005c 0x0000005c 5b jumpdest 0x0000005d 00 stop addtobalance 0x0000005e 5b jumpdest 0x0000005f 61 6b 00 push2 6b 00 0x00000062 60 04 push1 04 0x00000064 80 dup1 0x00000065 50 pop 0x00000066 50 pop 0x00000067 61 3e 01 push2 3e 01 0x0000006a 56 jump loc 0000006b 0x0000006b 5b jumpdest 0x0000006c 00 stop getbalance address 0x0000006d 5b jumpdest 0x0000006e 61 83 00 push2 83 00 0x00000071 60 04 push1 04 0x00000073 80 dup1 0x00000074 80 dup1 0x00000075 35 calldataload 0x00000076 90 swap1 0x00000077 60 20 push1 20 0x00000079 01 add 0x0000007a 90 swap1 0x0000007b 91 swap2 0x0000007c 90 swap1 0x0000007d 50 pop 0x0000007e 50 pop 0x0000007f 61 7d 01 push2 7d 01 0x00000082 56 jump loc 00000083 0x00000083 5b jumpdest 0x00000084 60 40 push1 40 0x00000086 51 mload 0x00000087 80 dup1 0x00000088 82 dup3 0x00000089 81 dup2 0x0000008a 52 mstore 0x0000008b 60 20 push1 20 0x0000008d 01 add 0x0000008e 91 swap2 0x0000008f 50 pop 0x00000090 50 pop 0x00000091 60 40 push1 40 0x00000093 51 mload 0x00000094 80 dup1 0x00000095 91 swap2 0x00000096 03 sub 0x00000097 90 swap1 0x00000098 f3 return loc 00000099 0x00000099 5b jumpdest 0x0000009a 33 caller 0x0000009b 73 ff ff ff ff push20 ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff 0x000000b0 16 and 0x000000b1 61 11 11 push2 11 11 0x000000b4 60 00 push1 00 0x000000b6 60 00 push1 00 0x000000b8 50 pop 0x000000b9 60 00 push1 00 0x000000bb 33 caller 0x000000bc 73 ff ff ff ff push20 ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff 0x000000d1 16 and 0x000000d2 81 dup2 0x000000d3 52 mstore 0x000000d4 60 20 push1 20 0x000000d6 01 add 0x000000d7 90 swap1 0x000000d8 81 dup2 0x000000d9 52 mstore 0x000000da 60 20 push1 20 0x000000dc 01 add 0x000000dd 60 00 push1 00 0x000000df 20 sha3 0x000000e0 60 00 push1 00 0x000000e2 50 pop 0x000000e3 54 sload 0x000000e4 60 40 push1 40 0x000000e6 51 mload 0x000000e7 80 dup1 0x000000e8 90 swap1 0x000000e9 50 pop 0x000000ea 60 00 push1 00 0x000000ec 60 40 push1 40 0x000000ee 51 mload 0x000000ef 80 dup1 0x000000f0 83 dup4 0x000000f1 03 sub 0x000000f2 81 dup2 0x000000f3 85 dup6 0x000000f4 88 dup9 0x000000f5 88 dup9 0x000000f6 f1 call 0x000000f7 93 swap4 0x000000f8 50 pop 0x000000f9 50 pop 0x000000fa 50 pop 0x000000fb 50 pop 0x000000fc 15 iszero 0x000000fd 15 iszero 0x000000fe 61 06 01 push2 06 01 0x00000101 57 jumpi loc 00000102 0x00000102 61 02 00 push2 02 00 0x00000105 56 jump loc 00000106 0x00000106 5b jumpdest 0x00000107 60 00 push1 00 0x00000109 60 00 push1 00 0x0000010b 60 00 push1 00 0x0000010d 50 pop 0x0000010e 60 00 push1 00 0x00000110 33 caller 0x00000111 73 ff ff ff ff push20 ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff 0x00000126 16 and 0x00000127 81 dup2 0x00000128 52 mstore 0x00000129 60 20 push1 20 0x0000012b 01 add 0x0000012c 90 swap1 0x0000012d 81 dup2 0x0000012e 52 mstore 0x0000012f 60 20 push1 20 0x00000131 01 add 0x00000132 60 00 push1 00 0x00000134 20 sha3 0x00000135 60 00 push1 00 0x00000137 50 pop 0x00000138 81 dup2 0x00000139 90 swap1 0x0000013a 55 sstore 0x0000013b 50 pop loc 0000013c 0x0000013c 5b jumpdest 0x0000013d 56 jump loc 0000013e 0x0000013e 5b jumpdest 0x0000013f 34 callvalue 0x00000140 60 00 push1 00 0x00000142 60 00 push1 00 0x00000144 50 pop 0x00000145 60 00 push1 00 0x00000147 33 caller 0x00000148 73 ff ff ff ff push20 ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff 0x0000015d 16 and 0x0000015e 81 dup2 0x0000015f 52 mstore 0x00000160 60 20 push1 20 0x00000162 01 add 0x00000163 90 swap1 0x00000164 81 dup2 0x00000165 52 mstore 0x00000166 60 20 push1 20 0x00000168 01 add 0x00000169 60 00 push1 00 0x0000016b 20 sha3 0x0000016c 60 00 push1 00 0x0000016e 82 dup3 0x0000016f 82 dup3 0x00000170 82 dup3 0x00000171 50 pop 0x00000172 54 sload 0x00000173 01 add 0x00000174 92 swap3 0x00000175 50 pop 0x00000176 50 pop 0x00000177 81 dup2 0x00000178 90 swap1 0x00000179 55 sstore 0x0000017a 50 pop loc 0000017b 0x0000017b 5b jumpdest 0x0000017c 56 jump loc 0000017d 0x0000017d 5b jumpdest 0x0000017e 60 00 push1 00 0x00000180 60 00 push1 00 0x00000182 60 00 push1 00 0x00000184 50 pop 0x00000185 60 00 push1 00 0x00000187 83 dup4 0x00000188 73 ff ff ff ff push20 ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff 0x0000019d 16 and 0x0000019e 81 dup2 0x0000019f 52 mstore 0x000001a0 60 20 push1 20 0x000001a2 01 add 0x000001a3 90 swap1 0x000001a4 81 dup2 0x000001a5 52 mstore 0x000001a6 60 20 push1 20 0x000001a8 01 add 0x000001a9 60 00 push1 00 0x000001ab 20 sha3 0x000001ac 60 00 push1 00 0x000001ae 50 pop 0x000001af 54 sload 0x000001b0 90 swap1 0x000001b1 50 pop 0x000001b2 61 b6 01 push2 b6 01 0x000001b5 56 jump loc 000001b6 0x000001b6 5b jumpdest 0x000001b7 91 swap2 0x000001b8 90 swap1 0x000001b9 50 pop 0x000001ba 56 jump details decompilation the decompile option will decompile the given function or contract and attempt to highlight vulnerabilities porosity abi abi code code decompile verbose 0 details summary click here to view b decompilation b summary porosity v0 1 https www comae io matt suiche comae technologies support comae io the ethereum bytecode commandline decompiler decompiles the given ethereum input bytecode and outputs the solidity code attempting to parse abi definition success hash 0x5fd8c710 function withdrawbalance if msg sender call gas 4369 value store msg sender store msg sender 0x0 l3 d8193 potential reetrant vulnerability found loc 5 hash 0xc0e317fb function addtobalance store msg sender store msg sender msg value return loc 4 hash 0xf8b2cb4f function getbalance address return store arg 4 loc 3 details develop osx cd porosity porosity make note you may need to install boost c http www boost org dependency using homebrew brew install boost using macports sudo port install boost | blockchain |
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MyRel | myrel welcome to myrel a web app developed for the database and web applications laboratory curricular unit https sigarra up pt feup en ucurr geral ficha uc view pv ocorrencia id 501685 at feup our goal with myrel is to provide a more personal and private space for users to connect with their friends and family unlike other social media platforms myrel allows users to create different relationships with their contacts and make posts that are only visible to certain groups of people this way users can share their thoughts and experiences with their close friends or just their family without the need to worry about their privacy while myrel is not yet a fully completed project we are constantly working to improve and expand its features to make it the best social media platform for maintaining close relationships with the people that matter most to you video yt video http img youtube com vi otrkb mhi5g 0 jpg https www youtube com watch v otrkb mhi5g feup lbaw 2023 myrel | server |
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datalab-ml-training | bbc datalab ml training objectives the goals of this training is to get you excited about data science give a quick introduction for some of the python s libraries available pandas data wrangling scikit learn ml matplotlib visualisation give a quick overview of an approach to tackling data science problems it will not make you an expert data scientist go into details or do the maths for the techniques algorithms we will use properly cover any deep learning neural networks setting up your environment this course is delivered using jupyter notebooks so if you re not familiar with them some helpful documentation is what is the jupyter notebook http jupyter notebook beginner guide readthedocs io en latest what is jupyter html and notebook basics http jupyter notebook readthedocs io en stable examples notebook notebook 20basics html the notebooks contain python code which you will run during the exercises this is done by highlighting the cell then clicking run in jupyter bear in mind that this code should be executed in order and each cell should complete before running the next cell dependencies this training requires a number of libraries which are installed for example with pip3 these libraries are jupyter http jupyter org an interactive programming environment that runs in the browser scikit learn http scikit learn org powerful and easy to use machine learning algorithms pandas https pandas pydata org a powerful way of handling dataframes which are two dimensional tabular data structures with labeled axes numpy http www numpy org scientific computing capability providing support for large multi dimensional arrays and matrices along with a large collection of high level mathematical functions to operate on these arrays scipy https www scipy org similar to numpy it gives you access to key mathematical modules such as optimization linear algebra integration and interpolation etc matplotlib https matplotlib org a plotting library for the python programming language and numpy jupyter with virtualenv this training also uses python 3 and a number of python libraries so before starting the training you will need to install python 3 following the python beginners guide https wiki python org moin beginnersguide download or you might find installing python 3 on mac os x http docs python guide org en latest starting install3 osx useful if you use mac os x install virtualenv https virtualenv pypa io using the installation https virtualenv pypa io en latest installation html documentation in your project directory create a new virtual environment by running virtualenv p python3 6 env enable your virtual environment by running source env bin activate install the dependencies dependencies by running pip3 install r requirements txt finally in order to start your development environment type jupyter notebook in your terminal this should automatically open a tab in your browser or you can visit localhost 8888 http localhost 8888 to shut down jupyter type ctrl c when you are finished with the training you can run deactivate to deactivate your virtualenv jupyter with docker if you are familiar with docker you can use the jupyter datascience notebook https hub docker com r jupyter datascience notebook image to spin up everything you need for the course as a starting point the following command creates a passwordless instance of jupyter at http localhost 8888 mapped to your current working directory bash docker run d rm p 127 0 0 1 8888 8888 name datascience notebook mount type bind source pwd target home jovyan jupyter datascience notebook start notebook sh notebookapp token jupyter with anaconda alternatively you can install anaconda https www anaconda com download macos which aims to simplify package management agenda the training is split into 4 courses one data exploration iplayerforecast course1 ipynb two data preparation iplayerforecast course2 ipynb three build a classifier iplayerforecast course3 ipynb four build a regressor iplayerforecast course4 ipynb this training is still work in progress please send us any feedback to datalab bbc co uk to help us improve it and if you found this training easy and had fun doing it why not join us https findouthow datalab rocks | ai |
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awesome-dl4nlp | awesome deep learning for natural language processing nlp awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome table of contents courses courses books books tutorials tutorials talks lectures talks frameworks models frameworks papers papers blog posts blog posts datasets datasets word embeddings word vectors word embeddings contributing contributing courses 1 nlp with deep learning cs224n from stanford winter 2019 course homepage http web stanford edu class cs224n index html a complete survey of the field with videos lecture slides and sample student projects course lectures https www youtube com watch v 8rxd5 xhemo list ploromvodv4rohcuxmzknm7j3fvwbby42z video playlist previous course notes https github com stanfordnlp cs224n winter17 notes probably the best book on dl for nlp course code https github com dsksd deepnlp models pytorch pytorch implementations of various deep nlp models in cs 224n 1 neural networks for nlp from carnegie mellon university course homepage http phontron com class nn4nlp2017 course lectures https www youtube com user neubig videos course code https github com neubig nn4nlp2017 code 1 deep learning for natural language processing from university of oxford and deepmind course homepage https www cs ox ac uk teaching courses 2016 2017 dl course slides https github com oxford cs deepnlp 2017 lectures course lectures https www youtube com playlist list pl613dyigmxozbtzhbyibqb0qtgk6ojbpm books 1 deep learning with text natural language processing almost from scratch with python and spacy https www amazon com deep learning text approach processing dp 1491984414 by patrick harrison and matthew honnibal 1 neural network methods in natural language processing https www amazon com gp product 1627052984 by yoav goldberg and graeme hirst 1 deep learning in natural language processing http www springer com us book 9789811052088 by li deng and yang liu 1 natural language processing in action https www manning com books natural language processing in action by hobson lane cole howard and hannes hapke 1 deep learning natural language processing in python by the lazyprogrammer kindle only 1 word2vec and word embeddings in python and theano https www amazon com deep learning language processing embeddings ebook dp b01kq0zn0a 1 from word2vec to glove in python and theano https www amazon com deep learning language processing word2vec ebook dp b01krboo4y 1 recursive neural networks recursive neural tensor networks in theano https www amazon com deep learning language processing recursive ebook dp b01ks5aexo 1 applied natural language processing with python https www amazon ca applied natural language processing python dp 1484237323 by taweh beysolow ii 1 deep learning cookbook https www amazon ca deep learning cookbook practical recipes dp 149199584x by douwe osinga 1 deep learning for natural language processing creating neural networks with python https www amazon ca deep learning natural language processing dp 148423684x by palash goyal sumit pandey karan jain 1 machine learning for text https www amazon ca machine learning text charu aggarwal dp 3319735306 by charu c aggarwal 1 natural language processing with tensorflow https www amazon ca natural language processing tensorflow language ebook dp b077q3vzfr by thushan ganegedara 1 fasttext quick start guide get started with facebook s library for text representation and classification https www amazon ca fasttext quick start guide representation dp 1789130999 1 hands on natural language processing with python https www amazon ca hands natural language processing python dp 178913949x 1 natural language processing in action seond edition https www manning com books natural language processing in action second edition by hobson lane and maria dyshel 1 getting started with natural language processing in action https www manning com books getting started with natural language processing by ekaterina kochmar 2 deep learning for natural language processing in action https www manning com books deep learning for natural language processing by stephan raaijmakers tutorials 1 text classification guide from google https developers google com machine learning guides text classification 1 deep learning for nlp with pytorch https pytorch org tutorials beginner deep learning nlp tutorial html talks 1 deep learning for natural language processing without magic http www socher org index php deeplearningtutorial deeplearningtutorial 1 a primer on neural network models for natural language processing https arxiv org abs 1510 00726 1 deep learning for natural language processing theory and practice tutorial https www microsoft com en us research publication deep learning for natural language processing theory and practice tutorial 1 tensorflow tutorials https www tensorflow org tutorials mandelbrot 1 practical neural networks for nlp https github com clab dynet tutorial examples from emnlp 2016 using dynet framework 1 recurrent neural networks with word embeddings http deeplearning net tutorial rnnslu html 1 lstm networks for sentiment analysis http deeplearning net tutorial lstm html 1 tensorflow demo using the large movie review dataset http ai stanford edu amaas data sentiment 1 lstmvis visual analysis for recurrent neural networks http lstm seas harvard edu client index html 1 using deep learning in natural language processing by rob romijnders from pydata amsterdam 2017 video https www youtube com watch v hvdpwoz swy slides https github com robromijnders talks blob master pydata dl nlp pdf 1 richard socher s talk on sentiment analysis question answering and sentence image embeddings https www youtube com watch v tdlmf8t4oqm 1 deep learning an interactive introduction for nlp ers http www slideshare net roelofp 220115dlmeetup 1 deep natural language understanding http videolectures net deeplearning2016 cho language understanding 1 deep learning summer school montreal 2016 http videolectures net deeplearning2016 montreal includes state of art language modeling 1 tackling the limits of deep learning for nlp by richard socher video https www youtube com watch v jywnmse4hqe slides https berkeley deep learning github io cs294 131 s17 slides socher talk pdf frameworks 1 overview of dl frameworks for nlp https medium com datamonsters 13 deep learning frameworks for natural language processing in python 2b84a6b6cd98 1 general frameworks 1 keras https keras io the python deep learning library emphasis on user friendliness modularity easy extensibility and pythonic 1 tensorflow https www tensorflow org a cross platform general purpose machine intelligence library with python and c api 1 pytorch http pytorch org pytorch is a deep learning framework that puts python first tensors and dynamic neural networks in python with strong gpu acceleration 1 specific frameworks 1 spacy https spacy io a python package designed for speed getting things dones and interoperates with other deep learning frameworks 1 genism topic modeling for humans https pypi python org pypi gensim a python package that includes word2vec and doc2vec implementations 1 fasttext https github com facebookresearch fasttext facebook s library for fast text representation and classification 1 built on tensorflow 1 syntaxnet https github com tensorflow models tree master research syntaxnet a toolkit for natural language understanding nlu 1 textsum https github com tensorflow models tree master research textsum a sequence to sequence with attention model for text summarization 1 skip thought vectors https github com tensorflow models tree master research skip thoughts implementation in tensorflow 1 activeqa active question answering https github com google active qa using reinforcement learning to train artificial agents for question answering 1 bert https github com google research bert bidirectional encoder representations from transformers for pre trained models 1 built on pytorch 1 pytext https github com facebookresearch pytext a deep learning based nlp modeling framework by facebook 1 allennlp https allennlp org an open source nlp research library 1 flair https github com zalandoresearch flair a very simple framework for state of the art nlp 1 fairseq https github com pytorch fairseq a sequence to sequence toolkit 1 fastai http docs fast ai text html simplifies training fast and accurate neural nets using modern best practices 1 transformer model http nlp seas harvard edu 2018 04 03 attention html annotated notebook implementation 1 deeplearning4j s nlp framework http deeplearning4j org nlp java implementation 1 dynet https github com clab dynet the dynamic neural network toolkit work well with networks that have dynamic structures that change for every training instance 1 deepnl https github com attardi deepnl a python library for nlp based on deep learning neural network architecture papers 1 deep or shallow nlp is breaking out http dl acm org citation cfm id 2874915 general overview of how deep learning is impacting nlp 1 natural language processing from research at google http research google com pubs naturallanguageprocessing html not all deep learning but mostly 1 context dependent recurrent neural network language model http www msr waypoint com pubs 176926 rnn ctxt pdf 1 translation modeling with bidirectional recurrent neural networks https www i6 informatik rwth aachen de publications download 936 sundermeyermartinalkhoulitamerwuebkerjoernneyhermann translationmodelingwithbidirectionalrecurrentneuralnetworks 2014 pdf 1 contextual lstm clstm models for large scale nlp tasks https arxiv org abs 1602 06291 1 lstm neural networks for language modeling http citeseerx ist psu edu viewdoc download doi 10 1 1 248 4448 rep rep1 type pdf 1 exploring the limits of language modeling http arxiv org pdf 1602 02410 pdf 1 conversational contextual cues https arxiv org abs 1606 00372 models context and participants in conversations 1 sequence to sequence learning with neural networks http papers nips cc paper 5346 sequence to sequence learning with neural networks pdf 1 efficient estimation of word representations in vector space http arxiv org pdf 1301 3781 pdf 1 learning character level representations for part of speech tagging http jmlr org proceedings papers v32 santos14 pdf 1 representation learning for text level discourse parsing http www cc gatech edu jeisenst papers ji acl 2014 pdf 1 fast and robust neural network joint models for statistical machine translation http acl2014 org acl2014 p14 1 pdf p14 1129 pdf 1 parsing with compositional vector grammars http www socher org index php main parsingwithcompositionalvectorgrammars 1 smart reply automated response suggestion for email https arxiv org abs 1606 04870 1 neural architectures for named entity recognition https arxiv org abs 1603 01360 state of the art performance in ner with bidirectional lstm with a sequential conditional random layer and transition based parsing with stack lstms 1 grammar as a foreign language https arxiv org abs 1412 7449 state of the art syntactic constituency parsing using generic sequence to sequence approach blog posts 1 natural language processing nlp progress https nlpprogress com tracking the most common nlp tasks including the datasets and the current state of the art 1 a review of the recent history of natural language processing http blog aylien com a review of the recent history of natural language processing 1 deep learning nlp and representations http colah github io posts 2014 07 nlp rnns representations 1 the unreasonable effectiveness of recurrent neural networks http karpathy github io 2015 05 21 rnn effectiveness 1 neural language modeling from scratch http ofir io neural language modeling from scratch a 1 1 machine learning for emoji trends http instagram engineering tumblr com post 117889701472 emojineering part 1 machine learning for emoji 1 teaching robots to feel emoji deep learning http getdango com emoji and deep learning html 1 computational linguistics and deep learning http www mitpressjournals org doi pdf 10 1162 coli a 00239 opinion piece on how deep learning fits into the broader picture of text processing 1 deep learning nlp best practices http ruder io deep learning nlp best practices index html 1 7 types of artificial neural networks for natural language processing https medium com datamonsters artificial neural networks for natural language processing part 1 64ca9ebfa3b2 1 how to solve 90 of nlp problems a step by step guide https blog insightdatascience com how to solve 90 of nlp problems a step by step guide fda605278e4e 2 7 applications of deep learning for natural language processing https machinelearningmastery com applications of deep learning for natural language processing datasets 1 dataset from one billion word language modeling benchmark http www statmt org lm benchmark 1 billion word language modeling benchmark r13output tar gz almost 1b words already pre processed text 1 stanford sentiment treebank https nlp stanford edu sentiment treebank html fine grained sentiment labels for 215 154 phrases in the parse trees of 11 855 sentences 1 chatbot data from kaggle https www kaggle com samdeeplearning deepnlp 1 a list of text datasets that are free public domain in alphabetical order https github com niderhoff nlp datasets 1 another list of text datasets that are free public domain in reverse chronological order https github com karthikncode nlp datasets 1 question answering datasets 1 quora s question pairs dataset https data quora com first quora dataset release question pairs identify question pairs that have the same intent 1 cmu s wikipedia factoid question answers https www cs cmu edu ark qa data 1 deepmind s algebra question answering https github com deepmind aqua 1 deepmind s from cnn dailymail question answering https github com deepmind rc data 1 microsoft s wikiqa open domain question answering https www microsoft com en us research publication wikiqa a challenge dataset for open domain question answering 1 stanford question answering dataset squad https rajpurkar github io squad explorer covering reading comprehension word embeddings and friends 1 the amazing power of word vectors https blog acolyer org 2016 04 21 the amazing power of word vectors from the morning paper blog 1 distributed representations of words and phrases and their compositionality https papers nips cc paper 5021 distributed representations of words and phrases and their compositionality pdf the original word2vec paper 1 word2vec parameter learning explained https arxiv org abs 1411 2738 an elucidating explanation of word2vec training 1 word embeddings in 2017 trends and future directions http ruder io word embeddings 2017 1 learning word vectors for 157 languages https arxiv org abs 1802 06893 1 glove global vectors for word representation http www nlp stanford edu pubs glove pdf a count based co occurrence model to learn word embeddings 1 doc2vec a gentle introduction to doc2vec https medium com scaleabout a gentle introduction to doc2vec db3e8c0cce5e distributed representations of sentences and documents https cs stanford edu quocle paragraph vector pdf 1 dynamic word embeddings for evolving semantic discovery https blog acolyer org 2018 02 22 dynamic word embeddings for evolving semantic discovery from the morning paper blog 1 ali ghodsi s lecture on word2vec part 1 https www youtube com watch v tsegsdvjjua part 2 https www youtube com watch v nuiruembaju 1 word2vec analogy demo http deeplearner fz qqq net 1 tensorflow embedding projector of word vectors http projector tensorflow org 1 skip thought vectors unsupervised learning of a generic distributed sentence encoder paper http arxiv org abs 1506 06726 code https github com ryankiros skip thoughts contributing have anything in mind that you think is awesome and would fit in this list feel free to send me a pull request license cc0 http i creativecommons org p zero 1 0 88x31 png http creativecommons org publicdomain zero 1 0 to the extent possible under law dr brian j spiering http www linkedin com in brianspiering has waived all copyright and related or neighboring rights to this work | ai |
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sql-challenge | sql homework employee database a mystery in two parts sql png sql png background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https help github com en github using git ignoring files https help github com en github using git ignoring files for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 submission create an image file of your erd create a sql file of your table schemata create a sql file of your queries optional create a jupyter notebook of the bonus analysis create and upload a repository with the above files to github and post a link on bootcamp spot copyright trilogy education services 2019 all rights reserved | server |
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CE-projects | ce projects cloud engineering projects with azubi africa | cloud |
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Kasuri | kasuri logo png https github com akiroz kasuri workflows test badge svg https img shields io npm v akiroz kasuri https www npmjs com package akiroz kasuri an opinionated type safe reactive module state management framework designed for complex embedded systems with a huge varity of i o and stateful components inspired by modern reactive ui frameworks and memory driven computing install yarn add akiroz kasuri concept concept png the system consists of a state fabric and compute logic split into multiple modules every module has its own state that lives inside the state fabric and could only modify its own state at the same time each module can read or subscribe to changes of all state in the whole system system state is managed in 2 nested levels modules and module state subscriptions listens to changes on the module state level although each module state can be arbitrarily nested system module1 state1 123 module2 state1 foo state2 x 0 y 0 z 0 motivation this project was developed for the specific needs of complex embedded systems with dozens of hardware integrations e g sensors actuators network devices displays etc several existing solutions have been evaluated used in the past ros robot operating system a distributed computing framework written in python c designed for robots provides node discovery pub sub and rpc infrastructures no predefined execution model io handling this option was rejected because ros is tied to a specific version of ubuntu concurrency in python c is hard computation power is limited on my hardware erlang otp the language runtime for erlang elixir with built in support for csp concurrency distributed node clustering io compute scheduling external monitoring and a database a solid choice for the job however the diversity of 3rd party libraries and availability of developers loses out when compared to more popular languages nodejs redux the language runtime for javascript with built in support for event based concurrency io compute scheduling a state management framework originally designed for ui programming with external tools for system introspection nodejs is a suitable runtime for the application however redux is way too complicated and redux devtools often hang or crash when there s hundreds thousands of actions per second thus kasuri aims to be a simpler version of redux for embedded systems note on state mutability it it worth noting that careful attention must be taken when using state values that aren t just simple values e g arrays nested objects if the state is updated via mutation of the previous state object references will not be updated causing the state subscription api to return the same value for both current and previous state in order to get correct values for the subscription api a new object must be created if your new value depends on the previous the use of immutable data structures is highly recommended immer is a good choice for creating immutable objects from mutations of previous state immer https immerjs github io immer project structuring the following project layout is recommended index ts entrypoint statemap ts exports an object mapping module name to the default state of all modules module1 state ts exports an object containing the default state of module1 module ts exports a module class that implements module1 logic a sample project could be found in the test directory api class module static defaultstate static defaultstate status pending statusmessage contains common default state modules all modules state should include these fields foostate module defaultstate mystate 123 getstate module string state string stalems number null returns state value from the fabric given the module and state name optionally discard stale values using stalems if the state is older than stalems milliseconds getstate returns undefined getupdatetime module string state string number get state last update time as unix timestamp milliseconds subscribestate module string state string listener current previous void subscribe to state changes listener takes both the current new and previous store values both contains state value and updatetime statechange module string state string promise current previous helper function that returns a promise of the next state change with the new and previous value and updatetime setstate update partial modulestate updates the state of this module given a map of key value pairs to set the update object a subset of the module s state swapstate state string swapfn value updatetime newval updates the state of this module given a state name and a swap function this swap function takes the state s current value updatetime time and return its new value note the swap function cannot be async async init method to be overwritten by subclasses this is called during system initialization subclasses should set the module s status state in this method to online if it s successful class kasuri constructor statemap modulemap constructs a new system from a default state map and a module object map the init method of each module will be called statemap module1 module defaultstate foo 1 bar hello world module2 module defaultstate modulemap module1 new module1 module2 new module2 store global state store contains all module state has the following schema modulename statename updatetime number unix timestamp in ms value t actual value of the state setstate module string update partial modulestate system wide version of module s setstate see module setstate getstate module string state string stalems number null same as module getstate getupdatetime module string state string number same as module getupdatetime subscribestate module string state string listener current previous void same as module subscribestate statechange module string state string promise current previous same as module statechange introspection the introspection server allows you to dump subscribe and set state in the state fabric const kasuri new kasuri const server await introspection server kasuri this server is intended to be used with the cli tool see kasuri help for details screenshot png introspection extensions the introspection server supports custom extensons to read format and modify the state fabric extension handlers can be added to the introspections server await introspection server kasuri extension mycustomext kasuri reqbody buffer buffer const params json parse reqbody tostring custom logic here the extension can be invoked cli stdin is passed as the request body and the result is piped to stdout echo foo 123 kasuri call mycustomext | os |
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DL-Notes-for-Interview | https github com elviswf deeplearningbookqa cn pdf github https github com exacity deeplearningbook chinese dl ml java android reference exacity deeplearningbook chinese https github com exacity deeplearningbook chinese elviswf deeplearningbookqa cn https github com elviswf deeplearningbookqa cn deeplearning latex http www codecogs com latex eqneditor php | ai |
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awesome-computer-vision | awesome computer vision awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of awesome computer vision resources inspired by awesome php https github com ziadoz awesome php for a list people in computer vision listed with their academic genealogy please visit here https github com jbhuang0604 awesome computer vision blob master people md contributing please feel free to send me pull requests https github com jbhuang0604 awesome computer vision pulls or email jbhuang vt edu to add links table of contents awesome lists awesome lists books books courses courses papers papers software software datasets datasets pre trained computer vision models pre trained computer vision models tutorials and talks tutorials and talks resources for students resources for students blogs blogs links links songs songs awesome lists awesome machine learning https github com josephmisiti awesome machine learning awesome deep vision https github com kjw0612 awesome deep vision awesome domain adaptation https github com zhaoxin94 awesome domain adaptation awesome object detection https github com amusi awesome object detection awesome 3d machine learning https github com timzhang642 3d machine learning awesome action recognition https github com jinwchoi awesome action recognition awesome scene understanding https github com bertjiazheng awesome scene understanding awesome adversarial machine learning https github com yenchenlin awesome adversarial machine learning awesome adversarial deep learning https github com chbrian awesome adversarial examples dl awesome face https github com polariszhao awesome face awesome face recognition https github com chanchichoi awesome face recognition awesome human pose estimation https github com wangzheallen awesome human pose estimation awesome medical imaging https github com fepegar awesome medical imaging awesome images https github com heyalexej awesome images awesome graphics https github com ericjang awesome graphics awesome neural radiance fields https github com yenchenlin awesome nerf awesome implicit neural representations https github com vsitzmann awesome implicit representations awesome neural rendering https github com weihaox awesome neural rendering awesome public datasets https github com awesomedata awesome public datasets awesome dataset tools https github com jsbroks awesome dataset tools awesome robotics datasets https github com sunglok awesome robotics datasets awesome mobile machine learning https github com fritzlabs awesome mobile machine learning awesome explainable ai https github com wangyongjie ntu awesome explainable ai awesome fairness in ai https github com datamllab awesome fairness in ai awesome machine learning interpretability https github com jphall663 awesome machine learning interpretability awesome production machine learning https github com ethicalml awesome production machine learning awesome video text retrieval https github com danieljf24 awesome video text retrieval awesome image to image translation https github com weihaox awesome image translation awesome image inpainting https github com 1900zyh awesome image inpainting awesome deep hdr https github com vinthony awesome deep hdr awesome video generation https github com matthewvowels1 awesome video generation awesome gan applications https github com nashory gans awesome applications awesome generative modeling https github com zhoubolei awesome generative modeling awesome image classification https github com weiaicunzai awesome image classification awesome deep learning https github com christoschristofidis awesome deep learning awesome machine learning in biomedical healthcare imaging https github com xindiwu awesome machine learning in biomedical healthcare imaging awesome deep learning for tracking and detection https github com abhineet123 deep learning for tracking and detection awesome human pose estimation https github com wangzheallen awesome human pose estimation awesome deep learning for video analysis https github com huaizhengzhang awsome deep learning for video analysis awesome vision language https github com yuewang cuhk awesome vision language pretraining papers awesome robotics https github com kiloreux awesome robotics awesome visual transformer https github com dk liang awesome visual transformer awesome embodied vision https github com changanvr awesome embodied vision awesome anomaly detection https github com hoya012 awesome anomaly detection awesome makeup transfer https github com thaoshibe awesome makeup transfer awesome learning with label noise https github com subeeshvasu awesome learning with label noise awesome deblurring https github com subeeshvasu awesome deblurring awsome deep geometry learning https github com subeeshvasu awsome deep geometry learning awesome image distortion correction https github com subeeshvasu awesome image distortion correction awesome neuron segmentation in em images https github com subeeshvasu awesome neuron segmentation in em images awsome delineation https github com subeeshvasu awsome delineation awesome imageharmonization https github com subeeshvasu awesome imageharmonization awsome gan training https github com subeeshvasu awsome gan training awesome document understanding https github com tstanislawek awesome document understanding books computer vision computer vision models learning and inference http www computervisionmodels com simon j d prince 2012 computer vision theory and application http szeliski org book rick szeliski 2010 computer vision a modern approach 2nd edition http www amazon com computer vision modern approach 2nd dp 013608592x ref dp ob title bk david forsyth and jean ponce 2011 multiple view geometry in computer vision http www robots ox ac uk vgg hzbook richard hartley and andrew zisserman 2004 computer vision http www amazon com computer vision linda g shapiro dp 0130307963 linda g shapiro 2001 vision science photons to phenomenology http www amazon com vision science phenomenology stephen palmer dp 0262161834 stephen e palmer 1999 visual object recognition synthesis lecture http www morganclaypool com doi abs 10 2200 s00332ed1v01y201103aim011 kristen grauman and bastian leibe 2011 computer vision for visual effects http cvfxbook com richard j radke 2012 high dynamic range imaging acquisition display and image based lighting http www amazon com high dynamic range imaging second dp 012374914x reinhard e heidrich w debevec p pattanaik s ward g myszkowski k 2010 numerical algorithms methods for computer vision machine learning and graphics https people csail mit edu jsolomon share book numerical book pdf justin solomon 2015 image processing and analysis https www amazon com processing analysis activate learning engineering dp 1285179528 stan birchfield 2018 computer vision from 3d reconstruction to recognition http web stanford edu class cs231a silvio savarese 2018 opencv programming learning opencv computer vision with the opencv library http www amazon com learning opencv computer vision library dp 0596516134 gary bradski and adrian kaehler practical python and opencv https www pyimagesearch com practical python opencv adrian rosebrock opencv essentials http www amazon com opencv essentials oscar deniz suarez dp 1783984244 ref sr 1 1 s books ie utf8 qid 1424594237 sr 1 1 keywords opencv essentials oscar deniz suarez m del milagro fernandez carrobles noelia vallez enano gloria bueno garcia ismael serrano gracia machine learning pattern recognition and machine learning http research microsoft com en us um people cmbishop prml index htm christopher m bishop 2007 neural networks for pattern recognition http www engineering upm ro master ie sacpi mat did info068 docum neural 20networks 20for 20pattern 20recognition pdf christopher m bishop 1995 probabilistic graphical models principles and techniques http pgm stanford edu daphne koller and nir friedman 2009 pattern classification http www amazon com pattern classification 2nd richard duda dp 0471056693 peter e hart david g stork and richard o duda 2000 machine learning http www amazon com machine learning tom m mitchell dp 0070428077 tom m mitchell 1997 gaussian processes for machine learning http www gaussianprocess org gpml carl edward rasmussen and christopher k i williams 2005 learning from data https work caltech edu telecourse html yaser s abu mostafa malik magdon ismail and hsuan tien lin 2012 neural networks and deep learning http neuralnetworksanddeeplearning com michael nielsen 2014 bayesian reasoning and machine learning http www cs ucl ac uk staff d barber brml david barber cambridge university press 2012 fundamentals linear algebra and its applications http www amazon com linear algebra its applications 4th dp 0030105676 ref sr 1 4 ie utf8 qid 1421433773 sr 8 4 keywords linear algebra and its applications gilbert strang 1995 courses computer vision eeng 512 csci 512 computer vision http inside mines edu whoff courses eeng512 william hoff colorado school of mines visual object and activity recognition https sites google com site ucbcs29443 alexei a efros and trevor darrell uc berkeley computer vision http courses cs washington edu courses cse455 12wi steve seitz university of washington visual recognition spring 2016 http vision cs utexas edu 381v spring2016 fall 2016 http vision cs utexas edu 381v fall2016 kristen grauman ut austin language and vision http www tamaraberg com teaching spring 15 tamara berg unc chapel hill convolutional neural networks for visual recognition http vision stanford edu teaching cs231n fei fei li and andrej karpathy stanford university computer vision http cs nyu edu fergus teaching vision index html rob fergus nyu computer vision https courses engr illinois edu cs543 sp2015 derek hoiem uiuc computer vision foundations and applications http vision stanford edu teaching cs131 fall1415 index html kalanit grill spector and fei fei li stanford university high level vision behaviors neurons and computational models http vision stanford edu teaching cs431 spring1314 fei fei li stanford university advances in computer vision http 6 869 csail mit edu fa15 antonio torralba and bill freeman mit computer vision http www vision rwth aachen de course 11 bastian leibe rwth aachen university computer vision 2 http www vision rwth aachen de course 9 bastian leibe rwth aachen university computer vision http klewel com conferences epfl computer vision pascal fua epfl computer vision 1 http cvlab dresden de courses computer vision 1 carsten rother tu dresden computer vision 2 http cvlab dresden de courses cv2 carsten rother tu dresden multiple view geometry https youtu be rdkwklfgmfo list pltbdjv 4f ejn6udz34tht9eviw7lbeo4 daniel cremers tu munich computational photography image manipulation and computational photography http inst eecs berkeley edu cs194 26 fa14 alexei a efros uc berkeley computational photography http graphics cs cmu edu courses 15 463 2012 fall 463 html alexei a efros cmu computational photography https courses engr illinois edu cs498dh3 derek hoiem uiuc computational photography http cs brown edu courses csci1290 james hays brown university digital computational photography http stellar mit edu s course 6 sp12 6 815 fredo durand mit computational camera and photography http ocw mit edu courses media arts and sciences mas 531 computational camera and photography fall 2009 ramesh raskar mit media lab computational photography https www udacity com course computational photography ud955 irfan essa georgia tech courses in graphics http graphics stanford edu courses stanford university computational photography http cs nyu edu fergus teaching comp photo index html rob fergus nyu introduction to visual computing http www cs toronto edu kyros courses 320 kyros kutulakos university of toronto computational photography http www cs toronto edu kyros courses 2530 kyros kutulakos university of toronto computer vision for visual effects https www ecse rpi edu rjradke cvfxcourse html rich radke rensselaer polytechnic institute introduction to image processing https www ecse rpi edu rjradke improccourse html rich radke rensselaer polytechnic institute machine learning and statistical learning machine learning https www coursera org learn machine learning andrew ng stanford university learning from data https work caltech edu telecourse html yaser s abu mostafa caltech statistical learning https class stanford edu courses humanitiesandscience statlearning winter2015 about trevor hastie and rob tibshirani stanford university statistical learning theory and applications http www mit edu 9 520 fall14 tomaso poggio lorenzo rosasco carlo ciliberto charlie frogner georgios evangelopoulos ben deen mit statistical learning http www stat rice edu gallen stat640 html genevera allen rice university practical machine learning http www cs berkeley edu jordan courses 294 fall09 michael jordan uc berkeley course on information theory pattern recognition and neural networks http videolectures net course information theory pattern recognition david mackay university of cambridge methods for applied statistics unsupervised learning http web stanford edu lmackey stats306b lester mackey stanford machine learning http www robots ox ac uk az lectures ml index html andrew zisserman university of oxford intro to machine learning https www udacity com course intro to machine learning ud120 sebastian thrun stanford university machine learning https www udacity com course machine learning ud262 charles isbell michael littman georgia tech convolutional neural networks for visual recognition https cs231n github io fei fei li andrej karphaty justin johnson stanford university machine learning for computer vision https youtu be qzmzfezxeki list pltbdjv 4f eiiongkls9okrbep8qr47wl rudolph triebel tu munich optimization convex optimization i http stanford edu class ee364a stephen boyd stanford university convex optimization ii http stanford edu class ee364b stephen boyd stanford university convex optimization https class stanford edu courses engineering cvx101 winter2014 about stephen boyd stanford university optimization at mit http optimization mit edu classes php mit convex optimization http www stat cmu edu ryantibs convexopt ryan tibshirani cmu papers conference papers on the web cvpapers http www cvpapers com computer vision papers on the web siggraph paper on the web http kesen realtimerendering com graphics papers on the web nips proceedings http papers nips cc nips papers on the web computer vision foundation open access http www cv foundation org openaccess menu py annotated computer vision bibliography http iris usc edu vision notes bibliography contents html keith price usc calendar of computer image analysis computer vision conferences http iris usc edu information iris conferences html usc survey papers visionbib survey paper list http surveys visionbib com index html foundations and trends in computer graphics and vision http www nowpublishers com cgv computer vision a reference guide http link springer com book 10 1007 978 0 387 31439 6 pre trained computer vision models list of computer vision models https github com shubham shahh open source models these models are trained on custom objects tutorials and talks computer vision computer vision talks http www computervisiontalks com lectures keynotes panel discussions on computer vision the three r s of computer vision https www youtube com watch v mqg6eoryriq jitendra malik uc berkeley 2013 applications to machine vision http videolectures net epsrcws08 blake amv andrew blake microsoft research 2008 the future of image search http videolectures net kdd08 malik fis q image jitendra malik uc berkeley 2008 should i do a phd in computer vision https www youtube com watch v m17ogxh3ny8 fatih porikli australian national university graduate summer school 2013 computer vision http www ipam ucla edu programs summer schools graduate summer school computer vision tab schedule ipam 2013 recent conference talks cvpr 2015 http www pamitc org cvpr15 jun 2015 eccv 2014 http videolectures net eccv2014 zurich sep 2014 cvpr 2014 http techtalks tv cvpr 2014 oral talks jun 2014 iccv 2013 http techtalks tv iccv2013 dec 2013 icml 2013 http techtalks tv icml 2013 jul 2013 cvpr 2013 http techtalks tv cvpr2013 jun 2013 eccv 2012 http videolectures net eccv2012 firenze oct 2012 icml 2012 http techtalks tv icml 2012 orals jun 2012 cvpr 2012 http techtalks tv cvpr2012webcast jun 2012 3d computer vision 3d computer vision past present and future https www youtube com watch v kyizmr917rc steve seitz university of washington 2011 reconstructing the world from photos on the internet https www youtube com watch v 04kgg3qexfi steve seitz university of washington 2013 internet vision the distributed camera http www technologyreview com video 426265 meet 2011 tr35 winner noah snavely noah snavely cornell university 2011 planet scale visual understanding https www youtube com watch v uhkca9 z1ps noah snavely cornell university 2014 a trillion photos https www youtube com watch v 6mwefpkufrc steve seitz university of washington 2013 computational photography reflections on image based modeling and rendering https www youtube com watch v j90 0ndk7xm richard szeliski microsoft research 2013 photographing events over time https www youtube com watch v zvpahzzvprk william t freeman mit 2011 old and new algorithm for blind deconvolution http videolectures net nipsworkshops2011 weiss deconvolution yair weiss the hebrew university of jerusalem 2011 a tour of modern image processing http videolectures net nipsworkshops2010 milanfar tmi peyman milanfar uc santa cruz google 2010 topics in image and video processing http videolectures net mlss07 blake tiivp andrew blake microsoft research 2007 computational photography https www youtube com watch v hjvni0mkmqk william t freeman mit 2012 revealing the invisible https www youtube com watch v bwniqy x98 fr do durand mit 2012 overview of computer vision and visual effects https www youtube com watch v re hvtytt i rich radke rensselaer polytechnic institute 2014 learning and vision where machine vision needs help from machine learning http videolectures net colt2011 freeman help q computer 20vision william t freeman mit 2011 learning in computer vision http videolectures net mlss08au lucey linv simon lucey cmu 2008 learning and inference in low level vision http videolectures net nips09 weiss lil q computer 20vision yair weiss the hebrew university of jerusalem 2009 object recognition object recognition http research microsoft com apps video dl aspx id 231358 larry zitnick microsoft research generative models for visual objects and object recognition via bayesian inference http videolectures net mlas06 li gmvoo q fei fei 20li fei fei li stanford university graphical models graphical models for computer vision http videolectures net uai2012 felzenszwalb computer vision q computer 20vision pedro felzenszwalb brown university 2012 graphical models http videolectures net mlss09uk ghahramani gm zoubin ghahramani university of cambridge 2009 machine learning probability and graphical models http videolectures net mlss06tw roweis mlpgm sam roweis nyu 2006 graphical models and applications http videolectures net mlss09us weiss gma q graphical 20models yair weiss the hebrew university of jerusalem 2009 machine learning a gentle tutorial of the em algorithm https nikola rt ee washington edu people bulyko papers em pdf jeff a bilmes uc berkeley 1998 introduction to bayesian inference http videolectures net mlss09uk bishop ibi christopher bishop microsoft research 2009 support vector machines http videolectures net mlss06tw lin svm chih jen lin national taiwan university 2006 bayesian or frequentist which are you http videolectures net mlss09uk jordan bfway michael i jordan uc berkeley optimization optimization algorithms in machine learning http videolectures net nips2010 wright oaml stephen j wright university of wisconsin madison convex optimization http videolectures net mlss07 vandenberghe copt q convex 20optimization lieven vandenberghe university of california los angeles continuous optimization in computer vision https www youtube com watch v ozqowozvdvg andrew fitzgibbon microsoft research beyond stochastic gradient descent for large scale machine learning http videolectures net sahd2014 bach stochastic gradient francis bach inria variational methods for computer vision https www youtube com playlist list pltbdjv 4f ej7a2iih5l5ztqqrwyjp2ri daniel cremers technische universit t m nchen lecture 18 missing from playlist https www youtube com watch v ggcbvpnd3si deep learning a tutorial on deep learning http videolectures net jul09 hinton deeplearn geoffrey e hinton university of toronto deep learning http videolectures net kdd2014 salakhutdinov deep learning q hidden 20markov 20model ruslan salakhutdinov university of toronto scaling up deep learning http videolectures net kdd2014 bengio deep learning yoshua bengio university of montreal imagenet classification with deep convolutional neural networks http videolectures net machine krizhevsky imagenet classification q deep 20learning alex krizhevsky university of toronto the unreasonable effectivness of deep learning http videolectures net sahd2014 lecun deep learning yann lecun nyu facebook research 2014 deep learning for computer vision https www youtube com watch v qgx57x0fbda rob fergus nyu facebook research high dimensional learning with deep network contractions http videolectures net sahd2014 mallat dimensional learning st phane mallat ecole normale superieure graduate summer school 2012 deep learning feature learning http www ipam ucla edu programs summer schools graduate summer school deep learning feature learning tab schedule ipam 2012 workshop on big data and statistical machine learning http www fields utoronto ca programs scientific 14 15 bigdata machine machine learning summer school https www youtube com channel uc3ywjsv5osdidanop8c1niq reykjavik iceland 2014 deep learning session 1 https www youtube com watch v juimbuvewbg yoshua bengio universtiy of montreal deep learning session 2 https www youtube com watch v fl w7 z3w3o yoshua bengio university of montreal deep learning session 3 https www youtube com watch v cohr7lagwa yoshua bengio university of montreal software annotation tools comma coloring http commacoloring herokuapp com annotorious https annotorious github io labelme http labelme csail mit edu release3 0 gtmaker https github com sanko shoko gtmaker external resource links computer vision resources https sites google com site jbhuang0604 resources vision jia bin huang uiuc computer vision algorithm implementations http www cvpapers com rr html cvpapers source code collection for reproducible research http www csee wvu edu xinl reproducible research html xin li west virginia university cmu computer vision page http www cs cmu edu afs cs project cil ftp html v source html general purpose computer vision library open cv http opencv org mexopencv http kyamagu github io mexopencv simplecv http simplecv org open source python module for computer vision https github com jesolem pcv ccv a modern computer vision library https github com liuliu ccv vlfeat http www vlfeat org matlab computer vision system toolbox http www mathworks com products computer vision piotr s computer vision matlab toolbox http vision ucsd edu pdollar toolbox doc index html pcl point cloud library http pointclouds org imageutilities https gitorious org imageutilities multiple view computer vision matlab functions for multiple view geometry http www robots ox ac uk vgg hzbook code peter kovesi s matlab functions for computer vision and image analysis http staffhome ecm uwa edu au 00011811 research matlabfns index html opengv http laurentkneip github io opengv geometric computer vision algorithms minimalsolvers http cmp felk cvut cz mini minimal problems solver multi view environment http www gcc tu darmstadt de home proj mve visual sfm http ccwu me vsfm bundler sfm http www cs cornell edu snavely bundler openmvg open multiple view geometry http imagine enpc fr moulonp openmvg multiple view geometry structure from motion library softwares patch based multi view stereo v2 http www di ens fr pmvs clustering views for multi view stereo http www di ens fr cmvs floating scale surface reconstruction http www gris informatik tu darmstadt de projects floating scale surface recon large scale texturing of 3d reconstructions http www gcc tu darmstadt de home proj texrecon awesome 3d reconstruction list https github com openmvg awesome 3dreconstruction list feature detection and extraction vlfeat http www vlfeat org sift http www cs ubc ca lowe keypoints david g lowe distinctive image features from scale invariant keypoints international journal of computer vision 60 2 2004 pp 91 110 sift http www robots ox ac uk vedaldi code siftpp html brisk http www asl ethz ch people lestefan personal brisk stefan leutenegger margarita chli and roland siegwart brisk binary robust invariant scalable keypoints iccv 2011 surf http www vision ee ethz ch surf herbert bay andreas ess tinne tuytelaars luc van gool surf speeded up robust features computer vision and image understanding cviu vol 110 no 3 pp 346 359 2008 freak http www ivpe com freak htm a alahi r ortiz and p vandergheynst freak fast retina keypoint cvpr 2012 akaze http www robesafe com personal pablo alcantarilla kaze html pablo f alcantarilla adrien bartoli and andrew j davison kaze features eccv 2012 local binary patterns https github com nourani lbp high dynamic range imaging hdr toolbox https github com banterle hdr toolbox semantic segmentation list of semantic segmentation algorithms http www it caesar com list of contemporary semantic segmentation datasets low level vision stereo vision middlebury stereo vision http vision middlebury edu stereo the kitti vision benchmark suite http www cvlibs net datasets kitti eval stereo flow php benchmark stero libelas library for efficient large scale stereo matching http www cvlibs net software libelas ground truth stixel dataset http www 6d vision com ground truth stixel dataset optical flow middlebury optical flow evaluation http vision middlebury edu flow mpi sintel optical flow dataset and evaluation http sintel is tue mpg de the kitti vision benchmark suite http www cvlibs net datasets kitti eval stereo flow php benchmark flow hci challenge http hci iwr uni heidelberg de benchmarks document challenging data for stereo and optical flow coarse2fine optical flow http people csail mit edu celiu opticalflow ce liu mit secrets of optical flow estimation and their principles http cs brown edu dqsun code cvpr10 flow code zip c matlab optical flow by c liu based on brox et al and bruhn et al http people csail mit edu celiu opticalflow parallel robust optical flow by s nchez p rez et al http www ctim es research works parallel robust optical flow image denoising bm3d ksvd super resolution multi frame image super resolution http www robots ox ac uk vgg software sr pickup l c machine learning in multi frame image super resolution phd thesis 2008 markov random fields for super resolution http people csail mit edu billf project 20pages srescode markov 20random 20fields 20for 20super resolution html w t freeman and c liu markov random fields for super resolution and texture synthesis in a blake p kohli and c rother eds advances in markov random fields for vision and image processing chapter 10 mit press 2011 sparse regression and natural image prior https people mpi inf mpg de kkim supres supres htm k i kim and y kwon single image super resolution using sparse regression and natural image prior ieee trans pattern analysis and machine intelligence vol 32 no 6 pp 1127 1133 2010 single image super resolution via a statistical model http www cs technion ac il elad various singleimagesr tip14 box zip t peleg and m elad a statistical prediction model based on sparse representations for single image super resolution ieee transactions on image processing vol 23 no 6 pages 2569 2582 june 2014 sparse coding for super resolution http www cs technion ac il elad various single image sr zip r zeyde m elad and m protter on single image scale up using sparse representations curves surfaces avignon france june 24 30 2010 appears also in lecture notes on computer science lncs patch wise sparse recovery http www ifp illinois edu jyang29 scsr htm jianchao yang john wright thomas huang and yi ma image super resolution via sparse representation ieee transactions on image processing tip vol 19 issue 11 2010 neighbor embedding http www jdl ac cn user hchang doc code rar h chang d y yeung y xiong super resolution through neighbor embedding proceedings of the ieee computer society conference on computer vision and pattern recognition cvpr vol 1 pp 275 282 washington dc usa 27 june 2 july 2004 deformable patches https sites google com site yuzhushome single image super resolution using deformable patches yu zhu yanning zhang and alan yuille single image super resolution using deformable patches cvpr 2014 srcnn http mmlab ie cuhk edu hk projects srcnn html chao dong chen change loy kaiming he xiaoou tang learning a deep convolutional network for image super resolution in eccv 2014 a adjusted anchored neighborhood regression http www vision ee ethz ch timofter accv2014 id820 supplementary index html r timofte v de smet and l van gool a adjusted anchored neighborhood regression for fast super resolution accv 2014 transformed self exemplars https sites google com site jbhuang0604 publications struct sr jia bin huang abhishek singh and narendra ahuja single image super resolution using transformed self exemplars ieee conference on computer vision and pattern recognition 2015 image deblurring non blind deconvolution spatially variant non blind deconvolution http homes cs washington edu shanqi work spvdeconv handling outliers in non blind image deconvolution http cg postech ac kr research deconv outliers hyper laplacian priors http cs nyu edu dilip research fast deconvolution from learning models of natural image patches to whole image restoration http people csail mit edu danielzoran epllcode zip deep convolutional neural network for image deconvolution http lxu me projects dcnn neural deconvolution http webdav is mpg de pixel neural deconvolution blind deconvolution removing camera shake from a single photograph http www cs nyu edu fergus research deblur html high quality motion deblurring from a single image http www cse cuhk edu hk leojia projects motion deblurring two phase kernel estimation for robust motion deblurring http www cse cuhk edu hk leojia projects robust deblur blur kernel estimation using the radon transform http people csail mit edu taegsang documents radondeblurringcode zip fast motion deblurring http cg postech ac kr research fast motion deblurring blind deconvolution using a normalized sparsity measure http cs nyu edu dilip research blind deconvolution blur kernel estimation from spectral irregularities http www cs huji ac il raananf projects deblur efficient marginal likelihood optimization in blind deconvolution http www wisdom weizmann ac il levina papers levinetalcvpr2011code zip unnatural l0 sparse representation for natural image deblurring http www cse cuhk edu hk leojia projects l0deblur edge based blur kernel estimation using patch priors http cs brown edu lbsun deblur2013 deblur2013iccp html blind deblurring using internal patch recurrence http www wisdom weizmann ac il vision blinddeblur html non uniform deblurring non uniform deblurring for shaken images http www di ens fr willow research deblurring single image deblurring using motion density functions http grail cs washington edu projects mdf deblurring image deblurring using inertial measurement sensors http research microsoft com en us um redmond groups ivm imudeblurring fast removal of non uniform camera shake http webdav is mpg de pixel fast removal of camera shake image completion gimp resynthesizer http registry gimp org node 27986 priority bp http lafarren com image completer imagemelding http www ece ucsb edu psen melding planarstructurecompletion https sites google com site jbhuang0604 publications struct completion image retargeting retargetme http people csail mit edu mrub retargetme alpha matting alpha matting evaluation http www alphamatting com closed form image matting http people csail mit edu alevin matting tar gz spectral matting http www vision huji ac il spectralmatting learning based matting http www mathworks com matlabcentral fileexchange 31412 learning based digital matting improving image matting using comprehensive sampling sets http www alphamatting com improvingmattingcomprehensivesamplingsets cvpr2013 zip image pyramid the steerable pyramid http www cns nyu edu eero steerpyr curvelab http www curvelet org edge preserving image processing fast bilateral filter http people csail mit edu sparis bf o 1 bilateral filter http www cs cityu edu hk qiyang publications code qx cvpr09 ctbf zip recursive bilateral filtering http www cs cityu edu hk qiyang publications eccv 12 rolling guidance filter http www cse cuhk edu hk leojia projects rollguidance relative total variation http www cse cuhk edu hk leojia projects texturesep index html l0 gradient optimization http www cse cuhk edu hk leojia projects l0smoothing index html domain transform http www inf ufrgs br eslgastal domaintransform adaptive manifold http inf ufrgs br eslgastal adaptivemanifolds guided image filtering http research microsoft com en us um people kahe eccv10 intrinsic images recovering intrinsic images with a global sparsity prior on reflectance http people tuebingen mpg de mkiefel projects intrinsic intrinsic images by clustering http giga cps unizar es elenag projects egsr2012 intrinsic contour detection and image segmentation mean shift segmentation http coewww rutgers edu riul research code edison graph based segmentation http cs brown edu pff segment normalized cut http www cis upenn edu jshi software grab cut http grabcut weebly com background algorithm html contour detection and image segmentation http www eecs berkeley edu research projects cs vision grouping resources html structured edge detection http research microsoft com en us downloads 389109f6 b4e8 404c 84bf 239f7cbf4e3d pointwise mutual information http web mit edu phillipi pmi boundaries slic super pixel http ivrl epfl ch research superpixels quickshift http www vlfeat org overview quickshift html turbopixels http www cs toronto edu babalex research html entropy rate superpixel http mingyuliu net contour relaxed superpixels http www vsi cs uni frankfurt de research current projects research superpixel segmentation seeds http www mvdblive org seeds seeds revised https github com davidstutz seeds revised multiscale combinatorial grouping http www eecs berkeley edu research projects cs vision grouping mcg fast edge detection using structured forests https github com pdollar edges interactive image segmentation random walker http cns bu edu lgrady software html geodesic segmentation http www tc umn edu baixx015 lazy snapping http research microsoft com apps pubs default aspx id 69040 power watershed http powerwatershed sourceforge net geodesic graph cut http www adobe com technology people san jose brian price html segmentation by transduction http www cs cmu edu olivierd video segmentation video segmentation with superpixels http www mpi inf mpg de departments computer vision and multimodal computing research image and video segmentation video segmentation with superpixels efficient hierarchical graph based video segmentation http www cc gatech edu cpl projects videosegmentation object segmentation in video http lmb informatik uni freiburg de publications 2011 ob11 streaming hierarchical video segmentation http www cse buffalo edu jcorso r supervoxels camera calibration camera calibration toolbox for matlab http www vision caltech edu bouguetj calib doc camera calibration with opencv http docs opencv org trunk doc tutorials calib3d camera calibration camera calibration html multiple camera calibration toolbox https sites google com site prclibo toolbox simultaneous localization and mapping slam community openslam https www openslam org kitti odometry benchmark for outdoor visual odometry codes may be available http www cvlibs net datasets kitti eval odometry php tracking odometry libviso2 c library for visual odometry 2 http www cvlibs net software libviso ptam parallel tracking and mapping http www robots ox ac uk gk ptam kfusion implementation of kinectfusion https github com gerhardr kfusion kinfu remake lightweight reworked and optimized version of kinfu https github com nerei kinfu remake lvr kinfu kinfu remake based large scale kinectfusion with online reconstruction http las vegas uni osnabrueck de related projects lvr kinfu infinitam implementation of multi platform large scale depth tracking and fusion http www robots ox ac uk victor infinitam voxelhashing large scale kinectfusion https github com nachtmar voxelhashing slambench multiple implementation of kinectfusion http apt cs manchester ac uk projects pamela tools slambench svo semi direct visual odometry https github com uzh rpg rpg svo dvo dense visual odometry https github com tum vision dvo slam fovis rgb d visual odometry https code google com p fovis graph optimization gtsam general smoothing and mapping library for robotics and sfm https collab cc gatech edu borg gtsam destination node 2f299 georgia institute of technology g2o general framework for graph optomization https github com rainerkuemmerle g2o loop closure fabmap appearance based loop closure system http www robots ox ac uk mjc software htm also available in opencv2 4 11 http docs opencv org 2 4 modules contrib doc openfabmap html dbow2 binary bag of words loop detection system http webdiis unizar es dorian index php p 32 localization mapping ratslam https code google com p ratslam lsd slam https github com tum vision lsd slam orb slam https github com raulmur orb slam single view spatial understanding geometric context http web engr illinois edu dhoiem projects software html derek hoiem cmu recovering spatial layout http web engr illinois edu dhoiem software counter php down varsha spatiallayout zip varsha hedau uiuc geometric reasoning http www cs cmu edu dclee code index html david c lee cmu rgbd2full3d https github com arron2003 rgbd2full3d ruiqi guo uiuc object detection inria object detection and localization toolkit http pascal inrialpes fr soft olt discriminatively trained deformable part models http www cs berkeley edu rbg latent voc dpm https github com rbgirshick voc dpm histograms of sparse codes for object detection http www ics uci edu dramanan software sparse r cnn regions with convolutional neural network features https github com rbgirshick rcnn spp net https github com shaoqingren spp net bing objectness estimation http mmcheng net bing comment page 9 edge boxes https github com pdollar edges reinspect https github com russell91 reinspect nearest neighbor search general purpose nearest neighbor search ann a library for approximate nearest neighbor searching http www cs umd edu mount ann flann fast library for approximate nearest neighbors http www cs ubc ca research flann fast k nearest neighbor search using gpu http vincentfpgarcia github io knn cuda nearest neighbor field estimation patchmatch http gfx cs princeton edu gfx pubs barnes 2009 par index php generalized patchmatch http gfx cs princeton edu pubs barnes 2010 tgp index php coherency sensitive hashing http www eng tau ac il simonk csh pmbp patchmatch belief propagation https github com fbesse pmbp treecann http www eng tau ac il avidan papers treecann code 20121022 rar visual tracking visual tracker benchmark https sites google com site trackerbenchmark benchmarks v10 visual tracking challenge http www votchallenge net kanade lucas tomasi feature tracker http www ces clemson edu stb klt extended lucas kanade tracking http www eng tau ac il oron elk elk html online boosting tracking http www vision ee ethz ch boostingtrackers spatio temporal context learning http www4 comp polyu edu hk cslzhang stc stc htm locality sensitive histograms http www shengfenghe com visual tracking via locality sensitive histograms html enhanced adaptive coupled layer lgtracker http www cv foundation org openaccess content iccv workshops 2013 w03 papers xiao an enhanced adaptive 2013 iccv paper pdf tld tracking learning detection http personal ee surrey ac uk personal z kalal tld html cmt clustering of static adaptive correspondences for deformable object tracking http www gnebehay com cmt kernelized correlation filters http home isr uc pt henriques circulant accurate scale estimation for robust visual tracking http www cvl isy liu se en research objrec visualtracking scalvistrack index html multiple experts using entropy minimization http cs people bu edu jmzhang meem meem html tgpr http www dabi temple edu hbling code tgpr htm cf2 hierarchical convolutional features for visual tracking https sites google com site jbhuang0604 publications cf2 modular tracking framework http webdocs cs ualberta ca vis mtf index html saliency detection attributes action reconition egocentric cameras human in the loop systems image captioning neuraltalk https github com karpathy neuraltalk optimization ceres solver http ceres solver org nonlinear least square problem and unconstrained optimization solver nlopt http ab initio mit edu wiki index php nlopt nonlinear least square problem and unconstrained optimization solver opengm http hci iwr uni heidelberg de opengm2 factor graph based discrete optimization and inference solver gtsam https collab cc gatech edu borg gtsam factor graph based lease square optimization solver deep learning awesome deep vision https github com kjw0612 awesome deep vision machine learning awesome machine learning https github com josephmisiti awesome machine learning bob a free signal processing and machine learning toolbox for researchers http idiap github io bob libsvm a library for support vector machines https www csie ntu edu tw cjlin libsvm datasets external dataset link collection cv datasets on the web http www cvpapers com datasets html cvpapers are we there yet http rodrigob github io are we there yet build which paper provides the best results on standard dataset x computer vision dataset on the web http www cvpapers com datasets html yet another computer vision index to datasets http riemenschneider hayko at vision dataset computervisiononline datasets http www computervisiononline com datasets cvonline dataset http homepages inf ed ac uk cgi rbf cvonline entries pl tag363 cv datasets http clickdamage com sourcecode cv datasets php visionbib http datasets visionbib com info index html visualdata http www visualdata io low level vision stereo vision middlebury stereo vision http vision middlebury edu stereo the kitti vision benchmark suite http www cvlibs net datasets kitti eval stereo flow php benchmark stero libelas library for efficient large scale stereo matching http www cvlibs net software libelas ground truth stixel dataset http www 6d vision com ground truth stixel dataset optical flow middlebury optical flow evaluation http vision middlebury edu flow mpi sintel optical flow dataset and evaluation http sintel is tue mpg de the kitti vision benchmark suite http www cvlibs net datasets kitti eval stereo flow php benchmark flow hci challenge http hci iwr uni heidelberg de benchmarks document challenging data for stereo and optical flow video object segmentation davis densely annotated video segmentation http davischallenge org segtrack v2 http web engr oregonstate edu lif segtrack2 dataset html change detection labeled and annotated sequences for integral evaluation of segmentation algorithms http www gti ssr upm es data lasiesta changedetection net http www changedetection net image super resolutions single image super resolution a benchmark https eng ucmerced edu people cyang35 eccv14 eccv14 html intrinsic images ground truth dataset and baseline evaluations for intrinsic image algorithms http www mit edu kimo publications intrinsic intrinsic images in the wild http opensurfaces cs cornell edu intrinsic intrinsic image evaluation on synthetic complex scenes http www cic uab cat datasets synthetic intrinsic image dataset material recognition opensurface http opensurfaces cs cornell edu flickr material database http people csail mit edu celiu cvpr2010 materials in context dataset http opensurfaces cs cornell edu publications minc multi view reconsturction multi view stereo reconstruction http vision middlebury edu mview saliency detection visual tracking visual tracker benchmark https sites google com site trackerbenchmark benchmarks v10 visual tracker benchmark v1 1 https sites google com site benchmarkpami vot challenge http www votchallenge net princeton tracking benchmark http tracking cs princeton edu tracking manipulation tasks tmt http webdocs cs ualberta ca vis trackdb visual surveillance virat http www viratdata org cam2 https cam2 ecn purdue edu saliency detection change detection changedetection net http changedetection net visual recognition image classification the pascal visual object classes http pascallin ecs soton ac uk challenges voc imagenet large scale visual recognition challenge http www image net org challenges lsvrc 2014 self supervised learning pass an an imagenet replacement for self supervised pretraining without humans https github com yukimasano pass scene recognition sun database http groups csail mit edu vision sun place dataset http places csail mit edu object detection the pascal visual object classes http pascallin ecs soton ac uk challenges voc imagenet object detection challenge http www image net org challenges lsvrc 2014 microsoft coco http mscoco org semantic labeling stanford background dataset http dags stanford edu projects scenedataset html camvid http mi eng cam ac uk research projects videorec camvid barcelona dataset http www cs unc edu jtighe papers eccv10 sift flow dataset http www cs unc edu jtighe papers eccv10 siftflow siftflowdataset zip multi view object detection 3d object dataset http cvgl stanford edu resources html epfl car dataset http cvlab epfl ch data pose ktti dection dataset http www cvlibs net datasets kitti eval object php sun 3d dataset http sun3d cs princeton edu pascal 3d http cvgl stanford edu projects pascal3d html nyu car dataset http nyc3d cs cornell edu fine grained visual recognition fine grained classification challenge https sites google com site fgcomp2013 caltech ucsd birds 200 http www vision caltech edu visipedia cub 200 html pedestrian detection caltech pedestrian detection benchmark http www vision caltech edu image datasets caltechpedestrians ethz pedestrian detection https data vision ee ethz ch cvl aess dataset action recognition image based video based hollywood2 dataset http www di ens fr laptev actions hollywood2 ucf sports action data set http crcv ucf edu data ucf sports action php image deblurring sun dataset http cs brown edu lbsun deblur2013 deblur2013iccp html levin dataset http www wisdom weizmann ac il levina papers levinetalcvpr09data rar image captioning flickr 8k http nlp cs illinois edu hockenmaiergroup framing image description kcca html flickr 30k http shannon cs illinois edu denotationgraph microsoft coco http mscoco org scene understanding sun rgb d http rgbd cs princeton edu a rgb d scene understanding benchmark suite nyu depth v2 http cs nyu edu silberman datasets nyu depth v2 html indoor segmentation and support inference from rgbd images aerial images aerial image segmentation https zenodo org record 1154821 wmn9khwnhip learning aerial image segmentation from online maps resources for students resource link collection resources for students http people csail mit edu fredo student html fr do durand mit advice for graduate students http www dgp toronto edu hertzman advice aaron hertzmann adobe research graduate skills seminars http www dgp toronto edu hertzman courses gradskills 2010 yashar ganjali aaron hertzmann university of toronto research skills http research microsoft com en us um people simonpj papers giving a talk giving a talk htm simon peyton jones microsoft research resource collection http web engr illinois edu taoxie advice htm tao xie uiuc and yuan xie ucsb writing write good papers http people csail mit edu fredo fredogoodwriting pdf fr do durand mit notes on writing http people csail mit edu fredo publi writing pdf fr do durand mit how to write a bad article http people csail mit edu fredo fredobadwriting pdf fr do durand mit how to write a good cvpr submission http billf mit edu sites default files documents cvprpapers pdf william t freeman mit how to write a great research paper https www youtube com watch v g3dkrstqdda simon peyton jones microsoft research how to write a siggraph paper http www slideshare net jdily how to write a siggraph paper siggraph asia 2011 course writing research papers http www dgp toronto edu hertzman advice writing technical papers pdf aaron hertzmann adobe research how to write a paper for siggraph http www computer org csdl mags cg 1987 12 mcg1987120062 pdf jim blinn how to get your siggraph paper rejected http www siggraph org sites default files kajiya pdf jim kajiya microsoft research how to write a siggraph paper www liyiwei org courses how siga11 liyiwei pptx li yi wei the university of hong kong how to write a great paper http www hagen informatik uni kl de bertram talks getpublished pdf martin martin hering hering bertram hochschule bremen university of applied sciences how to have a paper get into siggraph http www ui is s u tokyo ac jp takeo writings siggraph html takeo igarashi the university of tokyo good writing http www cs cmu edu pausch randy randy raibert htm marc h raibert boston dynamics inc how to write a computer vision paper http web engr illinois edu dhoiem presentations how 20to 20write 20a 20computer 20vison 20paper ppt derek hoiem uiuc common mistakes in technical writing http www cs dartmouth edu wjarosz writing html wojciech jarosz dartmouth college presentation giving a research talk http people csail mit edu fredo talkadvice pdf fr do durand mit how to give a good talk http www dgp toronto edu hertzman courses gradskills 2010 givinggoodtalks pdf david fleet university of toronto and aaron hertzmann adobe research designing conference posters http colinpurrington com tips poster design colin purrington research how to do research http people csail mit edu billf www papers doresearch pdf william t freeman mit you and your research http www cs virginia edu robins youandyourresearch html richard hamming warning signs of bogus progress in research in an age of rich computation and information http yima csl illinois edu psfile bogus pdf yi ma uiuc seven warning signs of bogus science http www quackwatch com 01quackeryrelatedtopics signs html robert l park five principles for choosing research problems in computer graphics https www youtube com watch v v2qaf8t8i6c thomas funkhouser cornell university how to do research in the mit ai lab http www cs indiana edu mit research how to html david chapman mit recent advances in computer vision http www slideshare net antiw recent advances in computer vision ming hsuan yang uc merced how to come up with research ideas in computer vision http www slideshare net jbhuang how to come up with new research ideas 4005840 jia bin huang uiuc how to read academic papers http www slideshare net jbhuang how to read academic papers jia bin huang uiuc time management time management https www youtube com watch v otugjssqot0 randy pausch cmu blogs learn opencv http www learnopencv com satya mallick tombone s computer vision blog http www computervisionblog com tomasz malisiewicz computer vision for dummies http www visiondummy com vincent spruyt andrej karpathy blog http karpathy github io andrej karpathy ai shack http aishack in utkarsh sinha computer vision talks http computer vision talks com eugene khvedchenya computer vision basics with python keras and opencv https github com jrobchin computer vision basics with python keras and opencv jason chin university of western ontario links the computer vision industry http www cs ubc ca lowe vision html david lowe german computer vision research groups companies http hci iwr uni heidelberg de links german vision awesome deep learning https github com christoschristofidis awesome deep learning awesome machine learning https github com josephmisiti awesome machine learning cat paper collection http www eecs berkeley edu junyanz cat cat papers html computer vision news http www rsipvision com computer vision news songs the fundamental matrix song http danielwedge com fmatrix the ransac song http danielwedge com ransac machine learning a cappella overfitting thriller https www youtube com watch v dqwi1kvmwrg licenses license cc0 http i creativecommons org p zero 1 0 88x31 png http creativecommons org publicdomain zero 1 0 to the extent possible under law jia bin huang www jiabinhuang com has waived all copyright and related or neighboring rights to this work | ai |
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greenwood | greenwood netlify status https api netlify com api v1 badges 6758148c 5c38 44d8 b908 ca0a1dad0f7c deploy status https app netlify com sites elastic blackwell 3aef44 deploys github release https img shields io github tag projectevergreen greenwood svg https github com projectevergreen greenwood tags github actions status https github com projectevergreen greenwood workflows master 20integration badge svg github issues https img shields io github issues pr raw projectevergreen greenwood svg https github com projectevergreen greenwood issues github license https img shields io badge license mit blue svg https raw githubusercontent com projectevergreen greenwood master license md lerna https img shields io badge maintained 20with lerna cc00ff svg https lerna js org overview greenwood is your workbench for the web focused on supporting modern web standards and development to help you create your next project for information on getting started reviewing our docs or to learn more about the project and how it works please visit our website https www greenwoodjs io features no bundle development https www greenwoodjs io about how it works pages are built on the fly html and markdown first authoring experience https www greenwoodjs io docs layouts and esm friendly optimized https www greenwoodjs io docs configuration optimization production builds no javascript by default prerendering support for web components extensible via plugins https www greenwoodjs io plugins supports ssg mpa spa and ssr project types https www greenwoodjs io docs layouts or a hybrid greenwood is currently working towards a 1 0 release https github com projectevergreen greenwood milestone 3 if you re interested in learning more about the web and web development at any skill level or interested in checking out our high level roadmap and how greenwood got where it is today you can read our state of greenwoodjs blog post https www greenwoodjs io blog state of greenwood 2022 we would love to have your help making greenwood getting started our website has a complete getting started http www greenwoodjs io getting started section that will walk you through creating a greenwood project from scratch you can follow along with or clone and go the companion repo https github com projectevergreen greenwood getting started installation greenwood can be installed with your favorite javascript package manager bash npm npm install greenwood cli save dev yarn yarn add greenwood cli dev then in your package json add the type field and scripts for the cli like so json type module scripts build greenwood build start greenwood develop serve greenwood serve greenwood build generates a production build of your project greenwood develop starts a local development server for your project greenwood serve generates a production build of your project and runs it on a nodejs based web server documentation all of our documentation is on our website https www greenwoodjs io which itself is built by greenwood see our website documentation to learn more about configuration pages templates component model styles and assets contributing we would love your contribution github contributing md to greenwood please check out our issue tracker for good first issue labels or feel to reach out to us on slack https join slack com t thegreenhouseio shared invite enqtmzcymze2mjk1mjgwltu5ymm1mdjimtg0odk4mja4nzuwnwfmzmmxndy5mtcwm2i0mjyxn2vhotewndu2ywqwowqzzmy1yzy4mwrlogi in the room greenwood or on twitter https twitter com prjevergreen built with greenwood site repo project details the greenhouse i o https www thegreenhouse io thegreenhouseio www thegreenhouse io https github com thegreenhouseio www thegreenhouse io personal portfolio blog website for thescientist13 greenwood maintainer contributary https www contributary community contributarycommunity www contributary community https github com contributarycommunity www contributary community a website spa for browsing open source projects that are open to contributions built with lit and hosted with aws s3 cloudfront analog studios https www analogstudios net analogstudiosri www analogstudios net https github com analogstudiosri www analogstudios net a local music studio spa website originally written in angular 2 recently migrated to lit it is currently transitioning to a hybrid static serverless website https github com analogstudiosri www analogstudios net discussions 37 showing off the full potential of greenwood and web components follow along for all the fun built a site with greenwood open a pr and add it here license see the license license md file for license rights and limitations mit | webcomponents jamstack hacktoberfest nodejs javascript css html esm ssg ssr lit | front_end |
Hyperparameter-Optimization-of-Machine-Learning-Algorithms | hyperparameter optimization of machine learning algorithms this code provides a hyper parameter optimization implementation for machine learning algorithms as described in the paper l yang and a shami on hyperparameter optimization of machine learning algorithms theory and practice https arxiv org abs 2007 15745 neurocomputing vol 415 pp 295 316 2020 doi https doi org 10 1016 j neucom 2020 07 061 to fit a machine learning model into different problems its hyper parameters must be tuned selecting the best hyper parameter configuration for machine learning models has a direct impact on the model s performance in this paper optimizing the hyper parameters of common machine learning models is studied we introduce several state of the art optimization techniques and discuss how to apply them to machine learning algorithms many available libraries and frameworks developed for hyper parameter optimization problems are provided and some open challenges of hyper parameter optimization research are also discussed in this paper moreover experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper parameter optimization this paper and code will help industrial users data analysts and researchers to better develop machine learning models by identifying the proper hyper parameter configurations effectively ps a comprehensive automated machine learning automl tutorial code can be found in automl implementation for static and dynamic data analytics https github com western oc2 lab automl implementation for static and dynamic data analytics including automated data pre processing automated feature engineering automated model selection hyperparameter optimization and automated model updating concept drift adaptation paper on hyperparameter optimization of machine learning algorithms theory and practice one column version arxiv https arxiv org abs 2007 15745 two column version elsevier https www sciencedirect com science article pii s0925231220311693 quick navigation section 3 important hyper parameters of common machine learning algorithms section 4 hyper parameter optimization techniques introduction section 5 how to choose optimization techniques for different machine learning models section 6 common python libraries tools for hyper parameter optimization section 7 experimental results sample code in hpo regression ipynb and hpo classification ipynb section 8 open challenges and future research directions summary table for sections 3 6 table 2 a comprehensive overview of common ml models their hyper parameters suitable optimization techniques and available python libraries summary table for sections 8 table 10 the open challenges and future directions of hpo research implementation sample code for hyper parameter optimization implementation for machine learning algorithms is provided in this repository sample code for regression problems hpo regression ipynb https github com liyanghart hyperparameter optimization of machine learning algorithms blob master hpo regression ipynb dataset used boston housing https scikit learn org stable modules generated sklearn datasets load boston html sample code for classification problems hpo classification ipynb https github com liyanghart hyperparameter optimization of machine learning algorithms blob master hpo classification ipynb dataset used mnist https scikit learn org stable modules generated sklearn datasets load digits html sklearn datasets load digits machine learning deep learning algorithms random forest rf support vector machine svm k nearest neighbor knn artificial neural networks ann hyperparameter configuration space ml model hyper parameter type search space rf classifier n estimators discrete 10 100 max depth discrete 5 50 min samples split discrete 2 11 min samples leaf discrete 1 11 criterion categorical gini entropy max features discrete 1 64 svm classifier c continuous 0 1 50 kernel categorical linear poly rbf sigmoid knn classifier n neighbors discrete 1 20 ann classifier optimizer categorical adam rmsprop sgd activation categorical relu tanh batch size discrete 16 64 neurons discrete 10 100 epochs discrete 20 50 patience discrete 3 20 rf regressor n estimators discrete 10 100 max depth discrete 5 50 min samples split discrete 2 11 min samples leaf discrete 1 11 criterion categorical mse mae max features discrete 1 13 svm regressor c continuous 0 1 50 kernel categorical linear poly rbf sigmoid epsilon continuous 0 001 1 knn regressor n neighbors discrete 1 20 ann regressor optimizer categorical adam rmsprop activation categorical relu tanh loss categorical mse mae batch size discrete 16 64 neurons discrete 10 100 epochs discrete 20 50 patience discrete 3 20 hpo algorithms grid search random search hyperband bayesian optimization with gaussian processes bo gp bayesian optimization with tree structured parzen estimator bo tpe particle swarm optimization pso genetic algorithm ga requirements python 3 5 keras https keras io scikit learn https scikit learn org stable hyperband https github com thuijskens scikit hyperband scikit optimize https github com scikit optimize scikit optimize hyperopt https github com hyperopt hyperopt optunity https github com claesenm optunity deap https github com rsteca sklearn deap tpot https github com epistasislab tpot contact info please feel free to contact me for any questions or cooperation opportunities i d be happy to help email liyanghart gmail com mailto liyanghart gmail com github liyanghart https github com liyanghart and western oc2 lab https github com western oc2 lab linkedin li yang https www linkedin com in li yang phd 65a190176 google scholar li yang https scholar google com eg citations user xefm7biaaaaj hl en and oc2 lab https scholar google com eg citations user oiebnboaaaaj hl en citation if you find this repository useful in your research please cite this article as l yang and a shami on hyperparameter optimization of machine learning algorithms theory and practice neurocomputing vol 415 pp 295 316 2020 doi https doi org 10 1016 j neucom 2020 07 061 article yang2020295 title on hyperparameter optimization of machine learning algorithms theory and practice author li yang and abdallah shami volume 415 pages 295 316 journal neurocomputing year 2020 issn 0925 2312 doi https doi org 10 1016 j neucom 2020 07 061 url http www sciencedirect com science article pii s0925231220311693 | hyperparameter-optimization machine-learning-algorithms hyperparameter-tuning machine-learning tuning-parameters grid-search random-search optimization hpo bayesian-optimization genetic-algorithm particle-swarm-optimization hyperband random-forest knn svm python-examples python-samples deep-learning artificial-neural-networks | ai |
CHP014-Unity-step-by-step- | chp014 unity step by step unity in embedded system design and robotics a step by step guide | os |
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CEN414-Database-Assignment | cen414 database assignment this is the database assignment in which mongodb was assigned to me done by odumuyiwa ayomikun 17cj022522 computer engineering 400 level for this assignment i built a user interface that would allow the user to access the data i stored in my mongodb they would also be able to performs various queries as well | server |
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Computer-Vision-with-OpenCV-3-and-Qt5 | computer vision with opencv 3 and qt5 this is the code repository for computer vision with opencv 3 and qt5 https www packtpub com application development computer vision opencv 3 and qt5 utm source github utm medium repository utm campaign 9781788472395 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book developers have been using opencv library to develop computer vision applications for a long time however they now need a more effective tool to get the job done and in a much better and modern way qt is one of the major frameworks available for this task at the moment instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 chapter 11 has no code file rest all chapter code files are present in their respective folder the code will look like the following include mainwindow h include int main int argc char argv qapplication a argc argv mainwindow w w show return a exec although every required tool and software the correct version and how it is installed and configured is covered in the initial chapters of the book the following is a list that can be used as a quick reference a regular computer with a more recent version of windows macos or linux such as ubuntu operating system installed on it microsoft visual studio on windows xcode on macos cmake qt framework opencv framework to get an idea of what a regular computer is these days you can search online or ask a local shop however the one you already have is most probably enough to get you started related products mastering opencv 3 second edition https www packtpub com application development mastering opencv 3 second edition utm source github utm medium repository utm campaign 9781786467171 machine learning for opencv https www packtpub com big data and business intelligence machine learning opencv utm source github utm medium repository utm campaign 9781783980284 computer vision with python 3 https www packtpub com application development computer vision python 3 utm source github utm medium repository utm campaign 9781788299763 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to download a free pdf copy of this book i p align center a href https packt link free ebook 9781788472395 https packt link free ebook 9781788472395 a p | ai |
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river | p align center img height 220px src docs img logo svg alt river logo p p align center tests a href https github com online ml river actions workflows ci yml img src https github com online ml river actions workflows ci yml badge svg alt ci pipeline a documentation a href https riverml xyz img src https img shields io website label docs style flat square url https 3a 2f 2friverml xyz 2f alt documentation a discord a href https discord gg qnmrkezman img src https dcbadge vercel app api server qnmrkezman style flat square alt discord a roadmap a href https github com orgs online ml projects 3 img src https img shields io website label roadmap style flat square url https github com orgs online ml projects 3 alt roadmap a pypi a href https pypi org project river img src https img shields io pypi v river svg label release color blue style flat square alt pypi a pepy a href https pepy tech project river img src https static pepy tech badge river style flat square alt pepy a black a href https github com psf black img src https img shields io badge code 20style black 000000 svg alt black a mypy a href http mypy lang org img src http www mypy lang org static mypy badge svg alt mypy a license a href https opensource org licenses bsd 3 clause img src https img shields io badge license bsd 203 clause blue svg style flat square alt bsd 3 license a p br p align center river is a python library for a href https www wikiwand com en online machine learning online machine learning a it aims to be the most user friendly library for doing machine learning on streaming data river is the result of a merger between a href https github com maxhalford creme creme a and a href https github com scikit multiflow scikit multiflow scikit multiflow a p quickstart as a quick example we ll train a logistic regression to classify the website phishing dataset http archive ics uci edu ml datasets website phishing here s a look at the first observation in the dataset python from pprint import pprint from river import datasets dataset datasets phishing for x y in dataset pprint x print y break age of domain 1 anchor from other domain 0 0 empty server form handler 0 0 https 0 0 ip in url 1 is popular 0 5 long url 1 0 popup window 0 0 request from other domain 0 0 true now let s run the model on the dataset in a streaming fashion we sequentially interleave predictions and model updates meanwhile we update a performance metric to see how well the model is doing python from river import compose from river import linear model from river import metrics from river import preprocessing model compose pipeline preprocessing standardscaler linear model logisticregression metric metrics accuracy for x y in dataset y pred model predict one x make a prediction metric metric update y y pred update the metric model model learn one x y make the model learn metric accuracy 89 28 of course this is just a contrived example we welcome you to check the introduction https riverml xyz dev introduction installation section of the documentation for a more thorough tutorial installation river is intended to work with python 3 8 and above installation can be done with pip sh pip install river there are wheels available https pypi org project river files for linux macos and windows this means you most probably won t have to build river from source you can install the latest development version from github as so sh pip install git https github com online ml river upgrade pip install git ssh git github com online ml river git upgrade using ssh this method requires having cython and rust installed on your machine features river provides online implementations of the following family of algorithms linear models with a wide array of optimizers decision trees and random forests approximate nearest neighbors anomaly detection drift detection recommender systems time series forecasting bandits factorization machines imbalanced learning clustering bagging boosting stacking active learning river also provides other online utilities feature extraction and selection online statistics and metrics preprocessing built in datasets progressive model validation model pipelines check out the api https riverml xyz latest api overview for a comprehensive overview should i be using river you should ask yourself if you need online machine learning the answer is likely no most of the time batch learning does the job just fine an online approach might fit the bill if you want a model that can learn from new data without having to revisit past data you want a model which is robust to concept drift https www wikiwand com en concept drift you want to develop your model in a way that is closer to what occurs in a production context which is usually event based some specificities of river are that it focuses on clarity and user experience more so than performance it s very fast at processing one sample at a time try it you ll see it plays nicely with the rest of python s ecosystem useful links documentation https riverml xyz package releases https pypi org project river history awesome online machine learning https github com online ml awesome online machine learning 2022 presentation at gaia https www youtube com watch v nzftmjniakk list pliu25 fciwnaz5pqwpihmpcmofyoesj8c index 5 online clustering algorithms evaluation metrics applications and benchmarking https dl acm org doi 10 1145 3534678 3542600 from kdd 22 https kdd org kdd2022 contributing feel free to contribute in any way you like we re always open to new ideas and approaches open a discussion https github com online ml river discussions new if you have any question or enquiry whatsoever it s more useful to ask your question in public rather than sending us a private email it s also encouraged to open a discussion before contributing so that everyone is aligned and unnecessary work is avoided feel welcome to open an issue https github com online ml river issues new choose if you think you ve spotted a bug or a performance issue our roadmap https github com orgs online ml projects 3 query is 3aopen sort 3aupdated desc is public feel free to work on anything that catches your eye or to make suggestions please check out the contribution guidelines https github com online ml river blob main contributing md if you want to bring modifications to the code base affiliations p align center img width 70 src https docs google com drawings d e 2pacx 1vsagehwajdsb0c24en fhwaf9djzbyh5yju7lk0snowd2m9uv9tufm u77k6obqtyn2mp05avf6tcjc pub w 2073 h 1127 alt affiliations p citation if river has been useful to you and you would like to cite it in a scientific publication please refer to the paper https www jmlr org papers volume22 20 1380 20 1380 pdf published at jmlr bibtex article montiel2021river title river machine learning for streaming data in python author montiel jacob and halford max and mastelini saulo martiello and bolmier geoffrey and sourty raphael and vaysse robin and zouitine adil and gomes heitor murilo and read jesse and abdessalem talel and others year 2021 license river is free and open source software licensed under the 3 clause bsd license https github com online ml river blob main license | incremental-learning machine-learning python online-learning online-statistics data-science streaming online-machine-learning streaming-data concept-drift real-time-processing stream-processing | ai |
aws-machine-learning-university-accelerated-cv | logo data mlu logo png machine learning university accelerated computer vision class this repository contains slides notebooks and datasets for the machine learning university mlu computer vision class our mission is to make machine learning accessible to everyone we have courses available across many topics of machine learning and believe knowledge of ml can be a key enabler for success this class is designed to help you get started with computer vision learn about widely used machine learning techniques and apply them to real world problems youtube watch all computer vision class video recordings in this youtube playlist https www youtube com playlist list pl8p z6c4gcuu4knhhcoujujfz2ttqu ta from our youtube channel https www youtube com channel uc12lqyqtqybxatys9aa7nuw playlists playlist https img youtube com vi 6cfi2co2ai 0 jpg https www youtube com playlist list pl8p z6c4gcuu4knhhcoujujfz2ttqu ta course overview there are three lectures and one final project for this class lecture 1 title studio lab intro to ml intro to computer vision neural networks gluon open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day1 nn ipynb br pytorch open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks pytorch mla cv day1 nn ipynb convolutional neural networks gluon open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day1 cnn ipynb br pytorch open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks pytorch mla cv day1 cnn ipynb final project gluon open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day1 final project ipynb br pytorch open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks pytorch mla cv day1 final project ipynb lecture 2 title studio lab image datasets training neural networks modern cnns lenet and alexnet gluon open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day2 transfer learning ipynb br pytorch open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks pytorch mla cv day2 transfer learning ipynb model fine tuning open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day2 autogluon cv mla cv day2 autogluon cv ipynb lecture 3 title studio lab advanced cnns vggnet and resnet gluon open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day3 resnet ipynb br pytorch open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks pytorch mla cv day3 resnet ipynb object detection open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated cv blob master notebooks mla cv day3 yolo ipynb semantic segmentation final project practice working with a real world computer vision dataset for the final project final project dataset is in the data final project dataset folder https github com aws samples aws machine learning university accelerated cv tree master data final project dataset for more details on the final project check out this notebook https github com aws samples aws machine learning university accelerated cv blob master notebooks mla cv day1 final project ipynb interactives visuals interested in visual interactive explanations of core machine learning concepts check out our mlu explain articles https mlu explain github io to learn at your own pace contribute if you would like to contribute to the project see contributing contributing md for more information license the license for this repository depends on the section data set for the course is being provided to you by permission of amazon and is subject to the terms of the amazon license and access https www amazon com gp help customer display html nodeid 201909000 you are expressly prohibited from copying modifying selling exporting or using this data set in any way other than for the purpose of completing this course the lecture slides are released under the cc by sa 4 0 license the code examples are released under the mit 0 license see each section s license file for details | machine-learning computer-vision deep-learning python gluon mxnet gluoncv | ai |
IOT-ESP8266-Google-Home | iot esp8266 google home youtube tutorial https www youtube com watch v uvg0rurkkgy in this project you will beable to control esp8266 with google home without opening a firewall port or setuping a revers proxy 1 download this project and unzip 2 deploy this project to heroku by clicking this button deploy https www herokucdn com deploy button svg https heroku com deploy 3 copy following folders to c program files x86 arduino libraries or install the libraries via the arduino library manager arduinowebsockets websockets by markus sattler arduinojson arduinojson by benolt blanchon 4 update and flash your esp8266 with esp8266 websocketclient esp8266 websocketclient esp8266 websocketclient ino 5 create an api ai agent and import matrix ai zip and setup google integration and webhook 6 test and enjoy troubleshooting 1 make sure google home device is logged in to same account as your api ai account 2 if unable to trigger relay power cycle the esp8266 3 if you are still unable to trigger the relay then restart heroku app by selecting option restat all dynos and then power cycle the esp8266 3 if you see crash errors in arduino ide serial monitor the use a better power supply 4 use a saprate power source for relay or use 5volts power source and use 3 3 voltage regulator to power esp8266 from same power supply 5 make sure you enter the correct url for webhook in api ai 6 make sure google action option is enabled 7 if it still does not work then go to church and pray to god | server |
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fund-protocol | coinalpha fund protocol a blockchain protocol for tokenized hedge funds this open source protocol enables asset managers to create a blockchain based vehicle that manages capital contributed by external investors the protocol utilizes the blockchain to perform functions such as segregated asset custody net asset value calculation fee accounting and management of investor in flows and out flows the goal of this project is to eliminate the setup and operational costs imposed by middlemen in traditional funds while maximizing transparency and liquidity for investors for more information about the project please see the our wiki https github com coinalpha fund protocol wiki installation geth ethereum client for testnet and live brew tap ethereum ethereum brew install ethereum testrpc ethereum client for local testing npm install g ethereumjs testrpc truffle deployment and testing framework use v4 0 0 beta 0 which ships with solc v0 4 15 npm install g truffle 4 0 0 beta 0 libraries and dependencies npm install testing local 1 run testrpc with a 1 second block time and increased block gas limit to allow for simulation of time based fees testrpc b 1 l 7000000 2 in another terminal window truffle console 3 truffle test to run all tests testnet 1 run geth testnet rpc rpcapi eth net web3 personal 2 in another terminal window truffle console 3 web3 eth accounts and check that you have at least 4 accounts each account should have more than 5 test eth 4 unlock your primary account web3 personal unlockaccount web3 eth accounts 0 insert your password here 15000 5 follow manual testing workflows in js fund test js ethereum bridge oraclize ethereum bridge is used for connecting to oraclize from a non public blockchain instance e g testrpc this is used for testing the datafeed contracts 1 in a separate folder from this repo clone the repo git clone https github com oraclize ethereum bridge 2 setup cd ethereum bridge npm install 3 when running testrpc use the same mnemonic to keep the oraclizeaddrresolver address constant testrpc l 7000000 p 7545 a 50 mnemonic coinalpha 4 run node bridge a 49 h localhost 7545 dev a 49 uses the 49th testrpc account for deploying oraclize the 9th account should not be used for any other purposes and port 7545 5 after starting the bridge take note of this message please add this line to your contract constructor oar oraclizeaddrresolveri 0x6f485c8bf6fc43ea212e93bbf8ce046c7f1cb475 6 add this line into datafeel sol | blockchain blockchain-protocol ethereum-contract ethereum-blockchain solidity-contracts | blockchain |
GCP | cloud computing provides on demand service broad access network resources can be accessed from any part of the world using an internet connection resource pooling resources are shared with the customers rapid elasticity measured service pay from what you use google cloud platform availability scalability insfrastructure needs lets users focus on the development that configuring the infrastructure resource hierarchy each gcp resource compute engine stackdriver monitoring cloud sql etc is attached to a single gcp project which may or may not be arranged within folders representing the departments in an organisation div align center p organisation individual user p img src https render githubusercontent com render math math uparrow p folder p img src https render githubusercontent com render math math uparrow p project p img src https render githubusercontent com render math math uparrow p resource p div example div align center p google p img src https render githubusercontent com render math math uparrow p workspace p img src https render githubusercontent com render math math uparrow p gmail p img src https render githubusercontent com render math math uparrow p compute engine p div using folders requires there to be an organization node to be present at the top of the hierarchy identify and access management control access to resource following the principal of least privilages while assigning resources to roles div align center p resource img src https render githubusercontent com render math math leftrightarrow access type img src https render githubusercontent com render math math leftrightarrow user identity p div custom roles can only be assigned at project or organizational level and not on folders can give roles permissions to resources through service accounts eg giving a vm instance permission to access a cloud storage bucket and not allowing external connection to it service account is also a resource it can have iam policies attached to it stackdriver workspace cloud monitoring debugging logging allows to store search and set alerts for log data within gcp monitoring provides visibility into the performance uptime and overall health of cloud applications by collecting metrics events and metadata from gcp trace error reporting debugger profiler compute options compute enginer for creating monolithic applications already managing vms in on premise environment or other cloud platform and to obtain similar performance kubernetes engine if application is containerized and micro service based app engine lets user concentrate only on the application development and code without having to configure any aspect of the infrastructure appengine flexible runs on containers more feature support for ab testing rerouting and so on cloud functions to write simple single purpose functions attached to events empted from cloud services event driven can only write code funcion in certain set of languages short run application cloud run managed service for stateless containers minimum management google compute engine is used for provisioning and managing cloud vm instances load balancing and autoscaling can be implemented when provisioning multiple vm instances autoscaling alter the number of actively running instances based on the load on application resources can attach storage to vms network connectivity and configuration can be done to allow access on specific ports expose instance to a specific ip address reserved or ephemeral machine families available general purpose best price performance ratio compute optimized for scientific research gaming applications memory optimized when lot of ram required gpu public image and custom images can be installed in vms for a specific vm external ip which is unique for all resources is internet accessible whereas internal ip two separate corporate resources can have same internal ip address is only accessibly within a subnet vpc in the google cloud network static reserved external ip addresses can be switched from one vm instance to another within the same project and they remain attached to the vm even when the vm has been stopped a particular billing characteristic to be noted on static ip addresses is that the user organisation is billed for the static ip when it is not in use startup script allows to install latest image package updates and install and run webserver templates during vm instance creation but this increases boot up time instance template can be used to speed up the vm creation process by having all properties pre configured the same can also be used to created managed instance groups instance templates are immutable custom images with os patches and softwares pre installed can be created from an instance persistant disk snapshot another image or file in cloud storage that can be shared accross projects an image can be hardened with specific corporate security standards and distributed for efficient and smooth workspace setup custom images are preferred over startup scripts since they do not have bootup overhead although custom images facilitate installation of os patches and softwares creation and manipulation of files and or services need to be done using startup scripts while creating an instance or an instance template using the custom image live migration to keep vm instances running when a host system needs to be updated the vm instances are migrated to another host within the same zone not supported by gpus and preemptible vms and this can be configured using availability policies the two available important policies include on host maintainance default migrate other options terminate and automatic restart to automatically restart vm instances that terminated due to non user initiated reasons maintenance event hardware failure etc unmanaged instance groups they are used when we would like to do most of the management ourselves though they aren t really recommended and are designed for heterogenous clusters eg migrating a particular cluster that is on premise and you d like it to run exactly as it is on google cloud pre emptible machines they cost 70 less than regularly charged vm no control over the shut down of vm can be shut down by gcp at any time preempted within 24hrs with 30s warning cannot alter number of available vms used for fault tolerant application cost sensitive scenario when workload is not immediate such as non immediate batch processing jobs restrictions not always available no slas can t be converted to regular vms no automatic restarts free tier credits are not applicable to launch these cloud run it runs containers not vms supports autoscaling but requires restructuring application as docker image to implement it it is a managed service for running containers and they must be stateless can t have persistant disk attached app engine app engine offers nosql databases in memory caching load balancing health checks logging and user authentication to applications running in it types 1 standard language specific sandbox based service runs applications in a sandbox which has restrictions no write to local files but can write to database services request timeout period 60s and limit on 3rd party software supports only fixed set of languages go php java python node js net and ruby rapid scaling 2 flexible can specify custom docker image to run in compute engine machines no sandbox constraints allows icmp ssh connection to the vm instance takes longer to startup lets user specify the geographic region the app runs in gradual scaling with occassional fluctuations in demand kubernetes engine kubernetes lets authorized users manage and scale applications through apis that control clusters each cluster comprises of a master and one or more nodes kubernetes deploys containers inside of abstraction called pods a pod is the smallest deployable unit in kubernetes it generally has just one container but deeply integrated containers can be put in single pod a pod has unique ip address and ports which are ephemeral in nature the containers inside a port access resources using localhost network interface a deployment represents a group of replica of the same pod to connect publically to outside network the pods can be exposed using loadbalancer which creates a service with fixed ip address for pods internalloadbalancer nodeport loadbalancer commands that start with gloud cluster are used to manipulate the cluster themselves whereas kubectl commands are used to manipulate things inside the cluster such as the node pools pods etc anthos hybrid cloud service used to manage kubernetes clusters in any cloud platform from one location easy and efficient management of workload allows splitting the application workload between cloud and on premise high capacity systems tailered to company requirements when full scale migration on on prem system to cloud is not required maintain consistent policies and services accross all clusters regardless of where they are deployed shared services between the cloud and on prem gke deployments include cloud interconnect gcp marketplace stackdriver anthos configuration managemnet agent use policy repository hosted either on prem or on cloud to enforce policies in each environment to manage images and deployment configuration cloud pub sub allows to decouple services through asynchronous messaging some messages may be delivered more than once say frontend and backend in instances where you need to scale your application the frontend scales far more efficiently owing to their stateless nature compared to the backend cloud sql database service therefore it becomes necessary to make the frontend instead communicate to a cloud pub sub topic where tasks can be queued up and allowing the backend services to consume these tasks at whatever rate that it can without lose of data important from a scalability perspective note the maximum permissible size of data is 10mb larger data that needs etl processing of larger data cloud storage can be used where data is stored in a bucket cloud pub sub can be used in this case to send url that points to bucket and file for the next service process to use deployment manager provides repeatable deployment cloning and scaling of application instances with template files deployment manager is an infrastructure management system for gcp resources cloud source repository it is used when users don t want to host their own git instance and they want to integrate their source repositories with iam permissions managed services cloud dataproc managed hadoop and spark hive pig service big data processing scalable while jobs are running to migrate from onpremise hadoop cluster preparing data data processing pipelines supports spartsql and sparkml dataflow data processing pipelines realtime data handling stream processing and batch processing of data dataprep preparing data for machine learning datalab interactive tool for exploring data bigquery can specify region in which dataset is stored networking 4 categories of networking components available connect cloud vpn and cloud interconnect help connect on premise resources to gcp and establish a secure channel between them cloud vpc virtualized network subnets scale cloud load balancing scaling of backend instances based on incoming traffic cloud cdn allows caching objects closest to the user accessing it to reduce latency optimize standard network tier optimize for cost and premium network tier optimize for performance variants available secure identity aware proxy and cloud armour allows to secure applications on the internet to prevent unauthorized malicious activities cloud dedicated interconnect conforms to googles slas unlike cloud vpn directconnect or carrier connect for securing establishing connection between onpremise and google cloud networks cloud vpc they have routing tables for forwarding traffic from one instance to another within the same network even across subnetworks across gcp zones when dealing with several projects and need to establish communication between vpcs vpc peering is used to allow communication between two vpcs using a peer relation shared vpc is used when admin needs to use iam control over who can access what resource in another vpc firewall firewall can be configured to limit access to resources ingress or outgress tags can be used to reference firewall rules to instances that have the same tag cloud armour web application firewall at layer 7 osi model rules are associated with application logic layer to control the incoming data http traffic and so on protect webapplication against ddos attacks allows us to specify our own package check logic on incoming requests data storage big query uses columnar storage datastore firestore main usecase is to store structural data from appengine apps transactional sql like query cloud datastore databases can span app engine and compute engine applications unit size 1mb entity document based json formatted flexible schema bigtable sparsly populated rows nosql considered as persistant hashtable each row in the database can be sparsely populated and is looked using a single key hbase api organizing principle sparse multidimensional map cluster of vms that manage metadata about the data the data is stored in the colossus filesystem the way the keys are structured determines which block the data is going to be written to if all the keys are sequential then we get a hotspot were data is written to the same block cloud storage specify file using bytes of data and refered using unique key it s url easily incorporated into webapplication fully managed and scalable storage objects are immutable can t be edited but new versions are created object versioning can be enabled data in transit is encrypted by https access control list acl can be used to define level of access to objects in buckets using scope and permission allows setting life cycle management policies to cloud storage buckets to automatically delete create files to move file to nearline coldline storage after certain period of time types multiregional regional nearline for objects that are accessed once per month on average coldline for objects that are accessed once per year on average coldline and nearline have access cost per gb of data read migrating from on premise legacy hadoop cluster using hdfs hadoop filesystem to use this in gcloud we can map hdfs to through dataproc cloud storage multi region cloud storage buckets replicate the data redundantly across several regions for it to be accessible to users with low latency and high availability billing pricing calculator available to plan and estimate gcp products usage users organisations are billed by the second after a minimum period of 1 minute and stopped vm instances are not billed unless it has attached storage budget alerts can be created to be notified on the charges based on resource utilization sustained usage discounts sustained usage billing discounts for more than 25 usage in a month vary from 20 50 and the discount increases with usage applicable to both google compute engine and kubernetes engine does not apply to a2 and e2 machine types as well as instances created using app engine flexible and dataflow resources commited usage discounts require user organisation commitment based on predictable resource requirements from a certain period of time 1 3 years and the discount upto 70 can be obtained based on machine type and gpu same restrictions as those for sustained usage discount apply | cloud |
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scenic | scenic div style text align left img align right src https raw githubusercontent com google research scenic main images scenic logo png width 200 alt scenic logo img div scenic is a codebase with a focus on research around attention based models for computer vision scenic has been successfully used to develop classification segmentation and detection models for multiple modalities including images video audio and multimodal combinations of them more precisely scenic is a i set of shared light weight libraries solving tasks commonly encountered tasks when training large scale i e multi device multi host vision models and ii several projects containing fully fleshed out problem specific training and evaluation loops using these libraries scenic is developed in jax https github com google jax and uses flax https github com google flax contents what we offer what we offer sota models and baselines in scenic sota models and baselines in scenic philosophy philosophy getting started getting started scenic component design scenic component design citing scenic citing scenic what we offer among others scenic provides boilerplate code for launching experiments summary writing logging profiling etc optimized training and evaluation loops losses metrics bi partite matchers etc input pipelines for popular vision datasets baseline models https github com google research scenic tree main scenic projects baselines scenic baseline models including strong non attentional baselines sota models and baselines in scenic there are some sota models and baselines in scenic which were either developed using scenic or have been reimplemented in scenic projects that were developed in scenic or used it for their experiments vivit a video vision transformer https arxiv org abs 2103 15691 omninet omnidirectional representations from transformers https arxiv org abs 2103 01075 attention bottlenecks for multimodal fusion https arxiv org abs 2107 00135 tokenlearner what can 8 learned tokens do for images and videos https arxiv org abs 2106 11297 exploring the limits of large scale pre training https arxiv org abs 2110 02095 the efficiency misnomer https arxiv org abs 2110 12894 discrete representations strengthen vision transformer robustness https arxiv org abs 2111 10493 pyramid adversarial training improves vit performance https arxiv org abs 2111 15121 vut versatile ui transformer for multi modal multi task user interface modeling https arxiv org abs 2112 05692 clay learning to denoise raw mobile ui layouts for improving datasets at scale https arxiv org abs 2201 04100 zero shot text guided object generation with dream fields https arxiv org abs 2112 01455 multiview transformers for video recognition https arxiv org abs 2201 04288 polyvit co training vision transformers on images videos and audio https arxiv org abs 2111 12993 simple open vocabulary object detection with vision transformers https arxiv org abs 2205 06230 learning with neighbor consistency for noisy labels https arxiv org abs 2202 02200 token turing machines https arxiv org pdf 2211 09119 pdf vid2seq large scale pretraining of a visual language model for dense video captioning https arxiv org pdf 2302 14115 pdf avatar unconstrained audiovisual speech recognition https arxiv org abs 2206 07684 adaptive computation with elastic input sequence https arxiv org abs 2301 13195 location aware self supervised transformers for semantic segmentation https arxiv org abs 2212 02400 how can objects help action recognition https openaccess thecvf com content cvpr2023 html zhou how can objects help action recognition cvpr 2023 paper html verbs in action improving verb understanding in video language models https arxiv org abs 2304 06708 unified visual relationship detection with vision and language models https arxiv org abs 2303 08998 reveal retrieval augmented visual language pre training with multi source multimodal knowledge memory https arxiv org abs 2212 05221 audiovisual masked autoencoders https arxiv org abs 2212 05922 matformer nested transformer for elastic inference https arxiv org abs 2310 07707 more information can be found in projects https github com google research scenic tree main scenic projects list of projects hosted in scenic baselines that were reproduced in scenic vit an image is worth 16x16 words transformers for image recognition at scale https arxiv org abs 2010 11929 detr end to end object detection with transformers https arxiv org abs 2005 12872 deformable detr deformable transformers for end to end object detection https arxiv org abs 2010 04159 clip learning transferable visual models from natural language supervision https arxiv org abs 2103 00020 mlp mixer an all mlp architecture for vision https arxiv org abs 2105 01601 bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 how to train your vit data augmentation and regularization in vision transformers https arxiv org abs 2106 10270 big transfer bit general visual representation learning https arxiv org abs 1912 11370 deep residual learning for image recognition https arxiv org abs 1512 03385 u net convolutional networks for biomedical image segmentation https arxiv org abs 1505 04597 pct point cloud transformer https arxiv org abs 2012 09688 universal transformers https arxiv org abs 1807 03819 pondernet https arxiv org abs 2107 05407 masked autoencoders are scalable vision learners https arxiv org abs 2111 06377 more information can be found in baseline models https github com google research scenic tree main scenic projects baselines scenic baseline models a name philosophy a philosophy scenic aims to facilitate rapid prototyping of large scale vision models to keep the code simple to understand and extend we prefer forking and copy pasting over adding complexity or increasing abstraction only when functionality proves to be widely useful across many models and tasks it may be upstreamed to scenic s shared libraries a name getting start a getting started see projects baselines readme md for a walk through baseline models and instructions on how to run the code if you would like to contribute to scenic please check out the philisophy philosophy code structure code structure and contributing contributing md sections should your contribution be a part of the shared libraries please send us a pull request quickstart you will need python 3 9 or later download the code from github shell git clone https github com google research scenic git cd scenic pip install and run training for vit on imagenet shell python scenic main py config scenic projects baselines configs imagenet imagenet vit config py workdir note that for specific projects and baselines you might need to install extra packages that are mentioned in their readme md or requirements txt files here https colab research google com github google research scenic blob main scenic common lib colabs scenic playground ipynb is also a minimal colab to train a simple feed forward model using scenic a name code structure a scenic component design scenic is designed to propose different levels of abstraction to support hosting projects that only require changing hyper parameters by defining config files to those that need customization on the input pipeline model architecture losses and metrics and the training loop to make this happen the code in scenic is organized as either project level code which refers to customized code for specific projects or baselines or library level code which refers to common functionalities and general patterns that are adapted by the majority of projects the project level code lives in the projects directory div align center img src https raw githubusercontent com google research scenic main images scenic design jpg width 900 alt scenic design img div library level code the goal is to keep the library level code minimal and well tested and to avoid introducing extra abstractions to support minor use cases shared libraries provided by scenic are split into dataset lib implements io pipelines for loading and pre processing data for common computer vision tasks and benchmarks see tasks and datasets section all pipelines are designed to be scalable and support multi host and multi device setups taking care dividing data among multiple hosts incomplete batches caching pre fetching etc model lib provides several abstract model interfaces e g classificationmodel or segmentationmodel in model lib base models with task specific losses and metrics neural network layers in model lib layers focusing on efficient implementation of attention and transformer layers accelerator friendly implementations of bipartite matching algorithms in model lib matchers train lib provides tools for constructing training loops and implements several optimized trainers classification trainer and segmentation trainer that can be forked for customization common lib general utilities like logging and debugging modules functionalities for processing raw data etc project level code scenic supports the development of customized solutions for customized tasks and data via the concept of project there is no one fits all recipe for how much code should be re used by a project projects can consist of only configs and use the common models trainers task data that live in library level code or they can simply fork any of the mentioned functionalities and redefine layers losses metrics logging methods tasks architectures as well as training and evaluation loops the modularity of library level code makes it flexible for projects to fall placed on any spot in the run as is to fully customized spectrum common baselines such as a resnet and vision transformer vit are implemented in the projects baselines https github com google research scenic tree main scenic projects baselines project forking models in this directory is a good starting point for new projects citing scenic if you use scenic you can cite our white paper https openaccess thecvf com content cvpr2022 html dehghani scenic a jax library for computer vision research and beyond cvpr 2022 paper html here is an example bibtex entry bibtex inproceedings dehghani2021scenic author dehghani mostafa and gritsenko alexey and arnab anurag and minderer matthias and tay yi title scenic a jax library for computer vision research and beyond booktitle proceedings of the ieee cvf conference on computer vision and pattern recognition cvpr year 2022 pages 21393 21398 disclaimer this is not an official google product | jax computer-vision deep-learning research attention transformers vision-transformer | ai |
cw_word_embedding | cw word embedding | ai |
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Mobilewebapp | mobilewebapp information technology folder | server |
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CH_Pextra-Unity-step-by-step- | ch pextra unity step by step unity in embedded system design and robotics a step by step guide | os |
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RTOS_Bone | freertos port for beaglebone this is the freertos port for am335x base code forked from freertos port for omap3530 https github com hwoarang freertos ported freertos http www freertos org http www freertos org the download includes the kernel source code and a demo application directory structure the source directory contains the real time kernel source files the demo directory contains the demo application source file the tracecon directory contains the trace visualisation exe file see the readme files in the respective directories for further information license the freertos org source code is licensed by the modified gnu general public license gpl see license document included for more details | os |
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Assessment-2-Team-Project | assessment 2 team project introduction to information technology sp2 | server |
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wing-it2 | wing it2 wing information technology 2 | server |
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puewue-frontend | power dashboard a client side boilerplate for displaying near real time data from a data center interactive wheel graph and histograms for up to 4 metrics boilerplate html code for displaying these components on a web page configuration to bridge to a data center s json based api and not just limited to data centers use this in conjunction with the back end project https github com facebook puewue backend screenshot https github com facebook puewue frontend blob master screenshot png raw true getting started 1 install gulp and bower globally by running npm install g gulp npm install g bower 2 install dependencies by running npm install bower install from your project s root 3 build the project by running gulp or gulp production for production use from your project s root outputs to build which is referenced in demo index html 4 connect the dashboard to your api by providing your server and metric settings see configuration inside of demo application js 5 test your connect with a local http server e g simplehttpserver configuration powerdashboard all exposed configuration is sent when instantiating powerdashboard an example of how to instantiate this is available in demo application js javascript wheelgraphendpointalias 24 hours see configuration api url structure for more details wheelgraph true set to false to disable the wheel graph histograms true set to false to disable the set of histograms apiconfig this is your host no trailing slash host http localhost 3000 this is an optional prefix for the uri with no leading or trailing slash see configuration api url structure for more details uriprefix my directory metrics this references a key in your api responses see configuration api data point structure alias humidity name humidity by default the range of data will use the min and max values from the points provided mindomaindifference can compliment this by ensuring there s a minimum range mindomaindifference 0 05 alias pue name pue you can also define hard boundaries using domain domain min 1 0 max 1 2 markup configuration accordion metrics dom reference metric display description this should reference one of your available metrics as part of the overall group these will display in an accordion style ui data attributes metric your metric alias this should match powerdashboard configuration view mode temperature or data view mode percentage if one of your metrics is based around percentage or temperature you can utilise these view modes to renders the values accordingly 4 example histograms are available in the demo index html boilerplate date range filters dom reference date range nav a description its purpose is to pass a specific date range into the histogram and refresh the data with the following attributes data attributes range 12 days this is your endpoint alias unit days this is a natural language label which must be parseable into a date range e g days months years range value 12 an integer to compliment the data unit which is utilised by the graph to accurately render the data points steps 4 how many dividing lines should be on the histogram s x axis 4 example date ranges are available in the demo index html boilerplate histograms dom reference histogram description displays filtered metric data points on a histogram data attributes metric your metric alias this should match powerdashboard configuration 4 example histograms are available in the demo index html boilerplate api here are some instructions on ensuring your api is compatible with the power dashboard url structure urls are concatenated on the client side using a combination of server settings a metric alias and an endpoint alias host uriprefix endpoint alias json host see configuration powerdashboard apiconfig uriprefix see configuration powerdashboard apiconfig endpoint alias if it is the wheel graph which is requesting data this is controlled under configuration powerdashboard apiconfig wheelgraphendpointalias otherwise this comes from the date range filters see markup configuration date range filters data point structure as a response from each endpoint e g 1 year json power dashboard expects an json formatted array of data point objects ordered oldest first each data point object expects the following data javascript timestamp 1389087180000 my data point 135 my other point 4 timestamp 1389087190000 my data point 115 my other point 1 average data ranges histograms support average ranges which show a transparent range line behind the average data to show minimum and maximum values at a given time to output this in the api you format each data point object as the following javascript timestamp 1389087180000 my point 1 5 min my point 1 the same alias but prefixed with min max my point 2 this same alias but prefixed with max my other point 235 dependencies power dashboard has the following dependencies jquery underscore dom and js utilities d3 svg library used for the graphs backbone interfaces with the graph to display the current metrics and control the accordion ui sylvester vector calculation for graphs tween js tweening for the graphs utilising window requestanimationframe simple inheritance inheritance for js objects used for the app s components moment data time parsing normalize css a browser reset stylesheet modernizr browser feature detection animation frame shim for requestanimationframe function jquery tinypubsub a global publish subscribe pattern for some components licensing the source code is licensed under the bsd style license found in the license license file in the root directory of this source tree an additional grant of patent rights can be found in the patents patents file in the same directory | front_end |
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AugESC | augesc this is the official repository for the findings of acl 2023 paper augesc dialogue augmentation with large language models for emotional support conversation https arxiv org abs 2202 13047 data huggingface https huggingface co datasets thu coai augesc fine tuned model huggingface https huggingface co thu coai blenderbot 1b augesc citation bib inproceedings zheng etal 2023 augesc title augesc dialogue augmentation with large language models for emotional support conversation author zheng chujie and sabour sahand and wen jiaxin and zhang zheng and huang minlie booktitle findings of acl year 2023 inproceedings liu etal 2021 towards title towards emotional support dialog systems author liu siyang and zheng chujie and demasi orianna and sabour sahand and li yu and yu zhou and jiang yong and huang minlie booktitle acl year 2021 | ai |
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Materi-Training | kumpulan materi training kumpulan materi training iot image processing robotics computer vision | ai |
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text-generation-webui-macos | merged 1 5 1 macos merged version this is a development version and i have not added many changes i had planned please feel free to use at your own risk as there may be bugs not yet found items added to this version stop server under the sessions tab use with caution if in multi user will probably disable this if in multi user mode however it offers better shutdown than just killing the process on the server added python class for handling diverse gpu compute devices like cuda cpu or mps changed code to use torch device once set initially to a device will fall back to cpu items working and tested on macos more support for apple silicon m1 m2 processors working with new llama cpp python 0 1 81 works with llama2 models there ggml models will need conversion to gguf format if using llama cpp python 0 1 81 earlier version llama coo python still works have not concluded testing of library dependencies will have that updated in build instructions for oobagooba macos still mainly supporting ggml now gguf gg universal format files you will have to convert your ggml files to gguf format removed from this tried to continue what was already started in removing flexgen from the repo removed docker if someone wants to help maintain for macos let me know slowly removing information on cuda as it is not relevant to macos updated installation instructions for libraries in the oobabooga macos quickstart https github com unixwzrd oobabooga macos blob main macos apple silicon quickstart md and the longer building apple silicon support https github com unixwzrd oobabooga macos blob main macos apple silicon quickstart md ggml support is in this release and has not been extensively tested from the look of upstream commits there are some changes which must be made before this will work with llama2 models if you want the most recent version from the oobabooga reposiotry go here oobabooga text generation webgui https github com oobabooga text generation webui otherwise use these instructions i have on putting together the macos python environment these instructions are not only useful for setting up oobabooga but also for anyone working in data analytics machine learning deep learning scientific computing and other areas that can benefit from an optimized python gpu environment on apple silicon building apple silicon support for oobabooga text generation webui https github com unixwzrd oobabooga macos blob main macos install md oobabooga macos apple silicon quick start for the impatient https github com unixwzrd oobabooga macos blob main macos apple silicon quickstart md i will be updating this readme file with new information specifically regarding macos and apple silicon i would like to work closely with the oobabooga team and try to implement similar solutions so the web ui can have a similar look and feel maintaining and improving support for macos and apple silicon in this project has required significant research debugging and development effort if you find my contributions helpful and want to show your appreciation you can buy me a coffee sponsor this project or consider me for job opportunities while the focus of this branch is to enhance macos and apple silicon support i aim to maintain compatibility with linux and posix operating systems contributions and feedback related to linux compatibility are always welcome anyone who would like to assist with supporting apple silicon let me know there is much to do and i can only do so much by myself merged 1 5 1 macos merged version merged 151 macos merged version features features installation installation downloading models downloading models ggml models ggml models gpt 4chan gpt 4chan starting the web ui starting the web ui basic settings basic settings model loader model loader accelerate transformers acceleratetransformers accelerate 4 bit accelerate 4 bit llama cpp llamacpp autogptq autogptq exllama exllama gptq for llama gptq for llama deepspeed deepspeed rwkv rwkv rope for llama cpp and exllama only rope for llamacpp and exllama only gradio gradio api api multimodal multimodal presets presets contributing contributing community community credits credits features 3 interface modes default notebook and chat multiple model backends tranformers llama cpp autogptq gptq for llama exllama rwkv flexgen dropdown menu for quickly switching between different models lora load and unload loras on the fly load multiple loras at the same time train a new lora precise instruction templates for chat mode including alpaca vicuna open assistant dolly koala chatglm moss rwkv raven galactica stablelm wizardlm baize ziya chinese vicuna mpt incite wizard mega koalpaca vigogne bactrian h2o and openbuddy multimodal pipelines including llava and minigpt 4 https github com oobabooga text generation webui tree main extensions multimodal 8 bit and 4 bit inference through bitsandbytes cpu only mode for macos bitsandbytes does not support apple silicon gpu cpu mode for transformers models deepspeed zero 3 inference docs deepspeed md extensions docs extensions md custom chat characters docs chat mode md very efficient text streaming markdown output with latex rendering to use for instance with galactica https github com paperswithcode galai nice html output for gpt 4chan api including endpoints for websocket streaming see the examples https github com unixwzrd text generation webui blob main api examples to learn how to use the various features check out the documentation https github com unixwzrd text generation webui tree main docs installation currently only manual installation until an installer can be put together building apple silicon support for oobabooga text generation webui https github com unixwzrd oobabooga macos blob main macos install md oobabooga macos apple silicon quick start for the impatient https github com unixwzrd oobabooga macos blob main macos apple silicon quickstart md downloading models models should be placed inside the models folder hugging face https huggingface co models pipeline tag text generation sort downloads is the main place to download models these are some examples pythia https huggingface co models sort downloads search eleutherai 2fpythia deduped opt https huggingface co models search facebook opt galactica https huggingface co models search facebook galactica gpt j 6b https huggingface co eleutherai gpt j 6b tree main you can automatically download a model from hf using the script download model py python download model py organization model for example python download model py facebook opt 1 3b to download a protected model set env vars hf user and hf pass to your hugging face username and password or user access token https huggingface co settings tokens the model s terms must first be accepted on the hf website note you may want to create symbolic links in the model directory since sometimes there are multiple models downloaded with different quantizations or on external storage due to space on th edisk where you insatlled this if you have external storge like i do you can create a link by going to the models directory and creating the link to your model ggml models you can drop these directly into the models folder making sure that the file name contains ggml somewhere and ends in bin gpt 4chan details summary instructions summary gpt 4chan https huggingface co ykilcher gpt 4chan has been shut down from hugging face so you need to download it elsewhere you have two options torrent 16 bit https archive org details gpt4chan model float16 32 bit https archive org details gpt4chan model direct download 16 bit https theswissbay ch pdf notpdf gpt4chan model float16 32 bit https theswissbay ch pdf notpdf gpt4chan model the 32 bit version is only relevant if you intend to run the model in cpu mode otherwise you should use the 16 bit version after downloading the model follow these steps 1 place the files under models gpt4chan model float16 or models gpt4chan model 2 place gpt j 6b s config json file in that same folder config json https huggingface co eleutherai gpt j 6b raw main config json 3 download gpt j 6b s tokenizer files they will be automatically detected when you attempt to load gpt 4chan python download model py eleutherai gpt j 6b text only when you load this model in default or notebook modes the html tab will show the generated text in 4chan format details starting the web ui conda activate textgen cd text generation webui python server py then browse to http localhost 7860 theme dark optionally you can use the following command line flags basic settings flag description h help show this help message and exit notebook launch the web ui in notebook mode where the output is written to the same text box as the input chat launch the web ui in chat mode multi user multi user mode chat histories are not saved or automatically loaded warning this is highly experimental character character the name of the character to load in chat mode by default model model name of the model to load by default lora lora lora the list of loras to load if you want to load more than one lora write the names separated by spaces model dir model dir path to directory with all the models lora dir lora dir path to directory with all the loras model menu show a model menu in the terminal when the web ui is first launched no stream don t stream the text output in real time settings settings file load the default interface settings from this yaml file see settings template yaml for an example if you create a file called settings yaml this file will be loaded by default without the need to use the settings flag extensions extensions extensions the list of extensions to load if you want to load more than one extension write the names separated by spaces verbose print the prompts to the terminal model loader flag description loader loader choose the model loader manually otherwise it will get autodetected valid options transformers autogptq gptq for llama exllama exllama hf llamacpp rwkv accelerate transformers flag description cpu use the cpu to generate text warning training on cpu is extremely slow auto devices automatically split the model across the available gpu s and cpu gpu memory gpu memory gpu memory maximum gpu memory in gib to be allocated per gpu example gpu memory 10 for a single gpu gpu memory 10 5 for two gpus you can also set values in mib like gpu memory 3500mib cpu memory cpu memory maximum cpu memory in gib to allocate for offloaded weights same as above disk if the model is too large for your gpu s and cpu combined send the remaining layers to the disk disk cache dir disk cache dir directory to save the disk cache to defaults to cache load in 8bit load the model with 8 bit precision using bitsandbytes bf16 load the model with bfloat16 precision requires nvidia ampere gpu no cache set use cache to false while generating text this reduces the vram usage a bit with a performance cost xformers use xformer s memory efficient attention this should increase your tokens s sdp attention use torch 2 0 s sdp attention trust remote code set trust remote code true while loading a model necessary for chatglm and falcon accelerate 4 bit requires minimum compute of 7 0 on windows at the moment flag description load in 4bit load the model with 4 bit precision using bitsandbytes compute dtype compute dtype compute dtype for 4 bit valid options bfloat16 float16 float32 quant type quant type quant type for 4 bit valid options nf4 fp4 use double quant use double quant for 4 bit llama cpp flag description threads number of threads to use n batch maximum number of prompt tokens to batch together when calling llama eval no mmap prevent mmap from being used mlock force the system to keep the model in ram cache capacity cache capacity maximum cache capacity examples 2000mib 2gib when provided without units bytes will be assumed does not apply for apple silicon gpu since it uses unified memory n gpu layers n gpu layers number of layers to offload to the gpu only works if llama cpp python was compiled with apple silicon gpu support for blas and llama cpp using metal load the model and look for llama model load internal n layer in ths stderr and this will show you the number of layers in the model set this value to that number or possibly n 2 this si very sensitive now an will overrun your data area or tensor cache causing a segmentation fault n ctx n ctx size of the prompt context llama cpp seed seed seed for llama cpp models default 0 random n gqa n gqa grouped query attention must be 8 for llama2 70b rms norm eps rms norm eps must be 1e 5 for llama2 70b autogptq flag description triton use triton no inject fused attention disable the use of fused attention which will use less vram at the cost of slower inference no inject fused mlp triton mode only disable the use of fused mlp which will use less vram at the cost of slower inference no use cuda fp16 this can make models faster on some systems desc act for models that don t have a quantize config json this parameter is used to define whether to set desc act or not in basequantizeconfig exllama flag description gpu split comma separated list of vram in gb to use per gpu device for model layers e g 20 7 7 max seq len max seq len maximum sequence length gptq for llama flag description wbits wbits load a pre quantized model with specified precision in bits 2 3 4 and 8 are supported model type model type model type of pre quantized model currently llama opt and gpt j are supported groupsize groupsize group size pre layer pre layer pre layer the number of layers to allocate to the gpu setting this parameter enables cpu offloading for 4 bit models for multi gpu write the numbers separated by spaces eg pre layer 30 60 checkpoint checkpoint the path to the quantized checkpoint file if not specified it will be automatically detected monkey patch apply the monkey patch for using loras with quantized models quant attn triton enable quant attention warmup autotune triton enable warmup autotune fused mlp triton enable fused mlp deepspeed flag description deepspeed enable the use of deepspeed zero 3 for inference via the transformers integration nvme offload dir nvme offload dir deepspeed directory to use for zero 3 nvme offloading local rank local rank deepspeed optional argument for distributed setups rwkv flag description rwkv strategy rwkv strategy rwkv the strategy to use while loading the model examples cpu fp32 cuda fp16 cuda fp16i8 rwkv cuda on rwkv compile the cuda kernel for better performance rope for llama cpp and exllama only flag description compress pos emb compress pos emb positional embeddings compression factor should typically be set to max seq len 2048 alpha value alpha value positional embeddings alpha factor for ntk rope scaling scaling is not identical to embedding compression use either this or compress pos emb not both gradio flag description listen make the web ui reachable from your local network listen host listen host the hostname that the server will use listen port listen port the listening port that the server will use share create a public url this is useful for running the web ui on google colab or similar auto launch open the web ui in the default browser upon launch gradio auth user pwd set gradio authentication like username password or comma delimit multiple like u1 p1 u2 p2 u3 p3 gradio auth path gradio auth path set the gradio authentication file path the file should contain one or more user password pairs in this format u1 p1 u2 p2 u3 p3 api flag description api enable the api extension public api create a public url for the api using cloudfare api blocking port blocking port the listening port for the blocking api api streaming port streaming port the listening port for the streaming api multimodal flag description multimodal pipeline pipeline the multimodal pipeline to use examples llava 7b llava 13b presets inference settings presets can be created under presets as yaml files these files are detected automatically at startup the presets that are included by default are the result of a contest that received 7215 votes more details can be found here https github com oobabooga oobabooga github io blob main arena results md contributing pull requests suggestions and issue reports are welcome make sure to carefully search https github com oobabooga text generation webui issues existing issues before starting a new one if you have some experience with git testing an open pull request and leaving a comment on whether it works as expected or not is immensely helpful a simple way to contribute even if you are not a programmer is to leave a on an issue or pull request that you find relevant community i will be checking in on the oobabooga discord server at the mac setup channel discord https discord gg jwzcf2dpqn credits the devopers and maintainers of the original oobabooga repository oobabooga text generation webgui https github com oobabooga text generation webui gradio dropdown menu refresh button code for reloading the interface https github com automatic1111 stable diffusion webui godlike preset https github com koboldai koboldai client wiki settings presets code for some of the sliders https github com pygmalionai gradio ui | ai |
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codeshell | p align center img src https cdn uploads huggingface co production uploads 6489a27bd0b2fd1f3297e5ca 3lqsqrzlubhbn2dipn6ox png width 400 p p align center a href https huggingface co wisdomshell codeshell target blank hugging face a a href http se pku edu cn kcl target blank pku kcl a p div align center license https img shields io github license modelscope modelscope svg https github com wisdomshell codeshell blob main license h4 align center p a href https github com wisdomshell codeshell blob main readme md b b a a href https github com wisdomshell codeshell blob main readme en md english a p h4 div introduction codeshell http se pku edu cn kcl ai codeshell 70 tokens 8192 benchmark humaneval mbpp codeshell codeshell ide codeshell https github com wisdomshell codeshell a href https huggingface co wisdomshell codeshell target blank b codeshell base b a codelshell a href https huggingface co wisdomshell codeshell chat target blank b codeshell chat b a codelshell a href https huggingface co wisdomshell codeshell chat int4 target blank b codeshell chat 4bit b a codelshell 4bit a href https github com wisdomshell llama cpp for codeshell target blank b codeshell cpp b a codelshell cpp gpu cpp 8g codeshell main characteristics of codeshell codelshell humaneval mbpp 7b ide vs code jetbrains c codeshell token performance humaneval mbpp 7b codelllama starcoder codeshell codeshell 7b codellama 7b starcoder 7b humaneval 34 32 29 44 27 80 mbpp 38 65 37 60 34 16 multiple js 33 17 31 30 27 02 multiple java 30 43 29 24 24 30 multiple cpp 28 21 27 33 23 04 multiple swift 24 30 25 32 15 70 multiple php 30 87 25 96 22 11 multiple d 8 85 11 60 8 08 multiple jl 22 08 25 28 22 96 multiple lua 22 39 30 50 22 92 multiple r 20 52 18 57 14 29 multiple rkt 17 20 12 55 10 43 multiple rs 24 55 25 90 22 82 requirements python 3 8 and above pytorch 2 0 and above are recommended transformers 4 32 and above cuda 11 8 and above are recommended this is for gpu users flash attention users etc quickstart codeshell a href https huggingface co wisdomshell codeshell target blank hugging face a transformers codeshell codeshell chat pip install r requirements txt transformers codeshell code generation codeshell python import torch from transformers import automodelforcausallm autotokenizer device cuda if torch cuda is available else cpu tokenizer autotokenizer from pretrained wisdomshell codeshell 7b model automodelforcausallm from pretrained wisdomshell codeshell 7b trust remote code true torch dtype torch bfloat16 to device inputs tokenizer def merge sort return tensors pt to device outputs model generate inputs print tokenizer decode outputs 0 fill in the moddle codeshell fill in the middle python input text fim prefix def print hello world n fim suffix n print hello world fim middle inputs tokenizer input text return tensors pt to device outputs model generate inputs print tokenizer decode outputs 0 codeshell codeshell 7b chat python model automodelforcausallm from pretrained wisdomshell codeshell 7b chat trust remote code true torch dtype torch bfloat16 to device tokenizer autotokenizer from pretrained wisdomshell codeshell 7b chat history query response model chat query history tokenizer print response history append query response query python http server response model chat query history tokenizer print response history append query response vs code jetbrains codeshell 7b chat vscode https github com wisdomshell codeshell vscode intellij https github com wisdomshell codeshell intellij model quantization codeshell 4 bit 8 bit 4 bit 6g gpu codeshell python model automodelforcausallm from pretrained wisdomshell codeshell 7b chat int4 trust remote code true to device tokenizer autotokenizer from pretrained wisdomshell codeshell 7b chat int4 codeshell in c c gpu codeshell c c codeshell c c https github com wisdomshell llama cpp for codeshell demo web ui api ide demo web ui web https 127 0 0 1 8000 python demos web demo py cli demo demo python demos cli demo py api codeshell openai api python demos openai api py http codeshell curl http 127 0 0 1 8000 v1 chat completions h content type application json d model codeshell 7b chat messages role user content ide codeshell ide ide ide vscode https github com wisdomshell codeshell vscode intellij https github com wisdomshell codeshell intellij model details code shell gpt 2 grouped query attention rope hyper parameter hyper parameter value n layer 42 n embd 4096 n inner 16384 n head 32 num query groups 8 seq length 8192 vocab size 70144 data codeshell github big code stack starcoder codeshell minihash kenlm tokenizer codeshell starcoder chat tokenizer size chinese english code total starcoder 49152 1 22 3 47 3 30 2 66 codeshell 70020 1 50 3 47 3 30 2 95 license codeshell codeshell apache 2 0 codeshell codeshell 1 dau 100 2 3 codeshell opensource gmail com star history star history chart https api star history com svg repos wisdomshell codeshell type date https star history com wisdomshell codeshell date | ai |
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skiff-ui | logo https raw githubusercontent com skiff org skiff org github io main assets updates skiff ui og png skiff ui design system skiff ui is an open source react component library based on a collection of reusable interface elements used in skiff it offers a wide range of customizable components for web apps empowering you to create beautiful and user friendly interfaces license https img shields io badge license mit blue svg https github com skiff org skiff ui blob main license npm latest package https img shields io npm v skiff org skiff ui latest svg https www npmjs com package skiff org skiff ui average time to resolve an issue https isitmaintained com badge resolution skiff org skiff ui svg https isitmaintained com project skiff org skiff ui average time to resolve an issue open collective backers and sponsors https img shields io opencollective all skiff https opencollective com skiff contributor covenant https img shields io badge contributor 20covenant 2 1 4baaaa svg code of conduct md follow on twitter https img shields io twitter follow skiff hq svg label follow skiff https twitter com skiffprivacy documentation skiff ui documentation https skiff com ui installation inside your project run either of the commands below to add skiff ui bash install skiff ui with npm npm install skiff org skiff ui save bash install skiff ui with yarn yarn add skiff org skiff ui using skiff ui inside your app integrate skiff components into your project easily as shown below typescript import button type from skiff org skiff ui button onclick onclick type type secondary click me button edit button https codesandbox io static img play codesandbox svg https codesandbox io s button example rfp4jn themeprovider to display skiff ui components correctly add the themeprovider at the root of your app typescript import as react from react import themeprovider from skiff org skiff ui function app component return themeprovider component themeprovider feedback if you have any feedback please reach out to us at support skiff org contributing contributions are always welcome see contributing md contributing md for ways to get started please adhere to this project s code of conduct md code of conduct md related our open source repositories and libraries skiff mail https github com skiff org skiff mail skiff windows app https github com skiff org skiff windows app aead library https www npmjs com package skiff org typed envelopes prosemirror tables https github com skiff org prosemirror tables license mit https choosealicense com licenses mit | components design-system react react-components typescript | os |
gigaSec-Tech-Notes | quick note this repository will only be updated infrequently reasons are provided below to prevent any commit conflict issues to avoid the notification spam of those who enabled watch on this repo i have moved to marketing sector still deciding whether i should make my marketing notes public summary to fully take advantage of my notes use a markdown editor that supports hyperlink like obsidian https obsidian md notes aren t categorized into folders by complete intention and design categorization of each topics is possible by the file 2 notes moc technology md note taking method used zettelkasten https en wikipedia org wiki zettelkasten progressive summarization https fortelabs co blog series ps moc https medium com nickmilo22 in what ways can we form useful relationships between notes 9b9ec46973c6 how i take smart notes https www amazon com how take smart notes nonfiction dp 1542866502 topics of notes programming coding exercises vim git cybersecurity networking ctf challenges pc hardware windows and linux seo web dev artificial intelligence basically almost everything in tech screenshots warning light mode media below dark mode image https user images githubusercontent com 105108954 175243737 ea656a29 7057 4a95 aff8 9feaa0b78d51 png light mode https user images githubusercontent com 105108954 175758494 aa507a35 d91a 4570 80bb fac11c154f99 mp4 installation process to be able to use my notes i recommend installing obsidian https obsidian md as that s where i write and personally i think obsidian is the best md editor otherwise you can use any markdown editor that supports hyperlink feature 3 step process of installing obsidian 0 install the app https obsidian md 1 download this repository repo 2 unzip the downloaded repo file 3 launch the app and click open folder as vault choose the downloaded folder of this repo installation warning important to read it will show you a warning whether to trust author that s me i assure you that no plugins causes harm plugins are not written by me and are written by the community therefore open source check the installed plugins here tech notes obsidian plugins i don t trust you fair enough and that s a good thing to not trust strangers online d you can choose do not trust but some minor features might not work tho it doesn t spawn inconvience or just delete the obsidian directory after downloading it i believe the obsidian app itself will automatically create it for you at startup | cybersecurity markdown notes operating-system programming technology | server |
samples | pure sample code samples for the web development course | front_end |
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digitalpolicy | digital policy guide a digital servant s guide to u s federal information technology law and policy joining the united states federal civil service can be daunting particularly for technology professionals who have not navigated the intricacies of government policy before this guide is a very brief introduction to key laws policies and guidance surrounding the practice of information technology in government this guide should not be considered official guidance itself but rather a tool to guide the reader to official sources this guide should also not be considered comprehensive and may not be up to date in all cases it is recommended that one always consider primary official sources contributing if you see something wrong or missing please do let us know the easiest way is to just open an issue https github com krusynth digitalpolicy issues with your comment or question of course pull requests https docs github com en pull requests collaborating with pull requests with direct contributions are always welcome as well public domain for detailed license information see license license md all contributions to this project will be released under the cc0 dedication https creativecommons org share your work public domain cc0 by submitting a pull request you are agreeing to comply with this waiver of copyright interest installation this is a standard jekyll https jekyllrb com docs github pages https pages github com website as such you ll need the comptible version of ruby installed 2 7 3 via rvm or other means after that make sure bundler is installed and then bundle install the ruby dependencies as usual you probably won t need to install the javascript scss dependencies as they re statically included in the repo if you do you ll need node and either yarn or npm as a package manager and then simply install as the tool directs from the package json included here there s a couple of helper scripts included in the package json as well serve will destroy the local site folder and then serve the website via jekyll at http localhost 8000 http localhost 8000 linkpkg will copy the needed assets from node modules into the public assets folder but again you should not need to do this step unless you re updating adding a node package or something i will admit i have yet to find a decent way of including node packages into github pages sites here i ve only included the ones needed to compile the scss while the javascripts are all static copies in the assets folder i m open to suggestions here content all of the content pages are located in the content folder except the main index md file because github pages wants it at the root we try to strike a balance between creating individual pages for topics and consolidating topics to reduce sprawl for instance individual laws only have separate pages if those laws are complicated or span across multiple concerns such as fitara in other cases we simply refer directly to the laws in the relevant sections laws not related to technology generally won t have their own page unless they are special in some way pages with toc true in the front matter will automatically create a table of contents at the top of the page when combined with layout law or layout document only this is useful for very long pages browsing through the source you will find stub pages for content that we have not yet completed feel free to add to these as well references to laws policies should generally be linked to official government sources whenever possible uscode house gov https uscode house gov is the preferred option for the us code www ecfr gov https www ecfr gov for federal regulations congress gov https www congress gov for laws whitehouse gov https whitehouse gov for federal policies executive orders and so on acronyms icons to reduce duplicative references this website uses a series of data files that provide tagged acronyms that are replaced wherever they appear on the site for instance using fitara will expand the first reference on a given page to the federal information technology acquisition reform act fitara with a link to the aws fitara page subsequential refernces to fitara will retain the acronym and still provide a link acronyms without links simply use the abbr tag to provide hover state titles that expand the definition after the first reference on a page additionally there are a handful of special icons we call them glyphs that can be used including soapbox and warning for a megaphone or caution sign respectively the data directory contains all of these files for instance the blob ghpages data acronyms laws yml blob ghpages data acronyms laws yml file lists commonly referenced laws and policies these files are processed on page content via the processing directives found in includes filters this uses standard jekyll liquid tags no plugins needed since these terms are replaced in the order they are listed in the data files to avoid conflicts longer terms should be put before shorter terms for instance doed should be listed before doe to avoid replacing the latter first with the wrong term if you re adding new acronyms you typically need to restart jekyll between edits as these are processed when the site is rebuilt | government technology policy | server |
pysimplechain | pysimplechain python implementation of a blockchain in less than 200 lines of code a python implementation of a simple blockchain less than 200 lines of code build status build image description the pysimplechain implementation is focused almost exclusively in the hashed ledger feature it does not include any advanced feature like a distributed ledger or a consensus protocol via proof of work here you ll also find that the idea of the transaction is abstracted to a more general concept of message that can contain any type of data for that reason the goal of this project is to explain and to make clearer how is a blockchain structured at the very core it s not built with the intention to replicate an advanced blockchain like bitcoin or ethereum the following blockchain implemented in the simple chain py file is composed of 3 classes the message class the block class and the chain a message is the basic data container it is sealed when added to a block and has 2 hashes that identify it the payload hash and the block hash each message is linked to the previous message via hash pointers the prev hash attribute the validate message method will ensure the integrity of each message but will not check if the hash pointers are correct this is left to the validate method in the block class a block can contain 1 n messages that are linked sequentially one after the other when a block is added to the chain it s sealed and validated to ensure that the messages are correctly ordered and the hash pointers match once the block is sealed and hashed it is validated by checking the expected vs the actual a chain can contain 1 m blocks that are linked sequentially one after another the chain integrity can be validated at any time calling the validate method which will call each block s validate method and will raise an invalidblockchain exception interactivity a manager function is provided to interact with the blockchain via the terminal console the basic actions are add message to block allows to add a message to the current block add block to chain allows to add the current block to the chain if it s not empty show block asks for an index and if exists a block with that index returns some of the block attributes show chain returns some of the block attributes for each block in the chain validate integrity returns true if the integrity is validated terminates the program raisiing the appropiate exception otherwise exit terminates the program and deletes the blockchain contribute hey there new ideas are welcome open close issues fork the repo and share your code with a pull request clone this project to your computer git clone https github com ericalcaide pysimplechain meta eric alcaide https github com hypnopump eric alcaide https twitter com eric alcaide ericalcaide1 gmail com build image https img shields io travis rust lang rust master svg build status | blockchain |
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IETWebDev2019 | ietwebdev2019 the official web development workshop conducted by iet on campus bit mesra students from various branche and batches came to attend this workshop br workshop was divided into two parts basic and advanced br first couple of days focussed on frontend development br the next couple of days focussed on backend development br on the final day we deployed our backend web app on a virtual private server br students deployed their app on digital ocean br tech stack 1 basics of html css 2 bootstrap 3 jquery 4 javascript 5 nodejs 6 mongodb more specifically mlab 7 express framework | front_end |
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distortos | distortos object oriented c rtos for microcontrollers homepage https distortos org br documentation https distortos org documentation br source code github https github com distortec distortos br forum https groups google com d forum distortos br build test https github com distortec distortos workflows build 20test badge svg br test board generator https github com distortec distortos workflows test 20board 20generator badge svg br unit tests https github com distortec distortos workflows unit 20tests badge svg configuration building to configure build distortos you need cmake https cmake org version 3 8 or later a build tool supported by cmake https cmake org cmake help latest manual cmake generators 7 html manual cmake generators 7 it is highly recommended to use ninja https ninja build org arm none eabi bleeding edge toolchain https github com freddiechopin bleeding edge toolchain gcc version 5 or later distortos tries to follow typical cmake cross compiling workflow which means that you always have to use a so called toolchain file toolchain files in distortos also serve another purpose they select the board which is going to be used by your application 1 download source package of distortos in zip https github com distortec distortos archive master zip or tar gz https github com distortec distortos archive master tar gz format and extract it 2 create a build folder for example output 3 from within the build folder initialize it with cmake for example with cmake dcmake toolchain file source board st stm32f4discovery toolchain st stm32f4discovery cmake gninja if you want a default configuration or cmake c configurations st stm32f4discovery test distortosconfiguration cmake gninja if you want to start from a saved configuration 4 edit distortos configuration with a tool of your choice for example cmake gui a gui application or ccmake curses based application 5 execute selected build tool for example ninja or ninja v if you want to see all command lines while building you can obviously replace step 1 with git clone https github com distortec distortos steps 2 4 can be all done from within cmake gui after starting the application use browse source button to select the folder with distortos and browse build button to select the build folder then click on configure button in the cmakesetup window which appears select the generator of your choice and make sure that specify toolchain file for cross compiling is selected before going any further click next and specify the toolchain file which also selects the board for example source folder source board st stm32f4discovery toolchain st stm32f4discovery cmake and click finish button test application the default target of build all is just the static library with distortos libdistortos a if you want to build the test application specify distortostest as the target for example ninja distortostest if you use ninja tl dr wget https github com distortec distortos archive master tar gz tar xf master tar gz cd distortos master mkdir output cd output cmake dcmake toolchain file source board st stm32f4discovery toolchain st stm32f4discovery cmake gninja cmake gui ninja or wget https github com distortec distortos archive master tar gz tar xf master tar gz cd distortos master mkdir output cd output cmake c configurations st stm32f4discovery test distortosconfiguration cmake gninja cmake gui ninja generating board to generate a board you need python https www python org version 2 7 version 3 6 or later jinja2 https palletsprojects com p jinja template engine for python version 2 10 or later ruamel yaml https bitbucket org ruamel yaml yaml loader dumper package for python both jinja2 and ruamel yaml can be easily installed with pip install jinja2 pip install ruamel yaml or python m pip install jinja2 followed by python m pip install ruamel yaml on windows however they may also be available in the package manager of your system board generator scripts generateboard py takes a yaml file as an input and produces a folder containing various board files source files headers cmake files including cmake toolchain file and so on the input yaml file describes the board hardware in a tree like form the idea is very close to devicetree and in fact earlier versions of board generator used devicetree files to get an idea about the format of the board yaml files take a look at some of the existing files for example source board st stm32f4discovery st stm32f4discovery yaml which describes stm32f4discovery board from st or source chip stm32 stm32f4 chipyaml st stm32f407vg yaml which describes stm32f407vg chip used on this board there is also some documentation about yaml bindings in documentation yaml bindings assuming that you already have distortos either as part of your project or as a standalone folder the basic invocation of the board generator is just path to distortos scripts generateboard py path to board yaml or python path to distortos scripts generateboard py path to board yaml on windows for example scripts generateboard py source board st stm32f4discovery st stm32f4discovery yaml you may also generate so called raw boards using chip yaml file as the input directly for example scripts generateboard py source chip stm32 stm32f4 chipyaml st stm32f407vg yaml o output path of raw board | real-time embedded operating-system arm cortex-m arm-microcontrollers microcontrollers rtos microcontroller cpp cpp11 framework library | os |
embed-react | getting started with create react app this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in your browser the page will reload when you make changes you may also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started to learn react check out the react documentation https reactjs org code splitting this section has moved here https facebook github io create react app docs code splitting https facebook github io create react app docs code splitting analyzing the bundle size this section has moved here https facebook github io create react app docs analyzing the bundle size https facebook github io create react app docs analyzing the bundle size making a progressive web app this section has moved here https facebook github io create react app docs making a progressive web app https facebook github io create react app docs making a progressive web app advanced configuration this section has moved here https facebook github io create react app docs advanced configuration https facebook github io create react app docs advanced configuration deployment this section has moved here https facebook github io create react app docs deployment https facebook github io create react app docs deployment npm run build fails to minify this section has moved here https facebook github io create react app docs troubleshooting npm run build fails to minify https facebook github io create react app docs troubleshooting npm run build fails to minify | os |
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ConversaDocs | conversadocs chat with your documents using llama 2 falcon or openai by default the model llama2 7b chat is loaded for maximum privacy you can upload multiple documents at once to a single database every time a new database is created the previous one is deleted program that enables seamless interaction with your documents through an advanced vector database and the power of large language model llm technology description link colab notebook open in colab https colab research google com assets colab badge svg https colab research google com github r3gm conversadocs blob main conversadocs colab ipynb repository github repository https img shields io badge github repository black style flat square logo github https github com r3gm conversadocs online inference hf hugging face spaces https img shields io badge f0 9f a4 97 20hugging 20face spaces blue https huggingface co spaces r3gm conversadocs chat with different formats of documents image https github com r3gm conversadocs assets 114810545 1c6c426f c144 442e b71a 07867bdf68d3 summarize image https github com r3gm conversadocs assets 114810545 2ded3f4e a9d6 44db b73b b81c0abeeebb play chess with the llm image https github com r3gm conversadocs assets 114810545 877a6580 fcf9 4c0c b0d4 f5eb5e8a6199 news 2023 07 24 document summarization was added 2023 07 29 error with llama 70b was fixed 2023 08 07 chessboard was added for playing with a llm | ai |
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llmx | llmx an api for chat fine tuned language models pypi version https badge fury io py llmx svg https badge fury io py llmx a simple python package that provides a unified interface to several llm providers of chat fine tuned models openai azureopenai palm cohere and local huggingface models note llmx wraps multiple api providers and its interface may change as the providers as well as the general field of llms evolve there is nothing particularly special about this library but some of the requirements i needed when i started building this that other libraries did not have unified model interface single interface to create llm text generators with support for multiple llm providers python from llmx import llm gen llm provider openai support azureopenai models too gen llm provider palm or google gen llm provider cohere or palm gen llm provider hf model uukuguy speechless llama2 hermes orca platypus 13b device map auto run huggingface model locally unified messaging interface standardizes on the openai chatml message format and is designed for chat finetuned models for example the standard prompt sent a model is formatted as an array of objects where each object has a role system user or assistant and content see below a single request is list of only one message e g write code to plot a cosine wave signal a conversation is a list of messages e g write code for x update the axis to y etc same format for all models python messages role user content you are a helpful assistant that can explain concepts clearly to a 6 year old child role user content what is gravity good utils e g caching etc e g being able to use caching for faster responses general policy is that cache is used if config including messages is the same if you want to force a new response set use cache false in the generate call python response gen generate messages messages config textgeneratorconfig n 1 use cache true output looks like bash textgenerationresponse text message role assistant content gravity is like a magical force that pulls things towards each other it s what keeps us on the ground and stops us from floating away into space config textgenerationconfig n 1 temperature 0 1 max tokens 8147 top p 1 0 top k 50 frequency penalty 0 0 presence penalty 0 0 provider openai model gpt 4 stop none logprobs usage prompt tokens 34 completion tokens 69 total tokens 103 are there other libraries that do things like this really well yes i d recommend looking at guidance https github com microsoft guidance which does a lot more interested in optimized inference try somthing like vllm https github com vllm project vllm installation install from pypi please use python3 10 or higher bash pip install llmx install in development mode bash git clone cd llmx pip install e note that you may want to use the latest version of pip to install this package python3 m pip install upgrade pip usage set your api keys first for each service bash for openai and cohere export openai api key your key export cohere api key your key for palm via makersuite export palm api key your key for palm vertex ai setup a gcp project and get a service account key file export palm service account key file path to your service account key file export palm project id your gcp project id export palm project location your project location you can also set the default provider and list of supported providers via a config file use the yaml format in this sample config default yml file llmx configs config default yml and set the llmx config path to the path of the config file python from llmx import llm from llmx datamodel import textgenerationconfig messages role system content you are a helpful assistant that can explain concepts clearly to a 6 year old child role user content what is gravity openai gen llm provider openai openai config textgenerationconfig model gpt 4 max tokens 50 openai response openai gen generate messages config openai config use cache true print openai response text 0 content see the tutorial notebooks tutorial ipynb for more examples current work supported models x openai x palm makersuite https developers generativeai google api rest generativelanguage vertex ai https cloud google com vertex ai docs generative ai learn models x cohere x huggingface local caveats prompting llmx makes some assumptions around how prompts are constructed e g how the chat message interface is assembled into a prompt for each model type if your application or use case requires more control over the prompt you may want to use a different library ideally query the llm models directly inference optimization for hosted models gpt 4 palm cohere etc this library provides an excellent unified interface as the hosted api already takes care of inference optimizations however if you are looking for a library that is optimized for inference with local models e g huggingface tensor parrelization distributed inference etc i d recommend looking at vllm https github com vllm project vllm or tgi https github com huggingface text generation inference citation if you use this library in your work please cite bibtex software victordibiallmx author victor dibia license mit month 10 title llmx an api for chat fine tuned language models url https github com victordibia llmx year 2023 | cohere cohere-ai huggingface llm openai palm-api text-generation | ai |
iot_push | https github com quickmsg smqtt iot push netty mqtt 3 1 1 mqtt 1 2 3 netty4 1 final springboot mqtt 3 1 1 a name 1 mqtt a mqtt ibm im xmpp mqtt mqtt a name 2 a example iot push server starter test session test test yy qos0 qos1 qos2 ssl ws spring a name 3 a lombok springboot jdk8 ide yml properties yml https github com 1ssqq1lxr iot push blob master iot push server starter test src main resources application yml test jmeter mqtt https github com tuanhiep mqtt jmeter example iot push client starter test springboot yml https github com 1ssqq1lxr iot push blob master iot push client starter test src main resources application yml mqttlistener mqttmessagelistener topic autowired procuder producer java https github com 1ssqq1lxr iot push blob master iot push client starter test src main java com lxr iot example mqttmain java qq 789331252 image image icon jpg | mqtt spring-boot iot netty yml jdk8 | server |
AI-Chip | div align center h1 ai chip ics and ips h1 div div align center img src https github com basicmi deep learning processor list raw master resource ai chips png div br div align center editor a href https www linkedin com in shan tang 27342510 strong s t strong a linkedin div div align center strong welcome to my wechat blog a href https mp weixin qq com s axfibbqbdhtj2zt7u5wqbw https mp weixin qq com mp appmsgalbum action getalbum biz mzi3mdq2mja3oa scene 1 album id 1374108991751782402 count 3 wechat redirect starryheavensabove a for more ai chip related articles strong div div align center strong a href https mp weixin qq com s axfibbqbdhtj2zt7u5wqbw https mp weixin qq com mp appmsgalbum action getalbum biz mzi3mdq2mja3oa scene 1 album id 1374108991751782402 count 3 wechat redirect starryheavensabove a strong div div align center img src https github com basicmi deep learning processor list raw master resource qrcode for weichat 258 jpg height 100 div div align center h1 h1 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optalysys optaylsys a a href etacompute eta compute a a href achronix achronix a a href areanna areanna ai a a href neuroblade neuroblade a a href luminous luminous computing a a href efinix efinix a a href aistorm aistorm a a href sima sima ai a a href untether untether ai a a href grai grai matter lab a a href rain rain neuromorphics a a href abr applied brain research a a href xmos xmos a a href dinoplusai dinoplusai a a href furiosa furiosa ai a a href perceive perceive a a href simplemachines simplemachines a a href neureality neureality a a href analoginference analog inference a a href quadric quadric a a href edgeq edgeq a a href innatera innatera nanosystems a a href ceremorphic ceremorphic a a href aspinity aspinity a a href teramem teramem a href d matrix d matrix a a td tr table div align center h1 h1 div div align center h2 a name ic vendors a i ic vendors h2 div hr div align center h1 h1 div div align center h3 h3 div a name nvidia a div align center img src https github com basicmi deep learning processor list raw master resource nvidia logo png height 50 div div align center h3 h3 div div align center h3 gpu h3 div p a href https nvidianews nvidia com news nvidia microsoft accelerate cloud enterprise ai nvidia teams with microsoft to build massive cloud ai computer a p blockquote p tens of thousands of nvidia gpus nvidia quantum 2 infiniband and full stack of nvidia ai software coming to azure nvidia microsoft and global enterprises to use platform for rapid cost effective ai development and deployment p blockquote p strong a href https developer nvidia com blog nvidia hopper architecture in depth nvidia hopper architecture in depth a strong p blockquote p today during the 2022 nvidia gtc keynote address nvidia ceo jensen huang introduced the new nvidia h100 tensor core gpu based on the new nvidia hopper gpu architecture this post gives you a look inside the new h100 gpu and describes important new features of nvidia hopper architecture gpus p blockquote p a href https www anandtech com show 16610 nvidia unveils grace a highperformance arm server cpu for use in ai systems nvidia unveils grace a high performance arm server cpu for use in big ai systems a p blockquote p kicking off another busy spring gpu technology conference for nvidia this morning the graphics and accelerator designer is announcing that they are going to once again design their own arm based cpu soc dubbed grace after grace hopper the computer programming pioneer and us navy rear admiral the cpu is nvidia s latest stab at more fully vertically integrating their hardware stack by being able to offer a high performance cpu alongside their regular gpu wares according to nvidia the chip is being designed specifically for large scale neural network workloads and is expected to become available in nvidia products in 2023 p blockquote div align center h3 h3 div a name intel a div align center img src https github com basicmi deep learning processor list raw master resource intel logo png height 60 div div align center h3 h3 div a name mobileye a div align center h3 mobileye eyeq h3 div mobileye is currently developing its fifth generation soc the a href https www mobileye com our technology evolution eyeq chip eyeq 5 a to act as the vision central computer performing sensor fusion for fully autonomous driving level 5 vehicles that will hit the road in 2020 to meet power consumption and performance targets eyeq socs are designed in most advanced vlsi process technology nodes down to 7nm finfet in the 5th generation a name loihi 2 a div align center h3 loihi h3 div p a href https www intel com content www us en newsroom news intel unveils neuromorphic loihi 2 lava software html intel advances neuromorphic with loihi 2 new lava software framework and new partners a p blockquote p second generation research chip uses pre production intel 4 process grows to 1 million neurons intel adds open software framework to accelerate developer innovation and path to commercialization p blockquote p a name habana a p div align center h3 habana h3 div p strong a href https www intel com content www us en newsroom news vision 2022 habana gaudi2 greco html intel s habana labs launches second generation ai processors for training and inferencing a strong p blockquote p today at intel vision intel announced that habana labs its data center team focused on ai deep learning processor technologies launched its second generation deep learning processors for training and inference habana gaudi 2 and habana greco these new processors address an industry gap by providing customers with high performance high efficiency deep learning compute choices for both training workloads and inference deployments in the data center while lowering the ai barrier to entry for companies of all sizes p blockquote p a href https habana ai aws launches ec2 dl1 instances habana gaudi debuts in the amazon ec2 cloud a p blockquote p the primary motivation to create this new training instance class was presented by andy jassy in the 2020 re invent to provide our end customers with up to 40 better price performance than the current generation of gpu based instances p blockquote div align center h3 h3 div a name qualcomm a div align center img src https github com basicmi deep learning processor list raw master resource qualcomm logo png height 40 div div align center h3 h3 div a href https www forbes com cdn ampproject org c s www forbes com sites karlfreund 2022 11 16 qualcomm ups the snapgragon ai game amp qualcomm ups the snapgragon ai game a blockquote p the leader in premium mobile socs has applied ai across the entire platform p blockquote strong a href https www qualcomm com products technology processors cloud artificial intelligence cloud ai 100 qualcomm cloud ai 100 a strong blockquote p the qualcomm cloud ai 100 designed for ai inference acceleration addresses unique requirements in the cloud including power efficiency scale process node advancements and signal processing facilitating the ability of datacenters to run inference on the edge cloud faster and more efficiently qualcomm cloud ai 100 is designed to be a leading solution for datacenters who increasingly rely on infrastructure at the edge cloud p blockquote div align center h3 h3 div a name samsung a div align center img src https github com basicmi deep learning processor list raw master resource samsung logo png height 35 div div align center h3 h3 div strong a href https news samsung com global samsung brings on device ai processing for premium mobile devices with exynos 9 series 9820 processor samsung brings on device ai processing for premium mobile devices with exynos 9 series 9820 processor a strong fourth generation custom core and 2 0gbps lte advanced pro modem enables enriched mobile experiences including ar and vr applications br samsung resently unveiled a href https news samsung com global samsung optimizes premium exynos 9 series 9810 for ai applications and richer multimedia content the new exynos 9810 brings premium features with a 2 9ghz custom cpu an industry first 6ca lte modem and deep learning processing capabilities a div align center h3 h3 div a name amd a div align center img src https github com basicmi deep learning processor list raw master resource amd logo png height 35 div div align center h3 h3 div the soon to be released a href https www amd com en graphics instinct server accelerators amd instinct mi series accelerators a amd instinct accelerators are engineered from the ground up for this new era of data center computing supercharging hpc and ai workloads to propel new discoveries the amd instinct family of accelerators can deliver industry leading performance for the data center at any scale from single server solutions up to the world s largest supercomputers 1 with new innovations in amd cdna 2 architecture amd infinity fabric technology and packaging technology the latest amd instinct accelerators are designed to power discoveries at exascale enabling scientists to tackle our most pressing challenges p a name ibm a p div align center img src https github com basicmi deep learning processor list raw master resource ibm logo png height 40 div div align center h3 h3 div p a href https www ibm com blogs systems ibm telum processor the next gen microprocessor for ibm z and ibm linuxone meet the ibm artificial intelligence unit a p blockquote p it s our first complete system on chip designed to run and train deep learning models faster and more efficiently than a general purpose cpu p blockquote p a href https www ibm com blogs systems ibm telum processor the next gen microprocessor for ibm z and ibm linuxone ibm telum processor the next gen microprocessor for ibm z and ibm linuxone a p blockquote p the 7 nm microprocessor is engineered to meet the demands our clients face for gaining ai based insights from their data without compromising response time for high volume transactional workloads p blockquote p a href https www ibm com blogs research tag truenorth truenorth a is ibm s neuromorphic cmos asic developed in conjunction with the darpa a href https en wikipedia org wiki synapse synapse a program p blockquote p it is a manycore processor network on a chip design with 4096 cores each one simulating 256 programmable silicon neurons for a total of just over a million neurons in turn each neuron has 256 programmable synapses that convey the signals between them hence the total number of programmable synapses is just over 268 million 228 in terms of basic building blocks its transistor count is 5 4 billion since memory computation and communication are handled in each of the 4096 neurosynaptic cores truenorth circumvents the von neumann architecture bottlenecks and is very energy efficient consuming 70 milliwatts about 1 10 000th the power density of conventional microprocessors a href https en wikipedia org wiki truenorth wikipedia a p blockquote p a href https www research ibm com artificial intelligence ai hardware center ai hardware center a p blockquote p the ibm research ai hardware center is a global research hub headquartered in albany new york the center is focused on enabling next generation chips and systems that support the tremendous processing power and unprecedented speed that ai requires to realize its full potential p blockquote div align center h3 h3 div p a name marvell a p div align center img src https github com basicmi deep learning processor list raw master resource marvell logo png height 60 div div align center h3 h3 div p a href https www marvell com products data processing units html data processing units a p blockquote p built on seven generations of the industry s first most scalable and widely adopted data infrastructure processors marvell s octeon octeon fusion and armada platforms are optimized for wireless infrastructure wireline carrier networks enterprise and cloud data centers p blockquote div align center h3 h3 div div align center h2 a name tech giants a ii tech giants hpc vendors h2 div p hr p div align center h3 h3 div p a name google a p div align center img src https github com basicmi deep learning processor list raw master resource google logo png height 40 div div align center h3 h3 div p strong a href https www zdnet com article google tensor everything you need to know about the pixel 6 chip google tensor everything you need to know about the pixel 6 chip a strong p blockquote p google has taken the wraps off its latest pixel smartphones and among the changes the one with the biggest long term impact is the switch to in house silicon for the search giant p blockquote p a href https www hpcwire com 2021 05 20 google launches tpu v4 ai chips google launches tpu v4 ai chips a p blockquote p google ceo sundar pichai spoke for only one minute and 42 seconds about the company s latest tpu v4 tensor processing units during his keynote at the google i o virtual conference this week but it may have been the most important and awaited news from the event p blockquote p a href https cloud google com tpu cloud tpu a p blockquote p machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses we built the tensor processing unit tpu in order to make it possible for anyone to achieve similar breakthroughs cloud tpu is the custom designed machine learning asic that powers google products like translate photos search assistant and gmail here s how you can put the tpu and machine learning to work accelerating your company s success especially at scale p blockquote p a href https cloud google com edge tpu edge tpu a p blockquote p ai is pervasive today from consumer to enterprise applications with the explosive growth of connected devices combined with a demand for privacy confidentiality low latency and bandwidth constraints ai models trained in the cloud increasingly need to be run at the edge edge tpu is google s purpose built asic designed to run ai at the edge it delivers high performance in a small physical and power footprint enabling the deployment of high accuracy ai at the edge p blockquote p other references are br a href https mp weixin qq com s b22p26 delwfspy9kdjkha google tpu3 a br br a href https mp weixin qq com s kf l4u7jrxj8kf3pi8m5iw google tpu a br br a href https mp weixin qq com s lbqynsna6 joelz kq2w8a google a br br a href https mp weixin qq com s g bdlvsy cx4akitcwf7jq google tpu a br br a href https www linkedin com pulse should we all embrace systolic arrays chien ping lu should we all embrace systolic arrays a br p div align center h3 h3 div p a name amazon aws a p div align center img src https github com basicmi deep learning processor list raw master resource amazon aws png height 50 div div align center h3 h3 div p strong a href https aws amazon com cn machine learning trainium aws trainium a strong p blockquote p aws trainium is the second custom machine learning ml chip designed by aws that provides the best price performance for training deep learning models in the cloud trainium offers the highest performance with the most teraflops tflops of compute power for the fastest ml training in amazon ec2 and enables a broader set of ml applications the trainium chip is specifically optimized for deep learning training workloads for applications including image classification semantic search translation voice recognition natural language processing and recommendation engines p blockquote p a href https aws amazon com cn machine learning inferentia aws inferentia high performance machine learning inference chip custom designed by aws a p blockquote p aws inferentia provides high throughput low latency inference performance at an extremely low cost each chip provides hundreds of tops tera operations per second of inference throughput to allow complex models to make fast predictions for even more performance multiple aws inferentia chips can be used together to drive thousands of tops of throughput aws inferentia will be available for use with amazon sagemaker amazon ec2 and amazon elastic inference p blockquote div align center h3 h3 div p a name microsoft a p div align center img src https github com basicmi deep learning processor list raw master resource microsoft logo png height 60 div div align center h3 h3 div div align center h3 h3 div p a name apple a p div align center img src https github com basicmi deep learning processor list raw master resource apple logo png height 60 div div align center h3 h3 div div align center h3 h3 div p a name alibaba a p div align center img src https github com basicmi deep learning processor list raw master resource alibaba logo png height 60 div div align center h3 h3 div p a href https medium com syncedreview alibabas new ai chip can process nearly 80k images per second 63412dec22a3 alibaba s new ai chip can process nearly 80k images per second a p blockquote p at the alibaba cloud aliyun apsara conference 2019 pingtouge unveiled its first ai dedicated processor for cloud based large scale ai inferencing the hanguang 800 is the first semiconductor product in alibaba s 20 year history p blockquote div align center h3 h3 div p a name tencent cloud a p div align center img src https github com basicmi deep learning processor list raw master resource tencent cloud logo png height 30 div div align center h3 h3 div p a href https www datacenterdynamics com en news tencent reveals three data center chips for ai video transcoding and networking tencent reveals three data center chips for ai video transcoding and networking a p blockquote p the company claims that the zixiao ai chip is twice as good as comparable competing products video transcoding chip canghai was 30 percent better and smartnic xuanling was apparently four times as good it did not provide external benchmarks or specific product details p blockquote div align center h3 h3 div p br a name baidu a p div align center img src https github com basicmi deep learning processor list raw master resource baidu logo png height 40 div div align center h3 h3 div p a href https www reuters com technology baidu says 2nd gen kunlun ai chips enter mass production 2021 08 18 baidu says 2nd gen kunlun ai chips enter mass production a p blockquote p chinese tech giant baidu said on wednesday it had begun mass producing second generation kunlun artificial intelligence ai chips as it races to become a key player in the chip industry which beijing is trying to strengthen p blockquote div align center h3 h3 div p a name fujitsu a p div align center img src https github com basicmi deep learning processor list raw master resource fujitsu logo png height 40 div div align center h3 h3 div blockquote p this a href https www nextplatform com 2017 08 09 fujitsu bets deep leaning hpc divergence dlu that fujitsu is creating a is done from scratch and it is not based on either the sparc or arm instruction set and in fact it has its own instruction set and a new data format specifically for deep learning which were created from scratch japanese computing giant fujitsu which knows a thing or two about making a very efficient and highly scalable system for hpc workloads as evidenced by the k supercomputer does not believe that the hpc and ai architectures will converge rather the company is banking on the fact that these architectures will diverge and will require very specialized functions p blockquote div align center h3 h3 div p a name nokia a p div align center img src https github com basicmi deep learning processor list raw master resource nokia logo png height 30 div div align center h3 h3 div blockquote p nokia has developed the a href https networks nokia com 5g reefshark reefshark chipsets a for its 5g network solutions ai is implemented in the reefshark design for radio and embedded in the baseband to use augmented deep learning to trigger smart rapid actions by the autonomous cognitive network enhancing network optimization and increasing business opportunities p blockquote div align center h3 h3 div p a name facebook a p div align center img src https github com basicmi deep learning processor list raw master resource facebook logo png height 50 div div align center h3 h3 div p a href https www reuters com technology facebook developing machine learning chip information 2021 09 09 facebook developing machine learning chip the information a p blockquote p facebook inc fb o is developing a machine learning chip to handle tasks such as content recommendation to users the information reported on thursday citing two people familiar with the project p blockquote div align center h3 h3 div p a name tesla a p div align center img src https github com basicmi deep learning processor list raw master resource tesla logo png height 60 div div align center h3 h3 div p strong a href https www forbes com sites jamesmorris 2022 10 06 teslas biggest news at ai day was the dojo supercomputer not the optimus robot tesla s biggest news at ai day was the dojo supercomputer not the optimus robot a strong p blockquote p elon musk played ai day to the crowd with the focus on the optimus humanoid robot but while this could have a huge impact on our lives and society if it does enter mass production at the price musk suggested 20 000 another part of the presentation will have more immediate effects that was the status report on the dojo supercomputer it could really change the world much more quickly than a bipedal bot p blockquote p a href https semianalysis com tesla dojo ai super computer unique packaging and chip design allow an order magnitude advantage over competing ai hardware tesla dojo unique packaging and chip design allow an order magnitude advantage over competing ai hardware a p blockquote p tesla hosted their ai day and revealed the innerworkings of their software and hardware infrastructure part of this reveal was the previously teased dojo ai training chip tesla claims their d1 dojo chip has a gpu level compute cpu level flexibility with networking switch io p blockquote div align center h3 h3 div div align center h2 a name ip vendors a iii traditional ip vendors h2 div p hr p div align center h3 h3 div p a name arm a p div align center img src https github com basicmi deep learning processor list raw master resource arm logo png height 30 div div align center h3 h3 div a href https www arm com products silicon ip cpu ethos ethos n78 npu ethos n78 a p blockquote p specifically designed for inference at the edge the ml processor gives an industry leading performance of 4 6 tops with a stunning efficiency of 3 tops w for mobile devices and smart ip cameras p blockquote p strong a href https www anandtech com show 12791 arm details project trillium mlp architecture arm details project trillium machine learning processor architecture a strong p blockquote p arm s second generation highly scalable and efficient npu the ethos n78 enables new immersive applications with a 2 5x increase in single core performance now scalable from 1 to 10 top s and beyond through many core technologies it provides flexibility to optimize the ml capability with 90 configurations p blockquote div align center h3 h3 div p a name synopsys a p div align center img src https github com basicmi deep learning processor list raw master resource synopsys logo png height 40 div div align center h3 h3 div p strong a href https news synopsys com 2022 04 19 synopsys introduces industrys highest performance neural processor ip synopsys introduces industry s highest performance neural processor ip a strong p blockquote p new designware arc npx6 npu ip delivers up to 3 500 tops performance for automotive consumer and data center chip designs p blockquote div align center h3 h3 div p a name imagination a p div align center img src https github com basicmi deep learning processor list raw master resource imagination logo png height 60 div div align center h3 h3 div p a href https www imaginationtech com products ai ai processors a p blockquote p whether you want smartness residing in the palm of your hand consumer products or industrial robots or enabled by powerful servers in the cloud we can help you achieve your vision we enable the smartness in your products with our powervr neural network accelerators nna and gpus our nc sdk enables seamless deployment of ai acceleration on either our hardware ip either in isolation or combined our nna provides maximum efficiency with a scalable architecture which enables a wide range of smart edge and end point devices from low performance iot to high performance robotaxi p blockquote div align center h3 h3 div p a name ceva a p div align center img src https github com basicmi deep learning processor list raw master resource ceva logo png height 40 div div align center h3 h3 div p a href https www ceva dsp com app deep learning deep learning for the real time embedded world a p blockquote p one solution lies in supplying a dedicated low power ai processor for deep learning at the edge combined with a deep neural network dnn graph compiler p blockquote div align center h3 h3 div p a name cadence a p div align center img src https github com basicmi deep learning processor list raw master resource cadence logo png height 40 div div align center h3 h3 div p a href https www cadence com en us home tools ip tensilica ip tensilica ai platform html tensilica ai platform a p div align center h3 h3 div p a name verisilicon a p div align center img src https github com basicmi deep learning processor list raw master resource verisilicon logo png height 40 div div align center h3 h3 div p a href https www verisilicon com en ipportfolio vivantenpuip vivante npu ip a p blockquote p verisilicon s neural network processor npu ip is a highly scalable programmable computer vision and artificial intelligence processor that supports ai operations upgrades for endpoints edge devices and cloud devices designed to meet a variety of chip sizes and power budgets the vivante npu ip is a cost effective high quality neural network acceleration engine solution p blockquote div align center h3 h3 div div align center h2 a name startups a iv startups h2 div p hr p div align center h3 h3 div p a name cerebras a p div align center a href https www cerebras net img src https github com basicmi deep learning processor list raw master resource cerebras logo png height 50 a div div align center h3 h3 div p strong a href https www cerebras net press release cerebras unveils andromeda a 13 5 million core ai supercomputer that delivers near perfect linear scaling for large language models cerebras unveils andromeda a 13 5 million core ai supercomputer that delivers near perfect linear scaling for large language models a strong p blockquote p delivering more than 1 exaflop of ai compute and 120 petaflops of dense compute andromeda is one of the largest ai supercomputers ever built and is dead simple to use p blockquote p a href https www cerebras net blog cerebras sets record for largest ai models ever trained on single device cerebras sets record for largest ai models ever trained on single device a p blockquote p we are announcing the largest models ever trained on a single device using the cerebras software platform csoft our customers can easily train state of the art gpt language models such as gpt 3 i and gpt j ii with up to 20 billion parameters on a single cs 2 system running on a single cs 2 these models take minutes to set up and users can quickly move between models with just a few keystrokes with clusters of gpus this takes months of engineering work p blockquote p a href https www anandtech com show 17061 cerebras completes series f funding another 250m for 4b valuation cerebras completes series f funding another 250m for 4b valuation a p blockquote p the new series f funding round nets the company another 250m in capital bringing the total raised through venture capital up to 720 million p blockquote p a href https www anandtech com show 16626 cerebras unveils wafer scale engine two wse2 26 trillion transistors 100 yield cerebras unveils wafer scale engine two wse2 2 6 trillion transistors 100 yield a p blockquote p two years ago cerebras unveiled a revolution in silicon design a processor as big as your head using as much area on a 12 inch wafer as a rectangular design would allow built on 16nm focused on both ai as well as hpc workloads today the company is launching its second generation product built on tsmc 7nm with more than double the cores and more than double of everything p blockquote p a href https techcrunch com 2019 11 19 the cerebras cs 1 computes deep learning ai problems by being bigger bigger and bigger than any other chip the cerebras cs 1 computes deep learning ai problems by being bigger bigger and bigger than any other chip a p blockquote p today the company announced the launch of its end user compute product the cerebras cs 1 and also announced its first customer of argonne national laboratory p blockquote div align center h3 h3 div p a name graphcore a p div align center a href https www graphcore ai img src https github com basicmi deep learning processor list raw master resource graphcore logo png height 70 a div p strong a href https www eetimes com graphcore supercharges ipu with wafer on wafer graphcore supercharges ipu with wafer on wafer a strong p blockquote p graphcore unveiled its third generation intelligence processing unit ipu the first processor to be built using 3d wafer on wafer wow technology p blockquote p a href https www graphcore ai mk2 benchmarks mk2 performance benchmarks a p p a href https techcrunch com 2020 02 24 graphcore the ai chipmaker raises another 150m at a 1 95b valuation graphcore the ai chipmaker raises another 150m at a 1 95b valuation a p blockquote p graphcore the bristol based startup that designs processors specifically for artificial intelligence applications announced it has raised another 150 million in funding for r d and to continue bringing on new customers it s valuation is now 1 95 billion p blockquote div align center h3 h3 div p a href https mp weixin qq com s ch9h8dutonk 2zfkk5yu0g xpu graphcore ipu a give some analysis on its ipu architecture p p a href https mp weixin qq com s amuqeashqev3dnibh3scea graphcore ai a more analysis p p a href https mp weixin qq com s qp0zssa7sqwxdqwgeaxmog ai graphcore ipu a in depth analysis after more information was disclosed p div align center h3 h3 div p a name tenstorrent a p div align center a href http tenstorrent com img src https github com basicmi deep learning processor list raw master resource tenstorrent logo png height 100 a div div align center h3 h3 div p a href https www prnewswire com news releases tenstorrent raises over 200 million at 1 billion valuation to create programmable high performance ai computers 301295913 html tenstorrent raises over 200 million at 1 billion valuation to create programmable high performance ai computers a p blockquote p toronto may 20 2021 prnewswire tenstorrent a hardware start up developing next generation computers announced today that it has raised over 200 million in a recent funding round that values the company at 1 billion the round was led by fidelity management and research company and includes additional investments from eclipse ventures epic cg and moore capital p blockquote p a href https www anandtech com show 16709 an interview with tenstorrent ceo ljubisa bajic and cto jim keller an interview with tenstorrent ceo ljubisa bajic and cto jim keller a p div align center h3 h3 div p a name blaize a p div align center a href https www blaize com img src https github com basicmi deep learning processor list raw master resource blaize logo png height 40 a div div align center h3 h3 div p a href https www eetimes com automotive ai startup blaize closes 71 million funding round automotive ai startup blaize closes 71 million funding round a p blockquote p blaize formerly thinci has closed a series d round of funding at 71 million new investor franklin templeton and existing investor temasek led the round along with participation from denso and other new and existing investors this round brings blaize s total funding to around 155 million total p blockquote div align center h3 h3 div p a name koniku a p div align center a href http koniku io img src https github com basicmi deep learning processor list raw master resource koniku logo png height 50 a div div align center h3 h3 div blockquote p founded in 2014 newark california startup a href http koniku io koniku a has taken in 1 65 million in funding so far to become the world s first neurocomputation company the idea is that since the brain is the most powerful computer ever devised why not reverse engineer it simple right koniku is actually integrating biological neurons onto chips and has made enough progress that they claim to have astrazeneca as a customer boeing has also signed on with a letter of intent to use the technology in chemical detecting drones p blockquote div align center h3 h3 div p a name adapteva a p div align center a href http www adapteva com img src https github com basicmi deep learning processor list raw master resource adapteva logo png height 70 a div div align center h3 h3 div p a href http www adapteva com adapteva a has taken in 5 1 million in funding from investors that include mobile giant ericsson a href http www parallella org docs e5 1024core soc pdf the paper epiphany v a 1024 processor 64 bit risc system on chip a describes the design of adapteva s 1024 core processor chip in 16nm finfet technology p p a name mythic a p div align center a href https mythic ai img src https github com basicmi deep learning processor list raw master resource mythic logo png height 20 a div div align center h3 h3 div p a href https www linkedin com pulse era analog compute has arrived michael b henry the era of analog compute has arrived a p blockquote p resnet 50 in our prototype analog ai processor production release will support 900 1000 fps and int8 accuracy at 3w p blockquote p a href https venturebeat com 2021 06 07 mythic launches analog ai processor that consumes 10 times less power mythic launches analog ai processor that consumes 10 times less power a p blockquote p analog ai processor company mythic launched its m1076 analog matrix processor today to provide low power ai processing p blockquote div align center h3 h3 div p a name brainchip a p div align center a href http www brainchipinc com img src https github com basicmi deep learning processor list raw master resource brainchip logo png height 40 a div div align center h3 h3 div p a href hhttps venturebeat com 2022 01 18 brainchip launches neuromorphic process for ai at the edge brainchip launches neuromorphic process for ai at the edge a p blockquote p brainchip today announced the commercialization of its akida neural networking processor aimed at a variety of edge and internet of things iot applications brainchip claims to be the first commercial producer of neuromorphic ai chips which could deliver benefits in ultra low power and performance over conventional approaches p blockquote div align center h3 h3 div p a name deepvision a p div align center a href https deepvision io img src https github com basicmi deep learning processor list raw master resource deepvision logo png height 40 a div div align center h3 h3 div p a href ai processor chipmaker deep vision raises 35 million in series b funding ai processor chipmaker deep vision raises 35 million in series b funding a p blockquote p tiger global leads series b financing enabling deep vision to expand video analytics and natural language processing capabilities in edge computing applications p blockquote div align center h3 h3 div p a name groq a p div align center h2 a href http groq com groq a h2 div div align center h3 h3 div p strong a href https www eetimes com groq demos fast llms on 4 year old silicon groq demonstrates fast llms on 4 year old silicon a strong p blockquote p mountain view calif groq has repositioned its first generation ai inference chip as a language processing unit lpu and demonstrated meta s llama 2 70 billion parameter large language model llm running inference at 240 tokens per second per user groq ceo jonathan ross told ee times that the company had llama 2 up and running on the company s 10 rack 64 chip cloud based dev system in a couple of days this system is based on the company s first gen ai silicon released four years ago p blockquote p a href https www forbes com sites amyfeldman 2021 04 14 ai chip startup groq founded by ex googlers raises 300 million to power autonomous vehicles and data centers ai chip startup groq founded by ex googlers raises 300 million to power autonomous vehicles and data centers a p blockquote p jonathan ross left google to launch next generation semiconductor startup groq in 2016 today the mountain view california based firm said that it had raised 300 million led by tiger global management and billionaire investor dan sundheim s d1 capital as it officially launched into public view p blockquote div align center h3 h3 div p a name kneron a p div align center a href http www kneron com img src https github com basicmi deep learning processor list raw master resource kneron logo png height 60 a div div align center h3 h3 div p a href https www prnewswire com news releases kneron to accelerate edge ai development with more than 10 million usd series a financing 300556674 html kneron to accelerate edge ai development with more than 10 million usd series a financing a p div align center h3 h3 div p a name gti a p div align center a href https www gyrfalcontech ai img src https github com basicmi deep learning processor list raw master resource gti logo png height 40 a div div align center h3 h3 div p according to this article a href https www prnewswire com news releases gyrfalcon offers automotive ai chip technology 300860069 html gyrfalcon offers automotive ai chip technology a p blockquote p gyrfalcon technology inc gti has been promoting matrix based application specific chips for all forms of ai since offering their production versions of ai accelerator chips in september 2017 through the licensing of its proprietary technology the company is confident it can help automakers bring highly competitive ai chips to production for use in vehicles within 18 months along with significant gains in ai performance improvements in power dissipation and cost advantages p blockquote div align center h3 h3 div p a name sambanova a p div align center a href https sambanovasystems com img src https github com basicmi deep learning processor list raw master resource sambanova logo png height 40 a div div align center h3 h3 div p strong a href https venturebeat com ai sambanova unveils new ai chip to power full stack ai platform sambanova unveils new ai chip to power full stack ai platform a strong p blockquote p today palo alto based sambanova systems unveiled a new ai chip the sn40l which will power its full stack large language model llm platform the sambanova suite that helps enterprises go from chip to model building and deploying customized generative ai models p blockquote p a href https techcrunch com 2021 04 13 sambanova raises 676m at a 5 1b valuation to double down on cloud based ai software for enterprises sambanova raises 676m at a 5 1b valuation to double down on cloud based ai software for enterprises a p blockquote p sambanova a startup building ai hardware and integrated systems that run on it that only officially came out of three years in stealth last december is announcing a huge round of funding today to take its business out into the world the company has closed on 676 million in financing a series d that co founder and ceo rodrigo liang has confirmed values the company at 5 1 billion p blockquote p a href https sambanova ai articles introducing sambanova systems datascale a new era of computing introducing sambanova systems datascale a new era of computing a p blockquote p sambanova has been working closely with many organizations the past few months and has established a new state of the art in nlp this advancement in nlp deep learning is illustrated by a gpu crushing world record performance result achieved on sambanova systems dataflow optimized system p blockquote p a href https sambanova ai a new state of the art in nlp beyond gpus a new state of the art in nlp beyond gpus a p blockquote p sambanova has been working closely with many organizations the past few months and has established a new state of the art in nlp this advancement in nlp deep learning is illustrated by a gpu crushing world record performance result achieved on sambanova systems dataflow optimized system p blockquote div align center h3 h3 div p a name greenwaves a p div align center a href https greenwaves technologies com en greenwaves technologies 2 img src https github com basicmi deep learning processor list raw master resource greenwaves logo png height 50 a div div align center h3 h3 div p a href https www eetimes eu greenwaves shows off advanced audio demos greenwaves shows off advanced audio demos a p blockquote p the gap9 processor a successor to gap8 which targets computer vision in iot devices is an ultra low power neural network processor suitable for battery powered devices greenwaves vice president of marketing martin croome told ee times europe that the company decided to focus gap9 on the hearables market after receiving traction from this sector for gap8 p blockquote div align center h3 h3 div p a name lightelligence a p div align center a href https www lightelligence ai img src https github com basicmi deep learning processor list raw master resource lightelligence logo png height 60 a div div align center h3 h3 div p strong a href https www eetimes com optical computing chip runs hardest math problems 100x faster than gpus optical chip solves hardest math problems faster than gpus a strong p blockquote p optical computing startup lightelligence has demonstrated a silicon photonics accelerator running the ising problem more than 100 times faster than a typical gpu setup p blockquote div align center h3 h3 div p a name lightmatter a p div align center a href https www lightmatter ai img src https github com basicmi deep learning processor list raw master resource lightmatter logo png height 50 a div div align center h3 h3 div p a href https www eetimes com lightmatter raises more funding for photonic ai chip lightmatter raises more funding for photonic ai chip a p blockquote p ightmatter the mit spinout building ai accelerators with a silicon photonics computing engine announced a series b funding round raising an additional 80 million the company s technology is based on proprietary silicon photonics technology which manipulates coherent light inside a chip to perform calculations very quickly while using very little power p blockquote p a name hailo a p div align center a href https www hailotech com img src https github com basicmi deep learning processor list raw master resource hailo logo png height 60 a div div align center h3 h3 div p a href https www eetimes com unicorn ai chipmaker hailo raises 136 million unicorn ai chipmaker hailo raises 136 million a p blockquote p israeli ai chip startup hailo has raised 136 million in a series c funding round bringing the company s total to 224 million the company has also reportedly reached unicorn status p blockquote div align center h3 h3 div p a name tachyum a p div align center a href http www tachyum com img src https github com basicmi deep learning processor list raw master resource tachyum logo png height 40 a div div align center h3 h3 div p a href https www hpcwire com off the wire tachyum launches prodigy universal processor tachyum launches prodigy universal processor a p blockquote p may 11 2021 tachyum today launched the world s first universal processor prodigy which unifies the functionality of a cpu gpu and tpu in a single processor creating a homogeneous architecture while delivering massive performance improvements at a cost many times less than competing products p blockquote div align center h3 h3 div p a name alphaics a p div align center a href https www alphaics ai img src https github com basicmi deep learning processor list raw master resource alphaics logo png height 50 a div div align center h3 h3 div p a href https www eetimes com alphaics begins sampling its deep learning co processor alphaics begins sampling its deep learning co processor a p blockquote p alphaics a startup developing edge ai and learning silicon aimed at smart vision applications is sampling its deep learning co processor gluon that also comes with a software development kit p blockquote div align center h3 h3 div p a name syntiant a p div align center a href https www syntiant com img src https github com basicmi deep learning processor list raw master resource syntiant logo png height 30 a div div align center h3 h3 div p a href https semiengineering com syntiant analog deep learning chips syntiant analog deep learning chips a p blockquote p startup syntiant corp is an irvine calif semiconductor company led by former top broadcom engineers with experience in both innovative design and in producing chips designed to be produced in the billions according to company ceo kurt busch p blockquote div align center h3 h3 div p a name aictx a p div align center a href https aictx ai img src https github com basicmi deep learning processor list raw master resource aictx logo png height 40 a div div align center h3 h3 div p strong a href https www eetimes com document asp doc id 1333983 baidu backs neuromorphic ic developer a strong p blockquote p munich swiss startup aictx has closed a 1 5 million pre a funding round from baidu ventures to develop commercial applications for its low power neuromorphic computing and processor designs and enable what it calls neuromorphic intelligence it is targeting low power edge computing embedded sensory processing systems p blockquote p a name flexlogix a p div align center a href http www flex logix com nmax img src https github com basicmi deep learning processor list raw master resource flexlogix logo png height 40 a div div align center h3 h3 div p strong a href https www zdnet com article flex logix has two paths to making a lot of money challenging nvidia in ai flex logix has two paths to making a lot of money challenging nvidia in ai a strong p blockquote p the programmable chip company scores 55 million in venture backing bringing its total haul to 82 million p blockquote p a name pfn a p div align center a href https projects preferred jp mn core en img src https github com basicmi deep learning processor list raw master resource pfn logo png height 40 a div div align center h3 h3 div p strong a href https www preferred networks jp en news preferred networks develops a custom deep learning processor mn core for use in mn 3 a new large scale cluster in spring 2020 a strong p blockquote p dec 12 2018 tokyo japan preferred networks inc pfn head office tokyo president ceo toru nishikawa announces that it is developing mn core tm a processor dedicated to deep learning and will exhibit this independently developed hardware for deep learning including the mn core chip board and server at the semicon japan 2018 held at tokyo big site p blockquote p a name cornami a p div align center a href http cornami com img src https github com basicmi deep learning processor list raw master resource cornami logo jpg height 30 a div div align center h3 h3 div p strong a href https www zdnet com article ai startup cornami reveals details of neural net chip ai startup cornami reveals details of neural net chip a strong p blockquote p stealth startup cornami on thursday revealed some details of its novel approach to chip design to run neural networks cto paul masters says the chip will finally realize the best aspects of a technology first seen in the 1970s p blockquote p a name anaflash a p div align center a href http anaflash ai img src https github com basicmi deep learning processor list raw master resource anaflash logo png height 40 a div div align center h3 h3 div p strong a href https www smart2zero com news ai chip startup offers new edge computing solution ai chip startup offers new edge computing solution a strong p blockquote p anaflash inc san jose ca is a startup company that has developed a test chip to demonstrate analog neurocomputing taking place inside logic compatible embedded flash memory p blockquote p a name optalysys a p div align center a href https www optalysys com img src https github com basicmi deep learning processor list raw master resource optalysys logo png height 40 a div div align center h3 h3 div p strong a href https www globenewswire com news release 2019 03 07 1749510 0 en optalysys launches world s first commercial optical processing system the ft x 2000 html optalysys launches world s first commercial optical processing system the ft x 2000 a strong p blockquote p optalysys develops optical co processing technology which enables new levels of processing capability delivered with a vastly reduced energy consumption compared with conventional computers its first coprocessor is based on an established diffractive optical approach that uses the photons of low power laser light instead of conventional electricity and its electrons this inherently parallel technology is highly scalable and is the new paradigm of computing p blockquote p a name etacompute a p div align center a href https etacompute com img src https github com basicmi deep learning processor list raw master resource etacompute logo png height 80 a div div align center h3 h3 div p a href https spectrum ieee org tech talk semiconductors processors lowpower ai startup eta compute delivers first commercial chips low power ai startup eta compute delivers first commercial chips a p blockquote p the firm pivoted away from riskier spiking neural networks using a new power management scheme p blockquote p a href https spectrum ieee org tech talk semiconductors processors eta compute debuts spiking neural network chip for edge ai eta compute debuts spiking neural network chip for edge ai a p blockquote p chip can learn on its own and inference at 100 microwatt scale says company at arm techcon p blockquote p a name achronix a p div align center a href https www achronix com product speedster7t img src https github com basicmi deep learning processor list raw master resource achronix logo png height 30 a div div align center h3 h3 div p strong a href https www eetimes com document asp doc id 1334717 achronix rolls 7 nm fpgas for ai a strong p blockquote p achronix is back in the game of providing full fledged fpgas with a new high end 7 nm family joining the gold rush of silicon to accelerate deep learning it aims to leverage novel design of its ai block a new on chip network and use of gddr6 memory to provide similar performance at a lower cost than larger rivals intel and xilinx p blockquote p a name areanna a p div align center a href https areanna ai com img src https github com basicmi deep learning processor list raw master resource areanna logo png height 60 a div div align center h3 h3 div p strong a href https www eetimes com document asp doc id 1334947 startup runs ai in novel sram a strong p blockquote p areanna is the latest example of an explosion of new architectures spawned by the rise of deep learning the debut of a whole new approach to computing has fired imaginations of engineers around the industry hoping to be the next hewlett and packard p blockquote p a name neuroblade a p div align center a href https www neuroblade ai img src https github com basicmi deep learning processor list raw master resource neuroblade logo png height 120 a div div align center h3 h3 div p strong a href https www eetasia com news article neuroblade preps inference chip neuroblade preps inference chip a strong p blockquote p add neuroblade to the dozens of startups working on ai silicon the israeli company just closed a 23 million series a led by the founder of check point software and with participation from intel capital p blockquote p a name luminous a p div align center a href https www luminouscomputing com img src https github com basicmi deep learning processor list raw master resource luminous logo png height 90 a div div align center h3 h3 div p strong a href https www technologyreview com s 613668 ai chips uses optical semiconductor machine learning bill gates just backed a chip startup that uses light to turbocharge ai a strong p blockquote p luminous computing has developed an optical microchip that runs ai models much faster than other semiconductors while using less power p blockquote p a name efinix a p div align center a href https www efinixinc com img src https github com basicmi deep learning processor list raw master resource efinix logo png height 25 a div div align center h3 h3 div p strong a href https www zdnet com article chip startup efinix hopes to bootstrap ai efforts in iot chip startup efinix hopes to bootstrap ai efforts in iot a strong p blockquote p six year old startup efinix has created an intriguing twist on the fpga technology dominated by intel and xiliinx the company hopes its energy efficient chips will bootstrap the market for embedded ai in the internet of things p blockquote p a name aistorm a p div align center a href https aistorm ai img src https github com basicmi deep learning processor list raw master resource aistorm logo png height 60 a div div align center h3 h3 div p strong a href https venturebeat com 2019 02 11 aistorm raises 13 2 million for ai edge computing chips aistorm raises 13 2 million for ai edge computing chips a strong p blockquote p david schie a former senior executive at maxim micrel and semtech thinks both markets are ripe for disruption he along with wsi toshiba and arm veterans robert barker andreas sibrai and cesar matias in 2011 cofounded aistorm a san jose based artificial intelligence ai startup that develops chipsets that can directly process data from wearables handsets automotive devices smart speakers and other internet of things iot devices p blockquote p a name sima a p div align center a href http sima ai img src https github com basicmi deep learning processor list raw master resource sima logo png height 40 a div div align center h3 h3 div p strong a href https www businesswire com news home 20200512005313 en sima ai raises 30 million series investment led sima ai raises 30 million in series a investment round led by dell technologies capital a strong p blockquote p san jose calif business wire sima ai the company enabling high performance machine learning to go green today announced its machine learning soc mlsoc platform the industry s first unified solution to support traditional compute with high performance lowest power safe and secure machine learning inference delivering the highest frames per second per watt sima ai s mlsoc is the first machine learning platform to break the 1000 fps w barrier for resnet 501 in customer engagements the company has demonstrated 10 30x improvement in fps w through its automated software flow across a wide range of embedded edge applications over today s competing solutions the platform will provide machine learning solutions that range from 50 tops 5w to 200 tops 20w delivering an industry first of 10 tops w for high performance inference p blockquote p a href https www businesswire com news home 20191022005079 en sima ai e2 84 a2 introduces mlsoc e2 84 a2 sima ai introduces mlsoc first machine learning platform to break 1000 fps w barrier with 10 30x improvement over alternative solutions a p blockquote p sima ai the company enabling high performance machine learning to go green today announced its machine learning soc mlsoc platform the industry s first unified solution to support traditional compute with high performance lowest power safe and secure machine learning inference delivering the highest frames per second per watt sima ai s mlsoc is the first machine learning platform to break the 1000 fps w barrier for resnet 501 in customer engagements the company has demonstrated 10 30x improvement in fps w through its automated software flow across a wide range of embedded edge applications over today s competing solutions the platform will provide machine learning solutions that range from 50 tops 5w to 200 tops 20w delivering an industry first of 10 tops w for high performance inference p blockquote p a name untether a p div align center a href https untether ai img src https github com basicmi deep learning processor list raw master resource untether logo png height 40 a div div align center h3 h3 div p strong a href https venturebeat com 2021 07 20 untether ai nabs 125m for ai acceleration chips untether ai nabs 125m for ai acceleration chips a strong p blockquote p untether ai a startup developing custom built chips for ai inferencing workloads today announced it has raised 125 million from tracker capital management and intel capital the round which was oversubscribed and included participation from canada pension plan investment board and radical ventures will be used to support customer expansion p blockquote p a name grai a p div align center a href https www graimatterlabs ai img src https github com basicmi deep learning processor list raw master resource grai logo png height 40 a div div align center h3 h3 div p strong a href https venturebeat com 2019 09 18 grai matter labs reveals neuronflow technology and announces graiflow sdk grai matter labs reveals neuronflow technology and announces graiflow sdk a strong p blockquote p grai matter labs aka gml a neuromorphic computing pioneer today revealed neuronflow a new programmable processor technology and announced an early access program to its graiflow software development kit p blockquote p a name rain a p div align center a href http rain neuromorphics com img src https github com basicmi deep learning processor list raw master resource rain logo png height 40 a div div align center h3 h3 div p strong a href https www crunchbase com organization rain neuromorphics rain neuromorphics on crunchbase a strong p blockquote p we build artificial intelligence processors inspired by the brain our mission is to enable brain scale intelligence p blockquote p a name abr a p div align center a href https appliedbrainresearch com img src https github com basicmi deep learning processor list raw master resource abr logo png height 40 a div div align center h3 h3 div p strong a href https www crunchbase com organization applied brain research applied brain research on crunchbase a strong p blockquote p abr makes the world s most advanced neuromoprhic compiler runtime and libraries for the emerging space of neuromorphic computing p blockquote p a name xmos a p div align center a href https www xmos com img src https github com basicmi deep learning processor list raw master resource xmos logo png height 40 a div div align center h3 h3 div p strong a href https www eetimes com xmos adapts xcore into aiot crossover processor xmos adapts xcore into aiot crossover processor a strong p blockquote p ee times exclusive the new chip targets ai powered voice interfaces in iot devices the most important ai workload at the endpoint p blockquote p a href https venturebeat com 2020 02 12 xmos unveils xcore ai a powerful chip designed for ai processing at the edge xmos unveils xcore ai a powerful chip designed for ai processing at the edge a p blockquote p the latest xcore ai is a crossover chip designed to deliver high performance ai digital signal processing control and input output in a single device with prices from 1 p blockquote p a name dinoplusai a p div align center a href http dinoplus ai img src https github com basicmi deep learning processor list raw master resource dinoplusai logo png height 60 a div div align center h3 h3 div blockquote p we design and produce ai processors and the software to run them in data centers our unique approach optimizes for inference with the focus on performance power efficiency and ease of use and at the same time our approach enables cost effective training p blockquote p a name furiosa a p div align center a href https www furiosa ai img src https github com basicmi deep learning processor list raw master resource furiosa logo png height 60 a div div align center h3 h3 div blockquote p we build high performance ai inference coprocessors that can be seamlessly integrated into various computing platforms including data centers servers desktops automobiles and robots p blockquote p a name corerain a p div align center a href http www corerain com en img src https github com basicmi deep learning processor list raw master resource corerain logo png height 60 a div div align center h3 h3 div blockquote p corerain provides ultra high performance ai acceleration chips and the world s first streaming engine based ai development platform p blockquote p a name perceive a p div align center a href https perceive io img src https github com basicmi deep learning processor list raw master resource perceive logo png height 60 a div div align center h3 h3 div p a href https venturebeat com 2020 03 31 perceive emerges from stealth with ergo edge ai chip perceive emerges from stealth with ergo edge ai chip a p blockquote p on device computing solutions startup perceive emerged from stealth today with its first product the ergo edge processor for ai inference ceo steve teig claims the chip which is designed for consumer devices like security cameras connected appliances and mobile phones delivers breakthrough accuracy and performance in its class p blockquote p a name simplemachines a p div align center a href https www simplemachines ai img src https github com basicmi deep learning processor list raw master resource simplemachines logo png height 60 a div div align center h3 h3 div p a href https www design reuse com news 49012 simplemachines ai chip tsmc 16nm html simplemachines inc debuts first of its kind high performance chip a p blockquote p as traditional chip makers struggle to embrace the challenges presented by the rapidly evolving ai software landscape a san jose startup has announced it has working silicon and a whole new future proof chip paradigm to address these issues the simplemachines inc smi team which includes leading research scientists and industry heavyweights formerly of qualcomm intel and sun microsystems has created a first of its kind easily programmable high performance chip that will accelerate a wide variety of ai and machine learning applications p blockquote p a name neureality a p div align center a href https www neureality ai img src https github com basicmi deep learning processor list raw master resource neureality logo png height 60 a div div align center h3 h3 div p strong a href https techcrunch com 2022 12 06 neureality ai accelerator chips startup raises 35m neureality lands 35m to bring ai accelerator chips to market a strong p blockquote p neureality a startup developing ai inferencing accelerator chips has raised 35 million in new venture capital p blockquote p a href https www electronicsmedia info 2021 05 06 neureality unveiled nr1 p a novel ai centric inference platform neureality unveiled nr1 p a novel ai centric inference platform a p blockquote p neureality has unveiled nr1 p a novel ai centric inference platform neureality has already started demonstrating its ai centric platform to customers and partners neureality has redefined today s outdated ai system architecture by developing an ai centric inference platform based on a new type of system on chip soc p blockquote p a href https techcrunch com 2021 02 10 neureality raises 8m for its novel ai inferencing platform neureality raises 8m for its novel ai inferencing platform a p blockquote p neureality an israeli ai hardware startup that is working on a novel approach to improving ai inferencing platforms by doing away with the current cpu centric model is coming out of stealth today and announcing an 8 million seed round p blockquote p a name analoginference a p div align center a href https www analog inference com img src https github com basicmi deep learning processor list raw master resource analoginference logo png height 60 a div div align center h3 h3 div p strong a href https www eenewsanalog com news analog inference startup raises 106 million analog inference startup raises 10 6 million a strong p blockquote p the company is backed by khosla ventures and is developing its first generation of products for ai computing at the edge the company raised 4 5 million shortly after its formation in march 2018 so the latest tranche brings the total raised to date to 15 1 million p blockquote p a name quadric a p div align center a href https www quadric io img src https github com basicmi deep learning processor list raw master resource quatric logo png height 60 a div div align center h3 h3 div p strong a href https www hpcwire com off the wire quadric announces unified silicon and software platform optimized for on device ai quadric announces unified silicon and software platform optimized for on device ai a strong p blockquote p burlingame calif june 22 2021 quadric quadric io an innovator in high performance edge processing has introduced a unified silicon and software platform that unlocks the power of on device ai p blockquote p a name edgeq a p div align center a href https edgeq io img src https github com basicmi deep learning processor list raw master resource edgeq logo png height 60 a div div align center h3 h3 div p strong a href https techcrunch com 2021 01 26 edgeq reveals more details behind its next gen 5g ai chip edgeq reveals more details behind its next gen 5g ai chip a strong p blockquote p 5g is the current revolution in wireless technology and every chip company old and new is trying to burrow their way into this ultra competitive but extremely lucrative market one of the most interesting new players in the space is edgeq a startup with a strong technical pedigree via qualcomm that we covered last year after it raised a nearly 40 million series a p blockquote p a name innatera a p div align center a href http www innatera com img src https github com basicmi deep learning processor list raw master resource innatera logo png height 60 a div div align center h3 h3 div p strong a href https www eetimes com innatera unveils neuromorphic ai chip to accelerate spiking networks innatera unveils neuromorphic ai chip to accelerate spiking networks a strong p blockquote p innatera the dutch startup making neuromorphic ai accelerators for spiking neural networks has produced its first chips gauged their performance and revealed details of their architecture p blockquote p a name ceremorphic a p div align center a href https ceremorphic com img src https github com basicmi deep learning processor list raw master resource ceremorphic logo png height 60 a div div align center h3 h3 div p strong a href https www eetimes com redpine founder launches ai processor startup redpine founder launches ai processor startup a strong p blockquote p ceremorphic an ai chip startup emerging from stealth mode this week is readying a heterogeneous ai processor aimed at model training in data centers automotive high performance computing robotics and other emerging applications p blockquote p a name aspinity a p div align center a href https www aspinity com img src https github com basicmi deep learning processor list raw master resource aspinity logo png height 60 a div div align center h3 h3 div p strong a href https embeddedcomputing com technology analog and power analog semicundoctors sensors aspinity analog ml chip allows battery powered always on aspinity analog ml chip allows battery powered always on a strong p blockquote p machine learning ml is all about massive amounts of processing dsp etc right maybe not according to the team at aspinity the company continues to push ahead on the analog front the latest member of the company s analogml family the aml100 operates completely in the analog domain as a result it can reduce always on system power by 95 for the record we had to walk through this a couple of times before i believed them p blockquote p a name teramem a p div align center a href https www tetramem com img src https github com basicmi deep learning processor list raw master resource teramem logo png height 60 a div div align center h3 h3 div p strong a href https www tetramem com posts tetramem technology debut at linley tetramem enjoyed an exciting public debut of our analog in memory compute technology at the linley spring 2022 processor conference a strong p blockquote p p blockquote p a name d matrix a p div align center a href https www d matrix ai img src https github com basicmi deep learning processor list raw master resource d matrix logo png height 60 a div div align center h3 h3 div p strong a href www reuters com technology ai chip startup d matrix raises 110 mln with backing microsoft 2023 09 06 exclusive ai chip startup d matrix raises 110 million with backing from microsoft a strong p blockquote p sept 6 reuters silicon valley based artificial intelligence chip startup d matrix has raised 110 million from investors that include microsoft corp msft o at a time when many chip companies are struggling to raise cash p blockquote p a href https www forbes com sites karlfreund 2022 06 21 d matrix ai chip promises efficient transformer processing d matrix ai chip promises efficient transformer processing a p blockquote p the startup combines digital in memory compute and chiplet implementations for data center grade inference p blockquote div align center h3 h3 div p a name aichipcompilers a p div align center h2 ai chip compilers h2 div p hr 1 a href https github com pytorch glow pytorch glow a br 2 a href https tvm ai tvm end to end deep learning compiler stack a br 3 a href https www tensorflow org xla google tensorflow xla a br 4 a href https developer nvidia com tensorrt nvidia tensorrt a br 5 a href https github com plaidml plaidml plaidml a br 6 a href https github com nervanasystems ngraph ngraph a br 7 a href https github com tiramisu compiler tiramisu mit tiramisu compiler a br 8 a href https onnc ai onnc open neural network compiler a br 9 a href https mlir llvm org mlir multi level intermediate representation a br 10 a href http tensor compiler org the tensor algebra compiler taco a br 11 a href https facebookresearch github io tensorcomprehensions tensor comprehensions a br 12 a href https www polymagelabs com polymage labs a br 13 a href https octoml ai octoml a br 14 a href https www modular com modular ai a br div align center h3 h3 div p a name aichipbenchmarks a p div align center h2 ai chip benchmarks h2 div p hr 1 a href https dawn cs stanford edu benchmark index html dawnbench an end to end deep learning benchmark and competition image classification imagenet a br 2 a href https github com rdadolf fathom fathom reference workloads for modern deep learning methods a br 3 a href https mlperf org mlperf a broad ml benchmark suite for measuring performance of ml software frameworks ml hardware accelerators and ml cloud platforms a strong you can find latest mlperf results training 2 1 hpc 2 0 inference tiny 1 0 a href https mlcommons org en news mlperf training 4q2022 here a strong br strong you can find mlperf inference results v2 1 a href https mlcommons org en news mlperf inference v21 here a strong br strong you can find mlperf training results v1 0 a href https mlcommons org en news mlperf training 2q2022 here a strong br 4 a href https aimatrix ai en us index html ai matrix a br 5 a href http ai benchmark com index html ai benchmark a br 6 a href https github com aiiabenchmark aiia dnn benchmark aiiabenchmark a br 7 a href https www eembc org mlmark eembc mlmark benchmark a br div align center h3 h3 div p a name reference a p div align center h2 reference h2 div p hr div align center h3 h3 div 1 a href https meanderful blogspot jp 2017 06 fpgas and ai processors dnn and cnn for html fpgas and ai processors dnn and cnn for all a br 2 a href http www nanalyze com 2017 05 12 ai hardware startups new ai chips 12 ai hardware startups building new ai chips a br 3 a href http eyeriss mit edu tutorial html tutorial on hardware architectures for deep neural networks a br 4 strong a href https nicsefc ee tsinghua edu cn projects neural network accelerator neural network accelerator comparison a strong br 5 white paper on ai chip technologies 2018 you can download it from a href https cloud tsinghua edu cn f 9aa0a4f0a5684cc48495 dl 1 here a or a href https drive google com open id 1iedm0bpjvwl5mnsesrs92ecmoszg5vcm google drive a br 5 strong what we talk about when we talk about ai chip a href https mp weixin qq com s sbx5yz5d3gxalcl15do6oq 1 a a href https mp weixin qq com s zvgdgkpimirlfuew0ffoeg 2 a a href https mp weixin qq com s ckhs5yblcmur4h2bwubicw 3 a a href https mp weixin qq com s hfnhhawwytfrusd3hlmblw 4 a strong br 6 strong a href https birenresearch github io aichip paper list ai chip paper list a strong br 7 strong a href https khairy2011 medium com tpu vs gpu vs cerebras vs graphcore a fair comparison between ml hardware 3f5a19d89e38 tpu vs gpu vs cerebras vs graphcore a fair comparison between ml hardware a strong br div align center a href http www reliablecounter com target blank img src http www reliablecounter com count php page https basicmi github io deep learning processor list digit style plain 3 reloads 1 alt laptop title laptop border 0 a div | chip ai-chips machine-learning deep-learning processor | ai |
copykat | copykat plagiarism detection application using natural language processing this project is stricly for academic purposes artists of creation alphabetically niranjan singh subhodip banerjee | ai |
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moko-parcelize | moko parcelize img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko parcelize https repo1 maven org maven2 dev icerock moko parcelize kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name parcelize moko parcelize this is a kotlin multiplatform library that supports parcelize in common code table of contents features features requirements requirements installation installation usage usage samples samples set up locally setup locally contributing contributing license license features parcelize in common code specially for android target all kotlin multiplatform targets support requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko parcelize 0 9 0 enable kotlin parcelize https developer android com kotlin parcelize groovy apply plugin kotlin parcelize usage parcelize mark common code classes with the annotation parcelize like in the android code for automatically generated parcelable implementation kotlin parcelize data class user val firstname string val lastname string parcelable samples please see more examples in the sample directory sample set up locally the parcelize directory parcelize contains the parcelize library the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps for publish to mavenlocal repository run gradlew publishtomavenlocal dis main host true contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2019 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | parcelable android ios kotlin kotlin-native kotlin-multiplatform moko | front_end |
practical-front-end | visual code extensions prettier code formatter https marketplace visualstudio com items itemname esbenp prettier vscode blockman highlight nested code blocks https marketplace visualstudio com items itemname leodevbro blockman todo tree https marketplace visualstudio com items itemname gruntfuggly todo tree vscode icons https marketplace visualstudio com items itemname robertohuertasm vscode icons microsoft edge chromium tools for vscode https marketplace visualstudio com items itemname ms edgedevtools vscode edge devtools debugger for microsoft edge https marketplace visualstudio com items itemname msjsdiag debugger for edge debugger for chrome https marketplace visualstudio com items itemname msjsdiag debugger for chrome date libraries moment https github com moment moment date fns https github com date fns date fns install node js on ubuntu shell https linuxize com post how to install node js on ubuntu 22 04 curl sl https deb nodesource com setup 18 x sudo e bash sudo apt install nodejs node v npm v uninstall node js on ubuntu shell https bytexd com how to uninstall nodejs in ubuntu sudo apt get remove nodejs node v npm v | html css javascript npm babel gulp webpack eslint angular reactjs vuejs | front_end |
Machine-Learning-for-Cybersecurity-Cookbook | machine learning for cybersecurity cookbook a href https www packtpub com security machine learning for cybersecurity cookbook utm source github utm medium repository utm campaign 9781789614671 img src https www packtpub com media catalog product cache e4d64343b1bc593f1c5348fe05efa4a6 9 7 9781789614671 original jpeg alt machine learning for cybersecurity cookbook height 256px align right a this is the code repository for machine learning for cybersecurity cookbook https www packtpub com security machine learning for cybersecurity cookbook utm source github utm medium repository utm campaign 9781789614671 published by packt implement smart ai systems for preventing cyber attacks and detecting threats and network anomalies what is this book about organizations today face a major threat in terms of cybersecurity from malicious urls to credential reuse and having robust security systems can make all the difference with this book you ll learn how to use python libraries such as tensorflow and scikit learn to implement the latest artificial intelligence ai techniques and handle challenges faced by cybersecurity researchers this book covers the following exciting features learn how to build malware classifiers to detect suspicious activities apply ml to generate custom malware to pentest your security use ml algorithms with complex datasets to implement cybersecurity concepts create neural networks to identify fake videos and images secure your organization from one of the most popular threats insider threats defend against zero day threats by constructing an anomaly detection system detect web vulnerabilities effectively by combining metasploit and ml understand how to train a model without exposing the training data if you feel this book is for you get your copy https www amazon com dp 1789614678 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 from sklearn model selection import train test split import pandas as pd following is what you need for this book if you re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and ai you ll find this book useful familiarity with cybersecurity concepts and knowledge of python programming is essential 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 8 software and hardware list chapter software required os required 1 python environment version depends on recipe windows mac os x and linux any 2 cuckoo sandbox latest windows mac os x and linux any 3 upx packer 3 95 windows mac os x and linux any 5 kali linux 2019 3 windows mac os x and linux any 6 wireshark 3 0 6 windows mac os x and linux any 7 octave latest windows mac os x and linux any appendix virtualbox latest windows mac os x and linux any 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 9781789614671 colorimages pdf related products hands on machine learning for cybersecurity packt https www packtpub com in big data and business intelligence hands machine learning cybersecurity utm source github utm medium repository utm campaign 9781788992282 amazon https www amazon com dp 1788992288 hands on artificial intelligence for cybersecurity packt https www packtpub com in data hands on artificial intelligence for cybersecurity utm source github utm medium repository utm campaign 9781789804027 amazon https www amazon com dp 1789804027 get to know the author emmanuel tsukerman graduated from stanford university and obtained his ph d from uc berkeley in 2017 dr tsukerman s anti ransomware product was listed in the top 10 ransomware products of 2018 by pc magazine in 2018 he designed an ml based instant verdict malware detection system for palo alto networks wildfire service of over 30 000 customers in 2019 dr tsukerman launched the first cybersecurity data science course suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781789614671 https packt link free ebook 9781789614671 a p | ai |
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paste | h1 align center paste h1 p align center paste is a design system used to build accessible consistent and high quality customer experiences at twilio paste is open source and contributions are welcome p p align center a href https github com twilio labs github blob master code of conduct md img alt code of conduct src https img shields io badge f0 9f 92 96 code 20of 20conduct blueviolet svg style flat square a a href http makeapullrequest com img src https img shields io badge prs welcome brightgreen svg style flat square alt prs welcome a p hr usage for usage guidelines please see our documentation website https paste twilio design developing on paste getting started clone the repo then run the following commands from within the repo s folder yarn build builds all of paste then you can run a local storybook instance with yarn start storybook for more commands please reference the package json file https github com twilio labs paste blob main package json contributing before contributing please make sure that you read our contributing guidelines https github com twilio labs paste blob main contributing md and agree with our code of conduct https github com twilio labs github blob main code of conduct md changesets each change to a package must come with a changeset https github com atlassian changesets you should have an individual changeset for each package you have touched to create a changeset you can run yarn changeset follow the prompts shown to you in your terminal instructions on how to use the cli tool can be found here https github com atlassian changesets blob master packages cli readme md but all you should need to do is generate a changeset and commit it with your pull request hosted storybook storybook https main 5e53448165911c0022e68c74 chromatic com we use chromatic to host the storybook instance dependencies paste is a monorepo and has some specific requirements in how it handles dependencies dev deps each package can have dev dep requirements such as typescript as this is a monorepo using yarn workspaces there is no requirement to list these as package dev deps all dev deps are hoisted to the monorepo root declare them at the root package json file peer deps a special note about peer deps peer dependencies should be hoisted up the dependency tree if package a lists package b as a peer dep and package b lists package c as a peer dep package c must also be listed as a peer dep for package a all the way down the tree a real example might be a button button may only have a peer dependency on box but box has a peer dependency on theme design tokens and style props these child peer deps need to be hoisted to the button package as such button deps should look like name button peerdependencies twilio paste box 0 0 1 twilio paste theme 0 0 1 twilio paste design tokens 0 0 1 twilio paste style props 0 0 1 internal deps when a package has a peer dependency on another internal package in the mono repo it must also be listed as a dev dependency for compilation example name button peerdependencies react 16 8 6 react dom 16 8 6 twilio paste box 0 0 1 twilio paste theme 0 0 1 twilio paste design tokens 0 0 1 twilio paste style props 0 0 1 devdependencies twilio paste box 0 0 1 twilio paste theme 0 0 1 twilio paste design tokens 0 0 1 twilio paste style props 0 0 1 checking and fixing peer deps to ensure peer dependencies are listed correctly a check is run on the monorepo on every build bash yarn packages check if missing deps are detected you will be informed of which packages are missing what deps to fix missing peer deps run the fix command bash yarn packages fix releasing releasing paste happens via ci cd using changesets https github com atlassian changesets license mit license | design-system twilio ui-components ux-patterns | os |
phoenix-rtos-build | phoenix rtos build phoenix rtos building scripts | os |
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CDAC-DESD | cdac desd embedded system design modules | os |
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IotShark | cs219 f19 final project iotshark this is our final project for cs 219 for the fall 2019 quarter iotshark is a iot monitoring service that allows users to monitor their iot devices for trends in data sent received ordinarily setting up a man in the middle attack with proper configurations can take up quite a bit of time and may seem dauntingly impossible for those with little to no experience in computer security or even computer science iotshark aims to provide a nearly fully automated solution for a user to monitor their iot devices by simply running a single script the user merely has to select which device they wish to monitor and this program takes care of the rest of the heavy work by starting the arp poisoning setting up the packet forwarding and the man in the middle packet sniffer it also has an easy to understand and interactive web ui where a user can filter the packets based on the ports types and timestamps to get a broader understanding of how much and when things are being transmitted we also aim to classify certain kinds of data such as heartbeat messages data transfers and anomalies though the last one will likely be demonstrated on the un encrypted rpi test since it is difficult to do anomaly detection without huge amounts of data and we would require many devices and individuals to gather that much data how to run please note that the following instructions have only been verified on macos if you use linux or windows wsl your mileage may vary and it might not work 1 install the required libraries pip3 install r requirements txt 2 set up the ip forwarding sudo sysctl net inet ip forwarding 1 3 run the app sudo python3 iotshark py the main script create a python virtual envionment and install dependency packages sh virtualenv python which python3 venv source venv bin activate pip install r requirements txt make sure packet forwarding is enabled on your local machine this is necessary for man in the middle attack to work on macos this can be done with sh sudo sysctl net inet ip forwarding 1 run the main program iotshark py see that script for accepted options currently this program performs the following 4 actions 1 scan for all hosts either in the given subnet by the s option or a set of common residential subnets 2 discover the hardware vendor and os of each host 3 perform arp poisoning between the selected host and gateway router 4 output graphs of past captured data by the f option followed by relative path to csv file after arp poisoning is running you can examine traffic from the target device by wireshark with a display filter like ip src 192 168 0 215 or ip dst 192 168 0 215 and tcp port 443 data file format the captured data is stored in a csv file with the following format timestamp incoming bytes outgoing bytes srcport dstport transfer protocol connection protocol srcip dstip csv 123123213 0 240 36 80 65124 http udp 192 168 0 215 104 24 4 5 123123240 300 0 800 443 65125 https tcp 104 24 4 5 192 168 0 215 using the tool to sniff iot devices for example here is a long string that we can say to alexa echo dot google home while sniffing their traffic pay attention if the device is transmitting data before the wake word it is a dark and stormy night my friends and i just came back from the yosemite national park where the quick brown fox jumps over the lazy dog next week is thanksgiving black friday in 2019 is coming as well it s a good time to do something exciting such as taking a computer security class or a programming language class at ucla by the way the first airbus a380 jumbo jet is retiring we like flying in that plane wake word what is the weather like in los angeles on thanksgiving anyways we have boeing 787 dreamliners for cross continental flights the web and mobile system class with ravi is amazing we should upgrade the commercial laundry machine during the black friday sale the bright and sunny weather is coming back and a trip to joshua tree national park awaits well i just saw a slow cat crashed into a new android robot there are some other robots made by apple and amazon too | iot flask-application scapy pyshark python3 security-audit | server |
livewires | livewires getting started view livewires io guide http livewires io guide for setup instructions we ll post these up better momentarily | front_end |
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xamarin-mobile-app-dev | apress source code this repository accompanies xamarin mobile application development http www apress com 9781484202159 by daniel hermes apress 2015 cover image 9781484202159 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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control-logic-generation-prompts | control logic generation prompts this repository contains a collection of natural language prompts to generate control logic in iec 61131 3 https en wikipedia org wiki iec 61131 3 structured text https en wikipedia org wiki structured text using large language models https en wikipedia org wiki large language model llm the collection is structured in categories standard algorithms mathematical functions plc programming tasks process control sequential control interlocks diagnostics communication advanced process control various engineering inputs programmer support p align center img src st code jpg width 400 p the complete list of prompts prompts readme md contains their exact formulation the prompts are meant as initial examples to test the capabilities and limitations of llms in the context of control programming some of them are inspired by real engineering projects but they are abstracted to be generally usable the range of heterogeneous categories is intended to cover different aspects of control programming the prompts may be refined and enhanced to improve the quality of the generated answers domain specific prompt engineering patterns could be derived which could then inform control engineers in formulating specific prompts for a task at hand p align center img src screenshot1 jpg width 600 p for testing the capabilities of llms to generate source code in iec 61131 3 structured text notation we have fed the prompts to chatgpt https chat openai com chat with the gpt 4 https openai com research gpt 4 llm and saved prompts the generated answers an excel workbook chatgpt chatgpt prompts plc programming xlsm is available to filter and sort the results an exact replication of these answers is perhaps impossible due to the non deterministic nature of llms still it should be possible to produce at least similarly useful answers with the same prompts | ai |
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csc3520-fall2022 | csc3520 fall2022 course materials for the fall 2022 semester of csc 3520 back end web mobile app development | front_end |
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altschool-assignment | altschool assignment this is my first assignment in the cloud engineering bootcamp at altschool below is a step by step breakdown of the assingment task with attached picture illustrations of the commands on my linux vm first let us create a user called felix altschool with an expiration date of two weeks createuserwithtwoweekslifespan create user with two weeks lifespan 1 png let us confirm that the user has been created by running the following command cat etc passwd grep felix altschool confirm user creation confirm user is created png now let us prompt the user to provide password on sign in prompt user for password ask user for password on sign in png a quick rundown of everything we have done so far we have created a user felix altschool which has an expiration of two weeks we have also added a prompt for password to this user now let us confirm that all these features were applied correctly to this user confirm user expiry confirm account expiry png as shown in the image above the account expires on 1st september 2023 which is exactly two weeks from the day it was created the next step in the process we are going to create a group called altschool and then add the user to this group as shown in the image below in the first line we created the group altschool in the second command we added the user felix altschool to the group altschool this was confirmed in the third command which listed what groups felix altschool belonged group creation and modification group creation png the next step in our task is to restrict the group altschool to be able to only run cat command on etc we will achieve this by editing the sudoers file by running the following command sudo visudo this will open the sudoers file in vim and allow us to make the necessary changes as shown in the image below edit sudoers file edit sudoers1 png finally the last task in this series requires us to create a new user and ensure that this user does not have a home directory first let us create the user felix cloud engineer and confirm that it has the home directory before removing same create new user new user png in the first command above we created a new user and in the second command we checked to confirm that the user has a home directory now let us go ahead and remove the home directory for the user felix cloud engineer by running the following command sudo usermod d felix cloud engineer edit new user no home user png as seen above after running the first command and checking the user felix cloud engineer again the home directory is no longer available | cloud |
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Embedded-Systems-Practice-ELE323P | embedded systems practice ele323p this repository is a collection of my codes from the embedded systems practice course at the indian institute of information technology design and manufacturing iiitdm kancheepuram | os |
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deep-natural-language-processing | deep natural language processing natural language processing nlp is a field of computer science that studies how computers and humans interact in the 1950s alan turing published an article that proposed a measure of intelligence now called the turing test more modern techniques such as deep learning have produced results in the fields of language modeling parsing and natural language tasks this repository aims to to cover both traditional and deep learning based nlp tasks such as speech recognition trigger word detection question answering systems as well as more recent ones such as transfer learning techniques and unsupervised nlp implemented in tensorflow and pytorch | tensorflow deep-learning natural-language-tasks nlp chatbot image-captioning image-classification language-modeling machine-translation memory-networks music-generation question-answering recurrent-neural-networks long-short-term-memory-models gated-recurrent-units transformer-architecture attention-mechanism seq2seq-model text-summarization word-embeddings | ai |
icon-tools | icon tools bash script to process icons for mobile development use cases uses imagemagick https github com imagemagick imagemagick convert mogrify to manipulate the image input and cairosvg https github com kozea cairosvg to convert svg files to pdf and png example runs icon tools sh colorize file rgb 255 0 0 icon tools sh colorize file ff0000ff icon tools sh createicons file 48 icon tools sh createvectoricons file icon tools sh dominantcolor file icon tools sh resize file 24 icon tools sh square file transparent icon tools sh trim file dependencies the script uses basename dirname grep head mkdir mv rm sed sort convert mogrify and cairosvg homebrew macos brew install imagemagick build from source brew install cairo libffi python3 pip3 install cairosvg contributors big thanks to our contributors abdull https github com abdull | front_end |
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T-ex | t ex t ex is the open source fitness application that developed with kotlin everyday i motivate myself to complete it i try to push new commit everyday the backend side of the app is under development during the app under development i add back end side of the app in the name of backendt ex you can find it in my repository | server |
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Tensorflow-Computer-Vision-Tutorial | tutorials of computer vision cv using tensorflow in these tutorials we will learn to build several convolutional neural networks cnns developed recent years all methods mentioned below are working in progress later they will have their video and text tutorial in chinese visit python https morvanzhou github io for more visualize filters and convolutions visualization codes 000 visualization py cnns lenet codes 101 lenet py googlenet codes 102 googlenet py resnet codes 103 resnet py deepdream codes 104 deepdream py object detection wip generative adversarial networks wip visualization codes 000 visualization py a href codes 000 visualization py img class course image src a donation if this does help you please consider donating to support me for better tutorials any contribution is greatly appreciated div a href https www paypal com cgi bin webscr cmd donations amp business morvanzhou 40gmail 2ecom amp lc c2 amp item name morvanpython amp currency code aud amp bn pp 2ddonationsbf 3abtn donatecc lg 2egif 3anonhosted img style border radius 20px box shadow 0px 0px 10px 1px 888888 src https www paypalobjects com webstatic en us i btn png silver pill paypal 44px png alt paypal height auto a div div a href https www patreon com morvan img src https morvanzhou github io static img support patreon jpg alt patreon height 120 a div | computer-vision cnn resnet googlenet lenet deepdream | ai |
nft-deployment-amazon-managed-blockchain | deploy nft on ethereum using amazon managed blockchain this repository contains sample code to deploy erc 721 smart contracts on the ethereum blockchain network using amazon managed blockchain as a geth node it provides a reference architecture for deploying a contract minting nft tokens and querying token owners using serverless components the repo is structured as follows bash code of conduct md contributing md license readme md images serverless reference architecture architecture images arch800x400 png pre requisites 1 an aws account with a vpc and a public subnet ensure you have permissions to make objects in the s3 bucket public 2 aws sam cli install the aws sam cli 3 node js install node js 14 including the npm package management tool 4 docker install docker community edition deploy ethereum node and serverless components an aws serverless application model sam template is used to deploy the stack bash cd serverless sam build building codeuri users pravincv nft amb repo serverless lambdas nftmain runtime nodejs14 x metadata architecture x86 64 functions nftmain running nodejsnpmbuilder npmpack running nodejsnpmbuilder copynpmrc running nodejsnpmbuilder copysource running nodejsnpmbuilder npminstall running nodejsnpmbuilder cleanupnpmrc build succeeded on success completion of the build deploy the stack bash sam deploy guided capabilities capability named iam input the parameter values and confirm changes to deploy stack name nft stack aws region ap southeast 1 region where amazon managed blockchain is supported parameter pemail name example com email id that will receive amazon sns notifications parameter psubnetid subnet xxxxxxx public subnet where the aws fargate task will run parameter pnftbucketname nftmetadata bucket where nft metadata will be stored parameter pconfirmationblocks 50 number of confirmations on the blockchain to wait for parameter pclustername nftcluster aws ecs cluster name parameter pcontainername rinkeby name of container parameter prepositoryname nftrepository aws ecr where the docker images will be stored parameter piprangewhitelist ip address cidr that is allowed to invoke the api parameter pnetworkid n ethereum rinkeby default network is rinkeby can choose ropsten if desired parameter pinstancetype bc t3 large choose desired instance size parameter pavailabilityzone ap southeast 1a choose availability zone based on region confirm changes before deploy y n confirm changes y sam needs permission to be able to create roles to connect to the resources in your template allow sam cli iam role creation y n choose y save arguments to configuration file y n choose y sam configuration file samconfig toml default file to save the samconfig sam configuration environment default the following resources are created on deployment sam images sam800x400 png this creates the following resources nftmain aws lambda function that provides apis to deploy an erc 721 contract to the ethereum rinkeby test network mint a new token as well get the owner of a specified token minted the deploy api sends out an sns message on successful deployment of a contract invokefargatetask this lambda function is triggered by the aws sns message sent by the nftmain lambda on being triggered it invokes a task that awaits until the rinkeby blockchain has confirmed the deployment confirmationblocks passed as an environment variable number of times ethtxntopic amazon sns topic to which nftmain lambda published the transaction id on deployment of a contract ethconfirmationtopic amazon sns topic to which the task publishes a message on completing confirmationblocks number of confirmations on the rinkeby network cluster and taskdefinition cluster and task definition that provides the compute for the confirmation logic nftapi api created and exposed via the api gateway for users to invoke http post ecrrepository repository where the docker image for the task is stored to complete the serverless stack create a docker image and push to the ecrrepository by building tagging and pushing the image as below bash cd serverless 1 aws ecr get login password region region docker login username aws password stdin account id dkr ecr region amazonaws com 2 docker build t nftrepository 3 docker tag nftrepository latest account dkr ecr region amazonaws com nftrepository latest 4 docker push account dkr ecr region amazonaws com nftrepository latest to create a transaction on the ethereum rinkeby network a private public key pair is required generate the private key and upload to aws ssm parameter store as an encrypted string as ethsystemkey ensure the string excludes the first 2 characters 0x of the privatekey add some ethereum test tokens for the rinkeby network by entering the public key https faucets chain link rinkeby and requesting ethereum test tokens please note that for aws ssm paramter store might not be sufficient for some cases where wallets bear real funds ethereumkey images eth800x400 png deploying a test contract minting an nft and checking token ownership now we are ready to deploy an erc 721 smart contract to the network bash nftmain nftsamples build nft baseuri json contracts nftsample sol nft baseuri sol aws web3 http provider js deploy contract js get owner js index js mint nft js package lock json package json utils js txconfirmation index js the contract to be deployed is stored in the nftmain directory under nftsamples we ll be using curl x post to deploy and mint the nft obtain the api endpoint i e invokeurl from amazon api gateway apis stages on the aws console bash cd serverless test events serverless test events deploy json getowner json mint json edit the deploy json file to fill up the needed values bash cat deploy json requesttype deploy tokenname coolnft name of nft tokenticker symb symbol metadatafilename metadata json name of metadata file metadata metadata associated with nft description useful description image url of the image preferably an ipfs hash name coolnft curl x post https api execute api region amazonaws com nftapi h content type application json d deploy json the deploy command returns a transaction id hash and the contract address bash transaction id 0x6b2af81c34055b678e5a8ae401f508fcbf950341df7e55e7372e80f75faf8afc contractaddress 0x8a3ce65b266e8de37fe7d2e4911ca4578b5a7445 cat mint json requesttype mint contractaddress 0x8a3ce65b266e8de37fe7d2e4911ca4578b5a7445 address of the contract address of the deployed contract mintaddress 0x905b8699e611a5f1f74bf5cc3cca2acd175ec0c0 public key of any another wallet address curl x post https api execute api region amazonaws com nftapi h content type application json d mint json the response contains the transaction id ash bash mint tx hash 0x960080892462ea76a90a5a00fe88ebc5d85d0b84d75e781ca5aa14e5829a0a14 cat getowner json requesttype mint contractaddress 0x8a3ce65b266e8de37fe7d2e4911ca4578b5a7445 address of the contract address of the deployed contract tokenid 0 id of the token that was minted previously curl x post https api region amazonaws com nftapi h content type application json d getowner json the response provides the address of the owner for whom the token was minted bash owner address 0x905b8699e611a5f1f74bf5cc3cca2acd175ec0c0 security see contributing contributing md security issue notifications for more information license this library is licensed under the mit 0 license see the license file | nft ethereum amazon-managed-blockchain aws rinkeby web3 | blockchain |
doc-blocks | div align center img src media logo mark svg alt doc blocks logo h1 doc blocks h1 div a design system for doc blocks ui components built on design systems cli https github com intuit design systems cli philosophy this repo contains a collection of components that make writing rich component documentation with storybook addon docs a breeze usage to use a component from this repo you will first need to install the component into your project for an example we will try to use the doc blocks row component sh npm i doc blocks row or with yarn yarn add doc blocks row then to use the component in your code just import it js import row from doc blocks row if you don t want to install all of the different components you can install doc blocks packages doc blocks readme md contributing to create a new component sh yarn run create follow the prompts and you will have a new sub package for your component sh first link the package yarn then start developing cd components component name yarn run dev before submitting a pull request ensure that all of the following work 1 yarn build 2 yarn lint 3 yarn test how to use the scripts inside the package json there are a bunch of scripts that this repo uses to run the project in development and to build the project below you can read a description of scripts you should use while developing in this project yarn dev builds everything and starts a storybook with all components yarn test run jest over the test files yarn lint lint all files using eslint yarn clean remove all dependencies build files and generated files from the project yarn create create a new components withing the repo yarn create package create a new package withing the repo yarn size determine how your changes effect the size of all sub packages package level scripts every command works at both the monorepo and package level the means you can run some script from just the package component for a faster development experience yarn dev build any dependency in the monorepo the component depends on and start a storybook with just the component yarn test test just the component yarn lint lint just the component yarn clean clean the generated files for just the component yarn size determine how your changes effect the size of the component contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href http hipstersmoothie com img src https avatars3 githubusercontent com u 1192452 v 4 s 100 width 100px alt br sub b andrew lisowski b sub a br a href https github com intuit doc blocks commits author hipstersmoothie title code a a href https github com intuit doc blocks commits author hipstersmoothie title documentation a a href example hipstersmoothie title examples a a href infra hipstersmoothie title infrastructure hosting build tools etc a td td align center a href https github com kharrop img src https avatars0 githubusercontent com u 24794756 v 4 s 100 width 100px alt br sub b kelly harrop b sub a br a href https github com intuit doc blocks commits author kharrop title documentation a a href https github com intuit doc blocks commits author kharrop title code a td td align center a href https github com kendallgassner img src https avatars githubusercontent com u 15275462 v 4 s 100 width 100px alt br sub b kendall gassner b sub a br a href https github com intuit doc blocks commits author kendallgassner title code a td td align center a href https github com fattslug img src https avatars githubusercontent com u 18297343 v 4 s 100 width 100px alt br sub b sean powell b sub a br a href https github com intuit doc blocks commits author fattslug title documentation a a href example fattslug title examples a a href https github com intuit doc blocks commits author fattslug title tests a a href https github com intuit doc blocks commits author fattslug title code a td td align center a href https github com noworries img src https avatars githubusercontent com u 803064 v 4 s 100 width 100px alt br sub b josh harwood b sub a br a href https github com intuit doc blocks commits author noworries title code a td tr table markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | storybook hacktoberfest | os |
System-Design | all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 7 orange svg style flat square contributors all contributors badge end a href contributing md img alt contributions welcome src https img shields io badge contributions welcome brightgreen style for the badge labelcolor black logo github a a href license img alt github license mit src https img shields io github license crio bytes system design style for the badge labelcolor black logo github a a href https twitter com crio do img alt twitter follow src https img shields io twitter follow crio do style for the badge color 09f labelcolor black logo twitter label crio do a introduction systems design is the process of defining the architecture https en wikipedia org wiki systems architecture modules interfaces and data https en wikipedia org wiki data for a system https en wikipedia org wiki system to satisfy specified requirements https en wikipedia org wiki requirement systems design could be seen as the application of systems theory https en wikipedia org wiki systems theory to product development https en wikipedia org wiki product development there is some overlap with the disciplines of systems analysis https en wikipedia org wiki systems analysis systems architecture https en wikipedia org wiki systems architecture and systems engineering https en wikipedia org wiki systems engineering background if the broader topic of product development https en wikipedia org wiki product development blends the perspective of marketing design and manufacturing into a single approach to product development then design is the act of taking the marketing information and creating the design of the product to be manufactured systems design is therefore the process of defining and developing systems https en wikipedia org wiki system to satisfy specified requirements https en wikipedia org wiki requirement of the user the basic study of system design is the understanding of component parts and their subsequent interaction with one another microbyte showcase section starts div align center img src https raw githubusercontent com crio bytes demo repo master maintainer 20resources img micro bytes header png div hr microbyte showcase sub section starts details summary b traffic cop learn load balancer using haproxy b summary sites like google which bring in high volume traffic may inadvertently be faced with frequent server upgrades so that page speed and in turn usability doesn t suffer for their loyal customers often however simple hardware upgrades aren t enough to handle the vast traffic that some sites draw so how do google ensures that it won t burst into figurative flames as page visits skyrocket the concept of load balancing comes into picture details nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp yakshit ravindra jain jnana cetana https github com jnana cetana microbyte showcase sub section ends microbyte showcase sub section starts details summary b exploring content delivery network b summary the internet is a constantly changing mechanism and new forms of data and content are constantly being created soon after it was made commercially available the problem of pushing massive amounts of data to the end user as fast as possible had to be solved enter cdns details nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp yakshit ravindra jain jnana cetana https github com jnana cetana microbyte showcase sub section ends microbyte showcase sub section starts details summary b exploring caching b summary in this microbyte we will commence by a small case study of caching in real life so the laymen can relate to it learn lru algorithm through visual representation talk less show more methodology and explore the application of cache in computer systems like linux page cache and dns cache details nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp yakshit ravindra jain jnana cetana https github com jnana cetana microbyte showcase sub section ends microbyte showcase section ends contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href http ak shaw portfolio netlify app img src https avatars0 githubusercontent com u 51538194 v 4 width 100px alt br sub b ayush kumar shaw b sub a br a href https github com crio bytes system design commits author ak shaw title code a a href https github com crio bytes system design commits author ak shaw title documentation a a href eventorganizing ak shaw title event organizing a a href ideas ak shaw title ideas planning feedback a a href maintenance ak shaw title maintenance a a href https github com crio bytes system design pulls q is 3apr reviewed by 3aak shaw title reviewed pull requests a td td align center a href https crio do img src https avatars0 githubusercontent com u 51743602 v 4 width 100px alt br sub b crio do b sub a br a href eventorganizing criodo title event organizing a td td align center a href https github com jnana cetana img src https avatars1 githubusercontent com u 72009286 v 4 width 100px alt br sub b yakshit ravindra jain b sub a br a href https github com crio bytes system design commits author jnana cetana title code a a href content jnana cetana title content a a href https github com crio bytes system design commits author jnana cetana title documentation a a href ideas jnana cetana title ideas planning feedback a td td align center a href https github com amoghrajesh img src https avatars2 githubusercontent com u 35884252 v 4 width 100px alt br sub b amogh rajesh desai b sub a br a href https github com crio bytes system design pulls q is 3apr reviewed by 3aamoghrajesh title reviewed pull requests a a href eventorganizing amoghrajesh title event organizing a td td align center a href https kevinpaulose05 github io img src https avatars3 githubusercontent com u 64629493 v 4 width 100px alt br sub b kevin paulose b sub a br a href https github com crio bytes system design pulls q is 3apr reviewed by 3akevinpaulose05 title reviewed pull requests a a href eventorganizing kevinpaulose05 title event organizing a td td align center a href https www youtube com channel uc9edh5byrct2winiji5qyig img src https avatars2 githubusercontent com u 62458868 v 4 width 100px alt br sub b sudhanshu tiwari b sub a br a href https github com crio bytes system design pulls q is 3apr reviewed by 3asudhanshutiwari264 title reviewed pull requests a a href eventorganizing sudhanshutiwari264 title event organizing a td td align center a href https github com archithdwij img src https avatars1 githubusercontent com u 30730368 v 4 width 100px alt br sub b archithdwij b sub a br a href https github com crio bytes system design pulls q is 3apr reviewed by 3aarchithdwij title reviewed pull requests a a href eventorganizing archithdwij title event organizing a td tr table markdownlint enable prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | hacktoberfest system-design | os |
Awesome-Parameter-Efficient-Transfer-Learning | awesome parameter efficient transfer learning a collection of parameter efficient transfer learning papers focusing on computer vision and multimodal domains content why parameter efficient why parameter efficient keywords convention keywords convention papers papers prompt prompt adapter adapter unified unified others others contribution contribution contributors contributors contributing to this paper list contributing to this paper list acknowledgement acknowledgement why parameter efficient pre training then fully fine tuning is a long standing paradigm in deep learning however as pre trained models are scaling up e g gpt 3 175b params fully fine tuning them on various downstream tasks has a high risk of overfitting moreover in practice it would be costly to train and store a large model for each task to overcome the above issues researchers started to explore parameter efficient transfer learning which aims at adapting large scale pre trained model to various downstream tasks by modifying as less parameter as possible inspired by the great advances in nlp domain and the continuous trend of scaling up models scholars in computer vision and multimodal domains also join the research craze keywords convention we follow the general idea of promptpapers https github com thunlp promptpapers to label the papers https img shields io badge coop blue the abbreviation of the work https img shields io badge image classification green the main explored task of the work https img shields io badge other orange other important information of the work papers prompt learning to prompt for vision language models ijcv 2022 arxiv 2109 01134 https img shields io badge coop blue https img shields io badge image classification green https img shields io badge text prompt orange kaiyang zhou jingkang yang chen change loy ziwei liu paper https arxiv org abs 2109 01134 code https github com kaiyangzhou coop prompting visual language models for efficient video understanding eccv 2022 arxiv 2112 04478 https img shields io badge action recognition action localization text video retrieval green https img shields io badge text prompt orange chen ju tengda han kunhao zheng ya zhang weidi xie paper https arxiv org abs 2112 04478 code https github com ju chen efficient prompt domain adaptation via prompt learning arxiv arxiv 2202 06687 https img shields io badge dapl blue https img shields io badge domain adaption green https img shields io badge text prompt orange chunjiang ge rui huang mixue xie zihang lai shiji song shuang li gao huang paper https arxiv org abs 2202 06687 code conditional prompt learning for vision language models cvpr 2022 arxiv 2203 05557 https img shields io badge cocoop blue https img shields io badge image classification green https img shields io badge visual conditional text prompt orange kaiyang zhou jingkang yang chen change loy ziwei liu paper https arxiv org abs 2203 05557 code https github com kaiyangzhou coop visual prompt tuning eccv 2022 arxiv 2203 12119 https img shields io badge vpt blue https img shields io badge image classification green https img shields io badge token level prompt orange menglin jia luming tang bor chun chen claire cardie serge belongie bharath hariharan ser nam lim paper https arxiv org abs 2203 12119 code https github com kmnp vpt exploring visual prompts for adapting large scale models arxiv 2203 17274 https img shields io badge image classification green https img shields io badge pixel level prompt orange hyojin bahng ali jahanian swami sankaranarayanan phillip isola paper https arxiv org abs 2203 17274 code https github com hjbahng visual prompting pro tuning unified prompt tuning for vision tasks arxiv 2207 14381 https img shields io badge protuning blue https img shields io badge image classification green xing nie bolin ni jianlong chang gaomeng meng chunlei huo zhaoxiang zhang shiming xiang qi tian chunhong pan paper https arxiv org abs 2207 14381 code p2p tuning pre trained image models for point cloud analysis with point to pixel prompting arxiv 2208 02812 https img shields io badge p2p blue https img shields io badge pointcloud green https img shields io badge 2d to 3d orange ziyi wang xumin yu yongming rao jie zhou jiwen lu paper https arxiv org abs 2208 02812 code https github com wangzy22 p2p class aware visual prompt tuning for vision language pre trained model arxiv 2208 08340 https img shields io badge cavpt blue https img shields io badge image classification green https img shields io badge joint prompt orange yinghui xing qirui wu de cheng shizhou zhang guoqiang liang yanning zhang paper https arxiv org abs 2208 08340 code prompt tuning with soft context sharing for vision language models arxiv 2208 13474 https img shields io badge softcpt blue https img shields io badge image classification green kun ding ying wang pengzhang liu qiang yu haojian zhang shiming xiang chunhong pan paper https arxiv org abs 2208 13474 code language aware soft prompting for vision language foundation models arxiv 2210 01115 https img shields io badge lasp blue https img shields io badge image classification green https img shields io badge language aware prompt orange adrian bulat georgios tzimiropoulos paper https arxiv org abs 2210 01115 code prompt learning with optimal transport for vision language models arxiv 2210 01253 https img shields io badge image classification green guangyi chen weiran yao xiangchen song xinyue li yongming rao kun zhang paper https arxiv org abs 2210 01253 code maple multi modal prompt learning arxiv 2210 03117 https img shields io badge maple blue https img shields io badge image classification green https img shields io badge pixel level prompt orange muhammad uzair khattak hanoona rasheed muhammad maaz salman khan fahad shahbaz khan paper https arxiv org abs 2210 03117 code https github com muzairkhattak multimodal prompt learning unified vision and language prompt learning arxiv 2210 07225 https img shields io badge upt blue https img shields io badge image classification green https img shields io badge joint prompt orange yuhang zang wei li kaiyang zhou chen huang chen change loy paper https arxiv org abs 2210 07225 code https github com yuhangzang upt cpl counterfactual prompt learning for vision and language models arxiv 2210 10362 https img shields io badge cpl blue https img shields io badge image classification green xuehai he diji yang weixi feng tsu jui fu arjun akula varun jampani pradyumna narayana sugato basu william yang wang xin eric wang paper https arxiv org abs 2210 10362 code https github com yuhangzang upt understanding and improving visual prompting a label mapping perspective arxiv 2211 11635 https img shields io badge ilm vp blue https img shields io badge image classification green https img shields io badge pixel level prompt orange aochuan chen yuguang yao pin yu chen yihua zhang sijia liu paper https arxiv org abs 2211 11635 code https github com optml group ilm vp texts as images in prompt tuning for multi label image recognition arxiv 2211 12739 https img shields io badge tai dpt blue https img shields io badge multilabel image classification green zixian guo bowen dong zhilong ji jinfeng bai yiwen guo wangmeng zuo paper https arxiv org abs 2211 12739 code https github com guozix tai dpt vop text video co operative prompt tuning for cross modal retrieval arxiv 2211 12764 https img shields io badge vop blue https img shields io badge text video retrieval green https img shields io badge joint prompt orange siteng huang biao gong yulin pan jianwen jiang yiliang lv yuyuan li donglin wang paper https arxiv org abs 2211 12764 code https github com bighuang624 vop unleashing the power of visual prompting at the pixel level arxiv 2212 10556 https img shields io badge evp blue https img shields io badge image classification green https img shields io badge pixel level prompt orange junyang wu xianhang li chen wei huiyu wang alan yuille yuyin zhou cihang xie paper https arxiv org abs 2212 10556 code https github com ucsc vlaa evp self supervised convolutional visual prompts arxiv 2303 00198 https img shields io badge out of distribution datasets green https img shields io badge out of distribution orange yun yun tsai chengzhi mao yow kuan lin junfeng yang paper https arxiv org abs 2303 00198 code multimodal prompting with missing modalities for visual recognition cvpr 2023 arxiv 2303 03369 https img shields io badge image classification green https img shields io badge missing modalities orange yi lun lee yi hsuan tsai wei chen chiu chen yu lee paper https arxiv org abs 2303 03369 code https github com yilunlee missing aware prompts from visual prompt learning to zero shot transfer mapping is all you need arxiv 2303 05266 https img shields io badge semap blue https img shields io badge image classification green ziqing yang zeyang sha michael backes yang zhang paper https arxiv org abs 2303 05266 code diversity aware meta visual prompting cvpr 2023 arxiv 2303 08138 https img shields io badge damvp blue https img shields io badge image classification green qidong huang xiaoyi dong dongdong chen weiming zhang feifei wang gang hua nenghai yu paper https arxiv org abs 2303 08138 code https github com shikiw dam vp patch token aligned bayesian prompt learning for vision language models arxiv 2303 09100 https img shields io badge pbprompt blue https img shields io badge image classification green xinyang liu dongsheng wang miaoge li zhibin duan yishi xu bo chen mingyuan zhou paper https arxiv org abs 2303 09100 code lion implicit vision prompt tuning arxiv 2303 09992 https img shields io badge lion blue https img shields io badge image classification green haixin wang jianlong chang xiao luo jinan sun zhouchen lin qi tian paper https arxiv org abs 2303 09992 code fine grained regional prompt tuning for visual abductive reasoning arxiv 2303 10428 https img shields io badge rgp blue https img shields io badge visual abductive reasoning green hao zhang basura fernando paper https arxiv org abs 2303 10428 code visual prompt multi modal tracking cvpr 2023 arxiv 2303 10826 https img shields io badge vipt blue https img shields io badge tracking green jiawen zhu simiao lai xin chen dong wang huchuan lu paper https arxiv org abs 2303 10826 code explicit visual prompting for low level structure segmentations cvpr 2023 arxiv 2303 10883 https img shields io badge evp blue https img shields io badge low level structure segmentation green weihuang liu xi shen chi man pun xiaodong cun paper https arxiv org abs 2303 10826 code https github com nifangbaage explict visual prompt clip goes 3d leveraging prompt tuning for language grounded 3d recognition arxiv 2303 11313 https img shields io badge cg3d blue https img shields io badge 3d recognition green deepti hegde jeya maria jose valanarasu vishal m patel paper https arxiv org abs 2303 11313 code https github com deeptibhegde clip goes 3d comments this works idea is similar to our text4point https arxiv org abs 2301 07584 multi modal prompting for low shot temporal action localization arxiv 2303 11732 https img shields io badge temporal action localization green chen ju zeqian li peisen zhao ya zhang xiaopeng zhang qi tian yanfeng wang weidi xie paper https arxiv org abs 2303 11732 code highlight enrich the meaning of an action class by querying the large scale language model to give a detailed action description troika multi path cross modal traction for compositional zero shot learning arxiv 2303 15230 https img shields io badge troika blue https img shields io badge compositional learning green siteng huang biao gong yutong feng yiliang lv donglin wang paper https arxiv org abs 2303 15230 code llama adapter efficient fine tuning of language models with zero init attention arxiv 2303 16199 https img shields io badge llama adapter blue https img shields io badge chatbot green renrui zhang jiaming han aojun zhou xiangfei hu shilin yan pan lu hongsheng li peng gao yu qiao paper https arxiv org abs 2303 16199 code https github com zrrskywalker llama adapter highlight tuning the llama 7b params to an excellent chatbot with only 1 2m trainable parameters and 1 hour fine tuning probabilistic prompt learning for dense prediction arxiv 2304 00779 https img shields io badge dense prediction green hyeongjun kwon taeyong song somi jeong jin kim jinhyun jang kwanghoon sohn paper https arxiv org abs 2304 00779 zero shot generative model adaptation via image specific prompt learning arxiv 2304 03119 https img shields io badge ipl blue https img shields io badge image synthesis green jiayi guo chaofei wang you wu eric zhang kai wang xingqian xu shiji song humphrey shi gao huang paper https arxiv org abs 2304 03119 code https github com picsart ai research ipl zero shot generative model adaptation instance aware dynamic prompt tuning for pre trained point cloud models arxiv 2304 07221 https img shields io badge idpt blue https img shields io badge point cloud green yaohua zha jinpeng wang tao dai bin chen zhi wang shu tao xia paper https arxiv org abs 2304 07221 code https github com zyh16143998882 idpt pvp pre trained visual parameter efficient tuning arxiv 2304 13639 https img shields io badge pvp blue https img shields io badge image classification green zhao song ke yang naiyang guan junjie zhu peng qiao qingyong hu paper https arxiv org abs 2304 13639 code approximated prompt tuning for vision language pre trained models arxiv 2306 15706 https img shields io badge apt blue https img shields io badge vqa visual reasoning retrieval green qiong wu shubin huang yiyi zhou pingyang dai annan shu guannan jiang rongrong ji paper https arxiv org abs 2306 15706 code adapter vl adapter parameter efficient transfer learning for vision and language tasks cvpr 2022 arxiv 2112 06825 yi lin sung jaemin cho mohit bansal paper https arxiv org abs 2112 06825 code https github com ylsung vl adapter https img shields io badge vl adapter blue https img shields io badge image and video qa caption visual reasoning green https img shields io badge multitask learning orange adaptformer adapting vision transformers for scalable visual recognition neurips 2022 arxiv 2205 13535 https img shields io badge adaptformer blue https img shields io badge action recognition green https img shields io badge temporal modeling orange shoufa chen chongjian ge zhan tong jiangliu wang yibing song jue wang ping luo paper https arxiv org abs 2205 13535 code https github com shoufachen adaptformer zero shot video question answering via frozen bidirectional language models neurips 2022 arxiv 2206 08155 https img shields io badge frozenbilm blue https img shields io badge vqa green antoine yang antoine miech josef sivic ivan laptev cordelia schmid paper https arxiv org abs 2206 08155 code https github com antoyang frozenbilm st adapter parameter efficient image to video transfer learning neurips 2022 arxiv 2206 13559 https img shields io badge st adapter blue https img shields io badge action recognition green https img shields io badge temporal modeling orange junting pan ziyi lin xiatian zhu jing shao hongsheng li paper https arxiv org abs 2206 13559 code https github com linziyi96 st adapter convolutional bypasses are better vision transformer adapters arxiv 2207 07039 https img shields io badge convpass blue https img shields io badge image classification green https img shields io badge convolution inductive bias orange shibo jie zhi hong deng paper https arxiv org abs 2207 07039 code https github com jieshibo petl vit conv adapter exploring parameter efficient transfer learning for convnets arxiv 2208 07463 https img shields io badge conv adapter blue https img shields io badge image classification green https img shields io badge design for convnet orange hao chen ran tao han zhang yidong wang wei ye jindong wang guosheng hu marios savvides paper https arxiv org abs 2208 07463 code effective adaptation in multi task co training for unified autonomous driving neurips 2022 arxiv 2209 08953 https img shields io badge lv adapter blue https img shields io badge detection segmentation green xiwen liang yangxin wu jianhua han hang xu chunjing xu xiaodan liang paper https arxiv org abs 2209 08953 code polyhistor parameter efficient multi task adaptation for dense vision tasks neurips 2022 arxiv 2210 03265 https img shields io badge polyhistor blue https img shields io badge dense vision tasks green https img shields io badge multitask learning orange yen cheng liu chih yao ma junjiao tian zijian he zsolt kira paper https arxiv org abs 2210 03265 code svl adapter self supervised adapter for vision language pretrained models arxiv 2210 03794 https img shields io badge svl adapter blue https img shields io badge image classification green omiros pantazis gabriel brostow kate jones oisin mac aodha paper https arxiv org abs 2210 03794 code https github com omipan svl adapter cross modal adapter for text video retrieval arxiv 2211 09623 https img shields io badge cm adapter blue https img shields io badge text video retrieval green https img shields io badge crossmodal orange haojun jiang jianke zhang rui huang chunjiang ge zanlin ni jiwen lu jie zhou shiji song gao huang paper https arxiv org abs 2211 09623 code https github com leaplabthu cross modal adapter vision transformers are parameter efficient audio visual learners arxiv 2212 07983 https img shields io badge lavish blue https img shields io badge audio visual tasks green https img shields io badge crossmodal orange yan bo lin yi lin sung jie lei mohit bansal gedas bertasius paper https arxiv org abs 2212 07983 code https github com genjib lavish take away message pre trained vision transformer can deal with audio data by representing 1d raw audio signal as 2d audio image multimodal video adapter for parameter efficient video text retrieval arxiv 2301 07868 https img shields io badge mv adapter blue https img shields io badge text video retrieval green https img shields io badge crossmodal orange bowen zhang xiaojie jin weibo gong kai xu zhao zhang peng wang xiaohui shen jiashi feng paper https arxiv org abs 2301 07868 code aim adapting image models for efficient video action recognition iclr 2023 arxiv 2302 03024 https img shields io badge aim blue https img shields io badge action recognition green https img shields io badge image2video orange taojiannan yang yi zhu yusheng xie aston zhang chen chen mu li paper https arxiv org abs 2302 03024 code https github com taoyang1122 adapt image models offsite tuning transfer learning without full model arxiv 2302 04870 https img shields io badge offsite tuning blue https img shields io badge both vision and language tasks green https img shields io badge privacy orange guangxuan xiao ji lin song han paper https arxiv org abs 2302 04870 code https github com mit han lab offsite tuning uniadapter unified parameter efficient transfer learning for cross modal modeling arxiv 2302 06605 https img shields io badge uniadapter blue https img shields io badge text video retrieval text image retrieval videoqa vqa green https img shields io badge crossmodal orange haoyu lu mingyu ding yuqi huo guoxing yang zhiwu lu masayoshi tomizuka wei zhan paper https arxiv org abs 2302 06605 code https github com rerv uniadapter towards efficient visual adaption via structural re parameterization arxiv 2302 08106 https img shields io badge repadapter blue https img shields io badge image classification green https img shields io badge re parameterization orange gen luo minglang huang yiyi zhou xiaoshuai sun guannan jiang zhiyu wang rongrong ji paper https arxiv org abs 2302 08106 code https github com luogen1996 repadapter t2i adapter learning adapters to dig out more controllable ability for text to image diffusion models arxiv 2302 08453 https img shields io badge t2i adapter blue https img shields io badge image generation green https img shields io badge diffusion model orange chong mou xintao wang liangbin xie jian zhang zhongang qi ying shan xiaohu qie paper https arxiv org abs 2302 08453 code https github com tencentarc t2i adapter knn adapter efficient domain adaptation for black box language models arxiv 2302 10879 https img shields io badge knn adapter blue https img shields io badge domain adaptation green https img shields io badge black box language model orange yangsibo huang daogao liu zexuan zhong weijia shi yin tat lee paper https arxiv org abs 2302 10879 code side adapter network for open vocabulary semantic segmentation arxiv 2302 12242 https img shields io badge side adapter network blue https img shields io badge segmentation green https img shields io badge open vocabulary orange mengde xu zheng zhang fangyun wei han hu xiang bai paper https arxiv org abs 2302 12242 code dual path adaptation from image to video transformers arxiv 2303 09857 https img shields io badge dualpath blue https img shields io badge action recognition green jungin park jiyoung lee kwanghoon sohn paper https arxiv org abs 2303 09857 code https github com park jungin dualpath highlight modeling temporal information in a separate path contrastive alignment of vision to language through parameter efficient transfer learning iclr 2023 arxiv 2303 11866 https img shields io badge lilt blue https img shields io badge retrieval and classification green zaid khan yun fu paper https arxiv org abs 2303 11866 code https github com codezakh lilt highlight aligning an already trained vision and language model with adapter a closer look at parameter efficient tuning in diffusion models arxiv 2303 18181 https img shields io badge diffusion model green chendong xiang fan bao chongxuan li hang su jun zhu paper https arxiv org abs 2303 18181 sparseadapter an easy approach for improving the parameter efficiency of adapters emnlp 2022 arxiv 2210 04284 https img shields io badge sparseadapter blue https img shields io badge glue 20benchmark green https img shields io badge pretrained 20language 20model orange shwai he liang ding daize dong miao zhang dacheng tao paper https arxiv org pdf 2210 04284 pdf code https github com shwai he sparseadapter parameter efficient model adaptation for vision transformers aaai 2023 arxiv 2203 16329 https img shields io badge pevit blue https img shields io badge image classification green https img shields io badge kadaptation orange xuehai he chunyuan li pengchuan zhang jianwei yang xin eric wang paper https arxiv org abs 2203 16329 code https github com eric ai lab pevit taca upgrading your visual foundation model with task agnostic compatible adapter arxiv 2306 12642 https img shields io badge taca blue https img shields io badge video text retrieval video recognition visual question answering green binjie zhang yixiao ge xuyuan xu ying shan mike zheng shou paper https arxiv org abs 2306 12642 code https github com tencentarc taca unified towards a unified view of parameter efficient transfer learning iclr 2022 arxiv 2110 04366 https img shields io badge translation summarization language understanding text classification green https img shields io badge unified view orange junxian he chunting zhou xuezhe ma taylor berg kirkpatrick graham neubig paper https arxiv org abs 2110 04366 code https github com jxhe unify parameter efficient tuning neural prompt search arxiv 2206 04673 https img shields io badge noah blue https img shields io badge image classification green https img shields io badge unified search framework orange yuanhan zhang kaiyang zhou ziwei liu paper https arxiv org abs 2206 04673 code https github com zhangyuanhan ai noah autopeft automatic configuration search for parameter efficient fine tuning arxiv 2301 12132 https img shields io badge autopeft blue https img shields io badge text classification green https img shields io badge unified search framework orange han zhou xingchen wan ivan vuli anna korhonen paper https arxiv org abs 2301 12132 code https github com cambridgeltl autopeft rethinking efficient tuning methods from a unified perspective arxiv 2303 00690 https img shields io badge image classification green https img shields io badge unified view orange zeyinzi jiang chaojie mao ziyuan huang yiliang lv deli zhao jingren zhou paper https arxiv org abs 2303 00690 code others check out thunlp deltapapers https github com thunlp deltapapers if you are interested in the progress of nlp domain lst ladder side tuning for parameter and memory efficient transfer learning neurips 2022 arxiv 2206 06522 https img shields io badge lst blue https img shields io badge glue vqa visual reasoning image caption green https img shields io badge side tuning orange yi lin sung jaemin cho mohit bansal paper https arxiv org abs 2206 06522 code https github com ylsung ladder side tuning scaling shifting your features a new baseline for efficient model tuning neurips 2022 arxiv 2210 08823 https img shields io badge ssf blue https img shields io badge image classification green https img shields io badge feature scale shift orange dongze lian daquan zhou jiashi feng xinchao wang paper https arxiv org abs 2210 08823 code https github com dongzelian ssf fact factor tuning for lightweight adaptation on vision transformer aaai 2023 arxiv 2212 03145 https img shields io badge fact blue https img shields io badge image classification green https img shields io badge tensor decomposition orange shibo jie zhi hong deng paper https arxiv org abs 2212 03145 code https github com jieshibo petl vit important channel tuning openreview https img shields io badge ict blue https img shields io badge image classification green hengyuan zhao pichao wang yuyang zhao fan wang mike zheng shou paper https openreview net forum id ttmyoodb9hz code mixphm redundancy aware parameter efficient tuning for low resource visual question answering arxiv 2303 01239 https img shields io badge mixphm blue https img shields io badge vqa green jingjing jiang nanning zheng paper https arxiv org abs 2303 01239 code revisit parameter efficient transfer learning a two stage paradigm arxiv 2303 07910 https img shields io badge image classification green hengyuan zhao hao luo yuyang zhao pichao wang fan wang mike zheng shou paper https arxiv org abs 2303 07910 code contribution contributors copy paste in your readme md file a href https github com jianghaojun awesome parameter efficient transfer learning graphs contributors img src https contrib rocks image repo jianghaojun awesome parameter efficient transfer learning a contributing to this paper list here is the tutorial of contributing to others projects https docs github com en get started quickstart contributing to projects first think about which category the work should belong to second use the same format as the others to describe the work note that there should be an empty line between the title and the author s list and take care of the indentation then add keywords tags keywords convention add the pdf link of the paper if it is an arxiv publication we prefer abs format to pdf format acknowledgement the structure of this repository is following thunlp deltapapers https github com thunlp deltapapers which focuses on collecting awesome parameter efficient transfer learning papers in nature language processing domain check out their repository if you are interested in the progress of nlp domain | computer-vision deep-learning machine-learning multimodal-deep-learning parameter-efficient-learning parameter-efficient-tuning transfer-learning | ai |
hv-uikit-react | p align center a href https lumada design github io uikit master img src https user images githubusercontent com 14975353 229386613 8f17d06d 9530 4e77 a173 dcb7587a85ea png alt ui kit logo width 250 a p h4 align center react ui library for the next design system h4 br div align center license https img shields io badge license apache 202 blue svg react https img shields io badge react 17 18 blue svg node https img shields io badge node 16 18 brightgreen svg master nightly https github com lumada design hv uikit react workflows master 20nightly badge svg div br next ui kit is hitachi vantara open source react component library that gives you the foundation to build your application faster and consistently why use the ui kit ready to go start your project with over 50 high quality react components out of the box composable compose your application ui with reusable building blocks accessible ui kit follows wai aria standards for all components themeable use next design system or customize it to match your design needs check the project status https lumada design github io uikit master path docs overview project status docs so you can stay up to date on the project development available packages components status and supported versions installation the fastest way to setup a new next ui kit project is by using hv uikit cli https github com lumada design hv uikit cli follow the link for usage documentation sh npx hitachivantara hv uikit cli latest create if you re adding ui kit to an existing project please follow our get started https lumada design github io uikit master path docs overview get started docs guide for more information if you re migrating from an older ui kit version please follow the migration guides https lumada design github io uikit master path docs overview migration from v4 x docs documentation see our documentation site here https lumada design github io uikit master for full how to docs and guidelines team table tr td align center a href https github com zettca img src https avatars githubusercontent com u 638946 v 4 width 64px alt br sub b bruno henriques b sub a br td td align center a href https github com francisco guilherme img src https avatars githubusercontent com u 14975353 v 4 width 64px alt br sub b francisco guilherme b sub a br td td align center a href https github com hqfox img src https avatars githubusercontent com u 19229133 v 4 width 64px alt br sub b henrique raposo b sub a br td td align center a href https github com mesteves22 img src https avatars githubusercontent com u 43220251 v 4 width 64px alt br sub b m rcia esteves b sub a br td td align center a href https github com plagoa img src https avatars githubusercontent com u 7498785 v 4 width 64px alt br sub b paulo lago b sub a br td tr table contributing please check out our contribution guidelines contributing md and let s build a better ui kit together we welcome all contributions you can help us fixing bugs or submit any new ideas as pull requests https github com lumada design hv uikit react blob master contributing md submitting a pull request or as github issues https github com lumada design hv uikit react blob master contributing md submitting an issue join and support the project contributors a href https github com lumada design hv uikit react graphs contributors img src https contrib rocks image repo lumada design hv uikit react a license this project is licensed under the terms of the apache 2 0 license license | react-components react javascript typescript design-systems | os |
Udacity-Natural-Language-Processing-Nanodegree | udacity natural language processing nanodegree this repository contains all my solutions to the tutorials and projects of the udacity natural language processing nanodegree link to course page https www udacity com course natural language processing nanodegree nd892 workspace for all the tutoraials and projects i used the provided and preconfigured workspaces of udacity | udacity nanodegree udacity-nanodegree natural-language-processing nlp deep-learning hmm machine-translation speech-recognition speech-to-text sentiment-analysis topic-modeling part-of-speech-tagger keras python3 | ai |
employee-database-data-engineering | sql challenge employee database a mystery in two parts where did the data come from the database consists of 6 tables imported from 6 csv files provided by monash university data analytics bootcamp what did i do in this challenge 1 inspect the csv files to design an er diagram using this tool http www quickdatabasediagrams com erd employeesql erd png 2 use the schema employeesql schema sql to create tables in a postgresql database 3 import each csv file into the corresponding sql table 4 query employeesql query sql the database to answer the following requirements list the following details of each employee employee number last name first name sex and salary list first name last name and hire date for employees who were hired in 1986 list the manager of each department with the following information department number department name the manager s employee number last name first name list the department of each employee with the following information employee number last name first name and department name list first name last name and sex for employees whose first name is hercules and last names begin with b list all employees in the sales department including their employee number last name first name and department name list all employees in the sales and development departments including their employee number last name first name and department name in descending order list the frequency count of employee last names i e how many employees share each last name 4 import the sql database into pandas using sqlalchemy use pandas and matplotlib to visualise and analysis the salary data please refer to the notebook employeesql bonus analysis ipynb contact email thao ph ha gmail com | server |
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Movies | movies offline this sample project is created to prove the usage some of the latest android technologies and concepts screenshots p align center img src https github com yehiahd movies blob master screenshots 01 png width 200 img src https github com yehiahd movies blob master screenshots 02 png width 200 img src https github com yehiahd movies blob master screenshots 03 png width 200 img src https github com yehiahd movies blob master screenshots 04 png width 200 p technologies concepts used kotlin br mvvm br rxjava br dagger2 br room br viewmodel br data binding br extra libraries used fastandroidnetworking https github com amitshekhariitbhu fast android networking for networking stuff br fastsave https github com yehiahd fastsave android for easy access to sp br picasso https github com square picasso for image loading br contact twitter https twitter com yehiahd facebook https www facebook com yehia hd linkedin https www linkedin com in yehiahd contribution suggestions ideas pulls and issues are very welcomed | server |
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sql-viewer | ui for uber queryparser https github com uber queryparser written in haskell using using react flux http hackage haskell org package react flux compiled to javascript with ghcjs https github com ghcjs ghcjs play with it here https dlthomas github io sql viewer built | front_end |
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CDE_GCP_07 | cde gcp 07 cloud data engineering gcp modules | cloud |
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tencentcloud-iot-sdk-embedded-c | c sdk c sdk v3 1 0 sdk sdk iot hub iot explorer c sdk https github com tencentyun qcloud iot explorer sdk embedded c c sdk c sdk 1 linux windows linux windows pc sdk linux toolchain glibc socket select io mutex cmakelists txt make settings sdk 2 rtos rtos c sdk rtos c sdk freertos rt thread tencentos tiny rtos rtos sdk newlib c lwip tcp ip 3 mcu mcu mcu wifi 2g 4g nb iot at mcu c sdk at socket freertos nonos hal docs at at mcu at sdk https github com tencentyun qcloud iot sdk tencent at based git sdk 1 cmakelists txt cmake cmakesettings json visual studio cmake cmake build sh linux cmake make settings linux makefile makefile linux makefile device info json debug dev info used off docs sdk external libs mbedtls samples include platform os linux windows freertos nonos tls mbedtls at sdk src sdk tools sdk sdk c sdk cmake makefile docs c sdk build sdk c sdk samples docs 3 2 0 sdk docs c sdk mtmc setup connect init params ota api sdk 3 0 3 ota ota api 3 0 2 samples ota ota mqtt sample c sdk 3 1 0 3 0 3 linux tools update from old sdk sh sdk qcloud err success qcloud ret success qcloud err mqtt reconnected qcloud ret mqtt reconnected qcloud err mqtt manually disconnected qcloud ret mqtt manually disconnected qcloud err mqtt connack connection accepted qcloud ret mqtt connack connection accepted qcloud err mqtt already connected qcloud ret mqtt already connected max size of device serc max size of device secret devcertfilename dev cert file name devprivatekeyfilename dev key file name devserc device secret max size of product key max size of product secret product key product secret debug elog debug info elog info warn elog warn error elog error disable elog disable log writter iot log gen qcloud iot dyn reg dev iot dynreg device iot system get time iot get systime bin bash sed i s qcloud err success qcloud ret success g grep rwl qcloud err success sed i s qcloud err mqtt reconnected qcloud ret mqtt reconnected g grep rwl qcloud err mqtt reconnected sed i s qcloud err mqtt manually disconnected qcloud ret mqtt manually disconnected g grep rwl qcloud err mqtt manually disconnected sed i s qcloud err mqtt connack connection accepted qcloud ret mqtt connack connection accepted g grep rwl qcloud err mqtt connack connection accepted sed i s qcloud err mqtt already connected qcloud ret mqtt already connected g grep rwl qcloud err mqtt already connected sed i s max size of device serc max size of device secret g grep rwl max size of device serc sed i s devcertfilename dev cert file name g grep rwl devcertfilename sed i s devprivatekeyfilename dev key file name g grep rwl devprivatekeyfilename sed i s devserc device secret g grep rwl devserc sed i s max size of product key max size of product secret g grep rwl max size of product key sed i s product key product secret g grep rwl product key sed i s debug elog debug g grep rwl debug sed i s info elog info g grep rwl info sed i s warn elog warn g grep rwl warn sed i s error elog error g grep rwl error sed i s disable elog disable g grep rwl disable sed i s log writter iot log gen g grep rwl log writter sed i s qcloud iot dyn reg dev iot dynreg device g grep rwl qcloud iot dyn reg dev sed i s iot system get time iot get systime g grep rwl iot system get time | mqtt coap tls sdk topic linux | server |
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