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front-end | front end developers reposit rio de tutoriais e refer ncias de desenvolvimento front end code hello world frontenders code br a href https www instagram com ana tech dev guide desenvolvimento web front end 18164552647134233 target blank guia no instagram a com 20 publica es br tutorial para devs iniciantes https www instagram com p ccg6lv7ufsy br refer ncias front end mdn web docs https developer mozilla org pt br br w3c https www w3c br br can i use https caniuse com br css tricks https css tricks com br web content accessibility guidelines wcag https www w3 org wai standards guidelines wcag br wcag guia de consulta r pida https guia wcag com br tutoriais em posts colaborativos import ncia do portf lio para pessoas desenvolvedoras kenzie academy brasil https www instagram com p chmxwtzakjo br tutorial front end frontin sampa https www instagram com p cvty0amdomw br como atua uma pessoa desenvolvedora front end fullture https www instagram com p czsfyenpxwx br meus artigos a import ncia do papel da pessoa desenvolvedora frontend revelo community https community revelo com a importancia do papel do desenvolvedor frontend br 10 ferramentas e refer ncias fundamentais para pessoas desenvolvedoras revelo community https community revelo com 10 ferramentas e referencias fundamentais para pessoas desenvolvedoras br sites para consulta web dev google https web dev br site do maujor https www maujor com br tableless https tableless com br br w3schools https www w3schools com br cursos web dev learn https web dev learn br freecodecamp https www freecodecamp org br discover rocketseat https app rocketseat com br discover br ferramentas convert your png to an svg adobe https www adobe com express feature image convert png to svg br contrast ratio https contrast ratio com br all online tools in one box 10015 tools https 10015 io br extens o do chrome devtools https developer chrome com docs devtools br codesandbox ferramenta de compartilhamento de c digo que permite criar e compartilhar projetos de desenvolvimento diretamente no navegador https codesandbox io br codepen plataforma para criar compartilhar e testar rapidamente c digos html css e javascript https codepen io br roadmaps frontend developer roadmap https roadmap sh frontend br the frontend learning roadmap por frontend masters https frontendmasters com guides learning roadmap br tech guide da alura https techguide sh pt br path front end br reposit rios recomendados css tools por leticia coelho e ana maria engenheiracoelho e anamariasilva https github com engenheiracoelho css tools br reposit rios para favoritar no github https www instagram com p ctf72kfdn0n br reposit rio frontin sampa lu s le o https github com luisleao frontinsampa br mdn web docs https github com mdn br womakerscode front end challenges https github com womakerscode challenges front end br felipe fialho frontend challenges https github com felipefialho frontend challenges br felipe fialho frontend feed https github 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frontendmentor io br jogos para aprender flexbox froggy https flexboxfroggy com br grid garden https cssgridgarden com br css diner https flukeout github io br artigos front end artigos de front end da alura https www alura com br artigos front end br reposit rios de organiza es front end front end brasil o mundo frontender dentro do github https github com frontendbr br frontin https github com frontinsampa br frontend masters https github com frontendmasters br tutoriais freecodecamp front end development https www freecodecamp org news tag front end development br the react beginner s guide for 2022 www freecodecamp org news react beginners guide br the react cheatsheet for 2022 https www freecodecamp org news the react cheatsheet br 10 react interview questions you should know in 2022 https www freecodecamp org news react interview questions to know br livros e e books livros de front end casa do c digo https www casadocodigo com br collections livros de front end br livros do 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tech dev img alt instagram src https img shields io badge ana tech dev 23e4405f svg style plastic logo instagram logocolor white color blue a | frontend front-end front-end-development github developer development-tools hacktoberfest hacktoberfest2022 hacktoberfest-accepted hacktoberfest10 hacktoberfest2023 | front_end |
blockchain-development | blockchain development a complimentary course on understanding of blockchain its application and development understanding of blockchain cryptocurrencies defi gamefi metaverse web3 custom blockchain development custom smart contract development projects like launching your own nfts tokens and blockchain prerequisites blockchain 101 technical background computer internet blockchain 201 technical background computer internet programming basics web basics html css javascript lessons blockchain 101 understanding blockchain lesson 01 introduction to course blockchain important of it as a career lessons lesson 01 md lesson 02 use cases of blockchain lessons lesson 02 md lesson 03 building blocks of blockchain lessons lesson 03 md lesson 04 how to use blockchain technology e g wallet bitcoin trading lessons lesson 04 md lesson 05 earning through blockchain technologies lessons lesson 05 md blockchain applications lesson 06 cryptocurrencies lessons lesson 06 md lesson 07 defi lessons lesson 07 md lesson 08 doas lessons lesson 08 md lesson 09 nfts lessons lesson 09 md lesson 10 gamefi metaverse lessons lesson 10 md blockchain 201 development blockchain lesson 11 building blockchain lessons lesson 11 md lesson 12 api access to our blockchain lessons lesson 12 md lesson 13 decentralized network lessons lesson 13 md lesson 14 syncing network lessons lesson 14 md lesson 15 consensus lessons lesson 15 md smart contract lesson 16 solidity lessons lesson 16 md lesson 17 custom smart contract lessons lesson 17 md lesson 18 token erc20 lessons lesson 18 md lesson 19 nft erc721 lessons lesson 19 md lesson 20 deployment lessons lesson 20 md guides these are guides which you can refer during the course to learn more about blockchain development blockchain guides blockchain md solidity guides solidity md hardhat guides hardhat md web3 guides web3 md javascript guides javascript md react guides react md projects we will try to cover at least 3 full fledged projects in this course 1 how to build your own blockchain projects project 01 readme md 2 how to launch your own token cryptocurrency projects project 02 readme md 3 how to launch your own nfts projects project 03 readme md technologies these are the technologies you will learn during this whole journey if you proceed development programming language solidity guides solidity md libraries frameworks web3 js guides web3 md hardhat guides hardhat md open zeppelin contracts testing chai contribution anyone can contribute to this course for contribution please read the contribution guidelines contributing md made the change pull the request and wait for approval author mubashir rasool razvi rizimore outlook com | blockchain roadmap course cryptography dapps smart-contracts security bitcoin ethereum | blockchain |
cloud-espm-scenarios | end to end sap hana cloud development scenarios espm is a reference application project showcasing end to end development scenarios for ui5 mobile and web applications using odata to connect to backend systems the reference applications like mobile shopping application https mobileshoppingespmhana hana ondemand com espm mobile shopping web sap ui xx fakeos ios launch with chrome web browser web shopping application https webshoppingespmhana hana ondemand com espm ui shopping web are based on the enterprise sales procurement model espm the end to end development scenarios descriptions of the espm references applications are available at sap community network http scn sap com community developer center cross technology quick start install and run all espm reference applications execute chapter 1 of http www sdn sap com irj scn index rid library uuid 8016f858 369d 3010 1f9c 9402c4ecad3f | front_end |
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calothea | calothea a calendar project in software engineering and databases on ntnu | server |
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Car-Model-Classification | grab ai for sea computer vision challenge cars dataset summary nowadays in computer vision deep learning approach is performing superior result and even better than human i decided to use deep learning approach to solve computer vision challenge in grab ai for sea there is already some published kernels in kaggle https www kaggle com jutrera stanford car dataset by classes folder kernels which i have referred to their approach https www kaggle com deepbear pytorch car classifier 90 accuracy as my starting point i have made some changes on training scheme and network architecture my approach of training scheme is better than baseline from the kaggle kernel by 0 27 while performing another two tasks using state of the art achitecture performance is improved by 1 66 i have also shown that not only we need to focus on performance but also focus on size and computational power i switched to lower resolution and state of the art deep architecture for mobile i managed to create a model that is efficient in terms of performance and computational power originality originality 1 multi task learning training scheme for car model car make and car type as the best of my knowledge there is no published solution on multi task learning on cars dataset using this scheme it has been proven test accuracy on car model is improved by at least 0 1 and the same model is able to perform classification on both car make and car type with a very high accuracy at the same time originality 2 compression mobilenetv2 by switching architecture to mobilenetv2 test accuracy is deteriotated by around 1 however with 10x smaller in model size weight pruning by weight pruning on mobilenetv2 model up to 40 test accuracy is kept at 88 refers to table below for more info architecture reference https pytorch org docs stable torchvision models html 1 resnet34 baseline from kaggle 2 resnext50 3 mobilenetv2 multi task learning architecture version 1 version 1 train on car model classification task only 196 classes 2 version 2 3 mtl version train on car model 196 classes car type 18 classes and car make 49 classes classification tasks this is inspired from yolo9000 https arxiv org abs 1612 08242 which they were using wordnet concept for object detection over 9000 classes the following equation is my final loss objective function in this solution l obj l model lambda type l type lambda make l make alt arch imgs arch png mtl architecture v1 from the figure above using output of base model and connect with two fully connected layers one for car type fc type and one for car make fc make then both of them are served as extra information to compute fc model the motivation is because i hope that car type and car make can act as a prior information to help improving in recognizing car model as a result it has been proven this solution can help improving score by at least 0 1 even though it is a minor improvement the model can classify car type and car make at the same time theorectically without using this scheme we can extract car make and car type from fc model however it is troublesome and it is more to programming instead of deep learning however using this scheme performance increased could be due to number of parameters increased to compute fc model therefore i made a better version which has shown in the figure below alt arch2 imgs arch2 png mtl architecture v2 number of parameters to compute fc model remained while error propagated from fc make and fc type flowed into fc model and hence extra gradient information to update weights as a result performance is improved this is also similar to inception v1 googlenet which they performed intermediate softmax branches at the middle dataset car model the cars dataset is from stanford cars dataset https ai stanford edu jkrause cars car dataset html contains 16 185 images of 196 classes of cars car make car type for each class first word in the class name represents car make and last word represents car type there are total 49 classes of car make and 18 classes of car type refers to datasets py for more info sample car make car type car make 1 audi 2 bmw 3 bentley car type 1 suv 2 sedan 3 convertible performance analysis number of parameters architecture number of parameters v1 m number of parameters v2 m resnet34 baseline 21 40 21 45 resnext50 23 45 23 60 mobilenetv2 2 509 2 608 car model make type test accuracy architecture image size version car model resnet34 baseline 224 1 87 10 2 87 50 400 1 92 14 2 92 41 mobilenetv2 224 1 87 30 2 88 01 400 1 91 89 2 91 49 resnext50 224 1 90 83 2 91 10 400 1 93 96 2 94 07 3 94 03 the table above shown test accuracy of different architecture and image size on version 1 and 2 for car model using mtl training scheme on resnet34 with image size of 400 performance is improved by 0 27 from 92 14 to 92 41 which has been proven that prior information of car make and car type are useful for final prediction of car model not only on baseline but performance on other architecture and image size also have shown improvement by at least 0 1 except for mobilenetv2 with image size of 400 by using state of the art deep architecture resnext50 the performance is even improved by 1 66 and 1 62 on version 2 and 3 tasks respectively and it is the best performance among all settings in terms of compression by using mobilenetv2 the performance on both version 1 and 2 are only deteriorated by around 1 while 10x smaller size than resnet34 and resnext50 however using lower resolution of image size of 224 the performance on both version 1 and 2 are dropped to 87 30 and 88 01 respectively architecture image size car make car type resnet34 224 92 81 94 40 400 96 29 96 65 mobilenetv2 224 92 65 93 58 400 95 21 95 57 resnext50 v2 224 95 63 96 11 400 97 71 97 72 resnext50 v3 400 97 57 97 44 the table above shown test accuracy of different architecture and image size on version 2 and 3 for car make and car type classification of car make and car type using resnext50 v2 with image size of 400 it has the best performance with 97 71 and 97 72 respectively while on v3 with lesser number of parameters has slightly lower performance which is 97 51 and 97 48 on car make and car type respectively weight pruning image size version accuracy prune rate 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 224 1 car model 87 32 86 98 86 62 85 19 78 05 57 87 1 63 0 42 0 52 2 car model 87 89 87 80 87 49 86 01 83 56 67 37 9 46 0 50 0 49 car make 92 39 92 33 91 97 90 56 88 12 72 99 20 73 7 75 7 75 car type 93 50 93 33 93 12 91 82 90 61 77 58 7 20 3 72 3 57 400 1 car model 91 92 91 47 90 86 88 46 80 21 30 29 1 21 0 50 0 50 2 car model 91 52 91 28 90 91 89 48 84 22 66 35 1 54 0 55 0 50 car make 94 96 94 68 94 24 92 42 87 32 68 46 7 93 3 20 2 91 car type 95 62 95 31 95 17 94 54 91 05 78 26 18 44 0 54 23 38 the table above shown test accuracy after weight pruning on mobilenetv2 using different prune rate mobilenetv2 can withstand up to 40 of weight pruning while maintaining performance of car model classification task for version 1 and 2 at 88 46 and 89 48 respectively usage only resnext50 v2 and v3 are uploaded in this repository test accuracy resnext50 v2 car model 94 07 car make 97 71 car type 97 72 test accuracy resnext50 v3 car model 94 03 car make 97 51 car type 97 48 requirements 1 python 3 6 5 2 torch 1 1 0 3 torchvision 0 3 0 4 numpy 5 pandas dataset processing if using stanford cars dataset https ai stanford edu jkrause cars car dataset html train dataset need to place in data cars train test dataset need to place in data cars test train annotation need to place in data devkit cars train annos csv test annotation need to place in data devkit cars test annos withlabels csv datasets py is responsible for loading dataset and data loader for training and testing modifying it if necessary network architecture the model creation is located in models as structured as below models models init py models network v1 py models network v2 py we can use any pretrained base network from torchvision models and plug into networkv1 or networkv2 depends on usage help python train py help python test py help python prune py help train python train py version 2 arch resnet34 imgsize 400 epochs 60 python train py version 2 arch resnext50 imgsize 400 epochs 60 python train py version 2 arch mobilenetv2 imgsize 224 epochs 60 continue training finetune 400x400 to 224x224 python train py version 2 arch resnext50 imgsize 224 epochs 30 finetune path logs resnext50 400 60 v2 1 best pth lr 0 001 testing with desired image size image size can be either 224 or 400 performance is not guarentee if using other size python test py config logs resnext50 400 40 v2 1 config json imgsize 400 predict car model make and type on a single image python predict py config logs resnext50 400 60 v2 1 config json imgpath data cars test 02022 jpg check pruning results python prune py config logs resnext50 400 60 v2 1 config json prune rate 0 1 python prune py config logs resnext50 400 60 v2 1 config json prune all conclusion 1 better deep architecture can yield better performance 2 higher resolution can yield better performance 3 multi task learning can improve model performance also can perform related tasks by the same model notes 1 all experiments are performed by 1 run only 2 experiment on v3 only performed using resnext50 3 tuning hyperparameter might improve the performance reference 1 https www kaggle com jutrera stanford car dataset by classes folder kernels 2 https www kaggle com deepbear pytorch car classifier 90 accuracy 3 https pytorch org docs stable torchvision models html 4 https arxiv org abs 1612 08242 5 3d object representations for fine grained categorization jonathan krause michael stark jia deng li fei fei 4th ieee workshop on 3d representation and recognition at iccv 2013 3drr 13 sydney australia dec 8 2013 https ai stanford edu jkrause cars car dataset html | deeplearning pytorch cars computervision computer-vision convolutional-neural-networks deep-learning image-classification | ai |
lambda-gen-ai-endpoint-blog | a step by step guide calling the jurassic 2 j2 ultra large language model through an aws lambda endpoint purpose of this guide this guide was created as a companion for the blog how to access the jurassic 2 j2 large language model via an aws lambda endpoint https aws amazon com blogs apn how to access the jurassic 2 large language model via an aws lambda endpoint the deployment guide will walk you through all the needed steps to deploy the solution referred to in the blog post prerequisites this section will cover any prereqs needed to run the solution ai21 labs account in order to use the jurassic 2 model you will need an api key from ai21 labs 1 sign up for a free account https www ai21 com studio pricing 2 access your api key here https studio ai21 com account api key you will need your ai21 labs api key in step 2 when deploying your cloudformation template note click the link for each step to go through the deployment guide step 1 deploying aws lambda layer docs layer md this section we will walk you through all the steps needed to deploy the lambda layer needed for the lambda function to include the ai21 lab s python sdk library this library is needed for the lambda function to communicate with the jurassic 2 model step 2 deploying cloudformation template docs cfn md this section will walk you through how to deploy the cloudformation template for the solution the cloudformation template will spin up all the needed resources for you to run this solution in a demo environment step 3 adding layer to lambda endpoint docs adding layer md this section will walk you through how to add the lambda layer you created in step 1 to the lambda function that was created when you deployed the cloudformation template in step 2 step 4 testing docs test md this section will walk you through how to test the lambda endpoint that will return results from the jurassic 2 llm step 5 cleanup docs cleanup md this section cover how to remove all aws resources that were created when we deployed the cloudformation template | ai genai generative-ai lambda lambda-functions large-language-models llms machine-learning machinelearning serverless serverless-functions | ai |
Mobile-iOS | awi project frontend what is the project the awi web application and interoperability project is a project that we had to do during our 4th year of engineering school the goal of this project is to create an rest api that will be used by a web app at the first semester and by a mobile app at the second semester this is the frontend part of the project for the ios app techlogies used a target blank href https developer apple com swift img alt github link src https img shields io badge swift 5 5 orange style for the badge logo swift a a target blank href https developer apple com documentation swiftui img alt github link src https img shields io badge swift ui ios 16 orange style for the badge logo apple a a target blank href https firebase google com img alt github link src https img shields io badge firebase 9 17 1 orange style for the badge logo firebase a a target blank href https github com elai950 alerttoast img alt github link src https img shields io badge alert toast grey style for the badge logo googlemessages a links a target blank href https github com romainfrezier mobile ios api img alt github link src https img shields io badge github backend git red style for the badge logo github a romain frezier robin hermet polytech montpellier | firebase-auth ios-app school-project swift swiftui | os |
Calore-Backend | p align center img width 400 height 141 src https raw githubusercontent com derida23 calore backend production docs readme calore jpg p p align center img src https img shields io badge backend blue style for the badge logo img src https img shields io badge javascript yellow style for the badge logo p p align justify this repository is inspired by a href https github com aldoignatachandra technical test be developer aldo backend test a in which there are point of sale features login register profile master data product and the main function is order i hope this rest api can be useful for you and i invite you to collaborate using this rest api for your needs to build web or mobile applications p requirments yarn v1 17 nodejs v8 sequelize v6 backend technology stack 1 javascript 2 nodejs with express js framework 3 rest api 4 mysql database 5 sequelize orm 6 cloudinary get started 1 clone repository 2 import database from docs db db calore sql or download database calore https github com derida23 calore backend tree production docs db 3 setup env file read env example for setup 4 open command or terminal and write yarn install 5 create cloudinary account to generate key to setup in env 6 write yarn dev in terminal api documentation 1 copy json file from docs to your machine 2 import json file to your postman collections features authentication user login get profile register role admin cashier update profile admin cashier data master data master tax address discount category uom cashier only access b get method b product product admin add product get product admin update product order order get order add order get order detail update order if you have a problem you can put it in the issue or contact me via the email listed on the profile | backend-api expressjs nodejs rest-api cashier point-of-sale express mysql-database cloudinary | front_end |
scikit-learn-mooc | scikit learn course a new session of the machine learning in python with scikit learn mooc https www fun mooc fr en courses machine learning python scikit learn is available starting on november 8th 2023 and will remain open on self paced mode enroll for the full mooc experience quizz solutions executable notebooks discussion forum etc the mooc is free and hosted on the fun mooc https fun mooc fr platform which does not use the student data for any other purpose than improving the educational material the static version of the course can be browsed online https inria github io scikit learn mooc course description the course description can be found here https inria github io scikit learn mooc index html follow the course online a few different ways are available launch an online notebook environment using binder https mybinder org badge logo svg https mybinder org v2 gh inria scikit learn mooc main filepath full index ipynb browse website https inria github io scikit learn mooc generated with jupyter book https jupyterbook org running the notebooks locally see instructions here local install instructions md contributing see contributing md contributing md how to cite us the mooc material is developed publicly under the cc by license https github com inria scikit learn mooc blob main license you can cite us through the project s zenodo archive using the following doi 10 5281 zenodo 7220306 https doi org 10 5281 zenodo 7220306 | machine-learning mooc python scikit-learn | ai |
impact-mobile-plugins | impact mobile plugins plugins for use in mobile game development via impactjs and ejecta usage drop in your lib plugins folder make sure to require plugins touch joystick and plugins touch joystick zone touch joystick gives the user full analog style movement via touch controls without having to worry about creating a clumsy on screen controller when the user touches the screen the origin of the touch is saved until they release the touch all movement with that finger is tracked relative to the origin and the plugin makes this available to the developer you can disable touches in specified regions by calling adddeadzone x y width height particularly useful in 2d top down perspective games touch joystick zone allows the developer to assign a zone in which analog touch inputs are accepted the origin is tracked like in touch joystick and the module returns the delta particularly useful for creating areas that respond to swipe or drag motions for example you could create a zone used to track the movement required to swing a sword also very useful for tracking short swipe motions license mit http mit license org | front_end |
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FR-SRGAN | fr srgan final project for mit 6 819 advances in computer vision required package pytorch skimage numpy cv2 tqdm usage todo overview this project is based on the following two papers photo realistic single image super resolution using a generative adversarial network https arxiv org abs 1609 04802 frame recurrent video super resolution https arxiv org abs 1801 04590 we are trying to combine these two models together in our project this is the where the name come from frame recurrent super resolution gan our final report is available here report https drive google com file d 19nxect98x1uljfuwggqmw4e2dadhvc0d view usp sharing dataset we used the dataset from toflow dataset http data csail mit edu tofu testset vimeo test clean zip which is about 15gb containing 7 8k video clips with 7 frames per clip results we trained our model on the dataset for 9 epochs and compared with default srgan model in the repo we did not retrain srgan in our dataset so the result is for reference only the following results are produced by 7 epoch model temporal profile srgan frvri4 png https s1 ax1x com 2018 12 21 frvri4 png https imgchr com i frvri4 frvsr frvyw9 png https s1 ax1x com 2018 12 21 frvyw9 png https imgchr com i frvyw9 fr srgan frv6zr png https s1 ax1x com 2018 12 21 frv6zr png https imgchr com i frv6zr ground truth frvsjj png https s1 ax1x com 2018 12 21 frvsjj png https imgchr com i frvsjj comparison frxiyq gif https s1 ax1x com 2018 12 21 frxiyq gif https imgchr com i frxiyq acknowledgement we referred to srgan implementation https github com leftthomas srgan by leftthomas in our project we would like to thank the author for his fabulous work | ai |
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portfolio-microverse | a name readme top a table of contents table of contents about the project about project built with built with live demo live demo getting started getting started setup setup prerequisites prerequisites run tests run tests authors authors future features future features contributing contributing show your support support acknowledgements acknowledgements license license project description portfolio a name about project a this website shows a sneak peek into the projects i have developed and deployed showcasing my skills in html and css with a passion for web development i have honed my expertise in these technologies to create stunning and functional websites you can hear me talk about my website here https www loom com share 42b00d2e473545c6a065885896772f58 sid 5790be37 9d3c 4916 9f02 b4816fdfed0e built with a name built with a html css linter html validation p align right a href readme top back to top a p live demo a name live demo a you can find a live version of the website here https arch noize github io portfolio microverse p align right a href readme top back to top a p key features use the figma design for the project use the semantics elements use the linter for fixing errors use the flexbox to place the items getting started getting started a name getting started a to get a local copy up and running follow these steps prerequisites in order to run this project you need example command sh 1 use a browser 2 use cable internet setup clone this repository to your desired folder example commands sh git clone git github com arch noize setup and mobile first git cd setup and mobile first install install this project with example command sh npm install usage to run the project execute the following command example command sh open the index with live server or if you don t have a live server extension open the index in the browser by clicking on the file run tests to run tests for the linter run the following command example command sh npx hint npx eslint deployment you can deploy this project using github pages render com netlify com furthermore you can find the website live here https arch noize github io portfolio microverse p align right a href readme top back to top a p authors a name authors a santiago ruido github arch noize https github com arch noize linkedin linkedin https www linkedin com in santiago ruido a1404880 p align right a href readme top back to top a p future features future features a name future features a add javascript add more styling add popup window for the deatils of each project p align right a href readme top back to top a p contributing contributing a name contributing a contributions issues and feature requests are welcome feel free to check the isuues pages https github com arch noize setup and mobile first issues p align right a href readme top back to top a p support show your support a name support a if you like this project kindly give it a star p align right a href readme top back to top a p acknowledgements acknowledgments a name acknowledgements a i would like to thank ekpenisi e raphael for the immense support p align right a href readme top back to top a p license license a name license a this project is mit mit md licensed p align right a href readme top back to top a p | front_end |
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tehno_shop-REACT | h1 tehnoshop com fully functional online shop made with react h1 p fully functional online shop where you can find your favorite product yo can login or register to page and buy your chosen product app builded with react frontend and strapi api for backend all arders are collected in backend and payments handled with stripe what is react react is an open source front end javascript library for building user interfaces or ui components it is maintained by facebook and a community of individual developers and companies react can be used as a base in the development of single page or mobile applications what is strapi strapi is a flexible open source headless cms that gives developers the freedom to choose their favorite tools and frameworks while also allowing editors to easily manage and distribute their content who use stripe millions of businesses of all sizes from startups to large enterprises use stripe s software and apis to accept payments send payouts and manage their businesses online used advanced css styles to make pages maximal representative and dynamic using css and javascript all effects and pop ups are made with javascript and css and styled components web pages are hosted using netiflay for frontend and heroku for backend also used the git source control that allows you to record changes to files over time and allows you to view changes and specific versions of those files later on all web page is made using the latest css technologies like grid system with a grid system this webpage is maximum responsive for different mobile devices with a minimum media query p h5 check demo span a href https tehno shop react netlify app target blank demo a span h5 p p s backend at start can be very slow thats because i use free plan of hosting with heroku after first time backend loaded it works perfect p br img src gitimages picture1 png width 1080 img src gitimages picture2 png width 1080 img src gitimages picture3 png width 1080 img src gitimages picture4 png width 1080 img src gitimages picture5 png width 1080 img src gitimages picture6 png width 1080 img src gitimages picture7 png width 1080 img src gitimages picture8 png width 1080 img src gitimages picture9 png width 1080 img src gitimages picture10 png width 1080 img src gitimages picture11 png width 1080 img src gitimages picture12 png width 1080 | server |
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emsys_21A_lab4 | lab4 describing combinatorial and sequential logic in verilog in this lab you will construct a basic alu like hardware module you will write a small program for it in an assembly language write the verilog hardware description for the module and simulate it with verilator setting up your hardware development environment for the lab work you need to remote connect to the linux lab machines in uni or you can physically come in and use the terminals in the lab session if you remote connect and don t use the physical linux machines in the labs i fully encourage you to embrace the command line and to do all of your development via ssh i understand that this might be daunting for some it was for me when i first started so i have made some videos a little further down this page to help connecting to a machine in the linux lab to connect to a lab machine you will need a few things 1 a linux lab account cosit should have emailed you with the details for this a week or two ago please let me know if you don t have one 2 the ability to connect to the university vpn 3 an ssh client with x11 forwarding setup on your local machine connecting to the uni vpn please follow the guides here http vpn swan ac uk i m sorry i know pulse secure is painful i wish they had not closed the ssh ports last year setting up ssh and x11 forwarding to setup ssh and x11 forwarding i have found the following tutorials helpful in the past for windows https youtu be flhvua 98sa t 151 for linux ubuntu https youtu be flhvua 98sa for mac https www cyberciti biz faq apple osx mountain lion mavericks install xquartz server once you have that setup on your machine pick a linux machine machine from here misc machine list md and try and connect basic bash and vim tutorial i encourage you to try and work via ssh using the bash terminal in case you are not familiar with this or need a quick refresher then please watch the following short 5 min video that will demonstrate some basic usage p align center a href http www youtube com watch feature player embedded v zfgbowdwgru target blank img src misc basic vim and bash usage png alt lesson video width 510 height 360 border 10 a p testing out your lab environment once you ve got ssh setup connected to a machine and familiarised yourself with bash and vim let s try simulating a hardware circuit please watch the following video p align center a href http www youtube com watch feature player embedded v r3rt3wbtrou target blank img src misc testing lab setup png alt lesson video width 510 height 360 border 10 a p github repos you should all now have the lab setup code pushed to your github repositories check to make sure that you have your github repo lab4 task1 your github repo lab4 task2 in each of these subdirectories you should see alu sv the verilog file where you will describe your alu program emsys the assembler like code that you can use to program your alu some files used to build and execute the verilog simulation to build your hardware and simulate it in the relevant tasks directory type make run to run a set of tests on the hardware type make test for each of the tests you should see the assembler line that was executed what the expected value was what the value your hardware produced was and if the test passed or failed task 1 designing logic for a simple alu like module in this first task you will design a simple alu like module alu stands for arithmetic logic unit which are small blocks of hardware within a cpu that perform different arithmetic operations on some inputs depending on a programmable opcode in each of these folders you can find the files used to describe the hardware run the simulation and program you hardware for both the tasks in this lab the following tutorial videos recapping what was covered in lecture 4 might be useful for this task 1 simple in n out https www youtube com watch v fnomnokp9mi 2 intermediate signals https www youtube com watch v ljmm6s6k2aw 3 bus signals and multiplexers https www youtube com watch v jb gnzetzts 4 a procedural blocks https www youtube com watch v xducyrsbpwk 4 b literal values concatenation https www youtube com watch v 3n8kvpveruo 4 c larger multiplexer https www youtube com watch v kyga2d 1dna the alu module will have three inputs a b and op there is a single output q a is the first 8 bit operand b is the second 8 bit operand op is a 3 bit op code that is used to select which arithmetic output is assigned to q how the op codes effect the assignments to q is described in the following table opcode binary assembler command details 000 add a b performs addition on values a and b and assigns the value to q q a b 001 sub a b performs addition on values a and b and assigns the value to q q a b 010 sr1 a shifts the value a one place to the right and assigns it to q 011 sl1 a shifts the value a one place to the left and assigns it to q 100 and a b performs a bitwise and operation on a and b and assigns it to q 101 or a b performs a bitwise or operation on a and b and assigns it to q 110 not used q should be 0 111 not used q should be 0 to test your alu small assembler programs can be written in the file task1 program emsys task1 program emsys these commands will be converted into op codes and fed into your hardware circuit during the simulation an example of this could be add 1 2 sub 100 50 sl1 3 after each of these commands executes in our alu hardware module we should see the following on the q output 3 after add 1 2 50 after sub 100 50 and 6 after sl1 3 the hardware a stub for the hardware module that you must complete can be found here task1 alu sv task1 alu sv in it you will find v module alu input logic 7 0 a input logic 7 0 b input logic 7 0 op output logic 7 0 q endmodule you need to complete the design for this module the assembly program written in task1 program emsys task1 program emsys will appear at the inputs of the module during simulation as a first step i recomend compiling a simple program and observing how the op a and b inputs are changing as you would expect to simulate the hardware and inspect the waveforms for task1 do the following 1 navigate into the task1 directory cd task1 2 type make to build the simulation and run it 3 once the simulation finishes type gtkwave wavedump vcd to view the waveform complete the verilog hardware description in task1 alu sv task1 alu sv to take the inputs and assign the appropriate value to the output q you do not need to write anything in the readme md files the hardware description is sufficient a possible design for your circuit could be as follows misc simple alu thing png some useful tips getting started verilog has operators for addition and subtraction you do not need to implement adder logic from first principles using logic gates first i would recommend simulating the hardware without any logic inside and make sure you understand how the input signals a b and op are changing in relation to the input program program emsys task 2 adding a working register we are now going to upgrade our alu from task 1 to contain a working register this is a single register that can store some data that we can operate over to do this we will need to add some simple sequential logic circuitry so that we can save some state the following videos recapping what was taught in lecture 5 might be helpful for this 1 sequential logic and d type flip flops https www youtube com watch v jls4mdr58c8 2 describing a counter circuit https www youtube com watch v u3k91t8aixa 3 write enable register https www youtube com watch v rlxe8sf2zpm misc simple alu working reg png instead of three inputs the alu now has only two an opcode input op a data input a in the diagram above you ll notice that the output of the multiplexer is now fed into an internal working register in the alu this register stores the value of the current operation the output of the register is fed into the input of the different operations e g add sr1 allowing us to operate on output of the previous operation for this version of the alu we require different opcodes below is a table of the opcode and their expected function where w is the value inside the working register opcode binary assembler command details 000 add a store the value w a in the working register output w a at q 001 sub a store the value w a in the working register output w a at q 010 sr1 save the value w 1 in the working register output w 1 at q 011 sl1 save the value w 1 in the working register output w 1 at q 100 and a save the value w a in the working register output w a at q 101 or a save the value w a in the working register output w a at q 110 set a stores the value a in the working register output 0 at q 111 working register remains unchanged output 0 at q complete the verilog hardware description in task2 alu sv task2 alu sv such that it passes the tests and test it with some of your own programs | os |
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DE-Project | de project data engineering project using prefect scheduling tool and mongodb as database and nodes js with react for data visualization 1 load 1 time batch data historical price data of chosen stocks using pymongo python to mongodb database downloaded from nasdaq com 2 schedule real time data loading from finnhub api to mongodb dataase using prefect orchestration tool updates data everey 1 minute 3 visaulize all data in react nodejs app screenshot of schedules in prefect img src images reactapp jpeg alt react app width 100 height auto screenshot of react app img src prefectschedules jpeg alt react app width 100 height auto | batch-processing data-science data-visualization databse dataengineering finhub-api mongodb mongodb-atlas mongoose prefect python reactjs real-time-processing stock-market | server |
Employee-Database-Construction | employee database construction the project involves data engineering six csv files of employee information a database is then created using postgresql where sql queries have been written for preliminary analysis data engineering upon inspection of the 6 csv files i designed an erd using http www quickdatabasediagrams com returning the image below erd https user images githubusercontent com 85002751 213859336 904493ad 1fa9 48f6 bfca 4b1fb02ef7a5 jpg the sql queries written to create the following tables can be found in schema sql upon completion the 6 csv files were loaded into the tables via postgresql data analysis sql queries found in query sql were made to conduct the following procedures 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 | data-engineering postgresql-database | server |
Resume_Filtering | img src https github com prateekguptaiiitk resume classifier blob develop skybits logo small png align left hspace 1 vspace 1 height 100 width 268 resume filtering using machine learning p 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 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 nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp resume filtering on the basis of job descriptions jds it was a summer 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 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 nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp internship project with a href http sky bits com skybits technologies pvt ltd a p language https img shields io badge language python3 blue svg github license https img shields io github license prateekguptaiiitk resume filtering svg https github com prateekguptaiiitk resume filtering blob develop license hits https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2fprateekguptaiiitk 2fresume filtering count bg 23d1c941 title bg 23555555 icon homebrew svg icon color 23e7e7e7 title hits edge flat false https hits seeyoufarm com binder https mybinder org badge logo svg https mybinder org v2 gh prateekguptaiiitk resume filtering develop introduction the main feature of the current project is that it searches the entire resume database to select and display the resumes which fit the best for the provided job description jd this is in its current form achieved by assigning a score to each cv by intelligently comparing them against the corresponding job description this reduces the window to a fraction of an original size of applicants resumes in the final window can be manually checked for further analysis the project uses techniques in machine learning and natural language processing to automate the process directory structure pre data cvs collectcv py jd csv model model training ipynb sentence extraction ipynb paragraph extraction from posts ipynb sample bitcoin stackexchange paras txt sample bitcoin stackexchange sentences txt scoring cv ranking ipynb using spacy model ipynb with word2vec ipynb context jpg prc data csv section extraction section extraction ipynb convertdocxtotext py convertpdftotext py extract py get jd ipynb pre directory details data https github com prateekguptaiiitk resume classifier tree develop data cvs contains 250 extracted resumes in text format from indeed com https www indeed com collectcv py python script to automate the process of extracting cvs from indeed com while this program is running every new text copied to clipboard is saved as a cv in cvs directory in text format jd csv csv file containing cleaned job descriptions from kaggle dataset can be found here https www kaggle com c job salary prediction data model https github com prateekguptaiiitk resume classifier tree develop model model training ipynb notebook to train the word2vec model using gensim the model was saved in model subdirectory locally sentence extraction ipynb notebook for extracting cleaned sentences from extracted paragraphs paragraph extraction from posts ipynb notebook for extracting paragraphs from posts xml sample bitcoin stackexchange sentences txt it is the sentences txt pure sentences file for bitcoin stackexchange com subdirectory of the dataset it was generated from the corresponding paras txt generated earlier using the code in sentence extraction from paras txt ipynb the process took around 12 5 hours to complete sample bitcoin stackexchange paras txt it is the paras txt paragraph in html tags file for bitcoin stackexchange com subdirectory of the dataset it was generated from the posts xml using the code in paragraph extraction from posts xml ipynb scoring https github com prateekguptaiiitk resume classifier tree develop scoring cv ranking ipynb notebook for ranking the cvs according to jds job description using spacy model ipynb demonstrates the need for a custom word2vector model rather than a general model trained otherwise the similarity values generated by en core web md spacy model trained on google news articles do not reflect the technological sharpness required for the project with word2vec ipynb demonstrates how to use word2vec to get similar words by words and similar words by vector it also implements sent2vec function this function takes a sentence as a argument and returns a average vector for the sentence root mean square is used to average the vectors the advantage of this function is to use it to find similar words for phrases which makes more sense while searching for roles etc for example web engineer will give engineer as a similar word context jpg pie chart showing the top three most frequent titles of job descriptions prc data csv csv file storing processed sections of different resumes section extraction https github com prateekguptaiiitk resume classifier tree develop section 20extraction section extraction ipynb notebook for extracting sections from different resumes convertdocxtotext py python script for converting a docx file to txt convertpdftotext py python scrtpt for converting a pdf file to txt extract py python script for extracting compressed files in 7z get jd ipynb notebook for cleaning and extracting relevant portions of original jd csv file from kaggle author table tr td img src https avatars2 githubusercontent com u 29523950 s 400 u 878e242ca2c624eb45a62bf62ae580a370b7a0ae v 4 width 180 prateek gupta p align center a href https github com prateekguptaiiitk img src http www iconninja com files 241 825 211 round collaboration social github code circle network icon svg width 36 height 36 a a href https twitter com prateek gupta21 img src https www shareicon net download 2016 07 06 107115 media svg width 36 height 36 a a href https www linkedin com in prateekjpg img src http www iconninja com files 863 607 751 network linkedin social connection circular circle media icon svg width 36 height 36 a p td tr table | nlp-machine-learning word2vec-model resumeclassifier spacy-nlp python3 gensim-word2vec | ai |
LLM4Mol | llm4mol llm large language model 4mol is a comprehensive repository dedicated to the collection and exploration of studies utilizing large language models for molecular design protein research and material science this repository serves as a central hub for researchers scientists and enthusiasts interested in leveraging the power of language models for advancing our understanding and applications in these domains discover state of the art techniques novel approaches and cutting edge research papers that harness the potential of ai powered language models in unraveling the complexities of biomedical text rna dna molecules peptides proteins antibody and materials join our vibrant community and contribute to the exciting advancements in the field of llm4mol updating recommendations and references generative ai and deep learning for molecular drug design https github com aspirincode papers for molecular design using dl list of papers about proteins design using deep learning https github com peldom papers for protein design using dl large language models in chemistry https github com alxfgh large language models in chemistry menu llm4biomedical text llm4biomedical text llm4small molecule llm4small molecule llm4rna dna llm4rnadna llm4peptide llm4peptide llm4protein llm4protein llm4antibody llm4antibody llm4clinical llm4clinical llm4chemistry llm4chemistry llm4material llm4material llm4biomedical text opportunities and challenges for chatgpt and large language models in biomedicine and health 2023 tian shubo qiao jin lana yeganova po ting lai qingqing zhu xiuying chen yifan yang et al arxiv 2306 10070 2023 https arxiv org abs 2306 10070 large language models are universal biomedical simulators 2023 schaefer moritz stephan reichl rob ter horst adele m nicolas thomas krausgruber francesco piras peter stepper christoph bock and matthias samwald biorxiv 2023 https doi org 10 1101 2023 06 16 545235 code https github com openbiolink simulategpt fine tuning large neural language models for biomedical natural language processing 2023 tinn robert hao cheng yu gu naoto usuyama xiaodong liu tristan naumann jianfeng gao and hoifung poon patterns 4 4 2023 https doi org 10 1016 j patter 2023 100729 code https aka ms huggingface a platform for the biomedical application of large language models 2023 lobentanzer sebastian and julio saez rodriguez arxiv 2305 06488v2 https arxiv org abs 2305 06488 code https github com biocypher chatgse large language models in biomedical natural language processing benchmarks baselines and recommendations 2023 chen qingyu jingcheng du yan hu vipina kuttichi keloth xueqing peng kalpana raja rui zhang zhiyong lu and hua xu arxiv 2305 16326v1 https arxiv org abs 2305 16326 code https github com qingyu qc gpt bionlp benchmark biomedgpt a unified and generalist biomedical generative pre trained transformer for vision language and multimodal tasks 2023 zhang k yu j yan z liu y adhikarla e fu s sun l arxiv 2305 17100v1 https arxiv org abs 2305 17100 code https github com taokz biomedgpt biomedlm a domain specific large language model for biomedical text 2022 paper https www mosaicml com blog introducing pubmed gpt code https huggingface co stanford crfm biomedlm llm4small molecule empowering molecule discovery for molecule caption translation with largelanguage models a chatgpt perspective 2023 jiatong li yunqing liu wenqi fan xiao yong wei hui liu jiliang tang qing li arxiv 2306 06615 2023 https arxiv org pdf 2306 06615 pdf code https github com phenixace molregpt enhancing activity prediction models in drug discovery with the ability to understand human language 2023 yin fang xiaozhuan liang ningyu zhang kangwei liu rui huang zhuo chen xiaohui fan huajun chen arxiv 2303 03363 2023 https arxiv org abs 2303 03363 code https github com ml jku clamp mol instructions a large scale biomolecular instruction dataset for large language models 2023 yin fang xiaozhuan liang ningyu zhang kangwei liu rui huang zhuo chen xiaohui fan huajun chen arxiv 2306 08018v1 https arxiv org abs 2306 08018 code https github com zjunlp mol instructions molregpt empowering molecule discovery for molecule caption translation with large language models a chatgpt perspective 2023 li jiatong yunqing liu wenqi fan xiao yong wei hui liu jiliang tang and qing li arxiv 2306 06615v1 https arxiv org abs 2306 06615 code https github com phenixace molregpt llm4rna dna hyenadna long range genomic sequence modeling at single nucleotide resolution 2023 eric nguyen michael poli marjan faizi armin thomas callum birch sykes michael wornow aman patel clayton rabideau stefano massaroli yoshua bengio stefano ermon stephen a baccus chris r arxiv 2306 15794v1 https arxiv org abs 2306 15794 dnabert pre trained bidirectional encoder representations from transformers model for dna language in genome 2021 ji yanrong zhihan zhou han liu and ramana v davuluri bioinformatics 37 15 2021 https doi org 10 1093 bioinformatics btab083 code https github com jerryji1993 dnabert llm4peptide lmpred predicting antimicrobial peptides using pre trained language models and deep learning 2022 dee william bioinformatics advances 2 1 2022 https doi org 10 1093 bioadv vbac021 code https github com williamdee1 lmpred amp prediction llm4protein protein protein interaction prediction is achievable with large language models 2023 hallee logan and jason p gleghorn biorxiv 2023 https doi org 10 1101 2023 06 07 544109 prediction of virus host association using protein language models and multiple instance learning 2023 liu dan francesca young david l robertson and ke yuan biorxiv 2023 https www biorxiv org content 10 1101 2023 04 07 536023v2 code https github com liudan111 evomil large language models generate functional protein sequences across diverse families 2023 madani ali ben krause eric r greene subu subramanian benjamin p mohr james m holton jose luis olmos jr et al nat biotechnol 2023 https doi org 10 1038 s41587 022 01618 2 code https github com salesforce progen llm4antibody on pre training language model for antibody 2023 wang danqing y e fei and hao zhou iclr 2023 https openreview net forum id zaq4lv55xhl code https github com dqwang122 eatlm efficient evolution of human antibodies from general protein language models 2023 hie brian l varun r shanker duo xu theodora uj bruun payton a weidenbacher shaogeng tang wesley wu john e pak and peter s kim nat biotechnol 2023 https www nature com articles s41587 023 01763 2 code https github com brianhie efficient evolution ablang an antibody language model for completing antibody sequences 2022 olsen tobias h iain h moal and charlotte m deane bioinformatics advances 2022 https doi org 10 1093 bioadv vbac046 code https github com oxpig ablang llm4clinical matching patients to clinical trials with large language models 2023 jin qiao zifeng wang charalampos s floudas jimeng sun and zhiyong lu arxiv 2307 15051 2023 https arxiv org abs 2307 15051 clinicalgpt large language models finetuned with diverse medical data and comprehensive evaluation 2023 wang danqing y e fei and hao zhou arxiv 2306 09968v1 https arxiv org abs 2306 09968 llm4chemistry chemcrow augmenting large language models with chemistry tools 2023 bran andres m sam cox andrew d white and philippe schwaller arxiv 2304 05376 2023 https arxiv org abs 2304 05376 code https github com ur whitelab chemcrow public llm4material large language models as master key unlocking the secrets of materials science with gpt 2023 xie tong yuwei wa wei huang yufei zhou yixuan liu qingyuan linghu shaozhou wang chunyu kit clara grazian and bram hoex arxiv 2304 02213v5 https arxiv org abs 2304 02213 matscibert a materials domain language model for text mining and information extraction 2022 gupta tanishq mohd zaki nm anoop krishnan and mausam npj comput mater 8 102 2022 https www nature com articles s41524 022 00784 w code https github com m3rg iitd matscibert | ai |
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udacity_udagram | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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jeesite | p align center img alt jeesite src https jeesite com assets images logo png width 120 height 120 style margin bottom 10px p h3 align center style margin 30px 0 30px font weight bold font size 30px jeesite h3 p align center a href https jeesite com docs upgrade target blank img alt jeesite v1 2 src https img shields io badge jeesite v1 2 success svg a nbsp a href https gitee com thinkgem jeesite stargazers target blank img alt star src https gitee com thinkgem jeesite badge star svg theme gvp a nbsp a href https gitee com thinkgem jeesite members target blank img alt fork src https gitee com thinkgem jeesite badge fork svg theme gvp a p br jeesite v4 x 5 x https gitee com thinkgem jeesite4 jeesite cloud https gitee com thinkgem jeesite4 cloud jeesite mobile https gitee com thinkgem jeesite4 uniapp jeesite pc https gitee com thinkgem jeesite client jeesite vue https gitee com thinkgem jeesite vue br jeesite java ee jeesite jeesite java jeesite jeesite spring framework java spring mvc mybatis apache shiro ehcahe activit javaee jeesite twitter bootstrap maven jeesite sys cms oa gen jeesite jeesite 1 2 3 4 5 6 7 8 sql 9 jeesite 1 apache license 2 0 http www apache org licenses license 2 0 2 java ee 3 mysql oracle sql server postgresql h2 4 5 6 7 8 taglib 9 ie7 chrome firefox ie6 1 spring framework 5 3 apache shiro 1 9 spring mvc 5 3 hibernate validator 5 2 sitemesh 2 4 activiti 5 21 spring task 4 1 mybatis 3 2 alibaba druid 1 0 ehcache 2 6 redis slf4j 1 7 logback apache commons jackson poi 3 9 2 js jquery 1 9 css twitter bootstrap 2 3 1 ui jquery validation plugin 1 11 ckeditor ckfinder jerichotab jingle jqgrid jquery jbox jquery select2 jquery ztree my97datepicker 4 java ee 7 servlet 3 1 jsp 2 1 mysql oracle sqlserver 2008 mysql 5 5 h2 java eclipse java ee 4 3 maven 3 1 git 1 java 2 3 4 sql 5 sha1 6 url 1 jdk1 8 maven3 0 mysql5 oracle10g tomcat8 5 2 src main resources jeesite properties 3 mysql oracle 4 bin init db bat linux mvn antrun run pinit db 5 bin package bat target jeesite war 6 jeesite war tomcat8 webapps tomcat8 7 tomcat thinkgem admin 1 jvm xmx512m xx maxpermsize 256m 2 tomcat server xml connector uriencoding utf 8 3 thinkgem admin https gitee com thinkgem jeesite tree master doc qq 127515876 209330483 223507718 709534275 730390092 183903863 github https github com thinkgem jeesite https gitee com thinkgem jeesite http jeesite com http www jeesite net jeesite thinkgem 163 com nbsp thinkgem github http www cnblogs com wenber p 3630921 html apache license 2 0 http www apache org licenses license 2 0 1 apache licence 2 3 4 notice notice apache licence notice apache licence 5 apache licence 6 jeesite 7 mybatis hibernate mybatis mybatis hibernate mybatis hibernate mybatis hibernate mybatis hibernate hibernate hibernate hibernate hibernate mybatis jeesite mysql oracle sql xml sql dbname sql thinkgem 163 com hibernate jeesite hibernate https gitee com thinkgem jeesite tree master hibernate | os |
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DataAspirant_codes | dataaspirant codes dataaspirant https www dataaspirant com blog total codes can be found here feel free to fork | python machine-learning data-science data-mining | ai |
ml-sound-classifier | machine learning sound classifier for live audio title title jpg picture on the left rembrandt portrait of an evangelist writing cited from wikipedia https commons wikimedia org wiki file rembrandt portrait of an evangelist writing jpg this is a simple fast for live audio in realtime customizable machine learning sound classifier mobilenetv2 light weight cnn model for mobile is used new alexnet based lighter faster cnn model is also provided tensorflow graph for runtime faster prediction and for portability freesound dataset kaggle 2018 pre built models are ready new raspberry pi realtime inference supported ensembling sequence of predictions moving average for stable results normalizing samplewise for robustness to level drift easy to customize example this project is a spin off from my solution for a competition kaggle freesound general purpose audio tagging challenge https www kaggle com c freesound audio tagging for trying in real environment the competition itself have provided dataset as usual then all the work have done with dataset s static recorded samples unlike competition this project is developed as working code exsample to record live audio and classify in realtime new alexnet based lighter model is also added and that makes it possible to run on raspberry pi application pull requests reports are welcome if you implemented on your mobile app or any embedded app like detecting sound of door open close and make notification with raspberry pi i really appreciate if you could let me know or you can pull request your application post as an issue here you can also reach me via twitter https twitter com nizumical 1 running classifier requirements install following modules python 3 6 tensorflow tested with 1 9 keras tested with 2 2 0 librosa tested with 0 6 0 pyaudio tested with 0 2 11 easydict tested with 1 4 pandas tested with 0 23 0 matplotlib tested with 2 2 2 imbalance learn for training only tested with 0 3 3 some others to be updated quick try simply running realtime predictor py will start listening to the default audio input to predict following result is an example when typing computer keyboard on my laptop which was runnning this you can see bus after many computer keyboard it appears many times with usual home environmental noise like sound of car running on the road neay by so it is expected python realtime predictor py using tensorflow backend fireworks 31 0 112626694 computer keyboard 8 0 13604443 computer keyboard 8 0 1880264 computer keyboard 8 0 20889345 computer keyboard 8 0 23447378 computer keyboard 8 0 27483797 computer keyboard 8 0 30053857 computer keyboard 8 0 31214702 computer keyboard 8 0 31657898 computer keyboard 8 0 30582297 computer keyboard 8 0 31986332 computer keyboard 8 0 289629 computer keyboard 8 0 22962372 computer keyboard 8 0 20274992 computer keyboard 8 0 2013374 computer keyboard 8 0 1868646 computer keyboard 8 0 17860062 computer keyboard 8 0 17743647 computer keyboard 8 0 18610674 bus 22 0 13272804 bus 22 0 14847495 bus 22 0 15111914 bus 22 0 16028993 bus 22 0 18327181 bus 22 0 1998493 bus 22 0 2240002 bus 22 0 2510223 bus 22 0 28142408 bus 22 0 32763514 bus 22 0 35531467 three example scripts for running classifier realtime predictor py this is basic example predicts 41 classes provided by freesound dataset deskwork detector py this is customization example to predict 3 deskwork sounds only uses emoji to express prediction probabilities premitive file predictor py this is very simple example no prediction ensemble is used predict from file only 2 applications as training examples example applications example apps md are provided as examples for some datasets these explains how to train models and cnn laser machine listener is an end to end example to showcase training converting model to pb and use it for prediction in realtime check example applications example apps md when you try to train model for your datasets 3 pretrained models following pretrained models are provided 44 1khz model is for high quality sound classification and 16khz model is for general purpose sounds file name dataset base model audio input mobilenetv2 fsd2018 41cls h5 fsdkaggle2018 mobilenetv2 44 1khz 128 n mels 128 time hop mobilenetv2 small fsd2018 41cls h5 fsdkaggle2018 mobilenetv2 16khz 64 n mels 64 time hop alexbased small fsd2018 41cls h5 fsdkaggle2018 alexnet 16khz 64 n mels 64 time hop about 9 000 samples from fsdkaggle2018 are used for training this number of samples 9 000 would not be enough to compare with imagenet so we cannot expect that these models have enough capability of representation but it s still better than starting from scratch in small problems 4 raspberry pi support see raspberrypi md raspberrypi md for the detail 5 tuning example behaviors followings are important 3 parameters that you can fine tune program behavior in your environment conf rt process count 1 conf rt oversamples 10 conf pred ensembles 10 conf rt oversamples this sets how many times raw audio capture will happen if this is 10 it happens 10 times per one second responce will be quicker with larger value as long as your environment is powerful enough conf rt process count this sets duration of audio conversion and following prediction this set count s of audio capture 1 means audio conversion and prediction happens with each audio capture if it is 2 once of prediction per twice of audio captures responce will also be quicker if you set smaller like 1 but usually prediction takes time and you might need to set this bigger if it s not so powerful on my macbookpro with 4 core cpu 1 is fine conf pred ensembles this sets number of prediction ensembles past to present predictions are held in fifo and this number is used as the size of this fifo ensemble is done by calclating geometric mean of all the predictions in the fifo for example if conf pred ensembles 5 past 4 predictions and present prediction will be used for calculating present ensemble prediction result 6 possible future applications following examples introduce possible impacts when you apply this kind of classifier in your applications app 1 iphone recording sound example fireworks detection a sample fireworks wav is recorded on iphone during my trip to local festival as a video this audio is cut from the video by quicktime so basically this is iphone recorded audio should be available in your iphone app so if you embed the prediction model your app will understand fireworks is going on cuda visible devices python premitive file predictor py sample fireworks wav using tensorflow backend fireworks 0 7465853 fireworks 0 7307739 fireworks 0 9102228 fireworks 0 83495927 fireworks 0 8383171 fireworks 0 87180334 fireworks 0 67869043 fireworks 0 8273291 fireworks 0 9263128 fireworks 0 6631776 fireworks 0 2644468 laughter 0 2599133 app 2 pc recording sound example deskwork deteection simple dedicated example is ready deskwork detector py will detect 3 deskwork sounds writing using scissors and computer keyboard typing and a sample desktop sounds wav is recorded on my pc that captured sound of 3 deskwork actions in sequence so this is the similar impact as iphone example we are capable of detecting specific action python deskwork detector py f sample desktop sounds wav using tensorflow backend writing 0 40303284 writing 0 53238016 writing 0 592431 writing 0 5818318 writing 0 60172915 writing 0 6218055 writing 0 6176261 writing 0 6429986 writing 0 64290494 writing 0 6534221 writing 0 70015717 writing 0 7130502 writing 0 72495073 writing 0 75500214 writing 0 7705893 writing 0 7760113 writing 0 79651505 writing 0 7876685 writing 0 8026823 writing 0 80895096 writing 0 8053692 writing 0 7975255 writing 0 77262956 writing 0 76053137 writing 0 7428124 writing 0 70416236 writing 0 6924045 writing 0 66129446 writing 0 6085751 writing 0 5530443 writing 0 50439316 writing 0 36155683 writing 0 25736108 writing 0 19337422 computer keyboard 0 17075704 computer keyboard 0 2984399 computer keyboard 0 42496625 computer keyboard 0 5532899 computer keyboard 0 64340276 computer keyboard 0 6802248 computer keyboard 0 66601527 computer keyboard 0 62935054 computer keyboard 0 58397347 computer keyboard 0 526138 computer keyboard 0 4632419 computer keyboard 0 42490804 computer keyboard 0 40419492 computer keyboard 0 38645235 computer keyboard 0 36492997 computer keyboard 0 33910704 computer keyboard 0 3251212 computer keyboard 0 30858216 computer keyboard 0 27181208 computer keyboard 0 263743 computer keyboard 0 23821306 computer keyboard 0 20494641 computer keyboard 0 12053269 computer keyboard 0 15198663 scissors 0 16703679 scissors 0 21830729 scissors 0 28535327 scissors 0 3840115 scissors 0 52598876 scissors 0 6164208 scissors 0 6412893 scissors 0 7195315 scissors 0 7480882 scissors 0 76995486 scissors 0 7952656 scissors 0 79389054 scissors 0 8043309 scissors 0 79848546 scissors 0 801064 scissors 0 79365206 scissors 0 7864271 scissors 0 71144533 scissors 0 70292735 scissors 0 6741176 scissors 0 6504746 scissors 0 6561931 scissors 0 6303668 scissors 0 6297095 scissors 0 6140897 scissors 0 57456887 scissors 0 5549676 future works documenting some more detail of training your dataset | ai |
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iot-hub-python-raspberrypi-client-app | iot hub raspberry pi 3 client application this repo contains the source code to help you get started with azure iot using the microsoft iot pack for raspberry pi 3 starter kit you will find the full tutorial on docs microsoft com https docs microsoft com en us azure iot hub iot hub raspberry pi kit c get started this repo contains a python application that runs on raspberry pi 3 with a bme280 temperature humidity sensor and then sends these data to your iot hub at the same time this application receives cloud to device messages from your iot hub and takes actions according to the c2d command step 1 set up your pi enable ssh on your pi follow this page https www raspberrypi org documentation remote access ssh to enable ssh on your pi enable i2c on your pi follow this page https www raspberrypi org documentation configuration raspi config md to enable i2c on your pi step 2 connect your sensor with your pi connect with a physical bme280 sensor and led you can follow the image to connect your bme280 and an led with your raspberry pi 3 bme280 https docs microsoft com en us azure iot hub media iot hub raspberry pi kit node get started 3 raspberry pi sensor connection png don t have a physical bme280 you can use the application to simulate temperature humidity data and send to your iot hub 1 open the config py file 2 change the simulated data value from false to true step 3 download and setup referenced modules 1 clone the client application to local sudo apt get install git core git clone https github com azure samples iot hub python raspberrypi client app git 2 because the azure iot sdks for python are wrappers on top of the sdks for c azure iot sdk c you will need to compile the c libraries if you want or need to generate the python libraries from source code cd iot hub python raspberrypi client app sudo chmod u x setup sh sudo setup sh in the above script we run setup sh without parameter so the shell will automatically detect and use the version of python installed search sequence 2 7 3 4 3 5 alternatively you can use a parameter to specify the python version which you want to use like this sudo setup sh python version p 2 7 3 4 3 5 known build issues 1 on building the python client library iothub client so on linux devices that have less than 1gb ram you may see build getting stuck at 98 while building iothub client python cpp as shown below 98 building cxx object python src cmakefiles iothub client python dir iothub client python cpp o if you run into this issue check the memory consumption of the device using free m command in another terminal window during that time if you are running out of memory while compiling iothub client python cpp file you may have to temporarily increase the swap space to get more available memory to successfully build the python client side device sdk library step 4 run your client application run the client application and you need to provide your azure iot hub device connection string note your connection string should be quoted in the command python app py your azure iot hub device connection string if you use the python 3 then you can use the command below python3 app py your azure iot hub device connection string if the application works normally then you will see the screen like this imgs success png | server |
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LaMP | lamp when large language models meet personalization this paper highlights the importance of personalization in the current state of natural language understanding and generation and introduces the lamp benchmark a novel benchmark for training and evaluating language models for producing personalized outputs lamp offers a comprehensive evaluation framework with diverse language tasks and multiple entries for each user profile it consists of seven personalized tasks spanning across three classification and four text generation tasks we further propose a retrieval augmentation approach that retrieves personalized items from user profiles to construct personalized prompts for large language models the experiments conducted to establish fine tuned and zero shot baseline results for the benchmark conclude that lms utilizing profile augmentation outperform their counterparts that do not factor in profile information data you can download all the datasets from the links provided here https lamp benchmark github io download however we provided the minimal ids to generate the dataset using our codes for the personalized email subject generation because this dataset is not publicly accessible follow the following section to generate that dataset lamp 6 personalized email subject generation avocado dataset the avocado https catalog ldc upenn edu ldc2015t03 dataset is not publicly accessible however we provided the samples id and the code we used to generate our dataset therefore if you get access to the dataset you can quickly generate the dataset with the same format as the other datasets in lamp using the following code python data avocado create avocado dataset py avocado files dir address to the directory containing zip files for avocado dataset avocado 1 0 2 data text extract addr a temp dir to extract the files for creating dataset output dir the directory to generate the final dataset input question file train the address to the train questions json file we provided in lamp input question file dev the address to the dev questions json file we provided in lamp input question file test the address to the test questions json file we provided in lamp evaluation the instructions for evaluating your results on the test set are provided here https lamp benchmark github io leaderboard in order to evaluate your results on the dev set we provided an evaluation script that can be found here evaluate all tasks together python eval eval all py golds zip address to all gold labels for all tasks zipped in a file preds zip address to all predictions for all tasks zipped in a file temp dir address to a temp dir for extracting files output file address to the results file evaluate one task python eval eval task py golds json address to gold labels for the task as a json file preds json address to predictions for the task as a json file task name name of the task lamp 1 lamp 2 lamp 3 lamp 4 lamp 5 lamp 6 lamp 7 output file address to the results file the pred files should follow the exact same format as the gold files task task name golds id sample 1 id output output of the model for the first sample id sample n id output output of the model for the n th sample reference lamp when large language models meet personalization https arxiv org abs 2304 11406 misc salemi2023lamp title la mp when large language models meet personalization author alireza salemi and sheshera mysore and michael bendersky and hamed zamani year 2023 eprint 2304 11406 archiveprefix arxiv primaryclass cs cl license lamp codes and data creation methods is licensed by attribution noncommercial sharealike 4 0 international cc by nc sa 4 0 see the cc by nc sa 4 0 txt cc by nc sa 4 0 txt file for details for the datasets in this benchmark you should follow their license acknowledgments this work was supported in part by the center for intelligent information retrieval in part by nsf grant 2143434 in part by the office of naval research contract number n000142212688 and in part by lowe s any opinions findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors | deep-learning information-retrieval machine-learning natural-language-processing nlp | ai |
ijburgcollege | ijburgcollege information technology software development | server |
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jdf | jdf npm version https badge fury io js jdf png http badge fury io js jdf build status https api travis ci org putaoshu jdf svg branch master https travis ci org putaoshu jdf npm https nodei co npm jdf png downloads true https nodei co npm jdf jdf jdf jingdong front end integrated solution ui changelog https github com putaoshu jdf blob master changelog md jdf nodejs python nodejs http nodejs org download node v4 2 6 v6 9 4 https nodejs org dist v6 9 4 python https www python org downloads python jdf npm install jdf g jdf v https github com putaoshu jdf blob master doc a tool develop md https github com putaoshu jdf blob master doc core dir standard md https github com putaoshu jdf blob master doc a tool config md https github com putaoshu jdf blob master doc a tool command md widget https github com putaoshu jdf blob master doc core widget md js https github com putaoshu jdf blob master doc core js md js https github com putaoshu jdf blob master doc core tpl md css https github com putaoshu jdf blob master doc core css md vm https github com putaoshu jdf blob master doc core vm md smarty https github com putaoshu jdf blob master doc core smarty md https github com putaoshu jdf blob master doc a tool format md server https github com putaoshu jdf blob master doc a tool server md lint https github com putaoshu jdf blob master doc a tool lint md livereload https github com putaoshu jdf blob master doc a tool livereload md csssprite https github com putaoshu jdf blob master doc a tool csssprite md windows mac linux vm smarty html sass less css es6 js css combo js css png cdn cmd id dependencies require png png24 png8 css background position base64 webp css gbk gb2312 utf8 utf8 bom utf8 css html js css html js css html js css lint chrome livereload jdf demo https qn kabuyun com jdf demo tar jdf windows jdf ui https qn kabuyun com jdf ui exe jdf sublime text2 https sublime wbond net packages jdf 20 20tool jdf https qn kabuyun com jdf build platform gif jdd https github com putaoshu jdd qq jdf 305542952 | jdf | front_end |
Harry-Potter-Cloak | harry potter cloak this is a beginner level computer vision project implemented using opencv with python as its language br h3 concepts used h3 br 1 image thresholding br 2 image masking br 3 space conversion br 4 video input br h5 system requirements python 3 opencv numpy installed h3 how to use this project h3 1 keep the pc stable and ensure that there in nothing initially infront of camera br 2 run the python script given above br 3 two windows will pop up the first one giving the hue saturation value of colour of sheet the second displaying output br 4 adjust the hsv values for the color of sheet to be in range br 5 enjoy the effect h4 screenshots h4 br p img src https raw githubusercontent com infoaryan harry potter cloak master screenshot1 png width 400 height 300 img src https raw githubusercontent com infoaryan harry potter cloak master screenshot2 png width 400 height 300 p | ai |
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CvPytorch | cvpytorch cvpytorch is an open source computer vision toolbox based on pytorch what s new 2022 10 16 release segnext models with segnext s segnext b segnext l on cityscapes https www cityscapes dataset com 81 22 82 49 82 57 miou 2022 10 04 release segnext models with segnext t on cityscapes https www cityscapes dataset com 79 83miou 2022 09 25 release objectbox models with objectbox l on coco http mscoco org 38 38map 2022 09 21 release yolox pai models with yolox pai s on coco http mscoco org 41 06map pls refer to conf coco pai yolox s yml conf coco pai yolox s yml 2022 09 18 release yolo7 models with yolov7 l on coco http mscoco org 45 26map 2022 09 12 release objectbox models with objectbox m on coco http mscoco org 36 41map 2022 08 03 release lpsnet models with lpsnet s on cityscapes https www cityscapes dataset com 56 66miou 2022 07 17 release fastestdet models on coco http mscoco org 11 42map 2022 07 10 release yolo6 models with yolov6 s on coco http mscoco org 39 63map 2022 06 11 release yolox models with yolox s on coco http mscoco org 38 36map 2022 05 31 release yolov5 models with yolov5 s on coco http mscoco org 36 10map pls refer to conf coco yolov5 s yml conf coco yolov5 s yml 2022 05 19 release efficientnet models with efficientnet b0 efficientnet b1 efficientnet b2 efficientnet b3 efficientnet b4 efficientnet b5 efficientnet b6 and efficientnet b7 on mini imagenet http image net org 85 08 85 60 85 74 86 06 88 69 85 62 85 76 and 85 54 macc 2022 05 16 release convnext models with convnext tiny convnext small convnext base and convnext large on mini imagenet http image net org 83 45 83 97 85 32 and 85 90 macc 2022 05 14 release vgg models with vgg11 vgg13 vgg16 and vgg19 on mini imagenet http image net org 58 52 62 32 56 45 and 50 36 miou 2022 05 12 release shufflenetv2 x0 5 and shufflenetv2 x1 0 models on mini imagenet http image net org 63 85 and 69 80macc 2022 05 11 release mobilenet v3 large models on mini imagenet http image net org 83 26macc 2022 05 11 release mobilenet v3 small models on mini imagenet http image net org 80 08macc 2022 04 27 release resnet50 models on mini imagenet http image net org 69 02macc 2022 04 27 release mobilenetv2 models on mini imagenet http image net org 77 47macc 2022 04 26 release nanodet plus 320 models with shufflenetv2 backbone on coco http mscoco org 25 89map 2022 04 25 release topformer models with topformer tiny on cityscapes https www cityscapes dataset com 71 00miou 2022 04 24 release topformer models with topformer small on cityscapes https www cityscapes dataset com 72 86miou 2022 04 22 release topformer models with topformer base on cityscapes https www cityscapes dataset com 74 60miou 2022 04 20 release sgcpnet models with mobilenet v3 on cityscapes https www cityscapes dataset com 56 47miou 2022 03 03 release regseg models with exp48 decoder26 on cityscapes https www cityscapes dataset com 73 76miou 2022 01 06 release fcos models with resnet50 backbone for 800x800 image on coco http mscoco org 36 88map 2021 07 23 release nanodet models with repvgg backbone on coco http mscoco org 27 16map 2021 07 23 release nanodet g models with cspnet backbone on coco http mscoco org 23 54map 2021 07 23 release nanodet models with efficientnet lite backbone on coco http mscoco org 25 65map 2021 07 22 release nanodet t models with transformer neck on coco http mscoco org 21 97map 2021 07 20 release nanodet 416 models with shufflenetv2 backbone on coco http mscoco org 23 30map 2021 07 07 release stdc models with stdc2 backbone on cityscapes https www cityscapes dataset com 73 36miou 2021 07 06 release stdc models with stdc1 backbone on cityscapes https www cityscapes dataset com 72 89miou 2021 07 05 release nanodet 320 models with shufflenetv2 backbone on coco http mscoco org 20 54map 2021 07 01 release deeplab v3 models with resnet50 backbone on cityscapes https www cityscapes dataset com 72 96miou 2021 06 28 release unet models on cityscapes https www cityscapes dataset com 56 90miou 2021 06 20 release pspnet models with resnet50 backbone on cityscapes https www cityscapes dataset com 72 59miou 2021 06 15 release deeplab v3 models with mobilenet v2 resnet50 and resnet101 backbone on cityscapes https www cityscapes dataset com 68 06 71 53 and 72 83miou dependencies python 3 8 pytorch 1 6 0 torchvision 0 7 0 tensorboardx 2 1 models image classification x vgg vgg very deep convolutional networks for large scale image recognition x resnet resnet deep residual learning for image recognition x densenet densenet densely connected convolutional networks x shufflenet shufflenet an extremely efficient convolutional neural network for mobile devices x shufflenet v2 shufflenet v2 practical guidelines for ecient cnn architecture design object detection ssd ssd single shot multibox detector faster r cnn faster r cnn towards real time object detection with region proposal networks yolov3 yolov3 an incremental improvement yolov5 fpn fpn feature pyramid networks for object detection x fcos fcos fully convolutional one stage object detection semantic segmentation fcn fully convolutional networks for semantic segmentation x deeplab v3 encoder decoder with atrous separable convolution for semantic image segmentation x pspnet pyramid scene parsing network x enet a deep neural network architecture for real time semantic segmentation x u net convolutional networks for biomedical image segmentation x segnet a deep convolutionalencoder decoder architecture for imagesegmentation instance segmentation x mask rcnn mask rcnn datasets pascal voc http host robots ox ac uk pascal voc cityscapes https www cityscapes dataset com ade20k http groups csail mit edu vision datasets ade20k coco http mscoco org pennped https www cis upenn edu jshi ped html camvid http mi eng cam ac uk research projects videorec camvid install training for this example we will use coco https github com ultralytics yolov5 blob master data get coco2017 sh dataset with yolov5l yaml feel free to use your own custom dataset and configurations single gpu bash python trainer py setting conf hymenoptera yml multiple gpus bash python m torch distributed launch nproc per node 2 trainer py setting conf hymenoptera yml inference todo x train custom data x multi gpu training x mixed precision training x warm up x gradient accumulation gradient checkpoint x clip grad model pruning sparsity quantization tensorrt deployment onnx and torchscript export class activation mapping cam test time augmentation tta license mit license copyright c 2020 min liu | ai |
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kornia-rs | kornia rs low level implementations for computer vision in rust continuous integration https github com kornia kornia rs actions workflows ci yml badge svg https github com kornia kornia rs actions workflows ci yml pypi version https badge fury io py kornia rs svg https badge fury io py kornia rs license https img shields io badge license apache 202 0 blue svg licence slack https img shields io badge slack 4a154b logo slack logocolor white https join slack com t kornia shared invite zt csobk21g cnydwe5fmvkcktierfgceq this project provides low level functionality for computer vision written in rust https www rust lang org to be consumed by machine learning and data science frameworks specially those working with images we mainly aim to provide i o functionality for images future video cameras and visualisation in future the library is written in rust https www rust lang org python bindings are created with pyo3 maturin https github com pyo3 maturin we package with support for linux amd64 arm64 macos and windows supported python versions are 3 7 3 8 3 9 3 10 3 11 installation from pip bash pip install kornia rs from source bash pip install maturin maturin build release out dist i which python3 pip install dist whl basic usage load an image that is converted to cv tensor wich is a centric structure to the dlpack protocol to share tensor data across frameworks with a zero copy cost python import kornia rs as k from kornia rs import tensor as cvtensor load an image with rust image rs as backend library cv tensor cvtensor k read image rs dog jpeg assert cv tensor shape 195 258 3 convert to dlpack to import to torch th tensor torch utils dlpack from dlpack cv tensor assert th tensor shape 195 258 3 assert np tensor shape 195 258 3 or to numpy with same interface np tensor np from dlpack cv tensor advanced usage encode or decoda image streams using the turbojpeg backend python load image using turbojpeg cv tensor k read image jpeg dog jpeg image np ndarray np from dlpack cv tensor hxwx3 encode the image with jpeg image encoder k imageencoder image encoder set quality 95 set the encoding quality get the encoded stream image encoded list int image encoder encode image tobytes image shape write to disk the encoded stream k write image jpeg dog encoded jpeg image encoded decode back the image image decoder k imagedecoder decoded tensor image decoder decode bytes image encoded decoded image np ndarray np from dlpack decoded tensor hxwx3 todo short mid terrm x infra automate packaging for manywheels x kornia integrate with the new image api x dlpack move dlpack implementation to dlpack rs x dlpack implement test for torch and numpy dlpack update dlpack version 0 8 dlpack implement dlpack to cv tensor todo not priority for now io implement image encoding and explore video viz fix minor issues and implement a full vizmanager to work on the browser tensor implement basic functionality to test add sub mul etc tensor explore xnnpack and openvino integration development to test the project in lyour local machine use the following instructions 1 clone the repository in your local directory bash git clone https github com kornia kornia rs git 2 1 optional build the devel dockerfile let s prepare the development environment with docker make sure you have docker in your system https docs docker com engine install ubuntu bash cd docker build devel sh kornia rs devel image kornia rs devel local devel sh 2 2 enter to the devel docker container bash devel sh 3 build the project you should now be inside the docker container bash maturin needs you to be a venv python3 m venv venv source venv bin activate build and generate linked wheels maturin develop extras dev 4 run the tests bash pytest test contributing this is a child project of kornia https github com kornia kornia join the community to get in touch with us or just sponsor the project https opencollective com kornia | ai |
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OnsenUI | p align center a href https onsen io target blank img width 140 src https onsenui github io art logos onsenui logo 1 png a p p align center a href https circleci com gh onsenui onsenui img src https circleci com gh onsenui onsenui svg style shield alt circle ci a a href https badge fury io js onsenui img src https badge fury io js onsenui svg alt npm version a a href https cdnjs com libraries onsen img src https img shields io cdnjs v onsen svg alt cdnjs a p onsen ui https onsen io cross platform hybrid app and pwa framework onsen ui is an strong open source strong framework that makes it easy to create native feeling progressive web apps pwas and hybrid apps the core library is written in strong pure javascript strong on top of a href http webcomponents org web components a and is strong framework agnostic strong which means you can use it with your favorite framework and its tools we provide some extra binding packages to make it easy to use onsen ui s api with many popular 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library to install bindings you can install react onsenui vue onsenui ngx onsenui or angularjs onsenui download or request from a cdn you can also take the necessary files from a cdn some of the options are unpkg https unpkg com onsenui jsdelivr https www jsdelivr com package npm onsenui and cdnjs https cdnjs com libraries onsen get the latest development build optionally you can download the latest development build here https onsenui github io latest build be careful usually everything there is already tested but it might be unstable sometimes examples with source code there are lots of sample applications written using onsen ui here are some examples https onsen io samples with source code and tutorials to give you an idea of what kind of apps you can create p align center a href https argelius github io angular2 onsenui pokedex target blank img src https onsen io images samples pokedex pikachu png a a href http argelius github io react onsenui redux weather demo html target blank img src https onsen io images samples react redux weather png a a href https frandiox github io onsenui youtube target blank img src https onsen io images samples youtube png a p onsen ui ecosystem because sometimes a ui framework may not be enough to make hybrid app development easy onsen ui comes with a complete ecosystem of well integrated tools meet monaca https monaca io p align center a href https monaca io target blank img width 300 src https onsenui github io art logos monaca logo 2 png a p developed by the onsen ui team monaca is a toolkit that makes hybrid mobile app development with phonegap cordova simple and easy onsen ui cordova templates debugging suite push notifications remote build back end solutions encryption version control continuous integration and more furthermore it provides multiple development environments with everything already configured and ready to go p align center a href https monaca io cloud html strong cloud ide strong a a href https monaca io cli html strong command line interface strong a a href https monaca io localkit html strong localkit gui strong a p example with cli sudo npm g install monaca monaca create helloworld and choose the starter template monaca preview preview on the browser monaca debug preview on a real device monaca remote build browser production build on the cloud see the onsen ui getting started page http onsen io v2 guide for more information browser support onsen ui is tested to work with the following browsers and mobile os android 4 4 4 ios 9 chrome safari contribution we welcome your contribution no matter how big or small please have a look at the contribution guide https github com onsenui onsenui blob master contributing md for details about project structure development environment test suite code style etc all the version updates are mentioned in the changelog https github com onsenui onsenui blob master changelog md | onsen-ui cordova react angular monaca hybrid-apps javascript webcomponents customelements vue material android ios html pwa | front_end |
azubi-cloud-engineering | azubi cloud engineering the azubi africa cloud engineering program aims to impart the skills needed to build deploy and design successful cloud architecture and to grow into a resilient cloud engineer in the long run the program is also tailored in such a way to accommodate people with limited it experience who want to develop their existing knowledge in the field and explore cloud engineering as a career the program is divided into two phases the platform agnostic and the project phase to prepare you adequately for a great life in tech | cloud |
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NomadAdvisor | nomad advisor application this repository contains a java application for the large scale and multistructured databases course assignment the workgroup must design a complete application which manages a big dataset stored in a document database costrains crud operation must be implemented some analytics and statistics on the dataset must be included as main functionalities of the application thus at least two aggregation pipelines must be designed at least three indexes must be defined replica set must be used define appropriately functional and non functional requirements some administration use cases must be defined dataset the dataset must be a real dataset characterized by a large volume at least 200mb documentation for more details regarding the project you can consult the official documentation docs documentation pdf credits d comola e petrangeli c aparo l fontanelli | server |
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ML-YouTube-Courses | ml youtube courses at dair ai we open ai education in this repo we index and organize some of the best and most recent machine learning courses available on youtube machine learning caltech cs156 learning from data caltech cs156 learning from data stanford cs229 machine learning stanford cs229 machine learning making friends with machine learning making friends with machine learning applied machine learning applied machine learning introduction to machine learning t bingen introduction to machine learning t bingen machine learning lecture stefan harmeling machine learning lecture stefan harmeling statistical machine learning t bingen statistical machine learning t bingen probabilistic machine learning probabilistic machine learning mit 6 s897 machine learning for healthcare 2019 mit 6s897 machine learning for healthcare 2019 deep learning neural networks zero to hero neural networks zero to hero by andrej karpathy mit deep learning for art aesthetics and creativity mit deep learning for art aesthetics and creativity stanford cs230 deep learning 2018 stanford cs230 deep learning 2018 introduction to deep learning mit introduction to deep learning cmu introduction to deep learning 11 785 cmu introduction to deep learning 11 785 deep learning cs 182 deep learning cs 182 deep unsupervised learning deep unsupervised learning nyu deep learning sp21 nyu deep learning sp21 foundation models foundation models deep learning t bingen deep learning t bingen deep learning playlist https www youtube com playlist list plzotaelrmxvpgu70zgsckrmdr0fteerui scientific machine learning parallel computing and scientific machine learning parallel computing and scientific machine learning practical machine learning evaluating and debugging generative ai evaluating and debugging generative ai chatgpt prompt engineering for developers chatgpt prompt engineering for developers langchain for llm application development langchain for llm application development langchain chat with your data langchain chat with your data building systems with the chatgpt api building systems with the chatgpt api langchain vector databases in production langchain vector databases in production building llm powered apps building llm powered apps full stack llm bootcamp full stack llm bootcamp full stack deep learning full stack deep learning practical deep learning for coders practical deep learning for coders stanford mlsys seminars stanford mlsys seminars machine learning engineering for production mlops machine learning engineering for production mlops mit introduction to data centric ai mit introduction to data centric ai natural language processing xcs224u natural language understanding 2023 xcs224u natural language understanding 2023 stanford cs25 transformers united stanford cs25 transformers united nlp course hugging face nlp course hugging face cs224n natural language processing with deep learning cs224n natural language processing with deep learning cmu neural networks for nlp cmu neural networks for nlp cs224u natural language understanding cs224u natural language understanding cmu advanced nlp 2021 2022 cmu advanced nlp multilingual nlp multilingual nlp advanced nlp advanced nlp computer vision cs231n convolutional neural networks for visual recognition cs231n convolutional neural networks for visual recognition deep learning for computer vision deep learning for computer vision deep learning for computer vision dl4cv deep learning for computer vision dl4cv reinforcement learning deep reinforcement learning deep reinforcement learning reinforcement learning lecture series deepmind reinforcement learning lecture series deepmind reinforcement learning polytechnique montreal fall 2021 reinforcement learning polytechnique montreal fall 2021 foundations of deep rl foundations of deep rl stanford cs234 reinforcement learning stanford cs234 reinforcement learning graph machine learning machine learning with graphs stanford machine learning with graphs stanford ammi geometric deep learning course ammi geometric deep learning course multi task learning multi task and meta learning stanford stanford cs330 deep multi task and meta learning others mit deep learning in life sciences mit deep learning in life sciences self driving cars t bingen self driving cars t bingen advanced robotics berkeley advanced robotics uc berkeley caltech cs156 learning from data an introductory course in machine learning that covers the basic theory algorithms and applications lecture 1 the learning problem lecture 2 is learning feasible lecture 3 the linear model i lecture 4 error and noise lecture 5 training versus testing lecture 6 theory of generalization lecture 7 the vc dimension lecture 8 bias variance tradeoff lecture 9 the linear model ii lecture 10 neural networks lecture 11 overfitting lecture 12 regularization lecture 13 validation lecture 14 support vector machines lecture 15 kernel methods lecture 16 radial basis functions lecture 17 three learning principles lecture 18 epilogue link to course https www youtube com playlist list pld63a284b7615313a stanford cs229 machine learning to learn some of the basics of ml linear regression and gradient descent logistic regression naive bayes svms kernels decision trees introduction to neural networks debugging ml models link to course https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu making friends with machine learning a series of mini lectures covering various introductory topics in ml explainability in ai classification vs regression precession vs recall statistical significance clustering and k means ensemble models link to course https www youtube com playlist list plrktj4ipxjpdxl0ntvnyqwkcyzhnuy2xg neural networks zero to hero by andrej karpathy course providing an in depth overview of neural networks backpropagation spelled out intro to language modeling activation and gradients becoming a backprop ninja link to course https www youtube com playlist list plaqhirjkxbuwi23v9cthsa9gvcauhrvkz mit deep learning for art aesthetics and creativity covers the application of deep learning for art aesthetics and creativity nostalgia art creativity evolution as data direction efficient gans explorations in ai for creativity neural abstractions easy 3d content creation with consistent neural fields link to course https www youtube com playlist list plcpmvp7ftsnibnwrnqjbdnrqo6qin3eyh stanford cs230 deep learning 2018 covers the foundations of deep learning how to build different neural networks cnns rnns lstms etc how to lead machine learning projects and career advice for deep learning practitioners deep learning intuition adversarial examples gans full cycle of a deep learning project ai and healthcare deep learning strategy interpretability of neural networks career advice and reading research papers deep reinforcement learning link to course https youtube com playlist list ploromvodv4roabxsyghtsbvuz4g yqhob link to materials https cs230 stanford edu syllabus applied machine learning to learn some of the most widely used techniques in ml optimization and calculus overfitting and underfitting regularization monte carlo estimation maximum likelihood learning nearest neighbours link to course https www youtube com playlist list pl2uml kcic0uly7icqdsigdmovaupqc83 introduction to machine learning t bingen the course serves as a basic introduction to machine learning and covers key concepts in regression classification optimization regularization clustering and dimensionality reduction linear regression logistic regression regularization boosting neural networks pca clustering link to course https www youtube com playlist list pl05ump7r6ij35shkldqccjsdntugy4fqt machine learning lecture stefan harmeling covers many fundamental ml concepts bayes rule from logic to probabilities distributions matrix differential calculus pca k means and em causality gaussian processes link to course https www youtube com playlist list plzrcxlf6ypbxs5oyoy3en 0u2fduit6gt statistical machine learning t bingen the course covers the standard paradigms and algorithms in statistical machine learning knn bayesian decision theory convex optimization linear and ridge regression logistic regression svm random forests boosting pca clustering link to course https www youtube com playlist list pl05ump7r6ij2xcvrrzlokx6eohwaga2cc practical deep learning for coders this course covers topics such as how to build and train deep learning models for computer vision natural language processing tabular analysis and collaborative filtering problems create random forests and regression models deploy models use pytorch the world s fastest growing deep learning software plus popular libraries like fastai and hugging face foundations and deep dive to diffusion models link to course part 1 https www youtube com playlist list plfyubjixbdtsvpqjsnjj pmdqb vyt5iu link to course part 2 https www youtube com watch v 7rmfsa24ls ab channel jeremyhoward stanford mlsys seminars a seminar series on all sorts of topics related to building machine learning systems link to lectures https www youtube com playlist list plsrtvum384i9pv10koj cqit9ofbjxekq machine learning engineering for production mlops specialization course on mlops by andrew ng link to lectures https www youtube com playlist list plkdae6sczn6gmoa0wbpjli3t34gd8l0ak mit introduction to data centric ai covers the emerging science of data centric ai dcai that studies techniques to improve datasets which is often the best way to improve performance in practical ml applications topics include data centric ai vs model centric ai label errors dataset creation and curation data centric evaluation of ml models class imbalance outliers and distribution shift course website https dcai csail mit edu lecture videos https www youtube com watch v ayzozzghzy4 list plnsypjg2dhqkdig0vvbn zneu0ynj1mo5 lab assignments https github com dcai course dcai lab machine learning with graphs stanford to learn some of the latest graph techniques in machine learning pagerank matrix factorizing node embeddings graph neural networks knowledge graphs deep generative models for graphs link to course https www youtube com playlist list ploromvodv4rplkxipqhjhpgdqy7imnkdn probabilistic machine learning to learn the probabilistic paradigm of ml reasoning about uncertainty continuous variables sampling markov chain monte carlo gaussian distributions graphical models tuning inference algorithms link to course https www youtube com playlist list pl05ump7r6ij2ye8rrjsb oldnbntaq bx mit 6 s897 machine learning for healthcare 2019 this course introduces students to machine learning in healthcare including the nature of clinical data and the use of machine learning for risk stratification disease progression modeling precision medicine diagnosis subtype discovery and improving clinical workflows link to course https www youtube com playlist list plul4u3cngp60b0pqxvqygndcyctdu1q5j introduction to deep learning to learn some of the fundamentals of deep learning introduction to deep learning link to course https www youtube com playlist list pltbw6njqru rwp5 7c0oivt26zgjg9ni cmu introduction to deep learning 11 785 the course starts off gradually from mlps multi layer perceptrons and then progresses into concepts like attention and sequence to sequence models link to course https deeplearning cs cmu edu f22 index html lectures https www youtube com playlist list plp 0k3kfddpxrmjgjm0p1wt6h gtqe8j9 tutorials recitations https www youtube com playlist list plp 0k3kfddpz8wxg8rqh0sen6x2l65huz deep learning cs 182 to learn some of the widely used techniques in deep learning machine learning basics error analysis optimization backpropagation initialization batch normalization style transfer imitation learning link to course https www youtube com playlist list pl iwqose6tfvmkkqhucjpaortijyt8a5a deep unsupervised learning to learn the latest and most widely used techniques in deep unsupervised learning autoregressive models flow models latent variable models self supervised learning implicit models compression link to course https www youtube com playlist list plwrjq4m4ujjpijp3691u qwwpgvkzslnp nyu deep learning sp21 to learn some of the advanced techniques in deep learning neural nets rotation and squashing latent variable energy based models unsupervised learning generative adversarial networks autoencoders link to course https www youtube com playlist list pllhtzkzzvu9e6xufg10tktwapkszczubi foundation models to learn about foundation models like gpt 3 clip flamingo codex and dino link to course https www youtube com playlist list pl9t0xvfp90gd8hox0kipbkjclx c3ja67 deep learning t bingen this course introduces the practical and theoretical principles of deep neural networks computation graphs activation functions and loss functions training regularization and data augmentation basic and state of the art deep neural network architectures including convolutional networks and graph neural networks deep generative models such as auto encoders variational auto encoders and generative adversarial networks link to course https www youtube com playlist list pl05ump7r6ij3ntwidtmbfvx7z 4wexrqd parallel computing and scientific machine learning the basics of scientific simulators introduction to parallel computing continuous dynamics inverse problems and differentiable programming distributed parallel computing physics informed neural networks and neural differential equations probabilistic programming aka bayesian estimation on programs globalizing the understanding of models link to course https www youtube com playlist list plcal7tjcwwygjdzoonlbgnvnzk0kb8vsa xcs224u natural language understanding 2023 this course covers topics such as contextual word representations information retrieval in context learning behavioral evaluation of nlu models nlp methods and metrics link to course https www youtube com playlist list ploromvodv4rowvldxftjtmor3krcwkjbp stanford cs25 transformers united this course consists of lectures focused on transformers providing a deep dive and their applications introduction to transformers transformers in language gpt 3 codex applications in vision transformers in rl universal compute engines scaling transformers interpretability with transformers link to course https www youtube com playlist list ploromvodv4rnijrchczutfw5itr z27cm nlp course hugging face learn about different nlp concepts and how to apply language models and transformers to nlp what is transfer learning bpe tokenization batching inputs fine tuning models text embeddings and semantic search model evaluation link to course https www youtube com playlist list plo2eipi jmqvwfqnduesu0npbatz9gp1o cs224n natural language processing with deep learning to learn the latest approaches for deep learning based nlp dependency parsing language models and rnns question answering transformers and pretraining natural language generation t5 and large language models future of nlp link to course https www youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj cmu neural networks for nlp to learn the latest neural network based techniques for nlp language modeling efficiency tricks conditioned generation structured prediction model interpretation advanced search algorithms link to course https www youtube com playlist list pl8pytp1v4i8akahej7loorlex pcxs xv cs224u natural language understanding to learn the latest concepts in natural language understanding grounded language understanding relation extraction natural language inference nli nlu and neural information extraction adversarial testing link to course https www youtube com playlist list ploromvodv4rpt5d0zs3yhbwsza8q dyij cmu advanced nlp to learn basics of modern nlp techniques multi task multi domain multi lingual learning prompting sequence to sequence pre training interpreting and debugging nlp models learning from knowledge bases adversarial learning link to 2021 edition https www youtube com playlist list pl8pytp1v4i8aysxn gkvgwxvluct9chj6 link to 2022 edition https www youtube com playlist list pl8pytp1v4i8d0ukqw2fehglrnldw9qk7z multilingual nlp to learn the latest concepts for doing multilingual nlp typology words part of speech and morphology advanced text classification machine translation data augmentation for mt low resource asr active learning link to 2020 course https www youtube com playlist list pl8pytp1v4i8chhppu6n1q9 04m96d9gt5 link to 2022 course https www youtube com playlist list pl8pytp1v4i8bhcpzfdkkdd1ontflcyzr7 advanced nlp to learn advanced concepts in nlp attention mechanisms transformers bert question answering model distillation vision language ethics in nlp commonsense reasoning link to course https www youtube com playlist list plwnsvgp6czadmqx6qevbar3 vdbiowhjl cs231n convolutional neural networks for visual recognition stanford s famous cs231n course the videos are only available for the spring 2017 semester the course is currently known as deep learning for computer vision but the spring 2017 version is titled convolutional neural networks for visual recognition image classification loss functions and optimization introduction to neural networks convolutional neural networks training neural networks deep learning software cnn architectures recurrent neural networks detection and segmentation visualizing and understanding generative models deep reinforcement learning link to course https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv link to materials http cs231n stanford edu 2017 deep learning for computer vision to learn some of the fundamental concepts in cv introduction to deep learning for cv image classification convolutional networks attention networks detection and segmentation generative models link to course https www youtube com playlist list pl5 tkqafazfbzxjbhtzdvcwe0zbhomg7r deep learning for computer vision dl4cv to learn modern methods for computer vision cnns advanced pytorch understanding neural networks rnn attention and vits generative models gpu fundamentals self supervision neural rendering efficient architectures link to course https www youtube com playlist list pl z2 u9mijdngfm7 f2fz9zxjvrp jhjv ammi geometric deep learning course to learn about concepts in geometric deep learning learning in high dimensions geometric priors grids manifolds and meshes sequences and time warping link to course https www youtube com playlist list pln2 demqetfslxw8yxp4q ii58wfdxb3c deep reinforcement learning to learn the latest concepts in deep rl intro to rl rl algorithms real world sequential decision making supervised learning of behaviors deep imitation learning cost functions and reward functions link to course https www youtube com playlist list pl iwqose6tfuriihcrlt wj9byivpbfgc reinforcement learning lecture series deepmind the deep learning lecture series is a collaboration between deepmind and the ucl centre for artificial intelligence introduction to rl dynamic programming model free algorithms deep reinforcement learning link to course https www youtube com playlist list plqymg7htrazdvh599eitlewsuosjbaodm evaluating and debugging generative ai you ll learn instrument a jupyter notebook manage hyperparameters config log run metrics collect artifacts for dataset and model versioning log experiment results trace prompts and responses for llms link to course https www deeplearning ai short courses evaluating debugging generative ai chatgpt prompt engineering for developers learn how to use a large language model llm to quickly build new and powerful applications link to course https www deeplearning ai short courses chatgpt prompt engineering for developers langchain for llm application development you ll learn models prompt and parsers memories for llms chains question answering over documents agents link to course https www deeplearning ai short courses langchain for llm application development langchain chat with your data you ll learn about document loading document splitting vector stores and embeddings retrieval question answering chat link to course https www deeplearning ai short courses building systems with chatgpt building systems with the chatgpt api learn how to automate complex workflows using chain calls to a large language model link to course https www deeplearning ai short courses building systems with chatgpt langchain vector databases in production learn how to use langchain and vector dbs in production llms and langchain learning how to prompt keeping knowledge organized with indexes combining components together with chains link to course https learn activeloop ai courses langchain building llm powered apps learn how to build llm powered applications using llm apis unpacking llm apis building a baseline llm application enhancing and optimizing llm applications link to course https www wandb courses courses building llm powered apps full stack llm bootcamp to learn how to build and deploy llm powered applications learn to spell prompt engineering llmops ux for language user interfaces augmented language models launch an llm app in one hour llm foundations project walkthrough askfsdl link to course https fullstackdeeplearning com llm bootcamp spring 2023 full stack deep learning to learn full stack production deep learning ml projects infrastructure and tooling experiment managing troubleshooting dnns data management data labeling monitoring ml models web deployment link to course https www youtube com playlist list pl1t8fo7arwlcwg04ognijy91pywmkt2lv introduction to deep learning and deep generative models covers the fundamental concepts of deep learning single layer neural networks and gradient descent multi layer neural networks and backpropagation convolutional neural networks for images recurrent neural networks for text autoencoders variational autoencoders and generative adversarial networks encoder decoder recurrent neural networks and transformers pytorch code examples link to course https www youtube com watch v 1nqczqdypp0 list pltkmizhvd 2kjtixow0zfhffbajjilh51 link to materials https sebastianraschka com blog 2021 dl course html self driving cars t bingen covers the most dominant paradigms of self driving cars modular pipeline based approaches as well as deep learning based end to end driving techniques camera lidar and radar based perception localization navigation path planning vehicle modeling control deep learning imitation learning reinforcement learning link to course https www youtube com playlist list pl05ump7r6ij321zzkxk6xcqxaaayjqbzr reinforcement learning polytechnique montreal fall 2021 designing autonomous decision making systems is one of the longstanding goals of artificial intelligence such decision making systems if realized can have a big impact in machine learning for robotics game playing control health care to name a few this course introduces reinforcement learning as a general framework to design such autonomous decision making systems introduction to rl multi armed bandits policy gradient methods contextual bandits finite markov decision process dynamic programming policy iteration value iteration monte carlo methods link to course https www youtube com playlist list plimtcgowf es jdf ucm60extcgzg67ua link to materials https chandar lab github io inf8953de foundations of deep rl a mini 6 lecture series by pieter abbeel mdps exact solution methods max ent rl deep q learning policy gradients and advantage estimation trpo and ppo ddpg and sac model based rl link to course https www youtube com playlist list plwrjq4m4ujjnymubm9rdmb3z9n5 0ily0 stanford cs234 reinforcement learning covers topics from basic concepts of reinforcement learning to more advanced ones markov decision processes planning model free policy evaluation model free control reinforcement learning with function approximation deep rl policy search exploration link to course https www youtube com playlist list ploromvodv4rosopzutgyctapigly2nd8u link to materials https web stanford edu class cs234 stanford cs330 deep multi task and meta learning this is a graduate level course covering different aspects of deep multi task and meta learning multi task learning transfer learning basics meta learning algorithms advanced meta learning topics multi task rl goal conditioned rl meta reinforcement learning hierarchical rl lifelong learning open problems link to course https www youtube com playlist list ploromvodv4rmc6zfymnd7ug3lvvwaity5 link to materials https cs330 stanford edu mit deep learning in life sciences a course introducing foundations of ml for applications in genomics and the life sciences more broadly interpreting ml models dna accessibility promoters and enhancers chromatin and gene regulation gene expression splicing rna seq splicing single cell rna sequencing dimensionality reduction genetics and variation drug discovery protein structure prediction protein folding imaging and cancer neuroscience link to course https www youtube com playlist list plypixjdtica5elzmwhl4hmeyle2azugvb link to materials https mit6874 github io advanced robotics uc berkeley this is course is from peter abbeel and covers a review on reinforcement learning and continues to applications in robotics mdps exact methods discretization of continuous state space mdps function approximation feature based representations lqr iterative lqr differential dynamic programming link to course https www youtube com playlist list plwrjq4m4ujjnbpjdt8wamrat4xkc639wf link to materials https people eecs berkeley edu pabbeel cs287 fa19 reach out on twitter https twitter com omarsar0 if you have any questions if you are interested to contribute feel free to open a pr with a link to the course it will take a bit of time but i have plans to do many things with these individual lectures we can summarize the lectures include notes provide additional reading material include difficulty of content etc you can now find ml course notes here https github com dair ai ml course notes | machine-learning deep-learning nlp natural-language-processing ai data-science | ai |
Smart_Charging_system | smart charging system the repository for the embedded system design project | os |
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MPid | mpid a microcontroller friendly pid module build status https travis ci org gbmhunter mpid png branch master https travis ci org gbmhunter mpid description a light weight fast pid library designed for use on embedded systems but can also run on any machine which has a g compiler for better performance and control the pid library supports a generic number type number type must support casting to doubles fixed point numbers are recommended for high speed operation doubles are recommended for non time critical algorithms relies on used calling pid run at a regular and fixed interval usually in the milli second range via an interrupt automatically adjusts kp ki and kd depending on the chosen time step zp zi and zd are the time step adjusted values do not try and make kp ki or kd negative this results in undefined behaviour smooth control derivative control is only active when at least two calls to run have been made does not assume previous input was 0 on first call which can cause a huge derivative jolt easy debugging you can print pid debug information by providing a callback via code pid setdebugprintcallback which supports method callbacks by utilizing the slotmachine cpp library the following code shows you how to assign a callback for debug printing c class printer public void printdebug const char msg std cout msg printer myprinter pid pidtest asign callback to printer s printdebug function this pidtest setdebugprintcallback slotmachine callbackgen printer void const char myprinter printer printdebug you can disable all debug info to free up some memory space by setting cp3id config include debug code in include config hpp to 0 the debug buffer size can be changed with cp3id config debug buff size again in config hpp code dependencies dependency delivery usage cstdint standard c library fixed width variable type definitions e g uint32 t massert external module providing runtime safety checks against this module munittest external module framework for unit tests building this example assumes you are running on a linux like system and have basic build tools like gcc and make installed as well as cmake sh git clone https github com gbmhunter mfixedpoint git cd mfixedpoint mkdir build cd build cmake make usage c create a pid object which uses double for all of it s calculations inputs and outputs pid double pidtest main set up pid controller non accumulating pidtest init 1 0 kp 1 0 ki 1 0 kd pid double pid direct control type pid double dont accumulate output control type 10 0 update rate ms 100 0 min output 100 0 max output 0 0 initial set point call every 10 0ms as set in pid init timerisr read input input readpin 2 perform one execution pidtest run input set output setpin 3 pidtest output see test pidtest cpp for more examples issues for known bugs desired enhancements e t c see github issues section changelog see changelog md | os |
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Diffusion4NLP-Papers | diffusion4nlp papers https img shields io static v1 label latest commit message 2022 10 18 color important https img shields io static v1 label newest message origin color red a paper list about diffusion models for natural language processing update news nov 9 2022 add 3 pre print papers oct 18 2022 add papers that attempt to apply diffusion models for nlp from scratch conference paper 1 diffusion lm improves controllable text generation xiang lisa li john thickstun ishaan gulrajani percy liang tatsunori b hashimoto in neuralps 2022 pdf https arxiv org pdf 2205 14217 pdf code https github com xiangli1999 diffusion lm 2 diffuseq sequence to sequence text generation with diffusion models shansan gong mukai li jiangtao feng zhiyong wu lingpeng kong iclr 2023 pdf https arxiv org pdf 2210 08933 pdf code https github com shark nlp diffuseq 3 latent diffusion energy based model for interpretable text modeling peiyu yu sirui xie xiaojian ma baoxiong jia bo pang ruiqi gao yixin zhu song chun zhu ying nian wu pdf https proceedings mlr press v162 yu22h yu22h pdf code https github com yupeiyu98 ldebm 4 analog bits generating discrete data using diffusion models with self conditioning ting chen ruixiang zhang geoffrey hinton pdf https openreview net pdf id jqj rlvxsj code https github com lucidrains bit diffusion 5 structured denoising diffusion models in discrete state spaces jacob austin daniel d johnson jonathan ho daniel tarlow rianne van den berg pdf https arxiv org pdf 2107 03006 pdf 6 composable text controls in latent space with odes guangyi liu zeyu feng yuan gao zichao yang xiaodan liang junwei bao xiaodong he shuguang cui zhen li zhiting hu pdf https arxiv org pdf 2208 00638 pdf 7 diffuser discrete diffusion via edit based reconstruction machel reid vincent j hellendoorn graham neubig iclr 2023 pdf https arxiv org pdf 2210 16886 pdf 8 self conditioned embedding diffusion for text generation robin strudel corentin tallec florent altch yilun du yaroslav ganin arthur mensch will grathwohl nikolay savinov sander dieleman laurent sifre r mi leblond pdf https arxiv org pdf 2211 04236v1 pdf 9 diffusionbert improving generative masked language models with diffusion models zhengfu he tianxiang sun qiong tang kuanning wang xuanjing huang and xipeng qiu acl 2023 pdf https www aclweb org anthology 2023 acl long 248 pdf 10 nuwa xl diffusion over diffusion for extremely long video generation shengming yin chenfei wu huan yang jianfeng wang xiaodong wang minheng ni zhengyuan yang linjie li shuguang liu fan yang jianlong fu ming gong lijuan wang zicheng liu houqiang li and nan duan acl 2023 pdf https www aclweb org anthology 2023 acl long 73 pdf 11 diffusemp a diffusion model based framework with multi grained control for empathetic response generation guanqun bi lei shen yanan cao meng chen yuqiang xie zheng lin and xiaodong he acl 2023 pdf https www aclweb org anthology 2023 acl long 158 pdf 12 diffusionner boundary diffusion for named entity recognition yongliang shen kaitao song xu tan dongsheng li weiming lu and yueting zhuang acl 2023 pdf https www aclweb org anthology 2023 acl long 215 pdf 13 what the daam interpreting stable diffusion using cross attention raphael tang linqing liu akshat pandey zhiying jiang gefei yang karun kumar pontus stenetorp jimmy lin and ferhan ture acl 2023 pdf https www aclweb org anthology 2023 acl long 310 pdf 14 ssd lm semi autoregressive simplex based diffusion language model for text generation and modular control xiaochuang han sachin kumar and yulia tsvetkov acl 2023 pdf https www aclweb org anthology 2023 acl long 647 pdf survey 1 diffusion models a comprehensive survey ofmethods and applications ling yang zhilong zhang shenda hong wentao zhang bin cui pdf https arxiv org pdf 2209 00796v3 pdf comprehensive dm papers 1 yangling0818 diffusion models papers survey taxonomy https github com yangling0818 diffusion models papers survey taxonomy 2 2 | ai |
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cc26xxware | this is a git versioned subset and slightly modified version of ti s cc26xxware this repository s sole purpose is to provide cc13xxware as a submodule for the contiki operating system https github com contiki os contiki new versions will only appear in this repository only if and when contiki s cc26xx port needs to use them modifications for current and upcoming versions only files used by contiki are included here documentation and files related to iar and ccs have been removed line endings have been converted to unix file permissions have been changed to 644 all sources are and will remain otherwise intact except in case where modifications are required by contiki cc26xxware is distributed by ti as part of ti rtos http www ti com tool ti rtos if you are looking for the latest version of cc26xxware do not clone this repository you should download ti rtos instead do not open pulls issues on this repository unless they are immediately related to using cc26xxware with contiki | os |
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Mobile-applications | mobile applications br xamarin app with web api mvvm patern and xamarin styles chat app with signalr navigation in xamarin forms | front_end |
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apis | features units based modular architecture loader provides multiple running modes provides app console brings app parts together automatically flexible settings system advanced routing with contracts resources ability to start multiple servers allows to listen multiple ports support http https ip4 ip6 etc cluster support domain support integrated daemonization logging multiple transports support websockets jsonp batch requests cors enabled number of useful handlers including authentication data parsing and validation response serialization and validation static files handling client libs for node js and browser security note all apis resources are by default fully cors and jsonp available you must use csrf protection for all your authenticated requests even for get requests and apis supports such protection by default too optionally you can check origin of your requests or disable cors jsonp functionality completely or for choosen request handlers by default apis philosophy is to allow cross origin requests and be ready for them also by default every resource will have options handler providing resource description and it will not be protected by authentication or something you can allways override this handler with null or your own variant handlers handler interface setup chain can be used to perform some interaction between handlers of chain used by impl and ret handle ctx async handle must not throw any exceptions use ctx error instead usually must call ctx next at some point test page to get test page on test page add to your contract js cont test apis testpage contract apps known by loader app app itself will be searched at cwd lib app cluster master cluster master app will be searched at cwd lib claster master daemon master daemon start stop app will be searched at cwd lib daemon master for any app if it cannot be found apis default will be used units known by loader core app known by app actually which also is loader the app itself core uncaught uncaught exception handler core logging logging subsystem core logging engines syslog syslog logging engine core mechanics web web mechanics enables responding on http requests core mechanics socket socket mechanics enables web socket communications runs on top of web mechanics core mechanics socket stat web socket statistics core settings settings will be searched at cwd lib settings core handler main app contract will be searched at cwd lib contract for both core settings and core handler units if unit cannot be found apis default will be used rest notes stateless all the way get must be cacheable think about url args how to provide cacheable structure put vs post put is safe to repeat example update something post is not safe to repeat can create copies example create delete is safe to repeat just ensures that it s deleted license mit | front_end |
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LAB2 | lab2 embedded systems design course lab2 description short video of experiment results and project source code | os |
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emio | logo docs res logo png continuous integration https github com viatorus emio actions workflows ci yml badge svg https github com viatorus emio actions workflows ci yml codecov https codecov io gh viatorus emio branch main graph badge svg token 7bqfk1pnlx https codecov io gh viatorus emio conan center https img shields io conan v emio https conan io center recipes emio em io is a safe and fast high level and low level character input output library for bare metal and rtos based embedded systems with a very small binary footprint cpp high level std string str emio format the answer is 42 format argument int answer emio result void scan res emio scan str the answer is answer scan input string if scan res emio print the answer is answer output to console without using heap emio static buffer 128 buf emio format to buf the answer is x 42 value buf view the answer is 0x2a low level emio writer wrt buf wrt write str in decimal value wrt write int 42 value wrt write char value buf view the answer is 0x2a in decimal 42 emio reader rdr 17c emio try uint32 t number rdr parse int uint32 t 17 emio try char suffix rdr read char c this library is in beta status please help to make it fly https github com viatorus emio milestone 1 api documentation docs api md try emio online https godbolt org z o5m7ce6m8 yet another character input output library bare metal and rtos based embedded systems do have special requirements which are mostly overlooked by the c standard its implementations and other libraries therefore this library has a very small binary footprint 38 times smaller than fmtlib returns a result object instead of throwing an exception provides a high level and low level api which can be used at compile time read more about it in the design docs design md document including emio in your project with cmake and fetch content cmake fetchcontent declare emio git tag main git repository https github com viatorus emio git git shallow true fetchcontent makeavailable emio download the single header file https viatorus github io emio generated with quom https github com viatorus quom from conan center https conan io center recipes emio a compiler supporting c 20 is required tested with gcc 11 3 and clang 15 contributing see the contributing docs contributing md document licensing em io is distributed under the mit license license md | os |
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CMU-15-721 | cmu 15 721 to become a better engineer i m taking the carnegie mellon university course 15 721 advanced database systems to learn olap oltp engineering i have a personal interest in high performance computing and there are many crossovers between that and the subjects taught in this course such as kernel bypass methods low latency networking parallel processing multithreading cache management and more which i will be learning prof andy pavlov teaches this course and he is incredibly knowledgable on the subject the lectures can be found on youtube here https www youtube com watch v lws8leqauvc list plse8odhjzxjyzllmbx3cr0sxwnrm7clfn lecture 4 analytical database indexes this lecture focuses on how to optimize sequential scans on a database i learned about zone maps which are essentially storing metadata inside a memory block which contains common aggregate data that might be queried often like min max avg sum and count this reduces the time complexity of scanning to o 1 in the case where you need the aggregate data i also learned about bit weaving which allows you to store your indexes as bit vectors in such a way that you can take advantage of bitwise operations to very efficiently check if the data you need is in the memory block exercises this course doesn t have labs or exercises so i ll be making my own exercise bloom filters one of the data structures mentioned in this course is the bloom filter the bloom filter is a probabalistic data structure this interested me because i had never heard of it before so i decided to make my own implementation you can watch me build a c implementation of this on youtube https youtu be i2p2zpir 9c si ix7ovq3f8fcdfmzu | server |
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Torpedo | torpedo torpedo is a framework to test the resiliency of the airship deployed environment it provides templates and prebuilt tools to test all components from hardware nodes to service elements of the deployed stack the report and logging module helps easy triage of design issues pre requisites label the nodes label the nodes on which argo to be run as argo enabled label the nodes on which metacontroller needs to be enabled as metacontroller enabled label the nodes on which torpedo should run as torpedo controller enabled label the nodes on which traffic and chaos jobs to run as resiliency enabled label the nodes on which log collector jobs to run as log collector enabled clone the git repository git clone https github com bm metamorph torpedo git install metacontroller kubectl create ns metacontroller cat torpedo metacontroller rbac yaml kubectl create n metacontroller f cat torpedo install metacontroller yaml kubectl create n metacontroller f install argo kubectl create ns argo cat torpedo install argo yaml kubectl create n argo f deploy torpedo controller cat torpedo torpedo crd yaml kubectl create f cat torpedo controller yaml kubectl create f apply torpedo rbac rules cat torpedo resiliency rbac yaml kubectl create f cat torpedo torpedo rbac yaml kubectl create f deploy torpedo kubectl create configmap torpedo metacontroller n metacontroller from file torpedo metacontroller torpedo metacontroller py cat torpedo torpedo controller yaml kubectl create n metacontroller f cat torpedo webhook yaml kubectl create n metacontroller f trigger the test suite cat test suite kubectl n metacontroller create f note in case ceph storage is used to create a pvc create a ceph secret in the namespace the pvc needs to created with same name as usersecretname as mentioned in the ceph storage class the ceph secret can be obtained by the following command kubectl exec it n ceph ceph mon pod ceph auth get key client admin base64 replace the key and name in torpedo secret yaml with the key generated in above command and the name mentioned in the ceph storage class respectively and execute the following command cat torpedo secret yaml kubectl create f test cases covered in torpedo 1 openstack openstack api get calls keystone service list mariadb keystone service list memcached keystone service list ingress keystone service list glance image list neutron port list nova server list cinder volume list heat stack list horizon get call on horizon landing page openstack rabbitmq glance rabbitmq post call to create and upload and delete an image neutron rabbitmq post call to create and delete a router nova rabbitmq post call to create and delete a server cinder rabbitmq post call to create a volume heat rabbitmq post call to create and delete a stack openstack api post calls glance post call to create and upload and delete an image neutron post call to create and delete a router neutron dhcp agent post call to create a virtual machine assign a floating ip to the virtual machine and initiate a ping request to the floating ip openvswitch db post call to create a virtual machine assign a floating ip to the virtual machine and initiate a ping request to the floating ip openvswitch daemon post call to create a virtual machine assign a floating ip to the virtual machine and initiate a ping request to the floating ip nova compute post call to create and delete a server nova scheduler post call to create and delete a server nova conductor post call to create and delete a server libvirt post call to create and delete a server cinder volume post call to create a volume cinder scheduler post call to create a volume heat post call to create and delete a stack 2 ucp ucp api get calls keystone keystone service list promenade get call to check health armada releases list drydock nodes list shipyard configdocs list barbican secrets list deckhand revisions list 3 kubernetes kubernetes proxy creates a pod and a service and initiate a ping request to the service ip kubernetes apiserver get call to the pod list kubernetes scheduler post call to create and delete a pod ingress get call to kube apiserver test suite description the test suite contains following sections 1 auth 2 job parameters 3 namespace 4 orchestrator plugin 5 chaos plugin 6 volume storage class 7 volume storage capacity 8 volume name auth auth section consists of keystone auth information in case of openstack and ucp and url and token in case of kubernetes auth auth url http keystone api openstack svc cluster local 5000 v3 username username password password user domain name default project domain name default project name admin job parameters job parameters section further consists 7 sections 1 name name for test case 2 service name of the service against which the tests to be run example nova cinder etc 3 component component of service against which the test to run example nova os api cinder scheduler 4 job duration duration for which the job needs to run both chaos and traffic jobs 5 count number of times chaos traffic should be induced on target service takes precedence only if job duration is set to 0 6 nodes used in case of node power off scenario defaults to none in normal scenarios takes a list of nodes with the following information ipmi ip ipmi ip of the target node password ipmi password of the target node user ipmi username of the target node node name node name of the target node 7 sanity checks a list of checks that needs to be performed while the traffic and chaos jobs are running defaults to none example get a list of pods nodes etc takes 3 parameters as input image image to be used to run the sanity checks name name of the sanity check command command to be executed 8 extra args a list of extra parameters which can be passed for a specific test scenario defaults to none namespace namespace in which the service to verify is running orchestrator plugin the plugin to be used to initiate traffic chaos plugin the plugin to be used to initiate chaos volume storage class storage class to be used to create a pvc used to choose the type of storage to be used to create pvc volume storage volume capacity of the pvc to be created volume name name of the volume pvc apiversion torpedo k8s att io v1 kind torpedo metadata name openstack torpedo test spec auth auth url http keystone api openstack svc cluster local 5000 v3 username admin password user domain name default project domain name default project name admin job params service nova component os api kill interval 30 kill count 4 same node true pod labels application nova component os api node labels openstack nova control enabled service mapping nova name nova os api max nodes 2 nodes ipmi ip ipmi ip node name node name user username password password sanity checks name pod list image kiriti29 torpedo traffic generator v1 command bin bash sanity checks sh pod list 2000 kubectl get pods all namespaces o wide extra args namespace openstack job duration 100 count 60 orchestrator plugin torpedo traffic orchestrator chaos plugin torpedo chaos volume storage class general volume storage 10gi volume name openstack torpedo test node power off testcase scenario in torpedo the framework aims at creating a chaos in a nc environment and thereby measuring the downtime before the cluster starts behaving normally parallely collecting all the logs pertaining to openstack api calls pods list nodes list and so on 1 the testcase initially creates a heat stack which in turn creates a stack of 10 vms before introducing any chaos ort tests 2 once the heat stack is completely validated we record the state 3 initiate sanity checks for a checking the health of openstack services keystone get call on service list glance get call on image list neutron get call on port list nova get call on server list heat get call on stack list cinder get call on volume list b checks on kubernetes pod list kubectl get pods all namespaces o wide node list kubectl get nodes rabbitmq cluster status kubectl exec it rabbitmq pod on target node n namespace rabbitmqctl cluster status ceph cluster status kubectl exec it ceph pod on target node n namespace ceph health 4 now we shutdown the node ipmi power off 5 parallely instantiate the heat stack creation and see how much time it takes for the heat stack to finish verify heat stack is created in 15 minutes config param if not re initiate the stack creation we try this in loop the test exits with a failure after 40 minutes time limit this is a configurable parameter 6 if the heat stack creation is complete then we bring up the shutdown node and repeat the steps 1 5 on other nodes 7 logs are captured with request response times failures success messages on the test requests 8 a report is generated based on the number of testcases that have passed or failed all the logs of sanity checks apache common log format the entire pod logs in all namespaces in the cluster the heat logs authors muktevi kiriti gurpreet singh hemanth kumar nakkina | resiliency chaos chaos-engineering kubernetes airship | cloud |
nlp_workshop | nlp workshop natural language processing workshop thinkingmachin es events clare corthell luminant data thinking machines http thinkingmachin es humans machines conference manila 18 february 2016 much of human knowledge is locked up in a type of data called text humans are great at reading but are computers this workshop leads you through open source data science libraries in python that turn text into valuable data then tours an open source system built for the wordnik dictionary to source definitions of words from across the internet goal learn the basics of text manipulation and analytics with open sources tools in python | ai |
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FlaskAPI-GCP-Applications | project description goal run an app on the cloud for api users to make requests and store user information rationale to achieve the goal efficiently this project proceeds with the following path application write an app with features in user authentication and form submission connection create a cloud database for storing app information we need to ensure a secured connection between the app and the database deployment deploy the app and expose it to the internet technology overview images technology png flask the application is a simple api written using the flask framework flask offers extensions like database access template engine user authentication etc terraform terraform is an infrastructure as code iac service that allows users to provision cloud infrastructures with reusable codes it uses configuration language to provision cloud infrastrucutres and can simultaneously plan and manage resources from different cloud providers like aws gcp and azure using terraform can significantly improve efficiency and reduce time in provisioning resources circumventing issues like manually updating configurations on the console or on the command line google cloud platform gcp cloudsql we will create a postgresql database instance with a user defined root user password and database this will be the database for storing user info kubernetes engine kubernetes creates clusters with nodes each node in a cluster has pods that can be aggregated as a service to be exposed to the internet we will spin up a kubernetes cluster with a single master node which will run the pod where our application will be located kubernetes can store sensitive information like password and credentials as secrets we will store database username and password in the pod as secrets artifact registry this is a container registry for storing container images kubernetes looks for images from the repository and run them on pods cloud build this is google s equivalent of docker and we will build the application as a container image here and push it to the registry google service account gsa cloud resources require permission to interact with other resources in particular we need to generate credential keys from service accounts with the following priviledges owner role for machines or softwares to provision and interact with infrastructures on behalf of gcp cloudsql client role for kubernetes to establish a tcp connection between the sql database and the application inside the cluster architecture infrastructures application images architecture png infrastructure overview images terraform workflow png terraform plans and provisions infrastructures using the main tf terraform main tf file in the terraform folder with variables tf terraform variables tf as input file the terraform tfvars terraform terraform tfvars file specifies any variables from variables tf terraform variables tf that don t have a default parameters the artifact registry cloudsql and gke folders are sub modules implemented in a similar fashion we only create the cloudsql database instance the kubernetes cluster and the container repository here the actual containerization process using docker cloud build will take place using github actions the github action configuration can be found in the cloudbuilds gke yaml github workflows cloudbuilds gke yaml file application overview the flask application is a simple api exposing the users to get and post method post users can input text to submit information to be store in the backend cloudsql postgre instance images api post png get users can click on the check button to view all the previously entered data in a tabular format upon request the application retrieves all data stored in the database and return them as such on the following page images api get png all the data are stored in the backend database images query png running the project 0 preparation create a new project keep note of the project id determine and keep note of the region and zone https cloud google com compute docs regions zones available enable api for cloudsql kubernetes engine cloud build and artifact registry 1 create service accounts and download credentials go to iam and create a service account called owner sa with owner role download a new key in json format and rename it owner sa key json create another service account called cloudsql sa with cloud sql client role download a new key in json format and rename it cloudsql sa key json images service accounts png 2 project environment setup fork and clone this repository in a local terminal create a folder called service accounts in the terraform folder move both owner sa key json and cloudsql sa key json files to the service accounts folder set environment variable for grant terraform permission to provision gcp resources bash export google application credentials path to flaskapi gcp applications terraform service accounts owner sa key json 3 update configurations update the variables in terraform tfvars terraform terraform tfvars where a todo comment indicates update the variables in cloudbuilds gke yaml github workflows cloudbuilds gke yaml where a todo comment indicates 3 github actions setup go to the forked repository settings and select action secrets create a secret called gke project and put in your project id as value create a secret called gke sa key and paste the content of the owner sa key json in as value images github secrets png 4 install requirements and initialize install gcloud https cloud google com sdk docs install install kubectl https cloud google com kubernetes engine docs how to cluster access for kubectl install terraform https www terraform io downloads 5 provision the infrastructures bash gcloud init set up your project id region and zone terraform init make sure you are in the terraform folder terraform workspace new dev the default workspace is default we will work in the dev workspace terraform plan let you preview the infrastructure changes terraform apply auto approve once it finishes building the terminal will output a list of infrastructure info bash terraform output if no outputs printed 6 update kubernetes configurations take note of the cloudsql instance name outputed by terraform update the lines in the deployment yaml deployment yaml file where a todo comment indicates store database username password and cloudsql client role credential key as kubernetes secrets bash gcloud container clusters get credentials flask cluster get credentials for kubectl to work with the cluster bash kubectl create secret generic postgres secret from literal username sql user from literal password sql pass from literal database db name bash kubectl create secret generic cloudsql sa from file service account json service accounts cloudsql sa key json 7 deploy the application using github actions commit and push the changes previously made to the configurations the github action will be triggered when the changes are pushed it will automatically containerize the application using cloud build and store it in the repository then it will deploy the container image to kubernetes and expose it to the internet through a service 8 access the application bash kubectl get services you may find the status pending under the external ip column wait and keep running the previous command until an ip address shows up copy the external ip address of the service flask app and access it from another tab images external ip png you should see the following web page show up images api welcome png click on the start button and you will be directed to the application as shown in the application overview section above 9 clean up the project bash terraform destroy auto approve delete the owner sa and cloudsql sa service accounts optional delete the credential keys and gihub action secrets optional | cloud |
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co-design | h1 align center co design h1 p align center strong co design is an open source design system built by a href https present do cobalt inc a strong p p align center a href https cobalt run img src https badgen net badge icon made 20by 20cobalt icon https caple static s3 ap northeast 2 amazonaws com cobalt badge svg label color 5b69c3 labelcolor 414c9a a a href https www npmjs com package co design core img src https img shields io npm v co design core svg alt latest npm version a a href https github com cobaltinc bloom blob master github contributing md img src https img shields io badge prs welcome brightgreen svg alt prs welcome a p rocket getting started package name description co design core packages co design core co design ui components for react co design hooks packages co design hooks useful react hooks page facing up license co design is made available under the mit license license | designsystem | os |
Dynamic-Database-Using-Cplusplus | dynamic database using c dynamic database for faculty of engineering ain shams university staff and students please check these videos 1 https www youtube com watch v ja1bpwfco3c 2 https www youtube com watch v qr5eq7brvf8 t | server |
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Surfsup | surfsup background basic climate analysis and data exploration of a climate database and design of a flask api based on the queries developed technologies jupyter notebook flask matplotlib numpy pandas datetime sqlalchemy analysis precipitaion analysis design a query to retrieve the last 12 months of precipitation data load the query results into a pandas dataframe and set the index to the date column sort the dataframe values by date plot the results using the dataframe plot method use pandas to print the summary statistics for the precipitation data station analysis design a query to calculate the total number of stations design a query to find the most active stations design a query to retrieve the last 12 months of temperature observation data tobs climate app design a flask api based on the queries use flask to create routes visualization precipitation analysis precipitation analysis https github com mddesta surfsup blob master output precipitation 20analysis png station usc00519281 temprature analysis station usc00519281 temprature analysis https github com mddesta surfsup blob master output station 20usc00519281 20temprature 20analysis png trip average temprature trip average temprature https github com mddesta surfsup blob master output trip 20avg 20temp png | flask-application flask-sqlalchemy pandas api-rest | server |
smockin | p align center img src public image smockin logo png width 400 p p align center version 2 20 1 p br new in version 2 20 0 ngrok is now built into smockin your api mocks can now be made publicly accessible in seconds change of jdk version java 11 or later is now required to build the main branch java 8 users can continue to build smockin from this branch https github com matthewgallina smockin tree jdk8 br br dynamic api s3 bucket mail server mocking for application development qa testing visit us https www smockin com contact us or follow us on twitter b smockin com b user guide https www smockin com help br br overview smockin is a development tool used to dynamically mock api endpoints s3 buckets email accounts featuring a rich ui an built in mock servers creating and managing mocks can be done quickly both with or without code whether you are a developer who needs to simulate restful api endpoints or an engineer working with complex microservice infrastructure smockin can help by mimicking any services that are either unavailable or otherwise too difficult or time consuming to set up smockin runs as a small web app which can be either installed locally onto a personal machine or hosted centrally and used by multiple users br dashboard https raw githubusercontent com mgtechsoftware smockin master public image dashboard png client call https raw githubusercontent com mgtechsoftware smockin master public image client call png br key features create dynamic api mocks to mimic real world application behaviour create manage s3 bucket mocks where an aws account may not be available new create manage mock mail inboxes run smockin centrally and create user accounts for your team import export mocks to share between your team version control monitor and log traffic going to the http or s3 mock servers choose whether to build your api mocks using javascript or without code a rich and complete ui solution br requirements java 8 maven 3 please note all bash scripts were written and tested on gnu bash version 3 2 57 1 release all bat files were were written and tested on windows 7 br quick start clone this repo git clone https github com mgtechsoftware smockin git change the current directory to smockin and build the project cd smockin mvn clean install run the install and start scripts for windows use the equivalent bat files install sh start sh finally from your browser open the dashboard http localhost 8000 index html if running smockin for the first time then please allow 20 30 secs for the app to fully start up br for further details please consult the installation https github com mgtechsoftware smockin wiki installation and getting started https github com mgtechsoftware smockin wiki api mock tutorial getting started guides in the wiki you can also check out the latest help guide here https www smockin com help br troubleshooting please consult the configuration troubleshooting https github com mgtechsoftware smockin wiki configuration troubleshooting guide br licence smockin is licensed in accordance with the terms of the apache license version 2 0 the full text of this license can be found at https www apache org licenses license 2 0 br acknowledgements third parties smockin is built upon the following frameworks spring boot https projects spring io spring boot hibernate http hibernate org spark http sparkjava com maven https maven apache org angularjs https angularjs org ui bootstrap https angular ui github io bootstrap vkbeautify https github com vkiryukhin vkbeautify h2 http www h2database com hikaricp https brettwooldridge github io hikaricp apache commons https commons apache org apache http components https hc apache org apache commons io https commons apache org proper commons io junit http junit org mockito http site mockito org apache activemq http activemq apache org apache ftpserver https mina apache org ftpserver project raml parser 2 https github com raml org raml java parser jasypt http www jasypt org java jwt https github com auth0 java jwt jwt decode https github com auth0 jwt decode jquery https jquery com code mirror https codemirror net s3proxy https github com gaul s3proxy greenmail https greenmail mail test github io greenmail java ngrok https github com alexdlaird java ngrok ngrok https ngrok com br br recent release features br important new in version 2 19 0 from 2 19 0 onwards smockin s internal h2 database has been upgraded to v2 1 moving away from v1 4 unfortunately v2 of h2 is not backwards compatible with v1 4 which means you will need to export your existing data and migrate this to the new db br note the latest version of smockin to continue using v1 4 of h2 will be kept here https github com matthewgallina smockin tree 2 18 4 br to upgrade to 2 19 0 please follow the steps below 1 in your current version of smockin 2 18 x or earlier use the export feature to backup any mock data your wish to keep 2 next go to the user home directory smockin is running from and rename the config directory smockin to smockin 2 18 4 3 download version 2 19 0 or later of smockin we recommend keeping the previous version of smockin on your system also for the short term so you can switch between versions until all of your data has been moved across 4 using the new version of smockin run the install script this will create a new smockin config directory containing the newer h2 database 5 launch the new version of smockin using one of the start run scripts 6 from the smockin dashboard use the import feature to save your mock data to the new database br unfortunately not all data can be automatically exported and so will need to be handled manually this includes mail mocks and messages server configuration key value data br should you wish to switch between versions of smockin as suggested above then this can be done by simply switching the smockin config directory in your user home and ensuring this aligns with version of smockin you wish to run a good practice we advise is to rename suffix any unused smockin config directories with the version of the smockin application they align too so as to make them easily identifiable for example smockin 2 18 4 unused config dir br smockin 2 19 0 unused config dir br smockin this is the active config dir br br new in version 2 18 0 introducing the email mock server 1 swap in the smockin mock mail server running on port 8003 as your application s delivery mail server 2 create email accounts in the smockin ui 3 view all received email messages from the ui including mail attachments 4 support for mail inbox auto generation so as to support random addresses during testing mail messages can be stored in memory or saved to database br br new in version 2 17 0 introducing s3 bucket mocking simply create a mock bucket build your s3 content and point your application to the s3 mock server featuring different synchronisation modes developing and testing around s3 has never been easier once you ve created your first bucket this can be accessed from port 8002 using any s3 client like so s3 client https raw githubusercontent com mgtechsoftware smockin master public image s3 client png br br new in version 2 11 0 introducing block mock and swap with a number of users discovering smockin as a great way to monitor live dev traffic you can now intercept and manipulate on the fly any requests via the live logging tool combined with the proxy server this now gives developers and qa engineers complete visibility and control over all http traffic running between your applications br br new in version 2 10 0 the smockin proxy server now supports multiple url destinations which can be mapped to specific paths for example say you are running 3 independent microservices for each of the following services v1 product localhost 9001 v1 customer localhost 9002 v1 address localhost 9003 you can now add these path to url mappings to smockin and then decide whether swap out any traffic between these services and the end user with mocked responses using various strategies this can be highly useful for either temporarily modifying an existing api or adding mocking an entirely new endpoint br br about smockin is designed and actively maintained by matthew gallina why the name smockin whilst it may sound like an old english middle ages embroidery technique the name actually came about more in relation to a classic jim carey movie quote smokin and yes far too much thought and time went into it | api raml api-mocking api-rest mock-server mocking java-8 simulation api-simulation api-mock mock-api s3-bucket java s3 | front_end |
ESD-E2020-code | embedded system design project an introduction to modern cmake can be found here https cliutils gitlab io modern cmake by default the system will cross compile for the zybo this can be disabled with cmake bbuild duse system compiler on setup before compiling please do cp cmake host vars cmake example cmake host vars cmake and modify as needed cmake basics to configure cmake bbuild this should be done when a cmakelists txt file changes to compile cmake build build quick deployment and test for easy testing of executables a custom target can be added in cmake with add deploy target executable name and invoked from the command line cmake build build target deploy executable name add libusb 1 0 to toolchain required for receiptprinter follow this guide https github com guino libusb arm i used the toolchain we where given by catalin and only used the libusb not libusb compat this seems to work | os |
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NewBank | swe2 2020 8 new bank project group 8 automation actions status build server and send to azure https github com swe2 2020 8 newbank workflows build server and send to azure badge svg build client offer artifact https github com swe2 2020 8 newbank workflows build client offer artifact badge svg build client add to release https github com swe2 2020 8 newbank workflows build client add to release badge svg to jump quickly to wiki https github com swe2 2020 8 newbank wiki for this document and others backlog sprint backlog and weekly reports https github com swe2 2020 8 newbank wiki newbank backlog and sprints page user stories and requirements https github com swe2 2020 8 newbank wiki user stories and requirements slack channel https join slack com t swe2 2020 8 shared invite zt gj3iswzo inc7jb3iasayq1pepr3jyq for discussions main board https github com orgs swe2 2020 8 projects 1 kanban board for management activities newbank code board https github com swe2 2020 8 newbank projects 1 kanban board for code works the code repository https github com swe2 2020 8 newbank issues https github com swe2 2020 8 newbank issues to identify activities including defects milestones https github com swe2 2020 8 newbank milestones to track weekly progress insights https github com swe2 2020 8 newbank pulse to see what s going on actions https github com swe2 2020 8 newbank actions for code automation gitops releases https github com swe2 2020 8 newbank releases to find the latest version of the code and a ready to run client working software server is running in http swe2 2020 8 southeastasia azurecontainer io interact directly with server using socat tcp4 swe2 2020 8 southeastasia azurecontainer io 80 download the latest java javafx newbankclient in the releases section https github com swe2 2020 8 newbank releases automation notes in order to understand how to run the server locally or how the automation has been built to deploy the server to azure click here https github com swe2 2020 8 newbank blob master server readme md and here https github com swe2 2020 8 newbank blob master client readme md to understand the automation for building and releasing the client newbankclient jar | cloud |
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E-Learning | e learning it is a basic learning management system developed in php and mysql a student can submit assignments and activities online posted by the professor | os |
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AdvancedML | advanced deep learning kaist course information instructor sung ju hwang sjhwang82 kaist ac kr tas seul lee animecult kaist ac kr and jaehyeong jo harryjo97 kaist ac kr office this is an on offline hybrid course building nubmer 9 room 9201 instructor 2nd floor tas office hours by appointment only grading policy absolute grading paper presentation 20 attendance and participation 20 project 60 tentative schedule dates topic 8 29 course introduction 9 1 review of deep learning basics video lecture 9 6 vision transformers lecture 9 8 vision transformers self supervised learning lecture 9 13 self supervised learning lecture 9 15 self supervised learning presentation 9 20 bayesian deep learning bayesian ml basics bayesian neural networks lecture 9 22 bayesian deep learning bayesian approximations uncertainties in prediction lecture 9 27 bayesian deep learning mcmc sampling for bayesian inference neural processes lecture 9 29 bayesian deep learning presentation 10 4 deep generative models advanced gans lecture 10 6 deep generative models advanced gans presentation initial proposal due 10 11 deep generative models diffusion models lecture 10 13 deep generative models diffusion models lecture 10 18 deep generative models diffusion models presentation 10 20 mid term presentation 10 25 large language models lecture 10 27 multimodal generative models lecture 11 1 large language models and multimodal generative models presentation 11 3 deep reinforcement learning deep rl basics lecture 11 8 deep reinforcement learning policy based rl model based rl lecture 11 10 deep reinforcement learning offline rl exploration rl via sequence modeling lecture 11 15 deep reinforcement learning presentation 11 17 meta learning lecture 11 22 meta learning presentation 11 24 continual learning lecture 11 29 continual learning presentation 12 1 robust deep learning lecture 12 6 robust deep learning presentation 12 8 deep graph learning lecture 12 13 deep graph learning presentation 12 15 final presentation reading list vision transformers dosovitskiy et al 21 https openreview net forum id yicbfdntty an image is worth 16x16 words transformers for image recognition at scale iclr 2021 touvron et al 21 http proceedings mlr press v139 touvron21a touvron21a pdf training data efficient image transformers distillation through attention icml 2021 liu et al 21 https openaccess thecvf com content iccv2021 papers liu swin transformer hierarchical vision transformer using shifted windows iccv 2021 paper pdf swin transformer hierarchical vision transformer using shifted windows iccv 2021 wu et al 21 https openaccess thecvf com content iccv2021 papers wu cvt introducing convolutions to vision transformers iccv 2021 paper pdf cvt introducing convolutions to vision transformers iccv 2021 dai et al 21 https proceedings neurips cc paper 2021 file 20568692db622456cc42a2e853ca21f8 paper pdf coatnet marrying convolution and attnetion for all data sizes neurips 2021 yang et al 21 https proceedings neurips cc paper 2021 file fc1a36821b02abbd2503fd949bfc9131 paper pdf focal attention for long range interactions in vision transformers neurips 2021 el nouby et al 21 https proceedings neurips cc paper 2021 file a655fbe4b8d7439994aa37ddad80de56 paper pdf xcit cross covariance image transformers neurips 2021 li et al 22 https openaccess thecvf com content cvpr2022 papers li mvitv2 improved multiscale vision transformers for classification and detection cvpr 2022 paper pdf mvitv2 improved multiscale vision transformers for classification and detection cvpr 2022 lee et al 22 https openaccess thecvf com content cvpr2022 papers lee mpvit multi path vision transformer for dense prediction cvpr 2022 paper pdf mpvit multi path vision transformer for dense prediction cvpr 2022 liu et al 22 https openaccess thecvf com content cvpr2022 papers liu a convnet for the 2020s cvpr 2022 paper pdf a convnet for the 2020s cvpr 2022 self supervised learning oliver et al 18 https papers nips cc paper 7585 realistic evaluation of deep semi supervised learning algorithms pdf realistic evaluation of deep semi supervised learning algorithms neurips 2018 berthelot et al 19 https papers nips cc paper 8749 mixmatch a holistic approach to semi supervised learning pdf mixmatch a holistic approach to semi supervised learning neurips 2019 sohn et al 20 https arxiv org pdf 2001 07685 pdf fixmatch simplifying semi supervised learning with consistency and confidence arxiv prepring 2020 dosovitskiy et al 14 https papers nips cc paper 5548 discriminative unsupervised feature learning with convolutional neural networks pdf discriminative unsupervised feature learning with convolutional neural networks nips 2014 pathak et al 16 https openaccess thecvf com content cvpr 2016 papers pathak context encoders feature cvpr 2016 paper pdf context encoders feature learning by inpainting cvpr 2016 norrozi and favaro et al 16 https arxiv org pdf 1603 09246 pdf unsupervised learning of visual representations by solving jigsaw puzzles eccv 2016 gidaris et al 18 https openreview net pdf id s1v4n2l0 unsupervised representation learning by predicting image rotations iclr 2018 he et al 20 https openaccess thecvf com content cvpr 2020 papers he momentum contrast for unsupervised visual representation learning cvpr 2020 paper pdf momentum contrast for unsupervised visual representation learning cvpr 2020 chen et al 20 https proceedings icml cc static paper files icml 2020 6165 paper pdf a simple framework for contrastive learning of visual representations icml 2020 mikolov et al 13 https arxiv org pdf 1301 3781 pdf efficient estimation of word representations in vector space iclr 2013 devlin et al 19 https www aclweb org anthology n19 1423 pdf bert pre training of deep bidirectional transformers for language understanding naacl 2019 clark et al 20 https openreview net pdf id r1xmh1btvb electra pre training text encoders as discriminators rather than generators iclr 2020 hu et al 20 https openreview net forum id hjlwwjsfdh strategies for pre training graph neural networks iclr 2020 chen et al 20 https proceedings icml cc static paper files icml 2020 6022 paper pdf generative pretraining from pixels icml 2020 laskin et al 20 https proceedings icml cc static paper files icml 2020 5951 paper pdf curl contrastive unsupervised representations for reinforcement learning icml 2020 grill et al 20 https arxiv org pdf 2006 07733 pdf bootstrap your own latent a new approach to self supervised learning neurips 2020 chen et al 20 https arxiv org pdf 2006 10029v1 pdf big self supervised models are strong semi supervised learners neurips 2020 chen and he 21 https openaccess thecvf com content cvpr2021 papers chen exploring simple siamese representation learning cvpr 2021 paper pdf exploring simple siamese representation learning cvpr 2021 tian et al 21 https arxiv org pdf 2102 06810 pdf understanding self supervised learning dynamics without contrastive pairs icml 2021 caron et al 21 https arxiv org pdf 2104 14294 pdf emerging properties in self supervised vision transformers iccv 2021 liu et al 22 https openreview net forum id 4azz9osqrar self supervised learning is more robust to dataset imbalance iclr 2022 bao et al 22 https openreview net pdf id p bhzsz59o4 beit bert pre training of image transformers iclr 2022 he et al 22 https openaccess thecvf com content cvpr2022 papers he masked autoencoders are scalable vision learners cvpr 2022 paper pdf masked autoencoders are scalable vision learners cvpr 2022 liu et al 22 https arxiv org pdf 2203 15508 pdf improving contrastive learning with model augmetnation arxiv preprint 2022 touvron et al 22 https arxiv org abs 2204 07118 deit iii revenge of the vit arxiv preprint 2022 bayesian deep learning kingma and welling 14 https arxiv org pdf 1312 6114 pdf auto encoding variational bayes iclr 2014 kingma et al 15 https arxiv org pdf 1506 02557 pdf variational dropout and the local reparameterization trick nips 2015 blundell et al 15 https arxiv org pdf 1505 05424 pdf weight uncertainty in neural networks icml 2015 gal and ghahramani 16 http proceedings mlr press v48 gal16 pdf dropout as a bayesian approximation representing model uncertainty in deep learning icml 2016 liu et al 16 https papers nips cc paper 6338 stein variational gradient descent a general purpose bayesian inference algorithm pdf stein variational gradient descent a general purpose bayesian inference algorithm nips 2016 mandt et al 17 https www jmlr org papers volume18 17 214 17 214 pdf stochastic gradient descent as approximate bayesian inference jmlr 2017 kendal and gal 17 https papers nips cc paper 7141 what uncertainties do we need in bayesian deep learning for computer vision pdf what uncertainties do we need in bayesian deep learning for computer vision icml 2017 gal et al 17 https papers nips cc paper 6949 concrete dropout pdf concrete dropout nips 2017 gal et al 17 http proceedings mlr press v70 gal17a gal17a pdf deep bayesian active learning with image data icml 2017 teye et al 18 http proceedings mlr press v80 teye18a teye18a pdf bayesian uncertainty estimation for batch normalized deep networks icml 2018 garnelo et al 18 http proceedings mlr press v80 garnelo18a garnelo18a pdf conditional neural process icml 2018 kim et al 19 http https arxiv org pdf 1901 05761 pdf attentive neural processes iclr 2019 sun et al 19 https arxiv org pdf 1903 05779 pdf functional variational bayesian neural networks iclr 2019 louizos et al 19 http papers nips cc paper 9079 the functional neural process pdf the functional neural process neurips 2019 zhang et al 20 https openreview net forum id rkes1rvtps cyclical stochastic gradient mcmc for bayesian deep learning iclr 2020 amersfoort et al 20 https arxiv org pdf 2003 02037 pdf uncertainty estimation using a single deep deterministic neural network icml 2020 dusenberry et al 20 https proceedings icml cc static paper files icml 2020 5657 paper pdf efficient and scalable bayesian neural nets with rank 1 factors icml 2020 wenzel et al 20 https proceedings icml cc static paper files icml 2020 3581 paper pdf how good is the bayes posterior in deep neural networks really icml 2020 lee et al 20 https arxiv org abs 2008 02956 bootstrapping neural processes neurips 2020 wilson et al 20 https proceedings neurips cc paper 2020 file 322f62469c5e3c7dc3e58f5a4d1ea399 paper pdf bayesian deep learning and a probabilistic perspective of generalization neurips 2020 izmailov et al 21 http proceedings mlr press v139 izmailov21a izmailov21a pdf what are bayesian neural network posteriors really like icml 2021 daxberger et al 21 http proceedings mlr press v139 daxberger21a daxberger21a pdf bayesian deep learning via subnetwork inference icml 2021 fortuin et al 22 https openreview net forum id xkjqjyqrjy bayesian neural network priors revisited iclr 2022 muller et al 22 https openreview net pdf id ksugkcbnf9 transformers can do bayesian inference iclr 2022 nguyen and grover 22 https proceedings mlr press v162 nguyen22b nguyen22b pdf transformer neural processes icml 2022 nazaret and blei 22 https proceedings mlr press v162 nazaret22a nazaret22a pdf variational inference for infinitely deep neural networks icml 2022 lotfi et al 22 https proceedings mlr press v162 lotfi22a lotfi22a pdf bayesian model selection the marginal likelihood and generalization icml 2022 alexos et al 22 https proceedings mlr press v162 alexos22a alexos22a pdf structured stochastic gradient mcmc icml 2022 deep generative models vaes autoregressive and flow based generative models rezende and mohamed 15 http proceedings mlr press v37 rezende15 pdf variational inference with normalizing flows icml 2015 germain et al 15 http proceedings mlr press v37 germain15 pdf made masked autoencoder for distribution estimation icml 2015 kingma et al 16 https papers nips cc paper 6581 improved variational inference with inverse autoregressive flow pdf improved variational inference with inverse autoregressive flow nips 2016 oord et al 16 http proceedings mlr press v48 oord16 pdf pixel recurrent neural networks icml 2016 dinh et al 17 https openreview net pdf id hkpbnh9lx density estimation using real nvp iclr 2017 papamakarios et al 17 https papers nips cc paper 6828 masked autoregressive flow for density estimation pdf masked autoregressive flow for density estimation nips 2017 huang et al 18 http proceedings mlr press v80 huang18d huang18d pdf neural autoregressive flows icml 2018 kingma and dhariwal 18 http papers nips cc paper 8224 glow generative flow with invertible 1x1 convolutions pdf glow generative flow with invertible 1x1 convolutions neurips 2018 ho et al 19 http proceedings mlr press v97 ho19a ho19a pdf flow improving flow based generative models with variational dequantization and architecture design icml 2019 chen et al 19 https papers nips cc paper 9183 residual flows for invertible generative modeling pdf residual flows for invertible generative modeling neurips 2019 tran et al 19 https papers nips cc paper 9612 discrete flows invertible generative models of discrete data pdf discrete flows invertible generative models of discrete data neurips 2019 ping et al 20 https proceedings icml cc static paper files icml 2020 647 paper pdf waveflow a compact flow based model for raw audio icml 2020 vahdat and kautz 20 https arxiv org pdf 2007 03898v1 pdf nvae a deep hierarchical variational autoencoder neurips 2020 ho et al 20 https proceedings neurips cc paper 2020 file 4c5bcfec8584af0d967f1ab10179ca4b paper pdf denoising diffusion probabilistic models neurips 2020 song et al 21 https openreview net forum id pxtig12rrhs score based generative modeling through stochastic differential equations iclr 2021 kosiorek et al 21 http proceedings mlr press v139 kosiorek21a kosiorek21a pdf nerf vae a geometry aware 3d scene generative model icml 2021 generative adversarial networks goodfellow et al 14 https papers nips cc paper 5423 generative adversarial nets pdf generative adversarial nets nips 2014 radford et al 15 https arxiv org abs 1511 06434 unsupervised representation learning with deep convolutional generative adversarial networks iclr 2016 chen et al 16 https papers nips cc paper 6399 infogan interpretable representation learning by information maximizing generative adversarial nets pdf infogan interpreting representation learning by information maximizing generative adversarial nets nips 2016 arjovsky et al 17 http proceedings mlr press v70 arjovsky17a arjovsky17a pdf wasserstein generative adversarial networks icml 2017 zhu et al 17 https arxiv org pdf 1703 10593 pdf unpaired image to image translation using cycle consistent adversarial networks iccv 2017 zhang et al 17 https arxiv org pdf 1706 03850 pdf adversarial feature matching for text generation icml 2017 karras et al 18 https openreview net forum id hk99zceab progressive growing of gans for improved quality stability and variation iclr 2018 choi et al 18 https openaccess thecvf com content cvpr 2018 papers choi stargan unified generative cvpr 2018 paper pdf stargan unified generative adversarial networks for multi domain image to image translation cvpr 2018 brock et al 19 https openreview net pdf id b1xsqj09fm large scale gan training for high fidelity natural image synthesis iclr 2019 karras et al 19 http openaccess thecvf com content cvpr 2019 papers karras a style based generator architecture for generative adversarial networks cvpr 2019 paper pdf a style based generator architecture for generative adversarial networks cvpr 2019 karras et al 20 https openaccess thecvf com content cvpr 2020 papers karras analyzing and improving the image quality of stylegan cvpr 2020 paper pdf analyzing and improving the image quality of stylegan cvpr 2020 sinha et al 20 https proceedings icml cc static paper files icml 2020 1324 paper pdf small gan speeding up gan training using core sets icml 2020 karras et al 20 https papers nips cc paper 2020 file 8d30aa96e72440759f74bd2306c1fa3d paper pdf training generative adversarial networks with limited data neurips 2020 liu et al 21 https openreview net pdf id 1fqg133qrai towards faster and stabilized gan training for high fidelity few shot image synthesis iclr 2021 esser et al 22 https openaccess thecvf com content cvpr2021 papers esser taming transformers for high resolution image synthesis cvpr 2021 paper pdf taming transformers for high resolution image synthesis cvpr 2021 hudson and zitnick 21 https arxiv org pdf 2103 01209 pdf generative adversarial transformers icml 2021 karras et al 21 https proceedings neurips cc paper 2021 file 076ccd93ad68be51f23707988e934906 paper pdf alias free generative adversarial networks neurips 2021 skorokhodov et al 22 https openaccess thecvf com content cvpr2022 papers skorokhodov stylegan v a continuous video generator with the price image quality cvpr 2022 paper pdf stylegan v a continuous video generator with the price image quality and perks of stylegan2 cvpr 2022 lin et al 22 https openreview net pdf id ufgmqim0a4b infinitygan towards infinite pixel image synthesis iclr 2022 lee et al 22 https openreview net pdf id dwg5rxg1ws vitgan training gans with vision transformers iclr 2022 yu et al 22 https openreview net forum id pfnyexj7z2 vector quantized image modeling with improved vqgan iclr 2022 franceschi et al 22 https proceedings mlr press v162 franceschi22a franceschi22a pdf a neural tangent kernel perspective of gans icml 2022 diffusion models song and ermon 19 https proceedings neurips cc paper 2019 file 3001ef257407d5a371a96dcd947c7d93 paper pdf generative modeling by estimating gradients of the data distribution neurips 2019 song and ermon 20 https papers nips cc paper 2020 file 92c3b916311a5517d9290576e3ea37ad paper pdf improved techniques for training score based generative models neurips 2020 ho et al 20 https proceedings neurips cc paper 2020 file 4c5bcfec8584af0d967f1ab10179ca4b paper pdf denoising diffusion probabilistic models neurips 2020 song et al 21 https openreview net forum id pxtig12rrhs score based generative modeling through stochastic differential equations iclr 2021 nichol and dhariwal 21 https proceedings mlr press v139 nichol21a nichol21a pdf improved denoising diffusion probabilistic models icml 2021 vahdat et al 21 https proceedings neurips cc paper 2021 file 5dca4c6b9e244d24a30b4c45601d9720 paper pdf score based generative modeling in latent space neurips 2021 dhariwal and nichol 21 https papers nips cc paper 2021 file 49ad23d1ec9fa4bd8d77d02681df5cfa paper pdf diffusion models beat gans on image synthesis neureips 2021 de bortoli et al 22 https proceedings neurips cc paper 2021 file 940392f5f32a7ade1cc201767cf83e31 paper pdf diffusion schrodinger bridge with application to score based generative modeling neurips 2021 ho and salimans 22 https arxiv org pdf 2207 12598 pdf classifier free diffusion guidance arxiv preprint 2022 dockhorn et al 22 https openreview net forum id czcer82cyc score based generative modeling with critically damped langevin diffusion iclr 2022 salimans and ho 22 https openreview net pdf id tidixipzhoi progressive distillation for fast sampling of diffusion models iclr 2022 chen et al 22 https openreview net forum id nioadkcedxb likelihood training of schrodinger bridge using forward backwrad sdes theory iclr 2022 deep reinforcement learning mnih et al 13 https arxiv org pdf 1312 5602 pdf playing atari with deep reinforcement learning nips deep learning workshop 2013 silver et al 14 http proceedings mlr press v32 silver14 pdf deterministic policy gradient algorithms icml 2014 schulman et al 15 https arxiv org pdf 1502 05477 pdf trust region policy optimization icml 2015 lillicrap et al 16 https arxiv org pdf 1509 02971 pdf continuous control with deep reinforcement learning iclr 2016 schaul et al 16 https arxiv org pdf 1511 05952 pdf prioritized experience replay iclr 2016 wang et al 16 http proceedings mlr press v48 wangf16 pdf dueling network architectures for deep reinforcement learning icml 2016 mnih et al 16 http proceedings mlr press v48 mniha16 pdf asynchronous methods for deep reinforcement learning icml 2016 schulman et al 17 https arxiv org pdf 1707 06347 pdf proximal policy optimization algorithms arxiv preprint 2017 nachum et al 18 https papers nips cc paper 7591 data efficient hierarchical reinforcement learning pdf data efficient hierarchical reinforcement learning neurips 2018 ha et al 18 https papers nips cc paper 7512 recurrent world models facilitate policy evolution pdf recurrent world models facilitate policy evolution neurips 2018 burda et al 19 https openreview net forum id rjnwdjaqyx large scale study of curiosity driven learning iclr 2019 vinyals et al 19 https www nature com articles s41586 019 1724 z grandmaster level in starcraft ii using multi agent reinforcement learning nature 2019 bellemare et al 19 https papers nips cc paper 8687 a geometric perspective on optimal representations for reinforcement learning pdf a geometric perspective on optimal representations for reinforcement learning neurips 2019 janner et al 19 http papers nips cc paper 9416 when to trust your model model based policy optimization pdf when to trust your model model based policy optimization neurips 2019 fellows et al 19 https papers nips cc paper 8934 virel a variational inference framework for reinforcement learning pdf virel a variational inference framework for reinforcement learning neurips 2019 kumar et al 19 https proceedings neurips cc paper 2019 file c2073ffa77b5357a498057413bb09d3a paper pdf stabilizing off policy q learning via bootstrapping error reduction neurips 2019 kaiser et al 20 https openreview net pdf id s1xcpjhtdb model based reinforcement learning for atari iclr 2020 agarwal et al 20 https proceedings icml cc static paper files icml 2020 5394 paper pdf an optimistic perspective on offline reinforcement learning icml 2020 lee et al 20 https proceedings icml cc static paper files icml 2020 3705 paper pdf batch reinforcement learning with hyperparameter gradients icml 2020 kumar et al 20 https proceedings neurips cc paper 2020 file 0d2b2061826a5df3221116a5085a6052 paper pdf conservative q learning for offline reinforcement learning icml 2020 yarats et al 21 https openreview net pdf id gy6 6stvgaf image augmentation is all you need regularizing deep reinforcement learning from pixels iclr 2021 chen et al 21 https proceedings neurips cc paper 2021 file 7f489f642a0ddb10272b5c31057f0663 paper pdf decision transformer reinforcement learning via sequence modeling neurips 2021 mai et al 22 https openreview net pdf id vrw3tvdfojq sample efficient deep reinforcement learning via uncertainty estimation iclr 2022 furuta et al 22 https openreview net forum id cajxvodl v generalized decision transformer for offline hindsight information matching iclr 2022 oh et al 22 https openreview net forum id wueiafqdy9h model augmented prioritized experience replay iclr 2022 rengarajan et al 22 https openreview net pdf id yj1wzgmvsmt reinforcement learning with sparse rewards using guidance from offline demonstration iclr 2022 patil et al 22 https proceedings mlr press v162 patil22a patil22a pdf align rudder learning from few demonstrations by reward redistribution icml 2022 goyal et al 22 https proceedings mlr press v162 goyal22a goyal22a pdf retrieval augmented reinforcement learning icml 2022 reed et al 22 https arxiv org pdf 2205 06175 pdf a generalist agent arxiv preprint 2022 memory and computation efficient deep learning han et al 15 https papers nips cc paper 5784 learning both weights and connections for efficient neural network pdf learning both weights and connections for efficient neural networks nips 2015 wen et al 16 https papers nips cc paper 6504 learning structured sparsity in deep neural networks pdf learning structured sparsity in deep neural networks nips 2016 han et al 16 https arxiv org pdf 1510 00149 pdf deep compression compressing deep neural networks with pruning trained quantization and huffman coding iclr 2016 molchanov et al 17 https arxiv org pdf 1701 05369 pdf variational dropout sparsifies deep neural networks icml 2017 luizos et al 17 https papers nips cc paper 6921 bayesian compression for deep learning pdf bayesian compression for deep learning nips 2017 luizos et al 18 https openreview net pdf id h1y8hhg0b learning sparse neural networks through l0 regularization iclr 2018 howard et al 18 https arxiv org pdf 1704 04861 pdf mobilenets efficient convolutional neural networks for mobile vision applications cvpr 2018 frankle and carbin 19 https https openreview net pdf id rjl b3rcf7 the lottery ticket hypothesis finding sparse trainable neural networks iclr 2019 lee et al 19 https openreview net pdf id b1vzqjacyx snip single shot network pruning based on connection sensitivity iclr 2019 liu et al 19 https openreview net pdf id rjlnb3c5ym rethinking the value of network pruning iclr 2019 jung et al 19 https arxiv org abs 1808 05779 learning to quantize deep networks by optimizing quantization intervals with task loss cvpr 2019 morcos et al 19 https papers nips cc paper 8739 one ticket to win them all generalizing lottery ticket initializations across datasets and optimizers pdf one ticket to win them all generalizing lottery ticket initializations across datasets and optimizers neurips 2019 renda et al 20 https openreview net pdf id s1gsj0nkvb comparing rewinding and fine tuning in neural network pruning iclr 2020 frankle et al 20 https proceedings icml cc static paper files icml 2020 5787 paper pdf linear mode connectivity and the lottery ticket hypothesis icml 2020 tanaka et al 20 https proceedings neurips cc paper 2020 file 46a4378f835dc8040c8057beb6a2da52 paper pdf pruning neural networks without any data by iteratively conserving synaptic flow neurips 2020 van baalen et al 20 https proceedings neurips cc paper 2020 file 3f13cf4ddf6fc50c0d39a1d5aeb57dd8 paper pdf bayesian bits unifying quantization and pruning neurips 2020 de jorge et al 21 https openreview net pdf id 9gsfouyupi progressive skeletonization trimming more fat from a network at initialization iclr 2021 stock et al 21 https openreview net pdf id dv19yyi1fs3 training with quantization noise for extreme model compression iclr 2021 lee et al 21 https arxiv org abs 1911 12990 semi relaxed quantization with dropbits training low bit neural networks via bit wise regularization iccv 2021 meta learning santoro et al 16 http proceedings mlr press v48 santoro16 pdf meta learning with memory augmented neural networks icml 2016 vinyals et al 16 https arxiv org pdf 1606 04080 pdf matching networks for one shot learning nips 2016 edwards and storkey 17 https arxiv org pdf 1606 02185 pdf towards a neural statistician iclr 2017 finn et al 17 https arxiv org pdf 1703 03400 pdf model agnostic meta learning for fast adaptation of deep networks icml 2017 snell et al 17 https arxiv org pdf 1703 05175 pdf prototypical networks for few shot learning nips 2017 nichol et al 18 https arxiv org pdf 1803 02999 pdf on first order meta learning algorithms arxiv preprint 2018 lee and choi 18 https arxiv org abs 1801 05558 gradient based meta learning with learned layerwise metric and subspace icml 2018 liu et al 19 https openreview net pdf id syvuric5k7 learning to propagate labels transductive propagation network for few shot learning iclr 2019 gordon et al 19 https openreview net pdf id hkxstoc5f7 meta learning probabilistic inference for prediction iclr 2019 ravi and beatson 19 https openreview net pdf id rkgpy3c5tx amortized bayesian meta learning iclr 2019 rakelly et al 19 http proceedings mlr press v97 rakelly19a rakelly19a pdf efficient off policy meta reinforcement learning via probabilistic context variables icml 2019 shu et al 19 https papers nips cc paper 8467 meta weight net learning an explicit mapping for sample weighting pdf meta weight net learning an explicit mapping for sample weighting neurips 2019 finn et al 19 http proceedings mlr press v97 finn19a finn19a pdf online meta learning icml 2019 lee et al 20 https openreview net pdf id rkezijbyvr learning to balance bayesian meta learning for imbalanced and out of distribution tasks iclr 2020 yin et al 20 https openreview net forum id bklefpeyws meta learning without memorization iclr 2020 raghu et al 20 https openreview net pdf id rkgmkcetpb rapid learning or feature reuse towards understanding the effectiveness of maml iclr 2020 iakovleva et al 20 https proceedings icml cc static paper files icml 2020 4070 paper pdf meta learning with shared amortized variational inference icml 2020 bronskill et al 20 https proceedings icml cc static paper files icml 2020 2696 paper pdf tasknorm rethinking batch normalization for meta learning icml 2020 rajendran et al 20 https proceedings neurips cc paper 2020 file 3e5190eeb51ebe6c5bbc54ee8950c548 paper pdf meta learning requires meta augmentation neurips 2020 lee et al 21 https openreview net forum id ws0ufjsnyjn meta gmvae mixture of gaussian vae for unsupervised meta learning iclr 2021 shin et al 21 http proceedings mlr press v139 shin21a shin21a pdf large scale meta learning with continual trajectory shifting icml 2021 acar et al 21 http proceedings mlr press v139 acar21b acar21b pdf memory efficient online meta learning icml 2021 lee et al 22 https openreview net forum id 01amrlen9wj online hyperparameter meta learning with hypergradient distillation iclr 2022 flennerhag et al 22 https openreview net forum id b ny3x071e5 boostrapped meta learning iclr 2022 yao et al 22 https openreview net forum id ajxwf7bvr8d meta learning with fewer tasks through task interpolation iclr 2022 guan and lu 22 https openreview net pdf id a3hhaedqajl task relatedness based generalization bounds for meta learning iclr 2022 continual learning rusu et al 16 https arxiv org pdf 1606 04671 pdf progressive neural networks arxiv preprint 2016 kirkpatrick et al 17 https arxiv org pdf 1612 00796 pdf overcoming catastrophic forgetting in neural networks pnas 2017 lee et al 17 https papers nips cc paper 7051 overcoming catastrophic forgetting by incremental moment matching pdf overcoming catastrophic forgetting by incremental moment matching nips 2017 shin et al 17 https papers nips cc paper 6892 continual learning with deep generative replay pdf continual learning with deep generative replay nips 2017 lopez paz and ranzato 17 https papers nips cc paper 7225 gradient episodic memory for continual learning pdf gradient episodic memory for continual learning nips 2017 yoon et al 18 https openreview net pdf id sk7ksfw0 lifelong learning with dynamically expandable networks iclr 2018 nguyen et al 18 https arxiv org pdf 1710 10628 pdf variational continual learning iclr 2018 schwarz et al 18 http proceedings mlr press v80 schwarz18a schwarz18a pdf progress compress a scalable framework for continual learning icml 2018 chaudhry et al 19 https openreview net pdf id hkf2 sc5fx efficient lifelong learning with a gem iclr 2019 rao et al 19 https papers nips cc paper 8981 continual unsupervised representation learning pdf continual unsupervised representation learning neurips 2019 rolnick et al 19 https papers nips cc paper 8327 experience replay for continual learning pdf experience replay for continual learning neurips 2019 jerfel et al 20 https papers nips cc paper 9112 reconciling meta learning and continual learning with online mixtures of tasks pdf reconciling meta learning and continual learning with online mixtures of tasks neurips 2019 yoon et al 20 https openreview net forum id r1gdj2ekpb scalable and order robust continual learning with additive parameter decomposition iclr 2020 remasesh et al 20 https arxiv org pdf 2007 07400 pdf anatomy of catastrophic forgetting hidden representations and task semantics continual learning workshop icml 2020 borsos et al 20 https proceedings neurips cc paper 2020 file aa2a77371374094fe9e0bc1de3f94ed9 paper pdf coresets via bilevel optimization for continual learning and streaming neurips 2020 mirzadeh et al 20 https proceedings neurips cc paper 2020 file 518a38cc9a0173d0b2dc088166981cf8 paper pdf understanding the role of training regimes in continual learning neurips 2020 saha et al 21 https openreview net pdf id 3aoj0rcnc2 gradient projection memory for continual learning iclr 2021 veinat et al 21 https openreview net pdf id ekv158tsfwv efficient continual learning with modular networks and task driven priors iclr 2021 madaan et al 22 https openreview net forum id 9hrka5pa7lw representational continuity for unsupervised continual learning iclr 2022 yoon et al 22 https openreview net forum id f9d 5wng4nv online coreset selection for rehearsal based continual learning iclr 2022 lin et al 22 https openreview net pdf id ievaf8i6jjo trgp trust region gradient projection for continual learning iclr 2022 wang et al 22 https proceedings mlr press v162 wang22v wang22v pdf improving task free continual learning by distributionally robust memory evolution icml 2022 kang et al 22 https proceedings mlr press v162 kang22b kang22b pdf forget free continual learning with winning subnetworks icml 2022 interpretable deep learning ribeiro et al 16 https arxiv org pdf 1602 04938 pdf why should i trust you explaining the predictions of any classifier kdd 2016 kim et al 16 https people csail mit edu beenkim papers kim2016nips mmd pdf examples are not enough learn to criticize criticism for interpretability nips 2016 choi et al 16 https papers nips cc paper 6321 retain an interpretable predictive model for healthcare using reverse time attention mechanism pdf retain an interpretable predictive model for healthcare using reverse time attention mechanism nips 2016 koh et al 17 https arxiv org pdf 1703 04730 pdf understanding black box predictions via influence functions icml 2017 bau et al 17 https arxiv org pdf 1704 05796 pdf network dissection quantifying interpretability of deep visual representations cvpr 2017 selvaraju et al 17 http openaccess thecvf com content iccv 2017 papers selvaraju grad cam visual explanations iccv 2017 paper pdf grad cam visual explanation from deep networks via gradient based localization iccv 2017 kim et al 18 http proceedings mlr press v80 kim18d kim18d pdf interpretability beyond feature attribution quantitative testing with concept activation vectors tcav icml 2018 heo et al 18 http papers nips cc paper 7370 uncertainty aware attention for reliable interpretation and prediction pdf uncertainty aware attention for reliable interpretation and prediction neurips 2018 bau et al 19 https openreview net pdf id hyg x2c5fx gan dissection visualizing and understanding generative adversarial networks iclr 2019 ghorbani et al 19 https papers nips cc paper 9126 towards automatic concept based explanations pdf towards automatic concept based explanations neurips 2019 coenen et al 19 https papers nips cc paper 9065 visualizing and measuring the geometry of bert pdf visualizing and measuring the geometry of bert neurips 2019 heo et al 20 https proceedings icml cc static paper files icml 2020 579 paper pdf cost effective interactive attention learning with neural attention processes icml 2020 agarwal et al 20 https arxiv org pdf 2004 13912v1 pdf neural additive models interpretable machine learning with neural nets arxiv preprint 2020 reliable deep learning guo et al 17 https arxiv org pdf 1706 04599 pdf on calibration of modern neural networks icml 2017 lakshminarayanan et al 17 http papers nips cc paper 7219 simple and scalable predictive uncertainty estimation using deep ensembles pdf simple and scalable predictive uncertainty estimation using deep ensembles nips 2017 liang et al 18 https openreview net pdf id h1vgkixrz enhancing the reliability of out of distrubition image detection in neural networks iclr 2018 lee et al 18 https openreview net pdf id ryiav2xaz training confidence calibrated classifiers for detecting out of distribution samples iclr 2018 kuleshov et al 18 https arxiv org pdf 1807 00263 pdf accurate uncertainties for deep learning using calibrated regression icml 2018 jiang et al 18 https papers nips cc paper 7798 to trust or not to trust a classifier pdf to trust or not to trust a classifier neurips 2018 madras et al 18 https papers nips cc paper 7853 predict responsibly improving fairness and accuracy by learning to defer pdf predict responsibly improving fairness and accuracy by learning to defer neurips 2018 maddox et al 19 https papers nips cc paper 9472 a simple baseline for bayesian uncertainty in deep learning pdf a simple baseline for bayesian uncertainty in deep learning neurips 2019 kull et al 19 https papers nips cc paper 9397 beyond temperature scaling obtaining well calibrated multi class probabilities with dirichlet calibration pdf beyond temperature scaling obtaining well calibrated multiclass probabilities with dirichlet calibration neurips 2019 thulasidasan et al 19 https papers nips cc paper 9540 on mixup training improved calibration and predictive uncertainty for deep neural networks pdf on mixup training improved calibration and predictive uncertainty for deep neural networks neurips 2019 ovadia et al 19 https papers nips cc paper 9547 can you trust your models uncertainty evaluating predictive uncertainty under dataset shift pdf can you trust your model s uncertainty evaluating predictive uncertainty under dataset shift neurips 2019 hendrycks et al 20 https openreview net pdf id s1gmrxhfvb augmix a simple data processing method to improve robustness and uncertainty iclr 2020 filos et al 20 https proceedings icml cc static paper files icml 2020 2969 paper pdf can autonomous vehicles identify recover from and adapt to distribution shifts icml 2020 robust deep learning szegedy et al 14 https arxiv org pdf 1312 6199 pdf intriguing properties of neural networks iclr 2014 goodfellow et al 15 https arxiv org pdf 1412 6572 pdf explaining and harnessing adversarial examples iclr 2015 kurakin et al 17 https openreview net pdf id bjm4t4kgx adversarial machine learning at scale iclr 2017 madry et al 18 https openreview net pdf id rjzibfzab toward deep learning models resistant to adversarial attacks iclr 2018 eykholt et al 18 https arxiv org pdf 1707 08945 pdf robust physical world attacks on deep learning visual classification athalye et al 18 https arxiv org pdf 1802 00420 pdf obfuscated gradients give a false sense of security circumventing defenses to adversarial examples icml 2018 zhang et al 19 http proceedings mlr press v97 zhang19p zhang19p pdf theoretically principled trade off between robustness and accuracy icml 2019 carmon et al 19 https papers nips cc paper 9298 unlabeled data improves adversarial robustness pdf unlabeled data improves adversarial robustness neurips 2019 ilyas et al 19 https papers nips cc paper 8307 adversarial examples are not bugs they are features adversarial examples are not bugs they are features neurips 2019 li et al 19 https papers nips cc paper 9143 certified adversarial robustness with additive noise pdf certified adversarial robustness with additive noise neurips 2019 tram r and boneh 19 https papers nips cc paper 8821 adversarial training and robustness for multiple perturbations pdf adversarial training and robustness for multiple perturbations neurips 2019 shafahi et al 19 https papers nips cc paper 8597 adversarial training for free pdf adversarial training for free neurips 2019 wong et al 20 https openreview net pdf id bjx040efvh fast is better than free revisiting adversarial training iclr 2020 madaan et al 20 https proceedings icml cc static paper files icml 2020 770 paper pdf adversarial neural pruning with latent vulnerability suppression icml 2020 croce and hein 20 http proceedings mlr press v119 croce20b croce20b pdf reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks icml 2020 maini et al 20 https proceedings icml cc static paper files icml 2020 3863 paper pdf adversarial robustness against the union of multiple perturbation models icml 2020 kim et al 20 https proceedings neurips cc paper 2020 file 1f1baa5b8edac74eb4eaa329f14a0361 paper pdf adversarial self supervised contrastive learning neurips 2020 wu et al 20 https proceedings neurips cc paper 2020 file 1ef91c212e30e14bf125e9374262401f paper pdf adversarial weight perturbation helps robust generalization neurips 2020 laidlaw et al 21 https openreview net pdf id dfwbosacjkn perceptual adversarial robustness defense against unseen threat models iclr 2021 pang et al 21 https openreview net pdf id xb8xvrtb8ce bag of tricks for adversarial training iclr 2021 madaan et al 21 http proceedings mlr press v139 madaan21a madaan21a pdf learning to generate noise for multi attack robustness icml 2021 mladenovic et al 22 https openreview net pdf id bygszbcm i online adversarial attacks iclr 2022 zhang et al 22 https openreview net pdf id w9g imphlqd how to robustify black box ml models a zeroth order optimization perspective iclr 2022 carlini and terzis 22 https openreview net pdf id ic4uhbq01mp poisoning and backdooring contrastive learning iclr 2022 croce et al 22 https proceedings mlr press v162 croce22a croce22a pdf evaluating the adversarial robustness of adaptive test time defenses icml 2022 zhou et al 22 https proceedings mlr press v162 zhou22m zhou22m pdf understanding the robustness in vision transformers icml 2022 graph neural networks li et al 16 https arxiv org pdf 1511 05493 pdf gated graph sequence neural networks iclr 2016 hamilton et al 17 https papers nips cc paper 6703 inductive representation learning on large graphs pdf inductive representation learning on large graphs nips 2017 kipf and welling 17 https openreview net pdf id sju4ayygl semi supervised classification with graph convolutional networks iclr 2017 velickovic et al 18 https openreview net pdf id rjxmpikcz graph attention networks iclr 2018 ying et al 18 https papers nips cc paper 7729 hierarchical graph representation learning with differentiable pooling pdf hierarchical graph representation learning with differentiable pooling neurips 2018 xu et al 19 https openreview net forum id rygs6ia5km how powerful are graph neural networks iclr 2019 maron et al 19 https papers nips cc paper 8488 provably powerful graph networks pdf provably powerful graph networks neurips 2019 yun et al 19 https papers nips cc paper 9367 graph transformer networks pdf graph transformer neteworks neurips 2019 loukas 20 https arxiv org pdf 1907 03199 pdf what graph neural networks cannot learn depth vs width iclr 2020 bianchi et al 20 https proceedings icml cc static paper files icml 2020 1614 paper pdf spectral clustering with graph neural networks for graph pooling icml 2020 xhonneux et al 20 https proceedings icml cc static paper files icml 2020 4075 paper pdf continuous graph neural networks icml 2020 garg et al 20 https proceedings icml cc static paper files icml 2020 2911 paper pdf generalization and representational limits of graph neural networks icml 2020 baek et al 21 https openreview net forum id jhcqxgaqign accurate learning of graph representations with graph multiset pooling iclr 2021 liu et al 21 http proceedings mlr press v139 liu21k liu21k pdf elastic graph neural networks icml 2021 li et al 21 http proceedings mlr press v139 li21o li21o pdf training graph neural networks with 1000 layers icml 2021 jo et al 21 https openreview net forum id vwgsqrorzz edge representation learning with hypergraphs neurips 2021 guo et al 22 https openreview net pdf id l4ihywgq6a data efficient graph grammar learning for molecular generation iclr 2022 geerts et al 22 https openreview net pdf id wizuem3tau expressiveness and approximation properties of graph neural networks iclr 2022 bevilacqua et al 22 https openreview net pdf id dfbkqark15w equivariant subgraph aggregation networks iclr 2022 jo et al 22 https proceedings mlr press v162 jo22a jo22a pdf score based generative modeling of graphs via the system of stochastic differential equations icml 2022 hoogeboom et al 22 https proceedings mlr press v162 hoogeboom22a hoogeboom22a pdf equivariant diffusion for molecule generation in 3d icml 2022 federated learning kone n et al 16 https arxiv org pdf 1610 02527 pdf federated optimization distributed machine learning for on device intelligence arxiv preprint 2016 kone n et al 16 https arxiv org abs 1610 05492 federated learning strategies for improving communication efficiency nips workshop on private multi party machine learning 2016 mcmahan et al 17 http proceedings mlr press v54 mcmahan17a mcmahan17a pdf communication efficient learning of deep networks from decentralized data aistats 2017 smith et al 17 https papers nips cc paper 7029 federated multi task learning pdf federated multi task learning nips 2017 li et al 20 https proceedings mlsys org static paper files mlsys 2020 176 paper pdf federated optimization in heterogeneous networks mlsys 2020 yurochkin et al 19 http proceedings mlr press v97 yurochkin19a yurochkin19a pdf bayesian nonparametric federated learning of neural networks icml 2019 bonawitz et al 19 https arxiv org pdf 1902 01046 pdf towards federated learning at scale system design mlsys 2019 wang et al 20 https openreview net forum id bkluqlsfds federated learning with matched averaging iclr 2020 li et al 20 https openreview net pdf id hjxnanvtds on the convergence of fedavg on non iid data iclr 2020 karimireddy et al 20 https proceedings icml cc static paper files icml 2020 788 paper pdf scaffold stochastic controlled averaging for federated learning icml 2020 hamer et al 20 https proceedings icml cc static paper files icml 2020 5967 paper pdf fedboost communication efficient algorithms for federated learning icml 2020 rothchild et al 20 https proceedings icml cc static paper files icml 2020 5927 paper pdf fetchsgd communication efficient federated learning with sketching icml 2020 fallah et al 21 https proceedings neurips cc paper 2020 file 24389bfe4fe2eba8bf9aa9203a44cdad paper pdf personalized federated learning with theoretical guarantees a model agnostic meta learning approach neurips 2020 reddi et al 21 https openreview net pdf id lkfg3lb13u5 adaptive federated optimization iclr 2021 jeong et al 21 https openreview net pdf id ce6cfxbh30h federated semi supervised learning with inter client consistency disjoint learning iclr 2021 yoon et al 21 https openreview net forum id svfh1 hyetf federated continual learning with weighted inter client transfer icml 2021 li et al 21 http proceedings mlr press v139 li21h li21h pdf ditto fair and robust federated learning through personalization icml 2021 neural architecture search zoph and le 17 https arxiv org abs 1611 01578 neural architecture search with reinforcement learning iclr 2017 baker et al 17 https openreview net pdf id s1c2cvqee designing neural network architectures using reinforcement learning iclr 2017 real et al 17 http proceedings mlr press v70 real17a real17a pdf large scale evolution of image classifiers icml 2017 liu et al 18 https openreview net pdf id bjqrkzba hierarchical representations for efficient architecture search iclr 2018 pham et al 18 http proceedings mlr press v80 pham18a html efficient neural architecture search via parameters sharing icml 2018 luo et al 18 https papers nips cc paper 8007 neural architecture optimization pdf neural architecture optimization neurips 2018 liu et al 19 https openreview net pdf id s1eyhoc5fx darts differentiable architecture search iclr 2019 tan et al 19 https arxiv org abs 1807 11626 mnasnet platform aware neural architecture search for mobile cvpr 2019 cai et al 19 https openreview net pdf id hylvb3aqym proxylessnas direct neural architecture search on target task and hardware iclr 2019 zhou et al 19 http proceedings mlr press v97 zhou19e zhou19e pdf bayesnas a bayesian approach for neural architecture search icml 2019 tan and le 19 http proceedings mlr press v97 tan19a tan19a pdf efficientnet rethinking model scaling for convolutional neural networks icml 2019 guo et al 19 https papers nips cc paper 8362 nat neural architecture transformer for accurate and compact architectures pdf nat neural architecture transformer for accurate and compact architectures neurips 2019 chen et al 19 https papers nips cc paper 8890 detnas backbone search for object detection pdf detnas backbone search for object detection neurips 2019 dong and yang 20 https openreview net forum id hjxyzkbkdr nas bench 201 extending the scope of reproducible neural architecture search iclr 2020 zela et al 20 https openreview net pdf id h1gdnyrkds understanding and robustifying differentiable architecture search iclr 2020 cai et al 20 https openreview net pdf id hylxe1hkws once for all train one network and specialize it for efficient deployment iclr 2020 such et al 20 https proceedings icml cc static paper files icml 2020 4522 paper pdf generative teaching networks accelerating neural architecture search by learning to generate synthetic training data icml 2020 liu et al 20 https www ecva net papers eccv 2020 papers eccv papers 123490766 pdf are labels necessary for neural architecture search eccv 2020 dudziak et al 20 https proceedings neurips cc paper 2020 file 768e78024aa8fdb9b8fe87be86f64745 paper pdf brp nas prediction based nas using gcns neurips 2020 li et al 20 https proceedings icml cc static paper files icml 2020 439 paper pdf neural architecture search in a proxy validation loss landscape icml 2020 lee et al 21 https openreview net forum id rkqufumuog3 rapid neural architecture search by learning to generate graphs from datasets iclr 2021 mellor et al 21 https arxiv org pdf 2006 04647 pdf neural architecture search without training icml 2021 large language models shoeybi et al 19 https arxiv org abs 1909 08053 megatron lm training multi billion parameter language models using model parallelism arxiv preprint 2019 raffel et al 20 https jmlr org papers volume21 20 074 20 074 pdf exploring the limits of transfer learning with a unified text to text transformer jmlr 2020 gururangan et al 20 https aclanthology org 2020 acl main 740 pdf don t stop pretraining adapt language models to domains and tasks acl 2020 brown et al 20 https papers nips cc paper 2020 file 1457c0d6bfcb4967418bfb8ac142f64a paper pdf language models are few shot learners neurips 2020 rae et al 21 https arxiv org abs 2112 11446 scaling language models methods analysis insights from training gopher arxiv preprint 2021 thoppilan et al 22 https arxiv org pdf 2201 11903 pdf lamda language models for dialog applications arxiv preprint 2022 wei et al 22 https openreview net pdf id gezrgcozdqr finetuned langauge models are zero shot learners iclr 2022 wang et al 22 https openreview net forum id pmqwkl1yctf language modeling via stochastic processes iclr 2022 alayrac et al 22 https arxiv org abs 2204 14198 flamingo a visual language model for few shot learning arxiv preprint 2022 chowdhery et al 22 https arxiv org abs 2204 02311 palm scaling langauge modeling with pathways arxiv preprint 2022 wei et al 22 https arxiv org pdf 2201 11903 pdf chain of thought prompting elicits reasoning in large language models neurips 2022 multimodal generative models li et al 19 https proceedings neurips cc paper 2019 file 1d72310edc006dadf2190caad5802983 paper pdf controllable text to image generation neurips 2019 ramesh et al 21 http proceedings mlr press v139 ramesh21a ramesh21a pdf zero shot text to image generation icml 2021 radford et al 21 http proceedings mlr press v139 radford21a radford21a pdf learning transferable visual models from natural language supervision icml 2021 ding et al 21 https proceedings neurips cc paper 2021 file a4d92e2cd541fca87e4620aba658316d paper pdf cogview mastering text to image generation via transformers neurips 2021 zou et al 22 https openaccess thecvf com content cvpr2022 papers zhou towards language free training for text to image generation cvpr 2022 paper pdf towards language free training for text to image generation cvpr 2022 rombach et al 22 https openaccess thecvf com content cvpr2022 papers rombach high resolution image synthesis with latent diffusion models cvpr 2022 paper pdf high resolution image synthesis with latent diffusion models cvpr 2022 nichol et al 22 https proceedings mlr press v162 nichol22a nichol22a pdf glide towards photorealistic image generation and editing with text guided diffusion models icml 2022 saharia et al 22 https arxiv org abs 2205 11487 photorealistic text to image diffusion models with deep language understanding arxiv preprint 2022 yu et al 22 https arxiv org abs 2206 10789 scaling autoregressive models for content rich text to image generation arxiv preprint 2022 deep learning for time series analysis che et al 18 http proceedings mlr press v80 che18a che18a pdf hierarchical deep generative models for multi rate multivariate time series icml 2018 campos et al 18 https openreview net pdf id hkwvaxycw skip rnn learning to skip state updates in recurrent neural networks iclr 2018 tatbul et al 18 https papers nips cc paper 7462 precision and recall for time series pdf precision and recall for time series neurips 2018 rangapuram et al 18 https papers nips cc paper 8004 deep state space models for time series forecasting pdf deep state space models for time series forecasting neurips 2018 deep learning for computer vision ren et al 15 https papers nips cc paper 5638 faster r cnn towards real time object detection with region proposal networks pdf faster r cnn towards real time object detection with region proposal networks nips 2015 liu et al 16 https arxiv org pdf 1512 02325 pdf ssd single shot multibox detector eccv 2016 long et al 15 https www cv foundation org openaccess content cvpr 2015 papers long fully convolutional networks 2015 cvpr paper pdf fully convolutional networks for semantic segmentation cvpr 2015 xu et al 15 http proceedings mlr press v37 xuc15 pdf show attend and tell neural image caption generation with visual attention icml 2015 held et al 16 http davheld github io goturn goturn pdf learning to track at 100 fps with deep regression networks eccv 2016 lin et al 17 http openaccess thecvf com content iccv 2017 papers lin focal loss for iccv 2017 paper pdf focal loss for dense object detection iccv 2017 chen et al 18 https arxiv org pdf 1802 02611 pdf encoder decoder with atrous separable convolution for semantic image segmentation eccv 2018 girdhar et al 18 http openaccess thecvf com content cvpr 2018 papers girdhar detect and track efficient pose cvpr 2018 paper pdf detect and track efficient pose estimation in videos cvpr 2018 | ai |
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car-sharing-blockchain | car sharing blockchain proof of concept system to renting cars with use of smart contracts prerequisites you do not need any extra libraries the only thing is installed python3 running we have provided example with renting one car for three days to run example git clone https github com jakubrog car sharing blockchain cd car sharing blockchain python main py | blockchain |
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Generative_AI_LLMs | generative ai with large language models deeplearning ai and amazon web services this repository contains all the resources i used and created for the generative ai with llms https www coursera org learn generative ai with llms about course on coursera course syllabus week 1 transformer architecture prompting and prompt engineering generative ai project lifecycle pre training llms week 2 instruction fine tuning model evaluation and benchmarks parameter efficient fine tuning peft lora and soft prompts week 3 reinforcement learning from human feedback rlhf proximal policy optimization model optimization for deployment llm application architecture you can view my course certificate here https coursera org verify cfqy6lpc25bv | aws generative-ai llms transformer | ai |
Zai_Azure_Resume | kudzai azure resume project my personal azure resume project creating an azure hosted website as part of showcasing my current skillset and the skills that i am learning and working towards for a career in cloud engineering i am utilizing azure cosmos db azure functions azure blob storage creating a cname record for the website and implementing https i am undertaking this also to get a start with web developing using html css and javascript notes frontend contains the website code in the index html file main js contains visitor counter code using java script created azure function using azure tools in visual studio code pointed the index html file to main js to get the counter attached to the website currently trying to get website working with azure function the function works but the site is showing the count this is due to cors i have updated the cors of the function to allow the url to access the function yet the issue persists | cloud |
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MDP-FE | hybrid app 1 | front_end |
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Image-Caption-Generator | image caption generator images imagecaption jpg raw true o reilly br this is implementation of a image caption generator from yumi s blog https fairyonice github io develop an image captioning deep learning model using flickr 8k data html which generates a caption based on the things that are present in the image image captioning is a challenging task where computer vision and natural language processing both play a part to generate captions this technology can be used in many new fields like helping visually impaired medical image analysis geospatial image analysis etc use cases some detailed usecases would be like an visually impaired person taking a picture from his phone and then the caption generator will turn the caption to speech for him to understand advertising industry trying the generate captions automatically without the need to make them seperately during production and sales doctors can use this technology to find tumors or some defects in the images or used by people for understanding geospatial images where they can find out more details about the terrain images usecase2 png raw true br dataset flickr 8k https forms illinois edu sec 1713398 this dataset includes around 1500 images along with 5 different captions written by different people for each image the images are all contained together while caption text file has captions along with the image number appended to it the zip file is approximately over 1 gb in size images dataset png raw true br flow of the project a cleaning the caption data b extracting features from images using vgg 16 c merging the captions and images d building lstm model for training e predicting on test data f evaluating the captions using bleu scores as the metric br steps to follow 1 cleaning the captions this is the first step of data pre processing the captions contain regular expressions numbers and other stop words which need to be cleaned before they are fed to the model for further training the cleaning part involves removing punctuations single character and numerical values after cleaning we try to figure out the top 50 and least 50 words in our dataset images top50 png raw true br 2 adding start and end sequence to the captions start and end sequence need to be added to the captions because the captions vary in length for each image and the model has to understand the start and the end 3 extracting features from images after dealing with the captions we then go ahead with processing the images for this we make use of the pre trained vgg 16 https github com fchollet deep learning models releases download v0 1 vgg16 weights tf dim ordering tf kernels h5 weights instead of using this pre trained model for image classification as it was intended to be used we just use it for extracting the features from the images in order to do that we need to get rid of the last output layer from the model the model then generates 4096 features from taking images of size 224 224 3 images vgg16 png raw true br 4 viewing similar images when the vgg 16 model finishes extracting features from all the images from the dataset similar images from the clusters are displayed together to see if the vgg 16 model has extracted the features correctly and we are able to see them together images cluster1 png raw true br 5 merging the caption with the respective images the next step involves merging the captions with the respective images so that they can be used for training here we are only taking the first caption of each image from the dataset as it becomes complicated to train with all 5 of them then we have to tokenize all the captions before feeding it to the model 6 splitting the data for training and testing the tokenized captions along with the image data are split into training test and validation sets as required and are then pre processed as required for the input for the model 7 building the lstm model images lstm png raw true br lstm model is been used beacuse it takes into consideration the state of the previous cell s output and the present cell s input for the current output this is useful while generating the captions for the images br the step involves building the lstm model with two or three input layers and one output layer where the captions are generated the model can be trained with various number of nodes and layers we start with 256 and try out with 512 and 1024 various hyperparameters are used to tune the model to generate acceptable captions images lstmmodel png raw true br 8 predicting on the test dataset and evaluating using bleu scores after the model is trained it is tested on test dataset to see how it performs on caption generation for just 5 images if the captions are acceptable then captions are generated for the whole test data images pred2 png raw true br these generated captions are compared to the actual captions from the dataset and evaluated using bleu https machinelearningmastery com calculate bleu score for text python scores as the evaluation metrics a score closer to 1 indicates that the predicted and actual captions are very similar as the scores are calculated for the whole test data we get a mean value which includes good and not so good captions some of the examples can be seen below good captions images good png raw true br bad captions images bad png raw true br hyper parameter tuning for the model images chart png raw true br images table png raw true br images tensorboard png raw true br conclusion implementing the model is a time consuming task as it involved lot of testing with different hyperparameters to generate better captions the model generates good captions for the provided image but it can always be improved | lstm-model image-captioning bleu-score caption-generator machine-learning computer-vision natural-language-processing flickr python jupyter-notebook | ai |
github-final-project | github final project a calculator that calculates simple interest given principal annual rate of interest and time period in years input p principal amount t time period in years r annual rate of interest output simple interest p t r | cloud |
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TensorRT-LLM | div align center tensorrt llm h4 a tensorrt toolbox for large language models h4 documentation https img shields io badge docs latest brightgreen svg style flat https nvidia github io tensorrt llm python https img shields io badge python 3 10 12 green https www python org downloads release python 31012 cuda https img shields io badge cuda 12 2 green https developer nvidia com cuda downloads trt https img shields io badge trt 9 1 green https developer nvidia com tensorrt version https img shields io badge release 0 5 0 green setup py license https img shields io badge license apache 202 blue license architecture docs source architecture md nbsp nbsp nbsp nbsp nbsp nbsp results docs source performance md nbsp nbsp nbsp nbsp nbsp nbsp examples examples nbsp nbsp nbsp nbsp nbsp nbsp documentation docs source div align left table of contents tensorrt llm overview tensorrt llm overview installation installation quick start quick start support matrix support matrix performance performance advanced topics advanced topics quantization quantization in flight batching in flight batching attention attention graph rewriting graph rewriting benchmarking benchmarking troubleshooting troubleshooting release notes release notes changelog changelog known issues known issues tensorrt llm overview tensorrt llm provides users with an easy to use python api to define large language models llms and build tensorrt https developer nvidia com tensorrt engines that contain state of the art optimizations to perform inference efficiently on nvidia gpus tensorrt llm also contains components to create python and c runtimes that execute those tensorrt engines it also includes a backend https github com triton inference server tensorrtllm backend for integration with the nvidia triton inference server https developer nvidia com nvidia triton inference server a production quality system to serve llms models built with tensorrt llm can be executed on a wide range of configurations going from a single gpu to multiple nodes with multiple gpus using tensor parallelism https docs nvidia com deeplearning nemo user guide docs en stable nlp nemo megatron parallelisms html tensor parallelism and or pipeline parallelism https docs nvidia com deeplearning nemo user guide docs en stable nlp nemo megatron parallelisms html pipeline parallelism the python api of tensorrt llm is architectured to look similar to the pytorch https pytorch org api it provides users with a functional tensorrt llm functional py module containing functions like einsum softmax matmul or view the layers tensorrt llm layers module bundles useful building blocks to assemble llms like an attention block a mlp or the entire transformer layer model specific components like gptattention or bertattention can be found in the models tensorrt llm models module tensorrt llm comes with several popular models pre defined they can easily be modified and extended to fit custom needs see below for a list of supported models models to maximize performance and reduce memory footprint tensorrt llm allows the models to be executed using different quantization modes see examples gpt examples gpt for concrete examples tensorrt llm supports int4 or int8 weights and fp16 activations a k a int4 int8 weight only as well as a complete implementation of the smoothquant https arxiv org abs 2211 10438 technique for a more detailed presentation of the software architecture and the key concepts used in tensorrt llm we recommend you to read the following document docs source architecture md installation for windows installation see windows windows tensorrt llm must be built from source instructions can be found here docs source installation md an image of a docker container with tensorrt llm and its triton inference server backend will be made available soon the remaining commands in that document must be executed from the tensorrt llm container quick start to create a tensorrt engine for an existing model there are 3 steps 1 download pre trained weights 2 build a fully optimized engine of the model 3 deploy the engine the following sections show how to use tensorrt llm to run the bloom 560m https huggingface co bigscience bloom 560m model 0 in the bloom folder inside the docker container you have to install the requirements bash pip install r examples bloom requirements txt git lfs install 1 download the model weights from huggingface from the bloom example folder you must download the weights of the model bash cd examples bloom rm rf bloom 560m mkdir p bloom 560m git clone https huggingface co bigscience bloom 560m bloom 560m 2 build the engine python single gpu on bloom 560m python build py model dir bloom 560m dtype float16 use gemm plugin float16 use gpt attention plugin float16 output dir bloom 560m trt engines fp16 1 gpu see the bloom example examples bloom for more details and options regarding the build py script 3 run the summarize py script can be used to perform the summarization of articles from the cnn daily dataset python python summarize py test trt llm hf model location bloom 560m data type fp16 engine dir bloom 560m trt engines fp16 1 gpu more details about the script and how to run the bloom model can be found in the example folder examples bloom many more models models than bloom are implemented in tensorrt llm they can be found in the examples examples directory support matrix tensorrt llm optimizes the performance of a range of well known models on nvidia gpus the following sections provide a list of supported gpu architectures as well as important features implemented in tensorrt llm devices tensorrt llm is rigorously tested on the following gpus h100 https www nvidia com en us data center h100 l40s https www nvidia com en us data center l40s a100 https www nvidia com en us data center a100 a30 https www nvidia com en us data center products a30 gpu v100 https www nvidia com en us data center v100 experimental if a gpu is not listed above it is important to note that tensorrt llm is expected to work on gpus based on the volta turing ampere hopper and ada lovelace architectures certain limitations may however apply precision various numerical precisions are supported in tensorrt llm the support for some of those numerical features require specific architectures fp32 fp16 bf16 fp8 int8 int4 volta sm70 y y n n y y turing sm75 y y n n y y ampere sm80 sm86 y y y n y y ada lovelace sm89 y y y y y y hopper sm90 y y y y y y in this release of tensorrt llm the support for fp8 and quantized data types int8 or int4 is not implemented for all the models see the precision docs source precision md document and the examples examples folder for additional details key features tensorrt llm contains examples that implement the following features multi head attention mha https arxiv org abs 1706 03762 multi query attention mqa https arxiv org abs 1911 02150 group query attention gqa https arxiv org abs 2307 09288 in flight batching paged kv cache for the attention tensor parallelism pipeline parallelism int4 int8 weight only quantization w4a16 w8a16 smoothquant https arxiv org abs 2211 10438 gptq https arxiv org abs 2210 17323 awq https arxiv org abs 2306 00978 fp8 https arxiv org abs 2209 05433 greedy search beam search rope in this release of tensorrt llm some of the features are not enabled for all the models listed in the examples examples folder models the list of supported models is baichuan examples baichuan bert examples bert blip2 examples blip2 bloom examples bloom chatglm 6b examples chatglm6b chatglm2 6b examples chatglm2 6b falcon examples falcon gpt examples gpt gpt j examples gptj gpt nemo examples gpt gpt neox examples gptneox llama examples llama llama v2 examples llama mpt examples mpt opt examples opt santacoder examples gpt starcoder examples gpt performance please refer to the performance docs source performance md page for performance numbers that page contains measured numbers for four variants of popular models gpt j llama 7b llama 70b falcon 180b measured on the h100 l40s and a100 gpu s advanced topics quantization this document docs source precision md describes the different quantization methods implemented in tensorrt llm and contains a support matrix for the different models in flight batching tensorrt llm supports in flight batching of requests also known as continuous batching or iteration level batching it s a technique docs source batch manager md that aims at reducing wait times in queues eliminating the need for padding requests and allowing for higher gpu utilization attention tensorrt llm implements several variants of the attention mechanism that appears in most the large language models this document docs source gpt attention md summarizes those implementations and how they are optimized in tensorrt llm graph rewriting tensorrt llm uses a declarative approach to define neural networks and contains techniques to optimize the underlying graph for more details please refer to doc docs source graph rewriting md benchmark tensorrt llm provides c benchmarks cpp readme md and python benchmarks python readme md tools to perform benchmarking note however that it is recommended to use the c version troubleshooting it s recommended to add options shm size 1g ulimit memlock 1 to the docker or nvidia docker run command otherwise you may see nccl errors when running multiple gpu inferences see https docs nvidia com deeplearning nccl user guide docs troubleshooting html errors for details when building models memory related issues such as 09 23 2023 03 13 00 trt e 9 gptlmheadmodel layers 0 attention qkv plugin v2 gemm 0 could not find any supported formats consistent with input output data types 09 23 2023 03 13 00 trt e 9 pluginv2builder cpp reportpluginerror 24 error code 9 internal error gptlmheadmodel layers 0 attention qkv plugin v2 gemm 0 could not find any supported formats consistent with input output data types may happen one possible solution is to reduce the amount of memory needed by reducing the maximum batch size input and output lengths another option is to enable plugins for example use gpt attention plugin release notes tensorrt llm requires tensorrt 9 1 0 4 and 23 08 containers change log tensorrt llm v0 5 0 is the first public release known issues report issues you can use github issues to report issues with tensorrt llm | ai |
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Upland-Task | upland task | cloud |
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laundry-app-api | giver trail backend my current node js project restful api integrated with react native app routes currently public but will be made private as development carries on 1 users create user email firstname lastname password pointesearned 1 currentcause 2 users user id displays the user s info excluding the password | server |
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awesome-IoT-hybrid | awesome iot hybrid awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome the missing awesome list collection of awesome iot and hybrid apps frameworks tools resources videos and shiny things iot iot os os frameworks tools frameworks tools resources websites projects resources websites projects iiot iiot hybrid desktop hybrid desktop hybrid mobile hybrid mobile tools plugins tools plugins miscellaneous miscellaneous iot tessel https tessel io arduino http www arduino cc beagleboard http beagleboard org bone hue http www developers meethue com raspberry pi https www raspberrypi org onion omega https www kickstarter com projects onion onion omega invention platform for the internet of video share particle https www particle io os riot os http www riot os org node os https node os com contiki os http www contiki os org raspbian http raspbian org project brillo https developers google com brillo balenaos https www balena io os frameworks tools cylonjs http cylonjs com node red http nodered org iot eclipse http iot eclipse org gladys project http gladysproject com lelylan https github com lelylan lelylan balenacloud https www balena io resources websites projects hackday https hackaday io projects instructables tech http www instructables com tag type id category technology hackster http www hackster io my controller https www mycontroller org home kaa project https www kaaproject org iiot industrial iot opc router https www opc router com iiot gateway workflow engine with various plug ins mqtt bridge opc ua bridge sql bridge rest bridge sap bridge hybrid desktop nw js https github com nwjs nw js electron https github com atom electron chromium embedded framework https bitbucket org chromiumembedded cef appjs http appjs com macgap https github com macgapproject hybrid mobile react native http facebook github io react native nativescript https www nativescript org phonegap http phonegap com corona http coronalabs com ionic http ionicframework com appcelerator http www appcelerator com intel xdk https software intel com en us html5 tools trigger io https trigger io crosswalk https crosswalk project org telerik platform http www telerik com platform meteor https www meteor com tabris js https tabrisjs com tools plugins cordova phonegap ibeacon plugin https github com petermetz cordova plugin ibeacon miscellaneous firefox os https www mozilla org en us firefox os leap motion https www leapmotion com contributing 1 fork it 2 create your branch git checkout b my new branch 3 commit your changes git commit am fix stuff 4 push to the branch git push origin my new branch 5 submit a pull request license the mit license mit copyright c 2014 michael lancaster permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | server |
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generator-web-extension | img align right src assets icon svg sanitize true alt logo https www npmjs com package generator web extension generator webextension npm package https badge fury io js generator web extension svg https www npmjs com package generator web extension build status https secure travis ci org webextension toolbox generator web extension png branch master https travis ci org webextension toolbox generator web extension build status https ci appveyor com api projects status ajr5bdyo5j15e6mf svg true https ci appveyor com project webextension toolbox generator web extension dependencies https david dm org webextension toolbox generator web extension status svg https david dm org webextension toolbox generator web extension devdependencies https david dm org webextension toolbox generator web extension dev status svg https david dm org webextension toolbox generator web extension type dev devdependencies https david dm org webextension toolbox generator web extension peer status svg https david dm org webextension toolbox generator web extension type peer js standard style https img shields io badge code 20style standard green svg style flat square https github com feross standard license https img shields io npm l generator web extension svg https github com webextension toolbox generator web extension blob master license advanced webextension generator that creates everything you need to get started with cross browser extension addon development under the hood it uses webextension toolbox https github com webextension toolbox webextension toolbox for compiling the extensions install shell npm install g yo generator web extension getting started 1 first make a new directory and cd into it mkdir my web extension cd 2 run yo web extension gif showing the demo https i imgur com lqtk588 gif options skip install skips the automatic execution of npm after scaffolding has finished license copyright 2018 henrik wenz this project is free software released under the mit license | web-extension chrome-extension firefox-extension opera-extension edge-extension yeoman yeoman-generator generator | front_end |
react-native-redux-toolkit-starter-app | react native redux toolkit start app omit in toc a react native boilerplate app to bootstrap your next app with redux toolkit and saga upgraded to the latest react native 0 69 x with brand new architecture fabric br div align center img src react native starter kit png width 100 div checkout also my brand new react native react query no redux toolkit here https github com irontony react native react query starter app license https img shields io github license irontony react native redux toolkit starter app license all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 2 screen svg style flat contributors sparkles all contributors badge end issues https img shields io github issues irontony react native redux toolkit starter app svg https github com irontony react native redux toolkit starter app issues img src https img icons8 com color 48 000000 travis ci png width 30px build https travis ci com irontony react native redux toolkit starter app svg branch master https travis ci com irontony react native redux toolkit starter app build https img shields io badge ios 20tested success brightgreen svg https github com irontony react native redux toolkit starter app build https img shields io badge android 20tested success brightgreen svg https github com irontony react native redux toolkit starter app a href https www buymeacoffee com irontony target blank img src https cdn buymeacoffee com buttons lato blue png alt buy me a coffee width 217px a table of contents omit in toc upgraded to the latest react native 0 69 x with brand new architecture fabric upgraded to the latest react native 069x with brand new architecture fabric checkout also my brand new react native react query no redux toolkit here checkout also my brand new react native react query no redux toolkit here installation inbox tray installation inbox tray rename project and bundles memo package rename project and bundles memo package ios android ios android environment setup globe with meridians environment setup globe with meridians scripts wrench scripts wrench run the app run the app generate app icons generate app icons generate splashscreen generate splashscreen to enabled react native fabric new architecture to enabled react native fabric new architecture setup ios setup ios typescript optional typescript optional roadmap running roadmap running screenshots screenshots contributors sparkles contributors sparkles license scroll license scroll installation inbox tray bash setup your project npx degit irontony react native redux toolkit starter app your new app cd your new app install dependencies yarn if needed setup ios development environment yarn setup ios see environment environment setup globe with meridians section for how to configure env variables see scripts scripts wrench section for how to run the app rename project and bundles memo package to rename the project and bundles just follow these steps ios android run npx react native rename name b bundle identifier from the project root example npx react native rename test new app b com testnewapp environment setup globe with meridians react native starter app environments variables management is based on a custom script and the app json config file define your environment variables inside app json inside the environments object under the desired environment key such as development staging or production and then run the app for the required env using one of the available run scripts e g ios dev if you want to use ides such xcode or android studio you have to set up the env variables with these commands yarn env dev to set the development env variables yarn env stage to set the staging env variables yarn env prod to set the production env variables scripts wrench run the app to run the app use one of the following scripts yarn android dev to start the app on android with the development environment variables yarn android stage to start the app on android with the staging environment variables yarn android prod to start the app on android with the production environment variables yarn ios dev to start the app on ios with the development environment variables yarn ios stage to start the app on ios with the staging environment variables yarn ios prod to start the app on ios with the production environment variables generate app icons to setup the app icons create an image at least 1024x1024px place it under assets folder as icon png run sh yarn assets icons generate splashscreen to setup the app splashscreen create an image at least 1242x2208px place it under assets folder as splashscreen png run sh yarn assets splashscreen to enabled react native fabric new architecture check the official documentation here https reactnative dev docs new architecture intro setup ios to setup the environment to run on ios run sh yarn setup ios this will run cocoapods to install all the required dependencies typescript optional the use of typescript in the project is not mandatory you can just write all your code using plain javascript our hint is to create all files as below files with logic and views with tsx extension files with stylesheet and others with ts extension to enable full typescript checks just open the tsconfig json file and chage as below br noimplicitany true set to true to be explicit and declare all types now br strict true enable it to use fully typescript set of invasive rules br remember the entry point file in the root of the project must be index js roadmap running initial setup br react native splashscreen https github com crazycodeboy react native splash screen br react native toolbox to generate splashscreen and icons automagically https github com panz3r react native toolbox br standard tree folders structure br react native 0 69 new architecture br redux toolkit br redux persist https github com rt2zz redux persist br react native debugger br react native flipper integration br redux saga br i18next br react navigation v6 br nativebase v3 as design system br env variables selection experimental way br typescript optional use read the doc above br ui kitten is not supported anymore screenshots div align center img src screenshots screenshot1 png width 50 div div align center img src screenshots screenshot2 png width 50 div div align center img src screenshots screenshot3 png width 50 div contributors sparkles thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href https github com irontony img src https avatars3 githubusercontent com u 3645225 v 4 width 100px alt br sub b irontony b sub a br a href ideas irontony title ideas planning feedback a a href https github com irontony react native redux toolkit starter app commits author irontony title code a a href https github com irontony react native redux toolkit starter app commits author irontony title documentation a a href https github com irontony react native redux toolkit starter app issues q author 3airontony title bug reports a a href maintenance irontony title maintenance a a href platform irontony title packaging porting to new platform a a href question irontony title answering questions a a href https github com irontony react native redux toolkit starter app pulls q is 3apr reviewed by 3airontony title reviewed pull requests a a href https github com irontony react native redux toolkit starter app commits author irontony title tests a a href example irontony title examples a td td align center a href http panz3r dev img src https avatars3 githubusercontent com u 1754457 v 4 width 100px alt br sub b mattia panzeri b sub a br a href ideas panz3r title ideas planning feedback a a href https github com irontony react native redux toolkit starter app commits author panz3r title documentation a a href tool panz3r title tools 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 license scroll licensed under mozilla public license version 2 0 license | react-native redux redux-saga reselect i18next splashscreen starter-kit redux-toolkit | front_end |
IIT | iit introduction to information technology | server |
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ucdp_udagram_restapi | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com grutt udacity c2 frontend a basic ionic client web application which consumes the restapi backend 2 the restapi backend https github com grutt udacity c2 restapi a node express server which can be deployed to a cloud service 3 the image filtering microservice https github com grutt udacity c2 image filter the final project for the course it is a node express application which runs a simple python script to process images tasks setup python environment you ll need to set up and use a virtual environment for this project to create a virtual environment run the following from within the project directory 1 install virtualenv dependency pip install virtualenv 2 create a virtual environment virtualenv venv 3 activate the virtual environment source venv bin activate note you ll need to do this every time you open a new terminal 4 install dependencies pip install r requirements txt when you re done working and leave the virtual environment run deactivate setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm init 2 install express npm i express save 3 install typescript dependencies npm i ts node dev tslint typescript types bluebird types express types node save dev 4 look at the package json file from the restapi repo and copy the scripts block into the auto generated package json in this project this will allow you to use shorthand commands like npm run dev create a new server ts file use our basic server as an example to set up this file for this project it s ok to keep all of your business logic in the one server ts file but you can try to use feature directories and app use routing if you re up for it use the restapi structure to guide you add an endpoint to handle post imagetoprocess requests it should accept two post parameter image url string a public url of a valid image file upload image signedurl string optional a url which will allow a put request with the processed image it should respond with 422 unprocessable if either post parameters are invalid it should require a token in the auth header or respond with 401 unauthorized it should be versioned the matching token should be saved as an environment variable tip we broke this out into its own auth router before but you can access headers as part of the req headers within your endpoint block it should respond with the image as the body if upload image signedurl is included in the request it should respond with a success message if upload image signedurl is not included in the request refactor your restapi server add a request to the image filter server within the restapi post feed endpoint it should create new signedurls required for the imagetoprocess post request body it should include a post request to the new server tip keep the server address and token as environment variables it should overwrite the image in the bucket with the filtered image in other words it will have the same filename in s3 deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service stand out postman integration tests try writing a postman collection to test your endpoint be sure to cover post requests with and without tokens post requests with valid and invalid parameters refactor data models try adding another column to your tables to save a separate key for your filtered image remember you ll have to rename the file before adding it to s3 advanced refactor data models try adding a second opencv filter script and add an additional parameter to select which filter to use as a post parameter | cloud |
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Attendance-Tracking-System-Using-Computer-Vision | attendance tracking system using computer vision alt text https www olloltd com images 2020 02 sssss gif introduction tracking systems nowadays are in high demand starting from the very basic attendance systems to most critical security systems with the powerful rise of ai computer vision these tracking systems have become more accurate more precise resulting in more reliability and stability our project is basically leveraging the power of image video detection recognition tracking we have applied the best in class computer vision algorithms to build a full attendance tracking system enabling the following features real time face detection recognition applied on video camera streams based on your computational resources power ability to add new employees to the system and retrain the algorithm using only 1 2 images a web application showing employees attendance activities extras faces detector recognizer final target monitor how much time does an employee stays in the work environment vs how much does he she waste per day either outside the environment or in fun area system architecture overview let s define it in simple forms any system has its own input data output data processing unit storage unit input data source for this project is mainly live streams videos from environment cameras desired output a reliable employees attendance activity database on a web application in between target build an attendance tracking system using computer vision storage videos hdd store employees final activity in a mysql database image https drive google com uc export view id 139turpwjpuxki8lcyqpixhkq7wdudzzt now that we have seen the overall system overview let s build the pipeline for this application system pipeline the project will mainly be separated into 4 modules module a getting data in either from videos or from images module b computer vision part face detector face recognizer module c attendance logic based on your environment module d web application attendance admin panel testing interface image https drive google com uc export view id 15d50d7xtine48o0 ei o4efjmcqlghgq let s dig into each module in a brief module a getting data in either from videos or from images since this is an attendance tracking system then we have some cameras installed at some location in your environment image https drive google com uc export view id 1b9ahrkxjq0hjb6pr2rker9q0xod1fk70 we have installed 6 cameras at the following critical locations a entrance door from outside camera 3 4 to get employees leaving environment b entrance door from inside camera 1 2 to get employees coming into the environment c fun area door camera 5 6 to capture employees going in or out of fun area you may ask why at these locations it s as simple as we want to know whenever an employee arrives to the environment using cameras 1 2 and whenever he she leaves using cameras 3 4 and how much time does he she wastes at fun area n b we have used 2 cameras at each zone to cover all possible face angels we need for each video frame to detect faces inside each frame recognize it you may think of a frame as an image module b computer vision part face detector face recognizer here we want to do 2 main tasks first detecting if an image contains a face or not detection then if the image has a face we want to know who is this person recognition module b 1 face detection at this step we have some input videos from different cameras videos are composed of frames you could think of each frame as a normal image and we just want to detect if there is a face in an image or not detection trials b 1 1 haar we have started our trials using traditional image processing algorithms haar it is a feature based cascade classifier using some basic filter we can get the region of the eyes is often darker than the region of the nose and cheeks the eyes are darker than the bridge of the nose image https drive google com uc export view id 1uv2h8a2gxll 23yfj5flz0njoys8y zo since that haar is very inefficient in difficult lighting conditions we didn t expect a good output from it also because haar features have to be determined manually there is a certain limit to the types of things it can detect if you give a classifier a network or any algorithm that detects faces edge and line features then it will only be able to detect objects with clear edges and lines even as a face detector if we manipulate the face a bit say cover up the eyes with sunglasses or tilt the head to a side a haar based classifier may not be able to recognize the face however haar has its own bright side when it s applied on high quality clear images since that we don t need to train haar features as they are manually selected we just need a relatively small amount of data for faces also haar execution speed is high and requires less computational resources when compared to other detection algorithms haar output on our environment image https drive google com uc export view id 1wyhh8uvqdhezy eoh4cxue n0u0n6lo as you can see haar failed to get our faces in this poor position lightning however it detected basic edges in the image b 1 2 yolo you only look once yolo is an object detector which detects objects in both images and video streams using deep learning opencv and python we call yolo as single stage detector there are other multi stage detectors that are more accurate but are very slow the you only look once or yolo family of models are a series of end to end deep learning models designed for fast object detection developed by joseph redmon et al and first described in the 2015 paper titled you only look once unified real time object detection details an object detector capable of super real time object detection obtaining 45 fps on a gpu image https drive google com uc export view id 1 0grqtez wmsr7h2w4aoiknqympjn9gd yolo detection method sliding window this approach involves a single deep convolutional neural network originally a version of googlenet later updated and called darknet based on vgg that splits the input into a grid of cells and each cell directly predicts a bounding box and object classification called sliding window the result is a large number of candidate bounding boxes that are consolidated into a final prediction by a post processing step since yolo is an object detector then it will be able to detect different objects in the image including people image https drive google com uc export view id 1cpmia x5qlozqiuarsyqgsiy eikohab perfect object human detection isn t it simple answer is yes it s very good but only in full object detection i mean the output of the detector is a box containing the person image https drive google com uc export view id 1pcdalqhllsphzhoai42u3nsucxpd gth when we feed these images to a classifier person a left person b right a classifier will always look at unique easy features at the beginning this includes the background out there t shirts colors etc which is not a good thing i e after some training whenever we pass to the classifier an image of any person standing for example at the location that has green background where person b stands right image the classifier will always predicts whoever passes by this location as person b because it fires based on the background not actual features b 1 3 segmentation now we have seen that yolo is a good detector but the detection outputs a full box including a background which we want to eliminate the segmentation concept comes into play here segmentation will allow us to crop the region of interest roi that we want from the image i e eliminating the whole background image https drive google com uc export view id 1u0vrzm2bqkewhpm lgoimszd4xt1t8mv this result is relatively perfect but actually we don t want neither the background nor the whole body and the detector still getting the whole body we just want the faces that s why we have checked for other face detectors and we found something called yolo faces b 1 4 yolo faces this is a special version of yolo object detectors that focuses only on detecting faces as an objects rather than detecting the whole body we have tried it however it wasn t very good in detecting all faces whenever they are aligned to the right or the left image https drive google com uc export view id 1cls2x4xn8oiqhofrogbvegfwmzztwg4i finally we have found a much better detector than most of the previously mentioned algorithms b 1 5 mtcnn multitask cascaded convolutional neural networks mtcnn the mtcnn is popular because it achieved then state of the art results on a range of benchmark datasets and because it is capable of also recognizing other facial features such as eyes and mouth called landmark detection image https drive google com uc export view id 1xnf6gmgnj06rev4izbgcxmymggh6hlrx obama face landmarks detection summary detection algorithm detection accuracy haar 70 yolo 92 segmentation 90 yolo faces 80 mtcnn 96 module b 2 face recognition at this step we have some images that contain boxes around any face in the picture now it s time to recognize the faces and know who is the exact person appearing this could be considered as a normal image classification problem where we can use either traditional ml algorithms or deep learning ones we have tried the very basic ml algorithms to the most advanced ones mixing between them here is our trials logistic regression random forests very poor accuracy neural networks poor accuracy on images convolutional neural networks cnn excellent accuracy on images however since this is an attendance system hence we have only a few registered images for each employee 1 4 images which aren t enough at all to train a cnn from scratch siamese networks based on 2 cnns good one with decent accuracy 84 cnn deep nn using transfer learning we have used a pre trained cnn to extract face features then we have applied the output features 128 embeddings to a nn which resulted in an overall accuracy 90 cnn svm using transfer learning we have used a pre trained cnn to extract face features then we have applied the output features 128 embeddings per face to svm which resulted in mostly an overall system accuracy 96 n b you may think how come cnn nn doesn t achieve high accuracy like cnn svm it s simply because deep learning requires alot of data to get good accuracy in the best scenario we will have only 5 images per employee and for example 100 employees i e 100 x 5 x 128 embeddings which is not enough for any deep learning model therefore we can conclude all of these by saying that the main pillars for the recognition phase are cnns transfer learning svm final pipeline image https drive google com uc export view id 1fq8enmrhulmsxcydyxkwkemvmt6f3lso module c attendance logic based on your environment this part is going to be purely sw logic covering 2 parts firstly what about the 4 error rate in our classification how can we make sure that the person recognized is 100 that person not 96 solved using confidence interval secondly we need some logic to handle the first seen last seen of a person including his her wasted time in fun area or outside the company then calculate the final working wasted hours let s start each of our cameras will produce a log file at the end of the day the logic module combines and process on them image https drive google com uc export view id 13zsinfqrzzai5ahgp3jeh5tce8e1t z4 sample log from camera id 1 before applying the confidence interval concept firstly we will combine all of the six log files produced by the 6 cameras and sort by the employee name timestamp the camera id image https drive google com uc export view id 1xekvxnxt19e1afeskhm4jawafm7yvf07 then we unify the cameras id s and reduce them from 6 ids to 3 ids since each 2 cameras capture the same area but from different angles 1 2 reduced to 1 3 4 reduced to 3 5 6 reduced to 6 image https drive google com uc export view id 1brawl76osgx8igb8uz9bzthuhlph13nb we have set our confidence interval threshold to be 5 which means if a person was detected in a video stream in 5 consecutive frames then most probably he she is the same person however if less than 5 then mostly due to light conditions or face angel the classifier model has mistaken the classification task therefore drop these log entries from your log file here is the final accurate log file image https drive google com uc export view id 19e8sz3hruv 15vrovg93lbokysgwiapj then by doing some pair successive subtractions we can obtain the time an employee spent in each area you can check the code at the github repository for further investigation and here is the final log file to be imported in the web application database containing all employees activity data note that we scaled the 8 of the working day to 8 minutes for computational reasons image https drive google com uc export view id 1tnbtjde3 n4kyzyy 44jxkmvpouz6shb now it s time to package all of this work inside a web application module d web application attendance admin panel now we have reached the state of having a really good model without real life application using it so it s useless so far in our case we have built a web application that track a company s employees activities attendance activities like when has employee arrive workplace how many hours the employee was off working and how many hours the employee was on desk and different types of activities that the employees usually do during a working day in the company s workplace in the web application module we will go through how we have built our web application using django framework activity tracking web application will help us to 1 add edit delete employees to our database 2 train our model for our newly added employees 3 test our model on a single picture 4 take a live snapshot and test the snapshot against our machine learning model web application database entities our web application will consist of employees activities that occurred by employees and employees pictures and test image model each of these models represents an entity with following responsibility a employee responsible for adding removing editing employees b activity responsible for employees activities c picture responsible for adding picture to an employee d test responsible for testing an image against our machine learning model each employee has a set of activities for each working day associated with him each employee has a set of pictures that the machine learning model will use to train and test the employee s face and a test entity to be able to test an image or video against machine learning model through the web application django comes with a grateful built in admin panel that will help us to build our web application admin panel quickly all we have to do is to register our models in the admin panel so the admin user will be able to make crud operations in all models django project installation to install django on our machine it s recommended to install it on python virtual environment i will suppose that you have python3 and virtual environment on linux machine already installed on your machine so we will create new virtual environment as following 1 make a new directory that will represent our web project and application mkdir attendance tracking system using computer vision 2 inside attendance tracking system using computer vision we will run the following to create our virtual environment with the name venv virtualenv venv 3 after creating our virtual environment we need to activate it we will run the following command in the same directory source venv bin activate 4 after activating our virtual environment we will need to install python pip if you have pip already installed you don t have to reinstall it sudo apt install python3 pip 5 now we need to install the requirements to get our web application working you will find the requirements file in the github repository link below after copying file to your project root directory run the below command to install packages pip install r path to requirements txt 6 now let s create our new project that will host our web application to create a new django project just run the following command note the tell django to create a project in the same directory which should be the root directory to our web application django admin createproject attendance tracking system using computer vision 7 a django project consists of multiple applications each application should represent a component of the project components so now we need to create an application that will represent our activity tracking application on the attendance tracking system project to create new application just run the following command manage py createapp activity tracking web app 8 after that you should have a specific folder for your machine learning function in the project root directory so you will add all your machine learning scripts in a subfolder named computer vision module 9 very good till now we have installed our project environment and initialize the project structure now you should see a file structure like the following in your project root directory gitignore manage py requirements txt activity tracking web app admin py apps py models py tests py views py init py migrations init py attendance tracking system using computer vision settings py urls py wsgi py init py computer vision module extract faces py load dataset py load faces py variables py 10 now we should run our local server to make sure that we have correctly set up our project run the following command to run local development server manage py runserver the output should be django version 2 2 4 using settings attendance tracking system using computer vision settings starting development server at http 127 0 0 1 8000 quit the server with ctrl break | ai |
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sod | h1 align center sod br br an embedded computer vision machine learning library br a href https sod pixlab io sod pixlab io a h1 api documentation https img shields io badge api 20documentation ready green svg https sod pixlab io api html dependency https img shields io badge dependency none ff96b4 svg https pixlab io downloads getting started https img shields io badge getting 20started now f49242 svg https sod pixlab io intro html license https img shields io badge license dual licensed blue svg https pixlab io downloads forum https img shields io gitter room nwjs nw js svg https community faceio net tiny dreal https pixlab io images logo png https pixlab io tiny dream output https i imgur com yibb8wr jpg introduction sod embedded features notable sod features programming with sod programming interfaces useful links other useful links sod embedded release 1 1 9 july 2023 changelog https sod pixlab io changelog html downloads https pixlab io downloads sod is an embedded modern cross platform computer vision and machine learning software library that exposes a set of apis for deep learning advanced media analysis processing including real time multi class object detection and model training on embedded systems with limited computational resource and iot devices sod was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products designed for computational efficiency and with a strong focus on real time applications sod includes a comprehensive set of both classic and state of the art deep neural networks with their a href https pixlab io downloads pre trained models a built with sod a href https sod pixlab io intro html cnn convolutional neural networks cnn a for multi class 20 and 80 object detection classification a href https sod pixlab io api html cnn recurrent neural networks rnn a for text generation i e shakespeare 4chan kant python code etc a href https sod pixlab io samples html decision trees a for single class real time object detection a brand new architecture written specifically for sod named a href https sod pixlab io intro html realnets realnets a multi class object detection https i imgur com mq98utv png cross platform dependency free amalgamated single c file and heavily optimized real world use cases includes detect recognize objects faces included at real time license plate extraction intrusion detection mimic snapchat filters classify human actions object identification eye pupil tracking facial body shape extraction image frame segmentation notable sod features built for real world and real time applications state of the art cpu optimized deep neural networks including the brand new exclusive a href https sod pixlab io intro html realnets realnets architecture a patent free advanced computer vision a href https sod pixlab io samples html algorithms a support major a href https sod pixlab io api html imgproc image format a simple clean and easy to use a href https sod pixlab io api html api a brings deep learning on limited computational resource embedded systems and iot devices easy interpolatable with a href https sod pixlab io api html cvinter opencv a or any other proprietary api a href https pixlab io downloads pre trained models a available for most architectures li cpu capable a href https sod pixlab io c api sod realnet train start html realnets model training a production ready cross platform high quality source code sod is dependency free written in c compile and run unmodified on virtually any platform amp architecture with a decent c compiler a href https pixlab io downloads amalgamated a all sod source files are combined into a single c file sod c for easy deployment open source actively developed maintained product developer friendly a href https sod pixlab io support html support channels a programming interfaces the documentation works both as an api reference and a programming tutorial it describes the internal structure of the library and guides one in creating applications with a few lines of code note that sod is straightforward to learn even for new programmer resources description a href https sod pixlab io intro html sod in 5 minutes or less a a quick introduction to programming with the sod embedded c c api with real world code samples implemented in c a href https sod pixlab io api html c c api reference guide a this document describes each api function in details this is the reference document you should rely on a href https sod pixlab io samples html c c code samples a real world code samples on how to embed load models and start experimenting with sod a href https sod pixlab io articles license plate detection html license plate detection a learn how to detect vehicles license plates without heavy machine learning techniques just standard image processing routines already implemented in sod a href https sod pixlab io articles porting c face detector webassembly html porting our face detector to webassembly a learn how we ported the a href https sod pixlab io c api sod realnet detect html sod realnets face detector a into webassembly to achieve real time performance in the browser other useful links resources description a href https pixlab io downloads downloads a get a copy of the last public release of sod pre trained models extensions and more start embedding and enjoy programming with a href https pixlab io sod copyright licensing a sod is an open source dual licensed product find out more about the licensing situation there a href https sod pixlab io support html online support channels a having some trouble integrating sod take a look at our numerous support channels face detection using realnets https i imgur com zlno8lz jpg | computer-vision library deep-learning image-processing object-detection c cpu real-time convolutional-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iot webassembly | server |
PDFBooks | hacking book pdf collection a live collection of pdf books based around hacking programming this is a git repo that i have setup dedicated to collecting and adding new books that are must reads for virtually everyone that is wanting to get into the hacking programming world all of the books in this repo can be found by doing a simple google dork search like so advanced penetration testing pdf and there are thousands of results for the pdf books you can read for free all i did was compile them all into 1 easy to find github repo but why i did this for a personal gain really i wanted to be able to have a list of pdf books i can read virtually anytime and anywhere i can easily pull these up on my phone tablet laptop or desktop and start reading hacking books the ultimate hackers collection if you are wanting to learn how to be a professional hacker in this folder you will find a slew of books that are based upon hacking sharpen your skills and learn new things with these pdf s download them for offline reading and on the go coding books code hard when we are talking about hacking we also need to have some understanding of programming or maybe you don t care about hacking so much as you do about programming what ever your case is these books covers it all from game development to website development mobile apps and even web apps live collection what the heck is a live collection do not fear and do not worry as i discover new books from people who would like to learn different things i add them to the repo i will do my best to add at least 2 books every week maybe more who knows the point is if you want to learn and grow your knowledge these books will help you get there contact support i am here to help we all need some help sometimes and maybe you would like to contribute and tell me about books i should have in this repo i would love to hear from you all you can easily connect with me by email cryptoh4ck3r proton me twitter https twitter com cryptoh4ck3r | front_end |
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nlp_multi_task_learning_pytorch | nlp multi task learning pytorch a basic multitask learning architecture for natural language processing of pytorch implementation the two tasks we use here is pos tagging and chunking as below we build the model normally several layers of rnn above embedding later we train specific task on different layer according to its complexity for example we think chunking is a higher task than pos tagging inspired by deep multi task learning with low level tasks supervised at lower layers http anthology aclweb org p16 2038 img https ws3 sinaimg cn large 006tnbrwgy1fuchyzqmynj30ik0aogm6 jpg running examples you can check several running examples in run sh i will explain one here echo joint training on the different level python main py data data the directory put the training data emsize 256 embedding size npos layers 1 number of pos tagging training layer nchunk layers 2 number of chunking training layer nhid 128 number of hidden states for rnn batch size 128 seq len 10 sequence length cuda enbale gpu train mode joint epochs 300 log interval 20 save result joint diff current done a basic architecture for pos tagging and chunking explorate the best hyperparameters of nn todo chunking exploration | ai |
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f18 | 301 moved f18 is now part of llvm and it is called flang in the llvm repository the code from this repository can now be found at https github com llvm llvm project tree main flang migration if you have a local fork of this repository or pull requests that need to be migrated to the llvm monorepo the following recipe may help you from your local f18 clone git clone https github com newren git filter repo tmp git filter repo tmp git filter repo git filter repo path rename flang force message callback return re sub b 0 9 b flang compiler f18 1 message refs branch name after this all the commits from the previous upstream f18 should match the ones in the monorepo now if you don t provide the refs option this will rewrite all the branches in your repo from there you should be able to rebase any of your branch commits on top of the llvm monorepo git remote set url origin git github com llvm llvm project git git fetch origin git rebase origin main i cherry picking commits should also work if you checkout the main branch from the monorepo you can git cherry pick sha1 from your rewritten branches you can also export patches with git format patch range and re apply it on the monorepo using git am patch file | front_end |
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mobile | websharper mobile mobile application development support for websharper ws manual docs downloads downloads license license git sources at github http github com intellifactory websharper mobile mercurial sources at bitbucket http bitbucket org intellifactory websharper mobile issue tracker issues docs http github com intellifactory websharper mobile blob master docs websharpermobile md license http github com intellifactory websharper mobile blob master license md ws http bitbucket org intellifactory websharper issues http github com intellifactory websharper mobile issues downloads http bitbucket org intellifactory websharper mobile downloads | front_end |
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zui | zealot the zealot monorepo is the home to javascript apps and libraries related to the zed https github com brimdata zed data platform this project is managed with the nx https nx dev monorepo tool inside the packages folder you ll find the source code for zed js the javascript library for browsers zed node the javascript library for node zed wasm the zed cli tools in the browser | data zed csv data-analytics data-viz data-wrangling electron-app json-inspector keyword-search table-view type-system zng zq zui super-structured-data | front_end |
moko-maps | moko maps https user images githubusercontent com 5010169 71351401 27c14d80 25a6 11ea 9183 17821f6d4212 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 maps https repo1 maven org maven2 dev icerock moko maps kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name maps mobile kotlin maps module this is a kotlin multiplatform library that provides controls of maps to common code table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features markers add markers to map from common code route draw route by waypoints from common code camera control camera zoom location from common code requirements gradle version 6 0 android api 16 ios version 9 0 installation root build gradle groovy allprojects repositories mavencentral maven url https mapbox bintray com mapbox if mapbox required project build gradle groovy dependencies commonmainapi dev icerock moko maps 0 6 0 commonmainapi dev icerock moko maps google 0 6 0 commonmainapi dev icerock moko maps mapbox 0 6 0 kotlin targets matching it is org jetbrains kotlin gradle plugin mpp kotlinnativetarget configureeach val target this as org jetbrains kotlin gradle plugin mpp kotlinnativetarget target binaries matching it is org jetbrains kotlin gradle plugin mpp framework configureeach val framework this as org jetbrains kotlin gradle plugin mpp framework val frameworks listof base maps map frameworkpath project file ios app pods googlemaps frameworkpath frameworks path let f it plus project file ios app pods mapbox ios sdk dynamic path let f it framework linkeropts frameworks with mobile multiplatform gradle plugin https github com icerockdev mobile multiplatform gradle plugin cocoapods configuration simplest build gradle kts kotlin cocoapods podsproject file ios app pods pods xcodeproj precompiledpod scheme googlemaps onlylink true podsdir listof file podsdir googlemaps base frameworks file podsdir googlemaps maps frameworks precompiledpod scheme mapbox onlylink true podsdir listof file podsdir mapbox ios sdk dynamic project podfile ruby pod googlemaps 3 7 0 pod mapbox ios sdk 5 5 0 googlemaps is static library that already linked in moko maps google remove duplicated linking post install do installer host targets installer aggregate targets select aggregate target aggregate target name include pods host targets each do host target host target xcconfigs each do config name config file config file frameworks delete googlemaps config file frameworks delete googlemapsbase config file frameworks delete googlemapscore xcconfig path host target xcconfig path config name config file save as xcconfig path end end end usage markers kotlin class markerviewmodel val mapscontroller googlemapcontroller viewmodel fun start viewmodelscope launch val marker1 mapscontroller addmarker image mr images marker latlng latlng latitude 55 045853 longitude 82 920154 rotation 0 0f println marker 1 pressed marker1 rotation 90 0f route kotlin class markerviewmodel val mapscontroller googlemapcontroller viewmodel fun start viewmodelscope launch val route mapscontroller buildroute points listof latlng latitude 55 032200 longitude 82 889360 latlng latitude 55 030853 longitude 82 920154 latlng latitude 55 013109 longitude 82 926480 linecolor color 0xcccc00ff markersimage mr images marker samples please see more examples in the sample directory sample set up locally before open project need to setup gradle properties with tokens mapbox tokens by guide https docs mapbox com android maps guides install mapbox secrettoken your secret mapbox key mapbox publictoken your public mapbox key google maps api key by guide https developers google com maps documentation android sdk get api key googlemaps apikey your api key ios info plist setup with tokens mglmapboxaccesstoken your public mapbox key googleapikey your api key add the following entry to your netrc file machine api mapbox com login mapbox password your secret mapbox key the maps directory maps contains the base classes for all maps providers the maps google directory maps google contains the google maps implementation the maps mapbox directory maps mapbox contains the mapbox implementation in sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps before compilation of ios target required pod install in sample ios app directory 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 | kotlin android ios moko kotlin-multiplatform kotlin-native | front_end |
prompt-lib | prompt lib a library for hassle free few shot prompting tldr this library makes it easy to write prompts for few shot prompting tasks it includes rate limiting and retry logic and makes it easy to write prompts for different tasks to get started run bash export openai api key sk xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx python prompt lib run inference py task id boolsimplify stream num prompt examples 1 name boolsimplify stream code davinci 002 s1 model name code davinci 002 max tokens 600 seed 1 num questions per thread 500 temperature 0 0 num inference examples 3 cot task is debug the outputs are stored in data logs boolsimplify stream code davinci 002 temp 0 0 seed 1 k all news aug 2023 added support for together ai api june 2023 added support for claude series of models from anthropic mar 2023 prompt lib now works with chatgpt gpt 4 the addition is backward compatible so you can simply use gpt 3 5 turbo or gpt 4 as the engine name however you may want to checkout how we convert the prompt to messages here https github com reasoning machines prompt lib blob e55c23080505daa1d3e73d26fadb5cd29bf07d25 prompt lib backends openai api py l169 colabs you can run these colabs directly in the browser supporting bulk inference is the main goal of prompt lib but these notebooks should give you a good idea of some of the functionality querying openai https github com reasoning machines prompt lib blob main notebooks queryopenai ipynb custom task https github com reasoning machines prompt lib blob main notebooks promptlibcustomtask ipynb if your goal is not to do bulk inference typically required for research papers benchmarking we recommend checking out https langchain readthedocs io running on a custom task prompt lib comes with a large number of tasks to register a new task please follow these instructions step 1 defining a custom task to begin we need a prompt a prompt is a list of examples where each example is an instantiation of the following py dataclass class example question str answer str thought str the question is the input to the model the answer is the expected output and the thought is an intermediate reasoning step the thought is optional and can be left as an empty string if not needed for direct prompting for example let us define a prompt for the task of boolean expression simplification the following is a list of examples for this task py example question a a a c d b answer false thought false expr is false example question a b a c answer a b c thought a b c is the same as a b a c example question a b a b answer a b thought expr expr is the same as expr example question a a b c c b a a b c c b answer true thought true expr is true the prompt can be added to any python file in scope but for the sake of this example we will add it to prompts boolsimplify boolsimplify py typically you will try multiple variations of a prompt for a task to keep things manageable we add a dictionary at the end of prompts boolsimplify boolsimplify py that gives a task id to each prompt py bool simple taskid to prompt boolsimplify stream bool simple examples note the id we give to our task boolsimplify stream by convention task ids have two parts dataset prompt type here the dataset is boolsimplify and the prompt type is stream this is important we expect dataset jsonl to be present in data tasks text file based prompt for many use cases you may not want to define a prompt in a text file this is easily done by specifying a path in bool simple taskid to prompt py bool simple taskid to prompt boolsimplify txt prompts boolsimplify boolsimplify txt all the steps below remain the same when running a job you need to specify boolsimplify txt as the task id step 2 registering the task the next step is to register the task the file task id to prompt maintains a global dictionary that maps task ids to prompts we can add our task to this dictionary by adding the following line to task id to prompt py from prompt lib prompts boolsimplify import boolsimplify taskid to prompt task id to prompt update bool simple taskid to prompt the task is now registered and can be used to run a custom task step 3 defining inputs outputs we want to evaluate the model on some test file and thus we need a file with inputs and expected outputs the file data tasks boolsimplify jsonl contains the inputs and outputs for the boolsimplify dataset the file is a jsonl and each line is a json object with the following keys py input a b a c target a b c a simple task file with 5 lines has been added to data tasks boolsimplify jsonl for this example you can add more lines to this file to evaluate the model on more examples step 4 running the task now that the task is registered and the inputs outputs are defined we can run the task the following command will run the task export your openai api key to the environment variable openai api key bash export openai api key sk xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx bash bash scripts run single task sh boolsimplify stream the outputs are stored in data logs boolsimplify stream structure of a prompt we highly recommend reading this document docs prompt md to understand the structure of a prompt better additional notes if you are using text prompts seeds are not supported instead you can use scripts shuffle prompt py to create a shuffled version of the prompt bash python prompt lib scripts shuffle prompt py prompt path quco prompts gsm function with comments txt seeds 1 2 3 example sep n n n note the n n n to separate examples this is needed for passing newlines to the script custom evaluation you can also run custom evaluation scripts this is highly recommended if you are running a large number of prompt variations consider writing the custom evaluation function in scripts eval py and then passing it as an argument eval function combined with wandb this can be a powerful way to track the performance of your prompts without having to track the outputs of each prompt variation multiple outputs you can specify num completions to get multiple outputs the output file is stored in the same location with an extra field generated answers that is a list of outputs the generated answer field is still present and is the first element of generated answers for example sh python3 prompt lib run inference py task id boolsimplify stream num prompt examples 1 name boolsimplify stream code davinci 002 s1 model name code davinci 002 max tokens 600 seed 1 num questions per thread 500 temperature 0 9 num inference examples 3 cot task is debug num completions 3 generates an output file data logs boolsimplify stream code davinci 002 temp 0 9 seed 1 k all 2023 01 02 10 16 39 outputs jsonl the timestamp will be different one entry in the file is prompt q a b a c na a b c is the same as a b a c the answer is a b c n n nq a b a b na expr expr is the same as expr the answer is a b n n nq a a a c d b na false expr is false the answer is false n n nq a a b c c b a a b c c b na true expr is true the answer is true question nq a b a b na answer a b generated answers expr expr is the same as expr the answer is a b expr expr is the same as expr the answer is a b b a is the same as a b the answer is b a generated answer expr expr is the same as expr the answer is a b is correct 1 todos allow formatting functions to be used for creating a prompt dynamically currently prompts are created either by reading from a file or by using prefixes for question answer this excludes use cases like https github com reasoning machines cocogen cache previous outputs in a memory during a run add example of using cached timestamp | commonsense-reasoning large-language-models few-shot-learning prompt-toolkit prompting | ai |
margdarsak | h1 align center roadmap h1 p align center welcome to the ask buddie roadmap repository feel free to access and contribute p p align center a href https github com askbuddie roadmap stargazers target blank img alt github stars src https img shields io github stars askbuddie roadmap style for the badge a a href https github com askbuddie roadmap network members target blank img alt github forks src https img shields io github forks askbuddie roadmap style for the badge a br p align center b community curated roadmaps and cheatsheets for various domains b p p table of contents c c 20programming readme md cpp c cpp readme md javascript web 20development javascript readme md reactjs web 20development frontend 20web 20development reactjs readme md backend web development web 20development backend 20web 20development readme md cyber security cyber 20security readme md mobile app development app 20development readme md machine learning machine 20learning readme md frontend web development web 20development frontend 20web 20development game development game 20development mernstack web 20development tech 20stacks mernstack readme md data analyst https github com gaurav shrestha roadmap blob main data 20analyst readme md data science data 20science readme md qa qa readme md bug bounty bug 20bounty readme md iot iot readme md dsa roadmap in 90 days dsa roadmap 90 days readme md devops https github com gaurav shrestha roadmap blob main devops readme md java java core java java core 20java readme md seo seo readme md django web 20development backend 20web 20development django readme md flask https github com gaurav shrestha roadmap blob main flask readme md bash scripting bash scripting readme md competitive programming https github com gaurav shrestha roadmap blob main competitive 20programming readme md affiliate marketing https github com askbuddie roadmap tree main affiliate 20marketing ui ux https github com askbuddie roadmap tree main ui ux contributing we welcome contributions of all kinds please read our contributing guidelines to get started br please try to include the learning resources in the roadmap for the beginners 1 fork this repository 2 add your roadmap to the respective folder please follow markdown syntax https www markdownguide org basic syntax 3 create a pull request 4 wait for review contributors thanks a href https github com askbuddie roadmap graphs contributors img src https contrib rocks image repo askbuddie roadmap a license mit license | hacktoberfest hacktoberfest2022 | server |
ui | stacks ui monorepo this project is a monorepo for all of our stacks ui related projects stacks ui the react component library built with emotion and theme ui stacks ui core the underlying css in js package this is a modification of theme ui stacks ui theme theme ui spec theme for the component library stacks ui utils utilities to help build out complex ui projects running locally this project uses lerna yarn workspaces changesets and tsdx for bundling clone the repo install deps via yarn | blockstack blockstack-ui react styled-components css-in-js styled-system | os |
My-Wallet-V3 | mywallet build status https travis ci org blockchain my wallet v3 svg branch master https travis ci org blockchain my wallet v3 coverage status https coveralls io repos github blockchain my wallet v3 badge svg branch master https coveralls io github blockchain my wallet v3 branch master javascript model for blockchain info wallet build install yarn https yarnpkg com en docs install sh yarn recommended can also use npm install npm run build tests sh npm test dev watch files and re build sh npm run build watch clean clean generated files sh make clean getting started load dist my wallet js optional set alias for modules you use javascript var mywallet blockchain mywallet var walletstore blockchain walletstore var spender blockchain spender var api blockchain api disable logout if desired for development work javascript mywallet disablelogout true set an interval since logout gets reactived by certain parts of the code window setinterval function mywallet disablelogout true 60000 my wallet communicates about its state with user defined event listeners setup a listener like so javascript function mylistenerfun eventname data handle events register listener function with mywallet mywallet addeventlistener mylistenerfun some events that we need to process event name our action did multiaddr populate wallet statistics on the ui hd wallets does not exist create an hd wallet on wallet decrypt finish get wallet transaction history to build an hd wallet with an existing legacy wallet we must initialize after receiving event notification from mywallet javascript var passphrase mywallet generatehdwalletpassphrase mywallet initializehdwallet passphrase null null successfun errorfun load a wallet from the server with no 2fa javascript var guid my wallet guid 1234 bcde var pass wallet password var twofactorcode null mywallet fetchwalletjson guid null null pass twofactorcode successfun need2fafun wrong2fafun othererrorfun do stuff with the wallet var legacyaddresses mywallet getlegacyactiveaddresses in order to fetch the wallet history make a call to get history javascript mywallet get history successfun errorfun get history will trigger the did multiaddr event on completion so the wallet stats and display can be updated security security issues can be reported to us in the following venues email security blockchain info bug bounty https hackerone com blockchain | bitcoin wallet javascript unit-tested ethereum | blockchain |
covaplad | covaplad this repo contains the source code for the covid volunteer and plasma donor system this was an academic project assigned by the department of electronics and computer engineering pulchowk campus http doece pcampus edu np under the subject database management system technologies used 1 django https www djangoproject com for the backend system 2 mariadb https mariadb org for the database server 3 html css and little bit of vanilla es5 javascript for making ajax requests usage install necessary requirements you need to have python https www python org as well as pip installed in your system to run this project mariadb https mariadb org also need to be explicitly installed and configured as per your convinience you can also install xampp https www apachefriends org index html which comes with mariadb https mariadb org and phpmyadmin https www phpmyadmin net if you do not like to tinker much with your system to install python dependencies follow the following steps note the following instructions assumed you have python 3 for the python command create a virtual environment sh python m venv venv activate the environment assuming you are using bash or zsh sh source venv bin activate install python dependencies sh pip install r requirements txt create a database first create a database in mariaddb and keep a file with name database conf in the root of the project with the contents similar to below client database covaplad user covaplad password secret key default character set utf8 the database field above is the name of the database you just created and user and password field is the username and the password of the user with all the previledges to the database just created make changes to the database to migrate the necessary changes to the newly created database run the following command sh manage py migrate note this only propagates the changes to the database schema to your newly created database it doesn t add any data entries to the database for a sample of data entries please contact the team first time setup during the first time setup the admin user should be created from the python shell so in order to create an admin user run the following command from the project root directory sh manage py shell this opens an interactive python shell in the python shell enter the following line by line python from address models import from user models import user from datetime import date c country name nepal c save p province name bagmati pradesh country c p save d district name kathmandu province p d save m municipality name nagarjun district d m save w ward number 1 municipality m w save user user username admin first name ram middle name bahadur last name thami email admin covaplad com phone number 9860807667 gender m temporary address w permanent address w user set password admin user dob date 1999 1 1 user is superuser true user is staff true user save now an admin user with username admin and password admin is created you can visit the admin site by going to admin in the website if you are logged in as the admin run backend server now the schema of various tables are setup in your newly created database you can now run the database server using the following command sh manage py runserver this will run a development server in localhost on port 8000 the development server will not be accessible on the lan just now | python django mariadb | server |
Machine-Learning | machine learning http i imgur com dpgtwn6 jpg 1 apriori https github com roc j machine learning tree master apriori 2 knn https github com roc j machine learning tree master knn 3 https github com roc j machine learning tree master taobao goods analysis | ai |
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zero-chain | zerochain build status https travis ci com layerxcom zero chain svg branch master https travis ci com layerxcom zero chain gitter https badges gitter im layerxcom zerochain svg https gitter im layerxcom zerochain utm source badge utm medium badge utm campaign pr badge div align center img src https user images githubusercontent com 20852667 60582364 9fff4100 9dc3 11e9 84f3 afedfa6d41d1 png width 1400px div br zerochain is a generic privacy protecting layer on top of substrate it provides some useful substrate modules and toolkit for protecting user s privacy and sensitive data stored on chain it is designed to get efficient zero knowledge proving reduce the on chain storage cost and bring the flexibility for developing applications have a look at zerochain book https layerxcom github io zerochain book for usage and more information about using zerochain status warning zerochain is alpha quality software improvements and fixes are made frequently for now only supported for the poc of confidential payment inspired by zether https crypto stanford edu buenz papers zether pdf paper balance for each account is encrypted div align center img src https user images githubusercontent com 20852667 54678399 6d00ac80 4b48 11e9 9c8d d1ec2b668761 png width 400px div transfer amount is encrypted div align center img src https user images githubusercontent com 20852667 54678984 9cfc7f80 4b49 11e9 9784 576dcaa35ca9 png width 400px div more features will be added muscle muscle initial setup curl https sh rustup rs ssf sh rustup update stable rustup update nightly rustup target add wasm32 unknown unknown toolchain nightly cargo nightly install git https github com alexcrichton wasm gc you will also need to install the following packages mac brew install cmake pkg config openssl git llvm linux sudo apt install cmake pkg config libssl dev git clang libclang dev building git clone git github com layerxcom zero chain git cd zero chain build sh cargo build release usage and tutorial documented in zerochain book https layerxcom github io zerochain book related repositories polkadot rs https github com layerxcom polkadot rs json rpc client for substrate api zface https github com layerxcom zero chain tree master zface in same repo currently an interface for interacting with zerochain librustzcash for zerochain https github com layerxcom librustzcash zcash s toolchains for zerochain documentations work in progress zerochain book https layerxcom github io zerochain book announcing zerochain applying zk snarks to substrate https medium com layerx announcing zerochain 5b08e158355d slide zerochain a privacy protecting layer on top of substrate https speakerdeck com osuke zerochain a privacy protecting layer on top of substrate slide zerochain a privacy layer for blockchain https speakerdeck com osuke zerochain a privacy layer for blockchain references substrate repo https github com paritytech substrate substrate developer hub https substrate dev zcash protocol specification https github com zcash zips blob master protocol protocol pdf zether https crypto stanford edu buenz papers zether pdf towards privacy in a smart contract world sonic https eprint iacr org 2019 099 pdf zero knowledge snarks from linear size universal and updatable structured reference strings contributing feel free to submit your own issues and prs for further discussions and questions talk to us on gitter https gitter im layerxcom zerochain maintainers osuke https twitter com zoom zoomzo | substrate zero-knowledge blockchain rust zk-snarks | blockchain |
web-dev | 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 | server |
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SQL | a mystery in two parts sql png sql sql png background this 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 work i designed the tables to hold data in the csvs import the csvs data sql data https github com ofunkey sql tree master sql data into a sql database and answer questions about the data in other words i performed 1 data modeling 2 data engineering 3 data analysis data modeling inspect the csvs and sketch out an erd of the tables and using a tool at http www quickdatabasediagrams com http www quickdatabasediagrams com data modeling sql employee erd data modeling jpg data engineering use the information to create a table schema for each of the six csv files specify data types primary keys foreign keys and other constraints import each csv file into the corresponding sql table data analysis 1 list the details of each employee employee number last name first name gender and salary data analysis1 sql images data analysis1 png 2 list employees who were hired in 1986 data analysis2 sql images data analysis2 png 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates data analysis3 sql images data analysis3 png 4 list the department of each employee with the following information employee number last name first name and department name data analysis4 sql images data analysis4 png 5 list all employees whose first name is hercules and last names begin with b data analysis5 sql images data analysis5 png 6 list all employees in the sales department including their employee number last name first name and department name data analysis6 sql images data analysis6 png 7 list all employees in the sales and development departments including their employee number last name first name and department name data analysis7 sql images data analysis7 png 8 in descending order list the frequency count of employee last names i e how many employees share each last name data analysis8 sql images data analysis8 png conclusion a technical report that outline the data engineering steps taken sql employee db sql https github com ofunkey sql blob master sql employee db sql | sql | server |
CG3002 | cg3002 ay1819 sem1 dance dance team 09 dancer this is the main repository for cg3002 project at the national university of singapore http www nus edu sg ay2018 2019 semester 1 cg3002 is the capstone module for the computer engineering http ceg nus edu sg programme project description this project detects labelled dance moves performed by user accomplished with the use of 1 arduino obtain values from sensor and output to raspberry pi 2 raspberry pi load sensor values to machine learning model send prediction to server 3 machine learning process raw data from sensor and output prediction for more instructions on the software component please see here software readme md how to use this project make sure you have installed the relevant dependencies for each component of this system first put all data under software data raw you can download the dataset as a zip file from here https drive google com open id 1osuyvorb94xz8rma6hx3gpqum hr1ifo unzip and put it under software data raw then run software extract raw py to extract features into software data extract alternatively you can download the extracted data as a zip file from here https drive google com open id 1fiplsw4cv4nv4n2kedfkprsubfygdref unzip and put it under software data extract after that run software train py to train the model which will be stored under the software model folder alternatively you can download the pre trained model from here https drive google com open id 1ezmiipoijjupz4yjbe7iztnooprhor6m put it under software model during demo do the following run arduinorpicommunications to start sending data from arduino to the raspberry pi run the following two programs at the same time python software main py python comms server client final eval server py ip address 3002 09 team members name sub team leow zheng yu hardware xiang hailin hardware gauri joshi software niu yunpeng software chua kun hong communications ang zhi yuan communications | arduino-mega raspberry-pi-3 motion-capture machine-learning embedded-systems | os |
gwtbootstrap3 | gwtbootstrap3 build status https travis ci org gwtbootstrap3 gwtbootstrap3 svg branch master https travis ci org gwtbootstrap3 gwtbootstrap3 maven central https maven badges herokuapp com maven central org gwtbootstrap3 gwtbootstrap3 badge svg style flat square https maven badges herokuapp com maven central org gwtbootstrap3 gwtbootstrap3 note this project is inactive gwtbootstrap3 is a wrapper for twitter bootstrap http getbootstrap com which helps you develop responsive mobile first html css and js projects on the web using java and google web toolkit gwt add gwtbootstrap3 to your project you can easily add gwtbootstrap3 to your project by including the library as a maven dependency xml dependency groupid org gwtbootstrap3 groupid artifactid gwtbootstrap3 artifactid version version version scope provided scope dependency final release 1 0 1 released on 14 november 2019 based on bootstrap v3 4 1 1 0 0 released on 26 august 2019 based on bootstrap v3 4 0 demo http gwtbootstrap3 github io gwtbootstrap3 demo the gwtbootstrap3 1 0 0 demo api docs http gwtbootstrap3 github io gwtbootstrap3 demo apidocs the gwtbootstrap3 1 0 0 api javadoc supported features https github com gwtbootstrap3 gwtbootstrap3 wiki supported features current releases supported features resources project wiki https github com gwtbootstrap3 gwtbootstrap3 wiki help with getting started and other useful project help join the chat at https gitter im gwtbootstrap3 gwtbootstrap3 https badges gitter im join 20chat svg https gitter im gwtbootstrap3 gwtbootstrap3 utm source badge utm medium badge utm campaign pr badge utm content badge | front_end |
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visa-chart-components | visa chart components image showing three examples of visa chart components for demonstration purposes only docs vcc components png clustered bar chart component visa chart components vcc is an accessibility focused framework agnostic set of data experience design systems components for the web vcc attempts to provide a toolset to enable developers to build equal data experiences for everyone everywhere visa chart components vcc is provided under the mit license license hr installation process to use vcc in your projects run yarn add visa charts or you can also install a single component by running yarn add visa bar chart we recommend leveraging bundles if you are going to install three 3 or more specific components for your project while we do deliver react visa charts react and angular visa charts angular bundles vcc components are compiled to standard web components within visa charts leveraging stencil js https stenciljs com and can be reused directly in many web environments and or frameworks bundles visa charts packages charts web components visa charts react packages charts react react visa charts angular packages charts angular angular visa charts r packages charts r r visachartr function in r visa charts python packages charts python python pyvisacharts package in python visa charts figma packages charts figma figma visa chart components plugin for figma components with ready status visa bar chart packages bar chart visa clustered bar chart packages clustered bar chart visa stacked bar chart packages stacked bar chart visa line chart packages line chart visa pie chart packages pie chart visa scatter plot packages scatter plot visa heat map packages heat map visa circle packing packages circle packing visa parallel plot packages parallel plot visa dumbbell plot packages dumbbell plot visa world map packages world map visa alluvial diagram packages alluvial diagram visa visa charts data table packages data table visa keyboard instructions packages keyboard instructions components with development status our utilities can also be leveraged directly visa visa charts utils packages utils visa visa charts utils dev packages utils dev hr development vcc is built as a monorepo containing a set of packages these packages include specific chart components e g visa bar chart our utilities e g visa visa charts utils as well as component bundles e g visa charts or visa charts react steps to get up and running for development note the initial install and build process can take some time clone the repo run yarn install run yarn dev i to install the monorepo run yarn dev b to build the packages across the monorepo you can also run yarn dev br to build copy the visa charts bundle to our r wrapper br single component development for development work on a single component we launch local stencil applications which allow for faster development iterations and features like hot reloading to run a single component development environment run the below command note these local dev applications are not included in our builds yarn dev ibsw visa component name the switches in the command relate to i install the component b build the component sw start watching the component you can run ibsw bsw or sw if you need to you can also build packages together once installed using commands like yarn build scope visa visa charts utils yarn build scope visa bar chart this can helpful if you are making changes to dependencies of the chart component for example our utilities package br running unit tests we have built extensive unit testing out for some of our components and are working towards propagating this across the rest to run unit tests the command is test all components at once yarn dev t test a specific component yarn dev test visa component name also in some cases component snapshots may need to be updated after changes have been implemented on components themselves take caution when updating testing snapshots in this situation run the update snapshot command as follows yarn dev updatesnapshot visa component name once you have finished running your tests you can leverage the below scripts to evaluate them yarn combine test results this script will combine all tests results across the monorepo both coverage and test summary failures yarn compare test results this script will do a snapshot test of your test results to the current snapshot yarn update test results this script will update the combined test snapshot useful in situations when you have added more tests cleaned up tests or enabled new components we use vscode https code visualstudio com as our development environment you can also leverage the built in debugging capability in this tool to evaluate the unit tests themselves br running components through yarn audit dependency check we enable a repo wide scan using yarn audit https classic yarnpkg com en docs cli audit to check for known dependency vulnerabilities to run the audit command s all components yarn dev a all or component specific yarn dev a visa component name once you have finished running your audit you can leverage the below scripts to evaluate the results yarn combine audit results this script will combine all audit results across the monorepo yarn compare audit results this script will do a snapshot test of your audit results to the current snapshot yarn update audit results this script will update the combined audit snapshot useful in situations when you have added more dependencies cleaned up dependencies or enabled new components br cleaning the repo caution this will require you to re install the entire monorepo which can take some time to clean repo we have clean command with options yarn dev c options here options can be all deletes all lock files node modules and build folders lock deletes only lock files build deletes only build folders and files node deletes only node modules folder across repo hr credits you can find license information for all dependencies included in our build here packages utils src utils license ts below is a list of key dependencies d3 js https d3js org stencil js https stenciljs com d3 svg annotation https d3 annotation susielu com numeral js http numeraljs com node uuid https github com uuidjs uuid vega label https github com vega vega label storybook https storybook js org htmlwidgets https www htmlwidgets org widget cookiecutter https github com jupyter widgets widget cookiecutter figma plugin api https www figma com plugin docs api api reference this project was is built with tireless efforts from chris demartini visa https github com chris demartini personal https github com demartsc david kutas visa https github com david kutas lilach manheim personal https github com lmanheim tanvi modi visa https github com tan modi stephanie modica personal https github com stephmod jaime tanner personal https github com tannerjaime frank elavsky personal https github com frankelavsky tica lin personal https github com ticahere basavaraj kabbure personal https github com basavarajk akhil gupta personal https github com akhil9tiet | visualization data-visualization d3 stencil charts graphs accessibility data-experience | os |
Natural-Language-Processing-with-TensorFlow | natural language processing with tensorflow this is the code repository for natural language processing with tensorflow https www packtpub com application development natural language processing tensorflow utm source github utm medium repository utm campaign 9781788478311 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 natural language processing nlp supplies the majority of data available to deep learning applications while tensorflow is the most important deep learning framework currently available natural language processing with tensorflow brings tensorflow and nlp together to give you invaluable tools to work with the immense volume of unstructured data in today s data streams and apply these tools to specific nlp tasks thushan ganegedara starts by giving you a grounding in nlp and tensorflow basics you ll then learn how to use word2vec including advanced extensions to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms chapters on classical deep learning algorithms like convolutional neural networks cnn and recurrent neural networks rnn demonstrate important nlp tasks as sentence classification and language generation you will learn how to apply high performance rnn models like long short term memory lstm cells to nlp tasks you will also explore neural machine translation and implement a neural machine translator after reading this book you will gain an understanding of nlp and you ll have the skills to apply tensorflow in deep learning nlp applications and how to perform specific nlp tasks instructions and navigations all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 the code will look like the following graph tf graph creates a graph session tf interactivesession graph graph creates a session to get the most out of this book we assume the following from the reader a solid will and an ambition to learn the modern ways of nlp familiarity with basic python syntax and data structures for example lists and dictionaries a good understanding of basic mathematics for example matrix vector multiplication optional advance mathematics knowledge for example derivative calculation to understand a handful of subsections that cover the details of how certain learning models overcome potential practical issues faced during training optional read research papers to refer to advances details in systems beyond what the book covers related products hands on deep learning with tensorflow https www packtpub com big data and business intelligence hands deep learning tensorflow utm source github utm medium repository utm campaign 9781787282773 deep learning with tensorflow second edition https www packtpub com big data and business intelligence deep learning tensorflow second edition utm source github utm medium repository utm campaign 9781788831109 beginning application development with tensorflow and keras https www packtpub com application development beginning application development tensorflow and keras utm source github utm medium repository utm campaign 9781789537291 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 9781788478311 https packt link free ebook 9781788478311 a p | tensorflow nlp-machine-learning rnn cnn tensorboard seq2seq lstm | ai |
tempNOC | final project group 6 team members hendy chua david lee huang purong civics ang project title vinochart description an app that allows users to create survey forms collect feedback and create organizational diagrams based on the feedback easily | os |
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SEI-Project-4 | general assembly project 4 overview this was my final project a complex full stack application built with django rest framework and react crumble is a full stack dating app which allows users to register and or login and find others who share the same interests 227cc555 765a 4f94 8be8 7b71ea49ef97 1 105 c https user images githubusercontent com 83225952 130610068 82aaf1c1 15b3 4c6c 82cf 8018bdc1f308 jpeg the app has been deployed with heroku and is available here https github com adrianp2021 sei project 4 brief and timeframe build a full stack application by making your own front end and back end use a python django api using django rest framework to serve your data from a postgres database consume your api with a separate front end built with react be a complete product which most likely means multiple relationships and crud functionality for at least a couple of models be deployed online so it s publicly accessible timeframe 8 days known bugs or errors wins and blockers future features key learnings | server |
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axelar-core | axelar core the axelar core app based on the cosmos sdk is the main application of the axelar network this repository is used to build the necessary binaries and docker image to run a core node how to build note for a release build checkout the release tag via git checkout vx y z first execute make build to create the axelard binary in the bin folder creating docker images to create a regular docker image for the node execute make docker image this creates the image axelar core latest to create a docker image for debugging with delve https github com go delve delve execute make docker image debug this creates the image axelar core debug latest smart contracts bytecode dependency in order to run build the project locally we need to import the bytecode from gateway smart contracts 1 find the specific version of the bytecode here contract version json 2 download the right version from the gateway workflow https github com axelarnetwork solidity cgp gateway actions workflows publish bytecode yaml example bytecode v4 3 0 3 unzip the json files under contract artifacts gateway 4 run make generate to generate x evm types contracts go interacting with a local node with a local dockerized node running the axelard binary can be used to interact with the node run bin axelard or bin axelard command help after building the binaries to get information about the available commands show api documentation execute go111module off go install u golang org x tools cmd godoc to ensure that godoc is installed on the host after the installation execute godoc http port index to host a local godoc server for example with port 8080 and godoc http 8080 index the documentation is hosted at http localhost 8080 pkg github com axelarnetwork axelar core the index flag makes the documentation searchable comments at the beginning of packages before types and before functions are automatically taken from the source files to populate the documentation see https blog golang org godoc for more information cli command documentation for the full list of available cli commands for axelard see here docs cli toc md test tools dev tool dependencies such as moq and goimports can be installed via make prereqs make sure they re on available on your path bug bounty and disclosure of vulnerabilities see the axelar documentation website https docs axelar dev bug bounty | blockchain |
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Inform | the inform app for macos what s new a new version of the inform app is now available here https github com tobylobster inform releases the minimum requirement is now macos 10 14 6 mojave users of older macos versions can of course continue using previous versions from here http inform7 com downloads the launcher screen shows the latest news from the iftf https iftechfoundation org colour schemes have been introduced see the new preferences pane support for dark mode support for basic inform fixes for skeins misdrawing in some cases apple silicon native while still supporting intel macs a great deal of modernisation has gone on under the hood but this still remains a work in progress support for inform 6 projects has been removed many thanks to maddthesane for a heroic number of modernisations tweaks and fixes work in the pipeline mac app store version longer term modernisation to swift and away from deprecated apis about inform inform is a design system for interactive fiction based on natural language a new medium of writing which came out of the text adventure games of the 1980s it has been used by many leading writers of if over the last twenty years for projects ranging from historical reconstructions through games to art pieces which have won numerous awards and competitions to learn more about the core of inform see https github com ganelson inform https github com ganelson inform for more inform resources see inform7 com http www inform7 com inform is free with no strings attached what you make with it is yours to publish on your website sell or give to your friends there s a vibrant community of users who welcome newcomers and the app will help you find a high traffic forum for discussions lastly inform is continuously maintained and developed all bug reports are examined and acted on and the app will show you how to post them version history app version inform version release date description 1 82 3 10 1 2 2022 8209 09 8209 05 official release supporting inform 10 1 2 notarized bug fix for previous version 1 82 2 10 1 2 2022 8209 09 8209 02 bugged not notarized 1 82 1 10 1 1 2022 8209 08 8209 21 official release supporting inform 10 1 1 1 82 0 10 1 0 2022 8209 08 8209 20 official release supporting inform 10 1 0 1 81 0 8209 beta1 10 1 0 nbsp beta 2022 8209 07 8209 30 this is a beta colour schemes dark mode basic inform apple silicon native support 1 68 1 6m62 2019 11 14 release with website bug fix 1 67 1 6m62 2019 10 25 macos catalina support 1 65 1 6m62 2016 10 21 macos sierra support also another bug fix for 10 6 8 bug 1895 1 64 1 6m62 2016 01 06 bugfixes fix for 10 6 8 support 1807 toolbar panel 1808 syntax colouring 1815 1 64 0 6m62 2015 12 24 includes new testing panel and support for extension projects 1 54 4 6l38 2014 11 21 first mac app store version released 1 53 6l38 2014 09 23 experimental sandboxed version getting closer to mac app store release distributed to testers to test if it works 1 52 6l38 2014 09 18 fixed previous version now signed properly and non sandboxed 1 51 6l38 2014 08 30 badly signed and sandboxed version 1 50 6l02 2014 05 07 first update modernising inform building the inform app see building the macos inform app documentation building inform md licensing see file copying toby nelson toby m nelson gmail com | os |
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fos | fos fast operating system readme zh md build status https secure travis ci org php php src png branch master https travis ci org dreamflyforever fos it s an open soruce project about code rtos real time operating systems named fos alias chinese operating system not copy other system supported isa openrisc riscv linux now it s function will be complete step by step i hope more and more people will be to make it better to build the fos tools need scons and system gcc cd to the rtos directory and run scons this will produce execution files named fos to run the fos for linux run the shell script start sh source tree kernel core kernel sources app user application sources cpu cpu architecture ports libc c function library middleware the component for fos sconstruct and config py compile tools start or1ksim sh run the system in the or1ksim contributions this software was written by shanjin yang license gpl any question please contact sjyangv0 qq com | rtos embeded-systems real-time opening-system risc-v | os |
Crowd_counting_from_scratch | crowd counting from scratch this is an overview and tutorial about crowd counting in this repository you can learn how to estimate number of pedestrians in crowd scenes through computer vision and deep leaning how to do crowd counting crowd counting has a long research history about twenty years ago or even earlier researchers have been interested in developing the method to count the number of pedestrians in the image automatically there are mainly three categories of methods to count pedestrians in crowd 1 pedestrian detector you can use traditional hog based detector or deeplearning based detector like yolos or rcnns but effect of this category of methods are seriously affected by occlusion in crowd scenes 2 number regression this category of methods just capture some features from original images and use machine learning models to map the relation between features and numbers an improved version via deep learning directly map the relation between original image and its numbers before deep learning regression based methods were sota and researchers are focus on finding more effective features to estimate more accuracy results but when deep learning get popular and achieve better results regression based methods get less attention because it is hard to capture effective hand crafted features 3 density map this category of methods are the mainstream methods in crowd counting nowadays compared with detector based methods and regression based methods density map can not only give the information of pedestrian numbers but also can reflect the distribution of pedestrians which can make the models to fit original images with opposite density better what is density map simply speaking we use a gaussian kernel to simulate a head in corresponding position of the original image after do this action for all heads in the image we then perform normalization in matrix which is composed by all these gaussian kernels the sample picture is as follows density map sample imgs density map sample png further there are three strategies to generate density map 1 fixed size density map use the same gaussian kernel to simulate all heads this method applies to scene without severe perspective distortion fixed kernel code generate density map same gaussian kernel py 2 perspective density map use the perspective map which is generated by linear regression of pedestrians height to generate gaussian kernels with different sizes to different heads this method applies to fixed scene perspective kernel code generate density map perspective gaussian kernel py and paper zhang cvpr2015 https www ee cuhk edu hk xgwang papers zhanglwycvpr15 pdf give detailed instruction about how to generate perspective density map 3 knn density map use the k nearest heads to generate gaussian kernels with different sizes to different heads this method applies to very crowded scenes k nearset kernel code generate density map k nearest gaussian kernel py and paper mcnn cvpr2016 https pdfs semanticscholar org 7ca4 bcfb186958bafb1bb9512c40a9c54721c9fc pdf give detailed instruction about how to generate k nearest density map dataloader for load image and its corresponding density map when finish generating density maps we need to program a dataloader to load image and its corresponding density map for forward and backward propagation every batch for images with same resolution we can use batch size 32 or 64 or even larger otherwise we just use batch size 1 we strongly recommend to use torch utils data dataset https pytorch org docs stable data html torch utils data dataset and torch utils data dataloader https pytorch org docs stable data html torch utils data dataloader to realize your own dataloader an example code for how to contrust an dataloader is in dataloader example code dataloader dataloader example py deep learning model for crowd counting for beginner paper mcnn cvpr2016 https pdfs semanticscholar org 7ca4 bcfb186958bafb1bb9512c40a9c54721c9fc pdf is the most suitable model to learn crowd counting the model is not complex and have an acceptable accuracy we provide an easy mcnn model code crowd model mcnn model py to let you know mcnn rapidly and an easy full realization of mcnn pytorch https github com commissarma mcnn pytorch if you want more accuracy result paper csrnet cvpr2018 https arxiv org abs 1802 10062 is a deeper model for crowd counting it uses dilated convolution to avoid the frequent pooling and upsample we provide an easy full realization of csrnet pytorch https github com commissarma csrnet pytorch some crowd counting dataset ucsd we provide an processed version with images and point annotations like other crowd counting dataset link https pan baidu com s 1rykwymyhmlr99w5ccexebq extraction code 4u66 but i haven t achieved the same performance as showed in paper mcnn cvpr2016 https pdfs semanticscholar org 7ca4 bcfb186958bafb1bb9512c40a9c54721c9fc pdf some tricks use data augumentation such as horizontal flip crop illumination change etc other crowd counting branch multi view crowd counting link multi view counting md learn more emsp if you want learn more about crowd counting you can visit awesome crowd counting https github com gjy3035 awesome crowd counting it collects the papers and opposite codes in the field of crowd counting | crowd-counting deep-learning computer-vision | ai |
e-commerce-Api | e commerce api e commerce app backend api s development with node js | server |
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vue-argon-design-system-nuxt | depreacted this repo is deprecated i will keep the source available as it might give you hints how to convert the vue argon design system https github com creativetimofficial vue argon design system to nuxt js vue argon design system nuxt vue argon design system for nuxt build setup bash install dependencies npm install serve with hot reload at localhost 3000 npm run dev build for production and launch server npm run build npm start generate static project npm run generate for detailed explanation on how things work checkout nuxt js docs https nuxtjs org | os |
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nucleus | nucleus the freshworks design system in ember build https github com freshdesk nucleus workflows build badge svg branch master this is the repository of the freshworks component library based on the unified freshworks design language https freshworks invisionapp com dsm freshworks d labs design system compatibility ember js v2 18 or above ember cli v2 18 or above node js v10 or above installation ember install freshworks package name package latest banner npm version https badge fury io js 40freshworks 2fbanner svg https www npmjs com package freshworks banner button npm version https badge fury io js 40freshworks 2fbutton svg https www npmjs com package freshworks button core npm version https badge fury io js 40freshworks 2fcore svg https www npmjs com package freshworks core icon npm version https badge fury io js 40freshworks 2ficon svg https www npmjs com package freshworks icon inline banner npm version https badge fury io js 40freshworks 2finline banner svg https www npmjs com package freshworks inline banner modal npm version https badge fury io js 40freshworks 2fmodal svg https www npmjs com package freshworks modal tabs npm version https badge fury io js 40freshworks 2ftabs svg https www npmjs com package freshworks tabs toast message npm version https badge fury io js 40freshworks 2ftoast message svg https www npmjs com package freshworks toast message toggle npm version https badge fury io js 40freshworks 2ftoggle svg https www npmjs com package freshworks toggle usage see the packages directory for a list of packages packages that can be installed individually contribution guidelines contribution guidelines for this project docs contributing md license this project is licensed under the mit license license md | emberjs ember-addon nucleus design-system component-library front-end | os |
MediAR | mediar project for ios engineering at carnegie mellon university made with dexter epouhe | os |
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Saylani-IT-Program-Assignments | saylani mass it program web and mobile hybrid development assignments all the tasks covers the assignments of a web mobile development consisting of the programming languages and tools used in software development | saylani-msit saylani msit | front_end |
Obstruction-Free-Photography | gpu accelerated obstruction free photography hacked together by david liao https github com davlia and zhan xiong chin https github com czxcjx tested on aws g2 2xlarge instance linux ip 172 31 53 81 4 4 0 53 generic 74 ubuntu smp fri dec 2 15 59 10 utc 2016 x86 64 x86 64 x86 64 gnu linux nvidia gk104gl grid 520 technical presentation presentation https docs google com presentation d 1gotx bjelcm1w14b2yrthjsyag2mvma fidksfwgc e edit usp sharing img src https i imgur com qjytng1 png width 300 img img src https i imgur com rac9bav png width 300 img build instructions just run make requires cuda opencv and cimg overview the field of computer vision is the dual to computer graphics in many cases the unknowns that we are solving for in optical models are swapped around for example the graphics problem given camera motion and lighting scene conditions generate a photorealistic image has the counterpart vision problem given photorealistic images and estimated lighting scene conditions calculate the camera motion in this project we take on the challenge of recovering a background scene from an image with both a background scene and an occluding layer e g fence glass pane reflection non symmetric objects the input to the algorithm is a series of images extracted from a camera that pans across a scene in planar motion the inspiration and intuition driving the solution of this challenge involves leveraging parallax motion of the foreground and background images to separate a foreground and background layer given the data parallel property of the challenge per pixel calculation this algorithm is a perfect target for gpu acceleration while the main focus is emphasized on the performance gained by implementing the algorithm on the gpu as opposed to the cpu we will briefly go over the algorithm algorithm outline let s briefly go over the algorithm to get an idea of what s going on under the hood description input a sequence of images we use 5 with the middle one being the reference frame assumptions there needs to be a distinct occluding layer and background layer the majority of the background and foreground motion needs to be uniform every background pixel is visible in at least one frame output a background image without obstruction pipeline 1 initialization edge detection edge detection edge detection sparse flow sparse flow layer separation layer separation dense flow interpolation dense flow interpolation warp matrix generation image warping warp matrix background and foreground initial estimated separation background and foreground initial estimation 2 optimization optimization minimize cost function optimization implementation here we describe how we implement each step in the pipeline edge detection https i imgur com mr0cnhl png we use canny s edge detection algorithm that has the sub pipeline as follows 1 grayscale conversion 2 noise removal smoothing 3 gradient calculation 4 non maximum edge suppression 5 hysteresis step 1 is an intensity average and steps 2 3 are convolutions using a gaussian kernel and sobel operators step 4 takes the edge direction and inspects pixels on both sides of the edge in the forward and reverse direction of the gradient and if the pixel is not a mountain it will be suppressed the end result is thinned edges that are pixel thick step 5 is a technique to reduce noise in the edges when thresholding when setting an intensity threshold it is possible that an edge dances around the boundaries of the threshold the result is a noisy dotted edge that we don t want instead we set a lower threshold and upper threshold where if an edge starts above the upper threshold but dips below the upper threshold while staying above the lower threshold it will still be considered an edge the below image borrowed from opencv docs gives a good idea of what this step does http docs opencv org 3 1 0 hysteresis jpg here the vertical represents the intensity while the horizontal maps to some 1d projected plane of the edge line a is considered an edge even though c is below the threshold line b however is not since it begins ends and maintains within the two thresholds sparse flow we then need to calculate how the image moves the original paper defines a markov random field over the edges constraining pixels to match the location they flow to as well as being similar to the flow of their neighboring pixels we were not able to replicate this method instead opting to use a more common optical flow algorithm the lucas kanade method https en wikipedia org wiki lucas e2 80 93kanade method to approximate this we made use of the opencv implementation for this step we run the lucas kanade algorithm on the set of pixels that make up the edges for each pixel the lucas kanade method assumes that the displacement of a small window e g 21x21 pixels around the window is constant between two frames and sets up a system of linear equations on the spatial and temporal gradients of the window of pixels then it uses least squares to solve this system of equations with the result being the optical flow of the chosen pixel while this is also ideal for gpu acceleration as each pixel can be treated independently we did not have time to write a kernel for this step also the method no longer maintains similarity between adjacent pixels so the flow may not be as continuous as the original paper s method layer separation https i imgur com b7sxyn8 png to then figure out which edges are part of the occluding layer and which edges are part of the background layer we use ransac to separate the vertices into two clusters for this we make the assumption that both the occluding layer and the background layer are translated in the same way between two frames this is also a simplification from the original paper s method which estimates a homography rather than a simple translation then we fix a percentage p of pixels that will be classified as part of the background layer we pick a random initial starting vector v then take the nearest p of motion vectors to it and classify that as our background layer then we take the mean of this newly computed background layer s optical flow and treat this as our new vector v repeating the process a fixed number of iterations we end up with a separation of pixels into background and foreground layer this did not always give stable results so we augmented this with additional heuristics such as separating based on the intensity of the pixels e g when the occluding layer is a fence or racket whiter areas tend to be part of the occluding layer showing wall or sky dense flow interpolation after we figure out how the edges move and separate the we interpolate sparse motion vectors of the background layer into a dense motion field basically we take the edge motion vectors and interpolate to figure out how every other pixel moves there were two methods we used to calculate this one was to do a k nearest neighbors weighted interpolation while the other was a naive average to generate an affine transformation matrix as it turns out the latter approximation works fine in the context of an initial estimate warp matrix we take the background motion field from the last step and generate a warp matrix basically inverting the motion matrix then apply it to all the non reference images to warp them to the reference image this has the effect of moving moving all the images in a way such that their backgrounds are stacked on top of each other their foreground as a result is the only moving layer background and foreground initial estimation after the last step we ve reduced the problem to a state where the camera perspective is static we can then estimate a pixel for each layer based on this information https i imgur com doo715o png mean based estimation if the occluding the layer is opaque we can take the average of all the pixels at a location if it is reflective we can take the minimum since reflection is additive light alternatively we can employ smarter techniques that take into account spatial coherence in this case we will select the pixel that has the most number of neightbors with a pixel intensity similar to it img src https i imgur com sgjj7zn png width 550 img spatial coherence based estimation optimization the quality of the image can be measured by an objective function defined over the foreground and background layers and motion fields as well as the alpha mask between the two layers then optimizing this objective function will in principle give a better image the original paper made use of an objective function that penalizes the following terms 1 differences between the original image and the combination of the estimated background and foreground 2 noisy alpha mask 3 noisy background foreground images 4 high gradients on the same pixel of the background and foreground image i e highly detailed of the image probably only belong to one layer rather than a mix of both 5 noisy motion fields they optimized this using iteratively reweighted least squares using the pyramid method to start from a coarse image and optimize on successively finer ones for our code we made use of the same objective function but we performed a naive gradient descent to optimize the objective function tweaking the parameters of the optimization we sometimes were able to obtain slightly better results but in general we did not find a significant improvement in image quality possibly due to the slow convergence of our naive gradient descent approach results the pipeline benchmarked exclusively for the individual kernel invocations the overhead costs were excluded in this analysis as they are not useful relevant the input used was img hanoix png where x is replaced by the image number please look at the entire recorded data set here https docs google com spreadsheets d 1 figxg5rl99xxkug dw7jfhtruxwwfpr utspinnjuk edit usp sharing note the tabs at the bottom performance analysis http i imgur com uehztlu png we note some of the more important numbers here at a glance it s pretty obvious that the pipeline hugely benefited from gpu acceleration the most notable is the gradient descent optimization step which received a huge speedup edge detection generating foreground and background estimates calculating spatial coherence and gradient descent are all low cost computations 5ms this makes sense as most of the work that they do is very computational they all have low branch counts and perform only the most basic of logical evaluations since the pipeline is sequential performance bottlenecks are distributed across each pipeline step and can be targets for future improvement the most notable bottleneck is the ransac algorithm since it is an iterative model tweaking iteration parameters would speed it up at the cost of accuracy at its current benchmarked state it is running 50 iterations so each iteration runs at about 2 ms time breakdown http i imgur com jtg5hbm png the overall time spent computing on the gpu was on ransac as well as gradient descent this is because gradient descent and ransac are both iterative methods that require multiple steps to convergence as a result we want high iterations for for both of these to get the best images on the other hand if we were to make this an real time pipeline we would choose to do 5 iterations to achieve sub 100ms time other images https i imgur com cbbqcz2 png reflection removal as well top left original image top right separation bottom left without gradient descent bottom right with gradient descent https i imgur com pnbd2qe png removing the spokes of a bike wheel from harrison college house bloopers img src http i imgur com 2mhwlro png width 600 img when canny edge detection goes wrong img src http i imgur com rs6v7kb png width 600 img a bug in the pipeline propagates and magnifies in the pipeline downstream http i imgur com ho0umij png maya scene where we have grounded camera movements as well as distances but still wrong output acknowledgements we want to give a big thanks to patrick cozzi for teaching the cis 565 course as well as benedict brown for giving us general cv tips | ai |
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