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ditto | div align center img src https raw githubusercontent com eclipse ditto master logo ditto fordarkbg svg sanitize true gh dark mode only alt ditto logo dark height 250 img src https raw githubusercontent com eclipse ditto master logo ditto svg sanitize true gh light mode only alt ditto logo light height 250 div eclipse ditto join the chat at https gitter im eclipse ditto https badges gitter im eclipse ditto svg https gitter im eclipse ditto utm source badge utm medium badge utm campaign pr badge utm content badge build status https github com eclipse ditto ditto workflows build badge svg https github com eclipse ditto ditto actions query workflow 3abuild maven central https img shields io maven central v org eclipse ditto ditto label maven https search maven org search q g org eclipse ditto docker pulls https img shields io docker pulls eclipse ditto things svg https hub docker com search q eclipse 2fditto type image license https img shields io badge license epl 202 0 green svg https opensource org licenses epl 2 0 lines of code https img shields io badge dynamic xml svg label lines 20of 20code url https 3a 2f 2fwww openhub net 2fprojects 2feclipse ditto xml 3fapi key 3d11ac3aa12a364fd87b461559a7eedcc53e18fb5a4cf1e43e02cb7a615f1f3d4f query 2fresponse 2fresult 2fproject 2fanalysis 2ftotal code lines colorb lightgrey https www openhub net p eclipse ditto eclipse ditto https www eclipse dev ditto is a technology in the iot implementing a software pattern called digital twins a digital twin is a virtual cloud based representation of his real world counterpart real world things e g devices like sensors smart heating connected cars smart grids ev charging stations an ever growing list of adopters https iot eclipse org adopters iot ditto makes use of ditto as part of their iot platforms if you re as well using it it would be super nice to show your adoption here https iot eclipse org adopters how to be listed as an adopter documentation find the documentation on the project site https www eclipse dev ditto https www eclipse dev ditto eclipse ditto explorer ui find a live version of the latest explorer ui https eclipse ditto github io ditto https eclipse ditto github io ditto primaryenvironmentname ditto sandbox you should be able to work with your locally running default using the local ditto environment and you can add additional environments to also work with e g with a deployed installation of ditto star history star history chart https api star history com svg repos eclipse ditto ditto type date https star history com eclipse ditto ditto date getting started in order to start up ditto via docker compose you ll need a running docker daemon docker compose installed for a single instance setup on a local machine at least 2 cpu cores which can be used by docker at least 4 gb of ram which can be used by docker you also have other possibilities to run ditto please have a look here https github com eclipse ditto ditto tree master deployment to explore them start ditto in order to start the latest built docker images from docker hub simply execute bash cd deployment docker docker compose up d check the logs after starting up bash docker compose logs f open following url to get started http localhost 8080 http localhost 8080 br or have a look at the hello world https www eclipse dev ditto intro hello world html additional deployment options deployment are also available if docker compose is not what you want to use development guide if you plan to develop extensions in ditto or to contribute some code the following steps are of interest for you warning if you just want to start use ditto please ingore the following sections build and start ditto locally in order to build ditto you ll need jdk 17 apache maven 3 8 x installed a running docker daemon in order to first build ditto and then start the built docker images 1 build ditto with maven bash mvn clean install skip tests bash mvn clean install dskiptests 2 build local ditto docker snapshot images bash build images sh if your infrastructure requires a proxy its host and port can be set using the p option like for example bash build images sh p 172 17 0 1 3128 please note that the given host and port automatically applies for http and https 3 start ditto with local snapshot images bash cd deployment docker the dev env file contains the snapshot number of ditto copy it to env so that docker compose uses it cp dev env env docker compose up d check the logs after starting up bash docker compose logs f you have now running a mongodb as backing datastore of ditto not part of ditto but started via docker ditto microservices policies things things search gateway connectivity an nginx acting as a reverse proxy performing a simple basic authentication listening on port 8080 including some static http api documentation on http localhost 8080 http localhost 8080 | eclipseiot iot internet-of-things digital-twin eclipse-ditto ditto hacktoberfest java mongodb akka digital-twins digital-shadow w3c web-of-things wot javascript | server |
Journeyly | project name reservation management system for a travel agency batch y2 s2 wd it 01 group leader it21008146 k s v perera it21008146 member 2 it21008696 m p c leo it21008696 member 3 it21105302 u s l fernando it21105302 member 4 it21008382 m m r muthusinghe it21008382 member 5 it20072810 g g k thilakshana it20072810 member 6 it21101274 t y r dolawatta it21101274 member 7 it21004636 g n p perera it21004636 member 8 it21024740 m g m p makawita it21024740 brief description of project this is a web application system for a travel agency providing variety of services to their clients mainly it provides clients to reserve flights reserve a place to stay hire taxies or rent vehicles and add reservations and buy tickets to the most iconic and famous places in sri lanka since all these services are integrated on the website and due to the user friendliness of the website it can save a lot of time being wasted in addition we also provide them with bundle packages which is more cost effective compared to making each and every reservation separately also this project contains the website content management which allows the editor to add modify delete content from the website along with the ceo dashboard and the financial management which handles all the financial tasks related to the operations of the agency this is a fully functional system with best user interaction and user experience which attracts clients to use the web services technologies used mern mongodb express js react js node js note the student s github account should be given in brackets e g asirirepos this ideally should be your student id | server |
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cs229-2018-autumn | cs229 autumn 2018 all lecture notes slides and assignments for cs229 machine learning http cs229 stanford edu course by stanford university the videos of all lectures are available on youtube https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu useful links cs229 summer 2019 edition https github com maxim5 cs229 2019 summer | machine-learning stanford-university neural-networks cs229 | ai |
goldenTicketMicroservices | microsservi os java com spring boot e spring cloud esse projeto foi desenvolvido com prop sitos educacionais apenas para a disciplina de sistemas distribu dos 2021 2 do curso de engenharia de computa o do instituto federal do sul de minas gerais o projeto conta com 3 microsservi os sendo o microsservi o de produto um crud completo cada microsservi o sobe sua pr pria inst ncia do banco h2 e todos os tr s se comunicam via rest com as facilidades do eureka server como funciona o microsservi o ticket funciona com um bilhete podendo ser encontrado em todos os produtos o que define se o bilhete premiado ou n o seu status ativo ou inativo o microsservi o produto possui um bilhete e os dois possuem o atributo produtoid em comum por fim o microsservi o cliente tamb m possui um bilhete com o atributo ticketid em comum podendo assim o cliente ter tirado um bilhete premiado pr requisitos jdk 11 ou superior devidamente instalada com suas vari veis de ambiente alguma ide de desenvolvimento java recomendado o intellij idea postman rodando minha cole o feita para testes https www getpostman com collections af67577bd3dee035fff9 instala o clonar o reposit rio na m quina abrir o intellij e importar os 4 projetos em paralelo rode cada uma das 4 em paralelo subir o eureka server primeiro com o eureka server up j possivel acessar a dashboard pelo endere o localhost 8762 e visualizar os microsservi os por fim abrir o postman file import e selecionar a aba link e colar o link disponibilizado acima perceba que foi mantido um padr o de url o ms de ticket vai subir na porta 6060 o de produto na 6161 e o de cliente na 6262 logo para acessar o banco de dados de cada um acessar a url localhost 6x6x h2 console no campo de url preencher com jdbc h2 file data xxx ms substituindo o nome do microsservi o se atente ao nome do ms ser o mesmo da porta acessada no campo usu rio preencher amaral e a senha deixar em branco pode ser necess rio inserir as tuplas em cada banco e para isso acesse a pasta properties de cada ms encontrando um arquivo data sql basta copiar esse c digo colar no h2 e rodar inserindo assim os dados no banco constru do com java 11 https dev java linguagem de programa o maven https maven apache org gerenciador de depend ncias java spring boot https spring io projects spring boot projeto do spring utilizado para construir as aplica es rest spring cloud https spring io projects spring cloud projeto do spring para comunica o http h2 database https www h2database com html main html banco de dados local para aplica es java autores ant nio zamai arquitetura desenvolvimento e documenta o zamaidev https github com zamaidev mateus nazar desenvolvimento mateusnazare https github com mateusnazare com por ant nio zamai https github com zamaidev | microservice spring-boot java spring-cloud restful-webservices | cloud |
my-first-repo | my first repo meta database engineering practice account | server |
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Chapter-4-5-6-CDS-ABAP-backend-code | chapter 4 5 6 cds abap backend code this repository contains all the backend code cds abap for the apps developed in the sap press book abap restful programming model abap development for sap s 4hana written by stefan haas bince mathew | server |
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Ionic2Weather | ionic2weather this is a starter template for the ionic2weather app from mobile app development with ionic 2 http www ionic2book com published by o reilly press how to use this template this template does not work on its own bash sudo npm install g ionic cordova ionic start ionic2weather sidemenu cd ionic2weather with ionic cli version 3 the use of third party templates was removed once you have created the starter ionic application using the steps above download this repo and copy over the src and www directories into the newly created folder replacing the initial src and www directory you will still need to follow any additional steps in the book | front_end |
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resources-for-flatiron | flatiron school s banner https upload wikimedia org wikipedia commons thumb 6 61 fs wiki png 330px fs wiki png visits badge https badges pufler dev visits chienleow resources for flatiron flatiron school resources feel free to submit a pull request if you want to add to this list of resources keep on coding table of contents details open summary table of contents summary ul li a href recorded lectures recorded lectures a li li a href assessment prep assessment prep a li li a href ruby ruby a li ul li a href intro to ruby intro to ruby a li li a href oo ruby oo ruby a li ul li a href rails rails a li ul li a href rails project rails project a li ul li a href javascript javascript a li ul li a href javascript project javascript project a li ul li a href react react a li ul li a href react redux final project react redux final project a li ul ul details recorded lectures lectures from flatiron school all topics google sheet https docs google com spreadsheets d 1a hlf6dura ghxctujnifl4 oju7latrgy lhvv54bq edit gid 1112612743 flatiron school denver video library https flatiron school denver firebaseapp com videos ahmed gaber s lectures https www youtube com channel ucmnrjlznmahlgpl 313kkjw playlists aysan isayo s lectures https www youtube com channel ucxwvma6qfunupivkctr8vkg videos evans wang s lectures https www youtube com channel ucwvpbpwglrxbllde xactyg videos assessment prep cli assessment prep https github com chienleow cli assessment prep sinatra assessment prep https github com chienleow sinatra assessment prep rails assessment prep https github com chienleow rails assessment prep ruby intro to ruby section 7 loops while and until loops lab lab walkthrough https youtu be q2zkff3s og section 8 iteration tic tac toe current player lab walkthrough https youtu be osmlmt 67gs section 9 ruby tic tac toe tic tac toe game status lab walkthrough https youtu be i8nxvn8f55g tic tac toe in ruby lab walkthrough https youtu be 5smwcv8vbfi procedural ruby section 9 data structures hash manipulation lab lab walkthrough https youtu be d6 nkh1lkri oo ruby intro to oo avi https youtu be uysgbtrjotc intro to oo avi most recent https youtu be xtlihmo1xt8 intro to oo avi 2 hours great explanation https youtu be h2618jco ea intro to oo aysan https www youtube com watch v sodz95bdx1m collaborating objects beth https youtu be vavun6c0bvc oo review mechanics when to use what avi https youtu be jrebj4icq8 rails section 1 intro to rails lecture avi 1 5 hours https www youtube com watch v kkq8lpeyw2g feature emb title section 4 intro to actionview rails url helpers lab walkthrough ayana 1 hour https www youtube com watch v xcszwc edbk feature youtu be section 5 crud todomvc great video explaining how url helpers work and crud lecture 1 assets and managing lists avi 1 hour https www youtube com watch v bltz dz8bsw feature emb title section 6 validations and forms activerecord validations went through types of validation helpers activemodel validator didn t quite work but tried the block version lecture luisa 1 hour https www youtube com watch v 5jn2g5ugoys feature emb title section 7 associations and rails rails active record associations lecture jennifer 1 hour https www youtube com watch v oww2vb0gkxg feature emb title rails associations and nested forms part 1 lecture aysan 1 hour https www youtube com watch v usinxlavjlc t 2143s rails associations and nested forms part 2 lecture aysan 1 hour https www youtube com watch v yatawoilts0 forms and basic associations lab the ternary operator in ruby medium article collection select https medium com dru edmondson the ternary operator in ruby 1e87b57f2fc9 section 10 routes and resources nested resources and namespace routes go to 21 34 for nested resources lecture avi 1 hour 40 mins https www youtube com watch v aemzxu55vlo feature emb title jenn hansen s rails lectures project build lists https docs google com document d 1vq3jxdxxmspqmexvmozab09kewcvhqpihyecckzgvx0 edit rails project rails project build from scratch avi rails build 1 find a pair avi https www youtube com watch v y9ym205nbaa feature emb title rails build 2 find a pair avi https www youtube com watch v jedxpw33qgu feature emb title rails build 3 find a pair avi https www youtube com watch v jfj8qg e1og feature emb title rails build 4 find a pair avi https www youtube com watch v k0itd43qr3q rails build 5 find a pair avi https www youtube com watch v qai 3ggk zm feature emb title rails build 6 find a pair avi https www youtube com watch v vsr3wwoxurq feature emb title javascript suggested js sections order 1 2 7 8 3 9 4 6 10 13 section 3 recognizing javascript what is lexical scope lexical scope kyle simpson video 5 mins https www youtube com watch v dhyhmp8esuk task lister lite lab walkthrough evans wang 30 mins https www youtube com watch v cavkz6pvf2e feature youtu be dom challenge lab walkthrough ahmed gaber 1 5 hours https www youtube com watch v odxpisfefca feature youtu be section 4 communicating with the server fetch and objects lecture aysan 1 hour https www youtube com watch v ru5tv3jvlyi t 43s ajax promises and fetch lecture avi 1 5 hours https www youtube com watch v 66nmdefvhhq feature emb title toy tale challenge lab walkthrough evans wang 1 hour https www youtube com watch v qht4kup zfs feature youtu be section 5 pulling it all together build the example lab walkthrough aysan 48 mins https www youtube com watch v djwpjlmkn6c section 6 rails as an api pokemon teams project lab walkthrough matteo 2 hours https www youtube com watch v skfcgwwve6a feature emb err woyt section 9 advanced functions callback functions freecodecamp article https www freecodecamp org news javascript callback functions what are callbacks in js and how to use them function declaration vs function expression freecodecamp article https www freecodecamp org news when to use a function declarations vs a function expression 70f15152a0a0 reduce lab programming with mosh js array reduce https www youtube com watch v g1c40tdp0bk javascript project javascript rails api project setup https github com learn co curriculum mod3 project week setup example flatiron school ayana s project build series seeda syllabus backend repo part 1 2 notes https github com ayanazaire seeda syllabus backend seeda syllabus frontend repo part 3 4 notes https github com ayanazaire seeda syllabus frontend js mantra when some event happens i want to make what kind of fetch and then manipulate the dom in what way part 1 planning and building a rails api https youtu be q5r7hsqdgfk part 2 routes controllers serializers https youtu be zjdfwbvfwai part 3 events fetch and dom manipulation using a rails api https youtu be goyf xqigye part 4 oojs refactor https youtu be eleimmg 8ck part 5 complex database relationships https youtu be u zrd8lvs7a part 6 intro to implementing bootstrap https youtu be 18l lrxmeiw part 7 implementing auth in js project using jwt https youtu be yigbmx49vce the wellbeing cookbook project build series by ally kadel video playlist https www youtube com playlist list plnuiyk37z4zg1 hogokagjm5e0ug1gxtl part 1 setting up the api https youtu be cvywglmuwnq part 2 setting up the client https youtu be ehvhvgg7dso part 3 working with objects https youtu be hlj5tklznuw part 4 displaying data in the dom https youtu be 6ngmncyf2is part 5 building a delete request https youtu be wgdc4futowc part 6 debugging delete and building a post request https youtu be dku6cx2kkqu part 7 finishing the post request https youtu be lveho7izdug part 8 updating the dom https youtu be h qdsdsnahe part 9 refactoring project structure https youtu be 71a8ta3t7l8 part 10 stylin https youtu be rwr6madmpcs aysan s project build rails api javascript application build https www youtube com watch v cxz fg2kgsy cernan s project build series part 1 build a rails api w a modular js frontend https www youtube com watch v secqorbcq9u part 2 build a rails api w a modular js frontend https www youtube com watch v o0fjpcf6s60 part 3 build a rails api w a modular js frontend https www youtube com watch v 1ql2 kiqz m react react lecture recordings and build series react lectures other react resources https docs google com spreadsheets d 1soqpq8j7czckqbgja9oczfeipnfsponszs4cryb3jom edit gid 0 nancy noyes lectures https docs google com spreadsheets d 1a hlf6dura ghxctujnifl4 oju7latrgy lhvv54bq edit gid 603692437 section 6 forms props state and events lab lab walkthrough seth 1 hour https www youtube com watch v vkgwpl9l4nw react redux final project expense tracker project build series https instruction learn co student video lectures query expense tracker expense tracker frontend repo https github com awilmerding1 expenses frontend expense tracker backend repo https github com awilmerding1 expenses backend expense tracker react redux project build part 1 https youtu be pkaainitbk0 part 2 https youtu be 4cad44cj gg part 3 https youtu be bhql1 aym2q part 4 https youtu be koklzqdctuc part 5 https youtu be o391wn4qm9e part 6 https youtu be v3ibpevplg8 part 7 https youtu be cefj0w2mnag part 8 https youtu be l51x1pl1gdu part 9 https youtu be xudhlzafuuu part 10 final https youtu be ivoeoq7og9m globetrotter app project build series https instruction learn co student video lectures query globetrotter globetrotter frontend repo https github com howardbdev globetrotter frontend globetrotter backend repo https github com howardbdev globetrotter backend | ruby ruby-on-rails javascript react redux sql flatiron-school flatiron-school-project | front_end |
FreeRTOS-SESIP | introduction this project is an example of an iot application using freertos this project was used to achieve sesip certification for the freertos kernel sesip is an optimized security evaluation methodology designed for iot devices for more information about sesip please see the globalplatform website https globalplatform org sesip the security target document which describes the details of the certification can be downloaded from the trustcb website https trustcb com download sesip 2000022 01 st wpdmdl 1930 a copy of the certificate https trustcb com download sesip 2000022 01 cert wpdmdl 1928 is also available for download from the trustcb website getting started for instructions describing how to operate this demo please refer to this documentation https github com freertos lab project freertos sesip tree main docs readme md getting help if you have any questions or need assistance troubleshooting your freertos project we have an active community that can help on the freertos community support forum https forums freertos org please also refer to faq http www freertos org faqhelp html for frequently asked questions | os |
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dl-tau-2017-2 | dl tau 2017 2 advanced natural language processing cosc 7336 introduction to deep learning for text analysis and understanding https fagonzalezo github io dl tau 2017 2 | ai |
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iOS-Feed | ci ios https github com omrank ios feed actions workflows ci ios yml badge svg https github com omrank ios feed actions workflows ci ios yml ci macos https github com omrank ios feed actions workflows ci macos yml badge svg https github com omrank ios feed actions workflows ci macos yml feed ios front end application on the surface a simple ios app that loads and displays images in the users feed under the hood the application of high level concepts of software design and architecture such as dependency inversion dependency injection single responsibility principle liskov substitution principle interface segregation principle clean architecture version control continuous integration and more to the process of engineering an ios mobile application with modular components that are decoupled from one another and are easy to refactor or replace following the principles of tdd all components are continously tested in isolation via unit tests allowing us to ensure that correct behaviors are achieved programming mistakes are caught and data races and memory leaks are protected against maintenanability is guaranteed in perpetuity by integration tests version control and the continuous integration pipeline this ensures the codebase is highly predictable easy to refactor and even easier to understand learning outcomes documentation of knowledge gained or refined during the process git branching rebasing merging resolving merge conflicts using local and remote repos for a safe development process pull requests on github ci using github actions for running tests prior to merging a pull requests integration tests end to end tests foundation s url loading system urlsession urlsessiontask urlsessionconfiguration the urlprotocol abstract class and how to use it to intercept network calls and simulate responses during testing coredata the stack nsmanagedobjectmodel nspersistentstorecoordinator nsmanagedobjectcontext the persistent container nspersistentcontainer entities properties and relationships object data models nsmanagedobject subclasses | tdd clean-architecture dependency-inversion ci | os |
alreadyme-ai-serving | serving alreadyme md ai model fastapi https img shields io badge fastapi 005571 logo fastapi https github com tiangolo fastapi docker https img shields io badge docker 230db7ed svg logo docker logocolor white https www docker com redoc https img shields io badge docs redoc blue https jolly zebra 41 redoc ly license https img shields io github license readme generator alreadyme ai research license fossa status https app fossa com api projects git 2bgithub com 2freadme generator 2falreadyme ai serving svg type shield https app fossa com projects git 2bgithub com 2freadme generator 2falreadyme ai serving ref badge shield black https img shields io badge code 20style black 000000 svg https github com psf black codefactor https www codefactor io repository github readme generator alreadyme ai serving badge https www codefactor io repository github readme generator alreadyme ai serving this repository is to serve alreadyme model https github com readme generator alreadyme ai research on fastapi requirements torch fastapi all omegaconf transformers loguru prerequisites before starting the server the fine tuned model weight is required while transformers pipeline has extremely slow we use pickling to enhance the initialization time because of that some conversion is needed python import torch from transformers import pipeline pipe pipeline text generation bloom 1b7 finetuned readme 270k steps torch dtype torch float16 device 0 torch save pipe bloom 1b7 finetuned readme 270k steps pipeline pt move the transformer model to app resources and change the path in app resources config yaml run the server we recommend to build a docker image instead using in local but it would be better to run before building the image to check any bug in the code and your fine tuned model start locally bash cd app uvicorn main app ip your ip address port your port build docker we do not provide any pre build image yet build your own image with custom fine tuned model bash docker build t alreadyme ai serving v0 1 2 f dockerfile build args cuda ver 11 6 1 build args cudnn ver 8 build args ubuntu ver 18 04 build args python ver 39 you can change the version of cuda cudnn ubuntu and python they can be useful for compatibility of different cloud environment after build your image run docker by bash docker run gpus all p 8080 80 alreadyme ai serving v0 1 2 the docker container will launch the server on port 80 so you should binding to your own port number e g 8080 documentation alreadyme ai serving supports openapi and you can see the documentation of the apis in your server if the server is running locally check out http 127 0 0 1 8080 docs for swagger or http 127 0 0 1 8080 redoc for redoc for convenience we hosted free redoc documentation page https jolly zebra 41 redoc ly you may login to see the details license alreadyme ai serving is released under the apache license 2 0 license can be found in here license fossa status https app fossa com api projects git 2bgithub com 2freadme generator 2falreadyme ai serving svg type large https app fossa com projects git 2bgithub com 2freadme generator 2falreadyme ai serving ref badge large | bloom markdown nlp black codex copilot deep-learning docker fastapi few-shot-learning gpt-3 huggingface language-model natural-language-processing pytorch readme-generator transformers | ai |
Udagram-Image-Filtering-Microservice | 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 | cloud |
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nak | nak build status https travis ci org scalanlp nak svg branch master https travis ci org scalanlp nak nak is a scala java library for machine learning and related tasks with a focus on having an easy to use api for some standard algorithms it is formed from breeze https github com scalanlp breeze liblinear java http liblinear bwaldvogel de and scalabha https github com utcompling scalabha it is currently undergoing a pretty massive evolution so be prepared for quite big changes in the api for this and probably several future versions we d love to have some more contributors if you are interested in helping out please see the helpwanted issues https github com scalanlp nak search q 23helpwanted type issues or suggest your own ideas what s inside nak currently provides implementations for k means clustering and supervised learning with logistic regression and support vector machines other models and algorithms that were formerly in breeze learn are now in nak see the nak wiki https github com scalanlp nak wiki for some preliminary and unfortunately sparse documentation the latest stable release of nak is 1 2 1 changes from the previous release include breeze learn pulled into nak k means from breeze learn and nak merged added locality sensitive hashing see the changelog https github com scalanlp nak wiki changelog for changes in previous versions using nak in sbt librarydependencies org scalanlp nak 1 2 1 in maven dependency groupid org scalanlp groupid artifactid nak artifactid version 1 2 1 version dependency example here s an example of how easy it is to train and evaluate a text classifier using nak see twentynewsgroups scala https github com scalanlp nak blob master src main scala nak example twentynewsgroups scala for more details scala def main args array string val newsgroupsdir new file args 0 implicit val isocodec scala io codec iso 8859 1 val stopwords set the a an of in for by on val traindir new file newsgroupsdir 20news bydate train val trainingexamples fromlabeleddirs traindir tolist val config liblinearconfig cost 5 0 val featurizer new bowfeaturizer stopwords val classifier trainclassifier config featurizer trainingexamples val evaldir new file newsgroupsdir 20news bydate test val maxlabelnews maxlabel classifier labels val comparisons for ex fromlabeleddirs evaldir tolist yield ex label maxlabelnews classifier evalraw ex features ex features val goldlabels predictions inputs comparisons unzip3 println confusionmatrix goldlabels predictions inputs questions or suggestions post a message to the scalanlp discuss https groups google com forum fromgroups forum scalanlp discuss mailing list or create an issue https github com scalanlp nak issues | ai |
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visionworkbench | contributing to vision workbench links project homepage home code repository repo bug tracker bugs how to submit patches one or more of fork the code http help github com forking on github and send a pull request http github com guides pull requests file them on the bug tracker bugs send them to the mailing list list mailing list subscription required send an empty email message with the subject subscribe without the quotes to vision workbench request lists nasa gov listr send your messages to vision workbench lists nasa gov list home https ti arc nasa gov tech asr groups intelligent robotics nasa vision workbench repo http github com visionworkbench visionworkbench bugs http github com visionworkbench visionworkbench issues list mailto vision workbench lists nasa gov listr mailto vision workbench request lists nasa gov | ai |
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dd-nerf-dataset | dronedeploy nerf dataset dronedeploy nerf dataset dnf contains a number of real world scenes captured by drone that include high resolution images camera poses intrinsics and extrinsics and dense color point clouds it is intended for computer vision research all reconstructions were created using dronedeploy https www dronedeploy com information name image images points size preview house1 house1 imgs house1 png raw true house1 220 3m 1 7gb pointcloud preview https drive google com file d 15ajlymev3gd dwsmhr3wtmchvkjq0rvx view usp sharing house2 house2 imgs house2 png raw true house2 952 25m 8 6gb pointcloud preview https drive google com file d 1uotqdkumqu2lxc5igly8qvww4qmldx7c view usp sharing house3 house3 imgs house3 png raw true house3 182 5m 1 4gb pointcloud preview https drive google com file d 1y8u9u0f7uu1yy35b 4qjnut5r12gdyeh view usp sharing house4 house4 imgs house4 png raw true house4 367 10m 5 2gb pointcloud preview https drive google com file d 1yjjtnuabczdzeepc1nqwzvqt6hrxf8vp view usp sharing ruins1 ruins1 imgs ruins1 png raw true ruins1 391 6m 3 1gb pointcloud preview https drive google com file d 1oy5otafheyts5l73i2btxk2p rmuh37n view usp sharing ruins2 ruins2 imgs ruins2 png raw true ruins2 1174 56m 9 3gb pointcloud preview https drive google com file d 1awi2eg69iyggzn0jcdqg45nv41kwo5zb view usp sharing ruins3 ruins3 imgs ruins3 png raw true ruins3 539 12m 4 4gb pointcloud preview https drive google com file d 1bmpbvfecn4hbksktb1bpfezsrjqx gre view usp sharing tower1 tower1 imgs tower1 png raw true tower1 783 42m 6 1gb pointcloud preview https drive google com file d 1d4v0 mqoyebqlsndanjkeyoksubzjs97 view usp sharing tower2 tower2 imgs tower2 png raw true tower2 684 16m 5 4gb pointcloud preview https drive google com file d 1mzu9sgt5unzv2pd9rfskics o181pt2k view usp sharing pipes1 pipes1 imgs pipes1 png raw true pipes1 98 7m 1 3gb pointcloud preview https drive google com file d 1zme6vzsdaqcncvhxka y n c2aptdurz view usp sharing download download links https drive google com drive folders 1kmhas5l9zy9ywhp6n4icm2xc7sieths7 https drive google com drive folders 1kmhas5l9zy9ywhp6n4icm2xc7sieths7 structure house1 cameras xml images 00001 jpg 00002 jpg 00003 jpg points points ply house2 house3 the cameras xml contains the poses of each camera along with the distortion parameters intrinsics and extrinsics see tools util py for working with this file directly the images directory contains the images and points points ply contains the dense pointcloud with x y z red green blue tools the tools directory contains an expanding collection of tools for working with and transforming the dataset contributions are welcome tools dd2ngp py create a cameras json ready for use with instant neural graphics primitives https github com nvlabs instant ngp the default cameras json is included in each zip of the dataset and uses the 50 nearest images to the center of the scene if you want to use different cameras you can use this script to re generate a cameras json usage sh instant ngp build testbed scene datasets house1 cameras json ngp imgs ngp png raw true ngp or sh python scripts run py mode nerf scene house1 cameras json screenshot transforms house1 cameras json width 1216 height 912 screenshot dir house1 output near distance 0 screenshot spp 16 script imgs script jpg raw true script license and citation bibtex misc pilkington2022 author nicholas pilkington title dronedeploy nerf dataset year 2022 month may publisher github journal github repository howpublished url https github com nickponline dd nerf dataset this work is made available for academic use under cc by nc sa https creativecommons org licenses by nc sa 4 0 for commercial use and queries please contact support dronedeploy com | ai |
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cs224d-deep-learning-for-nlp | cs224d deep learning for nlp notes code and other resources for auditing the course cs224d deep learning for natural language processing http cs224d stanford edu lectures lecture 2 simple word vector representations word2vec glove slides http cs224d stanford edu lectures cs224d lecture2 pdf video https www youtube com watch v t8tqzchnimk word2vec https code google com p word2vec glove global vectors for word representation http nlp stanford edu projects glove lecture 3 advanced word vector representations language models softmax single layer networks slides http cs224d stanford edu lectures cs224d lecture3 pdf video https www youtube com watch v t1j2q9 fgtm 22 efficient estimation of word representations in vector space http arxiv org pdf 1301 3781 pdf tomas mikolov kai chen greg corrado and jeffrey dean in proceedings of workshop at iclr 2013 distributed representations of words and phrases and their compositionality http arxiv org pdf 1310 4546 pdf tomas mikolov ilya sutskever kai chen greg corrado and jeffrey dean in proceedings of nips 2013 glove global vectors for word representation http nlp stanford edu projects glove glove pdf jeffrey pennington richard socher christopher d manning in proceedings of the 2014 conference on empirical methods in natural language processing improving word representations via global context and multiple word prototypes http www aclweb org anthology p12 1092 eric h huang richard socher christopher d manning andrew y ng 2012 the wordsimilarity 353 test collection http www cs technion ac il gabr resources data wordsim353 improving distributional similarity with lessons learned from word embeddings https levyomer files wordpress com 2015 03 improving distributional similarity tacl 2015 pdf omer levy yoav goldberg and ido dagan tacl 2015 other resources making sense of word2vec http radimrehurek com 2014 12 making sense of word2vec improving distributional similarity with lessons learned from word embeddings https levyomer files wordpress com 2015 03 improving distributional similarity tacl 2015 pdf omer levy yoav goldberg and ido dagan tacl 2015 getting started with word2vec and glove http textminingonline com getting started with word2vec and glove word2vec tutorial http radimrehurek com 2014 02 word2vec tutorial 2014 radim eh ek optimizing word2vec in gensim http radimrehurek com 2013 09 word2vec in python part two optimizing a glove implementation in python foldl http www foldl me 2014 glove python modern methods for sentiment analysis https districtdatalabs silvrback com modern methods for sentiment analysis michael czerny blue sky weakly supervised memory networks http arxiv org pdf 1503 08895v2 pdf sainbayar sukhbaatar arthur szlam jason weston 2015 when are tree structures necessary for deep learning of representations http arxiv org abs 1503 00185f 2015 jiwei li dan jurafsky eudard hovy extensions scaling recurrent neural network language models http arxiv org pdf 1502 00512v1 pdf will williams niranjani prasad david mrva tom ash tony robinson embedding based word similarity http irsrv2 cs biu ac il 9998 word machine linguistic regularities in sparse and explicit word representations http www cs bgu ac il yoavg publications conll2014analogies pdf 2014 omer levy and yoav goldberg word2vec http www reddit com r machinelearning comments 30m0eb what is the state of the art in language modeling embedding based word similarity http irsrv2 cs biu ac il 9998 word machine getting started with word2vec and glove http textminingonline com getting started with word2vec and glove lstm lstm networks for sentiment analysis http www deeplearning net tutorial lstm html lstm long short term memory http christianherta de lehre datascience machinelearning neuralnetworks lstm php recurrent neural networks theano keras theano based deep learning library https github com fchollet keras general deep learning university http memkite com deep learning bibliography glove global vectors for word representation pennington socher manning 2014 http blog mdda net ai 2014 10 13 glove what s wrong with deep learning what s wrong with convolutional nets http techtv mit edu collections bcs videos 30698 what s wrong with convolutional nets 2014 geoffrey hinton optimizing neural networks that generate images http www cs toronto edu tijmen tijmen thesis pdf 2014 tiijman tieleman transforming auto encoders http www cs toronto edu fritz absps transauto6 pdf 2011 g e hinton a krizhevsky s d wang | ai |
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SnakeGame | snakegame georgia tech embedded systems design project | os |
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it | it it information technology | server |
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RTXThread | https www cnblogs com yanye0xff p 17034882 html 1 rtxthread release make 2 eclipse ide for embedded c c developers file import project build project 2023 01 04 spi spi0 2 dma buffer 8bit 2022 10 13 keil mdk 2022 10 12 spi spi0 spi2 2 dma buffer 8 16bit src bsp driver drv spi c 2022 10 11 rt thread v4 1 1 finsh gd32f30x firmware library v2 1 4 | os |
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udagram-frontend | udagram simple frontend udagram is a simple cloud application developed along side 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 rajan98 udacity c2 frontend this repo a basic ionic client web application which consumes the restapi backend 2 the restapi backend https github com rajan98 udacity c2 restapi which is a node express server which can be deployed to a cloud service 3 the image filtering microservice https github com rajan98 udacity c2 image filter which is the final project for the course it is a node express application which runs a simple python script to process images getting setup tip this frontend is designed to work with the restapi backend https github com rajan98 udacity c2 restapi it is recommended you stand up the backend first test using postman and then the frontend should integrate installing node and npm this project depends on nodejs and node package manager npm before continuing you must download and install node npm is included from https nodejs com en download https nodejs org en download installing ionic cli the ionic command line interface is required to serve and build the frontend instructions for installing the cli can be found in the ionic framework docs https ionicframework com docs installation cli installing project dependencies this project uses npm to manage software dependencies npm relies on the package json file located in the root of this repository after cloning open your terminal and run bash npm install tip npm i is shorthand for npm install configure the backend endpoint ionic uses enviornment files located in src enviornments enviornment ts to load configuration variables at runtime by default environment ts is used for development and enviornment prod ts is used for produciton the apihost variable should be set to your server url either locally or in the cloud running the development server ionic cli provides an easy to use development server to run and autoreload the frontend this allows you to make quick changes and see them in real time in your browser to run the development server open terminal and run bash ionic serve building the static frontend files ionic cli can build the frontend into static html css javascript files these files can be uploaded to a host to be consumed by users on the web build artifacts are located in www to build from source open terminal and run bash ionic build todo 2 tasks i setup a clone set up protected branches dev staging master b npm ionic cli c run tests npm test identify broken function fix the function d write tests for form validation and re run tests | cloud |
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images-from-the-aether | images from the aether the toc below is auto generated by the vscode markdown toc plugin do not edit manually with the plugin installed this toc should regenerate as needed automatically when you save the file if not just re execute generate toc for markdown vscode markdown toc 1 purpose purpose 2 using the application usingtheapplication 3 the design and the result thedesignandtheresult 3 1 overview overview 3 2 starting out startingout 3 3 user input userinput 3 4 acting on user input actingonuserinput 3 5 fetching results fetchingresults 3 6 variations on a result variationsonaresult 3 7 api access apiaccess 3 8 the base modal component thebasemodalcomponent 3 9 state management statemanagement 4 more on managing search results moreonmanagingsearchresults 5 accessibility accessibility 6 user experience userexperience 6 1 input validation in detail inputvalidationindetail 6 2 other ux touch points otheruxtouchpoints 6 3 developer experience too developerexperiencetoo 7 performance performance 8 further points on maintainable design furtherpointsonmaintainabledesign 8 1 clean unit tests cleanunittests 8 2 entities are treated as entities not strings entitiesaretreatedasentitiesnotstrings 8 3 unit tests are phantom aware unittestsarephantom aware 8 4 css constants cssconstants 8 5 on file downloads onfiledownloads 8 6 more on file downloads moreonfiledownloads 8 7 beyond breadth unit tests have depth beyondbreadth unittestshavedepth 8 8 strict mode and other checks strictmodeandotherchecks 8 9 typescript type traps typescripttypetraps 8 10 unit tests test behavior rather than implementation unitteststestbehaviorratherthanimplementation 8 11 test environments and continuous integration testenvironmentsandcontinuousintegration 9 acknowledgements acknowledgements 10 default generated docs for this project defaultgenerateddocsforthisproject 10 1 development server developmentserver 10 2 code scaffolding codescaffolding 10 3 build build 10 4 running unit tests runningunittests 10 5 running end to end tests runningend to endtests 10 6 further help furtherhelp vscode markdown toc config numbering true autosave true vscode markdown toc config vscode markdown toc 1 a name purpose a purpose images from the aether is a cloud image viewer web application what another one there are plenty of image viewers out there of course and in fact i did not sit down to create one what i wanted to do was create a modern web app from scratch using best design principles and practices as a showcase piece for my portfolio and this seemed like a project that was small enough to make it the amount of code easy to digest for a reader yet still require quite a bit of different patterns interfaces and libraries to make it interesting so yes this is a real working web app not a toy or demo these are the high level requirements i began with display a photo collection keyed off a user specified string have a search bar that responds as the user types display results in a responsive grid use infinite scroll open an enlargement of a photo and include the author s name by clicking on one in the results image should be downloadable from that detail modal application should be accessible responsive and have strong ux fu that leaves a large number of details unspecified of course what i wanted to do was a clean tdd design so that at the end the unit tests itemize low level requirements providing a contract of the application s behavior guaranteed to be an accurate list of requirements as long as the tests pass my end result was a thorough but not quite complete list there are some things not really amenable to unit tests accessibility performance responsiveness of the display visual positioning where a button or field is positioned on the page alignment of elements etc and probably a few others 2 a name usingtheapplication a using the application you can certainly clone the repository and play with this application on your own machine or you can just launch it immediately in your browser at https msorens github io images from the aether courtesy of github pages https pages github com be advised however that you will need a pexels api key to do anything see api access below for further details 3 a name thedesignandtheresult a the design and the result what is the quickest way for you the reader to understand what this application does down to every nitty gritty detail sure you could read the code but it is quite a leap from that to really knowing what the app does or you could run the code but you really would not know how to exercise everything that the app does better than either of those come with me as i describe the behavior to you through unit tests and pictures 3 1 a name overview a overview there are just three components the appcomponent the main component manages a key for the api calls as well as the user input the viewphotoscomponent houses the collection of photos in an infinite scroll region it provides several visual cues no search started no results found end of results reached fetch in progress along with providing a modal to show details of a selected image the basemodalcomponent is a generic modal component that can be customized by providing content it is used both to display image details larger image author download link and for prompting the user for an initial api key and two services imageservice manages the api calls to test the api key and to load batches of photos keyservice manages storing and retrieving the api key in your browser s local storage next let us walk through the tests 3 2 a name startingout a starting out to begin i of course want the application to launch and it should display the name of the application and a search field for the user to interact with text section appcomponent display should create the app shows a search box shows the application name 3 3 a name userinput a user input i want to let the user type anything in the search box except that leading and or trailing whitespace should be ignored but that means i need to also make sure that whitespace elsewhere is still preserved and other special characters e g punctuation are also preserved this set of tests covers all those text section appcomponent user input normalizing input leading and trailing whitespace in user input ignored leading whitespace in user input ignored trailing whitespace in user input ignored user input used verbatim when no whitespace present intermediate whitespace in user input is preserved special characters used verbatim 3 4 a name actingonuserinput a acting on user input as the user types i want to respond briskly to fetch matching photos so i want to react to every keystroke yes but only when the user pauses say after a debounce period of 400 milliseconds text section appcomponent user input react to keystrokes images are fetched on every keystroke after debounce period images are not fetched if debounce period has not expired images are not fetched when input is empty 3 5 a name fetchingresults a fetching results once that debounce period has elapsed we call the api to fetch the first batch of photos here is a typical screen showing the input and output so far each image is annotated with an index number to give you a better sense of where you are in the results as you scroll only a small number of images are fetched at a time as you near the bottom of the scroll region another batch is fetched nominal results src assets readme graphics nominal results png subsequent batches are fetched automatically as the user scrolls to the end of the previous batch in the virtual scroll container this occurs by observing the events that are emitted as the user scrolls only when an event corresponds to the last photo of the previous batch do we fetch more which means there are a variety of times that we do not fetch more here are the tests text section viewphotoscomponent with mock api fetching more photos during scrolling more photos are fetched when final photo is encountered more photos are not fetched when any non final photo is encountered more photos are not fetched when initializing event triggers more photos are not fetched when end of input reached more photos are not fetched while still loading previous batch when the user has scrolled to the bottom and a request for more photos is initiated the next batch will arrive after some short delay based on the user s internet connection speed machine load etc that interval may be imperceptibly short or it may not while loading then it is helpful to have a visual cue a spinner to let the user know loading is in progress also once the final batch of photos arrives a visual cue indicating the end has been reached is handy this set of test verifies both those visual cues text section viewphotoscomponent with mock api status indicators displays end marker when collection exhausted logic displays end marker when collection exhausted rendering displays spinner while fetching photos here is a close up of the end marker in the same location as the spinner end results src assets readme graphics end results png with a collection of results to view one can select any image to open a detail view showing an enlargement of the selected photo along with some details of the photograph as detailed by these tests text section viewphotoscomponent with modal component detail view signals modal to open when user selects an image renders larger image of selected photo author name enclosed with link to author details renders with detail view author link will open in a new tab or window save button downloads image file url when clicked renders with detail view displays download icon from material icons save button tooltip displays filename for domain only file extension for domain only file no extension for domain path file for domain path file with spaces exception handles empty path gracefully with empty string exception handles empty url gracefully with empty string exception handles invalid url gracefully with empty string a typical representation detail view src assets readme graphics detail view png back to the main screen for a moment when the user enters some search string only the first batch of matches is fetched as described earlier but the back end already knows how many total matches are available so that information is passed along too and it is helpful to provide it to the user the total available count then is posted in the upper right of the screen while we could display it all the time it provides no value at certain times at initial program load while new data is being fetched etc these tests cover the various scenarios text section appcomponent display reporting of total available images shows no total matches while loading shows total available non zero matches after a search shows total with zero matches on http error shows total available zero matches after a search shows no total matches before a search 3 6 a name variationsonaresult a variations on a result seeing a collection of photos is ultimately what a user is after when using this application but there are several other things the user might see depending on circumstances 1 when first opening up the app nothing has been searched for so clearly there can be no results to display instead the user will see the no search started graphic a cute ish image that conveys the start here message 2 while the available collection of images is vast it is not limitless so it is possible that no images are found to match what the user types in that event the no results graphic appears 3 in order to access the photo collection one has to have a valid access key discussed in the next section if the key is not valid and one asks for some images the unauthorized graphic appears 4 inevitably there are other assorted error circumstances including no internet connection server limit exceeded etc for any of these unexpected errors a general error graphic appears here are thumbnails of those four displays composite view src assets readme graphics composite views png the set of unit tests below confirm each of those images appears at the correct time only one ever occurs at a time and none appear while loading is ongoing text section viewphotoscomponent with spy api result graphics displays no search started graphic with no input displays no results graphic with some input but no results displays unauthorized graphic for authorization failure displays note for authorization failure displays general error graphic for non authorization failure displays error code and message for non authorization failure displays general error graphic for empty response displays empty response for empty response does not display any graphic while loading 3 7 a name apiaccess a api access as mentioned in the last section the application requires an access key to obtain results from the pexels api while i could have hard wired a single access key that would be used by everyone who downloaded the app i thought it would yield a more interesting design by thinking about that api key as credentials for the application itself and as such it would not be overly burdensome to require each user to obtain and register their own key with the app thus upon startup the first thing that occurs is a check for a stored key if not found it pops up a modal asking the user to supply one initial no key present src assets readme graphics initial no key modal png that same modal may be re opened at any time by selecting the key icon in the upper right corner of the display these unit tests elaborate on the opening behavior of the modal for the api key text section appcomponent api key at startup retrieves the previously saved user entered key from browser local storage stores the user entered key in browser local storage key entering modal opening opens automatically when no key found stored does not open automatically when key is found stored button to re open key modal has label with key icon from material icons includes tooltip renders on main window opens the key entering modal manually when clicked that deceptively simple modal has a number of behaviors to provide a useful user experience the illustration my naive attempt at creating a kineograph https en wikipedia org wiki flip book and the unit tests following provide the complete story composite key modal src assets readme graphics composite key modal png text section appcomponent key modal has an input field to enter the key has buttons to save and to test the key buttons are initially disabled buttons reacts to presence of input test button has status indicators that are initially hidden test button reveals only spinner when api operation is in progress test button reveals only success indicator when api reports success test button reveals only failure indicator when api reports failure test button is disabled while test operation is in progress test button is not disabled when test operation completes 3 8 a name thebasemodalcomponent a the base modal component both the modal for the api key and the modal for the detail image view are using the same component the basemodalcomponent this component opens a dialog that appears to be on top of the main window greys out the contents of that main window and only allows actions within the dialog the inherent behaviors of this modal component available to any consumer component are covered by these tests text section basemodalcomponent modal opens on demand modal can be closed on demand modal closes itself upon pressing escape modal does not close upon pressing keys other than escape modal closes itself when clicking the background modal does not close when clicking deeper inside the modal body modal does not close when clicking the modal body 3 9 a name statemanagement a state management i am using ngxs store https github com ngxs store readme for the first time with this project having previously been an avid ngrx store https github com ngrx platform readme user both of these provide a powerful state management system based on redux for use in angular applications i came across ngxs recently in my search for a way to do reduce the boilerplate needed by ngrx you can compare and contrast the two systems on npmcompare com https npmcompare com compare ngrx store ngxs store and npmtrends com https www npmtrends com ngrx store vs ngxs store they both work on the same principles you dispatch an action that action causes side effects the store gets updated from those side effects and ultimately subscribers watching specific portions of the store render responses to those state changes in the ui there are two actions used by this simple application setsearchstring is called every time the user modifies the search string at the top of the application window after the debounce period discussed earlier it clears out the prior results if any and then stores the new string in the state the viewphotoscomponent watches for changes to that particular piece of the state when it sees a new value it invokes the next action fetchphotos text store actions setsearchstring action resets page counter to zero puts search string into state resets photos to empty list resets total to zero fetchphotos is invoked by either the presence of a new search string initiated by setsearchstring or by the user scrolling to the last photo of the prior results either way it liaises with the imageservice to fetch more data from the pexels api once that new data is received it determines whether it is the final batch or not to set the end flag and it adds a relative index number to each received photo so that can be displayed along with the photo in the ui text store actions fetchphotos action end of input end of input reflects in state with value true end of input reflects in state with value false annotates photos with sequence number stores new photos in state stores total in state increments page number with each subsequent fetch store actions fetchphotos action error return resets photos to empty list upon error resets total to zero upon error 4 a name moreonmanagingsearchresults a more on managing search results the heart of this application is managing a potentially huge collection of images searching for cat for example yields 8000 results the idea is to only fetch a small batch at a time and as the user scrolls down another batch is fetched then another etc the user can thus scroll through those 8000 images seamlessly this is the notion of infinite scrolling https builtin com ux design infinite scroll but if all those images were kept in memory batch after batch the browser would start to bog down and exhibit poor performance so the app then needs virtual scrolling https dev to adamklein build your own virtual scroll part i 11ib as well with a virtual scrolling container the app renders just the very small number of items that happen to be on screen at a given moment plus a few above and below that to allow for smooth transitions there are numerous scrolling libraries available that support either or both of those but mostly for a simple one dimensional list for this app we need a two dimensional grid of images that can be used with virtual infinite scrolling because the grid has multiple tiles across the page it is important for the app to also support responsive design https developer mozilla org en us docs learn css css layout responsive design meaning it adjusts the layout to accommodate whatever screen size and resolution the user is using practically speaking it should adjust the number of images across the page depending on the screen width and that is not just at page load time if the user changes the window size the layout should react accordingly with those aspirations in mind i went looking for an off the shelf component angular s own cdk virtual scroll viewport detailed in this great article from zoaib khan https zoaibkhan com blog create a fast infinite scrolling list in angular main problem with this approach supports list only not a grid ngx infinite scroll directive detailed here by christian nwamba https blog ag grid com implementing infinite loading in an angular store application main problem infinite but not virtual scroll so it keeps adding to the dom virtual scroller ngx virtual scroller from rinto jose https github com rintoj ngx virtual scroller main problem not responsive to window size changes od virtualscroll from onur do ang n l https github com dinony od virtualscroll main problem difficulty getting it running on my machine during my search for a good solution i had some useful conversations with some of the folks who have worked on this problem a special kudos in particular to zoaib khan who critiqued and tweaked code to help me get things going khan provided yet further assistance from his article fastest way to make a responsive card grid with css https zoaibkhan com blog fastest way to make a responsive card grid with css revealing an elegant css technique to add responsiveness to existing layout i finally settled on using the virtual scroller component incorporating with khan s tweaks for responsiveness this particular component had the added benefit of being able to handle non uniform sizing of images not every image in the grid has to be the same dimensions 5 a name accessibility a accessibility even in a small application such as this there are a number of required items to conform to wcag https www w3 org wai standards guidelines wcag accessibility requirements associate a label element with each input field note that every input field should have a corresponding label even if the label is not visible on screen so screen readers can properly make sense of things that is done by a handy bit of css i found for the task add an alt attribute to each image add context landmarks header main define tab navigable elements with tabindex attributes so while it may be more convenient to use a mouse to scroll through the infinite scroll region it is also possible to do so with the keyboard using successive presses of the tab key to accommodate those users unable to use a mouse set appropriate color contrast in this case when i initially added sequence numbers to each image they were just white characters on whatever background the image proffered but that made some of them harder to discern so i added a small background shading around the character with sufficient contrast to make them always readable by the way i use andi recommended by the gsa s own section508 gov https www section508 gov test web software as well as the very useful axe dev tools https www deque com axe devtools to identify such accessibility issues 6 a name userexperience a user experience first one ux touch point in detail to illustrate that some seemingly trivial things are not so trivial to implement that is followed by a list of other ux highlights 6 1 a name inputvalidationindetail a input validation in detail input fields both have validation and provide validation feedback to the user there are only two simple input fields in this application one for the search string and one for the api key it is sufficient that if they are non empty they should be considered valid and they should only be acted upon if they are valid as an example the buttons acting upon the api key are disabled until the input is valid which is to say non empty but even more than that the input fields themselves provide visual feedback on the valid invalid state of the input in this kineograph notice that the marker appears and turns green when input is valid but turns red when not note that you can use a required attribute to do all the work of checking empty or non empty but that is not the best ux in the last frame you see that if the user has typed only whitespace that is invalid too just takes a bit more handling to cover that case input validation feedback src assets readme graphics composite input validation png to make those red green bars i adapted a clever bit of css from angular s own development guide https angular io guide form validation control status css classes which you can see in app component css one useful change i made is that the field is not marked red until the user actually touches the field good ux dictates that one should not signal hey this is wrong when the user just arrives on the page and has not done anything yet 6 2 a name otheruxtouchpoints a other ux touch points here are some of the ux considerations applied to this application to provide the best possible ux some of these have already been mentioned throughout this narrative clear visual imagery for the non happy path which is to say any result other than a batch of images returned see variations on a result buttons are labeled with well known icons rather than words for both brevity and universality a spinner pops up during various operations to indicate the application is doing something a clear end marker pops up when all images for a given search have been retrieved images have sequence numbers visible so the user can maintain a sense of where they are when scrolling back and forth a total number of matches for the given search string is reported immediately giving the user a sense of how much there is to scroll through tooltips are used whenever there is additional helpful information to share with the user the api key modal test button shows both a spinner while testing and then positive or negative feedback right on the button when a result is determined 6 3 a name developerexperiencetoo a developer experience too ux is not just for users developer experience dx is important too i strive to make code welcoming in a sense so people who want to either read the code or just understand how the application works can easily dig in some examples a thorough read me file is supplied this one a table of contents is included easy to maintain with the markdown toc extension for vs code the documentation includes appropriate illustrations rather than trying to describe with lots and lots of words where do things come from borrowed or adapted code snippets include source references search for from http or see http in the code base to see examples comments explain where other types of things come from for instance i derived the bits in photo ts from some data conversions but rather than just provide the bits i included comments in that file explaining where they came from more on comments some comments are vital as in the previous bullet but care must be taken to use comments appropriately and only when necessary because too many comments can often be worse than not enough i wrote at length about this in fighting evil in your code comments on comments https www simple talk com opinion opinion pieces fighting evil code comments comments ci checks help minimize the developer burden automatically running checks on different browsers and even a markdownlint check too 7 a name performance a performance firstly as already discussed displaying a large growing collection of objects can quickly bog down the ui if implemented naively the virtual scrolling component largely takes care of this keeping what is actually being rendered to a very small essentially constant number of items just what is in the current viewport that keeps the ui feeling very brisk and peppy to the user secondly the fact that this large collection of objects is a set of images is a concern image files are much larger than text files of course so having to download a lot of them as the user scrolls back and forth might introduce noticeable lag times caching of these images is thus vital but the way i am using images in the app makes this issue moot the browser is in control of fetching the images as needed through what is rendered in the html template and by default browsers cache those image fetches the data i am manually fetching with each new search or each new batch is just image metadata author url etc which is relatively quite small so not a performance concern 8 a name furtherpointsonmaintainabledesign a further points on maintainable design 8 1 a name cleanunittests a clean unit tests i wanted the unit tests to be short and sweet and follow the useful arrange act assert pattern ideally just a couple lines for each as many of my tests were peeking at the dom i crafted a set of helpers in queryhelper ts to facilitate this in this example you can see how the findas and findallas generic helper functions make the test almost trivial i first do a sanity check that the input field is empty the standard dom query returns a generic htmlelement which does not have a value property so instead of just doing a find i use findas to cast it to an htmlinputelement in order to access its value i then do a findallas to find a set of buttons and confirm they are all disabled which similarly needs each object to be an htmlbuttonelement typescript it buttons are initially disabled fixture detectchanges expect findas htmlinputelement control bar input value tobe findallas htmlbuttonelement control bar button foreach button expect button disabled tobetrue 8 2 a name entitiesaretreatedasentitiesnotstrings a entities are treated as entities not strings often when writing a program we need to deal with a thing that has a well defined structure a bit of html or xml a url an entry in a log file etc since each of those is typically supplied as a string it is only natural that the developer treats them as strings but that can lead to what i call the subtle syntax catastrophe which i talk about in my article code smells raw strings and the subtle syntax catastrophe https www red gate com simple talk development dotnet development code smells raw strings subtle syntax catastrophe for the case at hand i had a need to extract the file name out of a url it is fairly straightforward to find the piece of string between the first virgule and question mark and know that that is the path to the file so the name of the file is the last piece on that path but that is dealing with the url as a raw string and there is no benefit to that rather treat it as a url create a first class url object and then just access the pathname property to get that file path you can see this in action in view photos component ts getfilename 8 3 a name unittestsarephantom aware a unit tests are phantom aware perhaps there is a industry term for this but i have coined the phrase phantom tests in my article the phantom menace in unit testing https www red gate com simple talk dotnet software testing the phantom menace in unit testing a phantom test is one that proves nothing because if the code under test did nothing the result would be the same i give a mention to this in base modal component spec ts 8 4 a name cssconstants a css constants native css does not support reusable variables or constants if you want to reuse a number in multiple places you just repeat that hard coded value over and over and fortunately sass does support constants except they call them variables the few global values i needed are recorded only once in variables scss and those named constants can then be used freely without any maintenance burden 8 5 a name onfiledownloads a on file downloads such a simple notion such a challenge to do in an in browser application in the early days in browser code could not interact with the filesystem for security reasons with the advent of html 5 however a simple mechanism was introduced to allow downloading a file using the download attribute in an anchor element html a href target url download image jpg download a initially that worked but then again for security reasons the download attribute became ignored for cross origin urls reference https developers google com web updates 2018 02 chrome 65 deprecations block cross origin a download patterns emerged to download the file in question asynchronously then feed it to an anchor element so as not to violate the cors constraint one way is using the built in fetch method from https dev to sbodi10 download images using javascript 51a9 javascript const image await fetch remoteurl const imageblog await image blob const imageurl url createobjecturl imageblog pass it to a generated anchor element alternately the axios library provides a slightly more convenient way to retrieve the remote data from https www delftstack com howto javascript javascript download use axios library to download files javascript axios url remoteurl method get responsetype blob then response pass it to a generated anchor element either way after retrieving the data it would be fed to an anchor element and one would then manually trigger the link by invoking its click method that one has to trigger the browser to do a file download via the dom rather than writing code to do it directly seemed rather peculiar to me looking further it turns out that a new native file system api was very recently developed but alas at the time of writing september 2021 it is so new that it is barely supported references can i use data at https caniuse com native filesystem api and draft report on file system access dated august 2021 at https wicg github io file system access as all the workarounds seemed somewhat kludgy i opted to just use the file saver https www npmjs com package file saver library which could do the whole operation in a single line of code see view photos component ts download 8 6 a name moreonfiledownloads a more on file downloads yes there is still more to say on this topic my initial thought was like a desktop application when the user selects to download something they are then presented with a file picker dialog allowing specifying the directory path and the file name through os specific design standards it turns out that displaying such a file picker though is again a large challenge the self same native file system api mentioned above will eventually allow that today it is so much simpler to just let the browser do its thing when you select something to download it puts it in the downloads folder if a file of the same name already exists the browser intelligently starts adding sequence numbers on a mac for instance chrome saves image jpg image 1 jpg image 2 jpg etc whereas firefox does the same thing sans the space character image jpg image 1 jpg image 2 jpg etc 8 7 a name beyondbreadth unittestshavedepth a beyond breadth unit tests have depth code coverage is a great metric for unit tests one should always strive for a high percentage just what that percentage should be 80 90 100 is open for debate but picking any reasonably high number is better than not however that metric only covers breadth of tests just as important is the aspect of depth that is all about the two pronged approach of boundary value analysis and equivalence class partitioning i talk about those in detail in the test quality section of my article go unit tests tips from the trenches https www red gate com simple talk devops testing go unit tests tips from the trenches as mentioned there data driven tests provide a good way to achieve depth one example of that approach to deep tests is in the normalizing input section in app component spec ts but test depth can also come from a series of related tests covering the same topic if you look above at the set of tests under the heading fetching more photos during scrolling you will find one test that checks for when more photos are fetched plus several related tests where more photos are not fetched you can peruse all the depth coverage tests in this test suite by searching for depth coverage 8 8 a name strictmodeandotherchecks a strict mode and other checks i am an ardent believer in using safety nets whenever possible let the machine do the work of checking rote things first and foremost the typescript compiler offers a strict mode in tsconfig json which is a composite of several important checks i am firmly of the opinion that one should never build a project without it the cost of finding errors grows exponentially as a project develops so if one can flip a switch to catch errors earlier that is a definite win i further rely on whatever linters i can find one for typescript of course but there is even a linter for markdown files such as the one you are reading called markdown lint https marketplace visualstudio com items itemname davidanson vscode markdownlint which helped me construct this file properly i use a spell checker for my code too code spell checker https marketplace visualstudio com items itemname streetsidesoftware code spell checker that not only helps prevent embarrassing typos being shown to all your users but it also occasionally resolves code bugs there are almost always bits of an application that rely on data names that the compiler just cannot validate a priori and if you mistype one you have a problem 8 9 a name typescripttypetraps a typescript type traps to say that typescript boosts javascript development to great heights cannot be overstated but strong typing is not a panacea it is a tool that must be used with care sometimes it can even fool you my canonical example is that if you use type assertions incorrectly you actually get weaker code see my slide deck typescript type traps https docs google com presentation d 1ir7p7v0tzo4cd4vctyub a5fhfnbsrytuocwlq6xksu edit slide id g5e2880a3d6 0 78 where i explain that and a few other vagaries of the language 8 10 a name unitteststestbehaviorratherthanimplementation a unit tests test behavior rather than implementation testing behavior in unit tests is much more robust and much more interesting than testing implementation it is more robust because testing behavior allows you to change the implementation if desired and still have all the tests pass without having to rewrite any and it is much more interesting because behavior oriented tests describe to any interested code readers something tangible some actual behavior that the code performs as opposed to say showing that some internal function with inputs x and y returns z exclusively focusing on behavior oriented tests allows me to do much more with unit tests than is traditionally the norm it can even supplant a portion of end to end testing in fact for this application i decided not to invest in end to end tests because my unit tests are sufficiently rich and robust on the other hand end to end tests alone would not be sufficient because they do not have the resolution to exercise all the nuances that unit tests do 8 11 a name testenvironmentsandcontinuousintegration a test environments and continuous integration a generated angular skeleton project by default sets up the karma test runner to run tests on chrome theoretically it should not matter which browser is targeted when running unit tests every browser should conform to the specs for javascript the dom etc but in the real world differences crop up either when a you least expect them or b they can be the most insidious so it is extremely useful to flag such differences as early as possible as such i have provided support for the triumvirate of browsers at the top of the food chain chrome firefox and edge running tests on each browser is as simple as npm run test chrome npm run test firefox or npm run test edge note that you have to already have the target browser installed on your system you can see the recipes for these commands in the scripts section of package json i am fairly fastidious when it comes to running unit tests continuously while i am writing code but i will admit i find it burdensome to remember to run the suite on different browsers invoking that best practice of being a lazy programmer see for example lazyprogrammer https www lazyprogrammer it i set up a continuous integration ci environment using github actions to automatically run tests against all three browsers for every pull request thus even if i forget to do it the machine will do it for me a powerful feature of github actions is that you can easily specify whatever target operating system you need so while i want my default environment to be linux it was simpler to run the tests for edge on a windows server the recipes for these ci checks are in actions yaml 9 a name acknowledgements a acknowledgements as the expression goes none of us is an island i would not have the acumen and skills of my craft honed as sharply today without having worked beside a number of keen and passionate individuals including in alphabetical order erin carver jamie degnan jay mundrawala joe dumoulin john knuth kaleb pederson kathie drake kimberly garmoe lance finfrock larry kippenham maggie walker mary jinglewski matt peck may pinyo nezar hussein ofira varga rick kurtz salim afiune scott mccabe sergei knezdov shadae holmes stephan renatus steven danna susan evans tara black tina saunders apologies to others i am undoubtedly forgetting 10 a name defaultgenerateddocsforthisproject a default generated docs for this project this project was generated with angular cli https github com angular angular cli version 11 2 6 10 1 a name developmentserver a development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files 10 2 a name codescaffolding a code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class guard interface enum module 10 3 a name build a build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build 10 4 a name runningunittests a running unit tests run ng test to execute the unit tests via karma https karma runner github io 10 5 a name runningend to endtests a running end to end tests run ng e2e to execute the end to end tests via protractor http www protractortest org 10 6 a name furtherhelp a further help to get more help on the angular cli use ng help or go check out the angular cli overview and command reference https angular io cli page | cloud |
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tokyo-olympic-azure-data-engineering-project | tokyo olympic azure data engineering project this is a data engineering project in ms azure cloud | cloud |
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Azure-RTOS-on-Azure-Sphere-Mediatek-MT3620 | page type sample languages c products azure rtos azure sphere description this sample demonstrates how azure sphere and azure rtos are able to run together on mt3620 development kit urlfragment azure rtos on azure sphere mediatek mt3620 sample mt3620 real time capability application using azure rtos this sample demonstrates how azure sphere https azure com sphere and azure rtos https azure com rtos are able to run together on mt3620 development kit this sample app for an mt3620 real time core running azure rtos that creates several threads and repeatedly transmits message over a uart these messages can be read in terminal application on a computer using a usb to serial adapter by default it uses the real time core s dedicated uart but if your hardware doesn t expose this uart s tx pin then the sample can be altered to use a different uart to use this sample clone the repository locally if you haven t already done so bash git clone recursive https github com azure samples azure rtos on azure sphere mediatek mt3620 contents file folder description demo threadx sample source code threadx azure rtos threadx source code submodule mt3620 m4 software mediatek 3620 hal submodule readme md this readme file prerequisites 1 seeed mt3620 development kit https aka ms azurespheredevkits or other hardware that implements the mt3620 reference development board rdb design https docs microsoft com azure sphere hardware mt3620 reference board design 1 a usb to serial adapter for example ftdi friend https www digikey com catalog en partgroup ftdi friend 60311 to connect the real time core uart to a usb port on your computer 1 a terminal emulator such as telnet or termite https www compuphase com software termite htm to display the output prepare your device to prepare your device on windows 1 right click the azure sphere developer command prompt shortcut and select more run as administrator the enable rt core debugging https docs microsoft com azure sphere app development build rt app tabs windows 2ccliv2beta pivots visual studio enable development and debugging parameter requires administrator privilege because it installs usb drivers for the debugger 1 enter the following azsphere command cmd azsphere device enable development enable rt core debugging 1 close the window after the command completes because administrator privilege is no longer required as a best practice you should always use the lowest privilege that can accomplish a task to prepare your device on linux 1 enter the following azsphere command cmd azsphere device enable development enable rt core debugging you may need to update the os by using if you have an older device cmd azsphere device recover set up hardware to display output to prepare your hardware to display output from the sample 1 attach the usb to serial adapter to your pc for seeed rdb 1 connect gnd on the usb to serial adapter to gnd on your dev kit on mt3620 rdb hardware gnd is header 2 pin 2 1 connect rx on the usb to serial adapter to txd0 on your dev kit on mt3620 rdb hardware txd0 is header 2 pin 3 pinout docs image mt3620 rdb headers png for avnet azure sphere starter kit 1 connect gnd on the usb to serial adapter to gnd on your dev kit on avnet azure sphere starter kit hardware gnd is click socket 1 header 2 pin 8 1 connect rx on the usb to serial adapter to txd0 on your dev kit on avnet azure sphere starter kit hardware txd0 is click socket 1 header 2 pin 4 pinout docs image avnet mt3620 sk ius0 png for windows 1 determine which com port the adapter uses on the pc if you re using the ftdi friend adapter start device manager select view devices by container and look for ft232r usb uart 1 on the pc start a terminal emulator and then open a 115200 8 n 1 terminal to the com port used by the adapter for linux 1 find out which port the serial device is connected to bash dmesg grep ttyusb the port should be named ttyusbn where n indicates the port number 1 set up a terminal program to monitor the port for example the following commands set up minicom https help ubuntu com community minicom to monitor port 5 but you can use any program you prefer bash sudo apt install minicom sudo minicom d dev ttyusb5 b 115200 build and run the sample see tutorial to learn how to build and deploy this sample tutorial build a real time capable application https docs microsoft com azure sphere install qs real time application tabs windows pivots visual studio note you need azure sphere sdk version 20 07 to build and run the sample observe the output the connected terminal emulator should display output from the demo threadx program the program outputs the threads status incrementally output docs image threadx counter png references install the sdk https docs microsoft com azure sphere install overview claim your device https docs microsoft com azure sphere install claim device update the os on an early dev kit https docs microsoft com azure sphere resources update old seeed os contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | azure-rtos azure-sphere mediatek | os |
full-stack-web-development-with-jakartaee-and-vue.js | apress source code this repository accompanies full stack web development with jakartaee and vue js http www apress com 9781484263419 by daniel andres pelaez lopez apress 2020 comment cover cover image 9781484263419 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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ultimate-coding-resources | h1 ultimate coding resources list h1 p a collection of the best resources for programming web development computer science and more p small disclaimer it is worth mentioning that a portion of resources mainly courses and books included have affiliate links or items mentioned are projects i have built that i directly benefit from their sale or use small github contributors https img shields io github contributors pizzapokerguy ultimate coding resources twitter follow https img shields io twitter follow pizzapokerguy label follow style social hr details open summary books summary ol li a href https amzn to 2n0dznb target blank clean architecture a by applying universal rules of software architecture you can dramatically improve developer productivity throughout the life of any software system now building upon the success of his best selling books clean code and the clean coder legendary software craftsman robert c martin uncle bob reveals those rules and helps you apply them li li a href https amzn to 2jijorz target blank clean code a handbook of agile software craftsmanship a even bad code can function but if code isn t clean it can bring a development organization to its knees every year countless hours and significant resources are lost because of poorly written code but it doesn t have to be that way li li a href https amzn to 2l6mce0 target blank code complete a widely considered one of the best practical guides to programming steve mcconnell s original code complete has been helping developers write better software for more than a decade li li a href https amzn to 2mli3ww target blank cracking the coding interview a learn how to uncover the hints and hidden details in a question discover how to break down a problem into manageable chunks develop techniques to unstick yourself when stuck learn or re learn core computer science concepts and practice on 189 interview questions and solutions li li a href https amzn to 2nphvrx target blank the clean coder a this book is packed with practical advice about everything from estimating and coding to refactoring and testing it covers much more than technique it is about attitude martin shows how to approach software development with honor self respect and pride work well and work clean communicate and estimate faithfully face difficult decisions with clarity and honesty and understand that deep knowledge comes with a responsibility to act li li a href http amzn to 2bncix2 target blank the complete software developer s career guide a early in his software developer career john sonmez discovered that technical knowledge alone isn t enough to break through to the next income level developers need soft skills like the ability to learn new technologies just in time communicate clearly with management and consulting clients negotiate a fair hourly rate and unite teammates and coworkers in working toward a common goal li li a href https amzn to 39o7hmm target blank the pragmatic programmer a dave thomas and andy hunt wrote the first edition of this influential book in 1999 to help their clients create better software and rediscover the joy of coding topics range from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse li li a href https amzn to 37jcaqj target blank the mythical man month essays on software engineering a few books on software project management have been as influential and timeless as the mythical man month with a blend of software engineering facts and thought provoking opinions fred brooks offers insight for anyone managing complex projects li li a href https amzn to 2s7bkek target blank soft skills the software developer s life manual a soft skills the software developer s life manual is a unique guide offering techniques and practices for a more satisfying life as a professional software developer in it developer and life coach john sonmez addresses a wide range of important soft topics li li a href https amzn to 35ddauu target blank html and css design and build websites a introduces html and css in a way that makes them accessible to everyone hobbyists students and professionals and it s full color throughout utilizes information graphics and lifestyle photography to explain the topics in a simple way that is engaging li li a href https amzn to 2fvyl0e target blank head first design patterns a brain friendly guide a at any given moment someone struggles with the same software design problems you have and chances are someone else has already solved your problem this edition of head first design patterns shows you the tried and true road tested patterns used by developers to create functional elegant reusable and flexible software li li a href https amzn to 2t4zrnk target blank grokking algorithms an illustrated guide for programmers and other curious people a grokking algorithms is a fully illustrated friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer you ll start with sorting and searching and as you build up your skills in thinking algorithmically you ll tackle more complex concerns such as data compression and artificial intelligence li li a href https amzn to 37kzsfq target blank a smarter way to learn javascript the new tech assisted approach that requires half the effort a learning javascript is hell because of two problems i remove the problems and you start having fun the first problem is retention you remember only ten or twenty percent of what you read that spells failure to become fluent in a computer language you have to retain pretty much everything li li a href https amzn to 2n3fnqx target blank code the hidden language of computer hardware and software a what do flashlights the british invasion black cats and seesaws have to do with computers in code they show us the ingenious ways we manipulate language and invent new means of communicating with each other and through code we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries li li a href https amzn to 2zyndsj target blank eloquent javascript a modern introduction to programming a javascript lies at the heart of almost every modern web application from social apps like twitter to browser based game frameworks like phaser and babylon though simple for beginners to pick up and play with javascript is a flexible complex language that you can use to build full scale applications li li a href https amzn to 2twfwpg target blank structure and interpretation of computer programs a structure and interpretation of computer programs has had a dramatic impact on computer science curricula over the past decade this long awaited revision contains changes throughout the text there are new implementations of most of the major programming systems in the book including the interpreters and compilers and the authors have incorporated many small changes that reflect their experience teaching the course at mit since the first edition was published a new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models objects with state concurrent programming functional programming and lazy evaluation and nondeterministic programming li li a href https www buymeacoffee com goalkickerbooks target blank goal kicker a programming notes for professionals books li li a href https launchschool com books target blank launchschool books a below are some books we wrote to supplement the courses at launch school some of the books take a very beginner friendly approach and are suitable for any level of technical proficiency some books may require prerequisite knowledge covered in a launch school course and may not be suitable for individual consumption li ol details details open summary courses summary ol li a href http dylanisrael com course fe interview target blank the 100 front end interview questions challenge a by the end of the course you ll be able to pass a front end technical screen covering the fundamentals of front end web development html css and javascript we also will dive into interview prep cultural questions and general hr screening questions li li a href http dylanisrael com course 100 algorithms target blank 100 algorithms challenge a technical interviews are the filter between good and great developers at least that is how the industry sees it in this course we will up your problem solving ability and speed with 100 algorithm problems and solutions li li a href https online learning harvard edu course cs50 introduction computer science target blank harvard cs50 a an introduction to the intellectual enterprises of computer science and the art of programming by harvard university li ol details details open summary sites summary details open summary sites coding platforms summary ol li a href https www freecodecamp org target blank freecodecamp a learn to code build projects earn certifications since 2014 more than 40 000 freecodecamp org graduates have gotten jobs at tech companies including li li a href https scrimba com target blank scrimba a our mind blowing learning experience makes it easier than ever to grow your coding skills so pick a course start learning and give your career the boost it deserves li li a href https codecombat com target blank codecombat a codecombat is a game based computer science program where students type real code and see their characters react in real time li li a href https www sololearn com target blank sololearn a join the largest community of mobile code learners today li li a href https www codingame com start target blank coding game a at codingame our goal is to let programmers keep on improving their coding skills by solving the world s most challenging problems learn new concepts and get inspired by the best developers li li a href https www theodinproject com target blank the odin project a the odin project is one of those what i wish i had when i was learning resources not everyone has access to a computer science education or the funds to attend an intensive coding school and neither of those is right for everyone anyway this project is designed to fill in the gap for people who are trying to hack it on their own but still want a high quality education li li a href https bento io target blank bento a bento s mission is to make technology education and opportunity available to all bento is a technology education company focused on educating and empowering the next generation of self taught developers li li a href https www codecademy com target blank codecademy a codecademy is an education company but not one in the way you might think we re committed to building the best learning experience inside and out making codecademy the best place for our team to learn teach and create the online learning experience of the future li li a href https pluralsight pxf io yaadk target blank pluralsight a at pluralsight we believe everyone should have the opportunity to create progress through technology and develop the skills of tomorrow with assessments learning paths and courses authored by industry experts our platform helps businesses and individuals benchmark expertise across roles speed up release cycles and build reliable secure products li li a href https teamtreehouse com target blank treehouse a our mission is to diversify the tech industry through accessible education and apprenticeship unlocking the door to opportunity and empowering people to achieve their dreams li li a href https frontendmasters com target blank frontendmasters a join now and learn straight from the experts who shape the modern web li li a href https linkedin learning pxf io ewx26 target blank linkedin learning a formally linda linkedin learning is one of the worlds largest course platforms with content being created monthly li li a href https designgurus org target blank design gurus a the ultimate resource to prepare for the system design and coding interview li li a href https www educative io target blank educative a level up your coding skills quickly and efficiently li li a href https www scaler com topics hubs target blank scaler a learn to code by reading quality content the ultimate resources to kick start your programming journey also help in job interview li li a href https thegymnasium com target blank gymnasium a aquent gymnasium offers free online courses for digital creative and communications professionals to equip them with in demand skills and advance their careers li li a href https hackr io target blank hackr io a we at hackr io aggregate courses from many providers to make it easy to find the best courses on almost any subject wherever they exist on the web li li a href https www codewars com target blank codewars a codewars is an educational community for computer programming on the platform software developers train on programming challenges known as kata these discrete programming exercises train a range of skills in a variety of programming languages and are completed within an online integrated development environment li li a href https www codementor io experts target blank codementor a get 1 on 1 expert help from senior developers to improve your coding skills or get help with your ongoing projects li li a href https www codeforhunger com target blank codeforhunger a learn to code learn coding from basics with examples and their solutions learn code with projects in c including li li a href https www interviewbit com practice target blank interviewbit a a site that gamifies the experience of practicing for interviews and includes lots of sample problems programming and company specific interview questions mock interviews and more li li a href https www codementor io events target blank codementor events a a developer community and virtual events platform where developers learn and share new tools technical concepts and career tips ol details details open summary sites algorithm platforms summary ol li a href https www algoexpert io codinggod target blank algoexpert a if you want to ace the tech interview being well versed in all common data structures and popular problem solving methods is paramount with 85 questions spanning 14 categories and 5 difficulty levels we ve got you covered li li a href https www hackerrank com target blank hackerrank a join over 7 million developers practice coding skills prepare for interviews and get hired li li a href https leetcode com target blank leetcode a leetcode is the best platform to help you enhance your skills expand your knowledge and prepare for technical interviews li li a href https www meetapro com utm source ucrgit target blank meetapro a an airbnb style mock interview coaching platform with mock interviews hosted by experienced faang interviewers li li a href https codesignal com target blank code signal a formerly codefights a grate algorithm site with a beautiful ui li li a href https www codewars com target blank code wars a codewars is a collective effort by its users they are creators authoring kata to teach various techniques solving kata with solutions that enlighten others and commenting with constructive feedback the leaders among them moderate the content and community li li a href https exercism io target blank exercism a level up your programming skills with 3 173 exercises across 51 languages and insightful discussion with our dedicated team of welcoming mentors exercism is 100 free forever li li a href https projecteuler net project euler a project euler is a series of challenging mathematical computer programming problems that will require more than just mathematical insights to solve although mathematics will help you arrive at elegant and efficient methods the use of a computer and programming skills will be required to solve most problems li li a href https www codechef com target blank code chef a codechef was created as a platform to help programmers make it big in the world of algorithms computer programming and programming contests at codechef we work hard to revive the geek in you by hosting a programming contest at the start of the month and two smaller programming challenges at the middle and end of the month we also aim to have training sessions and discussions related to algorithms binary search technicalities like array size and the likes li li a href https www coderbyte com coderbyte a coderbyte is a web application built to help you practice programming and improve your coding skills we offer a collection of code challenges and web development courses that can help you prepare for upcoming job interviews the coding challenges range in difficulty and they can all be completed straight in our online editor li li a href https edabit com edabit a we re like duolingo for learning to code when learning to code most people get stuck on the bridge between memorizing syntax and understanding the logic that makes it all work li li a href https www pramp com target blank pramp a join thousands of professionals practicing live mock interviews interview questions online with peers for free we help you prep land your dream tech job li li a href https www codility com target blank codility a codility is a software platform that helps tech recruiters and hiring managers assess their candidates skills by testing their code online li li a href https www codeabbey com target blank codeabbey a codeabbey hosts a series of simple to complex programming problems that allow the user to practice their ability to solve problems li li a href https adventofcode com target blank advent of code a advent of code is an advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like people use them as a speed contest interview prep company training university coursework practice problems or to challenge each other li li a href https backtobackswe com target blank backtobackswe a backtobackswe platform is built to give a huge edge to the people by teaching the mind and intuition interviewers probe for the goal of he platform is to make someone ready to pass software engineering interviews at the big 4 companies and any other large tech companies li li a href https www interviewbit com practice target blank interviewbit a a site that gamifies the experience of practicing for interviews and includes lots of sample problems programming language company specific interview questions mock interviews and more li ol details details open summary sites course sites summary ol li a href https www skillshare com r user dylanisrael skillshare a skillshare has a ton of free courses and access to tens of thousands of paid courses all for the low price of 10 a month li li a href https www udemy com target blank udemy a the leading global marketplace for learning and instruction by connecting students all over the world to the best instructors udemy is helping individuals reach their goals and pursue their dreams li li a href https thinkster io target blank thinkster a a better way to learn learn how real apps are built from scratch using the latest frameworks li li a href https frontendmasters com target blank frontend masters a advance your skills with in depth modern front end engineering courses li li a href https egghead io target blank egghead a egghead is a group of working web development professionals and open source contributors that provide you with concise information dense video courses on the best tools in the industry li li a href https laracasts com laracasts a push your web development skills to the next level through expert screencasts on laravel vue and so much more li li a href https www udacity com coupon udacaffil2020 target blank udacity a our 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blank flexbox froggy a welcome to flexbox froggy a game where you help froggy and friends by writing css code li li a href https ocw mit edu index htm target blank mit opencourseware a mit opencourseware is a web based publication of virtually all mit course content ocw is open and available to the world and is a permanent mit activity li li a href https www coursera org target blank coursera a join coursera for free and learn online build skills with courses from top universities like yale michigan stanford and leading companies like google and ibm li li a href https brilliant org target blank brilliant a build quantitative skills in math science and computer science with fun and challenging interactive explorations li li a href https www edx org target blank edx a edx is the trusted platform for education and learning founded by harvard and mit edx is home to more than 20 million learners the majority of top ranked universities in the world and industry leading companies li ol details details open summary sites career job sites summary ol li a href https www wayup com target blank wayup a is the industry leader for college students and recent grads to get hired li li a href https novoresume com novoresume a novore sume began in 2014 when andrei cristian and stefan noticed a common problem among several of their contacts despite having extensive work experience and impressive skill sets these individuals didn t know how to showcase their talents with a professional resume and cover letter sensing an opportunity to help others with their job search the three of them joined forces in exploring possible solutions as part of a university project li li a href https www glassdoor com index htm glassdoor a search millions of jobs and get the inside scoop on companies with employee reviews personalized salary tools and more li li a href https angel co angellist a apply privately to 130 000 tech startup jobs with one application see salary and equity upfront li li a href https www bls gov audience jobseekers htm u s bureau of labor statistics a the u s bureau of labor statistics provides a selection of useful reports on employment statistics and 10 year forecasts use them to determine if the career you ve chosen is experiencing or is likely to experience a talent shortage if so you can expect a higher starting salary or more appealing perks if your skills match up with employers needs li li a href https www payscale com payscale a what am i worth what your skills are worth in the market is constantly changing li li a href https www salary com salary a understand your worth and plan your next career move with easy to use tools li li a href https www linkedin com target blank linkedin a welcome to linkedin the world s largest professional network with nearly 660 million users in more than 200 countries and territories worldwide li li a href https resume github io target blank github resume a as a software startup owner i really enjoy when people 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blank stack overflow a founded in 2008 stack overflow is the largest most trusted online community for anyone that codes to learn share their knowledge and build their careers more than 50 million unique visitors come to stack overflow each month to help solve coding problems develop new skills and find job opportunities li li a href https devdocs io devdocs a fast offline and free documentation browser for developers search 100 docs in one web app li li a href https www 30secondsofcode org 30 seconds of code a short code snippets for all your development needs li li a href https 12factor net the twelve factor app a in the modern era software is commonly delivered as a service called web apps or software as a service the twelve factor app is a methodology for building software as a service apps li ol details details open summary sites freelance contract work summary ol li a href https www upwork com target blank upwork a to connect businesses with great talent to work without limits li 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our mission is to make web development faster by removing complexity we enable web developers to be more productive by simplifying collaboration we make it easier for teams to work on code together li li a href https www interviewbit com online c compiler target blank interviewbit a practice and execute c programs with our online compiler supports gcc c compiler li li a href https ide new cpp target blank interviewbit a an online compiler that helps code and run your programs online li ol details details open summary sites tools summary ol li a href https trello com target blank trello a whether it s for work a side project or even the next family vacation trello helps your team stay organized li li a href https slack com target blank slack a slack replaces email inside your company discuss collaborate share work is better without the inbox li li a href https github com target blank github a we re supporting a community where more than 40 million people learn share and work together to build software li li a href https hyper is target blank hyper terminal a the goal of the project is to create a beautiful and extensible experience for command line interface users built on open web standards in the beginning our focus will be primarily around speed stability and the development of the correct api for extension authors li li a href https justgetflux com target blank f lux a f lux fixes this it makes the color of your computer s display adapt to the time of day warm at night and like sunlight during the day li li a href https www getpostman com target blank postman a postman is the only complete api development environment today we have 8 million developers and over 400k companies using our comprehensive set of built in tools to support every stage of the api lifecycle li li a href https insomnia rest target blank insomnia a insomnia is a cross platform graphql and rest client available for mac windows and linux li li a href https www gitkraken com target blank gitkraken a unleash gitkraken three legendary developer productivity tools the git gui client for windows mac linux development li ol details details open summary sites misc summary ol li a href http bit ly 2puf8ek target blank autonomous a the goto place for automated standing desks treadmills and everything dev furniture related li li a href https unsplash com target blank unsplash a the internet s source of freely usable images li li a href https www humblebundle com partner coding360 humble bundle a although known for their games now adays ebook bundles around web development are becoming increasingly popular li li a href https www canva com canva a create beautiful designs with your team use canva s drag and drop feature and layouts to design share and print business cards logos presentations and more li ol details details details open summary youtube channels summary ol li a href https www youtube com user pizzapokerguy87 target blank dylan israel a dylan is a self taught programmer who produces some amazing content both informational and tutorials videos li li a href https www youtube com channel uc29ju8biph5as8ognqzwjya target blank traversy media a brad is also a self taught programmer who produces informational and tutorial based videos li li a href https www youtube com channel ucshz3rdocljdyutr rbubzw target blank program with erik a my name is erik hanchett and i m a web and java developer from reno nevada i ve been a programmer for many years now and i ve always been wanting to give back to the community li li a href https www youtube com channel ucsjbgttlrdami tdgpuv9 w target blank academind a web development tutorial videos li li a href https www youtube com channel ucvyrimvfunma1upldpzg5ow target blank designcourse a gary teach full stack development which means you will see tutorials regarding ui ux frontend dev backend dev graphic design and more li li a href https www youtube com channel ucaqjmy2djqwu3eqz0on83aw target blank eduonix learning solutions a eduonix learning solutions is an esteemed name in the e learning field and has a vast library of web designing and development courses to help cater the learning needs of all individuals li li a href https www youtube com channel uc8butisfwt wl7ev0huk0bq target blank freecodecamp org a hours and hours of tutorial videos li li a href https www youtube com channel uc ptwr16itqyyoglxyqmpzw target blank iamtimcorey a tim creates courses dedicated to c sql and more li li a href https www youtube com channel ucwv7vmbmwh4 v0zxdmdppba target blank programming with mosh a mosh produces amazing courses for both frontend and backend development li li a href https www youtube com channel ucw5yeuermmlnqo4oq8vwupg featured target blank the net ninja a tutorials on modern javascript beginner to advanced node js react vue js firebase mongodb html css plus loads more li li a href https www youtube com channel uclb90nqqcskpugdixsqez5q target blank dev ed a very funny and good 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ideas through to final artwork li ol details details open summary package managers summary ol li a href https yarnpkg com target blank yarn a yarn caches every package it downloads so it never needs to download it again li li a href https www npmjs com target blank npm a we re npm inc the company behind node package manager the npm registry and npm cli we offer those to the community for free but our day job is building and selling useful tools for developers like you ol details details open summary text editors summary ol li a href https code visualstudio com target blank vscode text editor a code editing redefined free built on open source runs everywhere li li a href https atom io target blank atom a teletype for atom makes collaborating on code just as easy as it is to code alone right from your editor li li a href https www sublimetext com target blank sublime a sublime text is a sophisticated text editor for code markup and prose you ll love the slick user interface extraordinary features and amazing performance li li a href https notepad plus plus org target blank notepad a notepad is a free as in free speech and also as in free beer source code editor and notepad replacement that supports several languages running in the ms windows environment its use is governed by gpl license li li a href https www vim org target blank vim a vim is a highly configurable text editor for efficiently creating and changing any kind of text it is included as vi with most unix systems and with apple os x li li a href http brackets io target blank brackets a a modern open source text editor that understands web design li ol details details open summary blogs summary ol li a href https www programwitherik com target blank program with erik a erik is a two time author youtuber podcastor an much more his blog includes tutorials rantings and musings on software development with a focus on javascript ember js vue js and node js li li a href https overreacted io target blank overreacted a dan abramov s i explain with words and code li li a href https css tricks com target blank css tricks a daily articles about css html javascript and all things related to web design and development li li a href https davidwalsh name target blank david walsh a a blog featuring tutorials about javascript html5 ajax php css wordpress and everything else development li li a href https www freecodecamp org news target blank freecodecamp a learn to code build projects earn certifications since 2015 40000 graduates have gotten jobs at tech companies including google apple amazon and more li li a href https blog webdevsimplified com target blank web dev simplified a the official web dev simplified blog by kyle cook short and simple articles on web development li li a href http blog cleancoder com target blank clean code a robert martin s uncle bob blog li li a href http codewall co uk target blank codewall a tutorials and technical articles spanning a range of web development technologies li ol details details open summary podcasts summary ol li a href https www selftaughtornot com target blank self taught or not a self taught or not covers the do s and don ts of software development hosted by dylan israel erik hanchett new episodes every monday li li a href https techjr dev target blanl tech jr a lee warrick and edwin otero explore what it s like to break into the tech field as junior developers as well as how to stay up to date on new technology li a href https 6figuredev com target blank the 6 figure dev a interviews developers to dive deep in their experiences how to increase your income and dive deep into their guests projects li li a href https podcast freecodecamp org freecodecamp a the official podcast of the freecodecamp open source community learn to code with free online courses programming projects and interview preparation for developer jobs li li a href https realtalkjavascript simplecast com target blank real talk javascript a dive into javascript and web development stories with hosts john papa ward bell and dan wahlin sponsored by nrwl io a weekly podcast where we tackle the challenges facing web developers today and invite expert guests on the show to share ktheir experience solving concrete problems while building real web applications li li a href https vanillajspodcast com target blank the vanilla js podcast a a show about javascript for people who hate the complexity of modern front end web development hosted by chris ferdinandi li li a href https egghead io podcasts target blank egghead io developer chats a humans talking about the business of building software constant learning and balancing it with everyday life li li a href https syntax fm target blank syntax fm a a tasty treats podcast for web developers by wes bos scott tolinski li li a href https frontendhappyhour com front end happy hour a a podcast featuring a panel of software engineers from netflix twitch atlassian airbnb talking over drinks about all things front end development li li a href https www codenewbie org podcast codenewbie a codenewbie podcast stories from people on their coding journey li li a href https www redhat com en command line heroes command line heroes a a podcast about the people who transform technology from the command line presented by red hat li ol details details open summary vscode extensions summary ol li a href https marketplace visualstudio com items itemname ritwickdey liveserver target blank live server a launch a development local server with live reload feature for static dynamic pages li li a href https marketplace visualstudio com items itemname shan code settings sync target blank settings sync a synchronize settings snippets themes file icons launch keybindings workspaces and extensions across multiple machines using github gist li li a href https marketplace visualstudio com items itemname coenraads bracket pair colorizer target blank bracket pair colorizer a a customizable extension for colorizing matching brackets li li a href https marketplace visualstudio com items itemname wallabyjs quokka vscode target blank quokka a live scratchpad for javascript li li a href https marketplace visualstudio com items itemname ms vsliveshare vsliveshare target blank live share a real time collaborative development from the comfort of your favorite tools li li a href https marketplace visualstudio com items itemname streetsidesoftware code spell checker target blank code spell checker a spelling checker for source code li li a href https marketplace visualstudio com items itemname esbenp prettier vscode overview target blank prettier a prettier is an opinionated code formatter it enforces a consistent style by parsing your code and re printing it with its own rules that take the maximum line length into account wrapping code when necessary li li a href https marketplace visualstudio com items itemname formulahendry auto rename tag target blank auto rename tag a automatically rename paired html xml tag same as visual studio ide does li li a href https marketplace visualstudio com items itemname dbaeumer vscode eslint target blank eslint a the extension uses the eslint library installed in the opened workspace folder if the folder doesn t provide one the extension looks for a global install version if you haven t installed eslint either locally or globally do so by running npm install eslint in the workspace folder for a local install or npm install g eslint for a global install li li a href https marketplace visualstudio com items itemname editorconfig editorconfig target blank editorconfig a this plugin attempts to override user workspace settings with settings found in editorconfig files no additional or vscode specific files are required as with any editorconfig plugin if root true is not specified editorconfig will continue to look for an editorconfig file outside of the project li li a href https marketplace visualstudio com items itemname auchenberg vscode browser preview target blank browser preview a browser preview for vs code enables you to open a real browser preview inside your editor that you can debug browser preview is powered by chrome headless and works by starting a headless chrome instance in a new process li li a href https marketplace visualstudio com items itemname msjsdiag debugger for chrome target blank chrome debugger a debug your javascript code in the chrome browser or any other target that supports the chrome debugger protocol li li a href https marketplace visualstudio com items itemname humao rest client target blank rest client a rest client allows you to send http request and view the response in visual studio code directly li li a href https marketplace visualstudio com items itemname wix vscode import cost target blank import cost a this extension will display inline in the editor the size of the imported package the extension utilizes webpack with babili webpack plugin in order to detect the imported size li li a href https marketplace visualstudio com items itemname kisstkondoros vscode codemetrics target blank code metrics a computes complexity in typescript javascript lua files li ol details details open summary conferences summary ol li a href https devfest withgoogle com target blank devfest a devfests are community led developer events hosted by google developer groups around the globe gdgs are focused on community building and learning about google s technologies li li a href https www ng conf org target blank ng conf a world s original angular conference li li a href https angelhack com target blank angelhack a the world s most diverse hacker community driving innovation of tech products through developer programs li ol details details open summary community summary details open summary community facebook groups summary ol li a href https www facebook com groups 190277791450732 code tech caffeine a coding noobs to senior devs to those just interested in technology this group is for you this group is devoted to sharing helpful and interesting items in software and technology li li a href https www facebook com groups 759985267390294 target blank hackathon hackers a this is the all inclusive hackathons group li li a href https www facebook com groups 318334558529855 target blank hacking and coding tutorials a a closed group for technical enthusiasts interesting in hacking and coding a place to openly discuss and share ideas and ask for help from real and certified hackers and programmers li li a href https www facebook com programmingjokes target blank programming jokes a 1000 jokes only programmers will get li li a href https www facebook com ilp18 target blank i love programming a welcome to one of the most popular facebook page related to programming here we share best tutorials news youtube videos and memes related to programming li ol details details open summary community forums subreddits etc summary ol li a href https www freecodecamp org forum target blank freecodecamp a 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meet people near you who share your interests li li a href https code org target blank code org a code org is a nonprofit dedicated to expanding access to computer science in schools and increasing participation by women and underrepresented minorities li li a href https www womenwhocode com target blank women who code a women who code wwcode is a global 501 c 3 non profit organization dedicated to inspiring women to excel in technology careers li li a href https dev to target blank dev to a dev is a community of software developers getting together to help one another out the software industry relies on collaboration and networked learning we provide a place for that to happen li ol details details details open summary chrome extensions summary ol li a href https chrome google com webstore detail lightshot screenshot tool mbniclmhobmnbdlbpiphghaielnnpgdp hl en target blank lightshot a simple and convenient screenshot tool select an area edit your screenshot and upload it to the server li li a href https chrome google com webstore detail json formatter bcjindcccaagfpapjjmafapmmgkkhgoa hl en target blank json formatter a makes json easy to read open source li li a href https chrome google com webstore detail form filler bnjjngeaknajbdcgpfkgnonkmififhfo target blank form filler a fill all inputs in a page with dummy data li li a href https chrome google com webstore detail wappalyzer gppongmhjkpfnbhagpmjfkannfbllamg hl en target blank wappalyzer a wappalyzer is a cross platform utility that uncovers the technologies used on websites it detects content management systems ecommerce platforms web frameworks server software analytics tools and many more li li a href https chrome google com webstore detail web developer checklist iahamcpedabephpcgkeikbclmaljebjp target blank web developer checklist a for web developers who wants to make sure they follow best practices this extension allows you to very easily discover problem areas in your website li li a href https chrome google com webstore detail ie tab hehijbfgiekmjfkfjpbkbammjbdenadd hl en 20 target blank ie tab a display web pages using ie within chrome use java silverlight activex sharepoint and more a top 10 extension since 2009 li li a href https chrome google com webstore detail lighthouse blipmdconlkpinefehnmjammfjpmpbjk target blank lighthouse a lighthouse is an open source automated tool for improving the performance quality and correctness of your web apps li li a href https chrome google com webstore detail web developer bfbameneiokkgbdmiekhjnmfkcnldhhm target blank web developer a the web developer extension adds a toolbar button to the browser with various web developer tools li ol details details open summary repos summary ol li a href https github com erikch devyoutubelist target blank dev youtube list a a curated list of amazing development channels on youtube these include web development back end development front end development live coders and more li li a href https github com ryanmcdermott clean code javascript target blank clean code javascript a software engineering principles from robert c martin s book clean code adapted for javascript this is not a style guide it s a guide to producing readable reusable and refactorable software in javascript li li a href https github com getify you dont know js target blank you don t know js a this is a series of books diving deep into the core mechanisms of the javascript language li li a href https github com badges shields target blank shields io a this is home to shields io a service for concise consistent and legible badges in svg and raster format which can easily be included in github readmes or any other web page li li a href https github com ossu computer science target blank open source society university a the ossu curriculum is a complete education in computer science using online materials it s not merely for career training or professional development it s for those who want a proper well rounded grounding in concepts fundamental to all computing disciplines and for those who have the discipline will and most importantly good habits to obtain this education largely on their own but with support from a worldwide community of fellow learners li li a href https github com marak faker js target blank faker js a generate massive amounts of fake data in the browser and node js li ol details details open summary memes summary ol li a href https martinfowler com bliki twohardthings html target blank two hard things a is about the two hardest things in software engineering li ol details | webdevelopment javascript programming resources learntocode | front_end |
lida | lida automatic generation of visualizations and infographics using large language models pypi version https badge fury io py lida svg https badge fury io py lida arxiv https img shields io badge arxiv 2303 02927 color svg https arxiv org abs 2303 02927 a target blank href https colab research google com github microsoft lida blob main notebooks tutorial ipynb img src https colab research google com assets colab badge svg alt open in colab a img src docs images lidascreen png width 100 lida is a library for generating data visualizations and data faithful infographics lida is grammar agnostic will work with any programming language and visualization libraries e g matplotlib seaborn altair d3 etc and works with multiple large language model providers openai azure openai palm cohere huggingface details on the components of lida are described in the paper here https arxiv org abs 2303 02927 and in this tutorial notebook notebooks tutorial ipynb see the project page here https microsoft github io lida for updates note on code execution to create visualizations lida generates and executes code ensure that you run lida in a secure environment features lida components docs images lidamodules jpg lida treats visualizations as code and provides a clean api for generating executing editing explaining evaluating and repairing visualization code x data summarization x goal generation x visualization generation x visualization editing x visualization explanation x visualization evaluation and repair x visualization recommendation x infographic generation beta pip install lida infographics python from lida import manager llm lida manager text gen llm openai palm cohere summary lida summarize data cars csv goals lida goals summary n 2 exploratory data analysis charts lida visualize summary summary goal goals 0 exploratory data analysis getting started setup and verify that your python environment is python 3 10 or higher preferably use conda https docs conda io en main miniconda html installing install the library via pip bash pip install lida once requirements are met setup your api key learn more about setting up keys for other llm providers here https github com victordibia llmx bash export openai api key your key alternatively you can install the library in dev model by cloning this repo and running pip install e in the repository root web api and ui lida comes with an optional bundled ui and web api that you can explore by running the following command bash lida ui port 8080 docs then navigate to http localhost 8080 in your browser to view the web api specification add the docs option to the cli command and navigate to http localhost 8080 api docs in your browser the fastest and recommended way to get started after installation will be to try out the web ui above or run the tutorial notebook notebooks tutorial ipynb building the web api and ui with docker the lida web api and ui can be setup using docker and the command below ensure that you have docker installed and you have set your openai api key environment variable bash docker compose up data summarization given a dataset generate a compact summary of the data python from lida import manager lida manager summary lida summarize data cars json generate data summary goal generation generate a set of visualization goals given a data summary python goals lida goals summary n 5 persona ceo with aerodynamics background generate goals add a persona parameter to generate goals based on that persona visualization generation generate refine execute and filter visualization code given a data summary and visualization goal note that lida represents visualizations as code python generate charts generate and execute visualization code charts lida visualize summary summary goal goals 0 library matplotlib seaborn ggplot visualization editing given a visualization edit the visualization using natural language python modify chart using natural language instructions convert this to a bar chart change the color to red change y axes label to fuel efficiency translate the title to french edited charts lida edit code code summary summary instructions instructions library library textgen config textgen config visualization explanation given a visualization generate a natural language explanation of the visualization code accessibility data transformations applied visualization code python generate explanation for chart explanation lida explain code charts 0 code summary summary visualization evaluation and repair given a visualization evaluate to find repair instructions which may be human authored or generated repair the visualization python evaluations lida evaluate code code goal goals i library library visualization recommendation given a dataset generate a set of recommended visualizations python recommendations lida recommend code code summary summary n 2 textgen config textgen config infographic generation wip given a visualization generate a data faithful infographic this methods should be considered experimental and uses stable diffusion models from the peacasso https github com victordibia peacasso library you will need to run pip install lida infographics to install the required dependencies python infographics lida infographics visualization charts 0 raster n 3 style prompt line art using lida with locally hosted llms huggingface lida uses the llmx https github com victordibia llmx library as its interface for text generation llmx supports multiple local models including huggingface models you can use the huggingface models directly assuming you have a gpu or connect to an openai compatible local model endpoint e g using the excellent vllm https vllm readthedocs io en latest library using huggingface models directly python from lida import llm text gen llm provider hf model uukuguy speechless llama2 hermes orca platypus 13b device map auto lida manager llm text gen now you can call lida methods as above e g sumamry lida summarize data cars csv using an openai compatible endpoint e g vllm server https vllm readthedocs io en latest getting started quickstart html openai compatible server python from lida import manager textgenerationconfig llm model name uukuguy speechless llama2 hermes orca platypus 13b model details name model name max tokens 2596 model provider openai parameters model model name assuming your vllm endpoint is running on localhost 8000 text gen llm provider openai api base http localhost 8000 v1 api key empty models model details lida manager text gen text gen important notes caveats faqs lida generates and executes code based on provided input ensure that you run lida in a secure environment with appropriate permissions lida currently works best with datasets that have a small number of columns 10 this is mainly due to the limited context size for most models for larger datasets consider preprocessing your dataset to use a subset of the columns lida assumes the dataset exists and is in a format that can be loaded into a pandas dataframe for example a csv file or a json file with a list of objects in practices the right dataset may need to be curated and preprocessed to ensure that it is suitable for the task at hand smaller llms e g oss llms on huggingface have limited instruction following capabilities and may not work well with lida lida works best with larger llms e g openai gpt 3 5 gpt 4 how reliable is the lida approach the lida paper https aclanthology org 2023 acl demo 11 describes experiments that evaluate the reliability of lida using a visualization error rate metric with the current version of prompts data summarization techniques preprocessing postprocessing logic and llms lida has an error rate of 3 5 on over 2200 visualizations generated compared to a baseline of over 10 error rate this area is work in progress can i build my own apps with lida yes you can either use the python api directly in your app or setup a web api endpoint and use the web api in your app see the web api web api and ui section for more details naturally some of these limitations could be addressed by a much welcomed pr documentation and citation a short paper describing lida accepted at acl 2023 conference is available here https arxiv org abs 2303 02927 bibtex inproceedings dibia2023lida title lida a tool for automatic generation of grammar agnostic visualizations and infographics using large language models author dibia victor booktitle proceedings of the 61st annual meeting of the association for computational linguistics volume 3 system demonstrations month jul year 2023 address toronto canada publisher association for computational linguistics url https aclanthology org 2023 acl demo 11 doi 10 18653 v1 2023 acl demo 11 pages 113 126 lida builds on insights in automatic generation of visualization from an earlier paper data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks https arxiv org abs 1804 03126 | datavisualization llm openai visualization cohere openai-api palm2 hacktoberfest | ai |
nanoQA | nanoqa img src utils logo jpg height 100 license https img shields io badge license apache 202 0 blue svg https github com miranthajayatilake kudle blob main license version https img shields io badge version 0 2 yellow discord https img shields io badge chat discord blueviolet https discord gg pu2yjdzscz chat with your data to find answers mag zap nanoqa uses large language models llms to build a question answering application on your own data please refer to this blog post for a comprehensive guide heavy exclamation mark link https medium com miranthaj building a chat ai to answer about your own data part i f05dde32ff1b demo img src utils demo gif br quick start create a virtual environment with python tested with python 3 10 9 on anaconda https www anaconda com pip install r requirements txt to install all dependecies make sure docker https www docker com is up and running in your local environment we use docker to set up elasticsearch https www elastic co guide en elasticsearch reference current docker html as our data store run bash datastore sh to pull and set up elasticsearch wait till this step is completed run python sample data py data faq covid https s3 eu central 1 amazonaws com deepset ai farm qa datasets documents small faq covid csv zip index qa this script will download a sample dataset of faqs on covid 19 and index it under index qa this dataset and index name are for demo purposes you can replace this with your own data and naming run streamlit run app py to spin up the user interface now you can provide your index name and start chatting with your data contributing i highly appreciate your contributions to this project in any amount possible this is still at an very basic stage suggestions on additional features and functionality are welcome general instructions on how to contribute are mentioned in contributing contributing md getting help please use the issues tracker of this repository to report on any bugs or questions you have also you can join the discord https discord gg pu2yjdzscz | chatgpt language-model machine-learning question-answering transformers | ai |
Archer-Project | archer project computing project for master of information technology in university of melbourne prefer operating system mac os warning using windows will lead to the result of missing python3 command which will makes admin upload certificate function not working properly installation 1 install python3 and setup your own virtual environment optional 2 install all flask extensions flask sqlalchemy flask bcrypt flask login flask wtf pdfcropmargins pypdf2 3 install pdftoppm by installing poppler mac user can install poppler by ruby e curl fssl https raw githubusercontent com homebrew install master install dev null 2 dev null brew install poppler 4 using command line to go to archer folder e g cd archer project archer and execute the program e g python runarcher py 5 go to your local host by browser default is 127 0 0 1 5000 system default admin account is admin archer com password is abcd1234 how to use pdfcrop 1 in this program we took advantage of a command line application pdfcropmargins to crop the margins of pdf files so you need to install pdfcropmargins pdftoppm and pypdf2 way to install pdfcropmargins pip install pdfcropmargins way to install pypdf2 pip install pypdf2 way to install pdftoppm ruby e curl fssl https raw githubusercontent com homebrew install master install dev null 2 dev null brew install poppler | server |
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ps-ux-design-systems | welcome to github pages you can use the editor on github https github com designgeneer ps ux design systems edit main readme md to maintain and preview the content for your website in markdown files whenever you commit to this repository github pages will run jekyll https jekyllrb com to rebuild the pages in your site from the content in your markdown files markdown markdown is a lightweight and easy to use syntax for styling your writing it includes conventions for markdown syntax highlighted code block header 1 header 2 header 3 bulleted list 1 numbered 2 list bold and italic and code text link url and image src for more details see github flavored markdown https guides github com features mastering markdown jekyll themes your pages site will use the layout and styles from the jekyll theme you have selected in your repository settings https github com designgeneer ps ux design systems settings pages the name of this theme is saved in the jekyll config yml configuration file support or contact having trouble with pages check out our documentation https docs github com categories github pages basics or contact support https support github com contact and we ll help you sort it out | os |
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ny_taxi_data | data engineering end to end project analysis of nyc taxi limousine commission data tech stack prefect workflow orchestration tool for data https www prefect io google bigquery google s fully managed serverless data warehouse that enables scalable analysis over petabytes of data dbt an open source command line tool that helps analysts and engineers transform data in their warehouse more effectively google data studio a web based data visualization tool that helps users build customized dashboards and easy to understand reports overview this is an end to end data engineering project data is being fetched in batches and processed to be made readily available for analysis and visualization this project intends to establish a pipeline in which new york taxi data is fetched from the nyc taxi limousine commision s website https www nyc gov site tlc about tlc trip record data page get processed and then stored into google s fully managed serverless data warehouse this data is then modelled using dbt to create several tables that then will be used by analysts to create dashboards and analysis this project was inspired by the project which was demonstrated in the data engineering zoomcamp run by data talks club dtc while this there are significant changes on the implementation majority of ideas were borrowed from dtc more information about both the boot camp and other dtc programs can be found here https datatalks club project s main components the implementation of this project will be done in three main parts first data aquisation processing and storage which covers the whole process of fetching and transforming the fetched data before being stored this whole process will be automated by orchestration and scheduling tool prefect second stage of this project will be modelling the stored data into tables that caters to the needs of the analysts using dbt the last stage will be to create visualization and reporting from the modelled data here we have used google s data studio as the reporting tool 1 data aquisation processing and storage with orchestration and scheduling in this part data is downloaded from the source processed and then stored in a storage this process is autmoated by using orchestratation tools there are multiple options of what tools you can use to orchestrate and monitor your scheduled data pipelines in this case prefect from https www prefect io was used after the pipeline was set a deployment with desired scheduling and monitoring was set configured and deployed altenative xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ccccxxxxx 1 1 data aquisation as explained above this data is being fetched from the nyc taxi limousine commission website since this data is is available as in parquet format then we can directly fetch it into a pandas dataframe the way data is fetched is different depending if we have the previous data already or not as discussed bellow 1 1 1 first time initial aquisation in the case that this is the first time we are loading this data then we will have to load all the previous datasets care has to be given to attend the fact that you might not want to load all the data set if you do not have enough storage 1 1 2 routine aquisation in this case data is loaded on a regular cadance ie once a month so we only load the data that was not loaded last time we loaded data the simple way to do so is by looking at the current date and then obtain the valu to represent the previous month and use that to select needed data set 1 2 processing after datqa aquisation now we can do some processing before we store this data into the google s bigquery it is very important not to do a lot of modification here because we going to store this data in its crude format and downstream will model few tables that we need to keep in this case in particular only few columns were renamed and some rows that could cause issues downstream were removed 1 3 data storage the slightly modified data now is ready to be stored in bigquery we will store this data into two different tables data from the green taxi will be stored in green rides table and that from the yellow taxi will be stored in yellow rides table 2 data modelling now that we have data in our storage we will have to prepare it to be able to be used in simple case the analysts can easilty access the data from this storage and build dashbord or perfom analysis without a problem but in cases where there is a lot of data from different sources a warehouse can easily end up being confusing and not having advantages we anticipate a warehouse to have hence it is very important to get into a behavior of modelling this data into the easily consumed form ie by combining some tables we are using dbt as a tool to model data dbt is very familiar among data engineers in data modelling data will be modelled as follows for context dbt uses the tables that we generated when we stored data in the data warehouse to generate either tables or views which can be used to generate final tables that our analysts will use in this case we have some views that are derived from the tables and then we are using those fews to create tables in core which are made available to the analysts 1 core zones ny table facts trip table monthly zone revenue table monthly zone riders table 1 staging stg greeen tripdata view stg yellow tripdata view seeding this is used to load files that do not change often seeding is used to upload data into the warehouse the difference is this is data that might not be changing often so we only do this once we are only seeding a look up file taxi zone lookup cv for the ny zones testing in our models is implemented as follows only the basic tests have been implemented via schema of the models as follows 1 stg green tripdata columns to be tested tripid tests 1 not null warn 1 unique warn vendorid tests 1 relationship with locationid warn dropoff locationid tests 1 relationship with locationid warn payment type tests 1 accepted values warn todo add more testing macros we only have few macros that decode some of the basic information which was not given directly get payment type description sql get ratecode description sql get triptype description sql get vendorid description sql 3 data visualization on google data studio i have demonstrated data visualization of this modelled data using google data studio for simplicity you can acess that visualizaion here https lookerstudio google com reporting 990a2679 8d75 413b 9ec7 80d3c2595987 how to implement this project 1 setting up environment and downloading data into your storage 1 clone the repository git clone https github com ndangalasinh ny taxi data git 1 create virtual env it is a good practice to create a virtual environment where we will implement this project there are a number of options to do this i am using conda you should feel free to use the method you are confident with if you do not have conda yet you should install it pip install conda then you can create you own virtual en conda create name myenv python 3 9 1 activate the virtual environment you have just created conda activate myenv 1 navigate into the cloned directory 1 install the required requirements from the requirements txt into your virtual env pip install r requirements txt 1 if you do not have a gcp account create one 1 what you need a valid gmail id credit card and a phone number while you can use the free tier but payment method is needed to set up 1 navigate to the gcp home page https cloud google com free 1 click on the start free button 1 sign in with your gmail id 1 select your country from the drop down accept the terms of service and then click on continue 1 enter the business name and enter payment method 1 start my free trial 1 complete the payment and your free trial will start small amount of money will be deducted and then it will be refunded later 1 settup a service account and download the key credentials in your local machine in json format 1 go to https console cloud google com apis credentials wizard 1 make sure your project is selected on the header of the page 1 from the select an api dropdown choose bigquery api 1 select application data for the type of data you will be accessing 1 select no i m not using them and click next 1 click next to create a new service account 1 type any name as the service account name 1 from the select a role dropdown choose bigquery admin and click continue 1 leave the grant users access to this service account fields blank 1 click done 1 create a service account key for your new project from the service accounts page 1 when downloading the json file make sure to use a filename you can easily remembe 1 start the prefect agent in your terminal prefect agent start default 1 start prefect server prefect server start 1 open the localhost by following the link shown on your terminal from step above 1 create gcp credentials block in your prefect ui 1 go to blocks 1 click the sign and search for gcp credentials 1 click add block to add this block 1 give the block a name its recommended to use gcp credentials or if you use other name remember to change the same in the etl py and otherxxxxxxxxx 1 now you can upload data to your storage for the initial load run etl py later we will deploy a job that will be uploading data after everymonth part 2 create a repository 1 create a personal repository on github pointing it to your local directory for this projet start a project inside dbt 1 create account with dbt cloud 1 create a new project in dbt cloud from account settings using the gear menu in the top right corner click new project 1 enter a project name and click continue 1 for the warehouse click bigquery then next to set up your connection 1 click upload a service account json file in settings 1 select the json file you downloaded in generate bigquery credentials and dbt cloud will fill in all the necessary fields 1 click test connection this verifies that dbt cloud can access your bigquery account 1 click next if the test succeeded if it failed you might need to go back and regenerate your bigquery credentials 1 under setup a repository select managed 1 type a name for your repo such as bbaggins dbt quickstart 1 click create it will take a few seconds for your repository to be created and imported 1 once you see the successfully imported repository click continue todo check the behavior when you connect and existing dbt repo part 3 1 automate data aquisation 1 automate data modelling create a deployment 1 in the upper left select deploy then click environments 1 click create environment 1 in the name field write the name of your deployment environment for example production 1 in the dbt version field select the latest version from the dropdown 1 under deployment credentials enter the name of the dataset you want to use as the target such as analytics this will allow dbt to build and work with that dataset for some data warehouses the target dataset may be referred to as a schema 1 click save scheduling a deployment 1 after creating your deployment environment you should be directed to the page for a new environment if not select deploy in the upper left then click jobs 1 click create one and provide a name for example production run and link to the environment you just created 1 scroll down to the execution settings section 1 under commands add this command as part of your job if you don t see it dbt build 1 select the generate docs on run checkbox to automatically generate updated project docs each time your job runs 1 for this exercise do not set a schedule for your project to run while your organization s project should run regularly there s no need to run this example project on a schedule scheduling a job is sometimes referred to as deploying a project 1 select save then click run now to run your job 1 click the run and watch its progress under run history 1 once the run is complete click view documentation to see the docs for your project part 4 1 data visualization | cloud |
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Stm32-FreeRTOS-TCPIP | stm32 freertos tcpip freertos tcpip keil proj on stm32f4 please install keil stm32f4xx dfp 2 9 0 pack and add the path yourdir pack keil stm32f4xx dfp 2 9 0 cmsis driver in your include path please put the network related tasks in the vapplicationipnetworkeventhook function in netinfoconfig c environment win7 keil5 v5 23 0 0 hardward stm32f4xx dp83848 dependency freertosv9 0 0 have been included in this package freertos plus tcp have been included in this package keil stm32f4xx dfp 2 9 0 pack please pre install related freertos tcp https www freertos org freertos plus freertos plus tcp index html some config proj structure proj structure pic 1 png include path include path pic 2 png detailed configuration tutorial csdn blog https blog csdn net weixin 37673586 article details 79253597 cn | os |
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NLP | nlp repository for natural language processing | ai |
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devnot-mentor-front-end | p align center img height 300 src https user images githubusercontent com 43035417 122687424 17337700 d21f 11eb 9d6b 26000d720291 jpg p devnot nedir g ncel yaz l m konular yla ilgili yaz lar yay nlayan yaz l m konferanslar bulu malar ve kamplar d zenleyen yaz l m geli tirici toplulu udur devnot mentor projesi nedir yaz l m alan nda kendini geli tirmek isteyen rencilerin mentilerin kendilerinden daha tecr beli yaz l mc lar mentorlar bulmalar n onlarla tan malar n ve d zenli g r meler yapmalar n sa layacak a k kaynak kod bir topluluk projesidir yaz l mc olmay veya mesle inde daha iyi noktalara gelmeyi hedefleyen ki ileri renci bu program kapsam nda bir mentorle e le tirerek d zenli g r meler yapmalar n ama lamaktay z bu g r melerde renciler mentorlerine sorular sorabilecek zmekte zorland klar konularda yard m isteyebilecek geli imleri i in uygun kaynak nerilerini dinleyebilecekler ba larken devnot mentor front end projesini al t rabilmek i in bilgisayar n za node js https nodejs org y klemelisiniz ba ml l klar y kleme ve dev ortam zerinde al t rma sh cd devnot mentor frontend npm install npm run dev prod veya staging ortam nda al t rmak i in sh npm run staging npm run production t m ortamlarda build ve minify i lemleri sh npm run build dev npm run build staging npm run build production paketler devnot mentor front end bir dizi a k kaynak projesi kullan r vuejs https vuejs org kullan c aray zlerini olu turdu umuz framework vuetify https vuetifyjs com material componentlere sahip bir vue ui k t phanesidir axios https github com imcvampire vue axios http istekleri get post put delete i in kullan lan k t phane vuex https vuex vuejs org vuex k t phanesi ile state y netimi i18n https kazupon github io vue i18n oklu dil deste i k t phanesi fontawesome https github com fortawesome vue fontawesome vekt r ikonlar d k mantasyon mimari assets foto raf ve stillerimizi saklad m z klas r services jwt yada api servislerini tan mlad m z klas r components sayfa bile enlerinin bulundu u klas r appbar sidebar cards etc entities form elemanlar i in tan mlanan entitiler plugins fontawesome vuetify gibi eklentilerin y netildi i klas r layout sayfa yerle imleri locales oklu dil deste i router projede kullan lacak routelar n tan mlamalar store store y netimi views bunu zaten biliyorsun routing layout ve views sayfalar m z do rudan app vue ye g ndermek yerine bir ara katman olu turduk b ylece vue sayfalar m z i in farkl i erik yer tutucular n tan mlayabiliriz kullan m ger ekten ok kolay reneceksiniz layout layout bir vue sayfas d r erik yer tutucunuzu olu turabilirsiniz html v app v app bar app v app bar v main v container router view router view v container v main v app route tan mlamalar router modules i erinde bir mod l olu turun javascript import layout from layout index vue const base path view name view component layout children path name component import views base view export default base layoutunuzu layout klas r nden import edin k k route tan mlay n ve layoutunuzu component olarak ekleyin children dizininin i erisindeki componentler sayfalar n z olmal mod llerinizi index js i erisinde import edin javascript import base from router modules base const routes base const router new vuerouter mode history base process env base url routes state y netimi vuex vue js uygulamalar i in bir durum y netimi modeli kitapl d r action type ve mutasyons type elerinizi store klas r nde bir magic string olarak tan mlay n actions tpye js i erisinde javascript region base export const base get method basegetmethod export const base post method basepostmethod export const base put method baseputmethod export const base delete method basedeletemethod endregion mutations type js i erisinde javascript region base export const base set method basesetmethod endregion modules klas r nde yeni bir mod l tan mlay n api servisini action ve mutation tan mlamalar n z import edin javascript import apiservice from services api service import base get method from store actions type import base set method from store mutations type actions sabitini tan mlay n javascript const actions base get method context return new promise resolve reject apiservice get users then response context commit base set method response resolve response catch err reject err api den bir data d nd nde mutation a commit at n javascript const mutations base set method state payload state data payload data state ve getters sabitlerini tan mlay n javascript const state data const getters getdata state return state data | front_end |
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eufemia | div align center a href https eufemia dnb no img src logo png height 100 a h1 dnb s design system h1 downloads https img shields io npm dt dnb eufemia style flat square npm version https img shields io npm v dnb eufemia style flat square last commit https img shields io github last commit dnbexperience eufemia style flat square eufemia actions https github com dnbexperience eufemia actions workflows actions yml badge svg https github com dnbexperience eufemia actions workflows actions yml codeql https github com dnbexperience eufemia actions workflows codeql analysis yml badge svg https github com dnbexperience eufemia actions workflows codeql analysis yml div this is a monorepo and uses yarn workspaces to manage the sub packages workspaces workspaces dnb eufemia https github com dnbexperience eufemia tree main packages dnb eufemia repo for the npm package dnb eufemia https www npmjs com package dnb eufemia design system portal https github com dnbexperience eufemia tree main packages dnb design system portal source code for the portal website eufemia dnb no https eufemia dnb no quick start bash yarn add dnb eufemia contribution find more information about how to contribute in eufemia portal contribute https eufemia dnb no contribute our contributors a href https github com dnbexperience eufemia graphs contributors img src https contrib rocks image repo dnbexperience eufemia a licence go to license https github com dnbexperience eufemia blob main license | dnb ux design-system react web accessibility a11y wcag uu universell-utforming | os |
feb | feb feb stands for front end boilerplate feb is a boileroplate let you easily create front end project and organize its structure for you and make you start coding fast it provides basic gulp tasks feb is using es6 and reactjs by default tools build system gulp http gulpjs com front end task runner and build system preprocessor babel https babeljs io javascript es6 preprocessor less http lesscss org css preprocessor modular development browserify http browserify org a super powerful module system testing mocha http mochajs org tdd bdd test framework sinon http sinonjs org spy and data mocking chai http chaijs com assertions library basic usage note it assumes that node js and npm have been installed in your system install global dependencies npm install g browserify gulp cli babel install feb command line tool npm install g feb cli create a project using command line tool and installl dependencies feb new hello feb cd hello feb npm install finally just fly gulp and then open your browser to load http localhost 8080 and change source codes of src how it works after running the gulp command things will happen 1 copy html html files directly placed in src will be copied into bin 2 compile scripts es6 files directly placed in src scripts will be compiled to es5 js files which will be placed in bin scripts but note that files placed in subfolders of src scripts won t be compiled but they can and should be required by entry javascript files placed directly in src scripts through browserify 3 compile less less files placed in src stylsheets will be compiled to css files which will be placed in bin stylesheets just like javascript only naked less files will be compiled 4 copy libraries and static resources copy lib assets folder and all their contents into bin 5 testing specs in test specs will be run and results will be shown in terminal 6 watch changes and livereload files s changes in src will make gulp rerun tasks above and if you start a local server and open your broswer to load pages in bin changes will trigger automatic refresh of browser if you have the livereload plugin installed in your chrome https chrome google com webstore detail livereload jnihajbhpnppcggbcgedagnkighmdlei hl en firefox https addons mozilla org zh cn firefox addon livereload you can read gulpfile babel js for more details distribution use gulp build to build and minify all stuff a dist folder will be created and ready for distribution examples todo license the mit license mit copyright c 2015 livoras 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 | front_end |
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maya-capture-gui | playblasting in maya done with gui a visual interface for maya capture https github com abstractfactory maya capture img align right src https cloud githubusercontent com assets 2439881 18627536 c1a6b4e4 7e5b 11e6 9c69 047bd5cbbce5 jpg warning preview release br features set up your playblasts visually with direct feedback produce consistent predictable playblasts callbacks to allow custom encoding prior to opening viewer avoid unwanted overscan playblast what you render br installation to install download this package and capture https github com abstractfactory maya capture and place both in a directory where maya can find them br usage to show the interface in maya run python import capture gui capture gui main br advanced callbacks register a pre view callback to allow a custom conversion or overlays on the resulting footage in your pipeline e g through ffmpeg python import capture gui use qt py to be both compatible with pyside and pyside2 maya 2017 from capture gui vendor qt import qtcore def callback options implement your callback here print callback before launching viewer debug print all options for example purposes import pprint pprint pprint options filename options filename print finished callback for video 0 format filename app capture gui main show false use qtcore qt directconnection to ensure the viewer waits to launch until your callback has finished this is especially important when using your callback to perform an extra encoding pass over the resulting file app viewer start connect callback qtcore qt directconnection show the app manually app show register preset paths register a preset path that will be used by the capture gui to load default presets from python import capture gui presets import capture gui path path to directory capture gui presets register path path after registering capture gui will automatically load the presets found in all registered preset paths capture gui main register tokens and translators register a token and translator that will be used to translate any tokens in the given filename python import capture tokens import capture gui this is an example function which retrieves the name of the current user def get user name import getpass return getpass getuser register the token user and pass the function which should be called when this token is present the label is for the right mouse button menu s readability capture tokens register token user lambda options get user name label insert current user s name known issues viewport plugin show menu close button sometimes appears off screen when torn off tearing off the show menu in the viewport plugin results in a menu with an off screen title bar when torn off near the top edge of the screen this makes it hard to close the menu to fix this either close the capture gui to close the menu or make a new torn off version of the show menu at a lower position on screen this will close the previous torn off menu | playblast maya gui | front_end |
CV-WINTER-CAMP-2021 | intel 2021 title image docs title image jpg pdf docs camp schedule pdf ide visual studio 2019 community edition https visualstudio microsoft com ru vs community git https git scm com downloads cmake https cmake org download test tasks lectures practices docs lectures readme md practices readme md gitter https badges gitter im cv summer camp community svg https gitter im cv summer camp community utm source badge utm medium badge utm campaign pr badge https gitter im cv summer camp community https gitter im cv summer camp community | ai |
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CarRental | car rental in react native | front_end |
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web3-bootcamp | open sourced turkish smart contract developer bootcamp itu blockchain img src itublockchain ef png alt header width 800 br our bootcamp contains different subjects about blockchain smart contract development knowledge we have a team of students working on this project who are experts in the selected subjects we started to work on expanding our training content which we started to prepare in february 2022 with the support we received from ethereum foundation you can reach the curriculum curriculum we have prepared in line with the education in this section after the completion of the specified contents we will work to expand it with pleasure please contact us mailto blockchain itu edu tr if you are willing to contribute this project our youtube channel https www youtube com c itublockchain follow us in twitter https twitter com itublockchain join our discord server get jr dev role and meet buidlers https discord gg 5hzq4qzbgs br meet the instructors all of the educational content are prepared by our board community members with 0xulas eth https twitter com ulerdogan alimsahin eth https twitter com aalimsahin tahos eth https twitter com 0xtahos zetsub0ii eth https twitter com hamza krb farhad https twitter com asgarovf br curriculum solidity basics this part contains basic solidity knowledge that required to understand the syntax of the language and write smart contracts headline topic instructor status code solidity basics introduction 0xulas eth https twitter com ulerdogan published https www youtube com watch v 7qet0ieh1v4 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 1 0x0 solidity basics variables scopes alimsahin eth https twitter com aalimsahin published https www youtube com watch v qe6 h1nbraw list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 2 1x0 solidity basics functions alimsahin eth https twitter com aalimsahin publihed https www youtube com watch v j b upnspws list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 3 1x1 solidity basics constructor alimsahin eth https twitter com aalimsahin published https www youtube com watch v xqr2tl4v0 c list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 4 1x2 solidity basics control structures 0xulas eth https twitter com ulerdogan published https www youtube com watch v a9ptee3cxsg list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 5 1x3 solidity basics mappings 0xulas eth https twitter com ulerdogan published https www youtube com watch v azddyhfesec list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 6 1x4 solidity basics structs 0xulas eth https twitter com ulerdogan published https www youtube com watch v lv qtwkp3my list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 7 t 2s 1x5 solidity basics modifiers 0xulas eth https twitter com ulerdogan published https www youtube com watch v 58mdet4hoig list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 8 t 2s 1x6 solidity basics events 0xulas eth https twitter com ulerdogan published https www youtube com watch v wm63xpvuekm list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 9 t 2s 1x7 solidity basics sending ethers alimsahin eth https twitter com aalimsahin published https www youtube com watch v tuqzmu4dyjc list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 11 1x8 solidity basics errors 0xulas eth https twitter com ulerdogan published https www youtube com watch v tuqzmu4dyjc list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 11 1x9 solidity basics libraries alimsahin eth https twitter com aalimsahin published https www youtube com watch v azsy27x8pxc list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 12 t 2s 1x10 solidity basics data locations zetsub0ii eth https twitter com hamza krb published https www youtube com watch v mzvzbp9l3y0 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 13 1x11 solidity basics inheritance alimsahin eth https twitter com aalimsahin published https www youtube com watch v ksrhlrhlti4 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 14 t 2s 1x12 solidity basics interacting contracts 0xulas eth https twitter com ulerdogan published https www youtube com watch v bwc rlkjs54 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 15 1x13 solidity basics interfaces zetsub0ii eth https twitter com hamza krb published https www youtube com watch v qzdvzd lczm list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 16 1x14 solidity basics call 0xulas eth https twitter com ulerdogan published https www youtube com watch v nbjiboxdpds list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 17 t 1s 1x15 solidity basics creating contracts 0xulas eth https twitter com ulerdogan published https www youtube com watch v 0stiyoi3kg8 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 19 t 3037s 1x16 hr development environments this part introduces the most popular developer tools libraries and environments for smart contract development also uses these tools for testing and network interaction headline topic instructor status code dev environments hardhat ethers 0xulas eth https twitter com ulerdogan published https www youtube com watch v 0stiyoi3kg8 list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 19 t 3037s 2x0 dev environments hardhat advanced 0xulas eth https twitter com ulerdogan published https www youtube com watch v fyaivvnkrbe ab channel itublockchain 2x1 dev environments hardhat vs foundry zetsub0ii eth https twitter com hamza krb published https www youtube com watch v 1h43hzpzevo ab channel itublockchain 2x2 dev environments foundry introduction tahos eth https twitter com 0xtahos published https www youtube com watch v kt1s2jkr0ns list plby2hxktgwn6v6rw ck86pg0cirtemb90 index 4 t 10s 2x3 dev environments foundry tests tahos eth https twitter com 0xtahos published https www youtube com watch v f0gvdvveyc4 list plby2hxktgwn6v6rw ck86pg0cirtemb90 index 5 2x4 dev environments foundry cheat codes tahos eth https twitter com 0xtahos published https www youtube com watch v lgztd14itwo ab channel itublockchain 2x5 dev environments foundry traces tahos eth https twitter com 0xtahos published https youtu be nv tcxfw mq 2x6 dev environments go ethereum introduction 0xulas eth https twitter com ulerdogan published https www youtube com watch v wssh4iw48tm ab channel itublockchain 2x7 dev environments go ethereum implementation 0xulas eth https twitter com ulerdogan published https www youtube com watch v xepba8cnszo ab channel itublockchain 2x8 hr frontend dapp development this part uses react for creating ui s for smart contracts and create web3 dapps also lastly introduces ethylene js https ethylene itublockchain com which is a react library prepared for us for react dapps headline topic instructor status code frontend dapp custom hooks init farhad https twitter com asgarovf published https www youtube com watch v rmoofobaryu list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 24 3x0 frontend dapp wallet conn ethers farhad https twitter com asgarovf published https www youtube com watch v qyc50netrmc list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 25 3x1 frontend dapp contract init farhad https twitter com asgarovf published https www youtube com watch v 15phvaardwu list plby2hxktgwn4cof 6a8ywlmrbox8 hs73 index 26 3x2 frontend dapp sending txs farhad https twitter com asgarovf published https www youtube com watch v hzcnt4n5nfs 3x3 frontend dapp redux toolkit farhad https twitter com asgarovf published https www youtube com watch v ge8qcu4q2yy 3x4 frontend dapp event listening farhad https twitter com asgarovf published https www youtube com watch v mux6y0vkgg 3x5 frontend dapp mm network management farhad https twitter com asgarovf published https www youtube com watch v 3ylyviv lxa 3x6 frontend dapp web3 modal farhad https twitter com asgarovf published https www youtube com watch v 0ftgf9jh0ug 3x7 frontend dapp intro to ethylene js farhad https twitter com asgarovf published https www youtube com watch v a ew z8tcqi 3x8 hr security this part includes content about ethereum smart contract security and introduces common mistakes or attacks then explains some tools headline topic instructor status code security security i zetsub0ii eth https twitter com hamza krb published https www youtube com watch v sqtw uknsoe ab channel itublockchain 4x0 security security ii zetsub0ii eth https twitter com hamza krb published https youtu be qm1h84z qtg 4x1 security security iii tahos eth https twitter com 0xtahos published https www youtube com watch v owamza4anvk 4x2 security security iv tahos eth https twitter com 0xtahos published https www youtube com watch v bvfdsp4rvu4 4x3 security slither zetsub0ii eth https twitter com hamza krb published https www youtube com watch v qsfp ql17uk 4x4 security echidna zetsub0ii eth https twitter com hamza krb published https www youtube com watch v xc yfcptmyi 4x5 security post mortem analyzes team otopsi otopsi 4x6 hr tutorials this part introduces some well known usecases and contracts headline topic instructor status code tutorials erc 20 alimsahin eth https twitter com aalimsahin published https www youtube com watch v qwrjpcqydte ab channel itublockchain 5x0 tutorials erc 721 alimsahin eth https twitter com aalimsahin published https www youtube com watch v zf4orrramo4 5x1 tutorials erc 1155 alimsahin eth https twitter com aalimsahin published https www youtube com watch v crx5icux3qe 5x2 tutorials marketplace contracts alimsahin eth https twitter com aalimsahin published https www youtube com watch v gsw1qqvdu2y 5x3 tutorials marketplace interface alimsahin eth https twitter com aalimsahin published https www youtube com watch v 0wmgdbdnd3e 5x4 tutorials multisig wallet 0xulas eth https twitter com ulerdogan published https www youtube com watch v xbmwzj0byva 5x5 tutorials simple game 0xulas eth https twitter com ulerdogan published https www youtube com watch v dtnfyq sxvu 5x6 tutorials staking contracts i alimsahin eth https twitter com aalimsahin published https www youtube com watch v bsgkohwdx1c ab channel itublockchain 5x7 tutorials staking contracts ii alimsahin eth https twitter com aalimsahin preparing 5x8 tutorials basic dex amm tahos eth https twitter com 0xtahos published https youtu be 6aufrt1envw 5x9 tutorials bridges introduction zetsub0ii eth https twitter com hamza krb published https youtu be z1zsacgvsga 5x10 tutorials bridge developing i zetsub0ii eth https twitter com hamza krb published https youtu be na37 ldutyg 5x11 tutorials bridge developing ii zetsub0ii eth https twitter com hamza krb preparing 5x12 tutorials patika q a staking 0xulas eth https twitter com ulerdogan published https www youtube com watch gst hr advanced concepts this part introduces some advanced concepts in smart contract developing headline topic instructor status code advanced concepts merkle trees 0xulas eth https twitter com ulerdogan published https youtu be 9ggkppui tw 6x0 advanced concepts evm explained tahos eth https twitter com 0xtahos published https www youtube com watch v txgivrhehiu ab channel itublockchain 6x1 advanced concepts gas optimizations tahos eth https twitter com 0xtahos published https www youtube com watch v 8npyt9fpkls ab channel itublockchain 6x2 advanced concepts chainlink application alimsahin eth https twitter com aalimsahin published https youtu be rf l6si 1hy 6x3 advanced concepts layerzero application alimsahin eth https twitter com aalimsahin published https www youtube com watch v ioxp875tmt8 6x4 advanced concepts assembly tahos eth https twitter com 0xtahos preparing 6x5 advanced concepts proxy contracts zetsub0ii eth https twitter com hamza krb preparing 6x6 hr br otopsi itu blockchain introduced a new series called otopsi https www youtube com playlist list plby2hxktgwn6xdcr2rhmhdmphuvt3sbnq img src videoimgs otopsi jpeg alt otopsi width 800 from now on post mortem analyzes will be carried out under this series with a larger team hr index topic link otopsi 0 nomad bridge go https www youtube com watch v yn2s5wkdaso list plby2hxktgwn6xdcr2rhmhdmphuvt3sbnq index 1 t 936s ab channel itublockchain otopsi 1 slope wallet go https www youtube com watch v zaazp8he8gi list plby2hxktgwn6xdcr2rhmhdmphuvt3sbnq index 2 t 6s ab channel itublockchain otopsi 2 tornado cash go https www youtube com watch v tyc3pblqgm0 list plby2hxktgwn6xdcr2rhmhdmphuvt3sbnq index 3 ab channel itublockchain otopsi 3 0 ftx collapse go https www youtube com watch v 3glux7jdad0 otopsi 3 1 ftx collapse and beyond go https www youtube com watch v qk0nsihtra4 otopsi 4 silicon valley bank go https www youtube com live icskx 0 z20 otopsi 5 euler finance hack go https www youtube com watch v o3xktcjktdy ab channel itublockchain | blockchain ethereum smart-contracts solidity web3 ethersjs golang javascript typescript goethereum foundry hardhat dapps-development testing | blockchain |
design-system | home office design system the design system for the uk home office this design system is for everyone its purpose is to extend the gov uk design system with work specific to the home office the system contains patterns components and styles to improve consistency across services and reduce duplication of work contributing you can contribute by discussing and proposing components and patterns read our contribution guidelines and then check the community backlog to see what s needed working on this repository this project is a monorepo managed via pnpm you can build multiple packages from this one repository packages come in the following varieties applications found in apps libraries found in lib components found in components note if you would like add more varieties you can do so by modifying the pnpm workspaces yaml and plopfile js getting started in order to work on this repository you will need to install pnpm once you have it installed you can pull down npm dependencies for the entire project by running shell pnpm install creating a new package to create a new package simply run shell npm run create this is all orchestrated from the plopfile js so you can modify how it works see plop installing new packages you will often need to install the libraries and components that we create into each other or into our applications this can be done just as you would install any other package shell pnpm install hods our new package under the hood this will create a symlink so you need not worry about updates this helps when working on mutliple packages at the same time our documentation this project comes with its own documentation application to allow you to easily document this project including any components that you create you can run it as you would any other application in this project shell cd apps docs npm run dev we publish our documentation site when pushing to the master branch design system for the uk home office https design homeoffice gov uk gov uk design system https design system service gov uk contribution guidelines https github com ukhomeoffice design system blob master contributing md community backlog https github com ukhomeoffice design system projects 1 pnpm https pnpm js org applications https not govuk netlify app libraries https not govuk netlify app components https not govuk netlify app apps apps lib lib components components pnpm workspaces yaml pnpm workspaces yaml plopfile js plopfile js install pnpm https pnpm js org en installation plop https plopjs com documentation application apps docs | os |
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llmux | llmux a large language model operating system for general purpose life long learning notice this project is the predecessor of npython https github com 20171130 npython and is no longer updated llmux is an operating system on large language models that provides an abstraction layer with basic funtionalities such as multiagent concurrency and communication long term memomy management device management etc it is designed to help building complex applications on large language model chatbots llmux is currently an experimental project under rapid development we encourage you to build applications and run experiments on llmux you can share your user experience report issues or start pull requests to help us improve llmux your contributions will be greatly appreciated install pip install r requirements txt quickstart you may read the technical note here https drive google com file d 1hfx bygppde97i7at4rwmbv0cho9o7cx view usp sharing first set up the environment variable openai api key run python3 demo py for a demo this script illustrates the basics of llmux you may read and modify it according to your needs you can start your message with to evaluate an expression call system exit to exit gracefully you can only see messages sent to you on the terminal to inspect all messages such as messages between a bot and the system or messages between bots inspect the output files by default the output path is home ubuntu llmux output you can modify this in demo py citations misc llmux title an operating system on large language models for general purpose lifelong learning author hangrui bi year 2023 todo 1 more testing and prompt engineering 2 improve the regex used for extracting codes in bot py 3 improve the format error messages distinguish error types identify unsuccessful function calls and warn 4 implemenet more devices 5 add an event handler to write old messages into the long term memory 6 implement advanced memory management compression and garbage collector 7 load balance among multiple backends 8 allow event handlers to filter messages within a peer 9 support large long term device that does not fit in ram | ai |
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Elixir | elixir gemini2 0 elixir also known as gemini is a technology designed to facilitate the training of large models on a small gpu cluster its goal is to eliminate data redundancy and leverage cpu memory to accommodate really large models in addition elixir automatically profiles each training step prior to execution and selects the optimal configuration for the ratio of redundancy and the device for each parameter this repository is used to benchmark the performance of elixir elixir will be integrated into colossalai for usability environment this version is a beta release so the running environment is somewhat restrictive we are only demonstrating our running environment here as we have not yet tested its compatibility we have set the cuda version to 11 6 and the pytorch version to 1 13 1 cu11 6 three dependent package should be installed from source colossalai https github com hpcaitech colossalai necessary just clone it and use pip install from the newest master branch apex https github com nvidia apex optional clone it checkout to tag 22 03 and install it xformers https github com facebookresearch xformers optional clone it checkout to tag v0 0 17 and install it finally install all packages in the requirements txt tools cuda memory profiling function cuda memory profiling in elixir tracer memory tracer can help you profile each kind of memory occupation during training it tells you the cuda memory occupation of parameters gradient and maximum size of activations generated during training moreover it is an efficient and fast tool which enables quickly profiling opt 175b model on a single gpu you can try it by yourself with the folder activation in the directory example i think you should have at least 16gb cuda memory to run the opt 175b example but that doesn t matter just try it first hardware performance profiling see the folder profile you can profile the aggregate bandwidth of gpu cpu communications and the aggreagte velocity of adam optimizers examples here is a simple example to wrap your model and optimizer for fine tuning https github com hpcaitech elixir tree main example fine tune python from elixir search import minimum waste search from elixir wrapper import elixirmodule elixiroptimizer model bertforsequenceclassification from pretrained bert base uncased optimizer torch optim adam model parameters lr 1e 4 eps 1e 8 sr minimum waste search model world size model elixirmodule model sr world group optimizer elixiroptimizer model optimizer here is an advanced example for performance which is used in our benchmarkhere https github com hpcaitech elixir blob main example common elx py python import torch import torch distributed as dist from colossalai nn optimizer import hybridadam from elixir wrapper import elixirmodule elixiroptimizer get the world communication group global group dist groupmember world get the communication world size global size dist get world size initialize the model in cpu model get model model name hybridadam allows a part of parameters updated on cpu and a part updated on gpu optimizer hybridadam model parameters lr 1e 3 sr optimal search model global size unified dtype torch float16 enable for fp16 training overlap true enable for overlapping communications verbose true print detailed processing information inp data proivde an example input data in dictionary format step fn train step provide an example step function model elixirmodule model sr global group prefetch true prefetch chunks to overlap communications dtype torch float16 use amp use fused kernels true enable fused kernels in apex optimizer elixiroptimizer model optimizer initial scale 64 loss scale used in amp init step true enable for the stability of training | efficient large-language-models memory-management | ai |
multichain | multichain layout declares the assets and chains that exist but provides no chain specific implementations infra defines a local deployment of the multichain using docker compose all underlying chains provide a dockerfile and service definition to make running node instances easy all chains need to add a dockerfile and service definition that allows the multichain to spin up a local development mode instance of the chain this is necessary for running comprehensive local test suites api defines the different compatibility apis that exist account address contract gas and utxo chains should implement the apis that are relevant to them for example bitcoin and its forks implements the address gas and utxo apis no actual implementations should be added to this folder chain defines all of the chain specific implementations of the apis each chain has its own sub package for example bitcoin bitcoin cash dogecoin and zcash are all chains that implement the address gas and utxo apis and each of these implementations are in chain bitcoin chain bitcoincash chain dogecoin and chain zcash respectively example the multichain is designed to be flexible enough to support any kind of chain anyone is free to contribute to the multichain by adding support for a new chain or improving support for an existing chain to show how this is done we will walk through an example adding support for dogecoin chains and assets before doing anything else let s add an enumeration for the asset and chain types which can be found in package multichain to avoid favouritism all assets and chains are listed in alphabetical order unless otherwise advised by an offiical team member the names and tickers found on https coinmarketcap com must be used adding an asset go enumeration of supported assets when introducing a new chain or new asset from an existing chain you must add a human readable string to this set of enumerated values assets must be listed in alphabetical order const bch asset bch bitcoin cash btc asset btc bitcoin doge asset doge dogecoin this is our new asset eth asset eth ether zec asset zec zcash adding a chain go enumeration of supported chains when introducing a new chain you must add a human readable string to this set of enumerated values chains must be listed in alphabetical order const bitcoin chain bitcoin bitcoincash chain bitcoincash dogecoin chain dogecoin this is our new chain ethereum chain ethereum zcash chain zcash docker next we need to setup a docker container in the infra folder this is needed for local test suites allowing for end to end integrated testing directly against a node doing this requires a couple of steps first we create a new dogecoin folder in the infra folder infra bitcoin bitcoincash dogecoin this is our new folder dockerfile this is our new dockerfile dogecoin conf run sh this is our new run file zcash env docker compose yaml the new folder must at least contain a dockerfile that installs the node and a run sh file that runs the nodes the node should be run in test mode the new folder can also contain other files that are specific to the needs of the chain being added in our case the dogecoin conf file is also needed to configure the node we will omit showing all the code here since there is quite a bit of it but you can check it out in the infra dogecoin folder second we add an entry to the env file our entry must include a private key that will have access to funds and the public address associated with that private key we will add sh dogecoin address that will receive mining rewards generally this is set to an address for which the private key is known by a test suite this allows the test suite access to plenty of testing funds export dogecoin pk crznrgh2ztcjupczkwbq2mjit8psfamtyryb1phg1vsrrcnbx4w4 export dogecoin address n3psspr4zqukwh4tcrjp9atwj4gmixqxmt last we add a service to the docker compose yaml file this allows the node to boot alongside the other nodes in the multichain this entry must expose the node for use in tests and must not overlap with other nodes that already exist ports are reserved on a first come first serve basis we will define the service as yaml dogecoin dogecoin build context dogecoin ports 0 0 0 0 18332 18332 entrypoint root run sh dogecoin address address api all chains should implement the address api luckily for dogecoin it is so similar to bitcoin that we can re export the bitcoin implementation without the need for custom modifications in chain dogecoin address go we add go package dogecoin import github com renproject multichain chain bitcoin type addressencoder bitcoin addressencoder addressdecoder bitcoin addressdecoder addressencodedecoder bitcoin addressencodedecoder these three interfaces allow users of the multichain to easily encode and decode dogecoin addresses other chains will need to provide their own implementations based on their chains address standards gas api most but not all chains should implement the gas api again dogecoin is so similar to bitcoin that we can re export the bitcoin implementation in chain dogecoin gas go go package dogecoin import github com renproject multichain chain bitcoin type gasestimator bitcoin gasestimator var newgasestimator bitcoin newgasestimator the interface allows users of the multichain to estimate gas prices although the current implementation is very simple the associated function allows users to construct an instance of the interface for dogecoin utxo api generally speaking chains fall into two categories account based or utxo based and some can even be both bitcoin and its forks are all utxo based chains as a fork of bitcoin dogecoin is a utxo based chain so we implement the utxo api to implement the utxo api we must implement the tx txbuilder and client interfaces more information can be found in the comments of api utxo folder again the implementation for dogecoin is trivial in chain dogecoin utxo we have go package dogecoin import github com renproject multichain chain bitcoin type tx bitcoin tx txbuilder bitcoin txbuilder client bitcoin client clientoptions bitcoin clientoptions var newtxbuilder bitcoin newtxbuilder newclient bitcoin newclient defaultclientoptions bitcoin defaultclientoptions up to this point we have done nothing but re export bitcoin so what makes dogecoin different and how can we express that difference well the chain dogecoin folder is the place where we must define anything else dogecoin users will need in the case of dogecoin the only thing that differentiates it from bitcoin is the chaincfg param object we define this in chain dogecoin dogecoin go go package dogecoin import github com btcsuite btcd chaincfg func init if err chaincfg register mainnetparams err nil panic err if err chaincfg register regressionnetparams err nil panic err var mainnetparams chaincfg params name mainnet net 0xc0c0c0c0 address encoding magics pubkeyhashaddrid 30 scripthashaddrid 22 privatekeyid 158 bip32 hierarchical deterministic extended key magics hdprivatekeyid 4 byte 0x02 0xfa 0xc3 0x98 starts with xprv hdpublickeyid 4 byte 0x02 0xfa 0xca 0xfd starts with xpub human readable part for bech32 encoded segwit addresses as defined in bip 173 dogecoin does not actually support this but we do not want to collide with real addresses so we specify it bech32hrpsegwit doge var regressionnetparams chaincfg params name regtest dogecoin has 0xdab5bffa as regtest same as bitcoin s regtest setting it to an arbitrary value leet hex dogecoin so that we can register the regtest network net 0xd063c017 address encoding magics pubkeyhashaddrid 111 scripthashaddrid 196 privatekeyid 239 bip32 hierarchical deterministic extended key magics hdprivatekeyid 4 byte 0x04 0x35 0x83 0x94 starts with xprv hdpublickeyid 4 byte 0x04 0x35 0x87 0xcf starts with xpub human readable part for bech32 encoded segwit addresses as defined in bip 173 dogecoin does not actually support this but we do not want to collide with real addresses so we specify it bech32hrpsegwit dogert most of the functions that we have re exported expected chaincfg params as an argument by defining one for regnet and mainnet users can construct dogecoin instances of the utxo api by using these params test suite 1 install docker 2 install docker compose 3 run docker 4 run test sh example output sh creating network docker default with the default driver building bitcoin successfully built 1ebb03faa04f successfully tagged docker bitcoin latest building bitcoincash successfully built e12e98011869 successfully tagged docker bitcoincash latest building zcash successfully built 56231a29ca2e successfully tagged docker zcash latest docker bitcoin 1 is up to date docker bitcoincash 1 is up to date docker zcash 1 is up to date waiting for multichain to boot run testmultichain running suite multichain suite stopping docker bitcoincash 1 done stopping docker zcash 1 done stopping docker bitcoin 1 done removing docker bitcoincash 1 done removing docker zcash 1 done removing docker bitcoin 1 done removing network docker default done | blockchain |
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Project1 | 18ss web project 1 br 2018 4 10 0 00 br 2018 4 10 br deadline project 5 br proejct br github repo br | front_end |
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sql-challenge | employee database a mystery in two parts analysis research project task as a data engineer at pewlett hackard regarding employees of the corporation from the 1980s and 1990s the database of these employees from that period are within six csv files objectives of the research project is to illustrate 1 data modeling 2 data engineering 3 data analysis the summary report will include the following below entity relationship er model of the employee database table schema of the employee database a sql file of queries for the employee database of the following queries below 1 list of employee details regarding employee number last name first name gender and salary 2 list of employees who were hired in 1986 3 list of manager details 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 4 list of employee details for each department with the following information employee number last name first name and department name 5 list of all employees whose first name is hercules and last names begin with b 6 list of all employees in the sales department including their employee number last name first name and department name 7 list of all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list of the frequency count of employee last names i e how many employees share each last name histogram to visualize the most common salary ranges by title and by all employees https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20senior 20engineer png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20assistant 20engineer png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20engineer png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20manager png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20technique 20leader png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20senior 20staff png https github com diannejardinez sql challenge blob master salary data output salary 20range 20for 20staff png https github com diannejardinez sql challenge blob master salary data output ave 20salary 20range 20for 20employees png bar chart of average salary by title https github com diannejardinez sql challenge blob master salary data output ave 20employee 20salary 20per 20position png employee database a mystery in two parts instructions background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data modeling 2 data engineering 3 data analysis instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints import each csv file into the corresponding sql table data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name gender and salary 2 list 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 and start and end employment dates 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https martin thoma com configuration files in python https martin thoma com configuration files in python for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 copyright trilogy education services 2019 all rights reserved | data-engineering data-analysis database erdiagram python-3 data-modeling entity-relationship-diagram sql-database sql matplotlib pandas jupyter-notebook | server |
redcross | p align center img src https res cloudinary com dtfbvvkyp image upload v1566331377 laravel logolockup cmyk red svg width 400 p p align center a href https travis ci org laravel framework img src https travis ci org laravel framework svg alt build status a a href https packagist org packages laravel framework img src https poser pugx org laravel framework d total svg alt total downloads a a href https packagist org packages laravel framework img src https poser pugx org laravel framework v stable svg alt latest stable version a a href https packagist org packages laravel framework img src https poser pugx org laravel framework license svg alt license a p about laravel laravel is a web application framework with expressive elegant syntax we believe development must be an enjoyable and creative experience to be truly fulfilling laravel takes the pain out of development by easing common tasks used in many web projects such as simple fast routing engine https laravel com docs routing powerful dependency injection container https laravel com docs container multiple back ends for session https laravel com docs session and cache https laravel com docs cache storage expressive intuitive database orm https laravel com docs eloquent database agnostic schema migrations https laravel com docs migrations robust background job processing https laravel com docs queues real time event broadcasting https laravel com docs broadcasting laravel is accessible powerful and provides tools required for large robust applications learning laravel laravel has the most extensive and thorough documentation https laravel com docs and video tutorial library of all modern web application frameworks making it a breeze to get started with the framework if you don t feel like reading laracasts https laracasts com can help laracasts contains over 1500 video tutorials on a range of topics including laravel modern php unit testing and javascript boost your skills by digging into our comprehensive video library laravel sponsors we would like to extend our thanks to the following sponsors for funding laravel development if you are interested in becoming a sponsor please visit the laravel patreon page https patreon com taylorotwell vehikl https vehikl com tighten co https tighten co kirschbaum development group https kirschbaumdevelopment com 64 robots https 64robots com cubet techno labs https cubettech com cyber duck https cyber duck co uk british software development https www britishsoftware co webdock fast vps hosting https www webdock io en devsquad https devsquad com userinsights https userinsights com fragrantica https www fragrantica com softonsofa https softonsofa com user10 https user10 com soumettre fr https soumettre fr codebrisk https codebrisk com 1forge https 1forge com tecpresso https tecpresso co jp runtime converter http runtimeconverter com webl agence https weblagence com invoice ninja https www invoiceninja com imi digital https www imi digital de earthlink https www earthlink ro steadfast collective https steadfastcollective com we are the robots inc https watr mx understand io https www understand io abdel elrafa https abdelelrafa com hyper host https hyper host appoly https www appoly co uk op gg https op gg http www yunruan ltd contributing thank you for considering contributing to the laravel framework the contribution guide can be found in the laravel documentation https laravel com docs contributions code of conduct in order to ensure that the laravel community is welcoming to all please review and abide by the code of conduct https laravel com docs contributions code of conduct security vulnerabilities if you discover a security vulnerability within laravel please send an e mail to taylor otwell via taylor laravel com mailto taylor laravel com all security vulnerabilities will be promptly addressed license the laravel framework is open sourced software licensed under the mit license https opensource org licenses mit redcross | server |
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PhpFromZero | phpfromzero banner image doc assets phpfromzero banner png p align center b a comprehensive project template for beginners to learn php web development from scratch b p p align center a href https github com dahkenangnon phpfromzero style text decoration none img src https img shields io github stars dahkenangnon phpfromzero style social alt github stars a a href https github com dahkenangnon phpfromzero blob master license style text decoration none img src https img shields io github license dahkenangnon phpfromzero alt license a p objective phpfromzero is a handcrafted project template aimed at helping beginners in php web development it provides a solid foundation for learning how to create powerful websites from scratch using an object oriented approach and the model view controller mvc design pattern without relying on external dependencies this project template is well documented and designed to be easily understandable for junior php developers by using phpfromzero you will gain hands on experience in creating php websites with an object oriented approach develop a clear understanding of how web applications work acquire the skills needed to learn and work with various php frameworks possess the knowledge to build your own php framework with ease features object oriented php learn how to structure your code using object oriented programming oop principles for improved maintainability and code organization mvc design pattern understand and implement the model view controller mvc design pattern to separate concerns and achieve better code structure documentation detailed documentation guides you through the project structure code explanations and usage instructions educational purpose phpfromzero is designed to facilitate learning and provide a solid foundation for beginners in php web development requirements to run phpfromzero make sure you have the following wamp or xampp installed and configured mysql database php 8 or newer attention phpfromzero requires php 8 or newer to run this project is used for educational purposes such as training my mentees use this template at your own risk if you want to use this template in a real project it is highly recommended to improve security and performance aspects for any questions or discussions feel free to contact dah kenangnon gmail com or join the discussion on the github repository https github com dahkenangnon phpfromzero discussions try it on your computer to run phpfromzero on your computer follow these steps 1 clone the repository git clone https github com dahkenangnon phpfromzero git 2 create a new database as mentioned in env local php 3 import the php from zero sql file into the newly created database 4 run the following command in the project directory bash php s localhost 9000 t public 5 open your browser and navigate to http localhost 9000 http localhost 9000 to view the pages please note that depending on your environment s configuration you may encounter some errors extending phpfromzero is designed to help junior developers understand the inner workings of web applications and create their own php projects with well structured and maintainable architecture it provides a simplified approach for educational purposes and may not be suitable for production environments thank you for choosing phpfromzero give it a star on github https github com dahkenangnon phpfromzero if you find it helpful we encourage you to give it a try explore the features and contribute to its improvement happy coding | php mvc oop poo php-project-example php-project | front_end |
task-management-frontend | task management application front end this application acts as the front end for the task management application developed throughout the nestjs zero to hero course produced by ariel weinberger | front_end |
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impy | h1 impy images in python h1 p impy is a library used for deep learning projects that use image datasets p ul li strong email strong lozuwaucb gmail com li li strong bug reports strong https github com lozuwa impy issues li ul p it provides p ul li data augmentation methods for images with bounding boxes the bounding boxes are also affected by the transformations so you don t have to label again li li fast image preprocessing methods useful in a deep learning context e g if your image is too big you need to divide it into patches li ul ol li a href installation installation a li li a href tutorial tutorial a li li a href documentation documentation a li li a href contribute contribute a li ol h1 id installation installation h1 h2 download the impy whl and use pip to install it h2 p follow the next steps p ul li download the impy whl from a href https github com lozuwa impy releases download impy 0 1 impy 0 1 py3 none any whl here a li ul ul li use pip to install the wheel li ul bash pip install impy 0 1 py3 none any whl h1 id tutorial tutorial h1 p impy has multiple features that allow you to solve several different problems with a few lines of code in order to showcase the features of impy we are going to solve common problems that involve both computer vision and deep learning p p we are going to work with a mini dataset of cars and pedestrians available a href https github com lozuwa cars dataset here a this dataset has object annotations that make it suitable to solve a localization problem p alt text static cars0 png raw true car s mini dataset img src static cars3 png alt cars height 600 width 800 img h2 object localization h2 p in this section we are going to solve problems related with object localization p h3 images are too big h3 p one common problem in computer vision and cnns is dealing with big images let s sample one of the images from our mini dataset p alt text static cars3 png raw true example of big image img src static cars1 png alt cars height 600 width 800 img p the size of this image is 3840x2160 it is too big for training most likely your computer will run out of memory in order to try to solve the big image problem we could reduce the size of the mini batch hyperparameter but if the image is too big it would still not work we could also try to reduce the size of the image but that means the image losses quality and you would need to label the smaller image again p p instead of hacking a solution we are going to solve the problem efficiently the best solution is to sample crops of a specific size that contain the maximum amount of bounding boxes possible crops of 1032x1032 pixels are usually small enough p p let s see how to do this with impy p ul li create a folder named b testing cars b then enter the folder li ul bash mkdir p pwd testing cars cd testing cars ul li download the cars dataset from a href https github com lozuwa cars dataset here a li ul bash git clone https github com lozuwa cars dataset ul li create a file named reducing big images py and put the next code li ul python import os from impy objectdetectiondataset import objectdetectiondataset def main define the path to images and annotations images path str os path join os getcwd cars dataset images annotations path str os path join os getcwd cars dataset annotations xmls define the name of the dataset dbname str carsdataset create an object of objectdetectiondataset obda any objectdetectiondataset imagesdirectory images path annotationsdirectory annotations path databasename dbname reduce the dataset to smaller rois of smaller rois of shape 1032x1032 offset list 1032 1032 images output path str os path join os getcwd cars dataset images reduced annotations output path str os path join os getcwd cars dataset annotations reduced xmls obda reducedatasetbyrois offset offset outputimagedirectory images output path outputannotationdirectory annotations output path if name main main ul li b note b the paths where we are going to store the reduced images don t exist let s create them li ul bash mkdir p pwd cars dataset images reduced mkdir p pwd cars dataset annotations reduced xmls ul li now we can run the script and reduce the images to smaller crops li ul bash python reducing big images py p impy will create a new set of images and annotations with the size specified by offset and will include the maximum number of annotations possible so you will end up with an optimal number of data points let s see the results of the example p img src static cars11 png alt cars height 300 width 300 img img src static cars12 png alt cars height 300 width 300 img img src static cars13 png alt cars height 300 width 300 img img src static cars14 png alt cars height 300 width 300 img img src static cars15 png alt cars height 300 width 300 img img src static cars16 png alt cars height 300 width 300 img img src static cars17 png alt cars height 300 width 300 img img src static cars18 png alt cars height 300 width 300 img img src static cars19 png alt cars height 300 width 300 img img src static cars20 png alt cars height 300 width 300 img img src static cars21 png alt cars height 300 width 300 img p as you can see the bounding boxes have been maintained and small crops of the big image are now available we can use this images for training and our problem is solved p p strong note strong that in some cases you are going to end up with an inefficient amount of crops due to overlapping crops in the clustering algorithm i am working on this and a better solution will be released soon nonetheless these results are still way more efficient than what is usually done which is crop each bounding box one by one this leads to inefficient memory usage repeated data points lose of context and simpler representation p h3 data augmentation for bounding boxes h3 p another common problem in computer vision and cnns for object localization is data augmentation specifically space augmentations e g scaling cropping rotation etc for this you would usually make a custom script but with impy we can make this easier p ul li create a json file named b augmentation configuration json b li ul bash touch augmentation configuration json ul li insert the following code in the b augmentation configuration json b file li ul json multiple image augmentations sequential image color augmenters sequential sharpening weight 2 0 save true restartframe false randomevent false bounding box augmenters sequential scale size 1 2 1 2 zoom true interpolationmethod 1 save true restartframe false randomevent false verticalflip save true restartframe false randomevent true image color augmenters sequential histogramequalization equalizationtype 1 save true restartframe false randomevent false bounding box augmenters sequential horizontalflip save true restartframe false randomevent false crop save true restartframe true randomevent false p let s analyze the configuration file step by step currently this is the most complex type of data augmentation you can achieve with the library p p note the file starts with multiple image augmentations then a sequential key follows inside sequential we define an array this is important each element of the array is a type of augmenter p p the first augmenter we are going to define is a image color agumenters which is going to execute a sequence of color augmentations in this case we have defined only one type of color augmentation which is sharpening with a weight of 2 0 p p after the color augmentation we have defined a bounding box augmenters which is going to execute a scale augmentation with zoom followed by a verticalflip p p we want to keep going so we define two more types of image augmenters another image color augmenters which applies histogramequalization to the image and another bounding box agumeneters which applies a horizontalflip and a crop augmentation p p note there are three types of parameters in each augmenter these are optional but i recommend specifying them in order to fully understand your pipeline these parameters are p ol li strong save strong saves the current transformation if true li li strong restart frame strong restarts the frame to its original space if true otherwise maintains the augmentation applied so far li li strong random event strong uses an stochastic function to randomize whether this augmentation might be applied or not li ol p as you have seen we can define any type of crazy configuration and augment our images with the available methods while choosing whether to save each augmentation restart the frame to its original space or randomize the event so we make things crazier get creative and define your own data augmentation pipelines p p once the configuration file is created we can apply the data augmentation pipeline with the following code p ul li after defining the augmentation file let s create the code to apply the augmentations create a file named b apply bounding box augmentations py b li ul ul li insert the following code to b apply bounding box augmentations py b li ul python import os from impy objectdetectiondataset import objectdetectiondataset def main define the path to images and annotations images path str os path join os getcwd cars dataset images annotations path str os path join os getcwd cars dataset annotations xmls define the name of the dataset dbname str carsdataset create an objectdetectiondataset object obda any objectdetectiondataset imagesdirectory images path annotationsdirectory annotations path databasename dbname apply data augmentation by using the following method of the objectdetectiondataset class configuration file str os path join os getcwd augmentation configuration json images output path str os path join os getcwd cars dataset images augmented annotations output path str os path join os getcwd cars dataset annotations augmented xmls obda applydataaugmentation configurationfile configuration file outputimagedirectory images output path outputannotationdirectory annotations output path if name main main ul li now execute the scrip running li ul bash python apply bounding box augmentations py p next i present the results of the augmentations note the transformation does not alter the bounding boxes of the image which saves you a lot of time in case you want to increase the representational complexity of your data p h3 sharpening h3 img src static carsshar png alt sharpened height 300 width 500 img h3 scaling image gets a little bit bigger h3 img src static carssc png alt vertical flip height 300 width 500 img h3 vertical flip h3 img src static carsvert png alt crop height 300 width 500 img h3 histogram equalization h3 img src static carshist png alt histogram equalization height 300 width 500 img h3 horizontal flip h3 img src static carshor png alt horizontal flip height 300 width 500 img h3 crop bounding boxes h3 img src static carscrop png alt crop height 300 width 500 img h1 id documentation documentation h1 h2 object detection dataset h2 h3 objectdetectiondataset class h3 p a class that holds a detection dataset parameters p ol li strong imagesdirectory strong a string that contains a path to a directory of images li li strong annotationsdirectory strong a string that contains a path to a directory of xml annotations li li strong databasename strong a string that contains a name li ol p class methods p h4 dataconsistency h4 p checks the consistency of the image files with the annotation files p h4 findemptyorwrongannotations h4 examines all the annotations in the dataset and detects if any is empty or wrong a wrong annotation is said to contain a bounding box coordinate that is either greater than width heigh respectively or is less than zero ol li strong removeempty strong a boolean that if true removes the annotations that are considered to be wrong or empty li ol h4 computeboundingboxstats h4 ol li strong savedataframe strong a boolean that if true saves the dataframe with the stats li li strong outputdirdataframe strong a string that contains a valid path li ol h4 saveboundingboxes h4 p saves the bounding boxes of the data set as images parameters p ol li strong outputdirectory strong a string that contains a path to a valid directory li li strong filterclasses strong a list of strings that contains classes that are supposed to be as labels in the dataset annotations li ol h4 reducedatasetbyrois h4 p iterate over images and annotations and execute redueimagedatapointbyroi for each one p ol li strong offset strong a list or tuple of ints li li strong outputimagedirectory strong a string that contains a valid path li li strong outputannotationdirectory strong a string that contains a valid path li ol h4 reduceimagedatapointbyroi h4 p p ol li strong imagepath strong a string that contains the path to an image li li strong annotationpath strong a string that contains a path to a xml annotation li li strong offset strong a list or tuple of ints li li strong outputimagedirectory strong a string that contains a valid path li li strong outputannotationdirectory strong a string that contains a valid path li ol h4 applydataaugmentation h4 p p ol li strong configurationfile strong a string that contains a path to a json file li li strong outputimagedirectory strong a string that contains a valid path li li strong outputannotationdirectory strong a string that contains a valid path li li strong threshold strong a float in the range 0 1 li ol h4 applycoloraugmentation h4 p p ol li strong frame strong a numpy tensor that contains an image li li strong augmentationtype strong a string that contains a valid impy augmentation type li li strong parameters strong a list of strings that contains the respective parameters for the type of augmentation li ol h4 applyboundingboxaugmentation h4 p p ol li strong frame strong a numpy tensor that contains an image li li strong boundingboxes strong a list of lists of ints that contains coordinates for a bounding box li li strong augmentationtype strong a string that contains a valid impy augmentation type li li strong parameters strong a list of strings that contains the respective parameters for the type of augmentation li ol h2 types of color augmentations h2 p all of the augmentations ought to implement the following parameters p ol li strong save strong saves the current transformation if true li li strong restart frame strong restarts the frame to its original space if true otherwise maintains the augmentation applied so far li li strong random event strong uses an stochastic function to randomize whether this augmentation might be applied or not li ol h3 invert color h3 p apply a bitwise not operation to the pixels in the image code example p json invertcolor cspace true true true h3 histogram equalization h3 p equalize the color space of the image code example p json histogramequalization equalizationtype 1 h3 change brightness h3 p multiply the pixel distribution with a scalar code example p json changebrightness coefficient 1 2 h3 random sharpening h3 p apply a sharpening system to the image code example p json sharpening weight 0 8 h3 add gaussian noise h3 p add gaussian noise to the image code example p json addgaussiannoise coefficient 0 5 h3 gaussian blur h3 p apply a gaussian low pass filter to the image code example p json gaussianblur sigma 2 h3 shift colors h3 p shift the colors of the image code example p json shiftblur h2 types of bounding box augmentations h2 p all of the augmentations ought to implement the following parameters p ol li strong save strong saves the current transformation if true li li strong restart frame strong restarts the frame to its original space if true otherwise maintains the augmentation applied so far li li strong random event strong uses an stochastic function to randomize whether this augmentation might be applied or not li ol h3 scale h3 p scales the size of an image and maintains the location of its bounding boxes code example p json scale size 1 2 1 2 zoom true interpolationmethod 1 h3 random crop h3 p crops the bounding boxes of an image specify the size of the crop in the size parameter code example p json crop size 50 50 h3 random pad h3 p pads the bounding boxes of an image i e adds pixels from outside the bounding box specify the amount of pixels to be added in the size parameter code example p json pad size 20 20 h3 flip horizontally h3 p flips the bounding boxes of an image in the x axis code example p json horizontalflip h3 flip vertically h3 p flips the bounding boxes of an image in the y axis code example p json verticalflip h3 rotation h3 p rotates the bounding boxes of an image anti clockwise code example p json rotation theta 0 5 h3 jitter boxes h3 p draws random squares of a specific color and size in the area of the bounding box code example p json jitterboxes size 10 10 quantity 5 color 255 255 255 h3 dropout h3 p set pixels inside the bounding box to zero depending on a probability p extracted from a normal distribution if p threshold then the pixel is changed code example p json dropout size 5 5 threshold 0 8 color 255 255 255 h1 id contribute contribute h1 p if you want to contribute to this library please follow the next steps so you can have a development environment p ul li install anaconda with python 3 7 tested with python 3 5 3 6 3 7 then create an empty environment li ul bash conda create name impy python3 7 ul li activate the conda environment li ul bash source activate impy ul li clone the repository li ul bash git clone https github com lozuwa impy ul li install the dependencies that are in setup py li ul ul li you are good to go note there are unit tests for each script inside the impy folder li ul h2 build the project h2 ul li go to impy s parent directory and run the following code li ul bash python setup py sdist bdist wheel ul li a folder named dist will appear it contains the whl and tar gz li ul | machine-learning dataset preprocessing raw-data tidy-data statistics exploratory-data-analysis | ai |
rps-cv | rps cv a rock paper scissors game using computer vision and machine learning on raspberry pi by julien de la bru re terreault drgfreeman tuta io animated screenshot img doc rps gif https www youtube com watch v ozo0 lx pma click on image to access video on youtube https www youtube com watch v ozo0 lx pma this project is showcased https www raspberrypi org magpi issues magpi74 pdf 5b 7b 22num 22 3a272 2c 22gen 22 3a0 7d 2c 7b 22name 22 3a 22fith 22 7d 2c787 5d in issue 74 https www raspberrypi org magpi issues 74 of the magpi the official magazine of the raspberry pi foundation https www raspberrypi org see my drgfreeman rps cv data science https github com drgfreeman rps cv data science repository where i posted different notebooks demonstrating some cool data science analysis on the image dataset resulting from this project magpi article in simplified chinese https github com tommyzihao magpi chinese blob master magpi74 18 19 e7 94 a8 e6 a0 91 e8 8e 93 e6 b4 be e8 b7 9f e4 ba ba e5 b7 a5 e6 99 ba e8 83 bd e7 8e a9 e7 8c 9c e6 8b b3 md contributed by tommy zihao https github com tommyzihao summary project origin this project results from a challenge my son gave me when i was teaching him the basics of computer programming making a simple text based rock paper scissors game in python at that time i was starting to experiment with computer vision with a raspberry pi and an old usb webcam so my son naively asked me could you make a rock paper scissors game that uses the camera to detect hand gestures i accepted the challenge and about a year and a lot of learning later i completed the challenge with a functional game overview of the game the game uses a raspberry pi computer and raspberry pi camera installed on a 3d printed support with led strips to achieve consistent images the pictures taken by the camera are processed and fed to an image classifier that determines whether the gesture corresponds to rock paper or scissors gestures the image classifier uses a support vector machine https en wikipedia org wiki support vector machine a class of machine learning https en wikipedia org wiki machine learning algorithm the image classifier has been priorly trained with a bank of labeled images corresponding to the rock paper scissors gestures captured with the raspberry pi camera how it works the image below shows the processing pipeline for the training of the image classifier top portion and the prediction of gesture for new images captured by the camera during the game bottom portion click here https raw githubusercontent com drgfreeman rps cv master img doc rps pipeline png for full size image rock paper scissors computer vision machine learning pipeline img doc rps pipeline png dependencies the project depends on and has been tested with the following libraries opencv 3 3 0 with bindings for python 3 python 3 4 numpy 1 13 0 scikit learn 0 18 2 scikit image 0 13 0 pygame 1 9 3 picamera follow this guide https www pyimagesearch com 2016 04 18 install guide raspberry pi 3 raspbian jessie opencv 3 for installation of opencv on the raspberry pi install python libraries within the same virtual environment as opencv using the pip install package name command picamera is installed by default on raspbian https www raspberrypi org downloads raspbian images hardware raspberry pi 3 model b or b computer https www raspberrypi org products raspberry pi 3 model b plus raspberry pi camera module v2 https www raspberrypi org products camera module v2 a green background to allow background subtraction in the captured images a physical setup for the camera to ensure consistent lighting and camera position the 3d models i used are available on thingiverse https www thingiverse com thing 2598378 camera lighting setup img doc hardware front jpg camera lighting setup img doc hardware rear jpg camera lighting setup img doc hardware top jpg program files capture py this file opens the camera in capture mode to capture and label images that will later be used to train the image classifier the captured images are automatically named and stored in a folder structure train py this script reads and processes the training images in preparation for training the image classifier the processed image data is then used to train the support vector machine image classifier the trained classifier is stored in the clf pkl file read by play py playgui py this file runs the actual rock paper scissors game using the camera and the trained image classifier in a graphical user interface gui images from each play are captured and added to the image bank creating additional images to train the classifier play py this file runs the actual rock paper scissors game similarly to playgui py except the game output is done in the terminal and opencv window no gui note that the due to memory limitations on the raspberry pi the train py script may not run properly on the raspberry pi with training sets of more than a few hundred images consequently it is recommended to run these on a more powerful computer this computer must also have opencv python 3 4 and the numpy scikit learn and scikit image python libraries installed library modules rpscv gui this module defines the rpsgui class and associated methods to manage the game graphical user interface gui rpscv imgproc this module provides the image processing functions used by the various other python files rpscv utils this module provides functions and constants used by the various other python files rpscv camera this module defines the camera class a wrapper around the picamera library with specific methods for the project such as white balance calibration ouput screenshots training mode typical output from train py on pc with intel core i7 6700 3 4ghz 16gb ram anaconda distribution rps cv jul rosalind pi git rps cv python train py 0 0 importing libraries 3 75 generating image data completed processing 1708 images rock 562 images paper 568 images scissors 578 images 99 51 generating test set 99 64 defining pipeline 99 64 defining cross validation 99 64 defining grid search grid search parameters gridsearchcv cv stratifiedkfold n splits 5 random state 42 shuffle true error score raise estimator pipeline steps pca pca copy true iterated power auto n components none random state none svd solver auto tol 0 0 whiten false clf svc c 1 0 cache size 200 class weight none coef0 0 0 decision function shape none degree 3 gamma auto kernel rbf max iter 1 probability false random state none shrinking true tol 0 001 verbose false fit params iid true n jobs 4 param grid clf c array 1 3 16228 10 31 62278 100 clf gamma array 0 0001 0 00032 0 001 0 00316 0 01 pca n components 60 pre dispatch 2 n jobs refit true return train score true scoring f1 micro verbose 1 99 64 fitting classifier fitting 5 folds for each of 25 candidates totalling 125 fits parallel n jobs 4 done 42 tasks elapsed 2 1min parallel n jobs 4 done 125 out of 125 elapsed 5 9min finished grid search best score 0 9910406616126809 grid search best parameters pca n components 60 clf c 10 0 clf gamma 0 00031622776601683794 458 66 validating classifier on test set classifier f1 score on test set 0 9922178988326849 confusion matrix 84 1 0 1 84 0 0 0 87 classification report precision recall f1 score support rock 0 99 0 99 0 99 85 paper 0 99 0 99 0 99 85 scissors 1 00 1 00 1 00 87 avg total 0 99 0 99 0 99 257 458 72 writing classifier to clf pkl 467 25 done play mode with graphical user interface playgui py initial screen initial screen img doc screen 0 0 png computer wins the play computer wins play img doc screen 1 0 png player wins the play player wins play img doc screen 2 3 png tie tie img doc screen 4 4 tie png game over player wins the game game over player wins img doc screen 3 5 game over png image capture mode capture py capture mode img doc screen capture py png play mode without gui play py play no gui img doc screen play py png | raspberry-pi raspberry-pi-camera rock-paper-scissors machine-learning computer-vision sklearn skimage opencv opencv-python game image-recognition image-classification | ai |
Karbowanec-Blockchain-Explorer | karbo blockchain explorer block explorer for karbo cryptonote based cryptocurrency installation 1 it takes data from daemon karbowanecd it should be accessible from the internet run karbowanecd with open port as follows bash karbowanecd restricted rpc enable cors enable blockchain indexes rpc bind ip 0 0 0 0 rpc bind port 32348 2 just upload to your website and change api variable in config js and in config php to point to your daemon docker we can use docker image to deploy an explorer node url can be replaced on the docker image build shell docker build build arg rpc node url http localhost 32348 or provided as env variable into docker container shell docker run karbo explorer env rpc node url http localhost 32348 | cryptocurrencies blockexplorer cryptonote | blockchain |
data-structure-with-python | div img src https github com roscibely algorithms and data structure blob main root ufersa png width 70 height 100 img src https www datacamp com datacamp png height 85 width 200 alt icon div algorithms and data structure with python 3 professora rosana rego https github com roscibely pex1241 algoritmos e estrutura de dados i bacharelado interdisciplinar em tecnologia da informa o ufersa parte 0 curso auxiliar dataquest https www dataquest io course introduction to python hourglass flowing sand tempo estimado 5h 1 vari veis https github com roscibely data structure with python tree develop root variaveis 2 if else elif https github com roscibely data structure with python tree develop root condicional 3 la os de repeti o https github com roscibely data structure with python tree root develop loops curso auxiliar dataquest https www dataquest io course for loops and conditional statements in python hourglass flowing sand tempo estimado 7h parte i 1 fun es https github com roscibely data structure with python tree main root functions curso auxiliar dataquest https www dataquest io course python functions and jupyter notebook hourglass flowing sand tempo estimado 7h 2 ponteiros https github com roscibely data structure with python tree develop root ponteiros 3 aloca o din mica https github com roscibely data structure with python tree main root alocacao dinamica 4 vetores https github com roscibely data structure with python tree develop root vetores 5 matrizes https github com roscibely data structure with python tree develop root matrizes 6 class https github com roscibely data structure with python tree develop root class parte ii 1 arquivos https github com roscibely data structure with python tree develop root arquivos 2 m dulos https github com roscibely data structure with python tree develop root modulos 3 complexidade de algoritmos https github com roscibely data structure with python blob develop root complexidade readme md curso auxiliar dataquest https www dataquest io course algorithm complexity hourglass flowing sand tempo estimado 9h 4 algoritmos de busca https github com roscibely data structure with python tree develop root busca 5 algoritmos de ordena o https github com roscibely data structure with python tree develop root ordenacao parte iii 1 dicion rios https github com roscibely data structure with python tree develop root dicionarios 2 estrutura de dados lista https github com roscibely data structure with python tree main root listas 3 estrutura de dados pilha https github com roscibely data structure with python tree develop root pilhas 4 estrutura de dados fila curso auxiliar dataquest https www dataquest io course data structures fundamentals hourglass flowing sand tempo estimado 6h datacamp curso data structures and algorithms in python https app datacamp com learn courses data structures and algorithms in python extra git e controle de vers o curso https www dataquest io course git and vcs materiais complementares introdu o ao google colab open in loom https img shields io badge video 83da77 style flat square logo loom https www loom com share 8a4f0d34b3cb4d9ea04b6dcf0b3d1aca google colab cont opcional open in loom https img shields io badge video 83da77 style flat square logo loom https www loom com share d96cb0af7d9c4416bfe8145c93248a11 hourglass flowing sand tempo estimado 2h inspira o para projetos estruturados com python 3 aplicativos m veis kivy guide https kivy org doc stable gettingstarted intro html kivy uma ferramenta que permite criar aplicativos m veis com python eles t m um guia para come ar desenvolvimento web bottle tutorial https bottlepy org docs dev tutorial app html bottle outro framework web para python aqui est um guia para come ar a us lo how to tango with django https www tangowithdjango com um guia para usar o django uma complexa estrutura da web em python jogos pygame tutorials https www pygame org wiki tutorials aqui est uma lista de tutoriais para pygame uma popular biblioteca python para fazer jogos making games with pygame https www amazon com making games python pygame sweigart dp 1469901730 um livro que ensina como fazer jogos em python div img src https github com roscibely algorithms and data structure blob develop root ufersa jpg width 700 height 250 div i ufersa campus pau dos ferros i | server |
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ES3F1_Labs | p align center img src lab 1 resource banner png p es3f1 high performance embedded systems design labs labs for es3f1 high performance embedded systems design toc lab 1 setting up the zybo z7 20 and programming the ps using the xilinx sdk lab 1 lab 3 setting up the pcam c5 and investigating video stream lab 3 moodle page https moodle warwick ac uk course view php id 32868 | os |
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go-ibax | ibax blockchain system platform go reference https pkg go dev badge github com ibax io go ibax svg https pkg go dev github com ibax io go ibax go report card https goreportcard com badge github com ibax io go ibax https goreportcard com report github com ibax io go ibax the most powerful infrastructure for applications on decentralized centralized ecosystems a powerful blockchain system platform with a new system framework and a simplified programming language it is including smart contract database table and interface build from source install go the build process for go ibax requires go 1 17 or higher if you don t have it download go 1 17 https go dev you ll need to add go s bin directories to your path environment variable e g by adding these lines to your etc profile for a system wide installation or home profile export path path usr local go bin export path path gopath bin if you run into trouble see the go install instructions https go dev dl compile export goproxy https athens azurefd net go111module on go mod tidy v go build run 1 create the node configuration file bash go ibax config 2 generate node keys bash go ibax generatekeys 3 generate the first block if you are creating your own blockchain network you must use the test true option otherwise you will not be able to create new accounts bash go ibax generatefirstblock test true 4 initialize the database bash go ibax initdatabase 5 starting go ibax bash go ibax start | blockchain cross-chain decentralized-exchange assets golang | blockchain |
Bloom-Filters-in-MapReduce | bloom filters in mapreduce a bloom filter is a space efficient probabilistic data structure that is used for membership testing to keep it simple its main usage is to remember which keys were given to it a bloom filter is space efficient meaning that it needs very little memory to remember what you gave it actually it has a fixed size so you even get to decide how much memory you want it to use independently of how many keys you will pass to it later of course this comes at a certain cost which is the possibility of having false positives but there can never be false negatives image https user images githubusercontent com 45880539 192534193 bf8f4aae b2dc 447e b42f 971ae6ff4dc3 png a bloom filter is a bit vector with elements it uses hash functions to map keys to the elements of the bit vector given a key every hash function 1 computes the corresponding output positions and sets the corresponding bit in that position to 1 if it is equal to 0 let s consider a bloom filter with the following characteristics number of bits in the bit vector number of hash functions number of keys added for membership testing false positive rate probability between 0 and 1 the relations between theses values can be expressed as image https user images githubusercontent com 45880539 192534832 37940420 4a60 49b2 beb4 5872f6ce2ed0 png to design a bloom filter with a given false positive rate you need to estimate the number of keys to be added to the bloom filter then compute the number of bits in the bloom filter and finally compute the number of hash functions to use a bloom filter has been builded over the ratings of movies listed in the imdb datasets the average ratings are rounded to the closest integer value and a bloom filter has been computed for each rating value in hadoop implementation the following classes has been used org apache hadoop mapreduce lib input nlineinputformat splits n lines of input as one split org apache hadoop util hash hash murmur hash the hash function family to use in the spark implementation analogous classes has been used | cloud |
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iot-sensor-tag | about this repo contains the code needed to connect to ibm s iot cloud https internetofthings ibmcloud com and publish and retrieve data from a texas instruments sensor tag http www ti com tool cc2541dk sensor for a demonstration of the applications within this repo you can watch the following video demo https i ytimg com vi lzuq85ejgwo 0 jpg time 1415806820552 http youtu be lzuq85ejgwo the publish directory https github com ibm bluemix iot sensor tag tree master publish contains a node js application that can be placed on any device connected to the internet and is capable of using bluetooth low energy le to connect to the sensor tag this application publishes sensor data from the sensor tag to the ibm iot cloud the subscribe directory https github com ibm bluemix iot sensor tag tree master subscribe contains a node js application which connects to the ibm iot cloud and uses the sensor tag data in various ways an application to control an html5 presentation deck with the sensor tag buttons an application which visualizes the temperature accelerometer gyroscope and magnetometer data from the sesnor tag the node red directory https github com ibm bluemix iot sensor tag tree master node red contains an exported flow which you can import into your node red instance of bluemix to see the data being published from the sensor tag here is a diagram that describes how the apps work with the ibm iot cloud and bluemix img src subscribe public sensor tag jpg other videos some of the content in these videos is outdated due to changes in bluemix but most of the content may still be relevant short version short version https i1 ytimg com vi uubp2ld5aqe 0 jpg v 53c552d1 https www youtube com watch v uubp2ld5aqe long version long version https i1 ytimg com vi u7vulzigju0 0 jpg v 53c552d1 https www youtube com watch v u7vulzigju0 license this code is licensed under apache v2 see the license file in the root of the repository | server |
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SavjeeCoin | p align center a href rel noopener img width 200px height 200px src https i imgur com velrxxl png alt project logo a p h3 align center savjeecoin h3 div align center github workflows ci yml https github com savjee savjeecoin actions workflows ci yml badge svg https github com savjee savjeecoin actions workflows ci yml coverage status https coveralls io repos github savjee savjeecoin badge svg branch master https coveralls io github savjee savjeecoin branch master github issues https img shields io github issues savjee savjeecoin svg https github com savjee savjeecoin issues github pull requests https img shields io github issues pr savjee savjeecoin svg https github com savjee savjeecoin pulls license https img shields io badge license mit blue svg license div for education purposes only this is by no means a complete implementation and it is by no means secure features simple proof of work algorithm verify blockchain to prevent tampering generate wallet private public key sign transactions getting started a name getting started a install library npm install save savjeecoin generate a keypair to make transactions on this blockchain you need a keypair the public key becomes your wallet address and the private key is used to sign transactions js const ec require elliptic ec const ec new ec secp256k1 const mykey ec genkeypair the mykey object now contains your public private key js console log public key mykey getpublic hex console log private key mykey getprivate hex create a blockchain instance now you can create a new instance of a blockchain js const blockchain transaction require savjeecoin const mychain new blockchain adding transactions js transfer 100 coins from my wallet to toaddress const tx new transaction mykey getpublic hex toaddress 100 tx sign mykey mychain addtransaction tx to finalize this transaction we have to mine a new block we give this method our wallet address because we will receive a mining reward js mychain minependingtransactions mykey getpublic hex video tutorial this source code comes from my video series on youtube https www youtube com watch v zvqczfzr124 list plzvrqmj9hditqzmbtfisdxfxul5k0f q4 you can check them here video 1 simple implementation video 2 adding proof of work https img youtube com vi zvqczfzr124 maxresdefault jpg https www youtube com watch v zvqczfzr124 https img youtube com vi hneate69814 maxresdefault jpg https www youtube com watch v hneate69814 video 3 mining rewards transactions video 4 signing transactions https img youtube com vi frv6cgxvq4i maxresdefault jpg https www youtube com watch v frv6cgxvq4i https img youtube com vi kwq84s13 hw maxresdefault jpg https www youtube com watch v kwq84s13 hw video 5 building a front end in angular https img youtube com vi aqv0wnpe 3g maxresdefault jpg https www youtube com watch v aqv0wnpe 3g | blockchain wallet cryptography | blockchain |
attifyos | attifyos v3 0 attifyos is a distro intended to help you perform security assessment and penetration testing of internet of things iot devices it saves you a lot of time by providing a pre configured environment with all the necessary tools loaded the new version is based on ubuntu 18 04 64 bit that also means that you ll receive updates for this version till april 2023 disclaimer make sure you only tests the target for which you have proper authorization of you hold all responsibilities of what you decide to do with it download links google drive https drive google com drive folders 1c5bkrpoctxqzodbf0a tt0unjx umkt3 https drive google com drive folders 1c5bkrpoctxqzodbf0a tt0unjx umkt3 https drive google com drive folders 1c5bkrpoctxqzodbf0a tt0unjx umkt3 tools included arduino baudrate bdaddr bettercap binwalk create ap cutter dspectrumgui dump1090 firmadyne firmware analysis toolkit firmware analysis toolkit fat firmware mod kit fmk ghidra gnuradio gqrx gr gsm gr paint hackrf tools hackrf tools inspectrum jadx kalibrate rtl killerbee libmpsse liquid dsp lte cell scanner nmap ook decoder qiling radare2 rfcat routersploit rtl 433 rtl sdr tools scapy spectrum painter ubertooth tools urh universal radio hacker credentials iot attify how does it look like attify os screenshot 1 images attifyos home png attify os screenshot 2 images attifyos fish autocomplete png attify os list of tools images attifyos tools png attify os screenshot 3 images attifyos bettercap png attify os screenshot 4 images attifyos inspectrum png credits aditya gupta adi1391 https twitter com adi1391 adi1391 and barun basak 0xec https twitter com 0xec 0xec of attify team https attify store com | embedded security iot iot-pentesting internet-of-things exploitation hardware hacking | server |
design-system-talks | design system talks know a talk that isn t listed below feel free to create a new pull request or open an issue a design system is a collection of documentation on principles and best practices that helps guide a team to build digital products they are often embodied in ui libraries and pattern libraries but can extend to include guides on other areas such as voice and tone talk speaker s introducing design systems into chaos https www youtube com watch v fzsi1bk brm diana mounter github thinking in symbols for universal design https www youtube com watch v z5xxgxbz3fo benjamin wilkins airbnb designing a design system https www youtube com watch v 7hyollo2gc4 jina bolton salesforce designing a design system for modular modules and building a team to build it https www youtube com watch v wsfk5rccxr4 jesiah mccann br marianne epstein br josh trout usa today living design systems https www youtube com watch v crp5cx7nzw jina bolton salesforce scaling design with systems https www youtube com watch v tuly1cym57g karri saarinen airbnb design systems at scale https www youtube com watch v kq48beotjyc sarah federman design systems are for people https www youtube com watch v ldctzdycy1k jina bolton design systems when and how much https www youtube com watch v hx02sal ih0 diana mounter design systems and creativity https www youtube com watch v bmrdrw93knw jina anne how to foster participation https www youtube com watch v 6xzhhhgtt9a inayaili de le n design systems for the rest of us https www youtube com watch v 60tmvbw1kfc siddharth kshetrapal minimising complexity https www youtube com watch v cg8yssvdd5c kellie matheson br richard hallows you ve built a design system now what https www youtube com watch v mpr4drniekq bethany sonefeld accessibility in the gov uk design system https www youtube com watch v oeymedpnpce nick colley design systems at deliveroo learnings and frustrations https www youtube com watch v 6c0yvuwjc84 raphael guilleminot br matt vagni your design system has a heart https www youtube com watch v j4tiaw2gqhm hana lodhi br antonas deduchovas delivering flexible cross platform design systems https www youtube com watch v luzbzaawtte charlie robbins design systems at scale https www youtube com watch v bltrdvksrsm sarah federman design system apis and the developer experience https www youtube com watch v hftvh9snezq diana mounter other resources awesome design systems https github com alexpate awesome design systems design systems handbook https www designbetter co design systems handbook | talks designsystems conference | os |
IOTstack | iotstack this project has been migrated please find the maintained version at https github com sensorsiot iotstack announcements migrated this project has been migrated please find the maintained version at https github com sensorsiot iotstack to migrate your current iotstack version you can run cd iotstack git remote set url origin https github com sensorsiot iotstack git git fetch origin master git reset hard origin master docker compose down menu sh docker compose up d this will override any changes you ve made locally on files git tracks if you have questions about migrating your current stack or why the migration occured please ask on the discord https discord gg zpkhnks any prs created or issues raised on this repo may not be answered old readme iotstack is a builder for docker compose to easily make and maintain iot stacks on the raspberry pi the bulk of the readme has moved to the wiki please check it out here https github com gcgarner iotstack wiki about docker stack for getting started on iot on the raspberry pi this docker stack consists of node red grafana influxdb postgres mosquitto mqtt portainer adminer openhab home assistant hassio zigbee2mqtt pi hole tasmoadmin parial wiki plex media server telegraf wiki coming soon rtl 433 espruinohub testing motioneye mariadb plex homebridge in addition there is a write up and some scripts to get a dynamic dns via duckdns and vpn up and running firstly what is docker the correct question is what are containers docker is just one of the utilities to run a container a container can be thought of as ultra minimal virtual machines they are a collection of binaries that run in a sandbox environment you download a preconfigured base image and create a new container only the differences between the base and your vm are stored containers don t have gui https en wikipedia org wiki graphical user interface s so generally the way you interact with them is via web services or you can launch into a terminal one of the major advantages is that the image comes mostly preconfigured there are pro s and cons for using native installs vs containers for me one of the best parts of containers is that it doesn t clutter your device if you don t need postgres anymore then just stop and delete the container it will be like the container was never there the container will fail if you try to run the docker and native vesions as the same time it is best to install this on a fresh system for those looking for a script that installs native applications check out peter scargill s script https tech scargill net the script tested platform raspberry pi 3b and 4b raspbian buster older pi s docker will not run on a pizero or a model 1 because of the cpu it has not been tested on a model 2 you can still use peter scargill s script https tech scargill net the script running under a virtual machine for those wanting to test out the script in a virtual machine before installing on their pi there are some limitations the script is designed to work with debian based distributions not all the container have x86 64 images for example portainer does not and will give an error when you try and start the stack please see the pinned issue 29 https github com gcgarner iotstack issues 29 there is more info there feature requests please direct all feature requests to discord https discord gg w45td83 youtube reference this repo was originally inspired by andreas spiess s video on using some of these tools some containers have been added to extend its functionality youtube video https www youtube com watch v jdv4x925au0 this is an alternative approach to the setup be sure to watch the video for the instructions just note that the network addresses are different see the wiki under docker networks youtube guide peyanski kiril made a youtube video on getting started using the project check it out here https youtu be 5jmnhuhv134 download the project 1 on the lite image you will need to install git first bash sudo apt get install git 2 download the repository with bash git clone https github com gcgarner iotstack git iotstack due to some script restraints this project needs to be stored in iotstack 3 to enter the directory run bash cd iotstack the menu i ve added a menu to make things easier it is good to familiarise yourself with the installation process the menu can be used to install docker and build the docker compose yml file necessary for starting the stack it also runs a few common commands i do recommend you start to learn the docker and docker compose commands if you plan on using docker in the long run i ve added several helper scripts have a look inside navigate to the project folder and run menu sh installing from the menu select the first option and follow the prompts build the docker compose file docker compose uses the docker compose yml file to configure all the services run through the menu to select the options you want to install docker commands this menu executes shell scripts in the root of the project it is not necessary to run them from the menu open up the shell script files to see what is inside and what they do miscellaneous commands some helpful commands have been added like disabling swap running docker commands from this point on make sure you are executing the commands from inside the project folder docker compose commands need to be run from the folder where the docker compose yml is located if you want to move the folder make sure you move the whole project folder starting and stopping containers to start the stack navigate to the project folder containing the docker compose yml file to start the stack run docker compose up d or scripts start sh to stop docker compose stop the first time you run start the stack docker will download all the images for the web depending on how many containers you selected and your internet speed this can take a long while the docker compose down command stops the containers then deletes them persistent data docker allows you to map folders inside your containers to folders on the disk this is done with the volume key there are two types of volumes modification to the container are reflected in the volume see wiki for further info wiki https github com gcgarner iotstack wiki add to the project feel free to add your comments on features or images that you think should be added contributions if you use some of the tools in the project please consider donating or contributing on their projects it doesn t have to be monetary reporting bugs and prs help improve the projects for everyone thanks mrmx oscrx brianimmel slyke augustasv paulf007 affankingkhan 877dev paraphraser stfnhmplr peyanski cmskedgell | docker raspberry-pi docker-compose-commands grafana influxdb portainer mosquitto postgres nodered mqtt | server |
SWEN304 | swen304 database system engineering | server |
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opendigitaltwins-dtdl | digital twins definition language the digital twins definition language dtdl is a language for describing models and interfaces for iot digital twins digital twins are models of entities in the physical environment such as shipping containers rooms factory floors or logical entities that participate in iot solutions using dtdl to describe a digital twin s capabilities enables the iot solutions to leverage the semantics of the entity dtdl is open to the community and microsoft welcomes collaboration with customers partners and the industry it is based on open w3c standards such as json ld https json ld org and rdf https www w3 org rdf which allow for easier adoption across services and tooling dart dtdl versions dtdl has evolved over time resulting in the next versions version docs notes v1 preview dtdlv1 md dtdl v1 preview dtdlv1 md out of support v2 dtdl v2 md dtdl v2 dtdl v2 md supported in adt iotcentral and iot plug and play v3 dtdl v3 md dtdl v3 dtdl v3 md supported in adt point right a simple example the next interface describes a thermostat reporting temperature as degreecelsius json context dtmi dtdl context 3 dtmi dtdl extension quantitativetypes 1 id dtmi com example thermostat 1 type interface displayname thermostat description reports current temperature contents type telemetry temperature name temperature displayname temperature description temperature in degrees celsius schema double unit degreecelsius cloud services using dtdl azure digital twins https azure microsoft com products digital twins azure iot plug and play https aka ms iotpnp azure device models repository https aka ms dmr azure iot central https azure microsoft com products iot central hammer and wrench developer tools for dtdl dtdl parser for net https aka ms dtdl parser vscode dtdl extension https aka ms dtdl vscode azure iot explorer https learn microsoft com azure iot fundamentals howto use iot explorer book modeling guides iot plug and play modeling guide https learn microsoft com azure iot develop concepts modeling guide adt modeling guide https learn microsoft com azure digital twins concepts models dtdl ontologies https learn microsoft com azure digital twins concepts ontologies | server |
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muta | p align center a href https github com nervosnetwork muta img src https github com nervosnetwork muta docs blob master static docs img muta logo1 png width 270 a h3 align center build your own blockchain today h3 p align center a href https opensource org licenses mit img src https img shields io badge license mit green svg a a href https github com nervosnetwork muta blob master rust toolchain img src https img shields io badge rustc nightly informational svg a a href https travis ci com nervosnetwork muta img src https travis ci com nervosnetwork muta svg branch master a a href https discord gg qxkft88 img src https img shields io discord 674846745607536651 logo discord alt chat on discord a a href https github com nervosnetwork muta img src https img shields io github stars nervosnetwork muta svg style social a a href https github com nervosnetwork muta img src https img shields io github forks nervosnetwork muta svg style social a p p align center developed by nervos br p p english readme cn md what is muta muta is a highly customizable high performance blockchain framework it has a built in bft like consensus algorithm overlord with high throughput and low latency and it can also support different virtual machines including ckb vm evm and wasm muta has interoperability across vms different virtual machines can be used in a muta based blockchain at the same time developed by the nervos team muta is designed to allow anyone in the world to build their own blockchain while enjoying the security and finality brought by nervos ckb developers can customize poa pos or dpos chains based on muta and use different economic models and governance models developers can also develop different application chains such as dex chains based on muta to implement a specific business logic muta s core design philosophy is to make the development of a blockchain state transition as flexible and simple as possible which means that while reducing the obstacles to build high performance blockchains it still maximizes its flexibility to facilitate developers to customize their business logic therefore as a highly customizable high performance blockchain framework muta provides a basic core component that a blockchain system needs and developers can customize the functional parts of the chain freely getting started muta documentation https nervosnetwork github io muta docs quickly build a simple chain and try some simple interaction please refer to quick start https nervosnetwork github io muta docs en us getting started md the basic core component muta provided muta provided all the core components needed to build a blockchain transaction pool https nervosnetwork github io muta docs en us transaction pool md p2p network https nervosnetwork github io muta docs en us network md consensus https nervosnetwork github io muta docs en us overlord md storage https nervosnetwork github io muta docs en us storage md customizable part developers can customize the functional parts of the chain by developing services service is an abstraction layer for extension in muta framework users can define block management add vms etc based on service each service as a relatively independent logical component can implement its specific function and at the same time different services can directly interact with each other so that more complex functional logic can be constructed more flexible is that services from different chains can also be reused which makes it easier for developers to build their own functional modules we provide detailed service development guides and some service examples service development guide https nervosnetwork github io muta docs en us service dev md service examples https nervosnetwork github io muta docs en us service eg md develop a dex chain https nervosnetwork github io muta docs en us dex md developer resources developer resources can be found here docs resources md who is using muta muta powers some open source projects p align left a href https www huobichain com img src https github com nervosnetwork muta docs blob master static docs img user s huobichain jpg width 150 a p is your project using muta edit this page with a pull request to add your logo tada how to contribute the contribution workflow is described in contributing md contributing md and security policy is described in security md security md | blockchain muta rust framework | blockchain |
Jaystalker | team 9 jaystalkers swen 514 term project members bash brian mirabito bjm9265 patrick dalesio pxd5295 noah kalinowski nwk6661 jacob scida jrs8979 ryan devoe rmd9481 | cloud |
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rucfs | rucfs rucfs r k f s is a read only un compressed file system it provides a simple and compact structure to store data up to 4gb it supports regular files nested directories and symbolic links very suit for embedded systems that pursues size and low memory such as stm32 esp etc badge https img shields io badge rucfs 1 0 lightgrey badge https img shields io badge license mit blue advantages for example this is the rucfs diagram of stm32f4x chips diagram img flash diagram svg prepare your rucfs image make sure you have installed python 3 bash usage mkfs rucfs py directory output tools mkfs rucfs py dir to pack rucfs img use rucfs on the chip c load a rucfs image rucfs ctx t ctx if rucfs ok rucfs load image ctx printf rucfs failed to load return 1 open the file rucfs file t file if rucfs ok rucfs fopen ctx hello world jpg file printf no such file or directory return 2 the description of the file char name file name uint8 t data file data uint32 t length file length printf ok file loaded length d length close the file rucfs fclose file todos big endian read write hal x benchmark and tests x mkfs rucfs x readme diagram license licensed under mit with thanks takayama lily https github com takayama lily lxy1226 https github com lxy1226 angelic47 https github com angelic47 | embedded filesystem stm32 | os |
voyager | p align center img src http chriscourses com images cc voyager svg p about voyager voyager is a node and sql based web application framework that provides everything you need to produce robust scalable database driven web apps including built in user authentication opt in simplistic database migrations knex http knexjs org easy to use orm sequelize http docs sequelizejs com clean full featured layout templating handlebars http handlebarsjs com robust front end build system webpack https webpack js org modern es6 syntax built on top of express and modeled after rails and laravel voyager provides a complete solution to getting up and running with a user based app as quickly as possible getting started 1 download the voyager command line interface cli https github com chriscourses voyager cli with npm npm install g voyager cli 2 create a new voyager project with or without auth voyager new newapp or voyager new newapp auth notice you must run the built in knex migrations and add a env file with valid credentials for the auth version to work further auth related instructions can be found under auth setup below 3 change directory to newapp and start the voyager server cd newapp voyager start 4 open up a new terminal tab and run webpack requires webpack installed globally webpack 5 your app should open up automatically at http localhost 3001 and you should see the voyager start up page auth setup to get started with voyager s built in authentication you must follow a few steps required to connect your app to a database and utilize key functionalities such as email confirmations and password resets 1 create a file called env in the root of your newly generated voyager project 2 copy the contents of env example and paste inside of the newly created env file db host db user db password db name mailgun key mailgun domain 3 create a mysql database for your app grab a mailgun api key https www mailgun com and insert the corresponding values into the env file a finished version will look something like this for a local server db host localhost db user root db password root db name newapp mailgun key key kfvud83k3kf3vbn22k223222 mailgun domain mailgun yourdomain com 4 install knex if you haven t already npm install knex g 5 run knex migrations knex migrate latest 6 restart the voyager server voyager start 7 open a new tab in terminal and start webpack webpack you should now have a fully functioning app with auth features such as user registration user login email confirmation and password reset functionality for more information and instruction regarding voyager auth check the chris courses youtube channel https www youtube com c chriscourses for voyager tutorials and more quick docs knex migration api run migrations knex migrate latest rollback last migrations knex migrate rollback license voyager is an open source framework licensed under the mit license https opensource org licenses mit | front_end |
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design-systems-in-finland | design systems in finland a curated list of finnish organizations or companies with large branches in finland that have or are actively working on a design system know a resource that isn t listed below feel free to create a new pull request https github com arielsalminen design systems in finland compare or open an issue https github com arielsalminen design systems in finland issues new what is a design system there isn t a standard definition of design system within our community and people use the term in different ways for the purposes of this list a design system is a set of interconnected patterns and shared practices that are coherently organized to achieve the purpose of digital products organizations name type url notes aalto https www aalto fi public https www aalto fi visual library abb https new abb com fi private n a edita publishing https www editapublishing fi private n a elisa https elisa fi public http stylebook elisa fi f secure https www f secure com private n a fortum https www fortum fi private n a fortum elemental system wip city of helsinki http www hel fi public http hds hel fi in alpha open source https github com city of helsinki helsinki design system helsinki university https www helsinki fi public https static helsinki fi ds alpha kesko https www kesko fi private n a wip l hitapiola https www lahitapiola fi public https www duetds com nordea https www nordea fi private n a nordhealth https nordhealth com public https nordhealth design wip nosto solutions http www nosto com private n a wip op ryhm https uusi op fi private n a oura https ouraring com private n a outotec http www outotec fi private n a posti https www posti fi private n a wip relex http www relex fi private n a fragments wip smartly https www smartly io private n a stora enso https www storaenso com en public https designsystem storaenso com suomi fi https designsystem suomi fi fi public https designsystem suomi fi fi open source telia https www telia fi public https teliads netlify app wip https www npmjs com package teliads components turva https www turva fi public https www duetds com theme turva umbra https umbra3d com private n a for web ios and ar hololens university of helsinki https www helsinki fi en public https universityofhelsinki github io styleguide open source https github com universityofhelsinki styleguide vaisala https www vaisala com en private n a veikkaus https www veikkaus fi private http design veikkaus fi url is shown in some blog posts but doesn t seem to be public verkkokauppa https www verkkokauppa com private n a yhdistysavain https www yhdistysavain fi private n a wip for 2 0 release zalando https www zalando fi public https fabric design github io styleguide open source tools name type url made by notes sc5 style guide generator http styleguide sc5 io open source http styleguide sc5 io sc5 https sc5 io while this is a style guide generator it could be used as a part of a bigger system gridlover https www gridlover net open source https www gridlover net tuomasj https twitter com tuomasj sakamies https twitter com sakamies establish a typographic system with modular scale vertical rhythm vaadin https vaadin com open source paid plan https vaadin com vaadin https vaadin com development platform for front end and back end devs vue design system https vueds com open source https vueds com ariel https social design systems ariel an open source tool for building design systems with vue js | os |
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66Days__NaturalLanguageProcessing | journey of 66daysofdata in natural language processing image https github com thinamxx 66days naturallanguageprocessing blob master images nlp jpg books 1 natural language processing with python https www nltk org book 2 natural language processing in action https www manning com books natural language processing in action 3 natural language processing with pytorch 4 natural language processing specialization https www coursera org specializations natural language processing research papers 1 teacher forcing in recurrent neural networks https towardsdatascience com what is teacher forcing 3da6217fed1c 2 exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 3 deep learning based text classification a comprehensive review https arxiv org abs 2004 03705 4 the illustrated transformer http jalammar github io illustrated transformer 5 nlp s imagenet moment https ruder io nlp imagenet 6 the illustrated bert elmo and co http jalammar github io illustrated bert 7 generalized language models https lilianweng github io lil log 2019 01 31 generalized language models html 8 efficient estimation of word representations in vector space https arxiv org abs 1301 3781 9 distributed representations of words and phrases and their compositionality http papers nips cc paper 5021 distributed representations of words and phrases and their compositionality pdf 10 glove global vectors for word representation https nlp stanford edu pubs glove pdf 11 a simple way to initialize recurrent networks of rectified linear units https arxiv org pdf 1504 00941 pdf 12 neural machine translation by jointly learning to align and translate https arxiv org pdf 1409 0473 pdf 13 dynamic memory networks for natural language processing https arxiv org abs 1506 07285 14 dynamic memory networks for natural language processing https arxiv org abs 1506 07285 15 recursive deep models for semantic compositionality over a sentiment treebank https nlp stanford edu socherr emnlp2013 rntn pdf 16 stanza a python natural language processing toolkit for many human languages https nlp stanford edu pubs qi2020stanza pdf 17 learning music helps you read using transfer to study linguistic structure in language models https nlp stanford edu pubs papadimitriou2020music pdf 18 improved semantic representations from tree structured long short term memory networks https nlp stanford edu pubs tai socher manning acl2015 pdf 19 the stanford corenlp natural language processing toolkit https nlp stanford edu pubs stanfordcorenlp2014 pdf 20 zero shot learning through cross modal transfer https nlp stanford edu pubs socherganjoomanningng nips2013 pdf 21 assessing the ability of lstms to learn syntax sensitive dependencies https arxiv org pdf 1611 01368 pdf 22 finding universal grammatical relations in multilingual bert https nlp stanford edu pubs chi2020finding pdf projects and notebooks 1 topic modeling with singular value decomposition and non negative matrix formation https github com thinamxx topicmodeling nlp blob master topic 20modeling 20with 20svd 20and 20nmf ipynb 2 sentiment classification of internet movie reviews database reviews https github com thinamxx internetmoviedatabase nlp blob master imdb ipynb 3 semantic analysis with lda lsa and ldia https github com thinamxx semanticanalysis lda ldia git 4 sentiment analysis of large movie dataset using rnn cnn and lstm https github com thinamxx neuralnetworks sentimentanalysis git 5 text generation using long short term memory or lstm https github com thinamxx neuralnetworks sentimentanalysis blob master generating 20text 20with 20lstm ipynb 6 chatbot with sequence to sequence networks https github com thinamxx chatbot sequence2sequence git 7 yelp reviews sentiment analysis https github com thinamxx yelpreviews analysis git 8 amazon reviews analysis https github com thinamxx amazonreviews analysis git 9 surname classification with demographics https github com thinamxx surnameclassification pytorch git 10 duplicate questions recognition using trax https github com thinamxx duplicatequestions recognition git day1 of 66daysofdata natural language processing natural language processing is a field of linguistics computer science and artificial intelligence concerned with the interactions between computers and human language in particular how to program computers to process and analyze large amounts of natural language data today i am learning natural language processing from very begining i have read and implemented the fundamentals of natural language processing here i have provided the short description of various libraries dependencies and modules required for natural language processing i have also presented the processing of text such as removing the retweet text removing the hyperlinks and removing hashtags i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics mentioned above excited about the days ahead topics fundamentals of natural language processing image https github com thinamxx 66daysofdata nlp blob master images day 201 png day2 of 66daysofdata string tokenization in natural language processing string tokenization is a process where the string is splitted into individual words or individual parts without blanks and tabs in the same step the words in the string is converted into lower case the tokenize module from nltk or naural language toolkit makes very easy to carry out this process in my journey of natural language processing today i have learned about string tokenization stop word and punctuation in natural language processing i have implemented tweettokenizer and presented the process to remove stopwords and punctuation from the tokenized tweets here in the snapshot i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics mentioned above excited about the days ahead topics fundamentals of natural language processing image https github com thinamxx 66daysofdata nlp blob master images day 202 png day3 of 66daysofdata stemming in natural language processing stemming is a process of converting a word to its most general form or stem it s basically the process of removing the suffix from a word and reduce it to it s root word it helps in reducing the size of vocabulary in my journey of natural language processing today i learned about stemming in natural language processing which is one of the most important steps while working with text i have presented the implementation of porter stemmer snowball stemmer and lancaster stemmer here in the snapshot i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics mentioned above excited for coming days porter stemmer it is one of the most common and gentle stemmer which is very fast but not very precise snowball stemmer it s actual name is english stemmer is more precise over large dataset lancaster stemmer it is very aggressive algorithm it will hugely trim down the working data which itself has pros and cons topics fundamentals of natural language processing image https github com thinamxx 66daysofdata nlp blob master images day 203 png day4 of 66daysofdata lemmatization in natural language processing lemmatization is the process of grouping together the inflected forms of words so that they can analysed as a single item identified by the word s lemma or a dictionary form it is the process where individual tokens from a sentence or words are reduced to their base form lemmatization is much more informative than simple stemming lemmatization looks at the surrounding text to determine a given words s part of speech where it doesn t categorize the phrases in my journey of natural language processing today i learned about lemmatization and it s simple implementation using spacy as well nltk i have covered the fundamentals of natural language processing such as tokenization stemming and lemmatization i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics mentioned above excited for the days ahead topics fundamentals of natural language processing image https github com thinamxx 66daysofdata nlp blob master images day 204 png day5 of 66daysofdata natural language processing natural language processing is a field of linguistics computer science and artificial intelligence concerned with the interactions between computers and human language in particular how to program computers to process and analyze large amounts of natural language data as a part of natural langauge processing journey i have started reading and implementing from the book natural language processing with python it s really amazing and i have encountered many basic functions which are unknown to me such as concordance function similar function common context function and a basic dispersion plot as well i will be using this book in my journey i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned above and below excited for the days ahead book natural language processing with python https www nltk org book day6 of 66daysofdata in my journey of natural language processing today i have explored about gutenberg corpus using nltk and python i have also learned about various interesting challenges with proper explanation of each topics under the hood of natural language processing from the book natural language processing with python i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned above and below word sense disambiguation in natural language processing word sense disambiguation is an open problem concerned with identifying which sense of a word is used in a sentence the solution to this issue impacts other computer related writing such as discourse improving relevance of search engines anaphora resolution coherence and inference pronounce resolution generating language output machine translation machine translation is a sub field of computational linguistics that investigates the use of software to translate text or speech from one language to another spoken dialog system textual entailment book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 206 png day7 of 66daysofdata on my journey of natural language processing today i have learned about different text corpora and basic corpus functionality defined in nltk from the book natural language processing with python i have also learned about loading own corpus plotting and tabulating distributions and generating random text with bigrams here i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned above and below the topics i have covered are summarized below web and chat text brown corpus reuters corpus inaugural address corpus annotated text corpora corpora in other languages conditional frequency distributions book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 207 png day8 of 66daysofdata on my journey of natural language processing today i have learned about processing raw text in natural language processing basically i have completed processing the text from electronic books and from html documents from the book natural language processing with python apart from that i have learned about wordnet the topics i have covered in wordnet are the wordnet hierarchy and semantic similarity semantic similarity is a metric defined over a set of documents or terms where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity example road and driving i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned above and below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 208 png day9 of 66daysofdata on my journey of natural language processing today i have learned about text processing with unicode and regular expressions for detecting word patterns with proper explanations within a program we can manipulate unicode just like normal strings unicode are encoded as stream of bytes examples ascii latin 2 utf 8 i have also implemented the regular expressions for detecting the word patterns using basic meta characters such as dollar sign matches the characters of the end of word dot symbol matches any single character caret symbol matches the characters of the start of word question mark specifies the previous character is optional plus sign means one or more instances of the preceding item sign means zero or more instances of the preceding item backslash means following character is deprived pipe symbol specifies the choice between left and right i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned above and below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 209 png day10 of 66daysofdata in my journey of natural language processing today i have learned about the various useful applications of regular expressions such as finding word stems regular expressions for tokenizing text normalizing text such as stemmers and lemmatization i have also read about the issues of tokenization tokenization turns out to be far more difficult task than one might have expected no single solution works well accross the board another issue of tokenization is the presence of contractions as well such as in didn t i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 2010 png day11 of 66daysofdata in my journey of natural language processing today i have completed all the preliminaries process or techniques required in natural language processing included in the book natural language processing with python i have completed the topics such as tokenization stemming lemmatization and text processing with regular expressions from this book apart from that i have also worked with various text corpora such as brown corpus inaugural address corpus reuters corpus and so on and i have completed first 110 pages of the book natural language processing with python i have plotted a simple bar plot using various categories of brown corpus presenting the frequency distribution of various words appearing inside the corpus i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 2011 20a png image https github com thinamxx 66daysofdata nlp blob master images day 2011 png day12 of 66daysofdata part of speech tagging the process of classifying words into their parts of speech and labeling them accordingly is known as part of speech tagging which is also known as pos tagging or simply tagging the collections of tags used for a particular task is known as tagset in my journey of natural language processing today i have learned about automatic tagging such as default tagger regular expression tagger and lookup tagger along with n gram tagging such as unigram tagger bigram tagger and so on i have also learned about combining taggers using backoff parameter i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 2012 png day13 of 66daysofdata supervised classification classification is the process of choosing the correct class label for a given input the classification is said to be supervised classification if it is built based on training corpora containing the correct label for each input one common example of classification is deciding whether an email is spam or not in my journey of natural language processing today i have learned about supervised classification i have covered the topics such as choosing right features document classification gender identification part of speech tagging using naive bayes classifier and decision trees classifier under the hood of supervised classification of natural language processing i have presented the basic implementation of naive bayes classifier in document classification i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 2013 png day14 of 66daysofdata in my journey of natural language processing today i have learned about sequence classification sentence segmentation and various evaluation methods under the hood of natural language processing i have covered the fundamental topics such as test data accuracy measure precision and recall confusion matrices cross validation decision trees and naive bayes with proper implementations which are so helpful for my understanding i have been in this journey for 2 weeks and i have covered all the fundamentals which are so relevant in natural language processing i have been following the natural language processing with python book and it really helps me a lot now i will be focusing more on implementations so that i will be following the course of fastai on natural language processing i have implemented the naive bayes classifier in text corpus and i hope you can gain insight about the implementation of naive bayes i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the book mentioned below excited about the days ahead book natural language processing with python https www nltk org book image https github com thinamxx 66daysofdata nlp blob master images day 2014 20a png day15 of 66daysofdata singular value decomposition or svd the words that appear most frequently in one topic would appear less frequently in the other otherwise that word wouldn t make a good choice to separate out the two topics therefore the topics are orthogonal the svd algorithm factorizes a matrix into one matrix with orthogonal columns and one with orthogonal rows along with diagonal matrix which contains the relative importance of each factor nonnegative matrix factorization or nmf non negative matrix factorization nmf is a factorization or constrain of non negative dataset nmf is non exact factorization that factors into one short positive matrix topic frequency inverse document frequency or tfidf tfidf is a way to normalize the term counts by taking into account how often they appear in a document and how long the document is and how common or rare the document is in my journey of natural language processing today i have learned and implemented about svd nmf and tfidf in topic modeling project i have captured just the overview of the implementations here i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the course mentioned below excited about the days ahead course fastai https docs fast ai topic modeling with svd and nmf https github com thinamxx naturallanguageprocessing nlp blob master topic 20modeling 20with 20svd 20and 20nmf ipynb image https github com thinamxx 66daysofdata nlp blob master images day 2015 png image https github com thinamxx 66daysofdata nlp blob master images day 2015 20a png day16 of 66daysofdata in my journey of natural language processing today i have learned about implementations of natural language processing from fastai course which has been published recently as the fastai course primarily focuses on the coding part and follows the top down aspect of learning and teaching it s bit complicated to learn than other courses fastai s api is really amazing and powerful as well i learned the basic steps of natural language processing with fastai such as word tokenization subword tokenization numericalization and preparing textblock and datablock i am currently working on sentiment analysis of imdb reviews using fastai i have shared the implementations of the word tokenization subword tokenization numericalization and process to prepare the textblock and datablock with fastai here in the snapshots i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the course mentioned below excited about the days ahead course fastai https docs fast ai sentiment classification of internet movie database reviews https github com thinamxx internetmoviedatabase nlp blob master imdb ipynb image https github com thinamxx 66daysofdata nlp blob master images day 2016 png day17 of 66daysofdata in my journey of natural language processing today i have learned about the implementation of fastai in preparing a sentiment classifier model i have prepared a model using fastai api which can classify sentiment of internet movie database reviews i e classifying the positive or negative sentiment fastai s api is really powerful and effective so that the model can classify the sentiment of internet movie database reviews with above 90 accuracy in just few lines of code i have learned about word tokenization subword tokenization numericalization textblock and datablock api and training the classifier model using fastai i have presented the snapshot of the implementation of fastai api in preparing the language model and training the model i have also presented the implementation of fastai api in preparing the classifier model using the language model and also the concept of unfreezing the model i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the course mentioned below excited about the days ahead course fastai https docs fast ai sentiment classification of internet movie database reviews https github com thinamxx internetmoviedatabase nlp blob master imdb ipynb image https github com thinamxx 66daysofdata nlp blob master images day 2017 png day18 of 66daysofdata in my journey of natural language processing today i have read a topic of natural language processing in the book dive into deep learning https d2l ai by aston zhang here i have covered the topics such as text processing machine translation natural language processing pretraining and word embedding the information and explanations were great and the code implementation is in mxnet i am not quite familiar with mxnet framework so i have just presented a small snapshot here apart from that today i have read a very small part of the book natural language processing in action https g co kgs u82sem by hobson lane i am so much thankful to anthony camarillo for sharing this book with me and i will give continuation with this book natural language processing in action https g co kgs u82sem i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days ahead books dive into deep learning https d2l ai natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2018 png day19 of 66daysofcode in my journey of natural language processing today i have read and implemented the first chapter of the book natural language processing in action https g co kgs u82sem in this chapter i have covered the topics such as natural language processing and the magic regular expressions finite state machine or fsm concept word order and grammar simple nlp chatbot pipeline and natural language iq i have presented the simple chatbot using regular expressions and finite state machine or fsm concept basically i will be working on much advanced chatbots using neural networks in coming days i hope you will also google out the fsm concept in nlp and also the implementation of regular expressions in fsm from here i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days ahead books natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2019 png day20 of 66daysofdata in my journey of natural language processing today i have read a couple of topics under the hood of natural language processing such as tokenized phrase dot product in natural language processing bag of words and overlapping token improvement with regex which is apart from regular expressions contractions extending vocabulary with ngrams i have also read and implemented the regex tokenizer tree bank tokenizer and casual tokenizer actually i am continuing my learning journey with a book natural language processing in action https g co kgs u82sem and lots of the preprocessing concepts which i have already read are coming along my way i prefer to go through the concepts again because i don t want to skip any topics from this book although the concepts might match along the way i won t repeat the same implementation in any of my snapshots i have presented the implementation of regex tokenizer tree bank tokenizer casual tokenizer and ngrams here in the snapshots these tokenization steps are more better than traditional tokenization steps using regular expressions i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days ahead books natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2020 png day21 of 66daysofdata in my journey of natural language processing today i have read and implemented about stopwords stemming lemmatization and sentiment analysis using vader approach which is the algorithm composed by human and also the machine learning approach with the help of naive bayes classifier i have completed the first 2 chapters of the book natural language processing in action https g co kgs u82sem and it is helping me a lot along my journey i have presented the implementation of vader approach in sentiment analysis along with naive bayes classifier and i have also included the implementation of casual tokenizer in the movies dataset i hope you will gain insights about the implementation of vader approach and naive bayes in sentiment analysis actually vader approach is not as efficient as machine learning approach such as naive bayes classifier i hope you will spend some time in learning about naive bayes in sentiment analysis along with vader approach i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2021 png day22 of 66daysofdata in my journey of natural language processing today i have read and implemented about bag of words vectorizing concept vector spaces cosine similarity zipf s law and inverse frequency concept text modeling tfidf relevance ranking and okapi bm25 concept i have completed the first three chapters of the book natural language processing in action https g co kgs u82sem and this chapter primarily focuses on the concept of vectorizing the tokens which are obtained after tokenization using tfidf vectorizer text vectorization is the process of converting text into numerical representation i have also read and implemented the concept of cosine similarity under the hood of natural language processing i have presented the implementation of tfidf vectorizer and also the process of tokenizing the text documents and removing the stopwords i have also implemented the cosine similarity using numpy and pure python as well in this snapshot i hope you will gain insights about text vectorization and tokenization from here i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2022 png day23 of 66daysofdata in my journey of natural language processing today i have started learning about semantic analysis i have read and implemented about topic vectors algorithms for scoring topics and semantic analysis such as latent semantic analysis lsa linear discriminant analysis lda and latent dirichlet allocation ldia today i primarily focused on reading and implementing about linear discriminant analysis lda lda is one of the most straight forward and fast dimension reduction and classification models in natural language processing semantic analysis is the process of relating the syntactic structures from the levels of phrases clauses sentences and paragraphs to the level of the writing as a whole and to their language dependent meanings i have presented the implementation of lda s working principal which states that computing the centroid of tfidf vectors for each side of the binary class here in the snapshot i hope you will gain insights about implementation of lda classifier and creating nlp pipeline of tokenizer and vectorizer from here and i hope you will spend some time in learning about semantic analysis as well i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days ahead books natural language processing in action https g co kgs u82sem image https github com thinamxx 66daysofdata nlp blob master images day 2023 png day24 of 66daysofdata in my journey of natural language processing today i have read and implemented latent semantic analysis lsa singular value decomposition svd principal component analysis pca truncated svd and latent dirichlet allocation ldia i have primarily focused on reading and implementing lsa and ldia for semantic analysis lsa works well with normalized tfidf vectors whereas ldia works well with raw bag of words bow count vectors semantic analysis is the process of relating the syntactic structures from the levels of phrases clauses sentences and paragraphs to the level of the writing as a whole and to their language dependent meanings i have presented the implementation of linear discriminant analysis lda while working with tfidf vectors and bow count vectors here in the snapshot i hope you will gain insights about the implementation of lda classifier along with ldia topic vectors and bow count vectors incase you want to see my notebook i have presented the overall implementation of semantic analysis with lsa lda and ldia with proper documentation here excited about the days to come books natural language processing in action https g co kgs u82sem semantic analysis https github com thinamxx semanticanalysis nlp git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2024 png day25 of 66daysofdata in my journey of natural language processing today i have started learning and implementing neural networks and deep learning for natural language processing i have completed the implementation of lsa and ldia in semantic analysis along with lda i have read the topics such as neural networks and perceptrons gradients local and global minimum and backpropagation under the hood of neural networks and deep learning actually i have primarily focused on reading the topics needed to understand the neural networks and deep learning rather than implementing the concepts i have presented the simple workflow of neural networks using keras api i will be implementing the keras api in natural language processing from today i hope you will also spend some time to learn the basic topics which i have mentioned above to understand the neural networks and deep learning excited to learn and implement neural networks for nlp in coming days book natural language processing in action https g co kgs u82sem image https github com thinamxx 66days naturallanguageprocessing blob master images day 2025 png day26 of 66daysofdata in my journey of natural language processing today i have read and implemented about word vectors softmax function negative sampling document similarity with doc2vec and google s word2vec glove and facebook s fasttext models which were pretrained on billions of text data i have primarily focused on learning and implementing the word2vec pretrained model today i am continuing my learning journey along with the book natural language processing in action https g co kgs u82sem word2vec is a model for natural language processing the word2vec algorithm uses a neural network model to learn word associations from a large corpus of text once word2vec model is trained it can detect synonymous words or suggest additional words for a partial sentence word2vec is a group of related models that are used to produce word embeddings i have presented the implementation to access the google s word2vec pretrained model and it s basic functions and the process to create own domain specific word2vec model i have also presented the implementation of doc2vec model here in the snapshot i hope you will also spend so time to learn about word2vec pretrained model excited about the days to come book natural language processing in action https g co kgs u82sem image https github com thinamxx 66days naturallanguageprocessing blob master images day 2026 png day27 of 66daysofdata in my journey of natural language processing today i have read and implemented about convolutional neural network for natural language processing i have covered the topics such as cnn building blocks step size or stride filter composition padding convolutional pipeline learning narrow windows and implementing the keras api under the hood of convolutional neural network for nlp i have started working on the sentiment analysis of large movie review dataset which was compiled for the 2011 paper learning word vectors for sentiment analysis since it is a very large dataset i have used just the subset of the dataset i will be implementing cnn for this project i have presented the basic implementations of approaching the dataset such as importing the dependencies processing the dataset with tokenization and google news pretrained model vectorization and splitting the dataset into training set and test set i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem sentiment analysis of large movie dataset https github com thinamxx convolutionalneuralnetwork andnlp git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2027b png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2027a png day28 of 66daysofdata in my journey of natural language processing today i have read and implemented about convolutional neural network for natural language processing i have covered the topics such as convolutional neural network architecture pooling dropout cnn parameters optimization and training cnn model under the hood of convolutional neural network for natural language processing i have presented the overall implementation of convolutional neural network here in the snapshot i have also presented the short info of all the parameters mentioned in the cnn model and the process of compiling and training the model as well i hope you will gain insights about the implementation of cnn model in sentiment analysis actually it is the continuation of yesterday s snapshots i hope you will also spend some time working on it i have completed working on the sentiment analysis of large movie review dataset i have prepared a model using convolutional neural network which can classify the sentiment of text data i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem sentiment analysis of large movie dataset https github com thinamxx convolutionalneuralnetwork andnlp git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2028 png day29 of 66daysofdata in my journey of natural language processing today i have read and implemented about simple recurrent neural network as well as bidirectional recurrent neural network here i have covered the topics such as backpropagation through time hyperparameters statefulness bidirectional networks and various other topics similar to convolutional neural network mentioned in the previous snapshot i have implemented the recurrent neural network in the same large movie review dataset to predict the sentiment of text data i have presented the overall implementation of rnn model here in the snapshot i have also presented the short info of all the parameters and codes mentioned here in the model i hope you will gain some insights about the implementation of rnn model in sentiment analysis although the implementation of rnn model is not so appreciable i hope you will spend some time understanding the working principle of rnn and working on the same i have completed working on the sentiment analysis of large movie review dataset using simple rnn as well as bidirectional rnn i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem sentiment analysis of large movie dataset https github com thinamxx convolutionalneuralnetwork andnlp git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2029 png day30 of 66daysofdata in my journey of natural language processing today i have read and implemented about long short term memory or lstm i have covered the topics such as lstm working principle backpropagation through time keras api and various other topics similar to cnn and rnn as mentioned in the previous snapshots under the hood of nlp actually i have primarily focused on implementing the lstm model in the same large movie review dataset to compare the effectiveness of cnn rnn and lstm on sentiment analysis of text data long short term memory or lstm is an artificial recurrent neural network or rnn architecture used in the field of deep learning unlike standard feedforward neural networks lstm has feedback connections i have presented the simple implementation of long short term memory or lstm model in the same large movie review dataset i have also presented the short info of all the parameters and codes mentioned here in the model i hope you will gain some insights about the implementation of lstm model in sentiment analysis i have completed working on the same with lstm model excited about the days ahead book natural language processing in action https g co kgs u82sem sentiment analysis of large movie dataset https github com thinamxx convolutionalneuralnetwork andnlp git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2030 png day31 of 66daysofdata long short term memory or lstm long short term memory or lstm is an artificial recurrent neural network or rnn architecture used in the field of deep learning unlike standard feedforward neural networks lstm has feedback connections it can not only process single data points but also entire sequences of data such as speech or video lstm is applicable for text generation as well today i have read and implemented about generating the text using long short term memory or lstm i have prepared a model using lstm which generates the text similar to william shakespeare s writing i have used the gutenberg corpus which contains the 3 plays of shakespeare to train the neural network i have presented the implementation of lstm model as well as the implementation of keras api in text generation here in the snapshot i have also presented the snapshot of generated text with the help of lstm model i have completed working on generating text with the help of lstm model i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem sentiment analysis of large movie dataset https github com thinamxx convolutionalneuralnetwork andnlp git image https github com thinamxx 66days naturallanguageprocessing blob master images 31a png image https github com thinamxx 66days naturallanguageprocessing blob master images 31b png day32 of 66daysofdata in my journey of natural language processing today i have read and implemented the topics such as encoder and decoder architecture in sequence to sequence models thought vector decoding thought using lstm sequence encoder and keras functional api in assembling sequence to sequence pipeline under the hood of natural language processing i have started working on building a chatbot using sequence to sequence neural networks and keras functional api i have presented the simple implementation of processing the text corpus and few steps to make the text data ready to train the sequence to sequence chatbot here in the snapshot i will be using the cornell dialog dataset for training the chatbot i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem chatbot with sequence to sequence networks https github com thinamxx chatbot git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2032 png day33 of 66daysofdata sequence to sequence model sequence to sequence neural networks can be built with a modular and reusable encoder and decoder architecture the encoder model generates a thought vector which is a dense and fixed dimension vector representation of the data the decoder model use thought vectors to generate output sequences in my journey of natural language processing today i have read and implemented various topics under sequence to sequence networks i have continued working on the chatbot using sequence to sequence learning i have used the keras functional api and cornell dialog dataset for training the model i have presented the implementation of thought encoder and thought decoder using keras functional api here in the snapshot i have also presented the techniques for training the model and generating the response sequences here i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing in action https g co kgs u82sem chatbot with sequence to sequence networks https github com thinamxx chatbot git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2033 png day34 of 66daysofdata in my journey of natural language processing today i have read about sentence segmentation named entity recognition understanding chatbot approaches and few nlp pipelines under the hood of natural language processing i have also started reading the book natural language processing with pytorch actually i had never worked with pytorch and never found any particular reason to start with pytorch but today i got motivated to start natural language processing with pytorch i will be reading and implementing natural language processing with pytorch from today i have presented the simple implementation of aiml bot and the vectorization concept here in the snapshot i am fond of revisiting the small concepts again and again so that i won t get stuck while implementing in real problems i am so excited to start natural language processing with pytorch i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing with pytorch image https github com thinamxx 66days naturallanguageprocessing blob master images day 2034a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2034b png day35 of 66daysofdata in my journey of natural language processing today i have read and implemented the supervised learning paradigm computational graphs pytorch basics and various nlp fundamentals such as tokenization lemmatization tagging and semantics i have also read and implemented about activation functions such as sigmoid tanh relu softmax and loss functions such as mean squared error and cross entropy under the hood of natural language processing with pytorch i have presented the implementation of pytorch in various activation functions and loss functions along with few preliminaries for understanding and working with pytorch here in the snapshots i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing with pytorch image https github com thinamxx 66days naturallanguageprocessing blob master images day 2035a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2035b png day36 of 66daysofdata regularization in machine learning regularization is the process of adding information in order to solve a well posed problems or to prevent overfitting in my journey of natural language processing today i have read and implemented about model selection approaches choosing loss functions choosing optimizers gradient based supervised learning evaluation metrics regularization and early stopping under the hood of natural language processing with pytorch i have started working on the yelp reviews dataset and the neural networks will be implemented using pytorch i have presented some data preprocessing techniques which i have implemented while working with yelp reviews dataset here in the snapshot i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing with pytorch yelp reviews sentiment analysis https github com thinamxx yelpreviews pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2036 png day37 of 66daysofdata in my journey of natural language processing today i have read and implemented about pytorch s data representation the vocabulary class the vectorizer class and the dataloader class under the hood of natural language processing with pytorch i am continuing my journey along with the book natural language processing with pytorch the vocabulary class the vocabulary class not only manages the bijection i e allowing user to add new tokens and have the index auto increment but also handles the special token called unk which stands for unknown by using the unk token it will be easy to handle tokens at test time that were never seen in training instance the vectorizer class the second stage of going from a text dataset to a vectorized minibatch is to iterate through the tokens of an input data point and convert each token to its integer form the result of this iteration should be a vector because this vector will be combined with vectors from other data points there is constraint that the vectors produced by the vectorizer should always have the same length the dataloader class the final step of text to vectorized minibatch pipeline is to actually group the vectorized datapoints because grouping into mini batches is a viatal part of training the neural networks pytorch provides a built in class called dataloader for coordinating the process i have presented the implementation of dataset class using pytorch here in the snapshot i have been working on the implementation of dataset class vectorizer class and dataloader class and i feel quite overwhelmed with the complexity of pytorch because i am not familiar with pytorch framework i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing with pytorch yelp reviews sentiment analysis https github com thinamxx yelpreviews pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2037a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2037b png day38 of 66daysofdata in my journey of natural language processing today i have read and implemented about perceptron classifier the training routine class using pytorch s implementation the training loop evaluation inference and inspection of the model prepared using pytorch under the hood of natural language processing with pytorch i am continuing my journey along with the book natural language processing with pytorch today i have continued working with yelp review dataset for sentiment analysis using pytorch i have presented the simple implementation of pytorch in training the classifier model along with the process of instantiating the dataset model loss optimizer and training state here in the snapshots actually it is the continuation of yesterday s snapshot i hope you will gain some insights and work on the same i hope you will also spend some time learning the topics from the books mentioned below excited about the days to come book natural language processing with pytorch yelp reviews sentiment analysis https github com thinamxx yelpreviews pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2038b png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2038a png day39 of 66daysofdata long short term memory or lstm model long short term memory or lstm is an artificial recurrent neural network or rnn architecture used in the field of deep learning lstm has feedback connections it can not only process single data points but also entire sequences of data such as speech or video in my journey of natural language processing today i have started working on new text dataset i e amazon electronics reviews dataset for analysis using only tensorflow and tensorboard i will be working on the same until i finish it completely apart from that today i have watched some videos on youtube and read some notebooks in kaggle under the hood of natural language processing i have presented the overall implementation of tensorflow and tensorboard in processing the data such as tokenization and encoding and the techniques for preparing lstm model here in the snapshots i hope you will gain some insights and work on the same excited about the days to come amazon reviews analysis https github com thinamxx amazonreviews analysis git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2039a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2039b png day40 of 66daysofdata in my journey of natural language processing today i have continued working on yesterday s notebook which was of text dataset i e amazon electronics reviews dataset for analysis using only tensorflow and tensorboard actually i spent most of my time in training the model and i tried to optimize the model to increase the speed of training but still the training procedure took most part of my time using gpu as well apart from that today i have spent some time working on greedy programming algorithms actually i got a chance to interview from one of the tech giants as well i had prepared a model using lstm which was trained on amazon reviews dataset this snapshot is the continuation of yesterday s snapshots so i have presented some basic workflow of model evaluation and deploying the trained model on unseen text data for analysis i have also presented the simple technique of data visualization for evaluating the model here in the snapshot i hope you will gain some insights and work on the same excited about the days to come amazon reviews analysis https github com thinamxx amazonreviews analysis git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2040a png day41 of 66daysofdata in my journey of natural language processing today i have read and implemented about feed forward networks for natural language processing using pytorch i have covered the topics such as the multi layer perceptron or mlp simple xor functions with mlp using pytorch and softmax activation function under the hood of natural language processing with pytorch i have started working in surname classification model inferring demographic information which has applications from product recommendations to ensuring fair outcomes for users across different demographics i will be using pytorch for building the classifier model i have presented the implementation of pytorch in building the simple mlp class here in the snapshots i have also presented the techniques for processing the raw dataset for surname classification project using pytorch i hope you will gain some insights and you will also spend some time learning about mlp and working on the same and get ready for building the model excited about the days ahead book natural language processing with pytorch surname classification with demographics pytorch https github com thinamxx surnameclassification pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2041a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2041b png day42 of 66daysofdata the vectorizer class the vocabulary converts individual tokens into integers and the surname vectorizer is responsible for applying the vocabulary and converting surname into vectors surnames are sequence of characters and each character is an individual token in the vocabulary in my journey of natural language processing today i have read and implemented about pytorch s data representation the vocabulary class the vectorizer class and the dataloader class under the hood of natural language processing with pytorch i have continued working in surname classification model inferring demographic information which has applications from product recommendations to ensuring fair outcomes for users across different demographics today i have worked out the implementation of dataset class vectorizer class vocabulary class and multi layer perceptron or mlp classifier model i have presented the implementation of vectorizer class using pytorch here in the snapshot i hope you will gain some insights from here and you will also spend some time working on the same excited about the days to come book natural language processing with pytorch surname classification with demographics pytorch https github com thinamxx surnameclassification pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2042 png day43 of 66daysofdata in my journey of natural language processing today i have read and implemented about mlp classifier the training routine class using pytorch s implementation the training loop evaluation inference and inspection of the model prepared using pytorch under the hood of natural language processing with pytorch i have continued working in surname classification model inferring demographic information which has applications from product recommendations to ensuring fair outcomes for users across different demographics today i have worked out the implementation of mlp classifier the training routine class inference and inspection of the model prepared using pytorch i have presented the simple implementation of mlp classifier model along with the snapshot of the inference and inspection of the model evaluation using pytorch actually it is the continuation of yesterday s snapshot i hope you will gain some insights and you will also spend some time working on the same excited about the days to come book natural language processing with pytorch surname classification with demographics pytorch https github com thinamxx surnameclassification pytorch git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2043a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2043c png day44 of 66daysofdata logistic regression logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable although many more complex extensions exist in regression analysis logistic regression is estimating the parameters of a logistic model in a form of binary regression in my journey of natural language processing today i have started learning from natural language processing specialization on coursera i have just started learning the first course and i will spend couple of weeks in this specialization i have covered the topics such as logistic regression and naive bayes along with various fundamental preprocessing steps under the hood of natural language processing this specialization is managed and organized by the team of andrew ng i e deeplearning ai so that i will gain more exposure to mathematics behind every topics which has very high importance i have presented the implementation of naive bayes classifier along with testing procedure here in the snapshot i hope you will also spend some time going through this specialization and i am so much excited about the days to come course natural language processing specialization coursera image https github com thinamxx 66days naturallanguageprocessing blob master images day 2044 png day45 of 66daysofdata knearest neighbors the knn algorithm is a simple supervised machine learning algorithm that can be used to solve both classification and regression problems knn works by finding the distances between a query and all the examples in the data selecting the specified number of examples or k closest to the query then votes for the most frequent label or averages the labels in my journey of natural language processing today i have completed the first course i e natural language processing with classification and vector spaces from natural language processing specialization on coursera i have covered the topics such as vector space models euclidean distance cosine similarity pca knn logistic regression naive bayes and many more in this course i have presented the simple implementation of knn along with techniques for creating hash table here in the snapshots actually i have presented this implementation on the basis of this course for self understanding so i have not included all the dependencies here in the snapshot i hope you will also spend some time going through this specialization excited about the days ahead course natural language processing specialization coursera natural language processing with classification and vector spaces https lnkd in dzw ubd image https github com thinamxx 66days naturallanguageprocessing blob master images day 2045 png day46 of 66daysofdata minimum edit distance the minimum edit distance between two strings is defined as the minimum number of editing operations like insertion deletion and substitution needed to transform one string into another it s working principle is applicable in building the auto correction model in my journey of natural language processing today i have started learning and implementing nlp from the second course of natural language processing specialization on coursera i e natural language processing with probabilistic models i have covered the topics such as autocorrect models using minimum edit distance algorithm pos tagging markov s models and the viterbi algorithm under the hood of natural language processing i will spend couple of weeks in this specialization i have presented the simple implementation of minimum edit distance algorithm whose working principle is applicable in auto correction model here in the snapshot i hope you will also spend some time learning about the topics mentioned above i hope you will also spend some time in this specialization i am excited about the days to come course natural language processing specialization coursera image https github com thinamxx 66days naturallanguageprocessing blob master images day 2046 png day47 of 66daysofdata continuous bag of words in the continuous bag of words model the distributed representations of context or surrounding words are combined to predict the word in the middle the model predicts the current word from a window of surrounding context words in my journey of natural language processing today i have learned about ngrams and probabilities sequence probabilities smoothing word embeddings cbow cleaning and tokenization from the course natural language processing with probabilistic models which is the second course in natural language processing specialization on coursera i have completed the two courses in this specialization i have presented the simple implementation of initializing the model softmax activation function forward propagation cost function and back propagation for training the cbow model here in the snapshot it presents the mathematics behind simple neural network which is crucial for understanding the implementation of neural networks and deep learning i hope you will gain some insights and you will also spend some time learning about the topics mentioned above excited about days ahead course natural language processing specialization coursera image https github com thinamxx 66days naturallanguageprocessing blob master images day 2047 png day48 of 66daysofdata gated recurrent unit a gated recurrent unit or gru is a gating mechanism in rnn similar to a long short term memory or lstm unit but without an output gate gru solves the vanishing gradient problem that can come with standard rnn by using an update gate and a reset gate in my journey of natural language processing today i have started learning and implementing nlp from the third course of natural language processing specialization on coursera i e natural language processing with sequence models i have covered the topics such as trax and neural networks dense and relu layers serial layers rnn and gru using trax deep and bidirectional rnn under the hood of natural language processing neural networks and trax trax is good for implementing new state of the art algorithms like transformers reformers and bert it is actively maintained by google brain team for advanced deep learning tasks i have presented the simple implementation of training gru model using trax here in the snapshot i hope you will gain some insights from here and you will also spend some time learning about trax frameworks in natural language processing and deep learning excited about the days ahead course natural language processing specialization coursera image https github com thinamxx 66days naturallanguageprocessing blob master images day 2048 png day49 of 66daysofdata in my journey of natural language processing today i have learned about lstms and named entity recognition rnns and vanishing gradients siamese neural networks triplets and one shot learning from the course natural language processing with sequence models which is the part of natural language processing specialization on coursera i have completed three courses in this specialization i have started working with quora questions answers dataset to build a lstm model that can identify the similar questions or the duplicate questions which is useful when we have to work with several versions of the same questions i will build this model using trax framework which is maintained by google brain team and good for implementing new state of the art algorithms like transformers reformers and bert i have presented the simple implementation of data preparation for training the lstm model using quora dataset here in the snapshot i hope you will gain some insights and you will also start working on the same excited about the days ahead course natural language processing specialization coursera duplicate questions recognition trax https github com thinamxx duplicatequestions recognition git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2049 png day50 of 66daysofdata siamese neural network siamese neural network is an artificial neural network which uses the same weight while working in tandem on two different input vectors to compute comparable output vectors in my journey of natural language processing today i have learned and implemented about data generators or iterators siamese neural networks lstms and triplet loss under the hood of natural language processing i have continued working with quora questions answers dataset to build a model using lstm that can identify the similar questions or the duplicate questions which is useful when we have to work with several versions of the same questions i have presented the implementation of siamese neural network using lstm model along with the implementation of triplet loss here in the snapshots i have also presented the implementation of training the model using data generators and other dependencies i have presented all the implementations using trax framework here i hope you will gain some insights from here and you will also continue working on the same excited about the days ahead course natural language processing specialization coursera duplicate questions recognition trax https github com thinamxx duplicatequestions recognition git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2050a png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2050b png day51 of 66daysofdata in my journey of natural language processing today i have learned and implemented about siamese neural networks and lstms implementation of trax in siamese neural networks under the hood of natural language processing i have completed working with quora questions answers dataset to build a lstm model that can identify the similar questions or the duplicate questions which is useful when we have to work with several versions of the same questions i have build a model using trax which can identify the duplicate questions neural networks and trax trax is good for implementing new state of the art algorithms like transformers reformers and bert it is actively maintained by google brain team for advanced deep learning tasks i have presented the implementation of siamese neural network using lstm model that can identify the similar or duplicate questions here in the snapshots i have also presented the output of the model here which fascinates me a lot i hope you will gain some insights from here and you will also spend some time working on the same excited about the days ahead course natural language processing specialization coursera duplicate questions recognition trax https github com thinamxx duplicatequestions recognition git image https github com thinamxx 66days naturallanguageprocessing blob master images day 2051 png image https github com thinamxx 66days naturallanguageprocessing blob master images day 2051b png day52 of 66daysofdata in my journey of natural language processing today i have learned about sequence to sequence models alignment attention models bleu or bilingual evaluation understudy rouge or recall oriented understudy greedy decoding random sampling temperature parameter beam search decoding minimum bayes risk or mbr and teacher forcing algorithm in lstm from the course natural language processing with attention models which is the last course of natural language processing specialization teacher forcing teacher forcing is the technique where the target word is passed as the next input to the decoder training with teacher forcing converges faster i have presented the simple implementation of neural machine translation model which uses attention along with the implementation of preparing attention input using encoder and decoder activations here in the snapshots i have presented this implementation on the basis of this course for self understanding so i have not included all the dependencies here in the snapshot i hope you will also spend some time going through the topics mentioned above excited about the days ahead course natural language processing specialization coursera teacher forcing in rnns https lnkd in dpw2fwc image https github com thinamxx 66days naturallanguageprocessing blob master images day 2052 png day53 of 66daysofdata transformer and natural language processing the transformer is a deep learning model which are designed to handle sequential data such as translation and summarization and allows much more parallelization than rnns such as lstm in my journey of natural language processing today i have learned about transformers and it s applications dot product multi head and causal attentions the transformer decoder and summarizer state of art transformers such as gpt2 or generative pretraining for transformer bert or bidirectional encoder representations from transformer t5 or text to text transfer transformer and multitask training strategy and glue benchmark from the course natural language processing with attention models which is the last course of natural language processing specialization on coursera i have presented the simple implementation of transformer language model using trax here in the snapshot i have also presented the implementation of positionalencoder feedforward and decoderblock which returns the list of layers required for the transformer model here i hope you will gain some insights and you will also spend some time learning about the transformer model excited about the days ahead course natural language processing specialization coursera image https github com thinamxx 66days naturallanguageprocessing blob master images day 2053 png day54 of 66daysofdata reformer the efficient transformer or the reformer is a transformer model which combines two crucial techniques to solve the problems of attention and memory allocation that limit transformer application to long context windows reformer uses lsh to reduce the complexity of attending over long sequences and reversible residual layers to more efficiently use the memory available in my journey of natural language processing today i have learned about long sequential data transformer complexity locality sensitive hashing or lsh attention reversible residual layers and reformer from the course natural language processing with attention models which is the last course of natural language processing specialization i have presented the simple implementation of reformer language model along with the implementation of training the reformer model using trax here in the snapshots i have also presented the implementation of forward reversible layer and reverse reversible layer here i hope you will gain some insights and you will also spend some time learning about the same excited about the days ahead course natural language processing specialization coursera natural language processing specialization completion https www coursera org account accomplishments specialization certificate 8lnp2u235swz image https github com thinamxx 66days naturallanguageprocessing blob master images day 2054 png day55 of 66daysofdata transfer learning transfer learning is a process where a model is first pretrained on a data rich task before being fine tuned on a downstream task and transfer learning has emerged as a powerful technique in natural language processing or nlp the effectiveness of transfer learning has given rise to a diversity of approaches methodology and practice in my journey of natural language processing today i have started reading the papers of transformers and natural language processing which is named as exploring the limits of transfer learning with a unified text to text transformer i have presented the implementation of encoder and decoder class which are the key components in any dominant sequence models i hope you will also spend some time reading about the transformer from the paper mentioned above excited about the days ahead exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 image https github com thinamxx 66days naturallanguageprocessing blob master images day 2055 png day56 of 66daysofdata in my journey of natural language processing today i have read a paper about deep learning and text classification which is named as deep learning based text classification a comprehensive review i have read about various text classification tasks such as sentiment analysis news categorization topic analysis and question answering i have also read about various deep learning models for text classification such as feed forward neural networks rnn based models cnn based models capsule neural networks models with attention transformers graph neural networks siamese neural networks and hybrid models from this paper i have presented the simple implementation of layer normalization sublayer connection encoder layer and feed forward layer using pytorch here in the snapshots i hope you will also spend time reading about deep learning and text classification from the paper mentioned above i am excited about the days ahead deep learning based text classification a comprehensive review https arxiv org abs 2004 03705 image https github com thinamxx 66days naturallanguageprocessing blob master images day 2056 png day57 of 66daysofdata in my journey of natural language processing today i have read a paper about deep learning and text classification which is named as deep learning based text classification a comprehensive review i have completed reading this paper i have read about unsupervised learning using autoencoders adversarial training reinforcement learning and few popular datasets such as yelp and imdb i have also read about popular metrics for text classification such as accuracy and error rate precision recall and f1 score from this paper i have also read one of the most popular articles named the illustrated transformer by jay alammar it teaches about the transformer from scratch i have read the topics such as encoder decoder self attention feed forward and multi head attention in this article i have presented the implementation of decoder class decoderlayer with feedforward and subsequent mask using pytorch here in the snapshots i hope you will spend some time reading the paper and article mentioned above excited about the days ahead deep learning based text classification a comprehensive review https arxiv org abs 2004 03705 the illustrated transformer http jalammar github io illustrated transformer image https github com thinamxx 66days naturallanguageprocessing blob master images day 2057 png day58 of 66daysofdata in my journey of natural language processing today i have read some articles about deep learning and natural language processing i have read an article named nlp s imagenet moment which is written by sebastian ruder here i have read about the recent advances in nlp such as ulmfit elmo openai transformer and the imagenet for language similarly i have read another article named as the illustrated bert elmo and co which is written by jay alammar here i have read about bert model architecture word embeddings transfer learning and the topics mentioned above i have also read another article named generalized language models which is written by lilian weng here i am reading about language models and i am still reading this article i have presented the implementation of attention and multi head attention class using pytorch here in the snapshot actually it is the continuation of yesterday s snapshot i hope you will also spend some time reading the articles mentioned above excited about the days ahead nlp s imagenet moment https ruder io nlp imagenet the illustrated bert elmo and co http jalammar github io illustrated bert generalized language models https lilianweng github io lil log 2019 01 31 generalized language models html image https github com thinamxx 66days naturallanguageprocessing blob master images day 2058 png day59 of 66daysofdata in my journey of natural language processing today i have read papers about skip gram and nlp i have read a paper which is named as efficient estimation of word representations in vector space here i have read about model architectures feed forward neural net language model recurrent neural net language model log linear models cbow models continuous skip gram model and various other related topics i have also read the paper which is named as distributed representations of words and phrases and their compositionality here i have read about skip gram model hierarchical softmax negative sampling subsampling and additive compositionality i have presented the implementation of positionwise feed forward class embedding positional encoding and feed forward using pytorch here in the snapshot actually it is the continuation of yesterday s snapshot i hope you will also spend some time reading the topics and papers mentioned above about skip gram model excited about the days ahead efficient estimation of word representations in vector space https arxiv org abs 1301 3781 distributed representations of words and phrases and their compositionality http papers nips cc paper 5021 distributed representations of words and phrases and their compositionality pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2059 png day60 of 66daysofdata in my journey of natural language processing today i have read papers about glove and recurrent neural network language model i have read a paper which is named as glove global vectors for word representation here i have read about matrix factorization methods the glove model and complexity of the model evaluation methods word analogies model analysis and various other related topics similarly i have read another paper which is named as a simple way to initialize recurrent networks of rectified linear units here i have read about language modeling and initialization speech recognition and various other related topics i have presented the implementation of transformer model batches and masking using pytorch here in the snapshot actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above i am excited about the days ahead glove global vectors for word representation https nlp stanford edu pubs glove pdf a simple way to initialize recurrent networks of rectified linear units https arxiv org pdf 1504 00941 pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2060 png day61 of 66daysofdata in my journey of natural language processing today i have read the paper about neural machine translation with attention and dynamic memory network i have read the paper which is named as neural machine translation by jointly learning to align and translate here i have read the topics related to neural machine translation such as rnn encoder and decoder translation and to align and qualitative and quantitative analysis and more related to the same i have also started reading another paper which is named as dynamic memory networks for natural language processing i have just started reading this paper and i will complete it soon i have presented the implementation of training the loop of transformer model along with the implementation of batching using pytorch here in the snapshots actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above i am excited about the days ahead neural machine translation by jointly learning to align and translate https arxiv org pdf 1409 0473 pdf dynamic memory networks for natural language processing https arxiv org abs 1506 07285 image https github com thinamxx 66days naturallanguageprocessing blob master images day 2061 png day62 of 66daysofdata in my journey of natural language processing today i have read the papers about dynamic memory networks and recursive neural networks i have read the paper which is named as dynamic memory networks for natural language processing here i have read about the dynamic memory networks and episodic memory module attention and memory update mechanism and various topics related to the same i have also read the paper which is named as recursive deep models for semantic compositionality over a sentiment treebank here i have read about semantic vector space and compositionality in vector space recursive neural tensor networks backpropagation and various topics related to the same i have presented the implementation of optimizer along with learning rates using pytorch here in the snapshots actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above excited about the days ahead dynamic memory networks for natural language processing https arxiv org abs 1506 07285 recursive deep models for semantic compositionality over a sentiment treebank https nlp stanford edu socherr emnlp2013 rntn pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2062 png day63 of 66daysofdata in my journey of natural language processing today i have read the papers about stanza nlp toolkit and transfer learning to study linguistic structure i have read the paper which is named as stanza a python natural language processing toolkit for many human languages here i have read about neural multilingual nlp pipeline dependency parsing and lemmatization ner and various topics related to the same i have also read the paper which is named as learning music helps you read using transfer to study linguistic structure in language models here i have read about lstm architecture and training random baselines recursive structure non linguistic structure and various topics related to the same i have presented the implementation of label smoothing and feed forward using pytorch here in the snapshots actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above excited about the days ahead stanza a python natural language processing toolkit for many human languages https nlp stanford edu pubs qi2020stanza pdf learning music helps you read using transfer to study linguistic structure in language models https nlp stanford edu pubs papadimitriou2020music pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2063 png day64 of 66daysofdata in my journey of natural language processing today i have read the papers about long short term memory and stanford corenlp toolkit i have read the paper which is named as improved semantic representations from tree structured long short term memory networks here i have read about bidirectional lstm and multilayer lstm tree structured lstm semantic relatedness and sentiment classification and various topics related to the same i have read another paper which is named as the stanford corenlp natural language processing toolkit here i have read about model design and development elementary usage annotators tokenization and lemmatization and various topics related to the same i have presented the implementation of data generator loss computation and greedy decoding in transformer model using pytorch here in the snapshot actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above excited about the days ahead improved semantic representations from tree structured long short term memory networks https nlp stanford edu pubs tai socher manning acl2015 pdf the stanford corenlp natural language processing toolkit https nlp stanford edu pubs stanfordcorenlp2014 pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2064 png day65 of 66daysofdata in my journey of natural language processing today i have read the papers about zero shot learning and syntax sensitive dependencies in lstm i have read the paper which is named as zero shot learning through cross modal transfer here i have read about zero shot and one shot learning word and image representations zero shot learning models novelty detection and various topics related to the same i have also read another paper which is named as assessing the ability of lstms to learn syntax sensitive dependencies here i have read about lstm model and baselines number prediction relative clauses language modeling and various topics related to the same i have presented the implementation of greedy decoding and iterators using pytorch here in the snapshot actually it is the continuation of previous snapshots i hope you will also spend some time reading about the topics and papers mentioned above excited about the days ahead zero shot learning through cross modal transfer https nlp stanford edu pubs socherganjoomanningng nips2013 pdf assessing the ability of lstms to learn syntax sensitive dependencies https arxiv org pdf 1611 01368 pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2065 png day66 of 66daysofdata today i have read the paper which is named as finding universal grammatical relations in multilingual bert here i have read about bert cross lingual probing universal grammatical relations finer grained analysis determiners and various topics related to the same i have also spent some time going through the documentation of my journey in github today i have completed this journey and i am so grateful and thankful to meet lots of amazing persons along the way actually ken jee is one of the amazing persons who started this journey he has always been supportive and a great example to the data science aspirants and i am so thankful to him i have been learning and implementing natural language processing for couple of months now i will continue my learning with new journey and new topics i hope you have also learned something interesting in natural language processing until this day personally i am so thankful to you the one who has come along this way and supported me since the start of the journey we will start to learn new topics with new hope and energy thank you finding universal grammatical relations in multilingual bert https nlp stanford edu pubs chi2020finding pdf image https github com thinamxx 66days naturallanguageprocessing blob master images day 2066 png | datascience machine-learning deeplearning python3 natural-language-processing | ai |
machine_learning_for_good | introduction to machine learning for good binder https mybinder org badge svg https mybinder org v2 gh deltaanalytics machine learning for good master open in colab https colab research google com assets colab badge svg https colab research google com github deltaanalytics machine learning for good center img src images delta octocat png width 50 center how can we use data for social impact data is powerful we believe that anyone can harness that power for change in this introductory course students will learn the foundational theory and the necessary coding skills to translate data into actionable insights students will learn the latest machine learning tools and algorithms data science is a highly interdisciplinary practice demanding critical thinking understanding of statistics and technical coding ability irresponsible application of powerful algorithms or an inadequate exploration of underlying assumptions can lead to spurious results in this course we emphasize the fundamentals of proper data science and expose students to what is possible using sophisticated machine learning methods each of the modules is hands on project based using real world data from kiva https www kiva org a non profit that connects people through lending to alleviate poverty who we are delta analytics http www deltanalytics org is a 501 c 3 san francisco bay area non profit dedicated to bringing rigorous data science to problem solving effecting change in nonprofits and the public sector and making data science an accessible and democratic resource for anyone with the same mission overview topics covered in this course include data cleaning supervised machine learning and unsupervised machine learning the slides that cover the theory of the topic are available here http www deltanalytics org curriculum html we present theory alongside real life data science examples will open doors to novices and professionals alike to harness the power of data for good weebly our website host has blocked traffic to certain countries we have submitted numerous complaints and apologize to students for the inconvenience caused until then you can access pdf of all course slides below module 1 introduction to machine learning https drive google com file d 1r4sby6dm6xjfqlh12tfb bf7wbvoin c view usp sharing module 2 machine learning deep dive https drive google com file d 1ez xqmayj77vervnrqmnfoj vbeoo5uw view usp sharing module 3 linear regression https drive google com file d 1kxbb7fps78xyfyutmgnlqjj3ldo0k3tb view usp sharing module 4 model selection and evaluation https drive google com file d 1esr4u566upiofcpfoitpusbao45mdj4o view usp sharing module 5 decision trees https drive google com file d 1sd ln we w3zo yzrmbe90h2i4 iefrs view usp sharing module 6 ensemble algorithms https drive google com file d 1g2at3s5cgu5hjmyt4x wivs0ruvi6z3s view usp sharing module 7 unsupervised algorithms https drive google com file d 1yda hhyp1v05qgvwlcvfnuuau67zl38n view usp sharing module 8 natural language processing pt 1 https drive google com file d 1y7gofnpfycsu1chweohmqhgxvi5kzprx view usp sharing module 9 natural language processing pt 2 https drive google com file d 1buj0fymzsxcfhna0ahwboxjht7v2fjj8 view usp sharing course outcomes by the end of the course students will be able to 1 explain the fundamental statistical and machine learning algorithms that underlying common data science methods 1 write code to clean process analyze and visualize real world data 1 be able to communicate with other data scientists using technical terms our students the course is intended for any and all individuals interested in harnessing data towards solving problems in their communities minimal prior coding or mathematical statistical experience is expected computer proficiency is necessary our teachers delta teaching fellows http www deltanalytics org teaching fellows html are all data professionals working in the san francisco bay area all of our time is donated for free to build out a curriculum that makes machine learning tools and knowledge more accessible to communities around the world you can learn more about our team here http www deltanalytics org teaching fellows html | data-science data-analysis data-mining non-profit python python3 data-visualisation | ai |
image-filter | image filter image filter a node express application which runs a simple script to process images an assignment for the udacity cloud engineering nanodegree deployment screenshot the deployment image is in the deployment image oluwatobi folder elastic beanstalk url the project has been deployed as an elastic beanstalk app on aws try it out at http oluwatobi image filter us east 1 elasticbeanstalk com filteredimage image url usage example http oluwatobi image filter us east 1 elasticbeanstalk com filteredimage image url https learncodeweb com wp content uploads 2021 02 how to define variables in typescript png | cloud |
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POPE | pope polling based object probing evaluation for object hallucination this repo provides the source code data of our paper evaluating object hallucination in large vision language models emnlp 2023 https arxiv org abs 2305 10355 inproceedings li hallucination 2023 title evaluating object hallucination in large vision language models author yifan li yifan du kun zhou jinpeng wang wayne xin zhao and ji rong wen booktitle the 2023 conference on empirical methods in natural language processing year 2023 url https openreview net forum id xozjw0kzxf img src assets pope png alt image 20230517233229650 style zoom 80 update 10 8 our work is accepted to emnlp 2023 6 25 we upload the caption result used in table 2 you can find them under caption data 6 3 we incorporate the code for using pope with seem to handle unannotated datasets 6 1 we release the code of pope collect ground truth objects pope can be easily built based on datasets with annotations about objects in the image with the help of automatic segmentation tools like seem you can also build pope on any dataset you want to test from annotations if you want to buile pope on datasets with object annotations e g coco you should first organize the annotations in a json file with the following format json image coco val2014 000000131089 jpg objects person baseball bat image coco val2014 000000393225 jpg objects bowl spoon carrot here the image is the filename of each image and the objects is the objects in the image extracted from annotations you can also add other keys e g image id in coco but the above two must be included we provide the annotation results of the validation set of coco 2014 under segmentation segmentation coco ground truth segmentation json we refer to lisaanne hallucination https github com lisaanne hallucination to collect objects from segmentations and captions from automatic segmentation results besides building pope with object annotations our method also supports building pope on raw images leveraing the automatic segmentation tools e g seem https github com ux decoder segment everything everywhere all at once we can first extract the objects in the image and then build the pope as the above method if you want to buile pope with seem you should first organize the annotations in a json file with the following format json image coco val2014 000000131089 jpg image coco val2014 000000393225 jpg build pope once you have the ground truth objects prepared you can build your own pope by running python python main py you can customize your pope by specifying these configs auto seg whether to use automatic segmentation results default false img path the path to the json file that contains the path of images to be segmented seg path the path to the segmentation path that containing ground truth objects in the image seg num the number of images to be segmented default 1000 sample num the number of negative objects to be sample for each image default 3 img num the number of images for building pope default 500 template the prompt template default is there a in the image dataset the dataset name used for the filename of the built pope default coco save path the save path of the built pope default output if you want to employ seem to segment images you can build pope by running python python main py auto seg true img path segmentation coco val images json seg num 1000 after the execution you will find 5 json files under output dataset dataset pope random json the pope built with random negative sampling strategy each question is a dict in the following format json question id 1 image coco val2014 000000016631 jpg text is there a person in the image label yes question id 2 image coco val2014 000000016631 jpg text is there a refrigerator in the image label no dataset pope popular json the pope built with popular negative sampling strategy dataset pope adversarial json the pope built with adversarial negative sampling strategy dataset ground truth objects json the appearance frequencies of all objects in the selected images dataset co occur json the co occurrence frequencies of all objects in the selected images evaluation now you can use built pope to evaluate lvlms and evaluate their object hallucination the answer of lvlms should be organized in a json file in the following format json question is there a bird in the image answer yes question is there a tree in the image answer no notice that the order of pope questions and the answer should be the same then you should specify the ans file i e the answer file of lvlms and label file i e the pope file in evaluate py and evaluate the results by running python python evaluate py the program will report the accuracy precision recall f1 score and yes ratio as metrics | ai |
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MemeRandomizer_Demo | memerandomizer demo demo for ui engineering guild presentation intro to ios dev for web devs demo starter in this folder you ll find an xcode project already set up for you to jump right in feel free to code along in this starter project as we go through the demo completed demo please reference this folder to see the completed project at any time | os |
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kukai | kukai development kukai is built using angular 15 install dependencies npm i run during development ng serve open latest audit least authority audit least 20authority 20 20tezos 20foundation 20kukai 20wallet 20final 20audit 20report pdf 2022 03 04 | kukai tezos wallet | blockchain |
mlreef | div align center a href https mlreef com img src https www mlreef com static e83d49c6147bb03391fc4e230da3d9a0 mlreef logo pos h 01 png width 250px a the collaboration platform for machine learning mlreef is an open source ml ops platform that helps you collaborate reproduce and share your machine learning work with thousands of other users important we are no longer supporting and updating this repository we are still actively working on this project but on our main repo at gitlab div mlreef mlreef is a ml dl development platform containing four main sections data management data management fully versioned data hosting and processing infrastructure publishing code repositories publishing code containerized and versioned script repositories for immutable use in data pipelines experiment manager experiment manager experiment tracking environments and results ml ops ml ops pipelines orchestration solution for ml dl jobs k8s cloud bare metal div align center sign up https mlreef com register start experimenting https docs mlreef com in minutes div to find out more about how mlreef can streamline your machine learning development lifecycle visit our homepage https www mlreef com data management a href https www mlreef com img src dashboard mlreef gif width 75 a host your data using git git lfs repositories work concurrently on data fully versioned or lfs version control full view on data processing and visualization history connect your external storage to mlreef and use your data directly in pipelines data set management access history pipelines publishing code adding your params via argparse python example params for a resnet50 script def process arguments args parser argparse argumentparser description resnet50 parser add argument input path action store help path to directory of images parser add argument output path action store default help path to output metrics parser add argument height action store default 224 help height of images int parser add argument width action store default 224 help width of images int parser add argument channels action store default 3 help channels of images 1 grayscale 3 rgb 4 rgba int parser add argument use pretrained action store default true help use pretrained resnet50 weights bool parser add argument epochs action store default 5 help number of epochs for training parser add argument batch size action store default 32 help batch size fed to the neural network int parser add argument validation split action store default 25 help fraction of images to be used for validation float parser add argument class mode action store default binary help categorical binary sparse input or none parser add argument learning rate action store default 0 0001 help learning rate of adam optimizer float parser add argument loss action store default sparse categorical crossentropy help loss function used to compile model params vars parser parse args args return params and publishing your scripts gets you the following containerization of your scripts always working scripts including easy hyperparameter access in pipelines execution environment including specific packages versions hyper parameters argparser for command line parameters with currently used values explicit parameters dictionary input validation and guides multiple containers based on version and code branches experiment manager complete experiment setup log full source control info including non committed local changes execution environment including specific packages versions hyper parameters full experiment output automatic capture artifacts storage and standard output logs performance metrics on individual experiments and comparative graphs for all experiments detailed view on logs and outputs generated extensive platform support and integrations supported all python based ml dl frameworks for example pytorch https pytorch org tensorflow https www tensorflow org keras https keras io or scikit learn https scikit learn org stable index html ml ops concurrent computing pipelining governance and control access and user management single permission management resource management model management mlreef architecture the mlreef ml components within the ml life cycle data storage components based currently on git and git lfs model development based on working modules published by the community or your team data management data processing data visualization experiment pipeline on hosted or on prem and model management ml ops orchestration experiment and workflow reproducibility and scalability a img src web public images ml coverage jpg width 75 a why mlreef mlreef is our solution to a problem we share with countless other researchers and developers in the machine learning deep learning universe training production grade deep learning models is a tangled process mlreef tracks and controls the process by associating code version control research projects performance metrics and model provenance we designed mlreef on best data science practices combined with the knowleged gained from devops and a deep focus on collaboration use it on a daily basis to boost collaboration and visibility in your team create a job in the cloud from any code repository with a click of a button automate processes and create pipelines to collect your experimentation logs outputs and data make you ml life cycle transparent by cataloging it all on the mlreef platform a href https www mlreef com img src web public images whymlreef png width 100 a getting started as a developer please read the contribution guidelines contribute md carefully clone the mlreef git repository https gitlab com mlreef mlreef to your local machine read the architecture document https docs mlreef com 99 development architecture md to start developing continue with the developer guide developer guide md canonical source the canonical source of mlreef where all development takes place is hosted on gitlab com mlreef mlreef http gitlab com mlreef mlreef license mit license see the license license md for more information documentation community and support more information in the official documentation http doc mlreef com and on youtube https www youtube com channel uctrukzs136dotzg95f79tka for examples and use cases check these use cases or start the tutorial after registring recommending netflix movies and tv shows https mlreef com erikaml recommending netflix movies and tv shows recognizing handwritten digits https mlreef com erikaml recognize handwritten digits painting like monet https mlreef com akane painting like monet if you have any questions post on our slack channel https app slack com client tsqqh4mt6 csf2j5upk or tag your questions on stackoverflow with mlreef tag for feature requests or bug reports please use gitlab issues https gitlab com mlreef mlreef issues additionally you can always reach out to us via hello mlreef com contributing merge requests are always welcomed heart see more details in the mlreef contribution guidelines contribute md | mlops mlops-environment artificial-intelligence machine-learning machine-learning-algorithms deep-learning deeplearning models tensorflow pytorch data-science mxnet reproducibility | ai |
demos | cloudscape design system demos this package contains demo pages that follow cloudscape design system https cloudscape design guidelines and use cloudscape components https cloudscape design components cloudscape is an open source design system for building intuitive engaging and inclusive user experiences at scale it consists of an extensive set of guidelines to create web applications along with the design resources and front end components to streamline implementation cloudscape was built for and is used by amazon web services aws products and services we created it in 2016 to improve the user experience across aws web applications and also to help teams implement those applications faster since then we have continued enhancing the system based on customer feedback and research you can view the demos running live from the the cloudscape website demos list https cloudscape design demos build the project to install the project dependencies and build all demos npm install npm run build the demo artifacts will be output to the build folder run dev server to start a dev server with live reload npm start the demos will be available at http localhost 9615 run tests npm test this will build the project by running the build script start a webpack dev server instance and run tests afterwards to run only tests without building the full project again npm run test no build security see contributing contributing md security issue notifications for more information license this library is licensed under the mit 0 license see the license file | design-system react | os |
teste-para-frontend | teste para candidatos vaga de desenvolvedor front end instru es 1 criar o html css responsivo com base no layout naveg vel 2 n s queremos avaliar o seu n vel de conhecimento em design responsivo responsive web design usando html5 css3 e grids caso queira utilize pr processadores less sass ou stylus esse ltimo o nosso favorito d 3 o grid do layout baseado no grid system do bootstrap 3 mas n o copie todo o bootstrap 3 para o projeto n s queremos ver voc fazendo os breakpoints e as modifica es dependendo do device 4 n o tenha pressa iremos avaliar a qualidade do seu c digo mesmo incompleto e principalmente a sua sem ntica material desktop https invis io n83owycz5 mobile https invis io za3pcies4 tablet e monitores menores https invis io 9y3pcj06b obs estas mesmas imagens ficam na pasta layouts aten o os recortes das imagens est o na pasta slices mas voc livre para usar um http lorempixel com da vida no lugar hospede o css dentro da pasta css n o necess rio fazer o carrossel rotate funcionar n o necess rio que a fonte seja exatamente a mesma usada na arte se quiser mant la a helvetica permitido usar qualquer editor de c digo e consultar a internet suporte de browsers ie9 chrome safari firefox links teis grid http getbootstrap com css grid paleta de cores usadas no layout https color adobe com pt teste folha web color theme 6584765 mussum ipsum gerador de lorem ipsum do mussum http mussumipsum com ao terminar o teste solicite um pull request em caso de d vidas envie e mail para l tec recrutamento frontend grupofolha com br boa sorte | front_end |
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BiblioReads | div align center img src public readme logo svg raw true width 256 height 256 alt div h1 align center biblioreads h1 div align center biblioreads is a free and open source alternative goodreads front end focused on privacy scrapes content from goodreads and presents the results without any of the bloat inspired by nitter https github com zedeus nitter libremdb https github com zyachel libremdb and other open source alternative front end projects github https github com nesaku biblioreads codeberg https codeberg org nesaku biblioreads div screenshots dark mode light mode public readme books dark png raw true public readme books light png raw true view more screenshots more screenshots features no ads simple we don t show ads no tracking or data collection no personal information is ever collected tracking cookies are not used no sign up required use biblioreads without signing up for an account and never deal with those annoying sign up popups all requests are proxied your requests are never directly made to goodreads lightweight modern design responsive design with built in light dark mode support pwa support installable as a pwa on desktop and mobile instances instance url country provider notes biblioreads eu org https biblioreads eu org us netlify run by me https github com nesaku biblioreads vercel app https biblioreads vercel app us vercel run by me https github com nesaku biblioreads mooo com https biblioreads mooo com globe with meridians cloudflare run by me https github com nesaku bl vern cc https bl vern cc us hetzner run by vern https vern cc biblioreads lunar icu https biblioreads lunar icu globe with meridians cloudflare run by lunar icu https lunar icu read whateveritworks org https read whateveritworks org globe with meridians cloudflare run by whateveritworks https www whateveritworks org biblioreads privacyfucking rocks https biblioreads privacyfucking rocks de 1blu de run by privacyfucking rocks https privacyfucking rocks read seitan ayoub lol https read seitan ayoub lol de hetzner run by unstablemaple https seitan ayoub lol onion bl vernccvbvyi5qhfzyqengccj7lkove6bjot2xhh5kajhwvidqafczrad onion http bl vernccvbvyi5qhfzyqengccj7lkove6bjot2xhh5kajhwvidqafczrad onion us hetzner run by vern https vern cc i2p vernapl3lpo3huqdx3pjzxqgdgavxjlmdskbvejh2gfqgmjuyvxq b32 i2p http vernapl3lpo3huqdx3pjzxqgdgavxjlmdskbvejh2gfqgmjuyvxq b32 i2p us hetzner run by vern https vern cc adding your instance if you d like to add your instance to the list please open an issue or pull request note if you collect any user data from your instance please set the applicable environment variables an example can be found in env local example automatic redirection libredirect use libredirect https github com libredirect libredirect to automatically redirect goodreads links to biblioreads greasemonkey userscript there is a userscript to redirect goodreads links to biblioreads the userscript can be found here https codeberg org zortazert greasemonkey redirect src branch main goodreads to biblioreads user js redirector you can use the redirector https github com einaregilsson redirector extension to redirect goodreads links to biblioreads with the configuration below note you can replace biblioreads eu org with any instance url to redirect all supported routes description goodreads to biblioreads pages example url https www goodreads com book show 5907 the hobbit include pattern https www goodreads com book work author series quotes list redirect to https biblioreads eu org 1 2 pattern type regular expression pattern description redirects all supported goodreads urls to biblioreads advanced options apply to x main window address bar to redirect search pages rule 1 description goodreads to biblioreads search page rule 1 example url https www goodreads com search q the hobbit qid include pattern https www goodreads com search q redirect to https biblioreads eu org search 1 pattern type regular expression pattern description redirects all goodreads search page urls to biblioreads advanced options apply to x main window address bar rule 2 description goodreads to biblioreads search page rule 2 example url https www goodreads com search utf8 e2 9c 93 q the hobbit search type books include pattern https www goodreads com search utf8 e2 9c 93 q redirect to https biblioreads eu org search 1 pattern type regular expression pattern description redirects all goodreads search page urls to biblioreads advanced options apply to x main window address bar faq how do i use this there are two ways you can use biblioreads 1 visit biblioreads eu org https biblioreads eu org or another instance and paste the goodreads book url into the input box or search for a book using the input box 2 replace the https www goodreads com of any book page url with https biblioreads eu org or another instance url then click the fetch data button for example www goodreads com book show 5907 the hobbit https www goodreads com book show 5907 the hobbit to biblioreads eu org book show 5907 the hobbit https biblioreads eu org book show 5907 the hobbit how does this work scraping since goodreads stopped issuing new developer keys for their public developer api https www goodreads com api back in december of 2020 and plans to retire the current version of their api which you can learn more about here https help goodreads com s article does goodreads support the use of apis biblioreads goes to the goodreads book page and scrapes the required content how did you come up with the name the biblio in biblioreads comes from the prefix biblio meaning book and reads comes from the ending of goodreads which is what this website is an alternative front end for what do you do with my data nothing since we don t collect any user data we can t use any user data why is this slower than goodreads when you make a request for a book on biblioreads your request needs to be first scraped by the scraper from goodreads before the result can be shown to you this causes a delay from when you make the request to when the result is shown why is this missing functionality if you have a certain feature that you would like to see feel free to open an issue on github https github com nesaku biblioreads issues or codeberg https codeberg org nesaku biblioreads issues comparison comparing biblioreads to goodreads speed the hunger games https www goodreads com book show 2767052 the hunger games book page tested on chrome v108 without scroll no throttling cache disabled performance biblioreads goodreads request count 45 190 data transferred 137 kb 24 8 mb domcontentloaded 191ms 1 79s finish time 3 27s 9 26s privacy goodreads collects information about your device and uses tracking cookies for advertising among other reasons this can be found in their privacy policy https www goodreads com about privacy blacklight https themarkup org blacklight found 16 ad trackers and 40 third party cookies see what cookies and trackers goodreads uses https themarkup org blacklight url www goodreads com see what cookies and trackers biblioreads uses https themarkup org blacklight url biblioreads eu org installation manual installation prerequisites node js https nodejs org en git https github com git guides install git 1 clone the git repository bash git clone https github com nesaku biblioreads git cd biblioreads 2 install start the project bash npm install npm run start or yarn install yarn start docker installation docker cli docker docker run d name biblioreads p 3000 3000 restart unless stopped nesaku biblioreads latest docker compose 1 download or copy docker compose yml from the repository 2 edit the default environment variables with your variables optional 3 run the command docker compose up d more information visit the docker image repo https github com nesaku biblioreads docker for more information development built using next js cheerio tailwindcss next themes getting started to run the development server bash git clone https github com nesaku biblioreads git cd biblioreads then npm install npm run dev or yarn install yarn dev if you want to use the api directly or run any kind of automation please host the project on your own server more screenshots dark mode light mode public readme author dark png raw true public readme author light png raw true public readme search dark png raw true public readme search light png raw true public readme quote dark png raw true public readme quote light png raw true public readme home dark png raw true public readme home light png raw true license licensed under the gnu agplv3 https www gnu org licenses agpl 3 0 en html please see license txt license txt for more information disclaimer biblioreads does not host any content all content on biblioreads is sourced from goodreads biblioreads is not affiliated with goodreads goodreads is a trademark of amazon technologies inc | alternative-frontends goodreads goodreads-api privacy scraper libre | front_end |
NLPwithPytorchCN | nlpwithpytorchcn natural language processing with pytorch delip rao et al https www oreilly com library view natural language processing 9781491978221 pytorch https www jianshu com p 553d0cd60b82 https www jianshu com p 56269eb3b4ef https www jianshu com p 3654606fed3c https www jianshu com p e7431bcff3a6 https www jianshu com p 2c73b5ba9ea7 https www jianshu com p ceb6956bcec0 https github com znsoftm nlpwithpytorchcn blob master e7 ac ac e4 ba 94 e7 ab a0 md https github com znsoftm nlpwithpytorchcn blob master e7 ac ac e5 85 ad e7 ab a0 md https github com znsoftm nlpwithpytorchcn blob master e7 ac ac e4 b8 83 e7 ab a0 md https yifdu github io 2018 12 28 natural language processing with pytorch https yifdu github io 2018 12 28 natural language processing with pytorch pytorch cs224n pytorch https github com znsoftm deepnlp models pytorch cs224n natural language processing nlp with pytorch http dl4nlp info en latest | ai |
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FreeRTOS | freertos port on mi v soft processors this repository contains the freertos example projects freertos on spike this folder contains the details of freertos running on spike simulator spike spike is the official risc v instruction set simulator spike supports rv32gc and rv64gc and has debug and gdb support find more details on spike here https github com riscv riscv isa sim freertos on mi v processor this folder contains freertos example projects running on a mi v soft processor it includes launchers for hardware deployment and for renode emulation which is bundled with softconsole 5 3 or higher group launcher miv rv32im freertos port test start renode emulator and attach will launch renode attach a gdb connection and then load the freertos binary to the emulation after a successful launch softconsole should break at main and should behave similarly to real hardware for details on how to use renode from within softconsole see softconsole s release notes | os |
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wiki | wiki this repository contains articles amp useful information about technologies what we are using teamavesta knowledgebase table of content engineering all the engineering related topics like devops sysops development will go here training training related meterials will go here links links about some useful articles blogs will go here engineering engineering readme md training training readme md links links readme md how to contribute step 1 clone avesta wiki repository to your local machine git clone https github com team avesta wiki git step 2 install sublime plugin for markdown preview using package control 1 install https packagecontrol io installation package control if you haven t yet 2 use kbd control kbd kbd shift kbd kbd p kbd then package control install package 3 look for markdown preview https github com revolunet sublimetext markdown preview and install it 4 you can use it to preview of your post for showing preview of your article open that file into sublime text and follow given steps optionally select some of your markdown for conversion use kbd control kbd kbd shift kbd kbd p kbd then markdown preview to show the follow commands you will be prompted to select which parser you prefer markdown preview preview in browser markdown preview export html in sublime text markdown preview copy to clipboard markdown preview open markdown cheat sheet step 3 make changes on existing topic or write new topic please create new article into appropriate folder if you want to write on training related topic create file into training folder you have to write in markdown template here is the markdown cheatsheet https github com adam p markdown here wiki markdown cheatsheet step 4 after making some changes you can add it to git branch git add step 5 commit your changes to git repo git commit m commit message step 6 pull new changes from online repo git pull master origin step 7 push your changes to online repo git push master origin | server |
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spare-blockchain | spare blockchain spare https i0 wp com spare farm wp content uploads 2021 05 banner logo png spare is the world s first block chain to utilize the revolutionary new consensus algorithm proof of space and time post and recycled crypto resources allowing it to be farmed on existing chia plots python 3 7 is required make sure your default python version is 3 7 by typing python3 running windows spare windows client can be downloaded from here https elasticbeanstalk us west 2 793349189011 s3 us west 2 amazonaws com spare win32 x64 zip macos build git clone https github com spare network spare blockchain cd spare blockchain sh install sh activate sh install gui sh cd spare blockchain gui npm install npm run build npm run electron ubuntu debian sudo apt get update sudo apt get upgrade y install git sudo apt install git y checkout the source and install git clone https github com spare network spare blockchain cd spare blockchain sh install sh activate the gui requires you have ubuntu desktop or a similar windowing system installed you can not install and run the gui as root sh install gui sh cd spare blockchain gui npm install npm run build npm run electron update cd spare blockchain activate spare stop d all deactivate git fetch git reset hard fetch head if you get release dev0 then delete the package lock json in spare blockchain gui and install sh again git status git status should say nothing to commit working tree clean if you have uncommitted changes release dev0 will be reported sh install sh activate spare init the gui requires you have ubuntu desktop or a similar windowing system installed you can not install and run the gui as root cd spare blockchain gui git fetch cd chmod x install gui sh install gui sh cd spare blockchain gui npm install npm run build npm run electron troubleshooting sometimes stray daemons left over from previously running processes will cause strange bugs errors when upgrading to a new version make sure all daemons and spare processes are killed before installing or upgrading this is normally done by executing spare stop d all from the upgrade example above but it doesn t hurt to double check using ps af grep spare to make sure there are no spare processes left running you may have to manually kill the spare daemon if an install and spare start was performed without first running spare stop d all if all else fails rebooting the machine and restarting the spare daemon processes usually does the trick centos red hat fedora sudo yum install epel release y sudo yum update y compiling python 3 7 is generally required on centos 7 7 and newer sudo yum install gcc openssl devel bzip2 devel zlib devel libffi libffi devel y sudo yum install libsqlite3x devel y possible that on some rhel based you also need to install sudo yum groupinstall development tools y sudo yum install python3 devel gmp devel boost devel libsodium devel y sudo yum install wget y sudo wget https www python org ftp python 3 7 7 python 3 7 7 tgz sudo tar zxvf python 3 7 7 tgz cd python 3 7 7 configure enable optimizations sudo make j nproc altinstall cd download and install the source version git clone https github com spare network spare blockchain git cd spare blockchain sh install sh activate the gui requires a windowing system to be installed you can not install and run the gui as root sh install gui sh cd spare blockchain gui npm install npm run build npm run electron amazon linux 2 sudo yum update y sudo yum install python3 git y git clone https github com spare network spare blockchain git cd spare blockchain sh install sh activate ansha abdul | blockchain |
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mlmm-evaluation | h1 align center p evaluation framework for multilingual large language models p h1 div align center a href https github com nlp uoregon mlmm evaluation blob main license img alt github src https img shields io badge code 20license apache 2 0 green svg a a href https github com nlp uoregon mlmm evaluation blob main data license img alt github data src https img shields io badge data 20license cc 20by 20nc 204 0 red svg a div overview this repo contains benchmark datasets and evaluation scripts for multilingual large language models llms these datasets can be used to evaluate the models across 26 different languages and encompass three distinct tasks arc hellaswag and mmlu this is released as a part of our okapi framework https github com nlp uoregon okapi for multilingual instruction tuned llms with reinforcement learning from human feedback arc https allenai org data arc a dataset with 7 787 genuine grade school level multiple choice science questions assembled to encourage research in advanced question answering hellaswag https allenai org data hellaswag hellaswag is a dataset for studying grounded commonsense inference it consists of 70k multiple choice questions about grounded situations each question comes from one of two domains activitynet or wikihow with four answer choices about what might happen next in the scene the correct answer is the real sentence for the next event the three incorrect answers are adversarially generated and human verified so as to fool machines but not humans mmlu https arxiv org pdf 2009 03300 pdf this dataset contains multiple choice questions derived from diverse fields of knowledge the test covers subjects in the humanities social sciences hard sciences and other essential areas of learning for certain individuals currently our datasets support 26 languages russian german chinese french spanish italian dutch vietnamese indonesian arabic hungarian romanian danish slovak ukrainian catalan serbian croatian hindi bengali tamil nepali malayalam marathi telugu and kannada p align center img src assets okapi languages png width 450 p these datasets are translated from the original arc hellaswag and mmlu datasets in english using chatgpt our technical paper for okapi to describe the datasets along with evaluation results for several multilingual llms e g bloom llama and our okapi models can be found here https arxiv org pdf 2307 16039 pdf usage and license notices our evaluation framework is intended and licensed for research use only the datasets are cc by nc 4 0 allowing only non commercial use that should not be used outside of research purposes install to install lm eval from our repository main branch run bash git clone https github com nlp uoregon mlmm evaluation git cd mlmm evaluation pip install e multilingual basic usage firstly you need to download the multilingual evaluation datasets by using the following script bash bash scripts download sh to evaluate your model on three tasks you can use the following script bash bash scripts run sh lang your model path for instance if you want to evaluate our okapi vietnamese model https huggingface co uonlp okapi vi bloom you could run bash bash scripts run sh vi uonlp okapi vi bloom leaderboard we maintain a leaderboard https huggingface co spaces uonlp open multilingual llm leaderboard for tracking the progress of multilingual llm acknowledgements our framework inherited largely from the lm evaluation harness https github com eleutherai lm evaluation harness repo from eleutherai please also kindly cite their repo if you use the code citation if you use the data model or code in this repository please cite bibtex article dac2023okapi title okapi instruction tuned large language models in multiple languages with reinforcement learning from human feedback author dac lai viet and van nguyen chien and ngo nghia trung and nguyen thuat and dernoncourt franck and rossi ryan a and nguyen thien huu journal arxiv e prints pages arxiv 2307 year 2023 | datasets evaluation evaluation-datasets evaluation-framework language-model large-language-models multilingual natural-language-processing nlp | ai |
Malware | malware this is a reading list for those interested in studying malware if you have any suggestions please send a pull request linux malware 2019 ieee s p lbm a security framework for peripherals within the linux kernel https www computer org csdl proceedings article sp 2019 666000a678 17d45w1oa3n 2018 ieee s p understanding linux malware https rud is dl ieee sp 2018 435301a870 pdf 2018 class 9 adversarial malware detection https secml github io class9 2017 usenix security understanding the mirai botnet https www usenix org system files conference usenixsecurity17 sec17 antonakakis pdf gclid 5b024b3b7304f5 75584304 5b024b3b730553 60141230 utm source xakep utm campaign mention135460 utm medium inline utm content lnk681856874400 2016 github mirai source code https github com jgamblin mirai source code blob master forumpost md 2016 blog mmd 0056 2016 linux mirai how an old elf malcode is recycled http blog malwaremustdie org 2016 08 mmd 0056 2016 linuxmirai just html iot malware 2020 s p security update labels establishing economic incentives for security patching of iot consumer products https www computer org csdl pds api csdl proceedings download article 349700a346 pdf token eyj0exaioijkv1qilcjhbgcioijiuzi1nij9 eyjpc3mioijjc2rsx2fwasisimf1zci6imnzzgxfyxbpx2rvd25sb2fkx3rva2vuiiwic3viijoiyw5vbnltb3vzqgnvbxb1dgvylm9yzyisimvtywlsijoiyw5vbnltb3vzqgnvbxb1dgvylm9yzyisimv4cci6mtu4njeznty0nn0 er0j42gk6of wct 7ckqabwg0zp b2fhkuajzxvhngs 2019 arxiv hades iot a practical host based anomaly detection system 2019 dsc honor among thieves towards understanding the dynamics and interdependencies in iot botnets https www cs ucf edu mohaisen doc dsc19iot pdf 2019 usenix cset idapro for iot malware analysis https www usenix org system files cset19 paper g pdf for iot devices extended version https arxiv org pdf 1905 01027 pdf 2019 asiaccs hades iot a practical host based anomaly detection systemfor iot devices https dl acm org doi pdf 10 1145 3321705 3329847 2019 ndss measurement and analysis of hajime a peer to peer iot botnet https www cs umd edu dml papers hajime ndss19 pdf 2019 ndss cleaning up the internet of evil things real world evidence on isp and consumer efforts to remove mirai https www ndss symposium org wp content uploads 2019 02 ndss2019 02b 2 cetin paper pdf 2018 usenix acquisitional rule based engine for discovering internet of thing devices https www usenix org system files conference usenixsecurity18 sec18 feng pdf 2018 arxiv a survey of machine and deep learning methods for internet of things iot security https arxiv org abs 1807 11023 2018 raid before toasters rise up a view into the emerging iot threat landscape https link springer com content pdf 10 1007 2f978 3 030 00470 5 26 pdf 2018 blackhat iot malware comprehensive survey analysis framework and case studies https i blackhat com us 18 thu august 9 us 18 costin zaddach iot malware comprehensive survey analysis framework and case studies wp pdf 2018 arxiv autobotcatcher blockchain based p2p botnet detection for the internet of things https arxiv org pdf 1809 10775 pdf 2017 milcom the mirai botnet and the iot zombie armies https cs gmu edu astavrou research themiraibotnetandtheiotzombiearmies pdf 2016 rapiditynetworks hajime analysis of a decentralized internet worm for iot devices https security rapiditynetworks com publications 2016 10 16 hajime pdf analyzing the propagation of iot botnets from dns leakage http www cs umd edu projects droot botnet pdf honey pot 2019 mobisys understanding fileless attacks on linux based iot devices with honeycloud https dl acm org doi pdf 10 1145 3307334 3326083 2018 arxiv thingpot an interactive internet of things honeypot https arxiv org pdf 1807 04114 pdf 2017 blackhat iotcandyjar towards an intelligent interaction honeypot for iot devices https www blackhat com docs us 17 thursday us 17 luo iotcandyjar towards an intelligent interaction honeypot for iot devices wp pdf 2017 cpss siphon towards scalable high interaction physical honeypots https dl acm org citation cfm id 3055192 2015 woot iotpot analysing the rise of iot compromises https www usenix org system files conference woot15 woot15 paper pa pdf sandbox 2020 s p pmp cost effective forced execution with probabilistic memory pre planning https www cs purdue edu homes zhan3299 res sp20 pdf 2019 lisa multiplatform linux sandbox for analyzing iot malware http excel fit vutbr cz submissions 2019 058 58 pdf 2018 thesis evaluating open source malware sandboxes with linux malware https pdfs semanticscholar org a0c5 f13e0313011b771b80fcc2346af73a127895 pdf ga 2 133957646 1445457844 1586099900 1317523662 1585860204 attack graphs 2018 tifs using bayesian networks for probabilistic identification of zero day attack paths https ieeexplore ieee org stamp stamp jsp arnumber 8327913 2018 dbsec assessing attack impact on business processes by interconnecting attack graphs and entity dependency graphs https ws680 nist gov publication get pdf cfm pub id 925926 2016 cns towards probabilistic identification of zero day attack paths http athena ecs csus edu sun paper zepro pdf 2013 journal of computer security aggregating vulnerability metrics in enterprise networks using attack graphs http www cse usf edu xou publications jcs13 pdf 2013 esorics patrol revealing zero day attack paths through network wide system object dependencies https s2 ist psu edu paper esorics2013 patrol pdf malware detection 2018 arxiv malicious software detection and classification utilizing temporal graphs of system call group relations https arxiv org pdf 1812 10748 pdf 2018 iot s p combining mud policies with sdn for iot intrusion detection https dl acm org citation cfm doid 3229565 3229571 2018 iot s p clear as mud generating validating and applying iot behavioral profiles https dl acm org citation cfm id 3229566 2018 arxiv device type specific anomaly detection diot a self learning system for detecting compromised iot devices https arxiv org abs 1804 07474 2018 ieee transactions on industrial informatics detection of malicious code variants based on deep learning https ieeexplore ieee org stamp stamp jsp arnumber 8330042 2018 ieee access malware visualization for fine grained classification https ieeexplore ieee org stamp stamp jsp arnumber 8290767 2015 international journal of information security malware analysis using visualized images and entropy graphs https www eecis udel edu cavazos cisc850 spring2017 papers entropy graphs pdf 2013 icassp large scale malware classification using random projections and neural networks https www cs toronto edu gdahl papers malwarerandomprojections icassp2013 pdf network level detection 2018 arxiv iot keeper securing iot communications in edge networks https arxiv org abs 1810 08415 systems security 2019 s p holmes real time apt detection through correlation of suspicious information flows https csdl computer org csdl proceedings sp 2019 6660 00 666000a430 pdf 2018 ccs tiresias predicting security events through deep learning https seclab bu edu people gianluca papers tiresias ccs2018 pdf 2018 ccs towards paving the way for large scale windows malware analysis generic binary unpacking with orders of magnitude performance boost https dl acm org citation cfm id 3243771 2018 ndss towards a timely causality analysis for enterprise security https www princeton edu yushan priotracker ndss18 pdf 2017 anatomy of an iot malware attack https developer ibm com articles iot anatomy iot malware attack 2014 tifs data centric os kernel malware characterization http citeseerx ist psu edu viewdoc download doi 10 1 1 708 4911 rep rep1 type pdf software and application security 2018 ccs lemna explaining deep learning based security applications http people cs vt edu gangwang ccs18 pdf 2018 ccs homonit monitoring smart home apps from encrypted traffic https dl acm org citation cfm id 3243820 2018 iot s p web based attacks to discover and control local iot devices http delivery acm org 10 1145 3230000 3229568 p29 acar pdf ip 130 127 49 160 id 3229568 acc public key a79d83b43e50b5b8 2eeb6dcc30042720a5 2e4d4702b0c3e38b35 2e4d4702b0c3e38b35 acm 1546977217 fd6741302d2616bb42e082b37886c2f7 android malware 2017 raid android malware clustering through malicious payload mining https pdfs semanticscholar org 6d4a eaa2daeb2a0f0a907bf456580bee64800b07 pdf 2014 ccs semantics aware android malware classification using weighted contextual api dependency graphs http lilicoding github io sa3repo papers 2014 zhang2014semantics pdf patches automated patch management and the challenges from iot https searchsecurity techtarget com tip automated patch management and the challenges from iot patch challenges securing the iot update process https www ntia doc gov files ntia publications iot patching capabilities 0717 pdf overall update process 7 patch command examples to apply diff patch files in linux https www thegeekstuff com 2014 12 patch command examples useful patch commands patch management is the catalyst for growth in the iot industry https datafloq com read patch management catalyst growth internet things 5106 software cloud management suite fixing upgrading and patching iot devices can be a real nightmare https www networkworld com article 3222651 internet of things fixing upgrading and patching iot devices can be a real nightmare html some iot devices are hard to update fundamentals of iot device management http iotdesign embedded computing com articles fundamentals of iot device management update time matters the connected world with great power comes great responsibility https www infosecurity magazine com opinions the connected world great power things are not smart enough to be managed easily comparison of open source configuration management software https en wikipedia org wiki comparison of open source configuration management software cves 2017 unauthorized flir lorex cloud access https depthsecurity com blog unauthorized flir cloud access 2016 security vulnerabilities published in 2016 cvss score 7 https www cvedetails com vulnerability list php vendor id 33 product id 47 version id page 1 hasexp 0 opdos 0 opec 0 opov 0 opcsrf 0 opgpriv 0 opsqli 0 opxss 0 opdirt 0 opmemc 0 ophttprs 0 opbyp 0 opfileinc 0 opginf 0 cvssscoremin 7 cvssscoremax 0 year 2016 month 0 cweid 0 order 1 trc 85 sha 0916de636a03e97ea8af9cba7dede14c7d7398d7 2016 trane iot vulnerabilities https blog talosintelligence com 2016 02 trane iot html vulnerability in trane thermostats 2013 dahua dvr authentication bypass cve 2013 6117 https depthsecurity com blog dahua dvr authentication bypass cve 2013 6117 2018 owasp iot vulnerabilities project https www owasp org index php owasp internet of things project tab iot vulnerabilities 2018 owasp iot attack surface areas project https www owasp org index php owasp internet of things project tab iot attack surface areas 2018 vulnogram https vulnogram github io firmware analysis 2018 ndss what you corrupt is not what you crash challenges in fuzzing embedded devices http s3 eurecom fr docs ndss18 muench pdf 2018 usenix security inception system wide security testing of real world embedded systems software https www usenix org system files conference usenixsecurity18 sec18 corteggiani pdf 2017 ifip sec towards automated classification of firmware images and identification of embedded devices http firmware re ifipsec17 2014 usenix security a large scale analysis of the security of embedded firmwares https www usenix org system files conference usenixsecurity14 sec14 paper costin pdf binary analysis 2017 ccs neural network based graph embedding for cross platform binary code similarity detection https arxiv org abs 1708 06525 adversarial examples for malware detection 2018 arxiv adversarial examples attacks on machine learning based malware visualization detection methods https arxiv org pdf 1808 01546 pdf | server |
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h2o-tutorials | h2o tutorials this document contains tutorials and training materials for h2o 3 if you find any problems with the tutorial code please open an issue in this repository for general h2o questions please post those to stack overflow using the h2o tag http stackoverflow com questions tagged h2o or join the h2o stream google group https groups google com forum forum h2ostream for questions that don t fit into the stack overflow format finding tutorial material in github there are a number of tutorials on all sorts of topics in this repo to help you get started here are some of the most useful topics in both r and python r tutorials intro to h2o in r https github com h2oai h2o tutorials blob master h2o open tour 2016 chicago intro to h2o r h2o grid search model selection in r https github com h2oai h2o tutorials blob master h2o open tour 2016 chicago grid search model selection r h2o deep learning in r http htmlpreview github io https github com ledell sldm4 h2o blob master sldm4 deeplearning h2o html h2o stacked ensembles in r http docs h2o ai h2o latest stable h2o docs data science stacked ensembles html h2o automl in r https github com h2oai h2o tutorials blob master h2o world 2017 automl readme md latinr 2019 h2o tutorial https github com ledell latinr 2019 h2o tutorial broad overview of all the above topics python tutorials intro to h2o in python https github com h2oai h2o tutorials blob master h2o open tour 2016 chicago intro to h2o ipynb h2o grid search model selection in python https github com h2oai h2o tutorials blob master h2o open tour 2016 chicago grid search model selection ipynb h2o stacked ensembles in python http docs h2o ai h2o latest stable h2o docs data science stacked ensembles html h2o automl in python https github com h2oai h2o tutorials blob master h2o world 2017 automl readme md most current material tutorials in the master branch are intended to work with the lastest stable version of h2o url training material https github com h2oai h2o tutorials blob master summary md latest stable h2o release http h2o ai download historical events tutorial versions in named branches are snapshotted for specific events scripts should work unchanged for the version of h2o used at that time h2o world 2017 training url training material https github com h2oai h2o tutorials tree master h2o world 2017 readme md wheeler 2 h2o release http h2o release s3 amazonaws com h2o rel wheeler 2 index html h2o world 2015 training url training material https github com h2oai h2o tutorials blob h2o world 2015 training summary md tibshirani 3 h2o release http h2o release s3 amazonaws com h2o rel tibshirani 3 index html requirements for most tutorials using python you can install dependent modules to your environment by running the following commands as current user pip install r requirements txt as root user sudo e pip install r requirements txt note if you are behind a corporate proxy you may need to set environment variables for https proxy accordingly if you are behind a corporate proxy export https proxy https user password proxy server proxy port as current user pip install r requirements txt if you are behind a corporate proxy export https proxy https user password proxy server proxy port as root user sudo e pip install r requirements txt | h2o tutorial machine-learning data-science deep-learning python r | ai |
Charlotte-Project | h1 align center charlotte project h1 h3 align center projeto desenvolvido em equipe para a cadeira de engenharia de software do 5 per odo da faculdade h3 p align center a href p gina de login login a nbsp nbsp nbsp nbsp nbsp nbsp a href p gina de registro resgistro a nbsp nbsp nbsp nbsp nbsp nbsp a href rea do administrador administrador a nbsp nbsp nbsp nbsp nbsp nbsp a href projeto projeto a nbsp nbsp nbsp nbsp nbsp nbsp a href ef b8 8f tecnologias tecnologias a nbsp nbsp nbsp nbsp nbsp nbsp a href colaboradores colaboradores a p br p gina de login p align center img alt visualiza o da tela de login src view public img login page png width 100 p br rea do administrador p align center img alt visualiza o da area do administrador src view public img adm page png width 100 p br projeto projeto de gerenciamento de entregadores com o intuito de ajudar na dificuldade em realizar os c lculos dos pagamentos dos entregadores e a falta de agilidade na gera o de relat rios automatizando a etapa de calcular e atualizar os dados dos entregadores br tecnologias o projeto foi realizado com as seguintes tecnologia php html css java br colaboradores nome danilo jean da silva github gigazin https github com gigazin nome fl vio v nicius da silva santos github fvssantos https github com fvssantos nome lucas antonio miranda de andrade github lucasantonio20 https github com lucasantonio20 nome luiz fernando leite github luizfernando o https github com luizfernando o nome luiz fernando soares ximenes accioly github nandincci https github com nandincci nome wallison willian de oliveira melo github wallisonwilliam https github com wallisonwilliam br | server |
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FreeRTOS-Cellular-Interface-Reference-Quectel-BG96 | freertos cellular interface reference quectel bg96 introduction this repository provides the reference implementation as cellular module ports for quectel bg96 https www quectel com product lte bg96 cat m1 nb1 egprs this repository should be used with freertos cellular interface https github com freertos freertos cellular interface see freertos cellular interface readme https github com freertos freertos cellular interface blob main readme md for more details use case there is also an use case at mqtt mutual auth demo with bg96 https github com freertos freertos tree main freertos plus demo freertos cellular interface windows simulator mqtt mutual auth demo with bg96 as a demonstration to run bg96 with freertos windows simulator how does community members to contribute a cellular module port we provide a repository freertos cellular interface community supported ports https github com freertos freertos cellular interface community supported ports for community members to contribute their modules see how to contribute a cellular module port https github com freertos freertos cellular interface community supported ports how to contribute a cellular module port for more details security see contributing contributing md security issue notifications for more information license the freertos cellular interface library is distributed under mit open source license the code in this repository is licensed under the mit license see the license license file | os |
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seer | seer software engineering evidence repository is a searchable database of evidence curated by software engineering experts from the software engineering research lab serl at aut created by team 11 for cise s2 2020 live demo https seer team11 herokuapp com | server |
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L2W-Resume-Website | learning2work resume portfolio website this is the resume portfolio website i created for the pearson learning2work cloud careers workshop which ran from july 19 2021 to july 23 2021 as part of the hiring process for the pearson learning2work cloud engineering apprenticeship | cloud |
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Birthday-Reminder-Application | birthday reminder application a simple android based reminder application through which users can set notifications for birthdays on a particular date and time br br b how it works b br br when the user clicks on the add birthday option a page is displayed where the user is provided with options to enter the name and select the date and time br android datepicker user interface control is used to select the date by day month and year br android timepicker user interface control is used for selecting the time in either 24 hour format or am pm mode br alarm manager is used to set the alarm br all information required to trigger the alarm are stored in the sqlite3 database br manifests file is updated with necessary declarations and permissions br screenshots p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657701812 png width 200 p align center home screen p p p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657701836 png width 200 p align center enter details p p p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657702169 png width 200 p align center select date p p p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657702186 png width 200 p align center select time p p p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657702202 png width 200 p align center view reminders p p p align center img src https github com dishaadiga birthday reminder application blob master screenshots screenshot 1657702220 png width 200 p align center notification p p | front_end |
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linkedin-skill-assessments-quizzes | linkedin skill assessments answers a id top page a owner https img shields io badge owner ebazhanov blue https github com ebazhanov admin https img shields io badge admin monkrus red https github com monkrus playground https img shields io badge resources playground orange https github com linkedin faq open issues https img shields io github issues ebazhanov linkedin skill assessments quizzes https github com ebazhanov linkedin skill assessments quizzes issues analytics https img shields io badge analytics ossinsight red https ossinsight io analyze ebazhanov linkedin skill assessments quizzes views https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2febazhanov 2flinkedin skill assessments quizzes count bg 2379c83d title bg 23555555 icon icon color 23e7e7e7 title hits edge flat false https hits seeyoufarm com telegram https badgen net badge icon telegram icon telegram label https t me lcpxl7kd7gvjmgm6 discord https img shields io badge discord join blue logo discord logocolor white color blue https discord gg nkcyjjw3mw all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 1562 orange svg style flat square contributors all contributors badge end remember i am not asking for any support or donation if you find any source asking for that it is fake warning disclaimer the owners of this repository are not liable for any illegal usage of the content provided in this repository the content is provided for informational and educational purposes only and any actions taken by users of this repository are the responsibility of the user by accessing this repository you agree to hold the owners harmless from any claims damages or expenses arising from the use of the information provided go to see the last contributor https github com ebazhanov linkedin skill assessments quizzes blob main readme md adding answers please add an explanation or reference link to your answers this will help anyone to better learn concepts they may not have a great understanding about use grammarly https www grammarly com to ensure your spelling and grammar are correct looking to participate in hacktoberfest 2023 please read the rules carefully hacktoberfest participation guidelines https hacktoberfest com participation announcements 1 column translation have links to quizzes in different languages like es fr it and de 2 if you want to meet each other or discuss quiz related problems or maybe ask for skills endorsement just join the discord chat 3 playground before taking quiz using md2practice web app https linkedin faq github io md2practice skill assessments quizzes web app https linkedin faq github io skill assessments quizzes linkedin quizzed with kodyfire terminal https github com linkedin faq linkedin quizzes kodyfire want to contribute here https github com linkedin faq is the source code need help telegram channel https img shields 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22 adobe xd adobe xd quiz es md img src assets it svg width 22 adobe xd adobe xd quiz it md img src assets fr svg width 22 adobe xd adobe xd quiz fr md 16 13 after effects after effects after effects quiz md needs updating img src assets es svg width 22 after effects after effects quiz es md img src assets it svg width 22 after effects after effects quiz it md img src assets fr svg width 22 after effects after effects quiz fr md 24 13 agile methodologies agile methodologies agile methodologies quiz md needs updating img src assets es svg width 22 agile methodologies agile methodologies quiz es md img src assets it svg width 22 agile methodologies agile methodologies quiz it md img src assets fr svg width 22 agile methodologies agile methodologies quiz fr md 116 116 its0x08 https github com its0x08 android android android quiz md img src assets es svg width 22 android android quiz es md img src assets it svg width 22 android android quiz it md img src assets fr svg width 22 android 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taletski https github com taletski procw https github com procw msteiner96 https github com msteiner96 declarck https github com declarck jquery jquery jquery quiz md 84 77 declarck https github com declarck json json json quiz md needs updating 87 86 ihamzakhanzada https github com ihamzakhanzada keynote keynote keynote quiz md 14 0 kotlin kotlin kotlin quiz md 78 78 itsneverlate https github com itsneverlate husseinhj https github com husseinhj linux linux linux quiz md 82 78 d4rio https github com d4rio amsal1 https github com amsal1 logic pro logic pro logic pro quiz md 82 78 machine learning machine learning machine learning quiz md 98 98 aaronwangj https github com aaronwangj antra0497 https github com antra0497 matlab matlab matlab quiz md 70 70 tm sanjay https github com tm sanjay maven maven maven quiz md 53 50 microsoft access microsoft access microsoft access quiz md 30 28 drmegalomaniac https github com drmegalomaniac microsoft azure microsoft azure microsoft azure quiz md 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ruslanbes https github com ruslanbes node js node js node js quiz md 79 76 pbachman https github com pbachman nosql nosql nosql quiz md 56 55 objective c objective c objective c quiz md 40 38 oop object oriented programming object oriented programming quiz md 102 82 declarck https github com declarck gaurovgiri https github com gaurovgiri php php php quiz md 89 79 ruslanbes https github com ruslanbes msteiner96 https github com msteiner96 pro tools pro tools pro tools quiz md 2 2 python python python quiz md img src assets es svg width 22 python python quiz es md 176 176 tik9 https github com tik9 amsal1 https github com amsal1 declarck https github com declarck tsg405 https github com tsg405 quickbooks quickbooks quickbooks quiz md needs updating 67 39 r r r quiz md 52 52 gregglind https github com gregglind react js reactjs reactjs quiz md img src assets es svg width 22 reactjs reactjs quiz es md 100 100 robtables https github com robtables bandinopla https github com bandinopla rest api rest api rest api quiz md 65 65 revit revit revit quiz md needs updating 14 0 ruby on rails ruby on rails ruby on rails quiz md 59 59 gudata https github com gudata rust rust rust quiz md 32 32 bobbybyrne https github com bobbybyrne emanuele em https github com emanuele em scala scala scala quiz md 52 48 search engine optimization seo search engine optimization search engine optimization quiz md 81 81 sharepoint sharepoint sharepoint quiz md needs updating 53 38 sketchup sketchup sketchup quiz md 2 2 solidworks solidworks solidworks quiz md needs updating 57 57 bhargavpatel1244 https github com bhargavpatel1244 spring framework spring framework spring framework quiz md 67 67 swift swift swift quiz md 67 67 transact sql t sql t sql t sql quiz md 45 42 beefydog https github com beefydog benvlodgi https github com benvlodgi unity unity unity quiz md needs updating 47 46 uno sebastian https github com uno sebastian visual basic for applications vba vba vba quiz md needs updating 36 34 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author siddhant40 title code a td td align center valign top width 14 28 a href https github com samara6855 img src https avatars githubusercontent com u 101248119 v 4 s 100 width 100px alt yasani samara simha reddy br sub b yasani samara simha reddy b sub a br a href design samara6855 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author samara6855 title code a td tr tr td align center valign top width 14 28 a href https github com biswabaibhab007 img src https avatars githubusercontent com u 94667039 v 4 s 100 width 100px alt biswa baibhab subudhi br sub b biswa baibhab subudhi b sub a br a href design biswabaibhab007 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author biswabaibhab007 title code a td td align center valign top width 14 28 a href https github com rs labhub img src https avatars githubusercontent com u 117426013 v 4 s 100 width 100px alt rohan sharma br sub b rohan sharma b sub a br a href design rs labhub title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author rs labhub title code a td td align center valign top width 14 28 a href https akxai vercel app img src https avatars githubusercontent com u 85357123 v 4 s 100 width 100px alt akshay aileni br sub b akshay aileni b sub a br a href design akxai title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author akxai title code a td td align center valign top width 14 28 a href https github com janumalaakhilendra img src https avatars githubusercontent com u 82641474 v 4 s 100 width 100px alt janumala akhilendra br sub b janumala akhilendra b sub a br a href design janumalaakhilendra title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author janumalaakhilendra title code a td td align center valign top width 14 28 a href https github com farukhs52 img src https avatars githubusercontent com u 129654632 v 4 s 100 width 100px alt farookh zaheer siddiqui br sub b farookh zaheer siddiqui b sub a br a href design farukhs52 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author farukhs52 title code a td td align center valign top width 14 28 a href https github com ackermanlevi1 img src https avatars githubusercontent com u 114604529 v 4 s 100 width 100px alt aryan br sub b aryan b sub a br a href design ackermanlevi1 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author ackermanlevi1 title code a td td align center valign top width 14 28 a href https github com saharshjain78 img src https avatars githubusercontent com u 117359137 v 4 s 100 width 100px alt saharsh jain br sub b saharsh jain b sub a br a href design saharshjain78 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author saharshjain78 title code a td tr tr td align center valign top width 14 28 a href https github com ayan joshi img src https avatars githubusercontent com u 96243602 v 4 s 100 width 100px alt ayan joshi br sub b ayan joshi b sub a br a href design ayan joshi title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author ayan joshi title code a td td align center valign top width 14 28 a href https github com himanshumahto img src https avatars githubusercontent com u 93067059 v 4 s 100 width 100px alt himanshu kumar mahto br sub b himanshu kumar mahto b sub a br a href design himanshumahto title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author himanshumahto title code a td td align center valign top width 14 28 a href https github com priyanshuthapliyal2005 img src https avatars githubusercontent com u 114170980 v 4 s 100 width 100px alt priyanshu thapliyal br sub b priyanshu thapliyal b sub a br a href design priyanshuthapliyal2005 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author priyanshuthapliyal2005 title code a td td align center valign top width 14 28 a href https github com piyushk77 img src https avatars githubusercontent com u 109459034 v 4 s 100 width 100px alt piyush katyal br sub b piyush katyal b sub a br a href design piyushk77 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author piyushk77 title code a td td align center valign top width 14 28 a href https github com margaret hu img src https avatars githubusercontent com u 70117942 v 4 s 100 width 100px alt margaret hu br sub b margaret hu b sub a br a href design margaret hu title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author margaret hu title code a td td align center valign top width 14 28 a href http youtube com linuxsploitofficial img src https avatars githubusercontent com u 68427058 v 4 s 100 width 100px alt bilal hameed br sub b bilal hameed b sub a br a href design linuxsploit title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author linuxsploit title code a td td align center valign top width 14 28 a href https github com ivikashgorai img src https avatars githubusercontent com u 114097162 v 4 s 100 width 100px alt vikash br sub b vikash b sub a br a href design ivikashgorai title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author ivikashgorai title code a td tr tr td align center valign top width 14 28 a href https github com rajarshi101 img src https avatars githubusercontent com u 109743720 v 4 s 100 width 100px alt rajarshi sengupta br sub b rajarshi sengupta b sub a br a href design rajarshi101 title design a a href https github com ebazhanov linkedin skill assessments quizzes commits author rajarshi101 title code a td tr tbody table markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome a id bottom page a go back to the top of the page top page contributor over time github contributor over time https contributor overtime api git contributor com contributors svg chart contributorovertime repo ebazhanov linkedin skill assessments quizzes https git contributor com chart contributorovertime repo ebazhanov linkedin skill assessments quizzes stargazers over time stargazers over time https starchart cc ebazhanov linkedin quiz assessments svg https starchart cc ebazhanov linkedin quiz assessments visualisation of this repository by gource https www youtube com watch v 24czvytc5d4 https www youtube com watch v 24czvytc5d4 gource gource gif | linkedin quiz-questions answers assessment quiz linkedin-questions hacktoberfest hacktoberfest2020 exam skills hacktoberfest2021 golang english france german hacktoberfest2022 hacktoberfest2023 | ai |
aws-mqtt | aws iot mqtt client npm https nodei co npm aws mqtt png global true https nodei co npm aws mqtt this module implements a client to connect to aws iot mqtt broker using websockets it can be used in a browser as well as in node js environment the main class exported from this package is a subclass of mqttclient https github com mqttjs mqtt js and exposes the same api what this package adds is the following aws iot broker url signing logic refreshing of credentials and re signing url on re connects custom websocket stream implementation that is more efficient then out of the box mqtt js websocket stream combo this package handles corking uncorking of the stream whereas websocket stream sends a separate frame per each byte written validated connection options that work with aws mqtt broker pins versions of mqtt and aws signature v4 to a known tested combo in the past lax version constraints broke this package nodeclient that works in node need to install ws separately motivation up until now an implementation of a realtime in browser application required the use of either an external service e g pusher https pusher com pubnub https www pubnub com or roll your own servers e g using socket io http socket io that maintain connections with browsers and need scaling to respond to the changes in number of active users using aws iot mqtt broker as the realtime backend provides a low cost scalable service for your application disclaimer aws documentation does not explicitly promote the use of iot mqtt broker as a general purpose pub sub broker only the use case of iot devices communication is explicitly described while technically there is nothing preventing general purpose browsers and servers connecting to the broker such as implemented by this library aws may change its terms of service or implement some broker constraints to prevent such use case use at your own risk usage npm install aws sdk aws mqtt save when using in a node environment not browser install websocket implementation e g ws npm install ws save in browser the example below assumes the use of babel webpack aws sdk now officially supports bundling with webpack with a few things to know http docs aws amazon com sdk for javascript v2 developer guide webpack html javascript import aws from aws sdk global import awsmqttclient from aws mqtt aws config region us east 1 your region aws config credentials see aws setup and security below const client new awsmqttclient region aws config region credentials aws config credentials endpoint iot us east 1 amazonaws com note see below on how to get the endpoint domain expires 600 sign url with expiration of 600 seconds clientid mqtt client math floor math random 100000 1 clientid to register with mqtt broker need to be unique per client will topic willmsg payload connection closed abnormally qos 0 retain false client on connect client subscribe mytopic client on message topic message console log topic message client on close client on offline the client object in the above example is a subclass of mqttclient class from mqtt js for events and api see the docs https github com mqttjs mqtt js api in node js the same usage as in browser but require a different module require aws mqtt lib nodeclient instead of require aws mqtt ws https github com websockets ws also needs to be installed install ws if you don t have it installed yet bash npm install ws 6 0 0 javascript in node v6 x const aws require aws sdk const awsmqttclient require aws mqtt lib nodeclient aws config region us east 1 aws config credentials const client new awsmqttclient region aws config region credentials aws config credentials endpoint iot us east 1 amazonaws com note get this value with aws iot describe endpoint clientid mqtt client math floor math random 100000 1 clientid to register with mqtt broker need to be unique per client will topic willmsg payload connection closed abnormally qos 0 retain false in aws lambda functions creating a nodeclient instance will set up internal timers and the node js process will not exit until you call client end this is fine if you are developing a long running server app that subscribes and or publishes messages for ephemeral functions such as aws lambda this approach will cause the function invocation to timeout to publish a single message to a topic and disconnect require publishmessage and use it like this javascript const aws require aws sdk const publishmessage require aws mqtt lib publishmessage aws config region us east 1 aws config credentials const config region aws config region credentials aws config credentials endpoint iot us east 1 amazonaws com publish returns a promise publishmessage config mytopic my message then console log console error using mqtt will the will option will send a message by the broker automatically when the client disconnect badly for more information of how to use it look the mqtt client https github com mqttjs mqtt js mqttclientstreambuilder options option on the mqtt js https github com mqttjs mqtt js documentation aws setup and security endpoint see https aws amazon com blogs iot aws iot core ats endpoints if you don t use ats endpoint as was specified in this readme some time ago you will get net err cert authority invalid error on connect aws iot describe endpoint endpoint type iot data ats policies there is an example cloudformation template tests cf stack yml and deploy script tests deploy sh to simplify creation of cognito identity pool first the required primer on aws javascript configuration http docs aws amazon com sdk for javascript v2 developer guide configuring the jssdk html there are multiple ways to configure aws based on the use case for example for usage in a web browser by un authenticated users the best practice is to use cognito identity when a cognito identity pool is created it s assigned an iam role that is used by un authenticated users that role needs to be given a minimum required permissions policy following the principle of least privilege read more on aws iot policies http docs aws amazon com iot latest developerguide iot policies html an example of the most permissive policy good for development to avoid head scratching on why things don t work not recommended for production json version 2012 10 17 statement effect allow action iot resource an example of a more restrictive policy statement for an un authenticated user tweak as you see fit note the use of iot clientid to dynamically limit the topics for more information see https docs aws amazon com iot latest developerguide example iot policies elements html https docs aws amazon com iot latest developerguide iot action resources html note how the format of resource is different for different actions json version 2012 10 17 statement effect allow action iot connect resource arn aws iot us east 1 client iot clientid effect allow action iot subscribe resource arn aws iot us east 1 topicfilter foo bar effect allow action iot receive resource arn aws iot us east 1 topic foo bar effect allow action iot publish resource arn aws iot us east 1 topic iot clientid quick example using cognito identity un authenticated users approach create a cognito identity pool in aws under cognito federated identities image of step1 https github com kmamykin aws mqtt raw master examples identitypoolstep1 jpg accept default create iam roles for authenticated and un authenticated identities image of step2 https github com kmamykin aws mqtt raw master examples identitypoolstep2 jpg go to iam roles and find the role for the un authenticated role created for the identity pool image of step3 https github com kmamykin aws mqtt raw master examples identitypoolstep3 jpg edit the inline policy to allow access to connect subscribe and publish to iot message broker note the policy shown below is very open for an un authenticated user you can start with that to make sure things work and then tighten it up image of step4 https github com kmamykin aws mqtt raw master examples identitypoolstep4 jpg get the identity pool id you just created bash aws cognito identity list identity pools max results 5 use the identity pool id in you app credentials javascript aws config region e g us west 2 aws config credentials new aws cognitoidentitycredentials identitypoolid pass aws config credentials to awsmqtt connect methods examples in examples folder there are two example projects chat the minimalistic example of using awsmqtt in browser with webpack node publisher contains two examples 1 timepublisher js how to connect to aws iot mqtt broker subscribe and publish messages 2 publish js how to publish one message and disconnect e g node publish js chat hello before running any of the examples copy examples config example js to examples config js and fill in your values frequently asked questions memory leak if your browser is reporting a memory leak this could be caused by your cleanup strategy of event listeners by default most browsers allow 10 event listeners to listen to a single emitting event from a singular source so if you have more than 10 listeners for an event ie client on message where message is the event then you will get this error if your client is disconnecting or reconnecting we recommend that you cleanup the old event listeners with client removealllisteners event where event could be message or other or client removelistener event fn frequently disconnecting there are a few reasons why your client may be disconnecting unexpectedly here are a few cases clientid in the connect configuration must be unique across each client if clients a and b use the same clientid then when b tries to connect client a will get disconnected by the broker because this package attempts to reconnect you when disconnected client a will reconnect which will cause client b to disconnect and an infinite cycle ensues note that clientid if not passed in the config will use mqtt client math floor math random 100000 1 to generate a unique clientid if you are passing in clientid to override the default make sure it is unique permissions with aws iam could be set up incorrectly | server |
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Computer-Vision-OpenCV3-Udemy | computer vision opencv3 python machine learning this course is taught by computer vision expert from university of edinberg the course is implementation oriented and less of theorey if you do not care about the theorey and are looking for a quick implememntation guide on popular opencv algorithms this is the course to take course website https www udemy com master computer vision with opencv in python what i learnt from this course key concepts of computer vision opencv perform image manipulations such as transformations cropping blurring thresholding edge detection and cropping segment images by understanding contours circle and line detection how to approximate contours do contour filtering and ordering as well as approximations feature detection sift surf fast brief orb for object detection implement object detection for faces people cars extract facial landmarks for face analysis applying filters and face swaps implement machine learning in computer vision for handwritten digit recognition implement facial recognition implement and understand motion analysis object tracking use basic computational photography techniques for photo restoration eliminate marks lines creases and smudges from old damaged photos getting started in deep learning | ai |
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Capstone_Robot_Arm | 2023 capston design 2023 1 inverse kinematics 3 5 https github com sw801733 capstone robot arm assets 104549849 64c4470a 2ccb 41ee adf5 a7d8bb7e0561 https www youtube com watch v ozfxrjzoal8 github inverse kinematics https github com sw801733 inverse kinematics object detection https github com taebee00 hw 3d https github com surpise hw 3d object detection https github com wooyoungman 2023 03 02 2023 06 02 249829064 4a85fba2 e642 46bc 990d b090e5c34f6f https github com sw801733 capstone robot arm assets 104549849 f1d231e2 db79 4d19 9bfe bcb6e114e8a2 1 2 3 inverse kinematics https github com sw801733 capstone robot arm assets 104549849 54f39c26 561d 4bb8 8ad7 1f2cf02f4414 https github com sw801733 capstone robot arm assets 104549849 9737f79b d4ae 4c01 8cd7 8b832110e7e6 1 z r z 2 1 x y r x r y 3 2 x y r x r y 4 3 5 x y r x r y 5 4 3d autodesk fusion 360 3d https github com taebee00 capstone robot arm assets 104549849 62fd2d3c c90a 48e5 b4e3 37f964f8c54f https github com taebee00 capstone robot arm assets 104549849 3c022401 e23d 462e 888f 298aa8991bbb https github com taebee00 capstone robot arm assets 104549849 29357338 210e 4e22 abd9 18fda63e682c https github com taebee00 capstone robot arm assets 104549849 ae1919b2 e7aa 497e a86a a75f768e23a9 jetson nano jetson inference camera capture dataset 1200 dataset 6 2 2 train valdation test https github com dusty nv jetson inference blob master docs pytorch collect detection md https github com sw801733 capstone robot arm assets 104549849 ff9a5511 6ec0 4758 b436 e0740d8d210b img src https github com sw801733 capstone robot arm assets 104549849 c9cb8214 5580 44db 9052 d326d1cd59cb width 50 height 50 br 4 1 15 inverse kinematics inverse kinematics inverse kinematics 1 2 3 3 3 3 inverse kinematics 4 2 2 inverse kinematics 3 3 inverse kinematics 2 2 inverse kinematics 3 3 inverse kinematics 2 2 https github com sw801733 capstone robot arm assets 104549849 cc449208 99a5 41a6 a564 286ea62717ce 3 3 https github com sw801733 capstone robot arm assets 104549849 e7a3dda3 87f2 4621 95bd 0372d952a844 1 theta 2 br cos c 2 a 2 b 2 2ab cos c br x 2 y 2 l 1 2 l 2 2 2l 1l 2cos 180 theta 2 br cos 180 theta 2 cos theta 2 br cos theta 2 frac x 2 y 2 l 1 2 l 2 2 2l 1l 2 br theta 2 arccos frac x 2 y 2 l 1 2 l 2 2 2l 1l 2 br br 2 theta 1 br sin frac sinb b frac sin c c br frac sin bar theta 1 l 2 frac sin 180 theta 2 sqrt x 2 y 2 frac sin theta 2 sqrt x 2 y 2 br bar theta 1 arcsin frac l 2sin theta 2 sqrt x 2 y 2 br theta 1 bar theta 1 alpha br alpha arctan frac y x br theta 1 arcsin frac l 2sin theta 2 sqrt x 2 y 2 arctan frac y x 1 theta 0 br tan theta 0 frac y x br theta 0 arctan frac y x br br 2 theta 1 theta 2 br 2 sqrt x 2 y 2 z 2 2 inverse kinematics br theta 2 arccos frac x 2 y 2 z 2 l 1 2 l 2 2 2l 1l 2 br theta 1 arcsin frac l 2 sin theta 2 x 2 y 2 z 2 arctan frac z sqrt x 2 y 2 | os |
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MAD_A2 | mad a2 | front_end |
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python-machine-learning-book-3rd-edition | python machine learning 3rd ed code repository python 3 6 https img shields io badge python 3 7 blue svg license https img shields io badge code 20license mit blue svg license txt code repositories for the 1st and 2nd edition are available at https github com rasbt python machine learning book and https github com rasbt python machine learning book 2nd edition python machine learning 3rd ed to be published december 12th 2019 paperback 770 pages publisher packt publishing language english isbn 10 1789955750 isbn 13 978 1789955750 kindle asin b07vblx2w7 img src other cover 1 jpg width 248 https www amazon com python machine learning scikit learn tensorflow dp 1789955750 links amazon page https www amazon com python machine learning scikit learn tensorflow dp 1789955750 packt page https www packtpub com data python machine learning third edition table of contents and code notebooks helpful installation and setup instructions can be found in the readme md file of chapter 1 ch01 readme md please note that these are just the code examples accompanying the book which we uploaded for your convenience be aware that these notebooks may not be useful without the formulae and descriptive text 1 machine learning giving computers the ability to learn from data open dir ch01 2 training machine learning algorithms for classification open dir ch02 3 a tour of machine learning classifiers using scikit learn open dir ch03 4 building good training sets data pre processing open dir ch04 5 compressing data via dimensionality reduction open dir ch05 6 learning best practices for model evaluation and hyperparameter optimization open dir ch06 7 combining different models for ensemble learning open dir ch07 8 applying machine learning to sentiment analysis open dir ch08 9 embedding a machine learning model into a web application open dir ch09 10 predicting continuous target variables with regression analysis open dir ch10 11 working with unlabeled data clustering analysis open dir ch11 12 implementing a multi layer artificial neural network from scratch open dir ch12 13 parallelizing neural network training with tensorflow open dir ch13 14 going deeper the mechanics of tensorflow open dir ch14 15 classifying images with deep convolutional neural networks open dir ch15 16 modeling sequential data using recurrent neural networks open dir ch16 17 generative adversarial networks for synthesizing new data open dir ch17 18 reinforcement learning for decision making in complex environments open dir ch18 br br raschka sebastian and vahid mirjalili python machine learning 3rd ed packt publishing 2019 book raschkamirjalili2019 address birmingham uk author raschka sebastian and mirjalili vahid edition 3 isbn 978 1789955750 publisher packt publishing title python machine learning 3rd ed year 2019 | machine-learning deep-learning tensorflow scikit-learn | ai |
openai_tools | license https img shields io badge license mit brightgreen license this repository contains basic scripts designed to investigate scientific publication pdfs utilizing the chatgpt api it has been created to share helpful code for navigating the literature please note that this code is experimental we encourage others to test it and if you find it useful feel free to share your feedback with us installing 1 you first need to create your conda environment as conda create name your env name python 3 10 2 then activate it conda activate your env name 3 install the package pip install 4 we rely on the adjusttext library for positioning labels that currently only work from the master branch so install it using pip install https github com phlya adjusttext archive master zip 5 go the script folder cd scripts 6 copy your openai api key in the env file you can find this here https platform openai com account api keys running scripts this package contains a list of classes to facilitate exploring the litterature using large language models currently we are focusing on openai api but we plan to extend in the future classes are documented and tested but we recommend using our scripts first in the script folder those scripts were designed to be simple to use and facilitate growing a local database of publication data 1 first after installing go the script folder cd scripts 2 you can use 3 scripts currently the first one is ran using python pdf summary py path pdf path to your pdf save summary true this will save a little text file along with your pdf with the same filename but with a txt extension the second one is ran using python list pdfs embedding py path folder path to your pdfs database path path to a folder the embedding is saved automatically in the folder with all your pdfs as tsne embeddings png currently this plot uses the filename of each pdf to assign a label the third one is ran using python pubmed embedding py pubmed query your query field abstract save path path to a png file database path path to a folder althought database path is optional we highly recommend choosing a folder on your local hard drive to store your paper database this will limit calls to the large language models and save you time papers embedding could scale to thousands of papers eventually if you process your entire litterature parameters for pdf summary path pdf path to a pdf file that you want to summarize type string default os path join script path example 2020 12 15 422967v4 full pdf save summary save the generated summary in a txt file alongside the pdf file type boolean default true save raw text save the raw text in a txt file along the pdf file type boolean default true cut bibliography try not to summarize the bibliography at the end of the pdf file type boolean default true chunk length determines the final length of the summary by summarizing the document in chunks more chunks result in a longer summary but may lead to inconsistency across sections typically 1 is a good value for an abstract and 2 or 3 for more detailed summaries type integer default 1 database path path to the database file this is an optional argument if path is not provided no database will be used or created if the path is provided the database will be created if it does not exist if it exists it will be loaded and used use this to grow your database of papers type str default none parameters for pubmed embedding pubmed query a query made to pubmed this can return a very large number of publications and they will be processed up to max results type str default none field you can embed the title or the abstract currently just give title or abstract your choice here will depends on how much details you want your embedding to rely on type str default abstract database path path to the database file this is an optional argument if path is not provided no database will be used or created if the path is provided the database will be created if it does not exist if it exists it will be loaded and used use this to grow your database of papers type str default none save path path to a local image file this is where the plot will be saved type str default none perplexity perplexity for the t sne plot default is 8 higher values will make the plot more spread out lower values will make the plot more clustered type int default 8 max results maximum number of results to fetch from pubmed type int default 100 examples paper summary see the example folder for example runs there is a typical short example length of a typical abstract and a long summary using chunk length 4 list pdfs embedding here is an example embedding for approximately 100 publications mostly around in vivo calcium imaging of neuronal activity example embedding example tsne embeddings example png pubmed embedding here is an example embedding for the following command python pubmed embedding py pubmed query in vivo two photon voltage imaging field abstract save path example twophotonvoltage html perplexity 5 below is a screenshot the generated plot is interactive https alleninstitute github io openai tools example twophotonvoltage html to explore the content of each paper download the html on your machine and open in a browser example embedding example twophotonvoltage png here is an second embedding for the following command for a very large number of paper python pubmed embedding py pubmed query mouse visual cortex field abstract save path example mouse invivo recordings html max result 3500 perplexity 30 below is a screenshot the generated plot is interactive https alleninstitute github io openai tools example mouse invivo recordings html to explore the content of each paper download the html on your machine and open in a browser example embedding example mouse invivo recordings png database the papers extractor utilizes a straightforward database created with the diskcache python package by specifying the database path the module will handle everything for you its purpose is to compile a collection of papers complete with statistics and numerical embeddings additionally it automatically regulates the number of calls made to llm apis effectively reducing costs any previously processed pdf is automatically cached to determine if a pdf was previously processed it uses a hash from the content of the pdf so you can freely rename them you can store your database or several at any location on your local drive using the database path parameter how to contribute 1 first go to tests and read the readme md 2 make a pr against main this will run ci through github actions credits this repository was started by jerome lecoq on april 12th 2023 please reach out jeromel alleninstitute org for any questions if this is useful to you wave are welcome | ai |
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cloud-applications-F22 | cloud applications f22 repository for content and examples for e6156 topics in sw engineering cloud applications | cloud |
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build-blockchain-insurance-app | warning this repository is no longer maintained warning this repository will not be updated i will check periodically for pull requests but do not expect a quick response read this in other languages readme cn md readme ja md build blockchain insurance application this project showcases the use of blockchain in insurance domain for claim processing in this application we have four participants namely insurance police repair shop and the shop furthermore each participant will own its own peer node the insurance peer is the insurance company providing the insurance for the products and it is responsible for processing the claims police peer is responsible for verifying the theft claims repair shop peer is responsible for repairs of the product while shop peer sells the products to consumer the value of running this network on the ibm blockchain platform is that you can easily customize the network infrastructure as needed whether that is the location of the nodes the cpu and ram of the hardware the endorsement policy needed to reach consensus or adding new organizations and members to the network note this code pattern can either be run locally or connected to the ibm blockchain platform b if you only care about running this pattern locally please find the local instructions here readme local md b audience level intermediate developers when the reader has completed this code pattern they will understand how to create a kubernetes cluster using the ibm kubernetes service create an ibm blockchain service and launch the service onto the kubernetes cluster create a network including all relevant components such as certificate authority msp membership service providers peers orderers and channels deploy a packaged smart contract onto the ibm blockchain platform by installing and instantiating it on the peers use the connection profile from ibm blockchain platform to create application admins and submit transactions from our client application view transaction details on our channel from ibm blockchain platform application workflow diagram workflow images app arch png 1 the blockchain operator creates a ibm kubernetes service cluster b 32cpu 32ram 3 workers recommended b and an ibm blockchain platform 2 0 service 2 the ibm blockchain platform 2 0 creates a hyperledger fabric network on an ibm kubernetes service and the operator installs and instantiates the smart contract on the network 3 the node js application server uses the fabric sdk to interact with the deployed network on ibm blockchain platform 2 0 4 the react ui uses the node js application api to interact and submit transactions to the network 5 the user interacts with the insurance application web interface to update and query the blockchain ledger and state included components ibm blockchain platform v2 beta https console bluemix net docs services blockchain howto ibp v2 deploy iks html ibp v2 deploy iks gives you total control of your blockchain network with a user interface that can simplify and accelerate your journey to deploy and manage blockchain components on the ibm cloud kubernetes service ibm cloud kubernetes service https www ibm com cloud container service creates a cluster of compute hosts and deploys highly available containers a kubernetes cluster lets you securely manage the resources that you need to quickly deploy update and scale applications featured technologies hyperledger fabric v1 4 https hyperledger fabric readthedocs io is a platform for distributed ledger solutions underpinned by a modular architecture that delivers high degrees of confidentiality resiliency flexibility and scalability node js https nodejs org is an open source cross platform javascript run time environment that executes server side javascript code react https reactjs org a declarative efficient and flexible javascript library for building user interfaces docker https www docker com docker is a computer program that performs operating system level virtualization it was first released in 2013 and is developed by docker inc watch the video multiple organization and multiple peer app demo 1 intro images part1 png https www youtube com watch v lr3efkayp0o watch the video ibm blockchain tutorial multiple organization and multiple peer app demo 2 build nodes images part2 png https www youtube com watch v wi6i28vrigs watch the video multiple organization and multiple peer app demo 1 intro images part3 png https www youtube com watch v 3drckqzvfqw prerequisites we find that blockchain can be finicky when it comes to installing node we want to share this stackoverflow response https stackoverflow com questions 49744276 error cannot find module api hyperledger composer because many times the errors you see with compose are derived in having installed either the wrong node version or took an approach that is not supported by compose ibm cloud account https cloud ibm com registration docker https www docker com products latest docker compose https docs docker com compose overview latest npm https www npmjs com get npm latest nvm latest node js https nodejs org en download node v8 9 x git client https git scm com downloads latest python https www python org downloads 2 7 x react https reactjs org 15 6 1 steps steps local network to run a local network you can find steps here readme local md steps cloud network 1 create ibm cloud services step 1 create ibm cloud services 2 build a network certificate authority step 2 build a network certificate authority 3 build a network create msp definitions step 3 build a network create msp definitions 4 build a network create peers step 4 build a network create peers 5 build a network create orderer step 5 build a network create orderer 6 build a network create and join channel step 6 build a network create and join channel 7 deploy insurance smart contract on the network step 7 deploy insurance smart contract on the network 8 connect application to the network step 8 connect application to the network 9 enroll app admin identities step 9 enroll app admin identities 10 run the application step 10 run the application important note this pattern is more advanced because it uses four organizations for this reason you will likely have to get a paid kubernetes cluster to run this pattern on the cloud since a free cluster will not have the cpu storage necessary to deploy all of the pods that we need to run this pattern there are other patterns that leverage a free kubernetes cluster and only two organizations so if you want to try that one out first go here https github com ibm blockchainbean2 step 1 create ibm cloud services create the ibm cloud kubernetes service https cloud ibm com catalog infrastructure containers kubernetes you can find the service in the catalog note that b for this code pattern we need to use the 32cpu 32gb ram cluster b once you reach the b create a new cluster page b you will need to do the following choose b standard b cluster type fill out cluster name choose geography b north america b choose location and availability b multizone b choose metro b dallas b choose worker nodes b dallas 10 only b choose master service endpoint b both private public endpoints b choose default worker pool b 1 12 7 stable default b choose master service endpoint b both private public endpoints b choose flavor b 32 cores 32gb ram ubuntu 18 b choose encrypt local disk b yes b choose worker nodes b 3 b click on b create cluster b b the cluster takes around 15 20 minutes to provision so please be patient b br p align center img src images gifs createcluster gif p br after your kubernetes cluster is up and running you can deploy your ibm blockchain platform v2 beta on the cluster the service walks through few steps and finds your cluster on the ibm cloud to deploy the service on br p align center img src images gifs deploy blockchain on cluster gif p br once the blockchain platform is deployed on the kubernetes cluster you can launch the console to start operating on your blockchain network br p align center img src images gifs launch ibm blockchain gif p br step 2 build a network certificate authority we will build a network as provided by the ibm blockchain platform documentation https console bluemix net docs services blockchain howto ibp console build network html ibp console build network this will include creating a channel with a single peer organization with its own msp and ca certificate authority and an orderer organization with its own msp and ca we will create the respective identities to deploy peers and operate nodes create your insurance organization ca click b add certificate authority b click b ibm cloud b under b create certificate authority b and b next b give it a b display name b of insurance ca specify an b admin id b of admin and b admin secret b of adminpw br p align center img src images gifs insuranceibp2 gif p br create your shop organization ca process is same as shown in gif above click b add certificate authority b click b ibm cloud b under b create certificate authority b and b next b give it a b display name b of shop ca specify an b admin id b of admin and b admin secret b of adminpw create your repair shop organization ca process is same as shown in gif above click b add certificate authority b click b ibm cloud b under b create certificate authority b and b next b give it a b display name b of repair shop ca specify an b admin id b of admin and b admin secret b of adminpw create your police organization ca process is same as shown in gif above click b add certificate authority b click b ibm cloud b under b create certificate authority b and b next b give it a b display name b of police ca specify an b admin id b of admin and b admin secret b of adminpw use your ca to register insurance identities select the b insurance ca b certificate authority that we created first we will register an admin for our insurance organization click on the b register user b button give an b enroll id b of insuranceadmin and b enroll secret b of insuranceadminpw click b next b set the b type b for this identity as client and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank we will repeat the process to create an identity of the peer click on the b register user b button give an b enroll id b of insurancepeer and b enroll secret b of insurancepeerpw click b next b set the b type b for this identity as peer and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank br p align center img src images gifs registerinsuranceadminpeer gif p br use your ca to register shop identities process is same as shown in gif above select the b shop ca b certificate authority that we created first we will register an admin for our insurance organization click on the b register user b button give an b enroll id b of shopadmin and b enroll secret b of shopadminpw click b next b set the b type b for this identity as client and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank we will repeat the process to create an identity of the peer click on the b register user b button give an b enroll id b of shoppeer and b enroll secret b of shoppeerpw click b next b set the b type b for this identity as peer and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank use your ca to register repair shop identities process is same as shown in gif above select the b repair shop ca b certificate authority that we created first we will register an admin for our insurance organization click on the b register user b button give an b enroll id b of repairshopadmin and b enroll secret b of repairshopadminpw click b next b set the b type b for this identity as client and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank we will repeat the process to create an identity of the peer click on the b register user b button give an b enroll id b of repairshoppeer and b enroll secret b of repairshoppeerpw click b next b set the b type b for this identity as peer and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank use your ca to register police shop identities process is same as shown in gif above select the b police ca b certificate authority that we created first we will register an admin for our insurance organization click on the b register user b button give an b enroll id b of policeadmin and b enroll secret b of policeadminpw click b next b set the b type b for this identity as client and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank we will repeat the process to create an identity of the peer click on the b register user b button give an b enroll id b of policepeer and b enroll secret b of policepeerpw click b next b set the b type b for this identity as peer and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank step 3 build a network create msp definitions create the insurance msp definition navigate to the b organizations b tab in the left navigation and click b create msp definition b enter the b msp display name b as insurance msp and an b msp id b of insurancemsp under b root certificate authority b details specify the peer ca that we created insurance ca as the root ca for the organization give the b enroll id b and b enroll secret b for your organization admin insuranceadmin and insuranceadminpw then give the identity name insurance admin click the b generate b button to enroll this identity as the admin of your organization and export the identity to the wallet click b export b to export the admin certificates to your file system finally click b create msp definition b br p align center img src images gifs insurancemsp gif p br create the shop msp definition same process as shown in gif above navigate to the b organizations b tab in the left navigation and click b create msp definition b enter the b msp display name b as shop msp and an b msp id b of shopmsp under b root certificate authority b details specify the peer ca that we created shop ca as the root ca for the organization give the b enroll id b and b enroll secret b for your organization admin shopadmin and shopadminpw then give the identity name shop admin click the b generate b button to enroll this identity as the admin of your organization and export the identity to the wallet click b export b to export the admin certificates to your file system finally click b create msp definition b create the repair shop msp definition same process as shown in gif above navigate to the b organizations b tab in the left navigation and click b create msp definition b enter the b msp display name b as repair shop msp and an b msp id b of repairshopmsp under b root certificate authority b details specify the peer ca that we created repair shop ca as the root ca for the organization give the b enroll id b and b enroll secret b for your organization admin repairshopadmin and repairshopadminpw then give the identity name repair shop admin click the b generate b button to enroll this identity as the admin of your organization and export the identity to the wallet click b export b to export the admin certificates to your file system finally click b create msp definition b create the police msp definition same process as shown in gif above navigate to the b organizations b tab in the left navigation and click b create msp definition b enter the b msp display name b as police msp and an b msp id b of policemsp under b root certificate authority b details specify the peer ca that we created police ca as the root ca for the organization give the b enroll id b and b enroll secret b for your organization admin policeadmin and policeadminpw then give the identity name police admin click the b generate b button to enroll this identity as the admin of your organization and export the identity to the wallet click b export b to export the admin certificates to your file system finally click b create msp definition b step 4 build a network create peers create an insurance peer on the b nodes b page click b add peer b click b ibm cloud b under create a new peer and b next b give your peer a b display name b of insurance peer on the next screen select insurance ca as your b certificate authority b then give the b enroll id b and b enroll secret b for the peer identity that you created for your peer insurancepeer and insurancepeerpw then select the b administrator certificate from msp b insurance msp from the drop down list and click b next b give the b tls enroll id b admin and b tls enroll secret b adminpw the same values are the enroll id and enroll secret that you gave when creating the ca leave the b tls csr hostname b blank the last side panel will ask you to b associate an identity b and make it the admin of your peer select your peer admin identity insurance admin review the summary and click b submit b br p align center img src images gifs addinsurancepeer gif p br create a shop peer same process as shown in gif above on the b nodes b page click b add peer b click b ibm cloud b under create a new peer and b next b give your peer a b display name b of shop peer on the next screen select shop ca as your b certificate authority b then give the b enroll id b and b enroll secret b for the peer identity that you created for your peer shoppeer and shoppeerpw then select the b administrator certificate from msp b shop msp from the drop down list and click b next b give the b tls enroll id b admin and b tls enroll secret b adminpw the same values are the enroll id and enroll secret that you gave when creating the ca leave the b tls csr hostname b blank the last side panel will ask you to b associate an identity b and make it the admin of your peer select your peer admin identity shop admin review the summary and click b submit b create a repair shop peer same process as shown in gif above on the b nodes b page click b add peer b click b ibm cloud b under create a new peer and b next b give your peer a b display name b of repair shop peer on the next screen select repair shop ca as your b certificate authority b then give the b enroll id b and b enroll secret b for the peer identity that you created for your peer repairshoppeer and repairshoppeerpw then select the b administrator certificate from msp b repair shop msp from the drop down list and click b next b give the b tls enroll id b admin and b tls enroll secret b adminpw the same values are the enroll id and enroll secret that you gave when creating the ca leave the b tls csr hostname b blank the last side panel will ask you to b associate an identity b and make it the admin of your peer select your peer admin identity repair shop admin review the summary and click b submit b create a police peer same process as shown in gif above on the b nodes b page click b add peer b click b ibm cloud b under create a new peer and b next b give your peer a b display name b of police peer on the next screen select police ca as your b certificate authority b then give the b enroll id b and b enroll secret b for the peer identity that you created for your peer policepeer and policepeerpw then select the b administrator certificate from msp b police msp from the drop down list and click b next b give the b tls enroll id b admin and b tls enroll secret b adminpw the same values are the enroll id and enroll secret that you gave when creating the ca leave the b tls csr hostname b blank the last side panel will ask you to b associate an identity b and make it the admin of your peer select your peer admin identity police admin review the summary and click b submit b step 5 build a network create orderer create your orderer organization ca click b add certificate authority b click b ibm cloud b under b create certificate authority b and b next b give it a unique b display name b of orderer ca specify an b admin id b of admin and b admin secret b of adminpw br p align center img src images gifs createorderer gif p br use your ca to register orderer and orderer admin identities shown in gif above in the b nodes b tab select the b orderer ca b certificate authority that we created first we will register an admin for our organization click on the b register user b button give an b enroll id b of ordereradmin and b enroll secret b of ordereradminpw click b next b set the b type b for this identity as client and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank we will repeat the process to create an identity of the orderer click on the b register user b button give an b enroll id b of orderer1 and b enroll secret b of orderer1pw click b next b set the b type b for this identity as peer and select org1 from the affiliated organizations drop down list we will leave the b maximum enrollments b and b add attributes b fields blank create the orderer organization msp definition navigate to the b organizations b tab in the left navigation and click b create msp definition b enter the b msp display name b as orderer msp and an b msp id b of orderermsp under b root certificate authority b details specify the peer ca that we created orderer ca as the root ca for the organization give the b enroll id b and b enroll secret b for your organization admin ordereradmin and ordereradminpw then give the b identity name b orderer admin click the b generate b button to enroll this identity as the admin of your organization and export the identity to the wallet click b export b to export the admin certificates to your file system finally click b create msp definition b br p align center img src images gifs orderermsp gif p br create an orderer on the b nodes b page click b add orderer b click b ibm cloud b and proceed with b next b give your peer a b display name b of orderer on the next screen select orderer ca as your b certificate authority b then give the b enroll id b and b enroll secret b for the peer identity that you created for your orderer orderer1 and orderer1pw then select the b administrator certificate from msp b orderer msp from the drop down list and click b next b give the b tls enroll id b admin and b tls enroll secret b adminpw the same values are the enroll id and enroll secret that you gave when creating the ca leave the b tls csr hostname b blank the last side panel will ask to b associate an identity b and make it the admin of your peer select your peer admin identity orderer admin review the summary and click b submit b br p align center img src images gifs createorderernode gif p br add organizations as consortium member on the orderer to transact navigate to the b nodes b tab and click on the b orderer b that we created under b consortium members b click b add organization b from the drop down list select insurance msp click b submit b repeat the same steps but add shop msp repair shop msp and police msp as well br p align center img src images gifs addconsortium gif p br step 6 build a network create and join channel create the channel navigate to the b channels b tab in the left navigation click b create channel b give the channel a name mychannel select the orderer you created orderer from the orderers drop down list select the msp identifying the organization of the channel creator from the drop down list this should be insurance msp insurancemsp associate available identity as insurance admin click b add b next to the insurance organization make the insurance organization an b operator b click b add b next to the shop organization make the shop organization an b operator b click b add b next to the repair shop organization make the repair shop organization an b operator b click b add b next to the police organization make the insurance organpoliceization an b operator b click b create b br p align center img src images gifs createchannel gif p br join your peer to the channel click b join channel b to launch the side panels select your orderer and click b next b enter the name of the channel you just created mychannel and click b next b select which peers you want to join the channel click insurance peer shop peer repair shop peer and police peer click b submit b br p align center img src images gifs joinchannel gif p br add anchor peers to the channel in order to communicate between organizations we need to enroll anchor peers from the channels tab click on the channel you have created mychannel from the channel overview page click on channel details scroll all the way down until you see anchor peers click add anchor peer and add the insurance police repair shop and shop peers select which peers you want to join the channel click insurance peer shop peer repair shop peer and police peer click b add anchor peer b if all went well your channel anchor peers should look like below br p align center img src images gifs addanchorpeer gif p br step 7 deploy insurance smart contract on the network install a smart contract clone the repository bash git clone https github com ibm build blockchain insurance app click the b smart contracts b tab to install the smart contract click b install smart contract b to upload the insurance smart contract package file click on b add file b and find your packaged smart contract it is the file in the build blockchain insurance app chaincodepackage directory select all peers we need to install the contract on each peer once the contract is uploaded click b install b br p align center img src images gifs installsmartcontract gif p br instantiate smart contract on the smart contracts tab find the smart contract from the list installed on your peers and click b instantiate b from the overflow menu on the right side of the row on the side panel that opens select the channel mychannel to instantiate the smart contract on click b next b select the organization members to be included in the policy insurancemsp shopmsp repairshopmsp policemsp click b next b give b function name b of init and leave b arguments b blank click b instantiate b br p align center img src images gifs instantiatecontract gif p br step 8 connect application to the network connect with sdk through connection profile under the instantiated smart contract click on connect with sdk from the overflow menu on the right side of the row choose from the dropdown for b msp for connection b insurancemsp choose from b certificate authority b dropdown insurance ca download the connection profile by scrolling down and clicking b download connection profile b this will download the connection json which we will use soon to establish connection you can click b close b once the download completes br p align center img src images gifs downloadconnection gif p br create insurance application admin go to the b nodes b tab on the left bar and under b certificate authorities b choose your b insurance ca b click on b register user b give an b enroll id b and b enroll secret b to administer your application users insuranceapp admin and insuranceapp adminpw choose client as b type b you can leave the b use root affiliation b box checked you can leave the b maximum enrollments b blank under b attributes b click on b add attribute b give attribute as hf registrar roles this will allow this identity to act as registrar and issues identities for our app click b add attribute b click b register b br p align center img src images gifs appadmin gif p br create shop application admin same process as shown above in the gif go to the b nodes b tab on the left bar and under b certificate authorities b choose your b shop ca b click on b register user b give an b enroll id b and b enroll secret b to administer your application users shopapp admin and shopapp adminpw choose client as b type b you can leave the b use root affiliation b box checked you can leave the b maximum enrollments b blank under b attributes b click on b add attribute b give attribute as hf registrar roles this will allow this identity to act as registrar and issues identities for our app click b add attribute b click b register b create repair shop application admin same process as shown above in the gif go to the b nodes b tab on the left bar and under b certificate authorities b choose your b repair shop ca b click on b register user b give an b enroll id b and b enroll secret b to administer your application users repairshopapp admin and repairshopapp adminpw choose client as b type b you can leave the b use root affiliation b box checked you can leave the b maximum enrollments b blank under b attributes b click on b add attribute b give attribute as hf registrar roles this will allow this identity to act as registrar and issues identities for our app click b add attribute b click b register b create police application admin same process as shown above in the gif go to the b nodes b tab on the left bar and under b certificate authorities b choose your b police ca b click on b register user b give an b enroll id b and b enroll secret b to administer your application users policeapp admin and policeapp adminpw choose client as b type b you can leave the b use root affiliation b box checked you can leave the b maximum enrollments b blank under b attributes b click on b add attribute b give attribute as hf registrar roles this will allow this identity to act as registrar and issues identities for our app click b add attribute b click b register b update application connection copy the connection profile you downloaded into the web www blockchain directory copy and paste everything in the connection profile and overwrite the ibpconnection json p align center img src images gifs ibpconnection gif p step 9 enroll app admin identities enroll insurnaceapp admin first navigate to the web www blockchain directory bash cd web www blockchain open the config json file and update the caname with the url of the b insurance b certificate authority from your ibpconnection json file save the file run the enrolladmin js script bash node enrolladmin js you should see the following in the terminal bash msg successfully enrolled admin user insuranceapp admin and imported it into the wallet p align center img src images gifs enrolladmin gif p enroll shopapp admin first change the appadmin appadminsecret and caname properties in your config json file so that it looks something like this your caname should be different than mine js connection file ibpconnection json appadmin shopapp admin appadminsecret shopapp adminpw orgmspid shopmsp caname https fa707c454921423c80ec3c3c38d7545c caf2e287 horeainsurancetest us south containers appdomain cloud 7054 username shopuser gatewaydiscovery enabled true aslocalhost false to find the other ca urls you will need to click on the nodes tab in ibm blockchain platform then on the shop ca and on the settings cog icon at the top of the page that will take you to the certificate authority settings as shown in the picture below and you can copy that endpoint url into your config json caname field p align center img src images gifs enrollshopadmin gif p br run the enrolladmin js script bash node enrolladmin js you should see the following in the terminal bash msg successfully enrolled admin user shopapp admin and imported it into the wallet enroll repairshopapp admin same process as shown in gif above first change the appadmin appadminsecret and caname properties in your config json file so that it looks something like this your caname should be different than mine js connection file ibpconnection json appadmin repairshopapp admin appadminsecret repairshopapp adminpw orgmspid repairshopmsp caname https fa707c454921423c80ec3c3c38d7545c caf2e287 horeainsurancetest us south containers appdomain cloud 7054 username repairuser gatewaydiscovery enabled true aslocalhost false run the enrolladmin js script bash node enrolladmin js you should see the following in the terminal bash msg successfully enrolled admin user repairshopapp admin and imported it into the wallet enroll policeapp admin same process as shown in gif above first change the appadmin appadminsecret and caname properties in your config json file so that it looks something like this your caname should be different than mine js connection file ibpconnection json appadmin policeapp admin appadminsecret policeapp adminpw orgmspid policemsp caname https fa707c454921423c80ec3c3c38d7545c caf2e287 horeainsurancetest us south containers appdomain cloud 7054 username policeuser gatewaydiscovery enabled true aslocalhost false run the enrolladmin js script bash node enrolladmin js you should see the following in the terminal bash msg successfully enrolled admin user policeapp admin and imported it into the wallet step 10 run the application navigate to the directory blockchain directory which contains the config js file https github com ibm build blockchain insurance app blob ubuntu local fix web www blockchain config js bash cd build blockchain insurance app web www blockchain in the editor of choice change line 8 https github com ibm build blockchain insurance app blob ubuntu local fix web www blockchain config js l8 of the config js file to iscloud true as shown in the image below is cloud images iscloud png if you are using mac save the changes otherwise if you are using an ubuntu system change line 9 https github com ibm build blockchain insurance app blob ubuntu local fix web www blockchain config js l9 of config js file to isubuntu true as shown in the image below is ubuntu images isubuntuandcloud png next from the blockchain directory navigate to the root project directory bash blockchain cd build blockchain insurance app login using your docker hub https hub docker com credentials bash docker login run the build script to download and create docker images for the orderer insurance peer police peer shop peer repairshop peer web application and certificate authorities for each peer this will run for a few minutes for mac user bash cd build blockchain insurance app build mac sh for ubuntu user make sure isubuntu true is saved in the line 9 https github com ibm build blockchain insurance app blob ubuntu local fix web www blockchain config js l9 of config js bash cd build blockchain insurance app build ubuntu sh you should see the following output on console creating repairshop ca creating insurance ca creating shop ca creating police ca creating orderer0 creating repairshop ca creating insurance ca creating police ca creating shop ca creating orderer0 done creating insurance peer creating insurance peer done creating shop peer creating shop peer done creating repairshop peer creating repairshop peer done creating web creating police peer creating web creating police peer done p align center img src images gifs buildapp gif p br wait for few minutes for application to install and instantiate the chaincode on network check the status of installation using command bash docker logs web on completion you should see the following output on console blockchain for insurance 2 1 0 serve app cross env node env production node bin server app app static js server running on port 3000 default channel not found attempting creation successfully created a new default channel joining peers to the default channel chaincode is not installed attempting installation base container image present info packager golang js packaging golang from bcins info packager golang js packaging golang from bcins info packager golang js packaging golang from bcins info packager golang js packaging golang from bcins successfully installed chaincode on the default channel successfully instantiated chaincode on all peers use the link http localhost 3000 to load the web application in browser the home page shows the participants peers in the network you can see that there is an insurance repair shop police and shop peer implemented they are the participants of the network blockchain insurance images home png imagine being a consumer hereinafter called biker that wants to buy a phone bike or ski by clicking on the go to the shop section you will be redirected to the shop shop peer that offers you the following products customer shopping images picture1 png you can see the three products offered by the shop s now in addition you have insurance contracts available for them in our scenario you are an outdoor sport enthusiast who wants to buy a new bike therefore you ll click on the bike shop section shopping images picture2 png in this section you are viewing the different bikes available in the store you can select within four different bikes by clicking on next you ll be forwarded to the next page which will ask for the customer s personal data bike shop images picture3 png you have the choice between different insurance contracts that feature different coverage as well as terms and conditions you are required to type in your personal data and select a start and end date of the contract since there is a trend of short term or event driven contracts in the insurance industry you have the chance to select the duration of the contract on a daily basis the daily price of the insurance contract is being calculated by a formula that had been defined in the chaincode by clicking on next you will be forwarded to a screen that summarizes your purchase and shows you the total sum bike insurance images picture4 png the application will show you the total sum of your purchase by clicking on order you agree to the terms and conditions and close the deal signing of the contract in addition you ll receive a unique username and password the login credentials will be used once you file a claim a block is being written to the blockchain note you can see the block by clicking on the black arrow on the bottom right at this point you should be able to go into your ibm blockchain platform console click on the channels and then be able to see the contract create block being added p align center img src images gifs runappibp gif p br for additional steps on how to file more claims and use the rest of the application please go here readme local md congratulations you ve successfully connection your react app to the ibm blockchain platform now each time you submit transactions with the ui they will be logged by the blockchain service additional resources following is a list of additional blockchain resources fundamentals of ibm blockchain https www ibm com blockchain what is blockchain hyperledger fabric documentation https hyperledger fabric readthedocs io hyperledger fabric code on github https github com hyperledger fabric troubleshooting run clean sh to remove the docker images and containers for the insurance network bash clean sh license this code pattern is licensed under the apache software license version 2 separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses contributions are subject to the developer certificate of origin version 1 1 dco https developercertificate org and the apache software license version 2 https www apache org licenses license 2 0 txt apache software license asl faq https www apache org foundation license faq html whatdoesitmean | blockchain blockchain-technology hyperledger-fabric docker docker-compose ibmcode | blockchain |
Java101_PatikaDev | java 101 patikadev patika dev https app patika dev egitimler adresindeki java ile backend web development patikas e itiminde kulland m kaynak kodlar i eren bir repo bu readme dosyas nda bu e itimdeki pratik ve devlerin cevaplar n bulacaks n z prat k dev prat k 1 https github com osmantuysuz java101 patikadev brain prati k 1 not ortalamas not ortalamas dev 1 https github com osmantuysuz java101 patikadev brain dev 1 v cut kitle i ndeksi hesaplama v cut kitle endeksi hesaplama prat k 2 https github com osmantuysuz java101 patikadev brain prati k 2 kdv hesaplama kdv hesaplama dev 2 https github com osmantuysuz java101 patikadev brain dev 2 manav kasa manav kasa prat k 3 https github com osmantuysuz java101 patikadev brain prati k 3 hipoten s bulma hipoten s bulma dev 3 https github com osmantuysuz java101 patikadev brain dev 3 u ak bileti fiyat hesaplama u ak bileti fiyat hesaplama prat k 4 https github com osmantuysuz java101 patikadev brain prati k 4 taksimetre taksimetre dev 4 https github com osmantuysuz java101 patikadev brain dev 4 in zodya hesaplama in zodya hesaplama prat k 5 https github com osmantuysuz java101 patikadev brain prati k 5 daire alan evre daire alan evre dev 5 https github com osmantuysuz java101 patikadev brain dev 5 art k y l hesaplama art k y l hesaplama prat k 6 https github com osmantuysuz java101 patikadev brain prati k 6 hesap makinesi hesap makinesi dev 6 https github com osmantuysuz java101 patikadev brain dev 6 girilen say lardan min ve max de erli bulan program girilen say lardan min ve max de erli bulan program prat k 7 https github com osmantuysuz java101 patikadev brain prati k 7 kullan c giri i kullan c giri i dev 7 https github com osmantuysuz java101 patikadev brain dev 7 m kemmel say bulan program m kemmel say bulan program prat k 8 https github com osmantuysuz java101 patikadev brain prati k 8 s n f ge me durumu s n f ge me durumu dev 8 https github com osmantuysuz java101 patikadev brain dev 8 ters gen yap m ters gen yap m prat k 9 https github com osmantuysuz java101 patikadev brain prati k 9 hava s cakl na g re etkinlik nerme hava s cakl na g re etkinlik nerme dev 9 https github com osmantuysuz java101 patikadev brain dev 9 1 100 aras ndaki asal say lar bulan program 1 100 aras ndaki asal say lar bulan program prat k 10 https github com osmantuysuz java101 patikadev brain prati k 10 say lar b y kten k e s ralayan program say lar b y kten k e s ralayan program dev 10 https github com osmantuysuz java101 patikadev brain dev 10 fibonacci serisi fibonacci serisi prat k 11 https github com osmantuysuz java101 patikadev brain prati k 11 bur bulan program bur bulan program dev 11 https github com osmantuysuz java101 patikadev brain dev 11 s hesab yapan program recursive metot s hesab yapan program recursive metot prat k 12 https github com osmantuysuz java101 patikadev brain prati k 12 girilen say lardan ift say lar bulan program girilen say lardan ift say lar bulan program dev 12 https github com osmantuysuz java101 patikadev brain dev 12 asal say bulan program recursive metot asal say bulan program recursive metot prat k 13 https github com osmantuysuz java101 patikadev brain prati k 13 tek say lar n toplam n bulan program tek say lar n toplam n bulan program dev 13 https github com osmantuysuz java101 patikadev brain dev 13 desene g re metot olu turma recursive metot desene g re metot olu turma recursive metot prat k 14 https github com osmantuysuz java101 patikadev brain prati k 14 girilen say dan k k 2nin kuvvetlerini bulan program girilen say dan k k 2 nin kuvvetlerini bulan program dev 14 https github com osmantuysuz java101 patikadev brain dev 14 maa hesaplay c maa hesaplay c prat k 15 https github com osmantuysuz java101 patikadev brain prati k 15 fakt riyel hesaplayan program fakt riyel hesaplayan program dev 15 https github com osmantuysuz java101 patikadev brain dev 15 diziyi k kten b y e s ralama diziyi k kten b y e s ralama prat k 16 https github com osmantuysuz java101 patikadev brain prati k 16 sl say hesaplayan program sl say hesaplayan program dev 16 https github com osmantuysuz java101 patikadev brain dev 16 dizideki elemanlar n frekans dizideki elemanlar n frekans prat k 17 https github com osmantuysuz java101 patikadev brain prati k 17 armstrong say lar bulan program armstrong say lar bulan program dev 17 https github com osmantuysuz java101 patikadev brain dev 17 matris transpozunu bulma matris transpozunu bulma prat k 18 https github com osmantuysuz java101 patikadev brain prati k 18 harmonik say lar bulan program harmonik say lar bulan program prat k 19 https github com osmantuysuz java101 patikadev brain prati k 19 y ld z ile gen yap m y ld z ile gen yap m prat k 20 https github com osmantuysuz java101 patikadev brain prati k 20 atm projesi atm projesi prat k 21 https github com osmantuysuz java101 patikadev brain prati k 21 ebob ve ekok bulan program ebob ve ekok bulan program prat k 22 https github com osmantuysuz java101 patikadev brain prati k 22 palindrom say lar palindrom say lar prat k 23 https github com osmantuysuz java101 patikadev brain prati k 23 recursive fibonacci serisi bulan program recursive ile fibonacci serisi prat k 24 https github com osmantuysuz java101 patikadev brain prati k 24 geli mi hesap makinesi geli mi hesap makinesi prat k 25 https github com osmantuysuz java101 patikadev brain prati k 25 renci bilgi sistemi renci bilgi sistemi prat k 26 https github com osmantuysuz java101 patikadev brain prati k 26 boks oyunu boks oyunu prat k 27 https github com osmantuysuz java101 patikadev brain prati k 27 dizideki elemanlar n ortalamas n bulan program dizideki elemanlar n ortalamas n bulan program prat k 28 https github com osmantuysuz java101 patikadev brain prati k 28 dizideki maksimum ve minimum de erleri bulan program dizideki maksimum ve minimum de erleri bulan program prat k 29 https github com osmantuysuz java101 patikadev brain prati k 29 ok boyutlu diziler ile a harfi yazd ran program ok boyutlu diziler ile a harfi yazd ran program prat k 30 https github com osmantuysuz java101 patikadev brain prati k 30 dizideki tekrar eden say lar bulan program dizideki tekrar eden say lar bulan program prat k 31 https github com osmantuysuz java101 patikadev brain prati k 31 say tahmin oyunu say tahmin oyunu prat k 32 https github com osmantuysuz java101 patikadev brain prati k 32 palindromik kelimleri bulan program palindromik kelimeleri bulan program b t rme projes https github com osmantuysuz java101 patikadev brain bi ti rme projesi may n tarlas oyunu may n tarlas oyunu brain prat k 1 not ortalamas question soru java dili ile not ortalamas hesaplama ad nda program yazal m erisinde matematik fizik kimya biyoloji t rk e ve tarih dersleri olan rencinin ald notlar girmesi sa layarak derslerin not ortalamas n ona s yleyelim warning f else kullanmadan sadece ko ullu ifadeler ileortalamas 60 tan b y k ise ona ba ar l de il ise ba ar s z cevab verelim green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tur int matematik fizik kimya biyoloji turkce tarih int toplam double sonuc boolean kosul kullan c dan de erleri al scanner input new scanner system in system out print matematik puan n z giriniz matematik input nextint system out print fizik puan n z giriniz fizik input nextint system out print kimya puan n z giriniz kimya input nextint system out print biyoloji puan n z giriniz biyoloji input nextint system out print t rk e puan n z giriniz turkce input nextint system out print tarih puan n z giriniz tarih input nextint hesaplamalar yap toplam matematik fizik kimya biyoloji turkce tarih sonuc toplam 6 ekrana kt lar yazd r system out println ders ortalaman z sonuc ekstra ko ul ile uygulamam z yazarsak kosul sonuc 60 system out println durum kosul true ba ar l ba ar s z details brain prat k 2 kdv hesaplama question soru java ile kullan c dan al nan para de erinin kdv li fiyat n ve kdv tutar n hesaplay p ekrana bast ran program yaz n warning e er girilen tutar 0 ve 1000 tl aras nda ise kdv oran 18 tutar 1000 tl den b y k ise kdv oran n 8 olarak kdv tutar hesaplayan program yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double tutar kdvlitutar fark kdvtutar1 0 18 kdvtutar2 0 08 boolean kosul1 kullan c dan kdv hesaplanacak miktar isteyelim scanner input new scanner system in system out print l tfen kdv hesaplanacak miktar girin tutar input nextdouble ko ullar m z yazal m kosul1 tutar 1000 sonu lar ekrana yazd ral m system out println kdv oran kosul1 true kdvtutar2 kdvtutar1 system out println kdv tutar kosul1 true tutar kdvtutar2 tutar kdvtutar1 system out println kdv li tutar kosul1 true tutar tutar kdvtutar2 tutar tutar kdvtutar1 details brain prat k 3 hipoten s bulma question soru java ile kullan c dan dik kenarlar n n uzunlu unu alan ve hipoten s hesaplayan program yaz n warning kenar uzunlu unu kullan c dan ald n z genin alan n hesaplayan program yaz n z pill form l genin evresi 2u pill form l u a b c 2 pill form l alan alan u u a u b u c green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m int a b double c kullan c dan verilerimizi alal m scanner input new scanner system in system out print birinci kenar giriniz a input nextint system out print kinci kenar giriniz b input nextint hipoten s hesaplayal m c math sqrt a a b b sonu lar ekrana yazal m system out println hipoten s c dev olarak verilen k s m de i kenleri olu tural m double alan u kullan c dan 3 kenar uzunlu u alal m system out println bir genin alan n n hesaplanmas system out print genin 1 kenar n giriniz a input nextint system out print genin 2 kenar n giriniz b input nextint system out print genin 3 kenar n giriniz c input nextint hesaplamalar yapal m u a b c 2 alan math sqrt u u a u b u c sonucu ekrana yazd ral m system out println girdi iniz kenar de erlerine g re alan alan details brain prat k 4 taksimetre question soru java ile gidilen mesafeye km g re taksimetre tutar n ekrana yazd ran program yaz n warning artlar pill taksimetre km ba na 2 20 tl tutmaktad r pill minimum denecek tutar 20 tl dir 20 tl alt nda ki cretlerde yine 20 tl al nacakt r pill taksimetre a l creti 10 tl dir green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double gidilecekkm kmbasinatutar 2 20 acilisucret 10 odenecektutar boolean kosul1 kullan c dan verilerimizi alal m scanner input new scanner system in system out print ka km yol gideceksiniz gidilecekkm input nextint taksimetre tutar hesaplayal m kosul1 gidilecekkm 20 sonu lar ekrana yazal m system out println taksimetre tutar kosul1 10 gidilecekkm kmbasinatutar 20 details brain prat k 5 daire alan evre question soru java ile yar ap n kullan c dan ald n z dairenin alan n ve evresini hesaplayan program yaz n warning yar ap r merkez a s s n n l s olan daire diliminin alan bulan program yaz n z pill alan form l r r pill evre form l 2 r pill say s n 3 14 al n z pill form l r r 360 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double pi 3 14 yaricap cevre alan merkezaci dairedilimi kullan c dan verilerimizi alal m scanner input new scanner system in system out print yar ap giriniz yaricap input nextdouble system out print merkez a y giriniz merkezaci input nextdouble hesaplamalar yapal m cevre 2 pi yaricap alan pi yaricap yaricap dairedilimi pi yaricap yaricap merkezaci 360 sonu lar ekrana yazal m system out println dairenin alan alan system out println dairenin evresi cevre system out println daire dilim l s dairedilimi details brain prat k 6 hesap makinesi question soru java ko ullu ifadeler ile basit hesap makinesi yap m warning videodaki hesap makinesini switch case kullanarak yap n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double sayi1 sayi2 sonuc char islem kullan c dan verilerimizi alal m scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextdouble system out print 2 say y giriniz sayi2 input nextdouble kullan c ya bilgiler verelim system out println system out println toplama system out println karma system out println arpma system out println b lme system out println hangi i lemi yapmak istiyorsunuz kullan c dan yapaca i lemi alal m islem input next charat 0 sonucu ekrana yazd ral m switch islem case sonuc sayi1 sayi2 system out println toplama sonucu sonuc break case sonuc sayi1 sayi2 system out println karma sonucu sonuc break case sonuc sayi1 sayi2 system out println arpma sonucu sonuc break case if sayi2 0 system out println bir say s f ra b l nemez break else sonuc sayi1 sayi2 system out println b lme sonucu sonuc break default system out println yanl bir i lem se tiniz break details brain prat k 7 kullan c giri i question soru java ko ullu ifadeler ile kullan c ad ve ifreyi kontrol eden program yap m warning e er ifre yanl ise kullan c ya ifresini s f rlay p s f rlamayaca n sorun e er kullan c s f rlamak isterse yeni girdi i ifrenin hatal girdi i ve unuttu u ifre ile ayn olmamas gerekti ini kontrol edip ifreler ayn ise ekrana ifre olu turulamad l tfen ba ka ifre giriniz sorun yoksa ifre olu turuldu yazan program yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i ken tan mlamalar string username password char sifrecevap scanner input new scanner system in kullan c dan username ve password giri inin istenmesi system out print l tfen kullan c ad n z giriniz username input nextline system out print l tfen ifrenizi giriniz password input nextline al nan bilgilerin kontrol ve hatal i lem cevaplar if username equals patika if password equals dev system out println sisteme ba ar l bir ekilde giri yapt n z else system out println hatal ifre giri i system out print ifrenizi s f rlamak ister misiniz e h sifrecevap input next charat 0 if sifrecevap e system out print l tfen yeni ifrenizi giriniz e er string newpassword input nextline olarak yaz lm olsayd sistem bu kodu atlayacakt bu nedenle input next olarak de er ald r ld string newpassword input next if newpassword equals password newpassword equals dev system out print ifre olu turulamad else system out print ifre olu turuldu else if sifrecevap h system out print ifre olu turma i lemi iptal edildi else system out print l tfen ge erli bir parametre giriniz e evet veya h hay r else system out println hatal kullan c ad giri i details brain prat k 8 s n f ge me durumu question soru java ko ullu ifadeler ile kullan c n n not durumuna g re s n f ge me durumunu hesaplayan program yap m pill dersler matematik fizik t rk e kimya m zik pill ge me notu 55 warning e er girilen ders notlar 0 veya 100 aras nda de il ise ortalamaya kat lmas n green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i ken tan mlamalar int turkce matematik fizik kimya muzik ortalama derssayisi 5 kullan c dan ders notlar n isteyelim scanner input new scanner system in system out print t rk e turkce input nextint system out print matematik matematik input nextint system out print fizik fizik input nextint system out print kimya kimya input nextint system out print m zik muzik input nextint al nan ders notlar n n 0 ile 100 aras nda oldu unu kontrol edelim 0 100 aras nda de ilse ortalama hesab na kat lmayacak if turkce 100 turkce 0 derssayisi turkce 0 if matematik 100 matematik 0 derssayisi matematik 0 if fizik 100 fizik 0 derssayisi fizik 0 if kimya 100 kimya 0 derssayisi kimya 0 if muzik 100 muzik 0 derssayisi muzik 0 ortalama hesab n yapal m ortalama turkce matematik fizik kimya muzik derssayisi sonucu yazd ral m if ortalama 55 system out println kald n z ortalaman z ortalama else if ortalama 55 ortalama 100 system out println ge tiniz ortalaman z ortalama details brain prat k 9 hava s cakl na g re etkinlik nerme question soru java ko ullu ifadeler ile hava s cakl na g re etkinlik neren program yap m warning ko ullar s cakl k 5 dan k k ise kayak yapmay ner s cakl k 5 ve 15 aras nda ise sinema etkinli ini ner s cakl k 15 ve 25 aras nda ise piknik etkinli ini ner s cakl k 25 ten b y k ise y zme etkinli ini ner warning ayn rnek zerinden if ko ullar ba ka hangi ekilde olu turulabilirdi farkl z m yollar bulunuz green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i ken tan mlamalar double havas cakligi kullan c dan hava durumunu isteyelim scanner input new scanner system in system out print l tfen hava durumunu giriniz havas cakligi input nextdouble al nan hava s cakl na g re etkinlik nerisi yapal m if havas cakligi 5 system out println kayak yapabilirsiniz else if havas cakligi 5 havas cakligi 15 system out println sinemaya gidebilirsiniz else if havas cakligi 15 havas cakligi 25 system out println pikni e gidebilirsiniz else if havas cakligi 25 system out println y zmeye gidebilirsiniz programda belirtilen uyar ya g re if ko ullar olmadan zmeye al al m boolean kosul1 havas cakligi 5 kosul2 havas cakligi 5 havas cakligi 15 kosul3 havas cakligi 15 havas cakligi 25 string sonuc ko ullar kontrol etme sonuc kosul1 kayak yapabilirsiniz kosul2 sinemaya gidebilirsiniz kosul3 pikni e gidebilirsiniz y zmeye gidebilirsiniz sonucu ekrana yazd ral m system out println sonuc details brain prat k 10 say lar b y kten k e s ralayan program question soru java ko ullu ifadeler ile girilen 3 say y b y kten k e s ralayan program yap m warning girilen 3 say y k kten b y e s ralayan program yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i ken tan mlamalar int sayi1 sayi2 sayi3 kullan c dan say lar isteyelim scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextint system out print 2 say y giriniz sayi2 input nextint system out print 3 say y giriniz sayi3 input nextint al nan say lar b y kten k e s ralay p yazd ral m system out println b y kten k e s ralama if sayi1 sayi2 sayi1 sayi3 if sayi2 sayi3 system out println sayi1 sayi2 sayi3 else system out println sayi1 sayi3 sayi2 else if sayi2 sayi1 sayi2 sayi3 if sayi1 sayi3 system out println sayi2 sayi1 sayi3 else system out println sayi2 sayi3 sayi1 else if sayi1 sayi2 system out println sayi3 sayi1 sayi2 else system out println sayi3 sayi2 sayi1 al nan say lar k kten b y e s ralayal m system out println k kten b y e s ralama if sayi1 sayi2 sayi1 sayi3 if sayi2 sayi3 system out println sayi1 sayi2 sayi3 else system out println sayi1 sayi3 sayi2 else if sayi2 sayi1 sayi2 sayi3 if sayi1 sayi3 system out println sayi2 sayi1 sayi3 else system out println sayi2 sayi3 sayi1 else if sayi1 sayi2 system out println sayi3 sayi1 sayi2 else system out println sayi3 sayi2 sayi1 details brain prat k 11 bur bulan program question soru java ko ullu ifadeler ile kullan c n n burcunu bulan program yap m pill ko burcu 21 mart 20 nisan pill bo a burcu 21 nisan 21 may s pill kizler burcu 22 may s 22 haziran pill yenge burcu 23 haziran 22 temmuz pill aslan burcu 23 temmuz 22 a ustos pill ba ak burcu 23 a ustos 22 eyl l pill terazi burcu 23 eyl l 22 ekim pill akrep burcu 23 ekim 21 kas m pill yay burcu 22 kas m 21 aral k pill o lak burcu 22 aral k 21 ocak pill kova burcu 22 ocak 19 ubat pill bal k burcu 20 ubat 20 mart warning ayn rne i switch case kullanmadan yap n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i ken tan mlamalar int ay gun kullan c dan ay ve g n de erlerini alal m scanner input new scanner system in system out print do du unuz ay giriniz ay input nextint system out print do du unuz g n giriniz gun input nextint switch case ile yapal m system out println switch case ile z m switch ay case 1 if gun 22 system out println kova burcusunuz else system out println o lak burcusunuz break case 2 if gun 20 system out println bal k burcusunuz else system out println kova burcusunuz break case 3 if gun 21 system out println ko burcusunuz else system out println kova burcusunuz break case 4 if gun 21 system out println bo a burcusunuz else system out println ko burcusunuz break case 5 if gun 22 system out println kizler burcusunuz else system out println bo a burcusunuz break case 6 if gun 23 system out println yenge burcusunuz else system out println kizler burcusunuz break case 7 if gun 23 system out println aslan burcusunuz else system out println yenge burcusunuz break case 8 if gun 23 system out println ba ak burcusunuz else system out println aslan burcusunuz break case 9 if gun 23 system out println terazi burcusunuz else system out println ba ak burcusunuz break case 10 if gun 22 system out println akrep burcusunuz else system out println terazi burcusunuz break case 11 if gun 23 system out println yay burcusunuz else system out println akrep burcusunuz break case 12 if gun 22 system out println o lak burcusunuz else system out println yay burcusunuz break switch case kullanmadan ayn rne i yapal m system out println f else ile z m if ay 1 gun 22 system out println kova burcusunuz else if ay 1 system out println o lak burcusunuz if ay 2 gun 20 system out println bal k burcusunuz else if ay 2 system out println kova burcusunuz if ay 3 gun 21 system out println ko burcusunuz else if ay 3 system out println bal k burcusunuz if ay 4 gun 21 system out println bo a burcusunuz else if ay 4 system out println ko burcusunuz if ay 5 gun 22 system out println kizler burcusunuz else if ay 5 system out println bo a burcusunuz if ay 6 gun 23 system out println yenge burcusunuz else if ay 6 system out println kizler burcusunuz if ay 7 gun 23 system out println aslan burcusunuz else if ay 7 system out println yenge burcusunuz if ay 8 gun 23 system out println ba ak burcusunuz else if ay 8 system out println aslan burcusunuz if ay 9 gun 23 system out println terazi burcusunuz else if ay 9 system out println ba ak burcusunuz if ay 10 gun 23 system out println akrep burcusunuz else if ay 10 system out println terazi burcusunuz if ay 11 gun 22 system out println yay burcusunuz else if ay 11 system out println akrep burcusunuz if ay 12 gun 22 system out println o lak burcusunuz else if ay 12 system out println yay burcusunuz details brain prat k 12 girilen say lardan ift say lar bulan program question soru java d ng ler ile kullan c n n girdi i say ya kadar ift olan say lar bulan program yaz yoruz warning java d ng ler ile 0 dan girilen say ya kadar olan say lardan 3 ve 4 e tam b l nen say lar n ortalamas n hesaplayan program yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int girilensayi kullan c dan say y alal m scanner input new scanner system in system out print l tfen say y giriniz girilensayi input nextint girilen say ya kadar olan ift say lar ekrana yazd ral m system out print girilen say ya kadar olan ift say lar for int i 1 i girilensayi i if i 2 0 system out print i system out println 3 e ve 4 e tam b l nenleri yazd ral m system out print girilen say ya kadar olan say lardan 3 e ve 4 e tam b l nenlerin ortalamas int ortalama 0 b len 0 for int i 1 i girilensayi i if i 3 0 i 4 0 ortalama i b len system out println ortalama b len details brain prat k 13 tek say lar n toplam n bulan program question soru java d ng ler ile negatif bir de er girilene kadar kullan c dan giri leri kabul eden ve girilen de erlerden tek say lar toplay p ekrana basan program yaz yoruz warning java d ng ler ile tek bir say girilene kadar kullan c dan giri leri kabul eden ve girilen de erlerden ift ve 4 n katlar olan say lar toplay p ekrana basan program yaz yoruz green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int girilensayi1 0 toplam 0 girilensayi2 0 bir d ng ile bu say lar negatif de er girilene kadar al p i inden tek olanlar toplayal m while girilensayi1 0 kullan c dan say y alal m scanner input new scanner system in system out print l tfen say y giriniz girilensayi1 input nextint if girilensayi1 2 1 toplam toplam girilensayi1 else continue system out println girilen tek say lar n toplam toplam system out println di er programa ge ildi toplam 0 tek bir say girilene kadar kullan c dan giri leri kabul eden ve girilen de erlerden ift ve 4 n katlar olan say lar toplay p ekrana basan program yaz yoruz while girilensayi2 2 0 kullan c dan say y alal m scanner input new scanner system in system out print l tfen say y giriniz girilensayi2 input nextint if girilensayi2 4 0 toplam girilensayi2 toplam else continue system out println girilen say lardan 4 n katlar toplam toplam details brain prat k 14 girilen say dan k k 2 nin kuvvetlerini bulan program question soru java d ng ler ile girilen say ya kadar olan 2 nin kuvvetlerini ekrana yazd ran program yaz yoruz warning java d ng ler ile girilen say ya kadar olan 4 ve 5 in kuvvetlerini ekrana yazd ran program yaz yoruz green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int girilensayi1 0 kullan c dan say y alal m scanner input new scanner system in system out print l tfen say y giriniz girilensayi1 input nextint bir d ng ile 2 nin kuvvetlerini bulup ekrana yazd ral m for int i 1 i girilensayi1 i i 2 system out println i java d ng ler ile girilen say ya kadar olan 4 ve 5 in kuvvetlerini ekrana yazd ran program yaz yoruz for int i 1 i girilensayi1 i i 4 system out println 4 n kuvvetleri i for int i 1 i girilensayi1 i i 5 system out println 5 n kuvvetleri i details brain prat k 15 fakt riyel hesaplayan program question soru java ile fakt riyel hesaplayan program yaz yoruz warning n elemanl bir k menin elemanlar ile olu turulacak r elemanl farkl gruplar n say s n in r li kombinasyonu olarak adland r l r n in r li kombinasyonu c n r eklinde g sterilir java ile kombinasyon hesaplayan program yaz n z pill kombinasyon form l c n r n r n r green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int girilensayi faktoryel 1 kullan c dan say y alal m scanner input new scanner system in system out print l tfen say y giriniz girilensayi input nextint fakt riyel hesaplayal m for int i 1 i girilensayi i faktoryel faktoryel i system out println girilensayi in fakt riyeli faktoryel kombinasyon hesaplama int eleman secim kombinasyon elemanfaktoriyel 1 secimfaktoriyel 1 elemansecimfaktoriyel 1 kullan c dan verileri alal m system out print l tfen eleman say s n giriniz eleman input nextint system out print l tfen se im say s n giriniz secim input nextint for int i 1 i eleman i elemanfaktoriyel elemanfaktoriyel i for int i 1 i secim i secimfaktoriyel secimfaktoriyel i for int i 1 i eleman secim i elemansecimfaktoriyel elemansecimfaktoriyel i system out println c eleman secim kombinasyonu elemanfaktoriyel secimfaktoriyel elemansecimfaktoriyel details brain prat k 16 sl say hesaplayan program question soru java ile kullan c n n girdi i de erler ile sl say hesaplayan program yaz yoruz warning java ile kullan c n n girdi i de erler ile sl say hesaplayan program for d ng s kullanarak yap n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class uslusayihesaplayanprogram public static void main string args int sayi1 kuvvet1 sayi2 kuvvet2 sayac 1 snc1 1 snc2 1 scanner input new scanner system in sl say hesaplayan program while ile z m system out print n sl sayi hesaplayan program while le z m n system out print l tfen taban say y giriniz sayi1 input nextint system out print l tfen kuvveti s giriniz kuvvet1 input nextint while sayac kuvvet1 snc1 sayi1 sayac system out println sayi1 kuvvet1 snc1 sl say hesaplayan program for ile z m system out print n sl sayi hesaplayan program for le z m n system out print l tfen taban say y giriniz sayi2 input nextint system out print l tfen kuvveti s giriniz kuvvet2 input nextint for int x 1 x kuvvet2 x snc2 sayi2 system out println sayi2 kuvvet2 snc2 details brain prat k 17 armstrong say lar bulan program question soru java d ng ler ile say n n armstrong say olup olmad n bulan program yaz yoruz mag armstrong say nedir n haneli bir say n n basamaklar n n n inci stlerinin toplam say n n kendisine e itse b yle say lara armstrong say denir rne in 407 say s n ele alal m 4 3 0 3 7 3 64 0 343 407 sonucunu verir bu da 407 say s n n armstrong bir say oldu unu g sterir 1342 say s na da bakal m 1 4 3 4 4 4 2 4 1 81 256 16 354 say s 1342 ye e it olmad i in armstrong say olmaz 1634 1 4 6 4 3 4 4 4 1 1296 81 256 1634 54748 5 5 4 5 7 5 4 5 8 5 3125 1024 16807 1024 32768 54748 pill rnek al ma kodu java import java util scanner public class main public static void main string args scanner input new scanner system in system out print say giriniz int number input nextint int basnumber 0 int tempnumber number int basvalue int result 0 int baspow while tempnumber 0 tempnumber 10 basnumber tempnumber number while tempnumber 0 basvalue tempnumber 10 1 1 1 1 1 4 baspow 1 for int i 1 i basnumber i baspow basvalue result baspow tempnumber 10 if result number system out println number say s bir armstrong say d r else system out println number say s bir armstrong say s de ildir int a 2451 basamaksayisi 0 numbercounter 0 basamak say s bulma lemi 2451 10 245 245 10 24 24 10 2 2 10 0 0 10 0 while a 0 a 10 numbercounter bir say n n son basama n bulma 2451 10 1 int b 2451 int c b 10 int sub 2 sup 3 int result 1 for int i 1 i sup i result sub system out println result green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class armstrongsayilaribulanprogram public static void main string args int sayi1 bsmksay1 0 hafizasay1 scanner input new scanner system in armstrong say lar bulan program system out print n armstrong sayilari bulan program n system out print l tfen bir say giriniz sayi1 input nextint hafizasay1 sayi1 for int x 0 sayi1 0 x sayi1 sayi1 10 bsmksay1 sayi1 hafizasay1 int bsmkmod1 bsmkdeger1 mod1 10 bolum1 1 gtoplam1 1 sonuc1 0 for int y 1 y bsmksay1 y bsmkmod1 sayi1 mod1 bsmkdeger1 bsmkmod1 bolum1 mod1 10 bolum1 10 for int z 1 z bsmksay1 z gtoplam1 bsmkdeger1 sonuc1 gtoplam1 gtoplam1 1 if sayi1 sonuc1 system out println sayi1 bir armstrong say d r else system out println sayi1 bir armstrong say d r de ildir basamak say lar n n toplam n hesaplayan program system out print n basamak sayilarinin toplamini hesaplayan program n int sayi2 bsmksay2 0 hafizasay2 system out print l tfen bir say giriniz sayi2 input nextint hafizasay2 sayi2 for int x 0 sayi2 0 x sayi2 sayi2 10 bsmksay2 sayi2 hafizasay2 int bsmkmod2 bsmkdeger2 mod2 10 bolum2 1 sonuc2 0 for int y 1 y bsmksay2 y bsmkmod2 sayi2 mod2 bsmkdeger2 bsmkmod2 bolum2 mod2 10 bolum2 10 sonuc2 bsmkdeger2 system out print basamak say lar n n toplam sonuc2 details brain prat k 18 harmonik say lar bulan program question soru java ile girilen say n n harmonik serisini bulan program yazaca z pill harmonik seri form l harmonik seri formul https raw githubusercontent com kodluyoruz taskforce main java101 pratik harmonic figures harmonic series gif green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class harmonikseribulanprogram public static void main string args double n toplam 0 scanner input new scanner system in system out print harmonik serisini bulmak istedi iniz say y n giriniz n input nextint for double x 1 x n x toplam 1 x system out print cevap toplam details brain prat k 19 y ld z ile gen yap m question soru java da d ng ler kullan larak y ld zlar ile gen yap yoruz pill rnek kod java import java util scanner public class main public static void main string args scanner klavye new scanner system in system out println bir say giriniz int n klavye nextint for int i 0 i n i for int j 0 j n i j system out print for int k 1 k 2 i 1 k system out print system out println warning java da d ng ler kullanarak y ld zlar ile elmas yap n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class yildizlarileucgenyapimi public static void main string args int katsay1 katsay2 scanner input new scanner system in y ld z ile gen yap m system out print n yildiz gen yapimi n system out print bir say giriniz katsay1 input nextint for int x 0 x katsay1 x for int y 0 y katsay1 x y system out print for int z 1 z 2 x 1 z system out print system out println y ld z elmas yap m system out print n yildiz elmas yapimi n system out print bir say giriniz katsay2 input nextint for int x 0 x katsay2 x for int y 0 y katsay2 x y system out print for int z 1 z 2 x 1 z system out print system out println for int x 0 x katsay2 x for int y 0 y x y system out print for int z 1 z 2 katsay2 2 x z system out print system out println details brain prat k 20 atm projesi question soru java d ng ler ile kullan c n n banka hesab n y netebildi i bir atm projesi yap yoruz pill rnek kod java import java util scanner public class main public static void main string args string username password scanner input new scanner system in int right 3 int balance 1500 int select while right 0 system out print kullan c ad n z username input nextline system out print parolan z password input nextline if username equals patika password equals dev123 system out println merhaba kodluyoruz bankas na ho geldiniz do system out println 1 para yat rma n 2 para ekme n 3 bakiye sorgula n 4 k yap system out print l tfen yapmak istedi iniz i lemi se iniz select input nextint if select 1 system out print para miktar int price input nextint balance price else if select 2 system out print para miktar int price input nextint if price balance system out println bakiye yetersiz else balance price else if select 3 system out println bakiyeniz balance while select 4 system out println tekrar g r mek zere break else right system out println hatal kullan c ad veya ifre tekrar deneyiniz if right 0 system out println hesab n z bloke olmu tur l tfen banka ile ileti ime ge iniz else system out println kalan hakk n z right warning ayn projedeki atm i lemlerini switch case kullanarak yap n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class atmprojesi public static void main string args string kullaniciadi1 kullaniciadi2 sifre1 sifre2 int kalanhak1 3 kalanhak2 3 islem1 islem2 bakiye1 5000 bakiye2 5000 paramiktari1 paramiktari2 scanner input new scanner system in atm projesi banka i lemleri if system out println n atm projesi banka lemler if n system out println merhabalar kodluyoruz bankas na ho geldiniz while kalanhak1 0 system out print kullan c ad n z kullaniciadi1 input nextline system out print ifreniz sifre1 input nextline if kullaniciadi1 equals patika sifre1 equals dev123 system out println sisteme ba ar l bir ekilde giri yapt n z do system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem1 input nextint if islem1 1 system out println bakiyeniz bakiye1 else if islem1 2 system out print l tfen yat rmak istedi iniz para miktar n giriniz paramiktari1 input nextint bakiye1 paramiktari1 system out println bakiyeniz bakiye1 else if islem1 3 system out print l tfen ekmek istedi iniz para miktar n giriniz paramiktari1 input nextint if paramiktari1 bakiye1 system out println bakiyeniz yetersiz else bakiye1 paramiktari1 system out println bakiyeniz bakiye1 while islem1 4 system out println yi g nler dileriz break else system out println hatal kullan c ad veya ifre giri i kalan hakk n z kalanhak1 if kalanhak1 0 system out println hesab n z bloke edilmi tir atm projesi banka i lemleri switch case system out println n atm projesi banka lemler switch case n system out println merhabalar kodluyoruz bankas na ho geldiniz while kalanhak2 0 system out print kullan c ad n z kullaniciadi2 input nextline system out print ifreniz sifre2 input nextline if kullaniciadi2 equals patika sifre2 equals dev123 system out println sisteme ba ar l bir ekilde giri yapt n z system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem2 input nextint do switch islem2 case 1 system out println bakiyeniz bakiye2 system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem2 input nextint break case 2 system out print l tfen yat rmak istedi iniz para miktar n giriniz paramiktari2 input nextint bakiye2 paramiktari2 system out println bakiyeniz bakiye2 system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem2 input nextint break case 3 system out print l tfen ekmek istedi iniz para miktar n giriniz paramiktari2 input nextint if paramiktari2 bakiye2 system out println bakiyeniz yetersiz else bakiye2 paramiktari2 system out println bakiyeniz bakiye2 system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem2 input nextint break case 4 break default system out println 1 bakiye sorgulama n 2 para yat rma n 3 para ekme n 4 k yap n system out print l tfen yapmak istedi iniz i lemi se iniz islem2 input nextint while islem2 4 system out print yi g nler dileriz break else system out println hatal kullan c ad veya ifre giri i kalan hakk n z kalanhak2 if kalanhak2 0 system out println hesab n z bloke edilmi tir details brain prat k 21 ebob ve ekok bulan program question soru java ile iki say n n ebob ve ekok de erlerini bulan program yaz yoruz pill ebob ki ya da daha fazla do al say n n ortak b lenlerinin en b y ne bu say lar n en b y k ortak b leni k saca ebob u denir mag rnek 18 ve 24 say lar n n en b y k ortak b lenini ad m ad m bulal m 18 in b lenleri 1 2 3 6 9 18 24 n b lenleri 1 2 3 4 6 8 12 24 bu ortak b lenlerin en b y 6 say s ebob tur pill ekok ki ya da daha fazla do al say n n ortak katlar n n en k ne bu say lar n en k k ortak kat k saca ekok u denir mag rnek 6 ve 8 say lar n n en k k ortak kat n ad m ad m bulal m 6 n n katlar 6 12 18 24 30 36 42 48 8 in katlar 8 16 24 32 40 48 56 64 bu ortak katlardan en k 24 say s ekok tur pill ekok n1 n2 ebob warning java ile iki say n n ebob ve ekok de erlerini while d ng s kullanarak yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int sayi1 sayi2 ebob 1 ekok boolean kucuksayi kullan c dan say y alal m scanner input new scanner system in system out print l tfen 1 say y say giriniz sayi1 input nextint system out print l tfen 2 say y say giriniz sayi2 input nextint kucuksayi sayi1 sayi2 if kucuksayi true for int i sayi1 i 1 i if sayi1 i 0 sayi2 i 0 ebob i system out print ebob ebob break else for int i sayi2 i 1 i if sayi2 i 0 sayi1 i 0 ebob i system out print ebob ebob break system out println system out println ekok sayi1 sayi2 ebob details brain prat k 22 palindrom say lar question soru java ile bir say n n palindrom say olup olmad n bulan bir program yap yoruz pill palindrom say nedir palindromik say iki taraftan okundu u zaman okunu y n yle ayn olan say lard r rnek 1 4 8 99 101 363 4004 9889 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test static boolean ispalindrom int number int temp number reversenumber 0 lastnumber while temp 0 system out println number temp lastnumber temp 10 system out println last number lastnumber reversenumber reversenumber 10 lastnumber system out println new number reversenumber temp 10 system out println new temp temp return true public static void main string args ispalindrom 2456 details brain prat k 23 recursive fibonacci serisi bulan program question soru java da recursive metotlar ile fibonacci serisi bulan program yap yoruz green square cevap details summary kodu g rmek i in t klay n z summary java package pratik23 public class recursivefibonacciserisi static int fbncc int sayi if sayi 1 sayi 2 return 1 return fbncc sayi 1 fbncc sayi 2 public static void main string args system out println fbncc 6 test etmek i in say de erini de i tirin details brain prat k 24 geli mi hesap makinesi question soru java ile kullan c n se ti i i lemleri yapan hesap makinesi yap yoruz pill hesap makinesinin fonksiyonlar 1 toplama lemi 2 karma lemi 3 arpma lemi 4 b lme i lemi 5 sl say hesaplama 6 faktoriyel hesaplama 7 mod alma 8 dikd rtgen alan ve evre hesab mag rnek kod java import java util scanner public class javapatika static void plus scanner scan new scanner system in int number result 0 i 1 while true system out print i say number scan nextint if number 0 break result number system out println sonu result static void minus scanner scan new scanner system in system out print ka adet say gireceksiniz int counter scan nextint int number result 0 for int i 1 i counter i system out print i say number scan nextint if i 1 result number continue result number system out println sonu result static void times scanner scan new scanner system in int number result 1 i 1 while true system out print i say number scan nextint if number 1 break if number 0 result 0 break result number system out println sonu result static void divided scanner scan new scanner system in system out print ka adet say gireceksiniz int counter scan nextint double number result 0 0 for int i 1 i counter i system out print i say number scan nextdouble if i 1 number 0 system out println b leni 0 giremezsiniz continue if i 1 result number continue result number system out println sonu result static void power scanner scan new scanner system in system out print taban de eri giriniz int base scan nextint system out print s de eri giriniz int exponent scan nextint int result 1 for int i 1 i exponent i result base system out println sonu result static void factorial scanner scan new scanner system in system out print say giriniz int n scan nextint int result 1 for int i 1 i n i result i system out println sonu result public static void main string args scanner scan new scanner system in int select string menu 1 toplama lemi n 2 karma lemi n 3 arpma lemi n 4 b lme i lemi n 5 sl say hesaplama n 6 faktoriyel hesaplama n 7 mod alma n 8 dikd rtgen alan ve evre hesab n 0 k yap do system out println menu system out print l tfen bir i lem se iniz select scan nextint switch select case 1 plus break case 2 minus break case 3 times break case 4 divided break case 5 power break case 6 factorial break case 0 break default system out println yanl bir de er girdiniz tekrar deneyiniz while select 0 warning ayn projeye mod almak ve dikd rtgen alan evre hesab n yapan metotlar yaz p men de i levsel hale getiriniz green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class gelismishesapmakinesi static void topla int sayi1 sayi2 sonuc scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextint system out print 2 say y giriniz sayi2 input nextint sonuc sayi1 sayi2 system out println sonu sonuc static void cikar int sayi1 sayi2 sonuc scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextint system out print 2 say y giriniz sayi2 input nextint sonuc sayi1 sayi2 system out println sonu sonuc static void carp int sayi1 sayi2 sonuc scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextint system out print 2 say y giriniz sayi2 input nextint sonuc sayi1 sayi2 system out println sonu sonuc static void bol int sayi1 sayi2 sonuc scanner input new scanner system in system out print 1 say y giriniz sayi1 input nextint system out print 2 say y giriniz sayi2 input nextint if sayi2 0 system out println s f ra b l nemez else sonuc sayi1 sayi2 system out println sonu sonuc static void usal int taban us sonuc 1 scanner input new scanner system in system out print taban say y giriniz taban input nextint system out print s say y giriniz us input nextint for int x 1 x us x sonuc taban system out println sonu sonuc static void faktoriyel int sayi sonuc 1 scanner input new scanner system in system out print say giriniz sayi input nextint for int x 1 x sayi x sonuc x system out println sonu sonuc static void mod int sayi mod sonuc scanner input new scanner system in system out print say y giriniz sayi input nextint system out print mod say s n giriniz mod input nextint sonuc sayi mod system out println sonu sonuc static void alanvecevre int sayi1 sayi2 alan cevre scanner input new scanner system in system out print 1 kenar giriniz sayi1 input nextint system out print 2 kenar giriniz sayi2 input nextint alan sayi1 sayi2 cevre 2 sayi1 sayi2 system out println alan alan system out println cevre cevre public static void main string args scanner input new scanner system in int islem while true system out println 0 program sonland r 1 toplama lemi 2 karma lemi 3 arpma lemi 4 b lme i lemi 5 sl say hesaplama 6 faktoriyel hesaplama 7 mod alma 8 dikd rtgen alan ve evre hesab n system out print l tfen bir i lem se iniz islem input nextbyte if islem 0 break switch islem case 1 topla break case 2 cikar break case 3 carp break case 4 bol break case 5 usal break case 6 faktoriyel break case 7 mod break case 8 alanvecevre break default system out println l tfen ge erli bir i lem numaras se iniz system out println hesap makinesi kapan yor details brain prat k 25 renci bilgi sistemi question soru pill course s n f zellikleri nitelikler name code prefix note teacher metotlar course addteacher printteacher pill teacher s n f zellikleri nitelikler name mpno branch metotlar teacher pill student s n f zellikleri nitelikler name stuno classes course1 course2 course3 avarage ispass metotlar student addbulkexamnote ispass calcavarage printnote course s n f na derse ait s zl notu k sm n girin ve ortalamaya etkisini her ders i in ayr ayr belirtin s zl notlar n da ortalamaya etkileme y zdesi ile dahil edin rnek fizik dersinine ait s zl notunun ortalamaya etkisi 20 ise s nav notunun etkisi 80 dir renci s zl den 90 s navdan 60 alm ise o dersin genel ortalamaya etkisi u ekilde hesaplan r fizik ortalamas 90 0 20 60 0 80 java public class student string name stuno int classes course mat course fizik course kimya double avarage boolean ispass student string name int classes string stuno course mat course fizik course kimya this name name this classes classes this stuno stuno this mat mat this fizik fizik this kimya kimya calcavarage this ispass false public void addbulkexamnote int mat int fizik int kimya if mat 0 mat 100 this mat note mat if fizik 0 fizik 100 this fizik note fizik if kimya 0 kimya 100 this kimya note kimya public void ispass if this mat note 0 this fizik note 0 this kimya note 0 system out println notlar tam olarak girilmemi else this ispass ischeckpass printnote system out println ortalama this avarage if this ispass system out println s n f ge ti else system out println s n fta kald public void calcavarage this avarage this fizik note this kimya note this mat note 3 public boolean ischeckpass calcavarage return this avarage 55 public void printnote system out println system out println renci this name system out println matematik notu this mat note system out println fizik notu this fizik note system out println kimya notu this kimya note java public class course teacher courseteacher string name string code string prefix int note public course string name string code string prefix this name name this code code this prefix prefix this note 0 public void addteacher teacher t if this prefix equals t branch this courseteacher t system out println lem ba ar l else system out println t name akademisyeni bu dersi veremez public void printteacher if courseteacher null system out println this name dersinin akademisyeni courseteacher name else system out println this name dersine akademisyen atanmam t r java public class teacher string name string mpno string branch public teacher string name string mpno string branch this name name this mpno mpno this branch branch java public class main public static void main string args course mat new course matematik mat101 mat course fizik new course fizik fzk101 fzk course kimya new course kimya kmy101 kmy teacher t1 new teacher mahmut hoca 90550000000 mat teacher t2 new teacher fatma ay e 90550000001 fzk teacher t3 new teacher ali veli 90550000002 kmy mat addteacher t1 fizik addteacher t2 kimya addteacher t3 student s1 new student nek aban 4 140144015 mat fizik kimya s1 addbulkexamnote 50 20 40 s1 ispass student s2 new student g d k necmi 4 2211133 mat fizik kimya s2 addbulkexamnote 100 50 40 s2 ispass student s3 new student hayta smail 4 221121312 mat fizik kimya s3 addbulkexamnote 50 20 40 s3 ispass brain prat k 26 boks oyunu question soru java s n flar ile boks ma n sim le eden program yaz yoruz mag dev projeye ilk kimin d v e ba layaca n 50 olas l k ile hesaplayan sistemi entegre ediniz java public class fighter string name int damage int health int weight double dodge public fighter string name int damage int health int weight double dodge this name name this damage damage this health health this weight weight this dodge dodge public int hit fighter foe system out println system out println this name foe name this damage hasar vurdu if foe dodge system out println foe name gelen hasar savurdu return foe health if foe health this damage 0 return 0 return foe health this damage public boolean dodge double randomvalue math random 100 0 0 to 99 9 return randomvalue this dodge java public class ring fighter f1 fighter f2 int minweight int maxweight public ring fighter f1 fighter f2 int minweight int maxweight this f1 f1 this f2 f2 this minweight minweight this maxweight maxweight public void run if checkweight while f1 health 0 f2 health 0 system out println yen round f2 health f1 hit f2 if iswin break f1 health f2 hit f1 if iswin break printscore else system out println sporcular n a rl klar uyu muyor public boolean checkweight return f1 weight minweight f1 weight maxweight f2 weight minweight f2 weight maxweight public boolean iswin if f1 health 0 system out println ma kazanan f2 name return true else if f2 health 0 system out println ma kazanan f2 name return true return false public void printscore system out println system out println f1 name kalan can t f1 health system out println f2 name kalan can t f2 health java public class main public static void main string args fighter marc new fighter marc 15 100 90 0 fighter alex new fighter alex 10 95 100 0 ring r new ring marc alex 90 100 r run brain prat k 27 dizideki elemanlar n ortalamas n bulan program question soru dizideki elemanlar n ortalamas n bulan program green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args int numbers 1 2 3 4 5 int sum 0 for int i 0 i numbers length i sum numbers i system out println sum numbers length details brain prat k 28 dizideki maksimum ve minimum de erleri bulan program question soru dizideki elemanlardan min ve max olanlar bulma green square cevap details summary kodu g rmek i in t klay n z summary java public class test public static void main string args int list 56 34 1 8 101 2 33 int min list 0 int max list 0 for int i list if i min min i if i max max i system out println min min system out println max max details brain prat k 29 ok boyutlu diziler ile a harfi yazd ran program question soru ok boyutlu diziler kullan larak y ld zlar ile ekrana b harfi yazan program yaz n z green square cevap details summary kodu g rmek i in t klay n z summary java public class test public static void main string args string letter new string 6 4 for int i 0 i letter length i for int j 0 j letter i length j if i 0 i 2 letter i j else if j 0 j 3 letter i j else letter i j for string row letter for string col row system out print col system out println details brain prat k 30 dizideki tekrar eden say lar bulan program question soru bir dizide tekrar eden say lar bulal m green square cevap details summary kodu g rmek i in t klay n z summary java import java sql array import java util arrays public class test static boolean isfind int arr int value for int i arr if i value return true return false public static void main string args int list 3 7 3 3 2 9 10 21 1 33 9 1 int duplicate new int list length int startindex 0 for int i 0 i list length i for int j 0 j list length j if i j list i list j if isfind duplicate i duplicate startindex list i break for int value duplicate if value 0 system out println value details brain prat k 31 say tahmin oyunu question soru java dilinde program n 0 100 aras nda rastgele se ti i bir say y kullan c n n tahmin etmesini istedi imiz bir say tahmin oyunu yap yoruz green square cevap details summary kodu g rmek i in t klay n z summary java import java util arrays import java util random import java util scanner public class test public static void main string args random rand new random int number rand nextint 100 int number int math random 100 scanner input new scanner system in int right 0 int selected int wrong new int 5 boolean iswin false boolean iswrong false system out println number while right 5 system out print l tfen tahmininizi giriniz selected input nextint if selected 0 selected 99 system out println l tfen 0 100 aras nda bir de er giriniz if iswrong right system out println ok fazla hatal giri yapt n z kalan hak 5 right else iswrong true system out println bir daha hatal giri inizde hakk n zdan d lecektir continue if selected number system out println tebrikler do ru tahmin tahmin etti ini say number iswin true break else system out println hatal bir say girdiniz if selected number system out println selected say s gizli say dan b y kt r else system out println selected say s gizli say dan k kt r wrong right selected system out println kalan hakk 5 right if iswin system out println kaybettiniz if iswrong system out println tahminleriniz arrays tostring wrong details brain prat k 32 palindromik kelimeleri bulan program question soru java dilinde kullan c n n girdi i kelimenin palindromik olup olmad bulan bir program yaz yoruz e er bir kelimenin tersten okunu u yine ayn kelimeyi veriyorsa o kelime palindromik kelime dir pill rnek abba asa kavak kayak green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test 1 z m string i inde gezerek harfleri kar la t rma static boolean ispalindrom string str int i 0 j str length 1 while i j if str charat i str charat j return false i j return true 2 z m gelen kelimeyi ters evirip ilk haliyle kar la t rma static boolean ispalindrom2 string str string reverse for int i str length 1 i 0 i reverse reverse str charat i if str equals reverse return true else return false public static void main string args string kelime scanner input new scanner system in system out print l tfen kelimeyi giriniz kelime input nextline system out println 1 z me g re if ispalindrom kelime true system out println girilen kelime kelimesi palindromdur else system out println girilen kelime kelimesi palindrom de ildir system out println 2 z me g re if ispalindrom2 kelime true system out println girilen kelime kelimesi palindromdur else system out println girilen kelime kelimesi palindrom de ildir details brain b t rme projes may n tarlas oyunu question soru java dilinde may n tarlas oyunu yapman z bekliyoruz warning oyun kurallar oyun metin tabanl d r ift boyutlu diziler zerinden oynanmal ve minesweeper s n f i erisinde tasarlanmal matris boyutunu yani sat r ve s tun say s n kullan c belirlemeli diziye ait eleman say s n n eyre i elemansayisi 4 kadar rastgele may n yerle tirilmeli rne in dizi 4x3 boyutunda ise eleman say s sat rsay s s tunsay s form l ile hesaplanmal ve boyutu 12 olmal bu durumda may n say s 12 4 3 adet olmal d r ipucu bu may nlar n konumlar n tutacak ikinci bir dizi olu turabilirsiniz kullan c matris zerinden bir nokta se meli nokta se imi i in sat r ve s tun de erlerini girmeli se ilen noktan n dizinin s n rlar i erisinde olup olmad n kontrol edilmeli ve ko ul sa lanmazsa tekrar nokta istenmeli kullan c n n girdi i noktada may n var ise oyunu kaybetmeli may n yok ise ilgili noktaya de en t m konumlar na bak lmal sa solu yukar s a a s sol st apraz sa st apraz sa alt apraz sol alt apraz ve etraf ndaki may nlar n say s n n toplam ilgili noktaya yaz lmal noktaya de en herhangi bir may n yok ise 0 de eri atanmal kullan c hi bir may na basmadan t m noktalar se ebilirse oyunu kazanmal pill kaybetme senaryosu may nlar n konumu may n tarlas oyuna ho geldiniz sat r giriniz 0 s tun giriniz 1 2 sat r giriniz 2 s tun giriniz 0 2 0 sat r giriniz 0 s tun giriniz 2 2 1 0 sat r giriniz 1 s tun giriniz 0 2 1 1 0 sat r giriniz 2 s tun giriniz 1 2 1 1 0 1 sat r giriniz 2 s tun giriniz 2 2 1 1 0 1 1 sat r giriniz 1 s tun giriniz 2 game over pill kazanma senaryosu may nlar n konumu may n tarlas oyuna ho geldiniz sat r giriniz 0 s tun giriniz 0 1 sat r giriniz 0 s tun giriniz 1 1 1 sat r giriniz 0 s tun giriniz 2 1 1 1 sat r giriniz 1 s tun giriniz 0 1 1 1 2 sat r giriniz 2 s tun giriniz 0 1 1 1 2 2 sat r giriniz 1 s tun giriniz 2 1 1 1 2 2 2 sat r giriniz 2 s tun giriniz 2 oyunu kazand n z 1 1 1 2 2 2 2 green square cevap details summary kodu g rmek i in t klay n z summary java import java util random import java util scanner public class minesweeper int column int row boolean maps int board int size minesweeper int row int column this row row this column column this maps new boolean row column this board new int row column this size row column void printminefield int array for int i 0 i array length i for int j 0 j array 0 length j system out print array i j system out println void minefield for int i 0 i maps length i for int j 0 j maps 0 length j if maps i j false system out print else system out print system out println void createminefield random random new random int randcolumn randrow int size this row this column 4 for int i 0 i maps length i for int j 0 j maps 0 length j board i j 0 maps i j false while size 0 randcolumn random nextint this row randrow random nextint this column if maps randrow randcolumn true maps randrow randcolumn true size void check int r int c if board r c 0 if c column 1 maps r c 1 true board r c if r row 1 maps r 1 c true board r c if c 0 maps r c 1 true board r c if r 1 0 if maps r 1 c true board r c if r 1 0 c 1 0 if maps r 1 c 1 true board r c if c 1 0 if maps r c 1 true board r c if c 1 column if maps r c 1 true board r c if c 1 column r 1 row if maps r 1 c 1 true board r c if r 1 row if maps r 1 c true board r c if r 1 0 c 1 column if maps r 1 c 1 true board r c if r 1 row c 1 0 if maps r 1 c 1 true board r c if r row c column if maps r 1 c 1 true board r c void run scanner input new scanner system in boolean game true int rownumber columnnumber createminefield size size 4 system out println may nlar n konumu minefield system out println may n tarlas oyununa ho geldiniz printminefield board do system out println system out print sat r say s n giriniz rownumber input nextint system out print s tun say s n giriniz columnnumber input nextint if maps rownumber columnnumber true system out println oyun bitti game false else check rownumber columnnumber printminefield board size if size 0 system out println oyunu kazand n z while game java import java util scanner public class test public static void main string args minesweeper mine new minesweeper 3 3 mine run details brain dev 1 v cut kitle ndeksi hesaplama question soru java ile kullan c dan boy ve kilo de erlerini al p bir de i kene atay n a a da ki form le g re kullan c n n v cut kitle ndeks de erini hesaplay p ekrana yazd r n warning form l kilo kg boy m boy m heavy check mark kt s l tfen boyunuzu metre cinsinde giriniz 1 72 l tfen kilonuzu giriniz 105 v cut kitle ndeksiniz 35 49215792320173 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double boy kilo kitleindeks kullan c dan verilerimizi alal m scanner input new scanner system in system out print boyunuzu metre cinsinden giriniz boy input nextdouble system out print kilonuzu giriniz kilo input nextdouble hesaplamalar yapal m kitleindeks kilo boy boy sonu lar ekrana yazal m system out println v cut kitle endeksiniz kitleindeks details brain dev 2 manav kasa question soru java ile kullan c lar n manavdan alm olduklar r nlerin kilogram de erlerine g re toplam tutar n ekrana yazd ran program yaz n pushpin meyveler ve kg fiyatlar armut 2 14 tl elma 3 67 tl domates 1 11 tl muz 0 95 tl patl can 5 00 tl heavy check mark rnek kt armut ka kilo 0 elma ka kilo 1 domates ka kilo 1 muz ka kilo 2 patl can ka kilo 3 toplam tutar 21 68 tl green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri olu tural m double armutkg 2 14 elmakg 3 67 domateskg 1 11 muzkg 0 95 patlicankg 5 00 tutar armut elma domates muz patlican kullan c dan verilerimizi alal m scanner input new scanner system in system out print armut ka kilo armut input nextdouble system out print elma ka kilo elma input nextdouble system out print domates ka kilo domates input nextdouble system out print muz ka kilo muz input nextdouble system out print patl can ka kilo patlican input nextdouble hesaplamalar yapal m tutar armut armutkg elma elmakg domates domateskg muz muzkg patlican patlicankg sonu lar ekrana yazal m system out println toplam tutar tutar details brain dev 3 u ak bileti fiyat hesaplama question soru java ile mesafeye ve artlara g re u ak bileti fiyat hesaplayan program yap n kullan c dan mesafe km ya ve yolculuk tipi tek y n gidi d n bilgilerini al n mesafe ba na cret 0 10 tl km olarak al n lk olarak u u un toplam fiyat n hesaplay n ve sonras nda ki ko ullara g re m teriye a a daki indirimleri uygulay n kullan c dan al nan de erler ge erli mesafe ve ya de erleri pozitif say yolculuk tipi ise 1 veya 2 olmal d r aksi takdirde kullan c ya hatal veri girdiniz eklinde bir uyar verilmelidir ki i 12 ya ndan k kse bilet fiyat zerinden 50 indirim uygulan r ki i 12 24 ya lar aras nda ise bilet fiyat zerinden 10 indirim uygulan r ki i 65 ya ndan b y k ise bilet fiyat zerinden 30 indirim uygulan r ki i yolculuk tipini gidi d n se mi ise bilet fiyat zerinden 20 indirim uygulan r mag pucu normal tutar mesafe 0 10 1500 0 10 150 tl ya ndirimi normal tutar ya ndirim oran 150 0 10 15 tl ndirimli tutar normal tutar ya ndirimi 150 15 135 tl gidi d n bilet ndirimi ndirimli tutar 0 20 135 0 20 27 tl toplam tutar 135 27 2 216 tl pill senaryo 1 mesafeyi km t r nden giriniz 1500 ya n z giriniz 20 yolculuk tipini giriniz 1 tek y n 2 gidi d n 2 toplam tutar 216 tl pill senaryo 2 mesafeyi km t r nden giriniz 500 ya n z giriniz 1 yolculuk tipini giriniz 1 tek y n 2 gidi d n 77 hatal veri girdiniz pill senaryo 3 mesafeyi km t r nden giriniz 200 ya n z giriniz 35 yolculuk tipini giriniz 1 tek y n 2 gidi d n 1 toplam tutar 20 0 tl green square cevap details summary kodu g rmek i in t klay n z summary java import java text decimalformat import java util scanner public class test public static void main string args dei kenler tan mland double mesafe yas mucret 0 10 ntutar yindirimi 0 itutar gdbindirimi ttutar int yoltip kullan c dan de erlerin ald r lmas icin scanner kullan ld virg llerden sonra kan 0 lar n silinmesi i in decimalformat eklendi scanner input new scanner system in decimalformat df new decimalformat kullan c dan km ya ve gidi d ne bilgisi istendi system out print mesafeyi km t r nden giriniz mesafe input nextint system out print ya n z giriniz yas input nextint system out print yolculuk tipini giriniz 1 tek y n 2 gidi d n yoltip input nextint cret hesaplamas ntutar mesafe mucret ndirimlerin hesaplanmas ve toplam tutara uygulanmas if yas 12 yindirimi ntutar 0 50 else if yas 12 yas 24 yindirimi ntutar 0 10 else if yas 65 yindirimi ntutar 0 30 itutar ntutar yindirimi switch yoltip case 1 gdbindirimi itutar 0 ttutar itutar gdbindirimi system out print ntoplam tutar df format ttutar tl break case 2 gdbindirimi itutar 0 20 ttutar itutar gdbindirimi ttutar ttutar 2 system out print ntoplam tutar df format ttutar tl break default system out println nhatal veri girdiniz break details brain dev 4 in zodya hesaplama question soru java ile kullan c dan do um tarihini al p in zodya de erini hesaplayan program yaz n z mag in zodya nedir 4000 bin y ld r kullan lan bir astroloji e ididir in astrolojisi ve insanlar 12 de i ik bur ve sembollerle tan mlar in zodya bu 12 burcun e it aral klarla 10 derece geni li inde s raland bir hayvan halkas d r ve y ld zlarla pek bir ilgisi yoktur mag in zodya nas l hesaplan r in zodya hesaplan rken ki inin do um y l n n 12 ile b l m nde kalana g re bulunur do um tarihi 12 0 maymun do um tarihi 12 1 horoz do um tarihi 12 2 k pek do um tarihi 12 3 domuz do um tarihi 12 4 fare do um tarihi 12 5 k z do um tarihi 12 6 kaplan do um tarihi 12 7 tav an do um tarihi 12 8 ejderha do um tarihi 12 9 y lan do um tarihi 12 10 at do um tarihi 12 11 koyun senaryo do um y l n z giriniz 1990 in zodya burcunuz at green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int dogumyili zodyak kullan c dan do um y l n alal m scanner input new scanner system in system out print l tfen do du unuz y l giriniz dogumyili input nextint zodyak hesaplayal m zodyak dogumyili 12 sonu lar ekrana yazd ral m switch zodyak case 0 system out println maymun break case 1 system out println horoz break case 2 system out println k pek break case 3 system out println domuz break case 4 system out println fare break case 5 system out println k z break case 6 system out println kaplan break case 7 system out println tav an break case 8 system out println ejderha break case 9 system out println y lan break case 10 system out println at break case 11 system out println koyun break default system out println hatal y l giri i break details brain dev 5 art k y l hesaplama question soru java ile kullan c n n girdi i y l n art k olup olmad n bulan program yaz n z mag art k y l nedir art k y l milad takvimde 365 yerine 366 g n olan y l bu fazladan g n art k g n normalde 28 g n olan ubat ay na 29 ubat n eklenmesi ile elde edilir mag art k y l nas l hesaplan r genel bir kural olarak art k y llar 4 rakam n n kat olan y llard r 1988 1992 1996 2000 2004 2008 2012 2016 2020 2024 gibi 100 n kat olan y llardan sadece 400 e kalans z olarak b l nebilenler art k y ld r rne in 1200 1600 2000 y llar art k y ld r ancak 1700 1800 ve 1900 art k y l de ildir sadece 400 e tam olarak b l nebilenlerin art k y l kabul edilmesinin nedeni bir astronomik y l n 365 25 g n de il yakla k olarak 365 242 g n olmas ndan kaynaklanan hatay gidermektir senaryo y l giriniz 2020 2020 bir art k y ld r y l giriniz 2021 2021 bir art k y ld r de ildir green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args de i kenleri tan mlayal m int yil kullan c dan bir y l alal m scanner input new scanner system in system out print l tfen y l giriniz yil input nextint art k y l hesab n yapal m ve sonu lar g sterelim if yil 4 0 yil 100 0 yil 4 0 yil 400 0 system out println yil art k y ld r else system out println yil art k y l de ildir details brain dev 6 girilen say lardan min ve max de erli bulan program question soru java ile klavyeden girilen n tane sayma say s ndan en b y k ve en k k say lar bulan ve bu say lar ekrana yazan program yaz n mag senaryo ka tane say gireceksiniz 4 1 say y giriniz 16 2 say y giriniz 22 3 say y giriniz 15 4 say y giriniz 100 en b y k say 100 en k k say 22 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args int sayin sayi buyuksay 0 kucuksay 0 scanner input new scanner system in system out print ka tane say gireceksiniz sayin input nextint for int x 1 x sayin x system out print x say y giriniz sayi input nextint if sayi buyuksay if kucuksay 0 kucuksay sayi buyuksay sayi if sayi kucuksay if buyuksay 0 buyuksay sayi kucuksay sayi system out println en b y k say buyuksay system out println en k k say kucuksay details brain dev 7 m kemmel say bulan program question soru klavyeden girilen bir say n n m kemmel say olup olmad n bulan ve say m kemmel say ise ekrana m kemmel say d r de ilse m kemmel say de ildir ifadelerini ekrana yazan program java dilinde yaz n z pill m kemmel say nedir bir say n n kendisi hari pozitif tam say arpanlar kalans z b len say lar n toplam kendisine e it olan say ya m kemmel say denir mag senaryo bir say giriniz 28 28 m kemmel say d r bir say giriniz 1 1 m kemmel say de ildir bir say giriniz 496 496 m kemmel say d r green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class mukemmelsayibulanprogram public static void main string args int sayi sonuc 0 scanner input new scanner system in system out print bir say giriniz sayi input nextint for int x 1 x sayi x if sayi x 0 sonuc x if sayi sonuc system out print sayi m kemmel say d r else system out print sayi m kemmel say de ildir details brain dev 8 ters gen yap m question soru java ile basamak say s n n kullan c dan al nan ve d ng ler kullan larak y ld zlar ile ekrana ters gen izen program yaz n pill rnek basamak say s 10 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class tersucgenyapimi public static void main string args int basamak scanner input new scanner system in system out print basamak say s n giriniz basamak input nextint for int x 1 x basamak x for int y 1 y x y system out print for int z 0 z 2 basamak 2 x z system out print system out println details brain dev 9 1 100 aras ndaki asal say lar bulan program question soru java ile 1 100 aras ndaki asal say lar ekrana yazd ran program yaz n z mag senaryo 2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67 71 73 79 83 89 97 green square cevap details summary kodu g rmek i in t klay n z summary java public class asalsayilaribulanprogram public static void main string args int sayac1 0 for int x 1 x 100 x for int y 1 y x y if x y 0 sayac1 if sayac1 2 system out print x sayac1 0 else sayac1 0 details brain dev 10 fibonacci serisi question soru java d ng ler ile fibonacci serisi bulan program yaz yoruz fibonacci serisinin eleman say s n kullan c dan al n pill fibonacci serisi nedir fibonacci serisi her say n n kendinden nceki ile toplanmas sonucu olu an bir say dizisidir bu ekilde devam eden bu dizide say lar birbirleriyle oranland nda alt n oran ortaya kar yani bir say kendisinden nceki say ya b l nd nde alt n orana gittik e yakla an bir dizi elde edilir fibonacci dizisi 0 dan ba lar ve sonsuza kadar her rakam bir nceki rakamla toplan r elde edilen sonu rakam n sa taraf na yaz l r fibonacci dizisinin ilk on say s u ekildedir 9 elemanl fibonacci serisi 0 1 1 2 3 5 8 13 21 34 0 1 1 1 1 2 1 2 3 2 3 5 3 5 8 5 8 13 13 8 21 21 8 34 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class fibonacciserisi public static void main string args int elemansay sayi1 0 sayi2 1 toplam 0 scanner input new scanner system in system out print fibonacci serisinin eleman say s n giriniz elemansay input nextint system out print sayi1 sayi2 for int x 2 x elemansay x toplam sayi1 sayi2 system out print toplam sayi1 sayi2 sayi2 toplam details brain dev 11 s hesab yapan program recursive metot question soru java dilinde taban ve s de erleri kullan c dan al nan s alma i lemini recursive metot kullanarak yapan program yaz n z mag senaryo taban de eri giriniz 2 s de erini giriniz 0 sonu 1 taban de eri giriniz 2 s de erini giriniz 3 sonu 8 taban de eri giriniz 5 s de erini giriniz 3 sonu 125 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class ushesabiyapanprogram static int us int taban int us if us 0 return taban us taban us 1 else return 1 public static void main string args int us taban scanner input new scanner system in system out print taban de eri giriniz taban input nextbyte system out print s de eri giriniz us input nextbyte system out println sonu us taban us test etmek i in say de erini de i tirin details brain dev 12 asal say bulan program recursive metot question soru java dilinde recursive metot kullanarak kullan c dan al nan say n n asal say olup olmad n bulan program yaz n mag senaryo say giriniz 22 22 say s asal de ildir say giriniz 2 2 say s asaldir say giriniz 39 39 say s asal de ildir say giriniz 17 17 say s asaldir green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class asalsayibulanprogram static int asal int x int y if y x if x y 0 return asal x y else return 0 return 1 public static void main string args int sayi scanner input new scanner system in system out print say giriniz sayi input nextint if asal sayi 2 1 system out println sayi say s asaldir else system out println sayi say s asal de ildir details brain dev 13 desene g re metot olu turma recursive metot question soru java dilinde kullan c dan al nan n de erine g re a a daki kurala uyan d ng kullanmadan bir recursive metot yaz n pill kural girilen say 0 veya negatif oldu u yere kadar girilen say dan 5 rakam n kar n her karma i lemi s ras nda ekrana son de eri yazd r n say negatif veya 0 olduktan sonra tekrar 5 ekleyin yine her ekleme i leminde say n n son de erini ekrana yazd r n mag senaryo n say s 16 kt s 16 11 6 1 4 1 6 11 16 n say s 10 kt s 10 5 0 5 10 n say s 25 kt s 25 20 15 10 5 0 5 10 15 20 25 n say s 5 kt s 5 0 5 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class desenegoremetotolusturma static int bescikar int n boolean dnoktasi int sayac n kullan c n n girmi oldu u say dnoktasi say n n 0 a e it veya k k olmas n n kontrol n yapan de i ken girilen say s f rdan b y k olaca ndan her zaman true sayac 0 veya eksi de erden nce ekrana bas lan say lar n say s saya de i keni kullan c dan al nan n de erine ba l olarak metot i erisinde de i ebilir ancak ba lang de eri her zaman 0 s f r olmal d r rnek 7 2 0 2 7 0 dan nce 2 say var 7 ve 2 sayac de i keni ile n say s art rlmaya ba land nda serinin sonsuza gitmesi engellenecek if dnoktasi if n 0 system out print n sayac return bescikar n 5 true sayac else return bescikar n false sayac else system out print n if sayac 0 sayac return bescikar n 5 false sayac else return 0 public static void main string args int n scanner input new scanner system in system out print n say s n input nextint system out print kt s bescikar n true 0 details brain dev 14 maa hesaplay c question soru java da employee ad nda fabrika al anlar n temsil eden ve metotlar ile al anlar n maa lar n hesaplayan bir s n f yazmal s n z bu s n f 4 nitelik ve 5 metoda sahip olacakt r pill s n f n nitelikleri name al an n ad ve soyad salary al an n maa workhours haftal k al ma saati hireyear e ba lang y l pill s n f n metotlar employee name salary workhours hireyear kurucu metot olup 4 parametre alacakt r tax maa a uygulanan vergiyi hesaplayacakt r al an n maa 1000 tl den az ise vergi uygulanmayacakt r al an n maa 1000 tl den fazla ise maa n n 3 kadar vergi uygulanacakt r bonus haftada 40 saatten fazla al ld nda saat ba na 30 tl olacak ekilde maa a uygulanacak bonus cretleri hesaplayacakt r raisesalary al an n i e ba lang y l na g re maa art n hesaplayacakt r uan ki y l 2021 olarak al n e er al an 10 y ldan az bir s redir al yorsa maa na 5 zam yap lacakt r e er al an 9 y ldan fazla ve 20 y ldan az al yorsa maa na 10 zam yap lacakt r e er al an 19 y ldan fazla al yorsa 15 zam yap lacakt r tostring al ana ait bilgileri ekrana bast racakt r mag rnek ad kemal maa 2000 0 al ma saati 45 ba lang y l 1985 vergi 60 0 bonus 150 0 maa art 300 0 vergi ve bonuslar ile birlikte maa 2090 0 toplam maa 2390 0 green square cevap details summary kodu g rmek i in t klay n z summary java import java util scanner public class test public static void main string args employee calisan1 new employee ahmet 2000 35 2015 employee calisan2 new employee osman 3000 50 2018 calisan1 tax calisan1 salary calisan1 bonus calisan1 workhours calisan1 raisesalary calisan1 hireyear calisan2 tax calisan2 salary calisan2 bonus calisan2 workhours calisan2 raisesalary calisan2 hireyear calisan1 infoemployee system out println calisan2 infoemployee java public class employee string name double salary int workhours int hireyear employee string name double salary int workhours int hireyear this name name this salary salary this workhours workhours this hireyear hireyear void tax double salary if salary 1000 this salary salary salary 0 03 else this salary this salary void bonus int workhours if this workhours 40 this salary this salary workhours 40 30 else this salary this salary void raisesalary int hireyear if 2021 hireyear 10 this salary this salary this salary 0 05 else if 2021 hireyear 10 2021 hireyear 20 this salary this salary this salary 0 1 else this salary this salary this salary 0 2 void infoemployee system out println ad this name system out println maa this salary system out println al ma saati this workhours system out println ba lang y l this hireyear details brain dev 15 diziyi k kten b y e s ralama question soru java dilinde dizideki elemanlar k kten b y e s ralayan program yaz n z dizinin boyutunu ve dizinin elemanlar n kullan c dan al n z mag senaryo dizinin boyutu n 5 dizinin elemanlar n giriniz 1 eleman 99 2 eleman 2 3 eleman 2121 4 eleman 1 5 eleman 0 s ralama 2121 2 0 1 99 dizinin boyutu n 6 dizinin elemanlar n giriniz 1 eleman 1000221 2 eleman 22 3 eleman 1 4 eleman 999 5 eleman 0 6 eleman 254 s ralama 1 0 22 254 999 1000221 green square cevap details summary kodu g rmek i in t klay n z summary java import java sql array import java util arrays import java util scanner public class test public static void main string args scanner input new scanner system in int diziuzunluk system out print dizi boyutunu giriniz diziuzunluk input nextint int dizi new int diziuzunluk for int i 0 i diziuzunluk i system out print i 1 eleman giriniz dizi i input nextint arrays sort dizi system out print s raland ktan sonra dizi for int i dizi system out print i details brain dev 16 dizideki elemanlar n frekans question soru java dilinde dizideki elemanlar n ka kez tekrar edildi ini yani frekanslar n bulan program yaz n z mag senaryo dizi 10 20 20 10 10 20 5 20 tekrar say lar 10 say s 3 kere tekrar edildi 20 say s 4 kere tekrar edildi 5 say s 1 kere tekrar edildi green square cevap details summary kodu g rmek i in t klay n z summary java import java sql array import java util arrays import java util scanner public class test public static void main string args int list 10 20 20 10 10 20 5 20 int counter 0 for int i 0 i list length i for int j 0 j list length j if list i list j for int k 0 k i k if list k list i counter 1 counter if counter 0 system out printf s say s s kere tekrar edildi n list i counter counter 0 details brain dev 17 matris transpozunu bulma question soru java dilinde ok boyutlu diziler ile olu turulmu matrisin transpozunu bulan program yaz n z matrisin transpozunu devri ini almak matrisin ayn numaral sat rlar ile s tunlar n n yer de i tirmesi demektir bu i lem sonucu elde edilen matris ba lang taki matrisin transpozudur devri idir bu a amada kxn lik bir matrisin transpozu devri i nxk lik bir matris olur rne in bir a matrisimiz olsun bu a matrisinin transpozu devri i a t a zeri t ile g sterilir rne in a a daki 2x3 lik a matrisinin transpozu devri i 2x3 l k bir a t matrisidir a matrisi https raw githubusercontent com kodluyoruz taskforce main java101 odev array transpose figures c1 png a matrisi transpoze https raw githubusercontent com kodluyoruz taskforce main java101 odev array transpose figures c2 png mag senaryo matris 2 3 4 5 6 4 transpoze 2 5 3 6 4 4 matris 1 2 3 4 5 6 transpoze 1 4 2 5 3 6 green square cevap details summary kodu g rmek i in t klay n z summary java import java sql array import java util arrays import java util scanner public class test public static void main string args int matris 2 3 4 5 6 4 7 8 9 9 8 7 int transpose new int matris 0 length matris length for int row 0 row transpose length row for int col 0 col transpose row length col transpose row col matris col row system out println matris for int row matris for int col row system out print col system out println system out println transpoze for int row transpose for int col row system out print col system out println details | server |
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