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space-kit | deprecated the space kit code has moved to an internal repository and this repository will no longer be maintained div align center img height 100 src https i imgur com yphoqoa png h1 align center space kit h1 p the home base for apollo s design system space kit provides essential design resources for our developers to use in the apollo branded interfaces that they create p a href https www npmjs com package apollo space kit title apollo space kit npm page img src https img shields io npm v apollo space kit svg a div getting started shell npm install apollo space kit emotion core emotion cache framer motion usage import things into your js app from the apollo space kit package all available exports are documented here exports js import apollo space kit reset css import this at app root import colors from apollo space kit function mycomponent return button style backgroundcolor colors indigo dark color white border 1px solid colors grey light button exports stylesheet reset todo move this to storybook docs a base stylesheet with a few sensible rules it uses normalize css https necolas github io normalize css to smooth out any inconsistencies between the ways that different browsers render elements it also applies box sizing border box to everything for a better element sizing experience https www paulirish com 2012 box sizing border box ftw lastly the stylesheet imports and sets our two main font families source sans pro https fonts google com specimen source sans pro and source code pro https fonts google com specimen source code pro you ll probably want to include this file once in your app ideally at the top most level for instance in a gatsby site that would be your layout js component https www gatsbyjs org docs layout components js webpack or similar js import apollo space kit reset css less less import inline node modules apollo space kit reset css faq my icons aren t showing up in the ui make sure that the icon component has a width and height applied to it that can mean applying classes or styles directly to the icon component or setting the component to have height 100 and width 100 and then applying a size to the containing element why can t i import from iconservices from apollo space kit icons my goal was to minimze the bundle size increase caused by using these icons if i had named exports from space kit icons then the user would have to make sure they are tree shaking to only import the icons they are actually using engine frontend is not yet tree shaking so we decided to not make the imports an option why does each icon have a named export instead of a default export the engine frontend team and apollo oss teams have decided to not use default exports this continues that trend developing space kit developing locally against storybook is easy run npm run storybook npm link run npm run watch to watch the entire project for changes and recompile on the fly you can use npm link to develop space kit features directly inside another project with a little bit of work npm link does not work out of the box however because of react hooks requiring that the same instance of react be used everywhere in an application we must use the same instance of react both in this project and in your consuming project if you re using webpack add resolve alias fields to your project s configuration like so js resolve alias used for npm link ing to space kit react path resolve node modules react used for npm link ing to space kit react refresh runtime path resolve node modules react refresh runtime tests we use jest and testing library for unit integration tests and will soon use chromatic https www chromaticqa com for visual regression testing integration tests the hardest part about writing tests is knowing what should be tested and what shouldn t test interactions test whatever you d test by hand that would give you release confidence a few dos and don ts do test interactions don t test anything that doesn t give you more confidence in other words do not add tests to increase code coverage do write test names that explain what is being tested what input is being given and what is expected https github com goldbergyoni javascript testing best practices ef b8 8f 11 include 3 parts in each test name do write simple tests with no abstractions https github com goldbergyoni javascript testing best practices ef b8 8f 0 the golden rule design for lean testing some resources on testing javascript testing best practices https github com goldbergyoni javascript testing best practices https kentcdodds com blog avoid nesting when you re testing https kentcdodds com blog avoid nesting when youre testing write fewer longer tests https kentcdodds com blog write fewer longer tests releases releases are handled automatically after merging prs by intuit auto https github com intuit auto you can split prs into a feature branch and then send that as a pr to main that ll use all the different prs to create the changelog semver each pr needs to have a semver lable in github so auto knows how to available labels are patch increment the patch version when merged minor increment the minor version when merged major increment the major version when merged prerelease create a pre release version when merged skip release preserve the current version when merged internal changes only affect the internal api documentation changes only affect the documentation use skip release if you don t want an automatic release with your pr there is a circleci job that checks that an appropriate label is on the pr it will always fail initially github actions are set up to re run the ci check when labels are added or removed it can be found in github main workflow github main workflow changelog the changelog will be updated automatically with the title of your pr used as the line item in the changelog the sections of the changelog will decided by the labels you gave your pr if you want to add more information for the changelog add a release notes section in your pr description https intuit github io auto pages auto changelog html additional release notes beta releases while local development should be done with npm link https docs npmjs com cli link sometimes we need to release pre release versions so you can see the effects of space kit changes in your project somewhere you can t use npm link like in a pull request in that case there are two options 1 all space kit prs automatically publish a canary build on npm and addd a link to that in your pr description 2 you can build your own canary anytime by running npx auto canary resources space kit s style guide zeplin https app zeplin io project 5c7dcb5ab4e654bca8cde54d screen 5cd0c46bce9a42346c709328 engine s style guide storybook https storybook apollographql com | os |
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ptom | ptom h2 android application h2 p information technology project p p decision support systems for public transportation on mobile application br p h2 apk h2 p need android 4 0 p p get apk a href https raw githubusercontent com prongbang ptom master ptom apk ptom a p h2 install on mobile h2 p 1 touch setting icon security unknown sources ok p p 2 touch file ptom apk from downloaded p p 3 touch install button p p 4 finish p | server |
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cl-mobile-syl | codelab mobile app development for ios and android course jrl493z wvu instructors ricky kirkendall rickykirkendall flocoapps com mailto rickykirkendall flocoapps com matt brumley matthewbbrumley gmail com mailto matthewbbrumley gmail com ta logan spears loganjspears gmail com mailto loganjspears gmail com location 4th floor evansdale crossing date time tuesday and thursday 4 00 5 15 pm office hours tuesday and thursday 3 00 4 00 pm slack team https codelabw16 slack com it is highly recommended that you use the slack channel for questions and discussions we will not be using ecampus in this class email ricky for an invitation to the slack team text lynda com lynda com subscription 25 month used for duration of class coding exercises a full list of coding exercises and their corresponding lessons can be found here https github com wvu ric cl mobile ex map the coding exercises are meant to be worked on primarily in class course description this course will help the student develop the knowledge and skills necessary to develop mobile application technology apps on android and ios platforms these skills will allow the student to explore creative commercial and entrepreneurial opportunities using these skills and their own creativity expected learning outcomes upon success completion of this mobile application development course 1 students will understand how the mobile apps are developed on ios and android 1 students will understand the concepts of developing a mobile apps 3 students will understand the major principles related to object oriented programing oop languages 4 students will determine the the viability of their app ideas 5 students will use industry integrated development environments ide and software development kits sdk along with various application programming interfaces api 6 students will learn to create an application prototype to communicate their vision to investors customers and other stakeholders 7 student will be able to think critically and conduct feasibility analyses of app ideas on their strengths weaknesses opportunities and threats 8 students will increase their proficiency in methods of developing applications that are technology driven in the app market 9 students will have gained experience in working within a team of their peers and mentors during group projects prerequisites math 126 http www math wvu edu mays 126a syllabus126a htm general mathematics cs101 http cs101 wvu edu media 19544 syllabus pdf or have completed 90 credit hours or more class format availability of a lab computers software that is needed for this course will be installed and available on the computers at codelab computers essential software includes java sdk android studio ide android sdk on pc machines and xcode ios objective c softwares on mac computers we recommend you to bring your laptops and install this software on your own laptops flipped classroom instructional content in this course will be primarily delivered through online modules in class time will be used for exercises discussions and project based learning grading system the final grade will be determined on the basis of the following percentages final grade breakdown attendance and class participation 25 homework and assignments 25 midterm exam 25 final project https github com wvu ric cl mobile final and presentation 25 grade range a 90 b 80 89 c 70 79 d 60 69 f 59 course outline 1 foundations of prorgamming 1 introduction 1 first programming language 1 final project submit app proposal 1 mobile app development for ios and android part 1 1 mobile programming language choose ios or android 2 developer tools 3 final project technical description 1 mobile app development for ios and android part 2 1 mobile app user interfaces 2 mobile app design 3 final project app mockups 1 final project 1 selected topics 2 data driven apps 3 final project coding attendance and instruction the instructor reserves the right to postpone or rearrange the presentation of material by making an announcement during a regularly scheduled class meeting examinations will not be moved without announcing the change at least one 1 week prior to the scheduled time no makeup exams offered unless prior notice was given or medical excuses the syllabus is a general guideline and does not constitute a contract between the student instructors and the university our attendance policy allows only up to 4 classes to be missed without formal excuse after four 4 absents class grade will be affected social justice and americans with disabilities act west virginia university is committed to social justice we concur with that commitment and expect to foster a nurturing learning environment based upon open communication mutual respect and non discrimination our university does not discriminate on the basis of race sex age disability veteran status religion sexual orientation color or national origin any suggestions as to how to further such a positive and open environment in this class will be appreciated and given serious consideration wvu recognizes the diversity of its students and the needs of those who wish to be absent from class to participate in days of special concern which are listed in the schedule of courses students should notify their instructors by the end of the second week of classes or prior to the first day of special concern whichever is earlier regarding day of special concern observances that will affect their attendance the instructor will make reasonable accommodation for tests or field trips that a student misses as a result of observing a day of special concern if you are a person with a disability and anticipate needing any type of accommodation in order to participate in this class please advise us and make appropriate arrangements with disability services 293 6700 course schedule weeks 1 4 introduction how to use github final app proposals programming language concepts syntax variables data types operations conditionals functions iterations strings collections style i o debugging oop thinking intro to algorithms weeks 4 8 xcode ios simulator writing code building code debugging code objective c concepts android studio writing code bulding projects debugging tools java concepts final technical description applying programming concepts to app ideas weeks 8 12 prototyping ios mvc structure ui elements storyboards view controllers navigation android mvc java style ui elements interface builder emulator action listeners final app mockups weeks 12 16 data driven apps with parse com selected topics relevant to final projects final app coding student signature got it x | front_end |
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lora-radio-driver | lora radio driver lora radio driver rtos rt thread lora tranceiver lora lora radio driver doc https github com forest rain lora radio driver tree master doc | os |
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Elidek | elidek project in the context of the databases class in school of electrical and computer engineering in national technical university of athens image 5 6 22 at 12 42 pm https user images githubusercontent com 57630709 172062588 425bdcfe e9cf 452e aa67 39767e86fe05 jpg installation instructions can be found in greek in the following pdf file pages 4 and 5 databasesreport pdf https github com goutzou elidek files 8840462 databasesreport pdf | server |
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govuk-theme | gov uk design system wordpress theme a wordpress theme built by dxw https dxw com that implements the gov uk design system https design system service gov uk via gov uk frontend https github com alphagov govuk frontend currently the theme applies v3 of gov uk frontend developing the theme install the dependencies npm install composer install compile the assets npm run build run the tests vendor bin kahlan spec run the linter vendor bin php cs fixer fix extending the theme this is intended to be used as a parent theme which is then extended re skinned via a child theme you can use the govuk theme class filter to help with this it is applied to all govuk prefixed classes so you can use this to either append a custom class to specific instances where those classes are applied append a custom class to all instances or replace those classes completely you can also replace any of the individual template components up to and including the overall layout template in the child theme as required note that the gds transport theme is only licensed for use on gov uk domains so if you re developing a theme for use elsewhere you ll need to replace it with a suitable alternative | govpress wordpress-theme gov | os |
react-Food_Order_App | h1 food order app h1 p this is a simple food order app that was built as a practice project to reinforce key concepts and aspects of web development the app allows users to choose from a list of meals and add them to a cart they can change the quantities and add multiple items the app also features a basic checkout form where the user can submit their order before submitting the order the user input is validated on the client side to ensure data accuracy once validated the order data is then submitted to a backend server using an http request firebase is used as the dummy backend server for this project the app also fetches meal data from the backend server allowing users to select from a list of available meals it was a practical exercise to practice all the previous concepts learned in the react course diving into the essence of each section and reinforcing core web development concepts p | server |
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ultraviolet | storybook assets logo dark svg gh dark mode only storybook assets logo light svg gh light mode only codecov https img shields io codecov c github scaleway ultraviolet github last commit https img shields io github last commit scaleway ultraviolet github closed issues https img shields io github issues closed scaleway ultraviolet github contributors https img shields io github contributors scaleway ultraviolet github commit activity https img shields io github commit activity m scaleway ultraviolet github https img shields io github license scaleway ultraviolet ultraviolet core ultraviolet core contains the core features of the ultraviolet ui library it is set of react library that can be used to build fast application ultraviolet ui https github com scaleway ultraviolet tree main packages ui the main library that includes a set of components and utilities to build fast application ultraviolet plus https github com scaleway ultraviolet tree main packages plus an extension of ui with more complex components ultraviolet form https github com scaleway ultraviolet tree main packages form a library to build forms with ultraviolet ui components it is using react final form under the hood ultraviolet themes https github com scaleway ultraviolet tree main packages themes a set of themes for the ultraviolet ui library default theme is included in ultraviolet ui ultraviolet icons https github com scaleway ultraviolet tree main packages icons a library that provides a set of icons to use with ultraviolet ui installation quick start sh pnpm add ultraviolet ui emotion react emotion styled documentation ultraviolet ui https github com scaleway ultraviolet tree main packages ui ultraviolet plus https github com scaleway ultraviolet tree main packages plus ultraviolet form https github com scaleway ultraviolet tree main packages form ultraviolet themes https github com scaleway ultraviolet tree main packages themes ultraviolet icons https github com scaleway ultraviolet tree main packages icons development before any command install dependencies running following command sh pnpm install storybook our storybook includes ultraviolet ui ultraviolet form and ultraviolet icons in order to start storybook without errors you will need to build the project once this is because ultraviolet form uses ultraviolet ui build to run sh pnpm build pnpm run start storybook documentation will then be available on http localhost 6006 http localhost 6006 test unit sh pnpm run test unit will run all tests pnpm run test unit update will update all snapshots pnpm run test unit watch will watch tests and only rerun the one who are modified pnpm run test unit coverage will generate a coverage report pnpm run testunit coverage coveragereporters lcov open coverage lcov report index html will generate an open an html code coverage report accessibility sh pnpm run test a11y will run all accessibility tests pnpm run test a11y src components alert will run accessibility test of alert component only lint sh pnpm run lint pnpm run lint fix build sh pnpm run build pnpm run build profile will open a visual representation of the modules inside the compile package use a locally built package you might want to test your local changes against a react application yalc https github com whitecolor yalc is a tool aiming to simplify working with local npm packages by providing a different workflow than npm pnpm link hence avoiding most of their issues with module resolving bash pnpm install global yalc make sure to have the yalc binary here is an example for using ultraviolet ui as a local package bash pnpm run build cd packages ui yalc publish now it s ready to install in your project cd project something yalc add ultraviolet ui cd ultraviolet if you do some changes into your package pnpm run build yalc publish push sig push will automatically update the package on projects where it have been added sig updates the signature hash to trigger webpack update you can redo the same with ultraviolet form if you want to test it warning since 1 0 0 pre 51 2021 04 23 https github com wclr yalc blob master changelog md 100pre51 2021 04 23 yalc publish needs the sig option to trigger webpack module actual update warning yalc create a yalc lock and updates the package json in the target project make sure to not commit these changes versioning we are using changeset https github com changesets changesets to manage our versioning once your modifications are ready to be released you can run pnpm run changeset to create a new changeset it will ask you to describe your changes and will create a new changeset file in the changesets folder documentation checkout our documentation website https storybook ultraviolet scaleway com contributing you can participate in the development and start contributing contributing md to it thanks a href https www chromatic com img src https user images githubusercontent com 321738 84662277 e3db4f80 af1b 11ea 88f5 91d67a5e59f6 png width 153 height 30 alt chromatic a thanks to chromatic https www chromatic com for providing the visual testing platform that helps us review ui changes and catch visual regressions | react ui scaleway ui-components ui-library reakit styled-components typescript form ultraviolet | os |
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 the image filtering microservice it is a node express application which runs a simple script to process images url the image filtering microserviceis deployed to aws elasticbeanstalk you can test in this link http uifm dev3 us east 1 elasticbeanstalk com how to use it in order to fetch a public image from internet you have to add some query url as it is explained in the initial screen http uifm dev3 us east 1 elasticbeanstalk com filteredimage image url http try with this http uifm dev3 us east 1 elasticbeanstalk com filteredimage image url https media timeout com images 103461302 750 422 image jpg github repo https github com angelvinyals udagram image filtering microservice git | cloud |
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FedKit | fedkit federated learning fl development kit for fedcampus a fatkid to support the fatcampus this repository contains the libraries used to implement the fl functionalities of the fedcampus app architecture backend the backend server uses django rest framework to provide persistent service and on demand backend configurable training the fl training steps are supported using the flower flower fl framework flower servers use grpc and are spawned on demand by the backend server located at backend please see backend readme md for information on how to set up the backend server mobile clients android client the android client package and demo is located at client to try out the android client please see client readme md to use the library in this repository to implement custom android flower clients please see client fedcampus readme md flutter android ios client the flutter client package to communicate with the backend and flower servers is at flutter the example flutter client for both android and ios is at fed kit client to try it out please see fed kit client readme md ml model generation we support tensorflow keras models for android and core ml models for ios we also include an example that converts a tensorflow model with mean square error to core ml using coremltools ml model for android the ml model generation script for android is located at gen tflite please see gen tflite readme md for information on how to create models and convert them to tflite files ml model for ios gen mlmodel is still a work in progress meanwhile you can construct core ml models manually using coremltools training procedure 1 client asks backend what which model to use based on its data type 1 client downloads that model if it does not have it 1 client asks backend for a flower server to train with that model development development on python code use python3 10 or above install dependencies using requirements txt s contributing please see contributing md for fedcampus contributing history this repository is moved from dyn flower android drf dyn flower android drf to fedkit fed kit please see the older git history in the former repository contributing https github com fedcampus meta blob main contributing md dyn flower android drf https github com fedcampus dyn flower android drf fed kit https github com fedcampus fedkit flower https flower dev | front_end |
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px2rem.scss | px2rem scss a href https www npmjs com package px2rem scss img src https img shields io npm dm px2rem scss svg alt downloads a a href https www npmjs com package px2rem scss img src https img shields io npm v px2rem scss svg alt version a self adaption plugin for mobile development install npm install px2rem scss save usage 1 in html head tag add viewport and designbasewidth as below html head meta charset utf 8 meta name viewport content width device width initial scale 1 0 minimum scale 1 0 maximum scale 1 0 user scalable 0 title demo title script var designbasewidth 375 script head 2 import px2rem config min js file html head meta charset utf 8 meta name viewport content width device width initial scale 1 0 minimum scale 1 0 maximum scale 1 0 user scalable 0 title demo title script var designbasewidth 375 script script src https unpkg com px2rem scss script head notice this plugin need calculate the html tag base font size before dom rendering otherwise web page will initial depend on font size 16px to calculate rem value this will cause page to rerender and repain 3 before you use mixins in px2rem scss plz import firstly scss import path to px2rem scss html include font dpr 16px include px2rem width 320px remarks px2rem scss provide two sass mixin http sass bootcss com docs sass reference mixins font dpr calculate font size px2rem convert px to rem notice the default base font size is 16px for html license mit | self-adaptive px2rem mobile-web scss | front_end |
ccda-samples | what s this repository this is a repository of c cda 2 1 xml files which are interoperable extracts from electronic health records ehrs 52 ehrs with 401 c cda samples are included here if you re wondering about the standard check out hl7 s website for the c cda 2 1 standard http www hl7 org implement standards product brief cfm product id 379 these samples are fictional many of them were created from test cases so you ll see the same patient data repeated from many ehrs you ll also see several different document types such as continuity of care documents ccds referral notes and discharge summaries where did you get the samples all of these samples have been copied unzipped renamed formatted and organized from the following repository https github com siteadmin 2015 c cda certification samples the original documents were entered into the public domain through that github repository maintained by the office of the national coordinator for health it onc we took what was available as of january 2018 so it doesn t represent all certified ehrs duplicates of the same vendor patient and document type have been removed please access the original repository for all testing data including duplicates why was this repository created we created this repository for a joint research study by the veterans health administration va diameter health dh intersystems and onc that research investigated issues regarding the interoperability of clinical data and was published at the american medical informatics association in 2018 the paper won the most distinguished research award for that year the authors on that study are john d amore dh omar bouhaddou va sandi mitchell va chun li dh russ leftwich intersystems todd turner va matt rahn onc and margaret donahue va for questions on that study please contact jdamore at diameterhealth com who also created this repo github jddamore the link to the full paper is here https www ncbi nlm nih gov pmc articles pmc6371305 this repository contains several directories beginning with z that have non xml files collected or created as part of the research study z infographic this includes a few graphics about human readable variation and the c cda testing architecture for va z test cases four test scenarios used in testing that represents almost half of the samples in the repository z test schematron each of the c cda documents was passed through the hl7 schematron as part of a research study the open source tooling to evaluate xml and is available at https github com ewadkins cda schematron the hl7 schematron is available at https gforge hl7 org gf project strucdoc scmsvn action browse path 2ftrunk 2fc cdar2 1 2fschematron 2f output of schematron validation was saved as json files organized by ehr directories how should this repository be used the c cda 2 1 documents in this repository were created for certification to the 2015 edition of meaningful use roughly correlates with stage 3 of meaningful use this repository could be used for application development software testing interoperability research or simply as a set of files for sample patient data we the authors do not represent any of the ehr vendors listed in the repository we also don t represent that theses are good examples of c cda documents while many follow the standard well some concerns relating to the interoperability of these c cda documents were identified in the research for examples reviewed by hl7 that represent best practices we recommend you go to http cdasearch hl7 org and of course there s an important legal disclaimer to all this please read this information 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 information or the use or other dealings in the information contact for questions on this repository or the research study please contact johnd at moreinformatics com github jddamore | server |
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mega-interview-guide | p align center img width 500 src assets img logos logo png alt mega interview guide logo p h1 align center the mega interview guide h1 p align center a humble guide to give developers the tools they need to nail technical interviews p p align center strong prs welcome strong p toc how to prepare how to prepare resources resources suggested reading suggested reading videos videos misc misc computer science computer science data structures data structures algorithms algorithms complexity complexity javascript web apis javascript web apis prototypal inheritance prototypal inheritance scope scope closures closures event bubbling event bubbling apply call and bind apply call and bind hoisting hoisting var let const var let const arrow functions arrow functions generator functions generator functions map vs object map vs object virtual dom virtual dom shadow dom shadow dom async await async await web workers web workers service workers service workers miscellaneous miscellaneous design patterns design patterns decorator decorator factory factory singleton singleton module module facade facade observer observer strategy pattern strategy pattern inversion of control inversion of control composition vs inheritance composition vs inheritance html css html css flexbox flexbox transforms transforms accessibility accessibility questions questions tocstop how to prepare invest time in preparing it s important for any engineer even senior ones to brush up on their interview skills coding skills and algorithms an interview is typically different from your day to day job practice answering many different coding questions practice answering a coding question with the most efficient bug free solution without using a compiler a few resources that offer coding questions to use for practice careercup https l facebook com l php u http 3a 2f 2fwww careercup com 2fpage h atnxgeu22051ck2jrqzuz0 xpfzm n44varqrrgan11c bj2vvn7wakmxepet56eziatvtq5pvqo sws4n6qtw0wlyqika349d8d17ymipgfcpjfwzh5ij6wh7pmpc mtcx2ywk5yovm8v s 1 topcoder https l facebook com l php u http 3a 2f 2fwww topcoder com 2f h atm84nvzqhwje5rs4kbb5nhbfgwbx2g 4i v4q3gw9i31bl155 2hlwalbaaz u1xigy8t7w1xfnghx7f9jt4izfuw oecggw0khns9mvkgq7tkfg9sqz oy3okpwcwownjoxmein1qmb7id s 1 project euler https l facebook com l php u http 3a 2f 2fprojecteuler net 2findex php h atpg4d f0q s08m pkcq6owpqh cuzelvdzo2hpdw2sz03dk80goaqbl45ueyk73stqcvhopxpg sd natxsuovwa7tkmiqhcj72omhzpwuay472i7xcejrivgdjulnsu39rl gh3g1t8a s s 1 or facebook code lab https l facebook com l php u https 3a 2f 2fcodelab interviewbit com 2f h atmmrfsahryeonw7hsluqsrhkv2q tqz63mro 4u9xfgmowjsv9yadpcd4pxmepnpm2rnxj8idkl23hwzzmtmfbwk0vau8myvxrps2jqy0tmhs4vje df8n0qag3d15gw4zkajgqkce2sani s 1 go over data structures algorithms and complexity be able to discuss the big o complexity of your approaches don t forget to brush up on your data structures like lists arrays hash tables hash maps stacks queues graphs trees heaps also sorts searches and traversals bfs dfs also review recursion and iterative approaches think about your 2 5 years career aspirations you will be asked to talk about your interest and your strengths as an engineer prepare 1 2 questions to ask your interviewer there is generally 5 minutes at the end of a typical interview for this resources suggested reading cracking the coding interview https l facebook com l php u http 3a 2f 2fwww amazon com 2fdp 2f0984782850 2f h atmzrtbftrzqclhngryrtjft xcq2o3nid4noee88unt61vmu5gfilsu62ceziwgbmgzesxhiio3myvajcowdug6cme2snfwxgoshyhdahwjiasz20a7ul x0os1 y4hnscjr2fmwv3qabqf s 1 introduction to algorithms https l facebook com l php u http 3a 2f 2fwww amazon com 2fdp 2f0262033844 h ator c75elaqf 869wl2veifzniqllc mnpqxeueusb7ovxcun7 lehuzucmjagz vvnnppra ogse4excdejtb2hxaqhszun9wu0en94hdmr58g7ibkpocfv2cns6eqcvch72wad9yfxcyi s 1 algorithms in c http www amazon com algorithms parts 1 5 bundle fundamentals dp 0201756080 data structures and algorithms https apps2 mdp ac id perpustakaan ebook karya 20umum dsa pdf and big o cheat sheet http bigocheatsheet com coursera algorithms part 1 https www coursera org learn algorithms part1 coursera algorithms part 2 https www coursera org learn algorithms part2 udacity intro to algorithms https www udacity com course intro to algorithms cs215 mit open courseware introduction to algorithms https ocw mit edu courses electrical engineering and computer science 6 006 introduction to algorithms spring 2008 you don t know js by kyle simpson https github com getify you dont know js cracking the javascript interview https medium com dev bits a perfect guide for cracking a javascript interview a developers perspective 23a5c0fa4d0d data structures and algorithms tutorial https www scaler com topics data structures cracking the coding interview js solutions https github com careercup ctci 6th edition javascript tech interview handbook https techinterviewhandbook org front end interview handbook https frontendinterviewhandbook com practice questions awesome javascript interviews https github com rohan paul awesome javascript interviews 500 data structures and algorithms https techiedelight quora com 500 data structures and algorithms interview questions and their solutions top 10 algos in interview questions http www geeksforgeeks org top 10 algorithms in interview questions hackerrank https www hackerrank com topcoder https community topcoder com tc module matchdetails rd 15712 codeforces http codeforces com contests leetcode https leetcode com interviewbit https www interviewbit com or kattis https open kattis com problems from cracking the coding interview book https books google co uk books about cracking the coding interview html id anhaxwaacaaj hl en list of acm icpc https icpc baylor edu worldfinals problems and code jam https code google com codejam past contests past questions videos clean code https www youtube com watch v rlflcwkxhj0 misc flexbox froggy http flexboxfroggy com computer science data structures data structures can implement one or more particular abstract data types adt which specify the operations that can be performed on a data structure and the computational complexity of those operations in comparison a data structure is a concrete implementation of the specification provided by an adt different kinds of data structures are suited to different kinds of applications and some are highly specialised to specific tasks for example relational databases commonly use b tree indexes for data retrieval while compiler implementations usually use hash tables to look up identifiers data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services usually efficient data structures are key to designing efficient algorithms some formal design methods and programming languages emphasise data structures rather than algorithms as the key organising factor in software design data structures can be used to organise the storage and retrieval of information stored in both main memory and secondary memory lists a linked list is a linear collection of data elements in which linear order is not given by their physical placement in memory each pointing to the next node by means of a pointer https en wikipedia org wiki pointer computer programming it is a data structure https en wikipedia org wiki data structure consisting of a group of nodes https en wikipedia org wiki node computer science which together represent a sequence https en wikipedia org wiki sequence under the simplest form each node is composed of data and a reference https en wikipedia org wiki reference computer science in other words a link to the next node in the sequence this structure allows for efficient insertion or removal of elements from any position in the sequence during iteration more complex variants add additional links allowing efficient insertion or removal from arbitrary element references linked lists are among the simplest and most common data structures they can be used to implement several other common abstract data types https en wikipedia org wiki abstract data type including lists https en wikipedia org wiki list abstract data type the abstract data type stacks https en wikipedia org wiki stack abstract data type queues https en wikipedia org wiki queue abstract data type associative arrays https en wikipedia org wiki associative array and s expressions https en wikipedia org wiki s expression though it is not uncommon to implement the other data structures directly without using a list as the basis of implementation stacks a stack is an abstract data type https en wikipedia org wiki abstract data type that serves as a collection https en wikipedia org wiki collection computing of elements with two principal operations push which adds an element to the collection and pop which removes the most recently added element that was not yet removed the order in which elements come off a stack gives rise to its alternative name lifo for last in first out additionally a peek https en wikipedia org wiki peek data type operation operation may give access to the top without modifying the stack the name stack for this type of structure comes from the analogy to a set of physical items stacked on top of each other which makes it easy to take an item off the top of the stack while getting to an item deeper in the stack may require taking off multiple other items first 1 https en wikipedia org wiki stack abstract data type cite note clrs 1 considered as a linear data structure https en wikipedia org wiki linear data structure or more abstractly a sequential collection the push and pop operations occur only at one end of the structure referred to as the top of the stack this makes it possible to implement a stack as a singly linked list https en wikipedia org wiki singly linked list and a pointer to the top element a stack may be implemented to have a bounded capacity if the stack is full and does not contain enough space to accept an entity to be pushed the stack is then considered to be in an overflow https en wikipedia org wiki stack overflow state the pop operation removes an item from the top of the stack javascript var stack stack push 2 stack is now 2 stack push 5 stack is now 2 5 var i stack pop stack is now 2 alert i displays 5 queues a queue is a particular kind of abstract data type https en wikipedia org wiki abstract data type or collection https en wikipedia org wiki collection abstract data type in which the entities in the collection are kept in order and the principal or only operations on the collection are the addition of entities to the rear terminal position known as enqueue and removal of entities from the front terminal position known as dequeue this makes the queue a first in first out fifo data structure https en wikipedia org wiki fifo computing and electronics in a fifo data structure the first element added to the queue will be the first one to be removed this is equivalent to the requirement that once a new element is added all elements that were added before have to be removed before the new element can be removed often a peek or front operation is also entered returning the value of the front element without dequeuing it a queue is an example of a linear data structure https en wikipedia org wiki linear data structure or more abstractly a sequential collection queues provide services in computer science https en wikipedia org wiki computer science transport https en wikipedia org wiki transport and operations research https en wikipedia org wiki operations research where various entities such as data objects persons or events are stored and held to be processed later in these contexts the queue performs the function of a buffer https en wikipedia org wiki buffer computer science queues are common in computer programs where they are implemented as data structures coupled with access routines as an abstract data structure https en wikipedia org wiki abstract data structure or in object oriented languages as classes common implementations are circular buffers https en wikipedia org wiki circular buffer and linked lists https en wikipedia org wiki linked list javascript var queue queue push 2 queue is now 2 queue push 5 queue is now 2 5 var i queue shift queue is now 5 alert i displays 2 priority queue priority queue is similar to queue where we insert an element from the back and remove an element from front but with a one difference that the logical order of elements in the priority queue depends on the priority of the elements the element with highest priority will be moved to the front of the queue and one with lowest priority will move to the back of the queue thus it is possible that when you enqueue an element at the back in the queue it can move to front because of its highest priority let s say we have an array of 5 elements 4 8 1 7 3 and we have to insert all the elements in the max priority queue first as the priority queue is empty so 4 will be inserted initially now when 8 will be inserted it will moved to front as 8 is greater than 4 while inserting 1 as it is the current minimum element in the priority queue it will remain in the back of priority queue now 7 will be inserted between 8 and 4 as 7 is smaller than 8 now 3 will be inserted before 1 as it is the 2nd minimum element in the priority queue naive approach suppose we have n elements and we have to insert these elements in the priority queue we can use list and can insert elements in o n time and can sort them to maintain a priority queue in o n logn time efficient approach we can use heaps to implement the priority queue it will take o log n time to insert and delete each element in the priority queue based on heap structure priority queue also has two types max priority queue and min priority queue example implementation javascript class maxheap constructor this heap getparent index return this heap index 1 2 getleftchild parentindex return this heap 2 parentindex 1 getrightchild parentindex return this heap 2 parentindex 2 bubbleup index const parentindex index 1 2 if parentindex 0 return if this getparent index this heap index this swap parentindex index this bubbleup parentindex sinkdown index const rightchildindex 2 parentindex 2 if this getrightchild index this heap length return if this getrightchild index this heap index this swap index rightchildindex this sinkdown rightchildindex swap indexone indextwo const temp this heap indexone this heap indexone this heap indextwo this heap indextwo temp push value this heap push value insert an element to the end of the heap if it s parent is smaller max heap or larger min heap it means we have a heap violation we should swap the child newly added value with the parent until we restore the heap property this bubbleup this heap length pop remove the first element in the heap swap the last element in the heap to the front sink down swapping with the right child until a position is found const max this heap shift this swap 0 this heap length this heap pop this sinkdown return max peek this heap 0 class priorityqueue note passing your heap implementation will allow you to switch from min to max heap with ease constructor heap this heap heap enqueue value this heap push value dequeue this heap pop value peek this heap peek const priorityqueue new priorityqueue new maxheap hash tables a hash table hash map is a data structure which implements an associative array abstract data type a structure that can map keys to values a hash table uses a hash function to compute an index into an array of buckets or slots from which the desired value can be found ideally the hash function will assign each key to a unique bucket but most hash table designs employ an imperfect hash function which might cause hash collisions where the hash function generates the same index for more than one key such collisions must be accommodated in some way in a well dimensioned hash table the average cost number of instructions for each lookup is independent of the number of elements stored in the table many hash table designs also allow arbitrary insertions and deletions of key value pairs at constant average cost per operation in many situations hash tables turn out to be more efficient than search trees or any other table lookup structure for this reason they are widely used in many kinds of computer software particularly for associative arrays database indexing caches and sets hash functions all a hash function does is turn a value into an array index the hash function in turn is composed of two parts the first part the hash code takes a value and returns an integer a function that always returned zero would be a hash coding function but it would be a terrible one more terrible ones include adding the ascii values of a string together multiplying the digits of a number etc a good hashing function minimizes the chance of a hash collision a hash collision is when two distinct values have the same hash code good hashing functions like sha 256 have extremely small probabilities of hash collisions i ve heard that it s much more likely that a giant asteroid will impact the earth than a collision of sha 256 ever happening in the wild the second part of hash functions is the compression mapping all this does is maps the integer to an array index using the modulus operator and the length of the underlying array a good hash function should be extremely fast to compute cryptographic strength has functions often are relatively slow to compute so faster ones are used that have higher collision rates so how do we manage collisions graphs a graph is an abstract data type https en wikipedia org wiki abstract data type that is meant to implement the undirected graph https en wikipedia org wiki graph discrete mathematics and directed graph https en wikipedia org wiki directed graph concepts from mathematics https en wikipedia org wiki mathematics specifically the field of graph theory https en wikipedia org wiki graph theory a graph data structure consists of a finite and possibly mutable set https en wikipedia org wiki set computer science of vertices or nodes together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph these pairs are known as edges arcs or lines for an undirected graph and as arrows directed edges directed arcs or directed lines for a directed graph the vertices may be part of the graph structure or may be external entities represented by integer indices or references https en wikipedia org wiki reference computer science a graph data structure may also associate to each edge some edge value such as a symbolic label or a numeric attribute cost capacity length etc https upload wikimedia org wikipedia commons thumb a a2 directed svg 250px directed svg png https upload wikimedia org wikipedia commons thumb a a2 directed svg 250px directed svg png trees a tree is a widely used abstract data type https en wikipedia org wiki abstract data type adt or data structure https en wikipedia org wiki data structure implementing this adt that simulates a hierarchical tree structure https en wikipedia org wiki tree structure with a root value and subtrees https en wikipedia org wiki subtrees of children with a parent node represented as a set of linked nodes https en wikipedia org wiki vertex graph theory a tree data structure can be defined recursively https en wikipedia org wiki recursion locally as a collection of nodes https en wikipedia org wiki node computer science starting at a root node where each node is a data structure consisting of a value together with a list of references to nodes the children with the constraints that no reference is duplicated and none points to the root alternatively a tree can be defined abstractly as a whole globally as an ordered tree https en wikipedia org wiki ordered tree with a value assigned to each node both these perspectives are useful while a tree can be analyzed mathematically as a whole when actually represented as a data structure it is usually represented and worked with separately by node rather than as a list of nodes and an adjacency list https en wikipedia org wiki adjacency list of edges between nodes as one may represent a digraph https en wikipedia org wiki tree data structure digraphs for instance for example looking at a tree as a whole one can talk about the parent node of a given node but in general as a data structure a given node only contains the list of its children but does not contain a reference to its parent if any terminoligy root the top node in a tree child a node directly connected to another node when moving away from the root parent the converse notion of a child siblings a group of nodes with the same parent descendant a node reachable by repeated proceeding from parent to child ancestor a node reachable by repeated proceeding from child to parent leaf a node with no children binary trees a binary tree is a tree https en wikipedia org wiki tree structure data structure https en wikipedia org wiki data structure in which each node has at most two children https en wikipedia org wiki child node which are referred to as the left child and the right child a recursive definition https en wikipedia org wiki recursive definition using just set theory https en wikipedia org wiki set theory notions is that a non empty binary tree is a triple https en wikipedia org wiki tuple l s r where l and r are binary trees or the empty set https en wikipedia org wiki empty set and s is a singleton set https en wikipedia org wiki singleton set some authors allow the binary tree to be the empty set as well from a graph theory https en wikipedia org wiki graph theory perspective binary and k ary trees as defined here are actually arborescences https en wikipedia org wiki arborescence graph theory a binary tree may thus be also called a bifurcating arborescence a term which appears in some very old programming books before the modern computer science terminology prevailed it is also possible to interpret a binary tree as an undirected https en wikipedia org wiki undirected graph rather than a directed graph https en wikipedia org wiki directed graph in which case a binary tree is an ordered https en wikipedia org wiki ordered tree rooted tree https en wikipedia org wiki rooted tree some authors use rooted binary tree instead of binary tree to emphasize the fact that the tree is rooted but as defined above a binary tree is always rooted a binary tree is a special case of an ordered k ary tree https en wikipedia org wiki k ary tree where k is 2 in computing binary trees are seldom used solely for their structure much more typical is to define a labeling function on the nodes which associates some value to each node binary trees labelled this way are used to implement binary search trees https en wikipedia org wiki binary search tree and binary heaps https en wikipedia org wiki binary heap and are used for efficient searching https en wikipedia org wiki search algorithm and sorting https en wikipedia org wiki sorting algorithm the designation of non root nodes as left or right child even when there is only one child present matters in some of these applications in particular it is significant in binary search trees in mathematics what is termed binary tree can vary significantly from author to author some use the definition commonly used in computer science but others define it as every non leaf having exactly two children and don t necessarily order as left right the children either https upload wikimedia org wikipedia commons thumb f f7 binary tree svg 384px binary tree svg png https upload wikimedia org wikipedia commons thumb f f7 binary tree svg 384px binary tree svg png binary search trees binary search trees bst are a particular type of data structure they allow fast lookup addition and removal of items and can be used to implement either dynamic sets of items or lookup tables that allow finding an item by its key e g finding the phone number of a person by name binary search trees keep their keys in sorted order so that lookup and other operations can use the principle of binary search when looking for a key in a tree or a place to insert a new key they traverse the tree from root to leaf making comparisons to keys stored in the nodes of the tree and deciding based on the comparison to continue searching in the left or right subtrees on average this means that each comparison allows the operations to skip about half of the tree so that each lookup insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree this is much better than the linear time required to find items by key in an unsorted array but slower than the corresponding operations on hash tables https upload wikimedia org wikipedia commons thumb d da binary search tree svg 400px binary search tree svg png https upload wikimedia org wikipedia commons thumb d da binary search tree svg 400px binary search tree svg png searching searching a binary search tree for a specific key can be programmed recursively or iteratively begin by examining the root node if the tree is null the key we are searching for does not exist in the tree otherwise if the key equals that of the root the search is successful and we return the node if the key is less than that of the root we search the left subtree similarly if the key is greater than that of the root we search the right subtree this process is repeated until the key is found or the remaining subtree is null if the searched key is not found after a null subtree is reached then the key is not present in the tree this is easily expressed as a recursive algorithm implemented in python python 1 def search recursively key node 2 if node is none or node key key 3 return node 4 if key node key 5 return search recursively key node left 6 key node key 7 return search recursively key node right the same algorithm can be implemented iteratively python 1 def search iteratively key node 2 current node node 3 while current node is not none 4 if key current node key 5 return current node 6 elif key current node key 7 current node current node left 8 else key current node key 9 current node current node right 10 return none heaps heap is a special case of balanced binary tree data structure where the root node key is compared with its children and arranged accordingly if has child node then key key as the value of parent is greater than that of child this property generates max heap based on this criteria a heap can be of two types max heap where the value of the root node is greater than or equal to either of its children maxheap assets img maxheap png min heap where the value of the root node is less than or equal to either of its children minheap assets img minheap png the heap is one maximally efficient implementation of an abstract data type https en wikipedia org wiki abstract data type called a priority queue https en wikipedia org wiki priority queue and in fact priority queues are often referred to as heaps regardless of how they may be implemented heaps are also crucial in several efficient graph https en wikipedia org wiki graph theory algorithms https en wikipedia org wiki algorithm such as dijkstra s algorithm https en wikipedia org wiki dijkstra 27s algorithm in a heap the highest or lowest priority element is always stored at the root a heap is not a sorted structure and can be regarded as partially ordered as visible from the heap diagram there is no particular relationship among nodes on any given level even among the siblings when a heap is a complete binary tree it has a smallest possible height a heap with n nodes always has log n height a heap is a useful data structure when you need to remove the object with the highest or lowest priority algorithms bubble sort a simple sorting algorithm that repeatedly steps through the list to be sorted compares each pair of adjacent items and swaps them if they are in the wrong order ie if 4 is next to 1 they will be swapped the pass through the list is repeated until no swaps are needed which indicates that the list is sorted the algorithm which is a comparison sort https en wikipedia org wiki comparison sort is named for the way smaller or larger elements bubble to the top of the list although the algorithm is simple it is too slow and impractical for most problems even when compared to insertion sort it can be practical if the input is usually in sorted order but may occasionally have some out of order elements nearly in position insertion sort insertion sort is a simple sorting algorithm https en wikipedia org wiki sorting algorithm that builds the final sorted array https en wikipedia org wiki sorted array or list one item at a time it is much less efficient on large lists than more advanced algorithms such as quicksort https en wikipedia org wiki quicksort heapsort https en wikipedia org wiki heapsort or merge sort https en wikipedia org wiki merge sort however insertion sort provides several advantages simple implementation efficient for quite small data sets much like other quadratic sorting algorithms more efficient in practice than most other simple quadratic i e o https en wikipedia org wiki big o notation n 2 algorithms such as selection sort https en wikipedia org wiki selection sort or bubble sort https en wikipedia org wiki bubble sort adaptive https en wikipedia org wiki adaptive sort i e efficient for data sets that are already substantially sorted the time complexity https en wikipedia org wiki time complexity is o nk when each element in the input is no more than k places away from its sorted position stable https en wikipedia org wiki stable sort i e does not change the relative order of elements with equal keys in place https en wikipedia org wiki in place algorithm i e only requires a constant amount o 1 of additional memory space online https en wikipedia org wiki online algorithm i e can sort a list as it receives it when people manually sort cards in a bridge hand most use a method that is similar to insertion sort merge sort merge sort also commonly spelled mergesort is an efficient general purpose comparison based https en wikipedia org wiki comparison sort sorting algorithm https en wikipedia org wiki sorting algorithm most implementations produce a stable sort https en wikipedia org wiki sorting algorithm stability which means that the implementation preserves the input order of equal https en wikipedia org wiki equality mathematics elements in the sorted output mergesort is a divide and conquer algorithm an example of merge sort first divide the list into the smallest unit 1 element then compare each element with the adjacent list to sort and merge the two adjacent lists finally all the elements are sorted and merged quick sort quicksort sometimes called partition exchange sort is an efficient sorting algorithm serving as a systematic method for placing the elements of an array in order quicksort is a comparison sort meaning that it can sort items of any type for which a less than relation formally a total order is defined in efficient implementations it is not a stable sort meaning that the relative order of equal sort items is not preserved quicksort can operate in place on an array requiring small additional amounts of memory to perform the sorting mathematical analysis of quicksort shows that on average the algorithm takes o n log n comparisons to sort n items in the worst case it makes o n2 comparisons though this behavior is rare quicksort first divides a large array into two smaller sub arrays the low elements and the high elements quicksort can then recursively sort the sub arrays the steps are pick an element called a pivot from the array partitioning reorder the array so that all elements with values less than the pivot come before the pivot while all elements with values greater than the pivot come after it equal values can go either way after this partitioning the pivot is in its final position this is called the partition operation recursively apply the above steps to the sub array of elements with smaller values and separately to the sub array of elements with greater values the base case of the recursion is arrays of size zero or one which never need to be sorted the pivot selection and partitioning steps can be done in several different ways the choice of specific implementation schemes greatly affects the algorithm s performance selection sort in computer science selection sort is a sorting algorithm specifically an in place comparison sort it has o n2 time complexity making it inefficient on large lists and generally performs worse than the similar insertion sort selection sort is noted for its simplicity and it has performance advantages over more complicated algorithms in certain situations particularly where auxiliary memory is limited the algorithm divides the input list into two parts the sublist of items already sorted which is built up from left to right at the front left of the list and the sublist of items remaining to be sorted that occupy the rest of the list initially the sorted sublist is empty and the unsorted sublist is the entire input list the algorithm proceeds by finding the smallest or largest depending on sorting order element in the unsorted sublist exchanging swapping it with the leftmost unsorted element putting it in sorted order and moving the sublist boundaries one element to the right searches binary search finds the position of a target value within a sorted array binary search compares the target value to the middle element of the array if they are unequal the half in which the target cannot lie is eliminated and the search continues on the remaining half until it is successful if the search ends with the remaining half being empty the target is not in the array traversals graph traversal means visiting every vertex and edge exactly once in a well defined order while using certain graph algorithms you must ensure that each vertex of the graph is visited exactly once the order in which the vertices are visited are important and may depend upon the algorithm or question that you are solving during a traversal it is important that you track which vertices have been visited the most common way of tracking vertices is to mark them breadth first search bfs bfs is the most commonly used approach to traverse a graph bfs is a traversing algorithm where you should start traversing from a selected node source or starting node and traverse the graph layerwise thus exploring the neighbour nodes nodes which are directly connected to source node you must then move towards the next level neighbour nodes as the name bfs suggests you are required to traverse the graph breadthwise as follows 1 first move horizontally and visit all the nodes of the current layer 2 move to the next layer https he s3 s3 amazonaws com media uploads fdec3c2 jpg https he s3 s3 amazonaws com media uploads fdec3c2 jpg the distance between the nodes in layer 1 is comparitively lesser than the distance between the nodes in layer 2 therefore in bfs you must traverse all the nodes in layer 1 before you move to the nodes in layer 2 traversing child nodes a graph can contain cycles which may bring you to the same node again while traversing the graph to avoid processing of same node again use a boolean array which marks the node after it is processed while visiting the nodes in the layer of a graph store them in a manner such that you can traverse the corresponding child nodes in a similar order in the earlier diagram start traversing from 0 and visit its child nodes 1 2 and 3 store them in the order in which they are visited this will allow you to visit the child nodes of 1 first i e 4 and 5 then of 2 i e 6 and 7 and then of 3 i e 7 etc to make this process easy use a queue to store the node and mark it as visited until all its neighbours vertices that are directly connected to it are marked the queue follows the first in first out fifo queuing method and therefore the neigbors of the node will be visited in the order in which they were inserted in the node i e the node that was inserted first will be visited first and so on bfs g s where g is the graph and s is the source node let q be queue q enqueue s inserting s in queue until all its neighbour vertices are marked mark s as visited while q is not empty removing that vertex from queue whose neighbour will be visited now v q dequeue processing all the neighbours of v for all neighbours w of v in graph g if w is not visited q enqueue w stores w in q to further visit its neighbour mark w as visited depth first search dfs the dfs algorithm is a recursive algorithm that uses the idea of backtracking it involves exhaustive searches of all the nodes by going ahead if possible else by backtracking here the word backtrack means that when you are moving forward and there are no more nodes along the current path you move backwards on the same path to find nodes to traverse all the nodes will be visited on the current path till all the unvisited nodes have been traversed after which the next path will be selected https he s3 s3 amazonaws com media uploads 9fa1119 jpg https he s3 s3 amazonaws com media uploads 9fa1119 jpg this recursive nature of dfs can be implemented using stacks the basic idea is as follows 1 pick a starting node and push all its adjacent nodes into a stack 2 pop a node from stack to select the next node to visit and push all its adjacent nodes into a stack 3 repeat this process until the stack is empty however ensure that the nodes that are visited are marked this will prevent you from visiting the same node more than once if you do not mark the nodes that are visited and you visit the same node more than once you may end up in an infinite loop iterative dfs iterative g s where g is graph and s is source vertex let s be stack s push s inserting s in stack mark s as visited while s is not empty pop a vertex from stack to visit next v s top s pop push all the neighbours of v in stack that are not visited for all neighbours w of v in graph g if w is not visited s push w mark w as visited recursive dfs recursive g s mark s as visited for all neighbours w of s in graph g if w is not visited dfs recursive g w complexity https en wikipedia org wiki big o notation bigo assets img bigo png big o notation is the language we use for articulating how long an algorithm takes to run it s how we compare the efficiency of different approaches to a problem with big o notation we express the runtime in terms of how quickly it grows relative to the input as the input gets arbitrarily large 1 how quickly the runtime grows some external factors affect the time it takes for a function to run the speed of the processor what else the computer is running etc so it s hard to make strong statements about the exact runtime of an algorithm instead we use big o notation to express how quickly its runtime grows 2 relative to the input since we re not looking at an exact number we need to phrase our runtime growth in terms of something we use the size of the input so we can say things like the runtime grows on the order of the size of the input o n o n or on the order of the square of the size of the input o n 2 o n2 3 as the input gets arbitrarily large our algorithm may have steps that seem expensive when nn is small but are eclipsed eventually by other steps as nn gets huge for big o analysis we care most about the stuff that grows fastest as the input grows because everything else is quickly eclipsed as nn gets very large if you know what an asymptote is you might see why big o analysis is sometimes called asymptotic analysis note often this worst case stipulation is implied sorting complexity assets img sorting complexity png examples this function runs in o 1 time or constant time relative to its input the input arraycould be 1 item or 1 000 items but this function would still just require one step javascript function printfirstitem arrayofitems console log arrayofitems 0 this function runs in o n time or linear time where n is the number of items in the array if the array has 10 items we have to print 10 times if it has 1 000 items we have to print 1 000 times javascript function printallitems arrayofitems arrayofitems foreach function item console log item here we re nesting two loops if our array has n items our outer loop runs n times and our inner loop runs n times for each iteration of the outer loop giving us n 2 total prints thus this function runs in o n 2 time or quadratic time if the array has 10 items we have to print 100 times if it has 1 000 items we have to print 1 000 000 times javascript function printallpossibleorderedpairs arrayofitems arrayofitems foreach function firstitem arrayofitems foreach function seconditem console log firstitem seconditem drop the constants when you re calculating the big o complexity of something you just throw out the constants so the below is o 2n which we just call o n javascript function printallitemstwice thearray thearray foreach function item o n console log item once more with feeling thearray foreach function item o n console log item o n o n o n 2 o n logarithms a common example of a logarithms is the binary search binary search is a technique used to search sorted data sets it works by selecting the middle element of the data set and compares it against a target value if the values match it will return success if the target value is higher than the value of the probe element it will take the upper half of the data set and perform the same operation against it likewise if the target value is lower than the value of the probe element it will perform the operation against the lower half it will continue to halve the data set with each iteration until the value has been found or until it can no longer split the data set this type of algorithm is described as o log n the iterative halving of data sets described in the binary search example produces a growth curve that peaks at the beginning and slowly flattens out as the size of the data sets increase e g an input data set containing 10 items takes one second to complete a data set containing 100 items takes two seconds and a data set containing 1000 items will take three seconds doubling the size of the input data set has little effect on its growth as after a single iteration of the algorithm the data set will be halved and therefore on a par with an input data set half the size this makes algorithms like binary search extremely efficient when dealing with large data sets javascript web apis https www sitepoint com 5 typical javascript interview exercises prototypal inheritance prototypal inheritance https developer mozilla org en us docs web javascript inheritance and the prototype chain when it comes to inheritance javascript only has one construct objects each object has a private property referred to as prototype which holds a link to another object called its prototype that prototype object has a prototype of its own and so on until an object is reached with null as its prototype by definition null has no prototype and acts as the final link in this prototype chain nearly all objects in javascript are instances of object https developer mozilla org en us docs web javascript reference global objects object which sits on the top of a prototype chain while this is often considered to be one of javascript s weaknesses the prototypal inheritance model is in fact more powerful than the classic model it is for example fairly trivial to build a classic model on top of a prototypal model what s the difference between class prototypal inheritance class inheritance a class is like a blueprint a description of the object to be created classes inherit from classes and create subclass relationships hierarchical class taxonomies instances are typically instantiated via constructor functions with the new keyword class inheritance may or may not use the class keyword from es6 classes as you may know them from languages like java don t technically exist in javascript constructor functions are used instead the es6 class keyword desugars to a constructor function class foo typeof foo function in javascript class inheritance is implemented on top of prototypal inheritance but that does not mean that it does the same thing javascript s class inheritance uses the prototype chain to wire the child constructor prototype to the parent constructor prototype for delegation usually the super constructor is also called those steps form single ancestor parent child hierarchies and create the tightest coupling available in oo design prototypal inheritance a prototype is a working object instance objects inherit directly from other objects instances may be composed from many different source objects allowing for easy selective inheritance and a flat prototype delegation hierarchy in other words class taxonomies are not an automatic side effect of prototypal oo a critical distinction instances are typically instantiated via factory functions object literals or object create scope scoping https spin atomicobject com 2014 10 20 javascript scope closures javascript has lexical scoping with function scope in other words even though javascript looks like it should have block scope because it uses curly braces a new scope is created only when you create a new function lexical scoping sometimes known as static scoping is a convention used with many programming languages that sets the scope range of functionality of a variable so that it may only be called referenced from within the block of code in which it is defined javascript var outerfunction function if true var x 5 console log y line 1 referenceerror y not defined var nestedfunction function if true var y 7 console log x line 2 x will still be known prints 5 if true console log y line 3 prints 7 return nestedfunction var myfunction outerfunction myfunction in this example the variable x is available everywhere inside of outerfunction also the variable y is available everywhere within the nestedfunction but neither are available outside of the function where they were defined the reason for this can be explained by lexical scoping the scope of variables is defined by their position in source code in order to resolve variables javascript starts at the innermost scope and searches outwards until it finds the variable it was looking for lexical scoping is nice because we can easily figure out what the value of a variable will be by looking at the code whereas in dynamic scoping the meaning of a variable can change at runtime making it more difficult closures closures https developer mozilla org en us docs web javascript closures a closure is a special kind of object that combines two things a function and the environment in which that function was created the environment consists of any local variables that were in scope at the time that the closure was created a closure is an inner function that has access to the outer enclosing function s variables scope chain the closure has three scope chains it has access to its own scope variables defined between its curly brackets it has access to the outer function s variables and it has access to the global variables the inner function has access not only to the outer function s variables but also to the outer function s parameters note that the inner function cannot call the outer function s arguments object however even though it can call the outer function s parameters directly a closure is created by adding a function inside another function a closure is the combination of a function and the lexical environment within which that function was declared javascript function init var name mozilla name is a local variable created by init function displayname displayname is the inner function a closure alert name use variable declared in the parent function displayname init event bubbling event bubbling http javascript info tutorial bubbling and capturing https www sitepoint com event bubbling javascript event bubbling relates to the order in which event handlers are called when one element is nested inside a second element and both elements have registered a listener for the same event a click for example but event bubbling is only one piece of the puzzle it is often mentioned in conjunction with event capturing and event propagation what is the event propagation let s start with event propagation this is the blanket term for both event bubbling and event capturing a click on an image does not only generate a click event for the corresponding img element but also for the parent a for the grandfather li and so on going all the way up through all the element s ancestors before terminating at the window object in dom terminology the image is the event target the innermost element over which the click originated the event target plus its ancestors from its parent up through to the window object form a branch in the dom tree for example in the image gallery this branch will be composed of the nodes img a li ul body html document window this branch is important because it is the path along which the events propagate or flow this propagation is the process of calling all the listeners for the given event type attached to the nodes on the branch each listener will be called with an event object that gathers information relevant to the event remember that several listeners can be registered on a node for the same event type when the propagation reaches one such node listeners are invoked in the order of their registration the propagation is bidirectional from the window to the event target and back this propagation can be divided into three phases 1 from the window to the event target parent this is the capture phase 2 the event target itself this is the target phase 3 from the event target parent back to the window the bubble phase the event capture phase in this phase only the capturer listeners are called namely those listeners that were registered using a value of true for the third parameter of addeventlistener https developer mozilla org en us docs web api eventtarget addeventlistener el addeventlistener click listener true if this parameter is omitted its default value is false and the listener is not a capturer so during this phase only the capturers found on the path from the window to the event target parent are called the event target phase in this phase all the listeners registered on the event target will be invoked regardless of the value of their capture flag the event bubbling phase during the event bubbling phase only the non capturers will be called that is only the listeners registered with a value of false for the third parameter of addeventlistener javascript el addeventlistener click listener false listener doesn t capture el addeventlistener click listener listener doesn t capture note that while all events flow down to the event target with the capture phase focus blur load and some others don t bubble up that is their travel stops after the target phase eventprop assets img eventprop png preventdefault vs stoppropagation stoppropagation stops the event from bubbling up the event chain preventdefault prevents the default action the browser makes on that event with stoppropagation only the buttons click handler is called and the divs click handler never fires where as if you just preventdefault only the browsers default action is stopped but the div s click handler still fires apply call and bind apply call and bind http javascriptissexy com javascript apply call and bind methods are essential for javascript professionals use bind when you want that function to later be called with a certain context useful in events use call or apply when you want to invoke the function immediately and modify the context call apply call the function immediately whereas bind returns a function that when later executed will have the correct context set for calling the original function this way you can maintain context in async callbacks and events example javascript function myobject element this elm element element addeventlistener click this onclick bind this false myobject prototype onclick function e var t this do something with t without bind the context of this function wouldn t be a myobject instance as you would normally expect a simple naive implementation of bind would be like javascript function prototype bind function ctx var fn this return function fn apply ctx arguments bind the bind method creates a new function that when called has its this keyword set to the provided value with a given sequence of arguments preceding any provided when the new function is called apply the apply method calls a function with a given this value and arguments provided as an array or an array like object https developer mozilla org en us docs web javascript guide indexed collections working with array like objects call the call method calls a function with a given this value and arguments provided individually call vs apply the difference is that apply lets you invoke the function with arguments as an array call requires the parameters be listed explicitly a useful mnemonic is a for a rray and c for c omma pseudo syntax thefunction apply valueforthis arrayofargs thefunction call valueforthis arg1 arg2 sample code javascript function thefunction name profession console log my name is name and i am a profession thefunction john fireman thefunction apply undefined susan school teacher thefunction call undefined claude mathematician thefunction call undefined matthew physicist used with the spread operator output my name is john and i am a fireman my name is susan and i am a school teacher my name is claude and i am a mathematician my name is matthew and i am a physicist hoisting variable and function hoisting http adripofjavascript com blog drips variable and function hoisting hoisting is a javascript mechanism where variables and function declarations are moved to the top of their scope before code execution function declarations and variable declarations are always moved hoisted invisibly to the top of their containing scope by the javascript interpreter function parameters and language defined names are obviously already there this means that code like this javascript function foo bar var x 1 is actually interpreted like this javascript function foo var x bar x 1 currying currying http www sitepoint com currying in functional javascript currying is a way of constructing functions that allows partial application of a function s arguments what this means is that you can pass all of the arguments a function is expecting and get the result or pass a subset of those arguments and get a function back that s waiting for the rest of the arguments javascript var greetcurried function greeting return function name console log greeting name the only problem is the syntax as you build these curried functions up you need to keep nesting returned functions and call them with new functions that require multiple sets of parentheses each containing its own isolated argument it can get messy to address that problem one approach is to create a quick and dirty currying function that will take the name of an existing function that was written without all the nested returns a currying function would need to pull out the list of arguments for that function and use those to return a curried version of the original function javascript var curryit function uncurried var parameters array prototype slice call arguments 1 return function return uncurried apply this parameters concat array prototype slice call arguments 0 to use this we pass it the name of a function that takes any number of arguments along with as many of the arguments as we want to pre populate what we get back is a function that s waiting for the remaining arguments javascript var greeter function greeting separator emphasis name console log greeting separator name emphasis var greethello curryit greeter hello greethello heidi hello heidi greethello eddie hello eddie practical applications javascript function converter tounit factor offset input offset offset 0 return offset input factor tofixed 2 tounit join var milestokm converter curry km 1 60936 undefined var poundstokg converter curry kg 0 4546 undefined var farenheittocelsius converter curry degrees c 0 5556 32 milestokm 10 returns 16 09 km poundstokg 2 5 returns 1 14 kg farenheittocelsius 98 returns 36 67 degrees c this relies on a curry extension of function although as you can see it only uses apply nothing too fancy javascript function prototype curry function if arguments length 1 return this nothing to curry with return function var method this var args toarray arguments return function return method apply this args concat slice apply null arguments var let const var the most important thing to know about the var keyword is that it is function scoped scope refers to how a computer keeps track of all the variables in a program specifically the environment in which each variable is accessible typically we talk about scope as either being global and therefore available everywhere or local and reserved for a specific block of code when using var our local scope is within a function this means that a variable with the same name can be used in two separate functions as each has their own local scope in fact we can use the same variable three times if we also include it in the global scope the function based nature of var also leads to javascript behavior such as hoisting https wsvincent com javascript hoisting where variable and function declarations are hoisted to the top of the scope before assignments hoisting hoisting is a behavior in javascript where variable and function declarations are hoisted to the top of their scope before code execution this can result in confusing behaviour such as the ability to call variables and functions before you wrote them let the let statement declares a local variable in a block scope it is similar to var in that we can optionally initialise the variable the main difference between let and var is that variable declared with let is block scoped and variables declared with var is function scoped const the const declaration creates a read only reference to a value it does not mean the value it holds is immutable just that the variable identifier cannot be reassigned for instance in the case where the content is an object this means the object s contents e g its parameters can be altered constants are block scoped much like variables defined using the let statement the value of a constant cannot change through re assignment and it can t be redeclared temporal dead zone let bindings are created at the top of the block scope containing the declaration commonly referred to as hoisting unlike variables declared with var which will start with the value undefined let variables are not initialized until their definition is evaluated accessing the variable before the initialization results in a referenceerror the variable is in a temporal dead zone from the start of the block until the initialization is processed javascript function do something console log bar undefined console log foo referenceerror var bar 1 let foo 2 arrow functions an arrow function expression has a shorter syntax than a function expression https developer mozilla org en us docs web javascript reference operators function and does not have its own this arguments https developer mozilla org en us docs web javascript reference functions arguments super https developer mozilla org en us docs web javascript reference operators super or new target https developer mozilla org en us docs web javascript reference operators new target these function expressions are best suited for non method functions and they cannot be used as constructors there is one subtle difference in behavior between ordinary function functions and arrow functions arrow functions do not have their own this value the value of this inside an arrow function is always inherited from the enclosing scope in contrast function functions receive a this value automatically whether they want one or not generator functions generators are functions which can be exited and later re entered their context variable bindings will be saved across re entrances generator functions are a feature introduced in es6 that allows a function to generate many values over time by returning an object which can be iterated over an iterable with a next method that returns objects the hidden power of generators https medium com javascript scene the hidden power of es6 generators observable async flow control cfa4c7f31435 generators in javascript especially when combined with promises are a very powerful tool for asynchronous programming as they mitigate if not entirely eliminate the problems with callbacks such as callback hell http callbackhell com and inversion of control https frontendmasters com courses rethinking async js callback problems inversion of control this pattern is what async functions are built on top of calling a generator function does not execute its body immediately an iterator https developer mozilla org en us docs web javascript reference iteration protocols iterator object for the function is returned instead when the iterator s next method is called the generator function s body is executed until the first yield https developer mozilla org en us docs web javascript reference operators yield expression which specifies the value to be returned from the iterator or with yield https developer mozilla org en us docs web javascript reference operators yield delegates to another generator function the next method returns an object with a value property containing the yielded value and a done property which indicates whether the generator has yielded its last value as a boolean calling the next method with an argument will resume the generator function execution replacing the yield expression where execution was paused with the argument from next a return statement in a generator when executed will make the generator finished i e the done property of the object returned by it will be set to true if a value is returned it will be set as the value property of the object returned by the generator much like a return statement an error thrown inside the generator will make the generator finished unless caught within the generator s body when a generator is finished subsequent next calls will not execute any of that generator s code they will just return an object of this form value undefined done true example return statement in a generator javascript function yieldandreturn yield y return r yield unreachable var gen yieldandreturn console log gen next value y done false console log gen next value r done true console log gen next value undefined done true map vs object the map object holds key value pairs any value both objects and primitive values https developer mozilla org en us docs glossary primitive may be used as either a key or a value a map object can iterate its elements in insertion order a for of loop will return an array of key value for each iteration objects are similar to maps in that both let you set keys to values retrieve those values delete keys and detect whether something is stored at a key because of this objects have been used as maps historically however there are important differences between objects and maps that make using a map better an object has a prototype so there are default keys in the map however this can be bypassed using map object create null the keys of an object are strings where they can be any value for a map you can get the size of a map easily while you have to manually keep track of size for an object use maps over objects when keys are unknown until run time and when all keys are the same type and all values are the same type use objects when there is logic that operates on individual elements virtual dom source https medium com deathmood how to write your own virtual dom ee74acc13060 virtual dom is any kind of representation of a real dom when we change something in our virtual dom tree we get a new virtual tree an algorithm then compares these two trees old and new finds differences and makes only necessary small changes to real dom so it reflects it s virtual counterpart so instead of updating the dom when your application state changes you simply create a virtual tree which looks like the dom state that you want virtual dom will then figure out how to make the dom look like this efficiently without recreating all of the dom nodes virtual dom allows you to update a view whenever state changes by creating a full virtual tree of the view and then patching the dom efficiently to look exactly as you described it this results in keeping manual dom manipulation and previous state tracking out of your application code promoting clean and maintainable rendering logic for web applications why virtual dom dom manipulation is the heart of the modern interactive web unfortunately it is also a lot slower than most javascript operations this slowness is made worse by the fact that most javascript frameworks update the dom much more than they have to for example let s say that you have a list that contains ten items you check off the first item most javascript frameworks would rebuild the entire list that s ten times more work than necessary only one item changed but the remaining nine get rebuilt exactly how they were before rebuilding a list is no big deal to a web browser but modern websites can use huge amounts of dom manipulation inefficient updating has become a serious problem manipulating the dom is slow manipulating the virtual dom is much faster because nothing gets drawn onscreen think of manipulating the virtual dom as editing a blueprint as opposed to moving rooms in an actual house how it helps in react when you render a jsx element every single virtual dom object gets updated this sounds incredibly inefficient but the cost is insignificant because the virtual dom can update so quickly once the virtual dom has updated then react compares the virtual dom with a virtual dom snapshot that was taken right before the update by comparing the new virtual dom with a pre update version react figures out exactly which virtual dom objects have changed this process is called diffing once react knows which virtual dom objects have changed then react updates those objects and only those objects on the real dom in our example from earlier react would be smart enough to rebuild your one checked off list item and leave the rest of your list alone this makes a big difference react can update only the necessary parts of the dom react s reputation for performance comes largely from this innovation in summary here s what happens when you try to update the dom in react 1 the entire virtual dom gets updated 2 the virtual dom gets compared to what it looked like before you updated it react figures out which objects have changed 3 the changed objects and the changed objects only get updated on the real dom 4 changes on the real dom cause the screen to change shadow dom shadow dom is used to encapsulate a dom subtree from the rest of the page useful if you are making plug and play widgets this encapsulation means your document stylesheet can t accidentally apply to encapsulated subtree your javascript can t accidentally modify parts inside shadow dom your ids wont overlap and so on following code will change the buttons text to japanese but interestingly host innertext will still give us hello world because the dom subtree under the shadow root is encapsulated html5 input type range video audio etc all uses shadow dom html button hello world button script var host document queryselector button var root host createshadowroot root textcontent script async await the await operator is used to wait for a promise it can only be used inside an async function the await expression causes async function execution to pause until a promise is resolved that is fulfilled or rejected and to resume execution of the async function after fulfillment when resumed the value of the await expression is that of the fulfilled promise if the promise is rejected the await expression throws the rejected value if the value of the expression following the await operator is not a promise it s converted to a resolved promise https developer mozilla org en us docs web javascript reference global objects promise resolve example if a promise is passed to an await expression it waits for the promise to be fulfilled and returns the fulfilled value javascript function resolveafter2seconds x return new promise resolve settimeout resolve x 2000 async function f1 var x await resolveafter2seconds 10 console log x 10 f1 if the value is not a promise it converts the value to a resolved promise and waits for it javascript async function f2 var y await 20 console log y 20 f2 if the promise is rejected the rejected value is thrown javascript async function f3 try var z await promise reject 30 catch e console log e 30 f3 web workers web workers is a simple means for web content to run scripts in background threads the worker thread can perform tasks without interfering with the user interface once created a worker can send messages to the javascript code that created it by posting messages to an event handler specified by that code and vice versa a worker is an object created using a constructor e g worker https developer mozilla org en us docs web api worker worker that runs a named javascript file this file contains the code that will run in the worker thread workers run in another global context that is different from the current window https developer mozilla org en us docs web api window data is sent between workers and the main thread via a system of messages both sides send their messages using the postmessage method and respond to messages via the onmessage event handler the message is contained within the message event s data attribute the data is copied rather than shared workers may in turn spawn new workers as long as those workers are hosted within the same origin as the parent page dedicated workers dedicated workers can be created like this javascript var myworker new worker worker js once created you can send messages to and from the worker via postmessage and listen on the on message event handler when you want to send a message to the worker you post messages to it like this in the context of main js javascript first onchange function myworker postmessage first value second value console log message posted to worker second onchange function myworker postmessage first value second value console log message posted to worker so here we have two input elements represented by the variables first and second when the value of either is changed myworker postmessage first value second value is used to send the value inside to the worker as an array you can send pretty much anything you like in the message in the worker we can respond when the message is received by writing an event handler block like this in the context of worker js javascript onmessage function e console log message received from main script var workerresult result e data 0 e data 1 console log posting message back to main script postmessage workerresult the onmessage handler allows us to run some code whenever a message is received with the message itself being available in the message event s data attribute here we simply multiply together the two numbers then use postmessage again to post the result back to the main thread back in the main thread we use onmessage again to respond to the message sent back from the worker javascript myworker onmessage function e result textcontent e data console log message received from worker here we grab the message event data and set it as the textcontent of the result paragraph so the user can see the result of the calculation notice that onmessage and postmessage need to be hung off the worker object when used in the main script thread but not when used in the worker this is because inside the worker the worker is effectively the global scope note when a message is passed between the main thread and worker it is copied not shared if you need to immediately terminate a running worker from the main thread you can do so by calling the worker s terminate https developer mozilla org en us docs web api worker method javascript myworker terminate the worker thread is killed immediately without an opportunity to complete its operations or clean up after itself in the worker thread workers may close themselves by calling their own close https developer mozilla org en us docs web api workerglobalscope method javascript close example example html the main page javascript var myworker new worker my task js myworker onmessage function oevent console log worker said oevent data myworker postmessage ali my task js the worker javascript postmessage i m working before postmessage ali onmessage function oevent postmessage hi oevent data for more information see mdn using web workers https developer mozilla org en us docs web api web workers api using web workers service workers serviceworkers https developer mozilla org en us docs web api serviceworker api essentially act as proxy servers that sit between web applications and the browser and network when available they are intended to amongst other things enable the creation of effective offline experiences intercepting network requests and taking appropriate action based on whether the network is available and updated assets reside on the server they will also allow access to push notifications and background sync apis a service worker is run in a worker context it therefore has no dom access and runs on a different thread to the main javascript that powers your app so it is not blocking it is designed to be fully async service workers only run over https for security reasons having modified network requests wide open to man in the middle attacks would be really bad throttle vs debounce throttle the original function be called at most once per specified period debounce the original function be called after the caller stops calling the decorated function after a specified period throttle implementation javascript function throttled delay fn let lastcall 0 return function args const now new date gettime if now lastcall delay return lastcall now return fn args debounce implementation javascript function debounce fn interval let timeouttoken return function args cleartimeout timeouttoken timeouttoken settimeout timeouttoken null fn apply this args interval miscellaneous difference between defer and async when you hit an web url in browser first the html content is downloaded and browser starts parsing the html during html parsing if it encounters a script block html parsing halts it makes a call to fetch the script if external and then executes the script before resuming html parsing if we use async html parsing doesn t stop whilst file is fetched but once it s fetched html parsing stops to execute the script if we use defer browser downloads the js during html parsing and executes the js only when html parsing is done http www growingwiththeweb com 2014 02 async vs defer attributes html pass by value pass by reference in javascript javascript uses a pass by value strategy for primitives but uses pass by reference for objects including arrays aka call by sharing https en wikipedia org wiki evaluation strategy call by sharing pass by reference is when variables passed in to functions are given the direct memory address this allows the function to manipulate the object or primitive as it exists outside the scope of the function pass by value means that when you pass that variable into a function its the equivalent of creating a new var and making a copy of the passed in var they re not necessarily the same exact variable in the same exact memory address but rather copies modifying the copy does not affect the original array methods stack methods array prototype pop https developer mozilla org en us docs web javascript reference global objects array pop removes the last element from an array and returns that element array prototype push https developer mozilla org en us docs web javascript reference global objects array push adds one or more elements to the end of an array and returns the new length of the array queue methods array prototype unshift https developer mozilla org en us docs web javascript reference global objects array unshift adds one or more elements to the front of an array and returns the new length of the array array prototype pop https developer mozilla org en us docs web javascript reference global objects array pop removes the last element from an array and returns that element misc array prototype reverse https developer mozilla org en us docs web javascript reference global objects array reverse reverses the order of the elements of an array in place the first becomes the last and the last becomes the first array prototype splice https developer mozilla org en us docs web javascript reference global objects array splice adds and or removes elements from an array array prototype slice https developer mozilla org en us docs web javascript reference global objects array slice extracts a section of an array and returns a new array memoization memoization is an optimisation technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again a memoized function remembers the results corresponding to some set of specific inputs subsequent calls with remembered inputs return the remembered result rather than recalculating it thus eliminating the primary cost of a call with given parameters from all but the first call made to the function with those parameters the set of remembered associations may be a fixed size set controlled by a replacement algorithm or a fixed set depending on the nature of the function and its use a function can only be memoized if it is referentially transparent that is only if calling the function has exactly the same effect as replacing that function call with its return value memoization is a way to lower a function s time cost in exchange for space cost that is memoized functions become optimised for speed in exchange for a higher use of computer memory space the time space cost of algorithms has a specific name in computing computational complexity all functions have a computational complexity in time i e they take time to execute and in space design patterns decorator decorator https addyosmani com resources essentialjsdesignpatterns book decoratorpatternjavascript https www scaler com topics design patterns decorator design pattern decorators are a structural design pattern that aim to promote code re use similar to mixins they can be considered another viable alternative to object sub classing classically decorators offered the ability to add behaviour to existing classes in a system dynamically the idea was that the decoration itself wasn t essential to the base functionality of the class otherwise it would be baked into the superclass itself they can be used to modify existing systems where we wish to add additional features to objects without the need to heavily modify the underlying code using them a common reason why developers use them is their applications may contain features requiring a large quantity of distinct types of object imagine having to define hundreds of different object constructors for say a javascript game the decorator pattern isn t heavily tied to how objects are created but instead focuses on the problem of extending their functionality rather than just relying on prototypal inheritance we work with a single base object and progressively add decorator objects which provide the additional capabilities the idea is that rather than sub classing we add decorate properties or methods to a base object so it s a little more streamlined javascript the constructor to decorate function macbook this cost function return 997 this screensize function return 11 6 decorator 1 function memory macbook var v macbook cost macbook cost function return v 75 decorator 2 function engraving macbook var v macbook cost macbook cost function return v 200 decorator 3 function insurance macbook var v macbook cost macbook cost function return v 250 var mb new macbook memory mb engraving mb insurance mb console log mb cost outputs 1522 console log mb screensize outputs 11 6 factory factory https addyosmani com resources essentialjsdesignpatterns book factorypatternjavascript the factory pattern is another creational pattern concerned with the notion of creating objects where it differs from the other patterns in its category is that it doesn t explicitly require us to use a constructor instead a factory can provide a generic interface for creating objects where we can specify the type of factory object we wish to be created imagine that we have a ui factory where we are asked to create a type of ui component rather than creating this component directly using the new operator or via another creational constructor we ask a factory object for a new component instead we inform the factory what type of object is required e g button panel and it instantiates this returning it to us for use when to use the factory pattern the factory pattern can be especially useful when applied to the following situations when our object or component setup involves a high level of complexity when we need to easily generate different instances of objects depending on the environment we are in when we re working with many small objects or components that share the same properties when composing objects with instances of other objects that need only satisfy an api contract aka duck typing to work this is useful for decoupling when not to use the factory pattern when applied to the wrong type of problem this pattern can introduce an unnecessarily great deal of complexity to an application unless providing an interface for object creation is a design goal for the library or framework we are writing i would suggest sticking to explicit constructors to avoid the unnecessary overhead due to the fact that the process of object creation is effectively abstracted behind an interface this can also introduce problems with unit testing depending on just how complex this process might be singleton singleton https addyosmani com resources essentialjsdesignpatterns book singletonpatternjavascript the singleton pattern is thus known because it restricts instantiation of a class to a single object classically the singleton pattern can be implemented by creating a class with a method that creates a new instance of the class if one doesn t exist in the event of an instance already existing it simply returns a reference to that object singletons differ from static classes or objects as we can delay their initialization generally because they require some information that may not be available during initialization time they don t provide a way for code that is unaware of a previous reference to them to easily retrieve them this is because it is neither the object or class that s returned by a singleton it s a structure think of how closured variables aren t actually closures the function scope that provides the closure is the closure in javascript singletons serve as a shared resource namespace which isolate implementation code from the global namespace so as to provide a single point of access for functions pros 1 better memory usage 2 low latency 3 needs initialisation once cons 1 they are generally used as a global instance why is that so bad because you hide the dependencies of your application in your code instead of exposing them through the interfaces making something global to avoid passing it around is a code smell 2 they violate the single responsibility principle https en wikipedia org wiki single responsibility principle by virtue of the fact that they control their own creation and lifecycle 3 they inherently cause code to be tightly coupled https en wikipedia org wiki coupling 28computer programming 29 this makes faking them out under test rather difficult in many cases 4 they carry state around for the lifetime of the application another hit to testing since you can end up with a situation where tests need to be ordered which is a big no no for unit tests why because each unit test should be independent from the other example javascript export default class singleton static instance constructor if instance return instance this state duke this instance this now let first new singleton let second new singleton console log first second output true module the module pattern was originally defined as a way to provide both private and public encapsulation for classes in conventional software engineering in javascript the module pattern is used to further emulate the concept of classes in such a way that we re able to include both public private methods and variables inside a single object thus shielding particular parts from the global scope what this results in is a reduction in the likelihood of our function names conflicting with other functions defined in additional scripts on the page privacy the module pattern encapsulates privacy state and organization using closures it provides a way of wrapping a mix of public and private methods and variables protecting pieces from leaking into the global scope and accidentally colliding with another developer s interface with this pattern only a public api is returned keeping everything else within the closure private this gives us a clean solution for shielding logic doing the heavy lifting whilst only exposing an interface we wish other parts of our application to use the pattern utilizes an immediately invoked function expression iife see the section on namespacing patterns for more on this where an object is returned it should be noted that there isn t really an explicitly true sense of privacy inside javascript because unlike some traditional languages it doesn t have access modifiers variables can t technically be declared as being public nor private and so we use function scope to simulate this concept within the module pattern variables or methods declared are only available inside the module itself thanks to closure variables or methods defined within the returning object however are available to everyone facade facade https addyosmani com resources essentialjsdesignpatterns book facadepatternjavascript when we put up a facade we present an outward appearance to the world which may conceal a very different reality this was the inspiration for the name behind the facade pattern this pattern provides a convenient higher level interface to a larger body of code hiding its true underlying complexity think of it as simplifying the api being presented to other developers something which almost always improves usability facades are a structural pattern which can often be seen in javascript libraries like jquery where although an implementation may support methods with a wide range of behaviors only a facade or limited abstraction of these methods is presented to the public for use this allows us to interact with the facade directly rather than the subsystem behind the scenes whenever we use jquery s el css or el animate methods we re actually using a facade the simpler public interface that avoids us having to manually call the many internal methods in jquery core required to get some behavior working this also avoids the need to manually interact with dom apis and maintain state variables the jquery core methods should be considered intermediate abstractions the more immediate burden to developers is the dom api and facades are what make the jquery library so easy to use to build on what we ve learned the facade pattern both simplifies the interface of a class and it also decouples the class from the code that utilizes it this gives us the ability to indirectly interact with subsystems in a way that can sometimes be less prone to error than accessing the subsystem directly a facade s advantages include ease of use and often a small size footprint in implementing the pattern observer observer https addyosmani com resources essentialjsdesignpatterns book observerpatternjavascript the observer is a design pattern where an object known as a subject maintains a list of objects depending on it observers automatically notifying them of any changes to state when a subject needs to notify observers about something interesting happening it broadcasts a notification to the observers which can include specific data related to the topic of the notification when we no longer wish for a particular observer to be notified of changes by the subject they are registered with the subject can remove them from the list of observers it s often useful to refer back to published definitions of design patterns that are language agnostic to get a broader sense of their usage and advantages over time the definition of the observer pattern provided in the gof book design patterns elements of reusable object oriented software is one or more observers are interested in the state of a subject and register their interest with the subject by attaching themselves when something changes in our subject that the observer may be interested in a notify message is sent which calls the update method in each observer when the observer is no longer interested in the subject s state they can simply detach themselves strategy pattern strategy defines a family of algorithms encapsulates each and makes them interchangeable strategy lets the algorithm vary independently form clients that use it this pattern seems to be very similar to factory command and others the main difference is that it is one to many pattern one object can have many strategies also this pattern is used to define algorithms it may be used for using optimal sorting algorithm for different types of data using different algorithms to count product discount based on user type using different algorithms to convert an image file to different format when to use it when you have a part of your class that s subject to change frequently or perhaps you have many related subclasses which only differ in behavior it s often a good time to consider using a strategy pattern another benefit of the strategy pattern is that it can hide complex logic or data that the client doesn t need to know about example javascript class greeter constructor strategy this strategy strategy greet return this strategy since a function encapsulates an algorithm it makes a perfect candidate for a strategy var politegreetingstrategy function console log hello var friendlygreetingstrategy function console log hey var boredgreetingstrategy function console log sup let s use these strategies var politegreeter new greeter politegreetingstrategy var friendlygreeter new greeter friendlygreetingstrategy var boredgreeter new greeter boredgreetingstrategy politegreeter greet hello friendlygreeter greet hey boredgreeter greet sup inversion of control ioc and dip are high level design principles which should be used while designing application classes these are principles so they only recommend certain best practices but do not provide any specific implementation details dependency injection di is a pattern and ioc container is a framework ioc principles and patterns assets img ioc principles and patterns png ioc is a design principle which recommends inversion of different kinds of controls in object oriented design to achieve loose coupling between the application classes here the control means any additional responsibilities a class has other than its main responsibility such as control over the flow of an application control over the dependent object creation and binding remember srp single responsibility principle if you want to do tdd test driven development then you must use ioc principle without which tdd is not possible dependency inversion principle dip principle also helps in achieving loose coupling between the classes it is highly recommended to use dip and ioc together in order to achieve loose coupling dip suggests that high level modules should not depend on low level modules both should depend on abstraction dependency injection dependency injection di is a design pattern used to implement ioc where it allows creation of dependent objects outside of a class and provides those objects to a class through different ways using di we move the creation and binding of the dependent objects outside of the class that depends on it dependency injection pattern involves 3 types of classes 1 client class the client class dependent class is a class which depends on the service class 2 service class the service class dependency is a class that provides service to the client class 3 injector class the injector class injects service class object into the client class types of dependency injection constructor injection in the constructor injection injector supplies service dependency through the client class constructor property injection in property injection aka setter injection injector supplies dependency through a public property of the client class method injection in this type of injection client class implements an interface which declares method s to supply dependency and the injector uses this interface to supply dependency to the client class dependency injection is effective in these situations 1 you need to inject configuration data into one or more components 2 you need to inject the same dependency into multiple components 3 you need to inject different implementations of the same dependency 4 you need to inject the same implementation in different configurations 5 you need some of the services provided by the container dependency injection is not effective if you will never need a different implementation you will never need a different configuration if you know you will never change the implementation or configuration of some dependency there is no benefit in using dependency injection ioc container ioc container a k a di container is a framework for implementing automatic dependency injection it manages object creating and its life time and also injects dependencies to the class ioc container creates an object of the specified class and also injects all the dependency objects through constructor property or method at run time and disposes it at the appropriate time this is done so that we don t have to create and manage objects manually all the containers must provide easy support for the following di lifecycle register the container must know which dependency to instantiate when it encounters a particular type this process is called registration basically it must include some way to register type mapping resolve when using ioc container we don t need to create objects manually container does it for us this is called resolution container must include some methods to resolve the specified type container creates an object of specified type injects required dependencies if any and returns it dispose container must manage the lifetime of dependent objects most ioc containers include different lifetime managers to manage an object s lifecycle and dispose of it composition vs inheritance prefer composition over inheritance as it is easy to modify later but do not use a compose always approach composition is simply when a class is composed of other classes or to say it another way an instance of an object has references to instances of other objects inheritance is when a class inherits methods and properties from another class to favor composition over inheritance is a design principle that gives the design higher flexibility it is more natural to build business domain classes out of various components than trying to find commonality between them and creating a family tree for example a gas pedal and a wheel share very few common traits yet are both vital components in a car what they can do and how they can be used to benefit the car is easily defined composition also provides a more stable business domain in the long term as it is less prone to the quirks of the family members in other words it is better to compose what an object can do has a than extend what it is is a initial design is simplified by identifying system object behaviors in separate interfaces instead of creating a hierarchical relationship to distribute behaviors among business domain classes via inheritance this approach more easily accommodates future requirements changes that would otherwise require a complete restructuring of business domain classes in the inheritance model additionally it avoids problems often associated with relatively minor changes to an inheritance based model that includes several generations of classes html css flexbox the flexible box module usually referred to as flexbox was designed as a one dimensional layout model and as a method that could offer space distribution between items in an interface and powerful alignment capabilities when we describe flexbox as being one dimensional we are describing the fact that flexbox deals with layout in one dimension at a time either as a row or as a column this can be contrasted with the two dimensional model of css grid layout https developer mozilla org en us docs web css css grid layout which controls columns and rows together when working with flexbox you need to think in terms of two axes the main axis and the cross axis the main axis is defined by the flex direction https developer mozilla org en us docs web css flex direction property and the cross axis runs perpendicular to it everything we do with flexbox refers back to these axes so it is worth understanding how they work from the outset the main axis is defined by flex direction which has four possible values row row reverse column column reverse should you choose row or row reverse your main axis will run along the row in the inline direction choose column or column reverse and your main axis will run from the top of the page to the bottom in the block direction the cross axis runs perpendicular to the main axis therefore if your flex direction main axis is set to row or row reverse the cross axis runs down the columns if your main axis is column or column reverse then the cross axis runs along the rows understanding which axis is which is important when we start to look at aligning and justifying flex items flexbox features properties that align and justify content along one axis or the other transforms the transform css https developer mozilla org en us docs web css property lets you rotate scale skew or translate a given element this is achieved by modifying the coordinate space of the css visual formatting model https developer mozilla org en us docs web css visual formatting model css transform matrix 1 2 3 4 5 6 transform translate 120px 50 transform scale 2 0 5 transform rotate 0 5turn transform skew 30deg 20deg transform scale 0 5 translate 100 100 accessibility being accessible is about making your website with all of its data and functions available for anyone no matter how they have to use your website katie cunningham accessibility handbook o reilly for a beginner friendly overview of accessibility the google web fundamentals guide https developers google com web fundamentals accessibility is an excellent starting point they explain the acessibility tree https developers google com web fundamentals accessibility semantics builtin the accessibility tree particularly well if you want to dive into a more hands on learning plan with recommended activities see julie grundy s accessibility learning plan for developers https github com stringyland a11y learning plan julie recommends resources and activities for writing semantic html creating accessible styles making images accessible learning the methods for hiding content building an accessible drop down menu building an accessible form providing accessible error messages accessibility testing questions how would you make these functions work javascript add 2 5 7 add 2 5 7 details summary show answer summary br basic function curry javascript function add addend return function summand return addend summand generic function curry supports both invocations javascript accepts partial list of arguments function add x y if typeof y undefined partial return function y return x y return x y details if we execute this javascript what will the browser s console show javascript var text outside function logit console log text var text inside logit details summary show answer summary br in javascript variables are hoisted to the top of the function that is unlike some other languages such as c a variable declared within a function is within scope throughout the function so the compiler sees your function like this javascript function logit var text console log text text inside no semicolon after a function declaration when you declare text as a local variable inside logit it shadows the variable in the outer scope and when a variable is declared it is initialized to undefined that s why undefined gets printed if you want to keep text in the outer scope just leave off the var declaration inside the function javascript var text outside function logit console log text logs outside now text inside logit details get nth fibonacci number details summary show answer summary br javascript function fib n if n 2 return 1 let x 0 let y 1 for var i 0 i n i let tempy y y tempy x x tempy return y details write a function that deeply flattens an array recursively details summary show answer summary br javascript function flatten array return array reduce sum el let flattenedarr array isarray el flatten el el return sum concat flattenedarr flatten 1 2 3 4 5 1 2 3 4 5 4 5 1 2 3 4 5 1 2 3 4 5 4 5 details write a function that deeply flattens an array iteratively details summary show answer summary br javascript function flatten array let clonedlist array slice 0 const flatlist while clonedlist length const item clonedlist shift if item instanceof array true clonedlist item concat clonedlist else flatlist push item return flatlist flatten 1 2 3 4 5 1 2 3 4 5 4 5 1 2 3 4 5 1 2 3 4 5 4 5 details write a function to find the 2nd largest element in a binary search tree details summary show answer summary br javascript const min 0 const max 1000 function rand min max return math random max min min class node constructor value this value value this left null this right null function insert root key const node new node key if root return node if root value key root left root left node else if root value key root right root right node else if root value key insert root left key else if root value key insert root right key return root function getmaxnode node if node return null if node right return node return getmaxnode node right function findsecondlargest node if node return null if the current node is the max the second largest is the max node on the left tree if node left node right return getmaxnode node left if the right child node is the max the current node is the second largest if node right if node right left node right right return node otherwise move on to the right child return findsecondlargest node right build bst let bst for var i 0 i 10 i const randno rand min max bst insert bst randno console log largest getmaxnode bst value console log second largest findsecondlargest bst value details given 2 identical dom trees but not equal and one element of the first dom tree how would you find this element in the second dom tree example dom html div id root1 div div div div div div id node1 div div div div div div id root2 div div div div div div id node2 div div div div div details summary show answer summary br javascript o o o o o o o o o o o o a b o o o o x o o y o o y find a b x function getpath rootnode startingnode const path let currentnode startingnode while currentnode rootnode const parentnode currentnode parentelement const childindex array from parentnode children indexof currentnode we only care about the index path push childindex currentnode parentnode return path function findnode rootnode path let currentnode rootnode while path length const childindex path path length 1 const childnode currentnode childen childindex currentnode childnode path pop return currentnode function find roota rootb nodex const path getpath roota nodex const nodey findnode rootb path return nodey details write an event emitter that supports subscribing unsubscribing and emitting events your implementation should fire the callback supplied as part of your subscription to be invoked with foo and bar as parameters for example emitter emit event name foo bar reference https www glassdoor com au interview write an emitter class emitter new emitter 1 support subscribing to events sub emitter subscribe eve qtn 1793084 htm details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details given an input array and another array that describes a new index for each element mutate the input array so that each element ends up in their new index discuss the runtime of the algorithm and how you can be sure there won t be any infinite loops reference https www glassdoor com au interview given an input array and another array that describes a new index for each element mutate the input array so that each ele qtn 446534 htm details summary show answer summary br javascript let arr a b c d e f const indices 2 3 4 0 5 1 should return d f a b c e 1 o n space and o n time solution javascript const indicesmap indices reduce map item index map set item index return map new map arr indices map val index return arr indicesmap get index console log arr 2 o n2 n n time solution javascript arr indices map val index return arr indices indexof index details implement a square root function details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details write a function to check that a binary tree is a valid binary search tree details summary show answer summary br https www interviewcake com question javascript bst checker this question is designed to trick you you must keep track of the min and max nodes do not evaluate only the children nodes in relation to the value of the parent node for more information please see https www geeksforgeeks org a program to check if a binary tree is bst or not consider this bst 3 2 5 1 4 notice how the 4 is in the left sub tree as a child of 2 it should actually be in the right subtree as a left hand child of 5 the following solution is correct but not efficient it runs slowly since it traverses over some parts of the tree many times a better solution looks at each node only once do not use this solution javascript definition for a binary tree node function treenode val this val val this left this right null function isvalidbst root min 0 max 9999 if root return true if root val max root val min return false if root left isvalidbst root left min root val return false if root right isvalidbst root right root val max return false return true the trick is to write a utility help function that traverses the tree keeping track of the narrowing min and max allowed values as it goes looking at each node only once do not use this solution this is a more efficient and correct solution javascript var isvalidbst function root min max return isbstutil root min max var isbstutil function root min max if root return true if root val min root val max return false return isbstutil root left min root val 1 isbstutil root right root val 1 max but the best solution is to do an in order traversal of the given tree and store the result in a temp array then check if the array is sorted if it is then the tree is a valid bst this is the general idea but we can improve upon it further but removing the temp array and just checking the previously visited node value if the value is less than the previous value then tree is not bst time complexity o n in order traversal 1 traverse the left subtree i e call inorder left subtree 2 visit the root 3 traverse the right subtree i e call inorder right subtree javascript var isvalidbst function root if root return true const stack let prev while root stack length 0 while root null stack push root root root left root stack pop if prev root val prev val return false prev root root root right return true details delete a node from a singly linked list given only variable pointing to that node reference https www interviewcake com question javascript delete node details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details write a function for reversing a linked list do it in place reference https www interviewcake com question javascript reverse linked list your function will have one input the head of the list your function should return the new head of the list here s a sample linked list node class javascript function linkedlistnode value this value value this next null details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details you have a linked list and want to find the kth to last node reference https www interviewcake com question javascript kth to last node in singly linked list write a function kthtolastnode that takes an integer k and the headnode of a singly linked list and returns the kth to last node in the list details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details verify a prime number details summary show answer summary br the simplest approach is to check if the number is divisible by every integer less than itself javascript const isprime num for let i 2 i num i if num i 0 return false return num 1 a more efficient approach is to check if the number is divisible by every integer up to its square root if n a b where a b a is always going to be less or equal to the square root of n javascript const isprime num const limit math sqrt num for let i 2 i limit i if num i 0 return false return num 1 a slightly more efficient approach is also to avoid all even numbers since they are divisible by 2 hence can t be prime javascript const isprime num if num 2 0 return num 2 const limit math sqrt num for let i 3 i limit i 2 if num i 0 return false return num 1 we can keep creating slightly more efficient versions by eliminating multiples of known primes for 2 3 it boils down to 6k i we can even write more efficient versions following the same pattern p1 p2 pn k i javascript const isprime num if num 2 0 return num 2 if num 3 0 return num 3 const limit math sqrt num for let i 5 i limit i 6 if num i 0 num i 2 0 return false return num 1 details remove duplicate members from an array details summary show answer summary br reference https wsvincent com javascript remove duplicates array the simplest approach is to use the set object which lets you store unique values of any type in other words set will automatically remove duplicates for us javascript const names john paul george ringo john let unique new set names console log unique john paul george ringo another option is to use filter javascript const names john paul george ringo john let x names names filter v i names indexof v i x names john paul george ringo and finally we can use foreach javascript const names john paul george ringo john function removedups names let unique names foreach function i if unique i unique i true return object keys unique removedups names john paul george ringo details swap two numbers without using a temp variable details summary show answer summary br javascript a a b b a b a a b details reverse a string in javascript details summary show answer summary br since strings in javascript are immutable first convert the string into an array of characters do the in place reversal on that array and re join that array into a string before returning it this isn t technically in place and the array of characters will cost o n additional space but it s a reasonable way to stay within the spirit of the challenge https www interviewcake com question javascript reverse string in place javascript function reversestring str step 1 use the split method to return a new array var splitstring str split h e l l o step 2 use the reverse method to reverse the new created array var reversearray splitstring reverse o l l e h step 3 use the join method to join all elements of the array into a string var joinarray reversearray join olleh step 4 return the reversed string return joinarray olleh reversestring hello details reverse words in a sentence details summary show answer summary br similar to the above but take note of the space to denote a new word javascript function reversestring str return str split reverse join reversestring hello world my name is daniel del core details find the first non repeating char in a string details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details remove duplicate characters from a sting details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details how can you verify that a word is palindrome details summary show answer summary br javascript function palindrome str string clean up to support sentences etc const cleanstr str tolowercase const reversestr cleanstr split reverse join return cleanstr reversestr palindrome race car true details generate random between 5 to 7 by using defined function details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details find missing number from unsorted array of integers details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details get two numbers that equal to a given number details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details find the largest sum of any two elements details summary show answer summary br javascript function findlargestsum arr const largest secondlargest arr reduce largest secondlargest currentvalue if currentvalue largest return currentvalue largest if currentvalue secondlargest return largest currentvalue return largest secondlargest number min value number min value return largest secondlargest findlargestsum 7 5 3 1 4 34 8 9 6 0 43 details check whether a given string is a substring of bigger string details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details get permutations of a string details summary show answer summary br there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details how would you handle 5000 promises details summary show answer summary br group the promises into batches of 50 for example and recursively execute each batch until completion or error if an error occurs you could either retry with that batch or throw an error to the client as timeouts and other server errors are likely to occur don t forget if duplicate requests occur you can simply memoize the function and safely return the same result to side step the network request and improve performance there s no answer here yet suggest one by creating a pull request https github com danieldelcore mega interview guide pulls details | interview-questions interview frontend questions-and-answers questions data-structures javascript sort algorithms computer-science | front_end |
front-end-roadmap | front end roadmap https objtube github io front end roadmap up https www bilibili com video bv1zz4y1h7ru 1 2 3 4 fork fork github git clone bash git clone https github com objtube front end roadmap fork bash code git add git commit m what did i do git push origin master pull request github pull request open in gitpod https gitpod io button open in gitpod svg https gitpod io https github com objtube front end roadmap heart roadmap jpeg | front-end roadmap html css javascript learning | front_end |
kaleidoscope | kaleidoscope https user images githubusercontent com 72175053 229659242 27090171 2005 4d1c 9de3 0a5b02ef8ceb png kaleidoscope pypi https img shields io pypi v kscope pypi python version https img shields io pypi pyversions kscope github https img shields io github license vectorinstitute kaleidoscope doi https img shields io badge doi in progress blue documentation https img shields io badge api reference lightgrey svg https kaleidoscope sdk readthedocs io en latest a user toolkit for analyzing and interfacing with large language models llms overview kaleidoscope provides a few high level apis namely model instances shows a list of all active llms instantiated by the model service load model loads an llm via the model service generate returns an llm text generation based on prompt input module names returns all modules names in the llm neural network get activations retrieves all activations for a set of modules edit activations manipulates activations for a set of modules kaleidoscope is composed of the following components python sdk a frontend python library for interacting with llms available in a separate repository at https github com vectorinstitute kaleidoscope sdk model service a backend utility that loads models into gpu memory and exposes an interface to recieve requests gateway service a controller service that interfaces between the frontend user tools and model service getting started instructions for setting up gateway service install bash git clone https github com vectorinstitute kaleidoscope git start gateway container bash cp kaleidoscope web env example kaleidoscope web env sudo docker compose f kaleidoscope web docker compose yaml up install kaleidoscope sdk toolkit the kaleidoscope sdk toolkit is a python module that provides a programmatic interface for interfacing with the services found here you can download and install the sdk from its own repository https github com vectorinstitute kaleidoscope sdk contributing contributing to kaleidoscope is welcomed see contributing contributing md for guidelines license mit license citation reference to cite when you use kaleidoscope in a project or a research paper sivaloganathan j coatsworth m willes j choi m shen g 2022 kaleidoscope http vectorinstitute github io kaleidoscope computer software vector institute for artificial intelligence retrieved from https github com vectorinstitute kaleidoscope git | deep-learning distributed-computing llm machine-learning neural-networks nlp python tensor | ai |
Fall18_MAD_examples | fall18 mad examples atls 4120 mobile app development class examples | front_end |
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cloud_assignment | cloud assignment altschool cloud engineering assignment for mr madu valentine | cloud |
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-Full-Stack-Web-Development-with-Django-2.x-and-Angular-8 | full stack web development with django 2 x and angular 8 full stack web development with django 2 x and angular 8 published by packt this is the code repository for full stack web development with django and angular 8 https www packtpub com web development full stack web development with django and angular 8 video published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the video course from start to finish about the video course in this course you ll take a tour of web development with django 2 x and angular 8 we ve included an additional bonus section on the features forthcoming in django 3 x designed to help you pre empt upgrade requirements and considerations well in advance you ll learn to build a database driven web app for seamless full stack web development we teach you the best practices you ll need to adopt and provide you with hands on experience by building a flight reservation application from the ground up you ll connect django to a mongodb database to help efficiently store and track data you ll create a beautifully styled web application using angular 8 for an amazing ui experience all by using this popular front end tool by the end of this course you ll be able to build a full fledged and rich web application with django angular and gain an early insight into what s coming in django 3 x h2 what you will learn h2 div class book info will learn text ul li create a data driven web application li connect django to databases such as sqlite to help store and track data li effectively utilize the template system li configure angular to work with the django framework li create an amazing user interface ui components with angular 8 li explore the free django admin functionality li bonus section get an early introduction to what s new in the forthcoming django 3 x release li ul div instructions and navigation assumed knowledge this course targets python developers who favor web development with django and now want to expand their horizons by building amazing front ends for their websites using angular 8 technical requirements ul b minimum hardware requirements b li for successful completion of this course students will require the computer systems with at least the following li operating system windows 10 li processor 1 4 ghz 32 bit x86 li memory 4gb li storage 2gb li ul ul b recommended hardware requirements b li os osx yosemite or above windows 7 or above li processor 2 ghz li memory 8 gb of ram li storage sdd 128 gb or above li ul ul b software requirements b li os windows 10 li browser chrome li visual studio code ide latest version li python 3 5 or higher li django 2 1 or higher li li li li angular 8 li li li pip 10 x or higher li li node js lts 8 9 or higher installed li npm 5 5 1 or higher installed li ul related products c programming by example video https www packtpub com application development c programming example video high performance computing with python 3 x video https www packtpub com application development high performance computing python 3x video utm source github utm medium repository utm campaign 9781789956252 functional programming in 7 days video https www packtpub com application development functional programming 7 days video utm source github utm medium repository utm campaign 9781788990295 | front_end |
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guidance | div align right a href https guidance readthedocs org img src https readthedocs org projects guidance badge version latest style flat a div div align center picture source media prefers color scheme dark srcset docs figures guidance logo blue dark svg img alt guidance src docs figures guidance logo blue svg width 300 picture div br where there is no guidance a model fails but in an abundance of instructions there is safety a href notebooks proverb ipynb gpt 11 14 a it expands the api of language models so you can craft rich output structure design precise tool use create multi agent interactions and much more all while using clear code and maximum inference efficiency b guidance b enables you to control modern language models more effectively and efficiently than traditional prompting or chaining guidance programs allow you to interleave generation prompting and logical control into a single continuous flow matching how the language model actually processes the text simple output structures like chain of thought https arxiv org abs 2201 11903 and its many variants e g art https arxiv org abs 2303 09014 auto cot https arxiv org abs 2210 03493 etc have been shown to improve llm performance the advent of more powerful llms like gpt 4 https openai com research gpt 4 allows for even richer structure and guidance makes that structure easier and cheaper features x simple intuitive syntax based on handlebars https handlebarsjs com templating x rich output structure with multiple generations selections conditionals tool use etc x playground like streaming in jupyter vscode notebooks x smart seed based generation caching x support for role based chat models e g chatgpt https beta openai com docs guides chat x easy integration with hugging face models including guidance acceleration notebooks guidance acceleration ipynb for speedups over standard prompting token healing notebooks token healing ipynb to optimize prompt boundaries and regex pattern guides notebooks pattern guides ipynb to enforce formats install python pip install guidance live streaming a href https github com guidance ai guidance blob main notebooks proverb ipynb notebook a speed up your prompt development cycle by streaming complex templates and generations live in your notebook at first glance guidance feels like a templating language and just like standard a href https handlebarsjs com handlebars a templates you can do variable interpolation e g proverb and logical control but unlike standard templating languages guidance programs have a well defined linear execution order that directly corresponds to the token order as processed by the language model this means that at any point during execution the language model can be used to generate text using the gen command or make logical control flow decisions this interleaving of generation and prompting allows for precise output structure that produces clear and parsable results python import guidance set the default language model used to execute guidance programs guidance llm guidance llms openai text davinci 003 define a guidance program that adapts a proverb program guidance tweak this proverb to apply to model instructions instead proverb book chapter verse updated where there is no guidance gen rewrite stop n gpt select chapter 9 or 10 or 11 select gen verse execute the program on a specific proverb executed program program proverb where there is no guidance a people falls nbut in an abundance of counselors there is safety book proverbs chapter 11 verse 14 img src docs figures proverb animation gif width 404 after a program is executed all the generated variables are now easily accessible python executed program rewrite a model fails nbut in an abundance of instructions there is safety chat dialog a href https github com guidance ai guidance blob main notebooks chat ipynb notebook a guidance supports api based chat models like gpt 4 as well as open chat models like vicuna through a unified api based on role tags e g system system this allows interactive dialog development that combines rich templating and logical control with modern chat models python connect to a chat model like gpt 4 or vicuna gpt4 guidance llms openai gpt 4 vicuna guidance llms transformers vicuna your path vicuna 13b device map auto experts guidance system you are a helpful and terse assistant system user i want a response to the following question query name 3 world class experts past or present who would be great at answering this don t answer the question yet user assistant gen expert names temperature 0 max tokens 300 assistant user great now please answer the question as if these experts had collaborated in writing a joint anonymous answer user assistant gen answer temperature 0 max tokens 500 assistant llm gpt4 experts query how can i be more productive img src docs figures chat animation gif width 619 guidance acceleration a href https github com guidance ai guidance blob main notebooks guidance acceleration ipynb notebook a when multiple generation or llm directed control flow statements are used in a single guidance program then we can significantly improve inference performance by optimally reusing the key value caches as we progress through the prompt this means guidance only asks the llm to generate the green text below not the entire program this cuts this prompt s runtime in half vs a standard generation approach python we use llama here but any gpt style model will do llama guidance llms transformers your path llama 7b device 0 we can pre define valid option sets valid weapons sword axe mace spear bow crossbow define the prompt character maker guidance the following is a character profile for an rpg game in json format json id id description description name gen name age gen age pattern 0 9 stop armor select armor leather or chainmail or plate select weapon select weapon options valid weapons class gen class mantra gen mantra temperature 0 7 strength gen strength pattern 0 9 stop items geneach items num iterations 5 join gen this temperature 0 7 geneach generate a character character maker id e1f491f7 7ab8 4dac 8c20 c92b5e7d883d description a quick and nimble fighter valid weapons valid weapons llm llama img src docs figures json animation gif width 565 the prompt above typically takes just over 2 5 seconds to complete on a a6000 gpu when using llama 7b if we were to run the same prompt adapted to be a single generation call the standard practice today it takes about 5 seconds to complete 4 of which is token generation and 1 of which is prompt processing this means guidance acceleration delivers a 2x speedup over the standard approach for this prompt in practice the exact speed up factor depends on the format of your specific prompt and the size of your model larger models benefit more acceleration is also only supported for transformers llms at the moment see the notebook https github com guidance ai guidance blob main notebooks guidance acceleration ipynb for more details token healing a href https github com guidance ai guidance blob main notebooks art of prompt design prompt boundaries and token healing ipynb notebook a the standard greedy tokenizations used by most language models introduce a subtle and powerful bias that can have all kinds of unintended consequences for your prompts using a process we call token healing guidance automatically removes these surprising biases freeing you to focus on designing the prompts you want without worrying about tokenization artifacts consider the following example where we are trying to generate an http url string python we use stablelm as an open example but these issues impact all models to varying degrees guidance llm guidance llms transformers stabilityai stablelm base alpha 3b device 0 we turn token healing off so that guidance acts like a normal prompting library program guidance the link is a href http gen max tokens 10 token healing false program img src docs figures url with space png width 372 note that the output generated by the llm does not complete the url with the obvious next characters two forward slashes it instead creates an invalid url string with a space in the middle why because the string is its own token 1358 and so once the model sees a colon by itself token 27 it assumes that the next characters cannot be otherwise the tokenizer would not have used 27 and instead would have used 1358 the token for this bias is not just limited to the colon character it happens everywhere over 70 of the 10k most common tokens for the stablelm model used above are prefixes of longer possible tokens and so cause token boundary bias when they are the last token in a prompt for example the token 27 has 34 possible extensions the the token 1735 has 51 extensions and the space token 209 has 28 802 extensions guidance eliminates these biases by backing up the model by one token then allowing the model to step forward while constraining it to only generate tokens whose prefix matches the last token this token healing process eliminates token boundary biases and allows any prompt to be completed naturally python guidance the link is a href http gen max tokens 10 img src docs figures url without space png width 362 rich output structure example notebook notebooks anachronism ipynb to demonstrate the value of output structure we take a simple task https github com google big bench tree main bigbench benchmark tasks anachronisms from bigbench where the goal is to identify whether a given sentence contains an anachronism a statement that is impossible because of non overlapping time periods below is a simple two shot prompt for it with a human crafted chain of thought sequence guidance programs like standard handlebars templates allow both variable interpolation e g input and logical control but unlike standard templating languages guidance programs have a unique linear execution order that directly corresponds to the token order as processed by the language model this means that at any point during execution the language model can be used to generate text the gen command or make logical control flow decisions the select or select command this interleaving of generation and prompting allows for precise output structure that improves accuracy while also producing clear and parsable results python import guidance set the default language model used to execute guidance programs guidance llm guidance llms openai text davinci 003 define the few shot examples examples input i wrote about shakespeare entities entity i time present entity shakespeare time 16th century reasoning i can write about shakespeare because he lived in the past with respect to me answer no input shakespeare wrote about me entities entity shakespeare time 16th century entity i time present reasoning shakespeare cannot have written about me because he died before i was born answer yes define the guidance program structure program guidance given a sentence tell me whether it contains an anachronism i e whether it could have happened or not based on the time periods associated with the entities display the few shot examples each examples sentence this input entities and dates each this entities this entity this time each reasoning this reasoning anachronism this answer each place the real question at the end sentence input entities and dates gen entities reasoning gen reasoning anachronism select answer yes or no select execute the program out structure program examples examples input the t rex bit my dog img src docs figures anachronism png width 837 all of the generated program variables are now available in the executed program object python out answer yes we compute accuracy notebooks anachronism ipynb on the validation set and compare it to using the same two shot examples above without the output structure as well as to the best reported result here https github com google big bench tree main bigbench benchmark tasks anachronisms the results below agree with existing literature in that even a very simple output structure drastically improves performance even compared against much larger models model accuracy few shot learning with guidance examples no cot output structure notebooks anachronism ipynb 63 04 palm 3 shot https github com google big bench tree main bigbench benchmark tasks anachronisms around 69 guidance notebooks anachronism ipynb 76 01 guaranteeing valid syntax json example notebook notebooks guaranteeing valid syntax ipynb large language models are great at generating useful outputs but they are not great at guaranteeing that those outputs follow a specific format this can cause problems when we want to use the outputs of a language model as input to another system for example if we want to use a language model to generate a json object we need to make sure that the output is valid json with guidance we can both accelerate inference speed notebooks guidance acceleration ipynb and ensure that generated json is always valid below we generate a random character profile for a game with perfect syntax every time python load a model locally we use llama here guidance llm guidance llms transformers your local path llama 7b device 0 we can pre define valid option sets valid weapons sword axe mace spear bow crossbow define the prompt program guidance the following is a character profile for an rpg game in json format json description description name gen name age gen age pattern 0 9 stop armor select armor leather or chainmail or plate select weapon select weapon options valid weapons class gen class mantra gen mantra strength gen strength pattern 0 9 stop items geneach items num iterations 3 gen this geneach execute the prompt program description a quick and nimble fighter valid weapons valid weapons img src docs figures perfect syntax png width 657 python and we also have a valid python dictionary out variables img src docs figures json syntax variables png width 714 role based chat model example notebook notebooks chat ipynb modern chat style models like chatgpt and alpaca are trained with special tokens that mark out roles for different areas of the prompt guidance supports these models through a href notebooks api examples library role ipynb role tags a that automatically map to the correct tokens or api calls for the current llm below we show how a role based guidance program enables simple multi step reasoning and planning python import guidance import re we use gpt 4 here but you could use gpt 3 5 turbo as well guidance llm guidance llms openai gpt 4 a custom function we will call in the guidance program def parse best prosandcons options best int re findall r best d prosandcons 0 return options best define the guidance program using role tags like system system create plan guidance system you are a helpful assistant system generate five potential ways to accomplish a goal block hidden true user i want to goal generate potential options can you please generate one option for how to accomplish this please make the option very short at most one line user assistant gen options n 5 temperature 1 0 max tokens 500 assistant block generate pros and cons for each option and select the best option block hidden true user i want to goal can you please comment on the pros and cons of each of the following options and then pick the best option each options option index this each please discuss each option very briefly one line for pros one for cons and end by saying best x where x is the best option user assistant gen prosandcons temperature 0 0 max tokens 500 assistant block generate a plan to accomplish the chosen option user i want to goal create a plan here is my plan parse best prosandcons options please elaborate on this plan and tell me how to best accomplish it user assistant gen plan max tokens 500 assistant execute the program for a specific goal out create plan goal read more books parse best parse best a custom python function we call in the program img src docs figures chat reading png width 935 this prompt program is a bit more complicated but we are basically going through 3 steps 1 generate a few options for how to accomplish the goal note that we generate with n 5 such that each option is a separate generation and is not impacted by the other options we set temperature 1 to encourage diversity 2 generate pros and cons for each option and select the best one we set temperature 0 to encourage the model to be more precise 3 generate a plan for the best option and ask the model to elaborate on it notice that steps 1 and 2 were hidden which means gpt 4 does not see them when generating content that comes later in this case that means when generating the plan this is a simple way to make the model focus on the current step since steps 1 and 2 are hidden they do not appear on the generated output except briefly during stream but we can print the variables that these steps generated python print n join option d s i x for i x in enumerate out options option 0 set a goal to read for 20 minutes every day before bedtime option 1 join a book club for increased motivation and accountability option 2 set a daily goal to read for 20 minutes option 3 set a daily reminder to read for at least 20 minutes option 4 set a daily goal to read at least one chapter or 20 pages python print out prosandcons option 0 pros establishes a consistent reading routine cons may not be suitable for those with varying schedules option 1 pros provides social motivation and accountability cons may not align with personal reading preferences option 2 pros encourages daily reading habit cons lacks a specific time frame which may lead to procrastination option 3 pros acts as a daily reminder to prioritize reading cons may become repetitive and easy to ignore option 4 pros sets a clear daily reading target cons may be difficult to achieve on busy days or with longer chapters best 0 agents notebook notebooks chat ipynb we can easily build agents that talk to each other or to a user via the await command the await command allows us to pause execution and return a partially executed guidance program by putting await in a loop that partially executed program can then be called again and again to form a dialog or any other structure you design for example here is how we might get gpt 4 to simulate two agents talking to one another python import guidance import re guidance llm guidance llms openai gpt 4 role simulator guidance system you are a helpful assistant system user you will answer the user as role in the following conversation at every step i will provide you with the user input as well as a comment reminding you of your instructions never talk about the fact that you are an ai even if the user asks you always answer as role if first question you can also start the conversation if user the assistant either starts the conversation or not depending on if this is the first or second agent assistant ok i will follow these instructions if first question let me start the conversation now role first question if assistant then the conversation unrolls geneach conversation stop false user user set this input await input comment remember answer as a role start your utterance with role user assistant gen this response temperature 0 max tokens 300 assistant geneach republican role simulator role republican await missing true democrat role simulator role democrat await missing true first question what do you think is the best way to stop inflation republican republican input first question first question none democrat democrat input republican conversation 2 response strip republican first question first question for i in range 2 republican republican input democrat conversation 2 response replace democrat democrat democrat input republican conversation 2 response replace republican print democrat first question for x in democrat conversation 1 print republican x input print print x response democrat what do you think is the best way to stop inflation republican the best way to stop inflation is by implementing sound fiscal policies such as reducing government spending lowering taxes and promoting economic growth additionally the federal reserve should focus on maintaining a stable monetary policy to control inflation democrat i agree that sound fiscal policies are important in controlling inflation as a democrat i would emphasize the importance of investing in education healthcare and infrastructure to promote long term economic growth additionally we should ensure that the federal reserve maintains a balanced approach to monetary policy focusing on both controlling inflation and promoting full employment republican while investing in education healthcare and infrastructure is important we must also prioritize reducing the national debt and limiting government intervention in the economy by lowering taxes and reducing regulations we can encourage businesses to grow and create jobs which will ultimately lead to long term economic growth as for the federal reserve it s crucial to maintain a stable monetary policy that primarily focuses on controlling inflation as this will create a more predictable economic environment for businesses and consumers democrat while reducing the national debt and limiting government intervention are valid concerns democrats believe that strategic investments in education healthcare and infrastructure can lead to long term economic growth and job creation we also support a progressive tax system that ensures everyone pays their fair share which can help fund these investments as for the federal reserve we believe that a balanced approach to monetary policy focusing on both controlling inflation and promoting full employment is essential for a healthy economy we must strike a balance between fiscal responsibility and investing in our nation s future republican it s important to find a balance between fiscal responsibility and investing in our nation s future however we believe that the best way to achieve long term economic growth and job creation is through free market principles such as lower taxes and reduced regulations this approach encourages businesses to expand and innovate leading to a more prosperous economy a progressive tax system can sometimes discourage growth and investment so we advocate for a simpler fairer tax system that promotes economic growth regarding the federal reserve while promoting full employment is important we must not lose sight of the primary goal of controlling inflation to maintain a stable and predictable economic environment democrat i understand your perspective on free market principles but democrats believe that a certain level of government intervention is necessary to ensure a fair and equitable economy we support a progressive tax system to reduce income inequality and provide essential services to those in need additionally we believe that regulations are important to protect consumers workers and the environment as for the federal reserve we agree that controlling inflation is crucial but we also believe that promoting full employment should be a priority by finding a balance between these goals we can create a more inclusive and prosperous economy for all americans gpt4 bing last example here notebooks chat ipynb api reference all of the examples below are in this notebook notebooks tutorial ipynb template syntax the template syntax is based on handlebars https handlebarsjs com with a few additions when guidance is called it returns a program python prompt guidance what is example prompt what is example the program can be executed by passing in arguments python prompt example truth what is truth arguments can be iterables python people john mary bob alice ideas name truth description the state of being the case name love description a strong feeling of affection prompt guidance list of people each people this this is a comment the removes adjacent whitespace either before or after a tag depending on where you place it each list of ideas each ideas this name this description each prompt people people ideas ideas template objects docs figures template objs png notice the special character after each this can be added before or after any tag to remove all adjacent whitespace notice also the comment syntax this is a comment you can also include prompts programs inside other prompts e g here is how you could rewrite the prompt above python prompt1 guidance list of people each people this each prompt2 guidance prompt1 list of ideas each ideas this name this description each prompt2 prompt1 prompt1 people people ideas ideas generation basic generation the gen tag is used to generate text you can use whatever arguments are supported by the underlying model executing a prompt calls the generation prompt python import guidance set the default llm could also pass a different one as argument to guidance with guidance llm guidance llm guidance llms openai text davinci 003 prompt guidance the best thing about the beach is gen best temperature 0 7 max tokens 7 prompt prompt prompt generation1 docs figures generation1 png guidance caches all openai generations with the same arguments if you want to flush the cache you can call guidance llms openai cache clear selecting you can select from a list of options using the select tag python prompt guidance is the following sentence offensive please answer with a single word either yes no or maybe sentence example answer select answer logprobs logprobs yes or no or maybe select prompt prompt example i hate tacos prompt select docs figures select png python prompt logprobs yes 1 5689583 no 7 332395 maybe 0 23746304 sequences of generate select a prompt may contain multiple generations or selections which will be executed in order python prompt guidance generate a response to the following email email response gen response is the response above offensive in any way please answer with a single word either yes or no answer select answer logprobs logprobs yes or no select prompt prompt email i hate tacos prompt generate select docs figures generate select png python prompt response prompt answer that s too bad tacos are one of my favorite meals no hidden generation you can generate text without displaying it or using it in the subsequent generations using the hidden tag either in a block or in a gen tag python prompt guidance block hidden true generate a response to the following email email response gen response block i will show you an email and a response and you will tell me if it s offensive email email response response is the response above offensive in any way please answer with a single word either yes or no answer select answer logprobs logprobs yes or no select prompt prompt email i hate tacos prompt hidden1 docs figures hidden1 png notice that nothing inside the hidden block shows up in the output or was used by the select even though we used the response generated variable in the subsequent generation generate with n 1 if you use n 1 the variable will contain a list there is a visualization that lets you navigate the list too python prompt guidance the best thing about the beach is gen best n 3 temperature 0 7 max tokens 7 prompt prompt prompt best that it is a great place to being able to relax in the sun that it s a great place to calling functions you can call any python function using generated variables as arguments the function will be called when the prompt is executed python def aggregate best return n join x for x in best prompt guidance the best thing about the beach is gen best n 3 temperature 0 7 max tokens 7 hidden true aggregate best prompt prompt aggregate aggregate prompt function docs figures function png pausing execution with await an await tag will stop program execution until that variable is provided python prompt guidance generate a response to the following email email response gen response await instruction gen updated response stream true prompt prompt email hello there prompt await1 docs figures await1 png notice how the last gen is not executed because it depends on instruction let s provide instruction now python prompt prompt instruction please translate the response above to portuguese prompt await2 docs figures await2 png the program is now executed all the way to the end notebook functions echo stream todo scott chat see also this notebook notebooks chat ipynb if you use an openai llm that only allows for chatcompletion gpt 3 5 turbo or gpt 4 you can use the special tags system user and assistant python prompt guidance system you are a helpful assistant system user conversation question user assistant gen response assistant prompt prompt conversation question what is the meaning of life prompt chat1 docs figures chat1 png since partial completions are not allowed you can t really use output structure inside an assistant block but you can still set up a structure outside of it here is an example also in here notebooks chat ipynb python experts guidance system you are a helpful assistant system user i want a response to the following question query who are 3 world class experts past or present who would be great at answering this please don t answer the question or comment on it yet user assistant gen experts temperature 0 max tokens 300 assistant user great now please answer the question as if these experts had collaborated in writing a joint anonymous answer in other words their identity is not revealed nor is the fact that there is a panel of experts answering the question if the experts would disagree just present their different positions as alternatives in the answer itself e g some might argue others might argue please start your answer with answer user assistant gen answer temperature 0 max tokens 500 assistant experts query what is the meaning of life you can still use hidden blocks if you want to hide some of the conversation history for following generations python prompt guidance system you are a helpful assistant system block hidden true user please tell me a joke user assistant gen joke assistant block user is the following joke funny why or why not joke user assistant gen funny assistant prompt agents with geneach you can combine the await tag with geneach which generates a list to create an agent easily prompt guidance system you are a helpful assistant system geneach conversation stop false user set this user text await user text user assistant gen this ai text temperature 0 max tokens 300 assistant geneach prompt prompt user text hi there prompt notice how the next iteration of the conversation is still templated and how the conversation list has a placeholder as the last element python prompt conversation user text hi there ai text hello how can i help you today if you have any questions or need assistance feel free to ask we can then execute the prompt again and it will generate the next round python prompt prompt user text what is the meaning of life prompt see a more elaborate example here notebooks chat ipynb using tools see the using a search api example in this notebook notebooks chat ipynb | ai |
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jobseeker | jobseeker this is the source code and issue board for the bots for https remoted io remoted is an aggregator for remote jobs for it professionals software engineering database cloud security you are more than welcome to participate to help making https remoted io the best remote job board give feedback report bugs suggest improvements and ask questions here https github com remoted io jobseeker issues other useful links remoted io https remoted io remoted graphql api https remoted io graphql remoted io source code https github com remoted io remoted jobseeker is licensed as agpl https github com remoted io jobseeker blob master license md made with by andre pena https twitter com andrerpena | cloud |
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cms-fe-angular8 | angular8 for cms project technical structure angular8 ng zorro antd angular router angular http rxjs websocket webpack4 less install project address git clone git clone git github com xpioneer cms fe angular8 git install node modules with yarn yarn in your command terminal run start server http localhost 9100 yarn run start publish production yarn run build | typescript ant-design lazyload angular-router tslint websocket httpintercptors angular8 | os |
CardScryer | card scryer computer vision trading card classification amp appraisal dependencies python 2 7 10 x64 untested on x86 cv2 3 0 0 from http opencv org downloads html pyside pypi pil pypi imagehash pypi pandas pypi numpy pypi | ai |
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DB_Cloud-Engineering-Challenge | the following resources were provisioned on aws us west 1 for this project vpc and subnets private and public nat gateway routetable internet gateway security groups load balancer and target groups ec2 running the application steps to run and view the string hello greylogs on your browser 1 install and configure terraform 2 my resources are provisioned in us west 1 region 3 on aws console get the name of your already existing key pair in us west 1 region or you can create a new key pair 4 open the variables tf file in the folder containing the tf files 5 navigate to variable instance key name block and set the default to the name of your key pair e g default mykeypair 6 save changes made in variables tf file before you proceed 7 run the terraform workflow a terraform init b terraform validate c terraform plan d terraform apply 8 allow 3 munites for resources to complete initialization 9 on your terminal copy the output of elb dsn name e g this elb dns name my elb 1929531987 us west 1 elb amazonaws com 10 enter the dns name to your browser e g my elb 1929531987 us west 1 elb amazonaws com 11 notice the string hello greylogs appears on your browser 12 run terraform destroy on your terminal to delete created resources other specifications terraform version v1 0 2 operating system linux cloud 20architecture 20diagram jpg | cloud |
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reverse-engineering-sonoff-itead-cloud | reverse engineering sonoff itead cloud | cloud |
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analog-sea | analogsea this webpage is an exemplary solution for a coding challenge in thinkful s frontend web development course they say good artists invent and great artists steal so we decided to compliment ourselves by lifting this design and code with modifications from digitalocean s login page https cloud digitalocean com login or at any rate what it looked like on the night of june 24 2016 to one benjamin e white | front_end |
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jacinto-ai-devkit | jacinto ai devkit training quantization tools model zoo accuracy benchmarks for embedded ai development deprecation notice 16 august 2021 this repository and the repositories that it used to point to are now deprecated please visit our new landing page for developers at https github com texasinstruments edgeai if you still need to access the old documentation and repositories it is here readme old md they will not be maintained and may be potentially removed in the future | deeplearning deep-learning cnn detection object-detection segmentation semantic-segmentation pytorch tensorflow caffe 8-bit trained-quantization fpn resnet mobilenet jacinto jacinto7 quantization | ai |
Empirical | empirical logo doc empiricalbanner png empirical is a library of tools for developing useful efficient reliable and available scientific software the provided code is header only and encapsulated into the emp namespace so it is simple to incorporate into existing projects ci https github com devosoft empirical workflows ci badge svg https github com devosoft empirical actions query workflow 3aci branch 3amaster documentation status https readthedocs org projects empirical badge version latest https empirical readthedocs io en latest badge latest doi https zenodo org badge 24824563 svg https zenodo org badge latestdoi 24824563 codecov https codecov io gh devosoft empirical branch master graph badge svg https codecov io gh devosoft empirical dockerhub https img shields io badge dockerhub hosted blue https hub docker com r devosoft empirical documentation coverage https img shields io endpoint url https 3a 2f 2fraw githubusercontent com 2fdevosoft 2fempirical 2fgh storage 2fstats 2fdoc coverage json github contributors https img shields io github contributors devosoft empirical svg style flat square https github com devosoft empirical graphs contributors see our built with empirical gallery https empirical readthedocs io en latest builtwithempiricalgallery for examples of web tools built with empirical see our quick start guides https empirical readthedocs io en latest quickstartguides to start using the library starting out on a new project that will use empirical take a look at the cookiecutter empirical project template https github com devosoft cookiecutter empirical project for automatically setting up a directory structure with all the files you need to get started step by step instructions included tools in empirical include web enabled elements to facilitate compiling to javascript and with a full web interfaces using mozilla s emscripten compiler from c to high efficiency javascript debug tools to facilitate audited memory management these tools are turned off outside of debug mode allowing the full speed of raw pointers and normal standard library components a wide range of helper tools to streamline common scientific computing tasks such as configuration data management mathematical manipulations etc all of these are designed to be easy to use and efficient a powerful set of evolution tools for building artificial life or evolutionary computation software a signal action system that allows for efficient customization of tools and flexible software that can easily facilitate plug in functionality see https empirical readthedocs io en latest for more detailed documentation on the available source files directory structure folder description demos examples of mini projects using empirical doc documentation files including for auto generation of docs examples example code for many of the above tools sub directory structure parallels source include header files that make up the empirical library see below for details planning scattered notes about future development most notes found in github issue tracker tests unit tests third party non empirical dependencies sub folders in include emp directory sub folder description base debug tools used throughout empirical for fast and efficient memory management bits tools for conveniently and efficiently working with bit representations config tools to build a simple configuration system including compile time optimizations control signaling tools allowing for fast and dynamic interaction across components compiler components for compiling code data tools for easy data management and output datastructs a variety of useful data structures and tools for using data structures debug tools to facilitate debugging particularly of emscripten generated web code functional tools for handling functions sets of functions and memoization games implementations of simple games geometry geometry and physics based tools for implementing simple virtual worlds hardware implementation of basic virtual hardware that is easy to configure io tools for handling input output matching tools for tag based matching math generally useful tools for math and randomness meta helpful tools for template meta programming prefab prefabricated web elements to drop onto web pages scholar tools for tracking authors citations and bibliographies tools basic tools that are generally useful for scientific software web tools for building web interfaces more low level than in prefab evolve specialized tools for building artificial life or evolutionary computation projects in progress tools that are being worked on but not at all ready for public consumption development status empirical is under heavy development as such many source files have at least one of the following statuses status meaning design notes are in place but no or minimal working code exploratory some functionality may work but needs re engineering to get right alpha some basic functionality works but more features still need to be added and tested beta basic functionality is all in place but needs more thorough testing release well tested functionality and used in multiple projects at least by authors stable used by many non authors in assorted projects without fixes for extended period abandoned may have worked or may not no longer being developed broken once worked at least beta level but now needs to be repaired not abandoned cleanup at least beta but needs code cleanup fixing of warnings etc deprecated functionality has been replaced and should shift to replacement external part of another project cited above not developed as part of empirical levelization the structure of empirical is levelized to facilitate development and testing this means that all directories are on a level where dependencies are either internal to that directory or on lower levels likewise within a directory files have a level and depend only on other files at lower levels level folders 0 base 1 meta 2 tools 3 config control data games geometry hardware scholar 4 evolve data control web config control | front_end |
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DeepPixel | h1 align center deep pixel h1 issues https img shields io github issues smaranjitghose deeppixel pull requests https img shields io github issues pr smaranjitghose deeppixel forks https img shields io github forks smaranjitghose deeppixel stars https img shields io github stars smaranjitghose deeppixel license https img shields io github license smaranjitghose deeppixel https github com smaranjitghose deeppixel blob master license a package for making computer vision and deep learning with images simpler features we are looking to make easier explainable ai class activation maps for convolutional neural networks image quality assessment image enhancement image pre processing https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 0 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 0 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 1 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 1 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 2 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 2 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 3 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 3 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 4 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 4 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 5 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 5 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 6 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 6 https sourcerer io fame smaranjitghose smaranjitghose deeppixel images 7 https sourcerer io fame smaranjitghose smaranjitghose deeppixel links 7 license mit license https github com smaranjitghose deeppixel blob master license | deeplearning computer-vision image-processing python hacktoberfest | ai |
healthcareai-r | readme md is generated from readme rmd please edit the rmd and knit it to generate the md healthcareai img src man figures logo png align right travis ci build status https travis ci org healthcatalyst healthcareai r svg branch master https travis ci org healthcatalyst healthcareai r codecov badge https codecov io gh healthcatalyst healthcareai r branch master graph badge svg https app codecov io gh healthcatalyst healthcareai r cran status badge https www r pkg org badges version last release healthcareai https cran r project org package healthcareai cran downloads badge https cranlogs r pkg org badges grand total healthcareai https cranlogs r pkg org badges last week healthcareai license mit https img shields io badge license mit blue svg https github com healthcatalyst healthcareai r blob master license doi https zenodo org badge doi 10 5281 zenodo 999334 svg https doi org 10 5281 zenodo 999334 overview the aim of healthcareai is to make machine learning in healthcare as easy as possible it does that by providing functions to develop customized reliable high performance machine learning models with minimal code easily make and evaluate predictions and push them to a database understand how a model makes its predictions make data cleaning manipulation imputation and visualization as simple as possible usage healthcareai can take you from messy data to an optimized model in one line of code r models machine learn pima diabetes patient id outcome diabetes models algorithms trained random forest extreme gradient boosting and glmnet model name diabetes target diabetes class classification performance metric auroc number of observations 768 number of features 12 models trained 2018 09 01 18 19 44 models tuned via 5 fold cross validation over 10 combinations of hyperparameter values best model random forest aupr 0 71 auroc 0 84 optimal hyperparameter values mtry 2 splitrule extratrees min node size 12 make predictions and examine predictive performance r predictions predict models outcome groups true plot predictions man figures readme plot predictions 1 png learn more for details on what s happening under the hood and for options to customize data preparation and model training see getting started with healthcareai https docs healthcare ai articles site only healthcareai html as well as the helpfiles for individual functions such as machine learn predict model list and explore documentation of all functions as well as vignettes on various uses of the package are available at the package website https docs healthcare ai also be sure to read our blog https healthcare ai blog and watch our broadcasts https www youtube com channel ucgzuobs x712kbcl6rszfnq to learn more about what s new in healthcare machine learning and how we are using this toolkit to put machine learning to work in real healthcare systems get involved we have a slack community https healthcare ai slack com that is a great place to introduce yourself share what you re doing with the package ask questions and troubleshoot your code contributing if you are interested in contributing the package great please read the contributing https github com healthcatalyst healthcareai r blob master contributing md guide and look for issues with the help wanted tag https github com healthcatalyst healthcareai r labels help 20wanted feel free to tackle any issue that interests you those are a few issues that we feel would make a good place to start feedback your feedback is hugely appreciated it is makes the package work well and helps us make it more useful to the community both feature requests and bug reports should be submitted as github issues https github com healthcatalyst healthcareai r issues new bug reports should be filed with a minimal reproducable example https gist github com hadley 270442 the reprex package https github com tidyverse reprex is extraordinarily helpful for this please also include the output of sessioninfo or better yet devtools session info legacy version 1 of healthcareai has been retired you can continue to use it but its compatibility with changes in the r ecosystem are not guaranteed you should always be able to install it from github with install packages remotes remotes install github healthcatalyst healthcareai r v1 2 4 for an example of how to adapt v1 models to the v2 api check out the transitioning vignettes https docs healthcare ai articles site only transitioning html | r machine-learning healthcare | ai |
yearn-protocol | yearn protocol github license https img shields io badge license agpl blue svg https github com iearn finance yearn protocol blob master license lint https github com iearn finance yearn protocol workflows lint badge svg test https github com iearn finance yearn protocol workflows test badge svg yearn protocol is a set of ethereum smart contracts focused on creating a simple way to generate high risk adjusted returns for depositors of various assets via best in class lending protocols liquidity pools and community made yield farming strategies on ethereum before getting started with this repo please read andre s overview blog post https medium com iearn yearn finance v2 af2c6a6a3613 describing how yearn finance works the delegated vaults blog post https medium com iearn delegated vaults explained fa81f1c3fce2 explaining how the delegated vaults work yeth vault explained https medium com iearn yeth vault explained c29d6b93a371 describing how the yeth vault works requirements to run the project you need python 3 8 local development environment and node js 10 x development environment for ganache brownie local environment setup see instructions eth brownie https github com eth brownie brownie local env variables for etherscan api https etherscan io apis and infura https infura io etherscan token web3 infura project id local ganache environment installed with npm install g ganache cli 6 11 installation to run the yearn protocol pull the repository from github and install its dependencies you will need yarn https yarnpkg com lang en docs install installed bash git clone https github com iearn finance yearn protocol cd yearn protocol yarn install lock file compile the smart contracts bash brownie compile tests run tests bash brownie test s run tests with coverage bash brownie test s coverage formatting check linter rules for json and sol files bash yarn lint check fix linter errors for json and sol files bash yarn lint fix check linter rules for py files bash black check config black config toml fix linter errors for py files bash black config black config toml security for security concerns please visit bug bounty https github com iearn finance yearn protocol blob develop security md documentation you can read more about yearn finance on our documentation webpage https docs yearn finance discussion for questions not covered in the docs please visit our discord server http discord yearn finance | ethereum ethereum-contract defi blockchain | blockchain |
projectWiki | projectwiki this is class assignment for database management system ece 542 in nnamdi azikiwe university electronics and computer engineering department | server |
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CVGesture | multi hand gesture recognization base on opencv github license http dmlc github io img apache2 svg license cvgesture developed by open ai lab is an open source application that uses opencv open source computer vision library to detect and recognize different hand gestures as it does not use deep learning algorithms it can achieve real time detection 15 fps with a72x2 cores and recognition while preserving high accuracy cascade classifiers based on haar feature are trained to recognize different gestures the object detector has been initially proposed by paul viola https docs opencv org 2 4 modules objdetect doc cascade classification html viola01 and improved by rainer lienhart https docs opencv org 2 4 modules objdetect doc cascade classification html lienhart02 using haar feature achieves higher recognition accuracy than using lbp feature current version of cvgesture recognizes two different gestures palm and fist palm means hand facing camera with five fingers open in some countries this gesture stands for five fist means hand facing camera with five fingers clenched the best distance between hand and camera is within 1m multiple gestures can be recognized at the same time if there are several hands appearing in the camera theoretically there is no upper limit for detection and recognition of the amount of hands at the same time and more than one hand appearing in the camera will not slow down the recognition speed significantly the release version is 0 3 1 is based on rockchip rk3399 http www rock chips com plus 3399 html platform target os is ubuntu 16 04 you can download the source code from oaid cvgesture https github com oaid cvgesture documents installation instructions installation md accuracy test instructions accuracy test instruction md performance report pdf performance report pdf haar feature training https github com sandeep 777 handgesturedetection release history version 0 3 1 2018 02 01 fix some bugs in version 0 3 0 version 0 3 0 2018 01 25 add script to test accuracy of classifier version 0 2 0 2017 12 25 retrained haar cascade classifier to increase the performance version 0 1 0 2017 10 13 initial version supports palm and fist recognization issue report encounter any issue please report on issue report https github com oaid cvgesture issues issue report should contain the following information the exact description of the steps that are needed to reproduce the issue the exact description of what happens and what you think is wrong | ai |
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alaran_portfolio | alaran portfolio my portfolio on data engineering machine learning python backend amp cloud service | cloud |
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Leo | leo overview leo provides a framework and set of classes for more easily using uima as these include classes for programatically creating analysis aggregate and type descriptors and well as base classes for different readers and listeners if you use leo please cite cornia r patterson ov ginter t duvall sl rapid nlp development with leo in amia annu symp proc washington dc usa 2014 1356 can be retrieved from https knowledge amia org 56638 amia 1 1540970 t 005 1 1543914 f 005 1 1543915 a 288 1 1544807 an 288 1 1544808 qr 1 packages classes gov va vinci leo the top level package that contains client and service for creating a uima as client and uima as service gov va vinci leo ae annotation engine classes including the leobaseannotator and classes used to import third party annotators and for including remote services as annotation engines in the pipeline gov va vinci leo cr collection readers these are base classes and implementations of collection readers clients use to read data to send to uima as gov va vinci leo descriptors descriptor related classes and factories for building aggregate analysis engine collection reader or type system descriptors gov va vinci leo gov va vinci leo listener base listeners and example listeners clients use to process cas objects returned from the uima as service gov va vinci leo model pojo model objects used within leo gov va vinci leo tools utility classes and tools used within leo gov va vinci leo types types used in leo usage scaling with leo and uima as leo and uima as can be scaled in several ways scaling by deploying copies of a pipeline an aggregate pipeline can be deployed on uima as to create multiple copies of the pipeline via service service new service service deploy mytypesystemdescription numinstances myannotator1 myannotator2 myannotator3 this deploys multiple copies of the complete pipeline in a single jvm scaling by deploying copies of individual annotators primitive annotators can also be scaled instead of full pipeline copies for instance if annotator a is twice as slow as annotator b you may wish to deploy twice as many annotator a s than b s service service new service list for holding our annotators arraylist leoaedescriptor annotators new arraylist leoaedescriptor add annotatora leoaedescriptor annotatora new leoaedescriptor my package annotatora true annotatora setnumberofinstances 2 annotators add annotatora add annotatorb leoaedescriptor annotatorb new leoaedescriptor my package annotatorb true annotatorb setnumberofinstances 1 annotators add annotatorb create an aggregate of the components leoaedescriptor aggregate new leoaedescriptor annotators specifies a queue in front of all of the delegates aggregate setisasync true initialize the service service deploy aggregate scaling by multiple deployments finally in addition combination with the above you can launch multiple jvms running the same leo server application if all server applications use the same broker url the service is distributed via the broker to the multiple jvms tuning an individual jvm for optimal performance then deploying multiple instanes of the jvm is an effective way to scale note the jvms can also be located on different machines of physical networks as long as they are all pointing to the same broker url more information for more information about deploying clients and services using leo see the user guide in the documentation | ai |
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carbon-components-vue | carbon vue 3 p a href https github com carbon design system carbon blob master license img src https img shields io badge license apache 2 0 blue svg alt carbon is released under the apache 2 0 license a a href https github com carbon design system carbon actions workflows ci yml img src https github com carbon design system carbon actions workflows ci yml badge svg alt ci workflow status a a href https lerna js org img src https img shields io badge maintained 20with lerna cc00ff svg alt maintained with lerna a a href https github com carbon design system carbon blob master github contributing md img src https img shields io badge prs welcome brightgreen svg alt prs welcome a a href https discord gg j7jeuektrx img src https img shields io discord 689212587170201628 color 5865f2 alt chat with us on discord a p vue implementation of the carbon design system a collection of carbon components https github com carbon design system carbon components implemented using img src https vuejs org images logo png width 20 alt vue logo vue js https vuejs org carbon vue library a carbon community project the library http vue carbondesignsystem com provides front end developers engineers a collection of reusable vue components to build websites and user interfaces adopting the library enables developers to use consistent markup styles and behavior in prototype and production work community contributions needed as a community project contributions are not only welcome but essential for the maintenance and growth of this project install shell npm add carbon vue or shell yarn add carbon vue add to vue project src main js js import carbonvue3 from carbon vue import app from app vue const app createapp app app use carbonvue3 app mount app see hello carbon vue https github com ibm hello carbon vue3 for an example vue project with carbon add to nuxt project plugins carbon vue js js import carbonvue from carbon vue export default definenuxtplugin nuxtapp nuxtapp vueapp use carbonvue see hello carbon nuxt add to nuxt project coming soon vue 3 vue 2 support will end on dec 31 2023 learn more https vuejs org guide introduction html upgrading from vue 2 check out the migration guide https v3 migration vuejs org vue 2 components can be found on the vue2 branch https github com carbon design system carbon components vue tree vue2 vue 3 components for carbon 10 have reached parity with the vue 2 components more work is needed especially around accessibility if you want to improve vue 3 components follow these guidelines 1 fork this repo and checkout the main branch 2 look to see which components are currently being improved you can do this by looking in the issues list https github com carbon design system carbon components vue issues 3 if you want to improve a component look in the open issue list to see if someone else might already be working on it look for issues with a v3 vue3 label and the name of the component for example cvdatepicker improving accessibility 4 if no one else is already working on it create an issue using the vue 3 improve component and label it as above work on the component and create a pr when you are ready components are expected to be implemented as single file components https vuejs org guide scaling up sfc html using the vue composition api https vuejs org guide extras composition api faq html see cvcheckbox as an example the vue 2 components use the options api you should reference the dom in the react components storybook and be sure to include any accessibility improvements that might be there you should update the story and test cases for the component if applicable sometimes the story might need updating almost always the test cases for the component will need updates if you have question tag davidnixon in your issue and let me know how i can help changelog changelog md changelog md list of available components view available vue components here http vue carbondesignsystem com usage information is available in the notes provided with each story building and publishing the following steps will build and publish the packages clone or download the repo run yarn to install dependencies and bootstrap the packages run yarn build to build all the packages including the storybook if you just want to build an individual package you can limit the scope yarn build scope carbon vue yarn build scope storybook to start the storybook in a local server use yarn start how to run the storybook just follow the steps listed below and you will be able to run the storybook 1 after the checkout to the vnext branch in order to install the dependencies run the command yarn install on the root 2 now run the command cd storybook to enter the storybook folder then again run the command yarn install to install the dependencies inside the storybook folder 3 finally run the command yarn serve inside the storybook folder in other words these are the commands you re going to use in order of execution yarn install cd storybook yarn serve or yarn install yarn serve storybook | hacktoberfest carbon-components-vue carbon vue | os |
NLP-progress | tracking progress in natural language processing table of contents english automatic speech recognition english automatic speech recognition md ccg english ccg md common sense english common sense md constituency parsing english constituency parsing md coreference resolution english coreference resolution md data to text generation english data to text generation md dependency parsing english dependency parsing md dialogue english dialogue md domain adaptation english domain adaptation md entity linking english entity linking md grammatical error correction english grammatical error correction md information extraction english information extraction md intent detection and slot filling english intent detection slot filling md language modeling english language modeling md lexical normalization english lexical normalization md machine translation english machine translation md missing elements english missing elements md multi task learning english multi task learning md multi modal english multimodal md named entity recognition english named entity recognition md natural language inference english natural language inference md part of speech tagging english part of speech tagging md paraphrase generation english paraphrase generation md question answering english question answering md relation prediction english relation prediction md relationship extraction english relationship extraction md semantic textual similarity english semantic textual similarity md semantic parsing english semantic parsing md semantic role labeling english semantic role labeling md sentiment analysis english sentiment analysis md shallow syntax english shallow syntax md simplification english simplification md stance detection english stance detection md summarization english summarization md taxonomy learning english taxonomy learning md temporal processing english temporal processing md text classification english text classification md word sense disambiguation english word sense disambiguation md vietnamese dependency parsing vietnamese vietnamese md dependency parsing intent detection and slot filling vietnamese vietnamese md intent detection and slot filling machine translation vietnamese vietnamese md machine translation named entity recognition vietnamese vietnamese md named entity recognition part of speech tagging vietnamese vietnamese md part of speech tagging semantic parsing vietnamese vietnamese md semantic parsing word segmentation vietnamese vietnamese md word segmentation hindi chunking hindi hindi md chunking part of speech tagging hindi hindi md part of speech tagging machine translation hindi hindi md machine translation chinese entity linking chinese chinese md entity linking chinese word segmentation chinese chinese word segmentation md question answering chinese question answering md for more tasks datasets and results in chinese check out the chinese nlp https chinesenlp xyz website french question answering french question answering md summarization french summarization md russian question answering russian question answering md sentiment analysis russian sentiment analysis md summarization russian summarization md spanish named entity recognition spanish named entity recognition md entity linking spanish entity linking md entity linking summarization spanish summarization md portuguese question answering portuguese question answering md korean question answering korean question answering md nepali machine translation nepali nepali md machine translation bengali part of speech tagging bengali part of speech tagging md sentiment analysis bengali sentiment analysis md persian named entity recognition persian named entity recognition md natural language inference persian natural language inference md summarization persian summarization md turkish summarization turkish summarization md german question answering german question answering md summarization german summarization md arabic language modeling arabic language modeling md this document aims to track the progress in natural language processing nlp and give an overview of the state of the art sota across the most common nlp tasks and their corresponding datasets it aims to cover both traditional and core nlp tasks such as dependency parsing and part of speech tagging as well as more recent ones such as reading comprehension and natural language inference the main objective is to provide the reader with a quick overview of benchmark datasets and the state of the art for their task of interest which serves as a stepping stone for further research to this end if there is a place where results for a task are already published and regularly maintained such as a public leaderboard the reader will be pointed there if you want to find this document again in the future just go to nlpprogress com https nlpprogress com or nlpsota com http nlpsota com in your browser contributing guidelines results nbsp results reported in published papers are preferred an exception may be made for influential preprints datasets nbsp datasets should have been used for evaluation in at least one published paper besides the one that introduced the dataset code nbsp we recommend to add a link to an implementation if available you can add a code column see below to the table if it does not exist in the code column indicate an official implementation with official http link to implementation if an unofficial implementation is available use link http link to implementation see below if no implementation is available you can leave the cell empty adding a new result if you would like to add a new result you can just click on the small edit button in the top right corner of the file for the respective task see below click on the edit button to add a file img edit file png this allows you to edit the file in markdown simply add a row to the corresponding table in the same format make sure that the table stays sorted with the best result on top after you ve made your change make sure that the table still looks ok by clicking on the preview changes tab at the top of the page if everything looks good go to the bottom of the page where you see the below form fill out the file change information img propose file change png add a name for your proposed change an optional description indicate that you would like to create a new branch for this commit and start a pull request and click on propose file change adding a new dataset or task for adding a new dataset or task you can also follow the steps above alternatively you can fork the repository in both cases follow the steps below 1 if your task is completely new create a new file and link to it in the table of contents above 2 if not add your task or dataset to the respective section of the corresponding file in alphabetical order 3 briefly describe the dataset task and include relevant references 4 describe the evaluation setting and evaluation metric 5 show how an annotated example of the dataset task looks like 6 add a download link if available 7 copy the below table and fill in at least two results including the state of the art for your dataset task change score to the metric of your dataset if your dataset task has multiple metrics add them to the right of score 1 submit your change as a pull request model score paper source code wish list these are tasks and datasets that are still missing bilingual dictionary induction discourse parsing keyphrase extraction knowledge base population kbp more dialogue tasks semi supervised learning frame semantic parsing framenet full sentence analysis exporting into a structured format you can extract all the data into a structured machine readable json format with parsed tasks descriptions and sota tables the instructions are in structured readme md structured readme md instructions for building the site locally instructions for building the website locally using jekyll can be found here jekyll instructions md | natural-language-processing machine-learning named-entity-recognition machine-translation nlp-tasks dialogue | ai |
barista | barista components and design system circleci https circleci com gh dynatrace oss barista tree master svg style svg https circleci com gh dynatrace oss barista tree master build status https badge buildkite com 804634c49653559b831d816cc5f6f51f96050aa360f4dd0460 svg branch master https buildkite com dynatrace barista npm version https img shields io npm v dynatrace barista components contributors https img shields io github contributors dynatrace oss barista commit activity https img shields io github commit activity w dynatrace oss barista barista https barista dynatrace com is the name of dynatrace https www dynatrace com s design system that contains all ingredients to create extraordinary user experiences like a barista picks the best ingredients for their coffee and we love coffee one major part of the barista design system is our components library https github com dynatrace oss barista tree master libs barista components written in angular getting started new to barista see our getting started guide https github com dynatrace oss barista blob master documentation getting started md for detailed information about how to set up and use barista components within your project development contribution you would like to help us brew that s awesome if you are interested in contributing to our library see our guide for developers https github com dynatrace oss barista blob master development md and contribution guidelines https github com dynatrace oss barista blob master contributing md and always keep our coding standards https github com dynatrace oss barista blob master coding standards md in mind contributors these wonderful people make this happen contributors https contributors img firebaseapp com image repo dynatrace oss barista https github com dynatrace oss barista graphs contributors changelog to stay up to date with the newest fixes and features take a look at our changelog https github com dynatrace oss barista blob master changelog md browser support the barista components support the most recent two versions of all major browsers chrome firefox safari and edge we do not support any version of internet explorer thanks to browserstack for the awesome support of open source projects a href https www browserstack com rel nofollow img src https user images githubusercontent com 2905693 69604517 fe9cb400 101d 11ea 867c 95b16cf60368 png alt browserstack logo width 250px a licence apache version 2 0 https github com dynatrace oss barista blob master license | angular angular-components design-system component-library web typescript | os |
eos-sharp | this library is not actively maintained eos sharp c client library for eosio blockchains the library is based on https github com eosio eosjs and mit licensed install package eos sharp prerequisite to build visual studio 2017 instalation eos sharp is now available through nuget https www nuget org packages eos sharp install package eos sharp usage configuration in order to interact with eos blockchain you need to create a new instance of the eos class with a eosconfigurator example csharp eos eos new eos new eosconfigurator httpendpoint https nodes eos42 io mainnet chainid aca376f206b8fc25a6ed44dbdc66547c36c6c33e3a119ffbeaef943642f0e906 expireseconds 60 signprovider new defaultsignprovider myprivatekey httpendpoint http or https location of a nodeosd server providing a chain api chainid unique id for the blockchain you re connecting to if no chainid is provided it will get from the get info api call expireinseconds number of seconds before the transaction will expire the time is based on the nodeosd s clock an unexpired transaction that may have had an error is a liability until the expiration is reached this time should be brief signprovider signature implementation to handle available keys and signing transactions use the defaultsignprovider with a privatekey to sign transactions inside the lib api read methods getinfo call csharp var result await eos getinfo returns csharp class getinforesponse string server version string chain id uint32 head block num uint32 last irreversible block num string last irreversible block id string head block id datetime head block time string head block producer string virtual block cpu limit string virtual block net limit string block cpu limit string block net limit getaccount call csharp var result await eos getaccount myaccountname returns csharp class getaccountresponse string account name uint32 head block num datetime head block time bool privileged datetime last code update datetime created int64 ram quota int64 net weight int64 cpu weight resource net limit resource cpu limit uint64 ram usage list permission permissions refundrequest refund request selfdelegatedbandwidth self delegated bandwidth totalresources total resources voterinfo voter info getblock call csharp var result await eos getblock blockidornumber returns csharp class getblockresponse datetime timestamp string producer uint32 confirmed string previous string transaction mroot string action mroot uint32 schedule version schedule new producers list extension block extensions list extension header extensions string producer signature list transactionreceipt transactions string id uint32 block num uint32 ref block prefix gettablerows call json code accountname of the contract to search for table rows scope scope text segmenting the table set table table name tablekey unused so far lowerbound lower bound for the selected index value upperbound upper bound for the selected index value keytype type of the index choosen ex i64 limit indexposition 1 primary first 2 secondary index in order defined by multi index 3 third index etc encodetype dec hex reverse reverse result order showpayer show ram payer csharp var result await eos gettablerows new gettablerowsrequest json true code eosio token scope eos table stat returns csharp class gettablerowsresponse list object rows bool more using generic type csharp jsonproperty helps map the fields from the api public class stat public string issuer get set public string max supply get set public string supply get set var result await eos gettablerows stat new gettablerowsrequest json true code eosio token scope eos table stat returns csharp class gettablerowsresponse stat list stat rows bool more gettablebyscope call code accountname of the contract to search for tables table table name to filter lowerbound lower bound of scope optional upperbound upper bound of scope optional limit reverse reverse result order csharp var result await eos gettablebyscope new gettablebyscoperequest code eosio token table accounts returns csharp class gettablebyscoperesponse list tablebyscoperesultrow rows string more class tablebyscoperesultrow string code string scope string table string payer uint32 count getactions call accountname accountname to get actions history pos a absolute sequence positon 1 is the end last action offset the number of actions relative to pos negative numbers return pos offset pos positive numbers return pos pos offset csharp var result await eos getactions myaccountname 0 10 returns csharp class getactionsresponse list globalaction actions uint32 last irreversible block bool time limit exceeded error create transaction note using anonymous objects and or properties as action data is not supported on webgl unity exports use data as dictionary or strongly typed objects with fields csharp var result await eos createtransaction new transaction actions new list api v1 action new api v1 action account eosio token authorization new list permissionlevel new permissionlevel actor tester112345 permission active name transfer data new from tester112345 to tester212345 quantity 0 0001 eos memo hello crypto world data can also be a dictionary with key as string the dictionary value can be any object with nested dictionaries csharp var result await eos createtransaction new transaction actions new list api v1 action new api v1 action account eosio token authorization new list permissionlevel new permissionlevel actor tester112345 permission active name transfer data new dictionary string string from tester112345 to tester212345 quantity 0 0001 eos memo hello crypto world returns the transactionid custom signprovider is also possible to implement your own isignprovider to customize how the signatures and key handling is done example csharp summary signature provider implementation that uses a private server to hold keys summary class supersecretsignprovider isignprovider summary get available public keys from signature provider server summary returns list of public keys returns public async task ienumerable string getavailablekeys var result await httphelper getjsonasync secretresponse https supersecretserver com get available keys return result keys summary sign bytes using the signature provider server summary param name chainid eosio chain id param param name requiredkeys required public keys for signing this bytes param param name signbytes signature bytes param param name abinames abi contract names to get abi information from param returns list of signatures per required keys returns public async task ienumerable string sign string chainid list string requiredkeys byte signbytes var result await httphelper postjsonasync secretsignresponse https supersecretserver com sign new secretrequest chainid chainid requiredkeys requiredkeys data signbytes return result signatures create new eos client instance using your custom signature provider eos eos new eos new eosconfigurator signprovider new supersecretsignprovider httpendpoint https nodes eos42 io mainnet chainid aca376f206b8fc25a6ed44dbdc66547c36c6c33e3a119ffbeaef943642f0e906 combinedsignersprovider is also possible to combine multiple signature providers to complete all the signatures for a transaction example csharp eos eos new eos new eosconfigurator httpendpoint https nodes eos42 io mainnet chainid aca376f206b8fc25a6ed44dbdc66547c36c6c33e3a119ffbeaef943642f0e906 expireseconds 60 signprovider new combinedsignersprovider new list isignprovider new supersecretsignprovider new defaultsignprovider myprivatekey | blockchain |
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react | fabric react reacts components exported under fabric ds react development the project uses storybook https storybook js org for component development start the storybook instance by running the following command sh yarn dev documentation to start a local dev server for the documentation site run the following command sh yarn docs dev releases this project uses semantic release https github com semantic release semantic release to automate package publishing when making changes to the main or next branch it is recommended to branch off the next branch and follow conventional commits https www conventionalcommits org en v1 0 0 summary when making changes when your changes are ready for pull request this should be opened against the next branch read more in depth about fabric releases here https github com fabric ds issues blob 779d59723993c13d62374516259602d967da56ca rfcs 0004 releases md please note that the version published will depend on your commit message structure we use commitizen https github com commitizen cz cli to help follow this structure npm install g commitizen when installed you should be able to type cz or git cz in your terminal to commit your changes replacing git commit add and commit with commitizen https github com commitizen cz cli raw master meta screenshots add commit png https github com commitizen cz cli raw master meta screenshots add commit png | os |
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iot-plugandplay-models | azure iot plugandplay models repository this repository includes dtdl https aka ms dtdl models that are made publicly available on https devicemodels azure com https devicemodels azure com these models can be used to create azure iot plug and play https aka ms iotpnp solutions related tools samples and specs can be found in the azure iot plugandplay models tools https github com azure iot plugandplay models tools repo the current repo only stores dtdl models submit a model 1 create a github account if you do not have one yet join github https github com join learn more about github https docs microsoft com learn modules intro to git 1 sign contributor license agreement https cla opensource microsoft com azure iot plugandplay models pullrequest 93 1 fork the public github repo https github com azure iot plugandplay models https github com azure iot plugandplay models learn more about forking a repo https docs github com en github getting started with github fork a repo 1 clone the forked repo optionally create a new branch to keep your changes isolated from the main branch by forking and cloning the public github repo a copy of repo will be created in your github account and a local copy is created in your dev machine please use this local copy to make modifications 1 author a new device model with an unique id using digital twin model identifier https github com azure opendigitaltwins dtdl blob master dtdl v2 dtdlv2 md digital twin model identifier review the pr requirements pr reqs md for naming conventions tip dtdl editor for visual studio code https marketplace visualstudio com items itemname vsciot vscode vscode dtdl could help you with the language syntax including auto completion and also validate the syntax with dtdl v2 1 save the device model json file to a local folder e g c iot plugandplay models mythermostat json 1 validate the models locally using the dmr client tool to validate validate models 1 add the new interfaces to the dtmi folder using the folder filename convention see the import import a model to the dtmi folder command below 1 review and cross check with the pr requirements pr reqs md and ensure all elements are conform to dtdl v2 https aka ms dtdl specification 1 commit the changes locally and push to your fork 1 from your fork create a pr that targets the main branch learn more about pull request https docs github com en desktop contributing and collaborating using github desktop creating an issue or pull request creating a pull request the pr triggers a series of github actions that will validate the new submitted interfaces and make sure your pr satisfies all the checks microsoft will respond to a pr with all checks in 3 business days github operation workflow text iot plugandplay models repo microsoft fork pull request pr iot plugandplay models repo your github account clone commit push your development pc author device model import your model to the dtmi folder install the dmr client command line tool the tool used to validate the models during the pr checks can also be used to add and validate the dtdl interfaces locally the device models repository command line tool aka dmr client is published on nuget https www nuget org packages microsoft iot modelsrepository commandline and requires dotnet sdk 3 1 x 5 0 x or 6 0 x you can use the dotnet command line via the dotnet tool install command to install dmr client the following is an example to install dmr client as a global tool bash dotnet tool install g microsoft iot modelsrepository commandline version 1 0 0 beta 6 to learn how to install dmr client in a local context please see this guide https docs microsoft com en us dotnet core tools local tools how to use note previous versions of the tool prior to 1 0 0 beta 3 must first be uninstalled with dotnet tool uninstall g dmr client use the dotnet tool list g command to check the id of the tool you want to uninstall update to update the dmr client tool assuming a global install you can run the following command bash dotnet tool update g microsoft iot modelsrepository commandline version target version uninstall to uninstall the dmr client tool assuming a global install you can run the following command bash dotnet tool uninstall g microsoft iot modelsrepository commandline usage of dmr client after installing microsoft iot modelsrepository commandline the following models repository management commands should be available for usage via the dmr client alias text dmr client microsoft iot models repository commandline v1 0 0 beta 6 usage dmr client options command options debug shows additional logs for debugging default false silent silences command output on standard out default false version show version information h help show help and usage information commands export exports a model producing the model and its dependency chain in an expanded format the target repository is used for model resolution validate validates the dtdl model contained in a file when validating a single model object the target repository is used for model resolution when validating an array of models only the array contents is used for resolution import imports models from a model file into the local repository the local repository is used for model resolution target model files for import will first be validated to ensure adherence to iot models repository conventions index builds a model index file from the state of a target local models repository expand for each model in a local repository generate expanded model files and insert them in place the expanded version of a model includes the model with its full model dependency chain command guide import a model to the dtmi folder if you have your model already stored in json files you can use the dmr client import command to add those to the dtmi folder with the right file name run this from the local repo root folder bash dmr client import model file mythermostat json this command will rename and locate the file in the appropriate folder validate models you can validate your models with the dmr client validate command to validate a model file using the dtdl parser bash dmr client validate model file my model file json note the validation uses the latest dtdl parser version to ensure all the interfaces are compatible with the dtdl language spec to validate external dependencies those must exist in the local repo to validate those you can specify a local or remote folder to validate against validating a model file using the dtdl parser checking dependencies with the current folder as a local repo bash dmr client validate model file my model file json repo strict validation the device model repo includes additional requirements pr reqs md these can be validated with the strict flag validating a model file using the dtdl parser checking dependencies with the current folder as a local repo in strict mode bash dmr client validate model file my model file json repo strict export models models can be exported from a given repo local or remote to a single file using a json array retrieving an interface from a custom repo by dtmi bash dmr client export dtmi dtmi com example thermostat 1 repo https raw githubusercontent com azure iot plugandplay models main create index the model repo can host a index json file with all the ids avaialble in the repository read the index spec https github com azure iot plugandplay models tools wiki model index building a model index for the repository if models exceed the page limit new page files will be created relative to the root index bash dmr client index local repo building a model index with a custom page limit indicating max models per page bash dmr client index local repo page limit 100 create expanded files to expand all models from the root directory of a local models repository following azure iot conventions expanded models are inserted in place bash dmr client expand local repo the default local repo value is the current directory be sure to specify the root for local repo bash dmr client expand iot models repository sdks azure sdks focused on models repository consumption are available in the following languages platform package source samples net azure iot modelsrepository https www nuget org packages azure iot modelsrepository source https github com azure azure sdk for net tree master sdk modelsrepository azure iot modelsrepository samples https github com azure azure sdk for net tree master sdk modelsrepository azure iot modelsrepository samples java com azure azure iot modelsrepository https search maven org artifact com azure azure iot modelsrepository source https github com azure azure sdk for java tree master sdk modelsrepository azure iot modelsrepository samples https github com azure azure sdk for java tree master sdk modelsrepository azure iot modelsrepository src samples node azure iot modelsrepository https www npmjs com package azure iot modelsrepository source https github com azure azure sdk for js tree master sdk iot iot modelsrepository samples https github com azure azure sdk for js tree master sdk iot iot modelsrepository samples v1 python azure iot modelsrepository https pypi org project azure iot modelsrepository source https github com azure azure sdk for python tree master sdk modelsrepository azure iot modelsrepository samples https github com azure azure sdk for python tree master sdk modelsrepository azure iot modelsrepository samples fetch models without any sdk any http client can consume the models by applying the repository convention https github com azure iot plugandplay models tools wiki resolution convention to convert a dtmi to a relative path eg the interface bash dtmi azure devicemanagement deviceinformation 1 can be retrieved from here https devicemodels azure com dtmi azure devicemanagement deviceinformation 1 json bash https devicemodels azure com dtmi azure devicemanagement deviceinformation 1 json there are samples for net and node in the azure iot plugandplay models tools https github com azure iot plugandplay models tools github repository with code you can use to acquire models for your custom iot solution 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 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 | server |
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design | p align center img src design new svg p div align center h3 a href brand guide branding pdf brand guide a span span a href logo logo a span span a href illustrations illustrations a span span a href social media social media a span span a href source source files a h3 div table of contents toc start min 1 max 3 link true asterisk false update true overview overview structure and links structure and links brand guide brand guide logo logo illustrations illustrations social media social media source files source files other assets other assets brand information brand information brand name brand name brand tagline brand tagline brand mission brand mission brand personality brand personality brand inspiration brand inspiration target audience target audience competition competition toc end overview the purpose of this repo is to gather all the general assets rules and resources for the design and branding process in one place within this readme the structure section below aims to briefly lay out what the different folders are for and there are also some short sections below discussing some of the main asset types there is also a further section below outlining some important brand information structure and links folder name purpose blog blog assets for the joystream blog https blog joystream org brand guide brand guide the official joystream brand guide illustrations illustrations illustrations for use on our website blog posts and more logo logo the joystream logo in different formats icons new icons icons new icons a variety of vector icons in our new style other presentation other presentation templates for presentations other promotional other promotional exemplar joystream promotional materials social media social media assets for our social media accounts and github repos source source source files for all the assets found in this repo icons system icons icons system icons icons to be used in our applications e g pioneer brand guide our brand guide is the definitive source of information and guidance on how to use the assets you find in this repository before you begin using the assets here it is highly recommended to have a quick read of this document to get a sense of the rules and best practices for each asset type our brand guide can be found here brand guide branding pdf logo our logo is perhaps the most important asset from a branding perspective we have a number of different types of logo available for use emblem wordmark icon etc in a number of different color schemes so it is important to follow the rules for the particular variant of the logo you are using all of these rules can be found in the brand guide mentioned earlier p align center img src logo use jpeg p illustrations we have designed a number of illustrations which can be used in various situations they are probably most appropriate for communications with our stakeholders these communications can take the form of blog posts press releases social media posts newsletters and more br br p align center img src illustrations png 2x future testnets png width 200 p br social media the social media folder in this repo contains all of the assets necessary for our social media accounts these include cover images for profile pages as well as cover images for blog posts etc source files source material in the form of adobe illustrator and sketch files can be found in the source folder this is unlikely to be useful for the typical visitor to this repository but will probably be very helpful for designers other assets there are a selection of other assets available in this repository which do not warrant their own description yet as they are not needed yet more information will be added on these assets and their usage rules in the future p align center img src other promotional shirts png p brand information brand name joystream brand tagline the video platform dao brand mission for a background on the mission and high level description of what we are trying to achieve please consult our manifesto https github com joystream manifesto blob master paper pdf this document describes the problems we are trying to solve by developing the joystream protocol i we believe that art be it as literature music film games or performance is a critical tool through which societies establish a shared understanding of what is common knowledge among all members these expressions define values and narratives that end up shaping culture faith and even public policy it therefore matters deeply how it is financed distributed and paid for i brand personality dynamic ethical experimental honest open brand inspiration we are very inspired by the branding that you find in projects like aragon which is also a governance project they are very effective at putting the ethics and mission of their projects at the center of their communication and all of their public messages are coordinated around this they also have a very clear separation between the project and the different entities involved in organising the project target audience our primary audience for the next two years will be three categories of users we need to reach community builders users who also want to engage in actually running and guiding how the platform will work in other words people who want to engage in platform governance and work this refers to people who enjoy building fostering and participating in nascent communities they are driven by a sense of purpose and also the motivation to get in early on something that can become big and important these people are often highly social enjoy working in groups with common purpose self directed digital native and often outsiders in the offline world crypto workers these are people out to make a small buck they are everywhere in the crypto space and they are looking for opportunities to do work and make some income these are often in developing countries or they may be a bit younger or in some other way locked out of easy access to making a good income often they have very little physical or social capital just some simple tools and they often do manual tasks of various kinds the importance of this contingent is that they are eager and they are willing and able to do work for crypto something which is currently rare long tail content creators these are people with small and passionate audiences willing to put up with friction typically covering idiosyncratic areas of focus perhaps associated with the taboo non mainstream controversial inappropriate ideological or marginalised they will be suffering from monetisation censorship or distribution problems on other platforms competition the most natural competition to consider would be other projects trying to build more user controlled media platforms often using blockchain or other peer to peer technology as a result there are quite a few such projects emphasising different aspects of how they will be better than existing centralised platforms like youtube or soundcloud what sets us apart is that we focus very deeply on governance and how users will be in direct control of everything others focus on more detailed particular features like how monetisation or advertising will work or that users can get paid for providing bandwidth in the system | os |
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IntroToEmbeddedSystemsFinalProject_GameDesign | introtoembeddedsystemsfinalproject gamedesign a space invaders game designed as a final project for the ee319k intro to embedded systems at the university of texas austin https www youtube com watch v fq1ugt38cdk t 8s a video showing how the game is played | os |
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kura | eclipse kura p align center img src https www eclipse org kura content images kura logo 400 png alt kura logo width 500 p div align center github tag https img shields io github v tag eclipse kura label latest 20tag github https img shields io github license eclipse kura label license github issues https img shields io github issues raw eclipse kura label open 20issues github pull requests https img shields io github issues pr eclipse kura label pull 20requests color blue github contributors https img shields io github contributors eclipse kura label contributors github forks https img shields io github forks eclipse kura label forks jenkins https img shields io jenkins build joburl https 2f 2fci eclipse org 2fkura 2fjob 2fmultibranch 2fjob 2fdevelop label jenkins 20build logo jenkins jenkins https img shields io jenkins tests compact message failed label e2 9d 8c joburl https 2f 2fci eclipse org 2fkura 2fjob 2fmultibranch 2fjob 2fdevelop 2f label jenkins 20ci passed label e2 9c 85 skipped label e2 9d 95 logo jenkins br div eclipse kura from the maori word for tank container https maoridictionary co nz search keywords kura is an osgi based application framework for m2m service gateways kura aims at offering a java osgi based container for m2m applications running in service gateways kura provides or when available aggregates open source implementations for the most common services needed by m2m applications kura components are designed as configurable osgi declarative service exposing service api and raising events while several kura components are in pure java others are invoked through jni and have a dependency on the linux operating system for more information see the eclipse project proposal http www eclipse org proposals technology kura documentation user documentation https eclipse github io kura latest here you ll find information on how to use eclipse kura i e installation instructions informations on how to use the web ui and tutorials developer documentation https github com eclipse kura wiki the eclipse kura github wiki serves as a reference for developers who want to contribute to the eclipse kura project and or develop new add ons here you ll find eclipse kura development release model guidelines on how to import internal packages creating new bundles and development environment tips tricks docker containers documentation https hub docker com r eclipse kura the eclipse kura team also provides docker containers for the project informations on how to build and run them are available at the project s docker hub page developer quickstart guide https github com eclipse kura getting started a quick guide on how to setup the development environment and build the project is also provided in this readme additionally we provide two channels for reporting any issue you find with the project github issues https github com eclipse kura issues for bug reporting github discussions https github com eclipse kura discussions for receiving feedback making new proposals and generally talking about the project system requirements eclipse kura is compatible with java 8 bundle requiredexecutionenvironment javase 1 8 and osgi r6 development model development on kura follows a variant of the gitflow model https github com eclipse kura wiki new kura git workflow development is made on the develop branch eclipse kura tree develop the master branch is not used anymore getting started development for kura can be done in eclipse ide using the kura development environment in a gateway or in a docker container development environment supported development platforms the eclipse installer based setup works for the main used platforms like linux mac os and windows prerequisites before installing kura you need to have the following programs installed in your system jdk 1 8 maven 3 5 x installing prerequisites in mac os to install java 8 download the jdk tar archive from the adoptium project repository https adoptium net releases html variant openjdk8 jvmvariant hotspot once downloaded copy the tar archive in library java javavirtualmachines and cd into it unpack the archive with the following command bash sudo tar xzf archive name tar gz the tar archive can be deleted afterwards depending on which terminal you are using edit the profiles zshrc profile bash profile to contain bash adoptium jdk 8 export java 8 home library java javavirtualmachines archive name contents home alias java8 export java home java 8 home java8 reload the terminal and run java version to make sure it is installed correctly using brew https brew sh you can easily install maven from the command line bash brew install maven 3 5 run mvn version to ensure that maven has been added to the path if maven cannot be found try running brew link maven 3 5 force or manually add it to your path with bash export path usr local opt maven 3 5 bin path installing prerequisites in linux for java bash sudo apt install openjdk 8 jdk for maven you can follow the tutorial from the official maven http maven apache org install html site remember that you need to install the 3 5 x version build kura change to the new directory and clone the kura repo bash git clone b develop https github com eclipse kura git move inside the newly created directory and build the target platform bash mvn f target platform pom xml clean install build the core components bash mvn f kura pom xml clean install build the examples optional bash mvn f kura examples pom xml clean install build the target profiles bash mvn f kura distrib pom xml clean install dbuildall note you can skip tests by adding dmaven test skip true in the commands above and you can compile a specific target by specifying the profile e g praspberry pi armhf build scripts alternatively you can use the build scripts available in the root directory bash build all sh or bash build menu sh and select the profiles you want to build building eclipse kura containers the kura container build process currently only supports x86 containers following the instructions below will build two containers one based on alpine linux kura alpine x86 64 and another on ubi8 kura ubi8 x86 64 build kura as per our instructions https github com eclipse kura build kura to build the containers you ll need to change the target of the build the target profiles step like the following bash mvn f kura distrib pom xml clean install dbuildallcontainers note this build step requires docker to be a executable command on your system for instance if you are using podman please follow the emulating docker cli guide https podman desktop io docs migrating from docker emulating docker cli with podman before running the command above after this command runs images can be found in your preferred container engine image list eclipse ide the simplest way to start developing on eclipse kura is to use an eclipse installer https www eclipse org downloads based setup a detailed installation and setup guide is available on the official documentation https eclipse github io kura docs develop java application development development environment setup here you ll find a brief explaination of the required steps to correctly setup the environment proceed as follows install a jdk 8 distribution like eclipse temurin https adoptium net temurin releases version 8 for your specific cpu architecture and os start the eclipse installer switch to advanced mode top right hamburger menu advanced mode select eclipse ide for eclipse committers and configure the product version to be the version 2023 03 or newer select the eclipse kura installer from the list if this is not available add a new installer from https raw githubusercontent com eclipse kura develop kura setups kura setup then check and press the next button select the developer type user if you want to develop applications or bundles running on kura select this option it will install only the apis and the examples developer if you are a framework developer select this option it will download and configure the eclipse kura framework update eclipse kura git repository username and customize further settings if you like e g root install folder installation folder name to show these options make sure that the show all variables checkbox is enabled set the jre 1 8 location value to the installed local jdk 8 vm leave all bootstrap tasks selected and press the finish button accept all the licenses and wait for the installation to finish at first startup eclipse ide will checkout the code perform a full build and configure a few working sets when the tasks are completed in the ide open double click target platform target definition kura target platform equinox 3 16 0 and press set as target platform located at the top right of the window now you are ready to develop on eclipse kura to raise an issue please report a bug on github issues https github com eclipse kura issues new known issues currently the emulator web ui is not properly working on windows so with your setup you will be able to build and deploy you applications but not be able to use the eclipse ide based kura emulator the full build of kura is only supported for linux and mac os based systems currently the maven build on windows requires to disable the tests and will fail when it tries to create the installers for the target platforms contributing contributing to eclipse kura is fun and easy to start contributing you can follow our guide here contributing md target gateways installers eclipse kura provides pre built installers for common development boards check the following link https www eclipse org kura downloads php to download the desired installers docker image eclipse kura is available also as a docker container https hub docker com r eclipse kura to easily run use docker run d p 443 443 t eclipse kura | eclipseiot gateway iot internet-of-things java | server |
youtube-projects | projects these are project resources for the live youtube lectures | dependency-injection git system-design | os |
DeepNaturalLanguageProcessing | deepnaturallanguageprocessing python languagemodel https github com sthsf deepnaturallanguageprocessing tree master language model anna https github com sthsf deepnaturallanguageprocessing tree master language model anna textclassification text classification textrnn lstm bilstm https github com sthsf deepnaturallanguageprocessing tree master textclassification text classification textrnn 1 embedding lstm gru dropout lstm gru dropout 2 embedding bi directional lstm gru concat output average last output softmax layer textcnn https github com sthsf deepnaturallanguageprocessing tree master textclassification text classification textcnn sentiment analysis sentimentanalysis zh https github com sthsf deepnaturallanguageprocessing tree master sentiment analysis sentiment analysis zh embedding word segmentation chinesesegmentation bidirectional rnn ner | ai |
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NLP-Malware | network based malware detection using natural language processing this project illustrates a method that utilizes the ordering of network flows to classify malicious behavior the approach is lightweight and privacy preserving while also being resilient to encrypted packet payloads getting started prerequisites the project is written in python3 ensure you have the latest version of python3 and pip3 installed https www python org downloads the project relies on tshark for pre processing pcap files and p7zip to extract zip files on ubuntu these can be installed using sudo apt get install tshark p7zip besides these other required packages can be installed using pip3 pip3 install r requirements txt user directory structure ml model py file with ml functions preprocess process py process sh pcap pre processing pcap to ngrams py pcap conversion to ngrams f2nlib py p2flib py scripts run sh script to run tests on computecanada servers run all sh automate test running requirements txt readme md license md running tests 1 grab the ustc tfc2016 deeptraffic https github com echowei deeptraffic dataset 2 generate a ngram file using process sh 3 run model py with the ngram file 4 automate tests using custom bash scripts the ones included in the repository work on computecanada servers process sh path to dataset n this creates a file called n test csv in the dataset folder python3 model py path to test csv this should print the results on the screen license this project is licensed under the mit license see the license md license md file for details acknowledgments mitacs globalink https www mitacs ca en programs globalink uottawa https engineering uottawa ca school eecs and mhrd india https mhrd gov in for funding the project prof david knox https engineering uottawa ca people knox david at uottawa for project supervision computecanada https www computecanada ca home for access to their servers to run tests | ai |
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Arduino-Library | arduino library badge https www ardu badge com badge thinger io svg https docs thinger io sdk setup the arduino client library is an easy to use client library to connect your iot devices to the thinger io https thinger io thinger io iot cloud platform iot platform using iotmp protocol this is a library specifically designed for the arduino ide platformio so you can easily install it in your environment and start connecting your devices within minutes supported boards it supports multiple network interfaces like ethernet wifi and gsm so you can use it in several devices like the following espressif esp8266 ota support espressif esp32 ota support arduino nano rp2040 connect ota support arduino nano 33 iot ota support arduino portenta h7 ota support arduino mkr 1010 ota support arduino mkr nb 1500 ota support arduino mkr 1000 arduino gsm1400 mkrgsm arduino ethernet arduino wifi arduino adafruit cc3000 arduino enc28j60 arduino yun arduino gprs shield arduino tinygsm library for gsm modems using gprs sim800 sim900 ai thinker a6 a6c a7 neoway m590 arduino esp8266 as wifi modem via at commands using tinygsm library texas instruments cc3200 seeedstudio linkit one both gprs and wifi ota over the internet some devices can be directly updated remotely over the internet ota thinger io provides a visual studio code extension for the ota process from building the firmware flashing over the internet to remotelly rebooting the device more details here https marketplace visualstudio com items itemname thinger io thinger io https s3 eu west 1 amazonaws com thinger io files vscode iot ota gif documentation please refer to the following page for a full documentation of the arduino client library arduino client library documentation http docs thinger io arduino license img align right src https opensource org trademarks opensource osi approved license 100x137 png the class is licensed under the mit license http opensource org licenses mit copyright copy thinger io http thinger io 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 | arduino-library | server |
browser-diet | how to lose weight in the browser build status https secure travis ci org zenorocha browser diet svg branch master https travis ci org zenorocha browser diet the definitive front end performance guide image https cloud githubusercontent com assets 398893 3528424 3368fc18 078f 11e4 9226 cdda8e13e5c9 jpg contributing if you want to add new tips or edit the existing ones just go to the src documents https github com zenorocha browser diet blob master src documents folder there you can find all of them in markdown md https pt wikipedia org wiki markdown format remember to add references on each tip that you write at references https github com zenorocha browser diet wiki references wiki instructions how can i locally run the project 1 install git http git scm com downloads and nodejs https nodejs org download if you don t have them already 2 open your terminal and download docpad https github com docpad docpad through this command sh sudo npm install g docpad 6 78 4 3 now clone it sh git clone git github com your github username browser diet git 4 then go to the project s folder sh cd browser diet 5 install all dependencies sh npm install 6 and finally run sh docpad run now you can see the website running at localhost 9778 d how can i run another language version simply go to the docpad coffee https github com zenorocha browser diet blob master docpad coffee file and change the value of the currentlang variable then you just need to run docpad run again browser support we do care about it ie https cloud githubusercontent com assets 398893 3528325 20373e76 078e 11e4 8e3a 1cb86cf506f0 png chrome https cloud githubusercontent com assets 398893 3528328 23bc7bc4 078e 11e4 8752 ba2809bf5cce png firefox https cloud githubusercontent com assets 398893 3528329 26283ab0 078e 11e4 84d4 db2cf1009953 png opera https cloud githubusercontent com assets 398893 3528330 27ec9fa8 078e 11e4 95cb 709fd11dac16 png safari https cloud githubusercontent com assets 398893 3528331 29df8618 078e 11e4 8e3e ed8ac738693f png ie 9 latest latest latest latest structure this project uses docpad https github com bevry docpad a static generator in nodejs and here s the basic structure pre out src documents layouts partials package json pre out this is where the generated files are stored once docpad has been run however this directory is unnecessary for versioning so it is ignored gitignore https github com zenorocha browser diet blob master gitignore src documents https github com zenorocha browser diet blob master src documents contains all tips in markdown md http pt wikipedia org wiki markdown format in addition to images fonts css and js files src layouts https github com zenorocha browser diet tree master src layouts contains the default template src partials https github com zenorocha browser diet tree master src partials contains reusable blocks of code package json https github com zenorocha browser diet blob master package json lists all nodejs dependencies team browserdiet was made with love by these people and a bunch of awesome contributors https github com zenorocha browser diet graphs contributors creator zeno rocha http gravatar com avatar e190023b66e2b8aa73a842b106920c93 s 70 https github com zenorocha zeno rocha https github com zenorocha br liferay inc design briza bueno http gravatar com avatar c272a0a8972473fdf231f2b2d897c242 s 70 https github com brizabueno briza bueno https github com brizabueno br americanas authors davidson fellipe http gravatar com avatar 054c583ad5dc09a861874e14dcb43e4c s 70 https github com davidsonfellipe giovanni keppelen http gravatar com avatar 8f5c490b5b30ac6d655eced70cea4e5f s 70 https github com keppelen jaydson gomes http gravatar com avatar 572696200604e59baa59ee90d61f7d02 s 70 https github com jaydson davidson fellipe https github com davidsonfellipe br globo com giovanni keppelen https github com keppelen br planedia jaydson gomes https github com jaydson br terra reviewers marcel duran http gravatar com avatar 87ce1e1fe50034eb0f7b6eaeb5dd9a76 s 70 https github com marcelduran renato mangini http gravatar com avatar 61ffaf1af13c5626d3cd975275b2ddfd s 70 https github com mangini s rgio lopes http gravatar com avatar 3244c236c487924b4309fdd53cbb8633 s 70 https github com sergiolopes marcel duran https github com marcelduran br twitter renato mangini https github com mangini br google s rgio lopes https github com sergiolopes br caelum translations mike taylor http gravatar com avatar 8d8b46576d7de08d9e1500587c0fa8ce s 70 https github com miketaylr emilio lvarez http gravatar com avatar 87488b8b39dcff7dc6f34b062dbfce37 s 70 https github com ecoal95 lukasz jakub http gravatar com avatar 7351c99c580e5aaf32212220d66dc3ba s 70 https github com lukasz jakub adamczuk fordlee http gravatar com avatar a0f1f645387cf64667f804e134ce89d1 s 70 https github com fordlee404 nicolas carlo http gravatar com avatar 46978c87c5c75cea5d05a61fa8b7d95b s 70 https github com nicoespeon tomas dvorak http gravatar com avatar 3d725fd7dc3cec46f01a64fd000274b4 s 70 https github com todvora makoto tateno http gravatar com avatar 4317f6e3c1bb7c611d4e3b6c21834ced s 70 https github com makotot mike taylor https github com miketaylr br english emilio lvarez https github com ecoal95 br spanish lukasz jakub https github com lukasz jakub adamczuk br polish fordlee https github com fordlee404 br chinese nicolas carlo https github com nicoespeon br french tomas dvorak https github com todvora br czech makoto tateno https github com makotot br japanese credits docpad by benjamin lupton https github com balupton highlight js by ivan sagalaev https github com isagalaev fonts pacifico by vernon adams http www fontsquirrel com license pacifico open sans by steve matteson https www google com fonts specimen open sans horseshoes by lauren ashpole http www laurenashpole com licensing html illustrations the 56 geeks project by scott johnson http www myextralife com 56geeks flag icon set by gosquared https www gosquared com resources flag icons license code is under mit http zenorocha mit license org license and content is under creative commons by sa 3 0 http creativecommons org licenses by sa 3 0 deed en us | front_end |
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Leveraging-cache-and-MessagingQueue-to-scale-BlockchainNetwork | warning this repository is no longer maintained warning this repository will not be updated the repository will be kept available in read only mode leveraging the cache and messaging queue to scale a blockchain network read this in other languages readme ko md in this step we will configure redis and rabbitmq cluster in our architecture to control the flow of incoming request to blockchain network with the direct use of rest api calls it is not possible to control the number of requests sent to blockchain network this might cause errors such as read write conflicts etc in order to control the flow of request sent to blockchain network and scale our application we will use rabbitmq cluster with 3 nodes consisting of mirrored queues to queue the user requests and redis cluster with 6 nodes 3 leaders and 3 followers where results of execution are store for a short duration in architecture diagram we have rabbitmq producer present in api containers that queue the requests to rabbitmq cluster and rabbitmq consumers configured with an instance of fabric node sdk in task execution containers to consume the requests from users and send it blockchain network for execution you will find the configuration code for redis in backend utils util js you will find the configuration code for rabbitmq in rabbitclient utils util js included components hyperledger fabric docker hyperledger fabric sdk for node js application workflow diagram application workflow images arch png 1 issue a git clone https github com ibm leveraging cache and messagingqueue to scale blockchainnetwork 2 issue the command build sh to setup the network prerequisites docker https www docker com products v1 13 or higher docker compose https docs docker com compose overview v1 8 or higher steps 1 run build sh script to build and start the network 1 run the build sh script 2 check the logs to see the results 2 check the logs 3 test the blockchain network 3 test the blockchainnetwork 1 run the build sh script this accomplishes the following a clean up system by removing any existing blockchain docker images b generate certificates the crypto config yaml crypto configuration file defines the identity of who is who it tells peers and orderers what organization they belown to and what domain they belong to c create peers orderers and channel the configtx yaml file initializes a blockchain network or channel and services with an orderer genesis block which serves as the first block on a chain additionally membership services are installed on each channel peer in this case the shop and fitcoin peers d build docker images of the orderer peers channel network open a new terminal and run the following command bash export fabric cfg path pwd chmod x cryptogen chmod x configtxgen chmod x generate certs sh chmod x generate cfgtx sh chmod x docker images sh chmod x build sh chmod x clean sh build sh 2 check the logs command bash docker logs blockchain setup output bash ca registration complete ca registration complete 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 bcfit info packager golang js packaging golang from bcfit successfully installed chaincode on the default channel successfully instantiated chaincode on all peers blockchain network setup complete command bash docker ps output bash f4ddfcb1e4d8 haproxy 1 7 docker entrypoint 5 minutes ago up 5 minutes 0 0 0 0 3000 3000 tcp rabbitclient 5f40495511f1 backend node index js 5 minutes ago up 5 minutes fitcoin fitcoin backend 1 6ea304c78c40 backend node index js 5 minutes ago up 5 minutes 0 0 0 0 3030 3030 tcp fitcoin shop backend 1 ef481c334532 rabbit client node index js 5 minutes ago up 5 minutes 0 0 0 0 3003 3000 tcp rabbitclient3 51b7c6cee311 rabbit client node index js 5 minutes ago up 5 minutes 0 0 0 0 3002 3000 tcp rabbitclient2 1195c7cb43ca rabbit client node index js 5 minutes ago up 5 minutes 0 0 0 0 3001 3000 tcp rabbitclient1 15533fe3a151 redis server docker entrypoint 5 minutes ago up 5 minutes 6379 tcp 0 0 0 0 7000 7005 7000 7005 tcp fitcoin redis server 1 556840abfc4d dev shop peer bcfit 1 0e0d4e71de9ac7df4d0d20dfcf583e3e63227edda600fe338485053387e09c50 chaincode peer add 6 minutes ago up 6 minutes dev shop peer bcfit 1 8c594ddc16f4 haproxy 1 7 docker entrypoint 6 minutes ago up 6 minutes 0 0 0 0 5672 5672 tcp 0 0 0 0 15672 15672 tcp rabbitmq c59da84a4e7c rabbitmq 3 management usr local bin clus 6 minutes ago up 6 minutes 4369 tcp 5671 5672 tcp 15671 15672 tcp 25672 tcp rabbitmq2 f07024afd0f1 rabbitmq 3 management usr local bin clus 6 minutes ago up 6 minutes 4369 tcp 5671 5672 tcp 15671 15672 tcp 25672 tcp rabbitmq3 7ef2085afd54 rabbitmq 3 management docker entrypoint s 6 minutes ago up 6 minutes 4369 tcp 5671 5672 tcp 15671 15672 tcp 25672 tcp rabbitmq1 2a775a81c967 blockchain setup node index js 7 minutes ago up 7 minutes 3000 tcp blockchain setup 90136f4c90fe fitcoin peer peer node start 7 minutes ago up 7 minutes 0 0 0 0 8051 7051 tcp 0 0 0 0 8053 7053 tcp fitcoin peer 19e4890f71e3 shop peer peer node start 7 minutes ago up 7 minutes 0 0 0 0 7051 7051 tcp 0 0 0 0 7053 7053 tcp shop peer 654ada9fbbf6 ishangulhane fabric couchdb tini docker ent 7 minutes ago up 7 minutes 4369 tcp 9100 tcp 0 0 0 0 9984 5984 tcp shop statedb b19022ef3b2a ishangulhane fabric couchdb tini docker ent 7 minutes ago up 7 minutes 4369 tcp 9100 tcp 0 0 0 0 5984 5984 tcp ca datastore 6360ff012bbd fitcoin ca fabric ca server st 7 minutes ago up 7 minutes 0 0 0 0 8054 7054 tcp fitcoin ca 9d06dd0a009d orderer peer orderer 7 minutes ago up 7 minutes 0 0 0 0 7050 7050 tcp orderer0 0de13cd1ba31 shop ca fabric ca server st 7 minutes ago up 7 minutes 0 0 0 0 7054 7054 tcp shop ca 9dba93e63b5c ishangulhane fabric couchdb tini docker ent 7 minutes ago up 7 minutes 4369 tcp 9100 tcp 0 0 0 0 8984 5984 tcp fitcoin statedb command bash docker logs fitcoin fitcoin backend 1 output ca registration complete ca registration complete ca registration complete x awaiting rpc requests on clientclient0 x awaiting rpc requests on clientclient2 x awaiting rpc requests on clientclient1 command bash docker logs fitcoin shop backend 1 output ca registration complete ca registration complete starting socker server x awaiting rpc requests on clientclient0 3 test the blockchainnetwork in a separate terminal navigate to testapplication folder and run the following command npm install node index js navigate to url to view the blockchain blocks http localhost 8000 history html blocks images blocks png now navigate to url to perform operations on network http localhost 8000 test html note for this application the user queue value can be either user queue or seller queue sample enroll user request blocks images enroll png sample query request blocks images query user png sample invoke request blocks images invoke user png additional resources hyperledger fabric docs https hyperledger fabric readthedocs io en latest hyperledger composer docs https hyperledger github io composer latest introduction introduction html 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 | ibmcode blockchain-network rabbitmq-cluster hyperledger-fabric | blockchain |
FreeRTOS | h1 align center free real time operating system rtos h1 h1 align center h1 explanation of free rtos in arabic bash freertos 1 multitasking 2 task scheduling 3 define the priorities 4 task synchronization and communication 5 memory management 6 timer and tic management 7 portability read more about free rtos https en wikipedia org wiki freertos explanation and distribution of projects arduino https github com basilavad freertos tree main examples arduino arduinofreertos bash 1 support all arduino dual core and single core 2 esp32 https github com basilavad freertos tree main examples esp32 freertos bash 1 support all esp32 dual core and single core 2 esp32 has two 32 bit tensilica xtensa lx6 microprocessors which makes it a powerful dual core core0 and core1 microcontroller it is available in two variants single core and dual core but the dual core variant is more popular because there is no significant price difference esp32 function block diagram esp32 block diagram https github com basilavad freertos assets 69681817 dc83c3b1 b488 4d12 b306 ee6c3d44a592 installation use the arduino ide arduino ide https www arduino cc en software or platformio ide in vs code platformio ide https platformio org to update this code cpp git clone https github com basilavad freertos tree main usage include and call the header files inside your project cpp include freertos freertos h include freertos task h task function declarations cpp void core0task void parameter void core1task void parameter creat task functions core0task and core1task cpp void core0task void parameter for your code hear for example digitalwrite pin4 low vtaskdelay 15 porttick period ms void core1task void parameter for your code hear for example digitalwrite pin4 high vtaskdelay 10 porttick period ms initialize and creat the task functions inside the setup function cpp void setup task function task name stack size task priority task handle task core 0 or 1 for esp32 xtaskcreatepinnedtocore core0task core0task null configmax priorities 1 null 0 xtaskcreatepinnedtocore core1task core1task null configmax priorities 1 null 1 freertos library test and result period to frequency docfewc6qbgabf7mbxcl frequencyformula https github com basilavad freertos assets 69681817 4eaa119b 8f84 425e 9601 0dd5f6ed9c17 contributing pull requests are welcome for major changes please open an issue first to discuss what you would like to change please make sure to update tests as appropriate | os |
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MGameNotes | mgamenotes mobile game development of notes development notes sample code 1 http shadowkong com archives 1697 2 2dx lua c http shadowkong com archives 1716 3 lua require http shadowkong com archives 1741 4 http shadowkong com archives 1747 5 c primer http shadowkong com archives 1751 6 http shadowkong com archives 1758 7 http shadowkong com archives 1781 8 2dx gui http shadowkong com archives 1785 9 http shadowkong com archives 1808 10 2dx widget ccnode http shadowkong com archives 1812 about rect s bolg http shadowkong com | front_end |
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hadixsaab.github.io | hadixsaab github io toggle navigation asentus logo jeanclaude aoun web amp mobile developer push yourself your limits download my cv image about me i m jeanclaude aoun computer science graduate amp self learner started coding from a very young age love to code lt in order to bring my visions to life and to contribute in pushing the future of the tech community education bachelor of computer science lebanese university fs2 fanar 2015 2018 my skills building backend web apis with nodejs or php mysql mongodb firebase oauth2 unix shell ssh ftp ssl advanced modular javascript es6 angular framework html css js git json npm jquery bootstrap develop hybrid apps for ios and android using ionicframework angular cordova typescript sass artificial intelligence genetic algorithms evolutionary systems neural networks game development html5 canvas phaser io unity 3d augmented reality java rust python assembly swi prolog c android studio c photoshop illustrator final cut pro video editing | server |
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autoDevTools | npm version https img shields io npm v auto dev tool svg travis https img shields io travis rust lang rust svg https github com chokcoco autodevtools license https img shields io npm l express svg npm autodevtools https www npmjs com package auto dev tool autodevtools log install npm install auto dev tool save dev autodevtools js autodevtools js amd cmd usage html script type text javascript src autodevtool js script script type text javascript var devtool new autodevtool devtool init script amd html script src require js script script require autodevtool js function autodevtool var devtool new autodevtool devtool init script api log javascript var devtool new autodevtool devtool init var name title var data value devtool log name data javascript var a 10 devtool log a a error javascript try catch error devtool log error error json javascript var json a dev b tool devtool log json json how to open 1s https github com chokcoco autodevtools blob master images example demo jpg update dom mit license | front_end |
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bit-test | bit test bit for information technology | server |
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GameOn-website-FR | projet gameon 1 forkez ce repo 2 il est conseill d utiliser visualstudio code et vous pouvez utiliser docker mais ce n est pas obligatoire 3 il n y a aucune d pendance 4 vous ne devez utiliser que du css personnalis et du javascript pur sans jquery bootstrap ou autre librairie | front_end |
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awesome-python-htmx | pyhat awesome python htmx awesome https awesome re badge svg https github com sindresorhus awesome are you interested in the intersection of python and hypermedia driven applications https htmx org essays hypermedia driven applications head on over to the discussions tab https github com pyhat stack awesome python htmx discussions introduce yourself https github com pyhat stack awesome python htmx discussions 2 and let s get to work https github com pyhat stack awesome python htmx discussions 1 what is pyhat a name about a pyhat is more than just a snake with a hat it stands for python htmx asgi tailwind mdash a web stack that allows you to build powerful web applications using nothing more than drumroll python htmx and tailwind quick a name tools take me to the tools already a our goal we want to promote hypermedia driven applications that s it that s the goal okay well more specifically we want to promote htmx within the python ecosystem why should i care does any of this sound like you i want to stick with just python and html css but not sacrifice front end functionality https htmx org essays when to use hypermedia i don t want to have to use a complicated front end framework https htmx org essays a response to rich harris i don t enjoy agonizing over css class names https tailwindcss com docs utility first text you 20aren e2 80 99t 20wasting 20energy 20inventing 20class 20names i want to maintain a consistent design without any bikeshedding if the above sounds like you then you are in the right place maybe this isn t for me one of the links above goes to when should you use hypermedia https htmx org essays when to use hypermedia over at htmx org and is a pretty great read if you want to asses if this is for you it also expounds on the following points hypermedia might not be a good fit if your ui has many dynamic interdependencies i e google maps google sheets if you require offline functionality if your ui state is updated extremely frequently i e online game if your team is not on board but will it work in production yes here is a very good example https htmx org essays a real world react to htmx port of a project a company underwent using htmx with django in production you can also watch the original video from djangocon eu 2022 titled from react to htmx on a real world saas product we did it and it s awesome https www youtube com watch v 3gobi93tjzi details summary some highlights from the article summary the effort took about 2 months with a 21k loc code base mostly javascript no reduction in the application s user experience ux they reduced the code base size by 67 21 500 loc to 7200 loc they increased python code by 140 500 loc to 1200 loc a good thing if you prefer python to js they reduced their total js dependencies by 96 255 to 9 they reduced their web build time by 88 40 seconds to 5 first load time to interactive was reduced by 50 60 from 2 to 6 seconds to 1 to 2 seconds much larger data sets were possible when using htmx because react simply couldn t handle the data web application memory usage was reduced by 46 75mb to 45mb details glossary scroll asynchronous server gateway interface asgi a standard that allows an application to talk to a server allowing for multiple asynchronous events per application br component a reusable custom element within javascript it is a self contained element with its own properties methods that are reusable in this context the term is more broadly applied to any reusable elements which may include hypermedia or other design elements br dependency any application librariy or package that are required to run your application br fragments refers to partial content of a an html template see also template fragments br hypermedia medium of information including graphics audio video text and hyperlinks typically represented on the web as html br hypermedia driven application hda uses declarative html embedded syntax to achieve front end interactivity while interacting with the server in terms of hypermedia html instead of a non hypermedia format json br partials a loose term sometimes referring to partial content that can be displayed in a template or partial content to be generated from within a template block br server side rendering ssr generating static html markup on the server before it is rendered in the browser on the front end br single page application spa a web app implementation that loads a single web document and subsequently updates content through javascript apis br template fragments a relatively rare ssr template library feature that allow you to render a fragment or partial bit of the content within a template rather than the entire template usage a name usage a htmx can be used with any backend framework currently there is a lot of experimentation in the python space which is exciting but that also means that there are a lot of disparate approaches the best advice here is to get familiar with some of the core packages htmx tailwind then feel free to check out any of the packages below official resources htmx https htmx org htmx gives you access to ajax css transitions websockets and server sent events directly in html using attributes so you can build modern user interfaces with the simplicity and power of hypertext htmx has no outside dependencies outside of a vanilla javascript file referenced in your html head section tailwindcss https tailwindcss com docs installation rapidly build modern websites without ever leaving your html tailwind provides a standalone cli tool that does not require npm or any other javascript dependencies you can install it through pip using the pytailwindcss https pypi org project pytailwindcss library introductory resources how to create a django form using htmx in 90 seconds https www photondesigner com articles submit async django form with htmx a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a a simple short guide to start using htmx with django very quicky simple site https github com tataraba simplesite a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a provides thorough documentation on building a site from the ground up with fastapi jinja htmx and tailwind rapid prototyping with flask htmx and tailwind css https testdriven io blog flask htmx tailwind a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a in this tutorial you ll learn how to set up flask with htmx and tailwind css testdriven io django htmx and alpine js modern websites javascript optional https www saaspegasus com guides modern javascript for django developers htmx alpine a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a building a modern front end in django without reaching for a full blown javascript framework choosing the right tools for the job and bringing them into your project introductory courses htmx flask modern python web apps hold the javascript course https training talkpython fm courses htmx flask modern python web apps hold the javascript a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a htmx is one of the hottest properties in web development today and for good reason this framework along with the libraries and techniques introduced in this course will have you writing the best python web apps you ve ever written clean fast and interactive without all that frontend overhead talkpython training htmx django modern python web apps hold the javascript course https training talkpython fm courses htmx django modern python web apps hold the javascript similar to the course above except with django talkpython training bugbytes django htmx https www youtube com watch v ula0c rz6gk list pl 2ebedymibrbyz8gxhcnqsuv2dog4jxy a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a a phenomenal tutorial series on using django with htmx code with stein django ecommerce website htmx and tailwind https youtu be eoyfwkxxxxm si rovmhdlowsq3ihzq a great tutorial on building a django ecommerce website with htmx and tailwind design theory and patterns django htmx patterns https github com spookylukey django htmx patterns a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br a compilation of patterns for writing django projects that use htmx with complete example code htmx essays https htmx org essays br a collection of essays by carson gross the creator of htmx some specific essays of note for those not familiar with his teachings hypermedia driven applications https htmx org essays hypermedia driven applications this web stack could have been called pyhda this essay gives a great primer on how a pyhat application should look architecturally locality of behaviour lob https htmx org essays locality of behaviour a concept you will see referred to a lot around here the behaviour of a unit of code should be as obvious as possible by looking only at that unit of code splitting your data application apis going further https htmx org essays splitting your apis a great essay responding to a great article https max engineer server informed ui if you split your api into data and application apis you should consider changing your application api from json to hypermedia html using a hypermedia oriented library like htmx to reap the benefits of the hypermedia model simplicity reliability flexibility etc why we should stop using javascript according to douglas crockford inventor of json https youtu be lc5np9oqdhu br a short video of douglas crockford explaining why we should stop using javascript 3 irl use cases for python and htmx https www bitecode dev p 3 irl use cases for python and htmx from the author there is nothing htmx does that you couldn t do in another way but htmx pairs wonderfully with traditional server side frameworks and gives you clean correct results quite fast you won t get candy crush bling level with it but you will get something practical which is regularly all what i need third party packages a name tools a demos music binder https github com tataraba musicbinder a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a more advanced version of simple site repo https github com tataraba simplesite showcasing features like active search and infinite scroll you can open with a codespace in github without having to install anything locally bulldoggy the reminders app https github com automationpanda bulldoggy reminders app a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a bulldoggy is a small demo web app for tracking reminders uses htmx to handle get post patch requests in a fully functioning to do frontend owela club https github com adamchainz owela club a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a play the namibian game of owela against a terrible ai built using django and htmx templates fuzzy couscous https tobi de github io fuzzy couscous a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a a href https tailwindcss com target blank img src https img shields io badge tailwind css a9bbcc style flat logo tailwindcss logocolor black alt tailwind css a br a cli tool based on django s startproject template to bootstrap your django projects with a pyhat stack helper libraries starlette a href https www starlette io target blank img src https img shields io badge starlette a9bbcc style flat logo starlette logocolor black alt starlette a fastapi a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a flask a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a jinja a href https palletsprojects com p jinja target blank img src https img shields io badge jinja2 a9bbcc style flat logo jinja logocolor black alt jinja2 a django a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a tailwind a href https tailwindcss com target blank img src https img shields io badge tailwind css a9bbcc style flat logo tailwindcss logocolor black alt tailwind css a jinja2 fragments https github com sponsfreixes jinja2 fragments a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a a href https palletsprojects com p jinja target blank img src https img shields io badge jinja2 a9bbcc style flat logo jinja logocolor black alt jinja2 a br allows rendering individual blocks from jinja2 templates this library was created to enable the pattern of template fragments https htmx org essays template fragments with jinja2 extremely helpful when using htmx to enable locality of behavior https htmx org essays locality of behaviour jinja partials https github com mikeckennedy jinja partials a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a a href https palletsprojects com p jinja target blank img src https img shields io badge jinja2 a9bbcc style flat logo jinja logocolor black alt jinja2 a br when building real world web apps with flask jinja2 it s easy to end up with repeated html fragments just like organizing code for reuse it would be ideal to reuse smaller sections of html template code that s what this library is all about django render block https github com clokep django render block a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br allows rendering individual blocks from django templates this library was created to enable the pattern of template fragments https htmx org essays template fragments with django using django or jinja2 templates extremely helpful when using htmx to enable locality of behavior https htmx org essays locality of behaviour flask htmx https github com edmondchuc flask htmx a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a br a flask extension to work with htmx htmx flask https github com sponsfreixes htmx flask a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a br an extension for flask that adds support for htmx to your application it simplifies using htmx with flask by enhancing the global request object and providing a new make response function fastapi htmx https github com maces fastapi htmx a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a br an opinionated extension for fastapi to speed up development of lightly interactive web applications asgi htmx https github com florimondmanca asgi htmx a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a br htmx integration for asgi applications works with starlette fastapi quart or any other web framework supporting asgi that exposes the asgi scope inspired by django htmx django htmx https github com adamchainz django htmx a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br extensions for using django with htmx django siteajax https github com idlesign django siteajax a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br streamline your server and client interaction using declarative techniques in your html and helpful abstractions from siteajax in your python code powered by htmx hx requests https github com yaakovlowenstein hx requests a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br a package to simplify the usage of htmx with django easily add htmx requests witout needing additional urls and reduce clutter in views by offloading all responsibility to an hx request django cbv htmx https github com mixmash11 django cbv htmx a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br helps connect django class based views with htmx starlette htmx https github com lllama starlette htmx a href https www starlette io target blank img src https img shields io badge starlette a9bbcc style flat logo starlette logocolor black alt starlette a br a set of extensions for using htmx with starlette based on django htmx django template partials https github com carltongibson django template partials a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br reusable named inline partials for the django template language frameworks litestar https litestar dev a href https litestar dev target blank img src https img shields io badge litestar a9bbcc style flat logocolor black alt litestar a br litestar is a full on asgi web framework think fastapi sanic starlette etc so why is it included here with their most recent 2 0 release the creators have included htmx support out of the box a special htmxrequest provides easier access to hx request header objects and an htmxtemplate object that includes attributes for common htmx actions pushing url re swap re targets etc forge packages https www forgepackages com a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br forge is a set of django packages that work well together but can also be used independently these include some htmx tailwind specific packages highlighted below note that these are opinionated approaches but they provide a robust set of features to enhance your developer experience forge htmx https www forgepackages com docs forge htmx the forge htmx django package adds a couple of unique features for working with htmx one is template fragments and the other is view actions forge tailwind https www forgepackages com docs forge tailwind a href https tailwindcss com target blank img src https img shields io badge tailwind css a9bbcc style flat logo tailwindcss logocolor black alt tailwind css a use tailwind css with django without requiring javascript or npm django htmx ui https github com nikalexis django htmx ui a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a a href https palletsprojects com p jinja target blank img src https img shields io badge jinja2 a9bbcc style flat logo jinja logocolor black alt jinja2 a br a django app that combines and helps leverage the full stack django framework the frontend htmx framework the django htmx library and the jinja template engine it provides extended django views with htmx build in functionality crud views for django models extra mixins to use with your views to make life easier a ready to use jinja environment middlewares for automations and extra utils and decorators for common use cases components django dashboards https github com wildfish django dashboards a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br tools to help you build data dashboards in django django htmx autocomplete https github com phacdatahub django htmx autocomplete a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br a client side autocomplete component powered by htmx featuring multiselect search and is completely extensible tools django tailwind cli https oliverandrich github io django tailwind cli a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a a href https tailwindcss com target blank img src https img shields io badge tailwind css a9bbcc style flat logo tailwindcss logocolor black alt tailwind css a br an integration of tailwind css for django that is based on the precompiled versions of the tailwind css cli no js required pytailwindcss https github com timonweb pytailwindcss a href https tailwindcss com target blank img src https img shields io badge tailwind css a9bbcc style flat logo tailwindcss logocolor black alt tailwind css a br tailwind css is notoriously dependent on node js if you re a python developer this dependency may not be welcome in your team your docker container or your inner circle giving up node js means you won t be able to install plugins or additional dependencies for your tailwind css setup at the same time that might not be a dealbreaker you can still customize tailwind css via the tailwind config js file html form to dict https github com guettli html form to dict br do simple end to end testing of form handling without a real browser like selenium puppeteer playwright supports the action and method attributes of forms and additionaly the htmx attributes hx get hx post projects using pyhat or similar django requests tracker https github com bensi94 django requests tracker a href https docs djangoproject com en target blank img src https img shields io badge django a9bbcc style flat logo django logocolor black alt django a br a django development tool which collects and displays information on requests responses sql queries headers django settings and more the front end uses htmx idp z3 https gitlab com krr idp z3 a href https flask palletsprojects com en target blank img src https img shields io badge flask a9bbcc style flat logo flask logocolor black alt flask a br a software collection implementing the knowledge base paradigm using the fo language uses htmx for the front end jupyspace https github com davidbrochart jupyspace a href https fastapi tiangolo com target blank img src https img shields io badge fastapi a9bbcc style flat logo fastapi logocolor black alt fastapi a br a web server and client to manage conda forge environments from the browser and access them through jupyterlab uses htmx on the front end further reading awesome htmx https github com rajasegar awesome htmx maximizing productivity pycharm and htmx integration https oluwatobi dev blog maximizing productivity pycharm and htmx integration unsuck js https unsuckjs com 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Action-Recognition | action recognition objective given a video containing human body motion you have to recognize the action agent is performing solution approaches we started with action recognition from skeleton estimates of human body given 3d ground truth coordinates of human body obtained from kinect cameras we tried to use lstms as well as temporal convolutions for learning skeleton representation of human activity recognition we also tried fancier lstms as well where we projected the 3d coordinates onto x y plane y z plane z x plane followed by 1d convolutions and subsequently adding the outputs of the 4 lstms x y y z z x 3d additionally we tried variants where we chose three out of the four lstms and compared performance among different projections then we moved to action recognition from videos we used pretrained hourglass network to estimate joints at each frame in videos and used similar lstms to perform the task of action recognition dataset we have used ntu rgbd action https github com shahroudy nturgb d dataset in this project it consists of 60 classes of various human activities and consist of 56 880 action samples of these 60 classes we removed the last 11 classes consisting of multiple people we trained most our models on subsets of this dataset consisting of action label id drink water 0 throw 1 tear up paper 2 take off glasses 3 put something inside pocket take out something from pocket 4 pointing to something with finger 5 wipe face 6 falling 7 or action label id drink water 0 wear jacket 1 handwaving 2 kick something 3 salute 4 we have also trained a some models on the complete dataset using 49 classes pipeline the input is a sequence of frames i e video which first passes through a trained model available here https github com xingyizhou pytorch pose hg 3d this produces the estmates for the pose in 3d this 3d pose passes through our network which takes it various projections and is used as the main features to classify the action from the above 8 categories p align center img src plots readme out input gif alt input p p align center img src plots readme out xingy net png alt x net p 2d 3d input plots readme out output ske gif input plots readme out 3d ske gif predicted action tear up paper check the load testbed in notebook to verify this example p align center img src plots readme out main model0 png alt main model0 style width 1200px height 900px p we also tried many different variations for our classifier model which includes simple 2 layered lstm network another type of variation included lstm models based on only some of the 2d projection of the pose say xy or yz or zx etc some results data classifier results accuracy val train ground truth skeleton 5 classes single lstm 3d coordinates 75 5 79 5 ground truth skeleton 5 classes 2 stacked lstms 3d coordinates 77 1 80 4 ground truth skeleton 5 classes 3 stacked lstms 3d coordinates 77 2 85 6 ground truth skeletons 49 classes 2 stacked lstms 3d coordinates 59 7 72 5 hourglass predicted skeletons 8 classes 2 stacked lstms 3d coordinates 81 25 hourglass predicted skeletons 8 classes 2d 3d projection lstms 1d conv fusion 82 57 hourglass predicted skeletons 8 classes all 2d projection lstms 1d conv fusion 64 23 hourglass predicted skeletons 8 classes x y projection only 1d conv 75 36 hourglass predicted skeletons 8 classes y z projection only 1d conv 72 94 hourglass predicted skeletons 8 classes z y projection only 1d conv 73 86 for the above mentioned 8 classes some of the top accuracies models and their learning curve is shown below note that some of the models are not fully trained and will possibly score higher if training is completed br br b here are the plots of the losses and accuracies of some of the best models b br br i 3d 2d projections lstms 82 7 accuracy i p align float img src plots plots av classifierx3 png style width 300px img src plots plots acc classifierx3 png style width 300px p i all 2d projections 64 23 accuracy i p align float img src plots plots av classifierx32d all png style width 300px img src plots plots acc classifierx32d all png style width 300px p i simple 2 stacked lstm 81 25 accuracy i p align float img src plots plots lossforsimplelstm png style width 300px img src plots plots accuraciesforsimplelstm png style width 300px p i simple 2 stacked lstm on entire data 59 7 accuracy i p align float img src plots plots lossentiredatasimplelstm png style width 300px img src plots plots accuraciesentiredatasimplelstm png style width 300px p requirements kindly use the requirements txt to set up your machine for replicating this experiment instructions to train the models run python lstm classifierx3cuda one of model names py in the src folder this will start the training for 50 epochs and keep saving the best and the last model so far along with the accuracy and loss results in tr models and outputs respectively references for the purpose of this experiment to get the poses from the the images and videos we are using the awesome repository https github com xingyizhou pytorch pose hg 3d https github com xingyizhou pytorch pose hg 3d | action-recognition deep-learning lstm convolutional-networks ntu-rgbd skeleton-based-action-recognition | ai |
hxcuong | hxcuong information technology | server |
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nuclei-sdk | nuclei software development kit build documentation https github com nuclei software nuclei sdk workflows build 20documentation badge svg https github com nuclei software nuclei sdk actions query workflow 3a 22build documentation 22 build sdk https github com nuclei software nuclei sdk workflows build 20sdk badge svg https github com nuclei software nuclei sdk actions query workflow 3a 22build sdk 22 nuclei software development kit nuclei sdk is developed for developing and evaluating software using our fpga evaluation board nuclei sdk diagram doc source asserts images nuclei sdk diagram png this nuclei sdk is built based on the nmsis framework user can access all the apis provided by nmsis and also the apis that provided by nuclei sdk which mainly for on board peripherals access such as gpio uart spi and i2c etc we also intergated three rtoses into nuclei sdk which are freertos ucosii and rtthread you can easily find it in the os folder quick startup wanner to a try with nuclei sdk click quick start with nuclei sdk https doc nucleisys com nuclei sdk quickstart html to start up requirements ubuntu linux 16 04 lts or windows 7 linux gnu make 3 82 windows windows build tools https nucleisys com download php nuclei riscv gnu gcc toolchain https nucleisys com download php nuclei openocd https nucleisys com download php directory structure here is the directory structure for this nuclei sdk console nuclei sdk root application baremetal freertos ucosii rtthread build gmsl makefile base makefile conf makefile core makefile components makefile files makefile global makefile misc makefile rtos makefile rules makefile soc doc build source makefile requirements txt nmsis core dsp nn library os freertos ucosii rtthread soc gd32vf103 demosoc test core ctest h license readme md license makefile nmsis version package json sconscript readme md setup bat setup sh application this directory contains all the application softwares for this nuclei sdk the application code can be divided into mainly 4 parts which are baremetal applications which will provide baremetal applications without any os usage these applications will be placed in application baremetal folder freertos applications which will provide freertos applications using freertos rtos placed in application freertos folder ucosii applications which will provide ucosii applications using ucosii rtos placed in application ucosii folder rtthread applications which will provide rt thread applications using rt thread rtos placed in application rtthread folder soc this directory contains all the supported socs for this nuclei sdk the directory name for soc and its boards should always in lower case here we mainly support nuclei processor cores running on nuclei fpga evaluation board mcu200t ddr200t the support package placed in soc demosoc in each soc s include directory nuclei sdk soc h must be provided and include the soc header file for example soc demosoc common include nuclei sdk soc h in each soc board s include directory nuclei sdk hal h must be provided and include the board header file for example soc demosoc board nuclei fpga eval include nuclei sdk hal h build this directory contains the key part of the build system based on makefile for nuclei sdk nmsis this directory contains the nmsis header files which is widely used in this nuclei sdk you can check the nmsis version nmsis version file to know the current nmsis version used in nuclei sdk we will also sync the changes in nmsis project https github com nuclei software nmsis when it provided a new release os this directory provided three rtos package we suppported which are freertos ucosii and rt thread license nuclei sdk license file nmsis version nmsis version file it will show current nmsis version used in nuclei sdk package json platformio package json file for nuclei sdk used in nuclei plaform for platformio https platformio org platforms nuclei sconscript rt thread package scons build script used in rt thread package development https www rt thread org document site development guide package package makefile an external makefile just for build run debug application without cd to any coresponding application directory such as application baremetal helloworld setup sh nuclei sdk environment setup script for linux you need to create your own setup config sh shell nuclei tool root path to your tool root in the nuclei tool root for linux you need to have nuclei risc v gnu gcc toolchain and openocd installed as below nuclei tool root gcc bin include lib libexec riscv nuclei elf share openocd bin contrib distro info openulink scripts share setup bat nuclei sdk environment setup bat script for windows you need to create your own setup config bat bat set nuclei tool root path to your tool root in the nuclei tool root for windows you need to have nuclei risc v gnu gcc toolchain necessary windows build tools and openocd installed as below console nuclei tool root build tools bin gnu mcu eclipse licenses gcc bin include lib libexec riscv nuclei elf share openocd bin contrib distro info openulink scripts share how to use 1 create and modify your own setup config for linux create setup config sh in nuclei sdk root for windows create setup config bat in nuclei sdk root 2 source the environment script right in nuclei sdk root for linux source setup sh for windows setup bat 3 build and run application note by default the soc and board is set to demosoc and nuclei fpga eval if you don t pass any soc and board variable in make command it will use the default soc and board assume that you will run this application application baremetal helloworld cd application baremetal helloworld you can run make help to show help message we provided different nuclei core configurations core your core we supported see build makefile core such as core n305 we support three download modes download mode for different applications flashxip program will to be download into flash and run directly in flash flash program will be download into flash when running program will be copied to ilm ram and run in ilm ram ilm program will be download into ilm ram and run directly in ilm ram program lost when poweroff for example if you want to build your application for core n305 download ilm you can easily run this command shell make core n305 download ilm all if you want to upload your application for core n305 download ilm you can easily run this command shell make core n305 download ilm upload option 1 if you want to debug your application for core n305 download ilm first open a new terminal in the same application folder and run make core n305 download ilm run openocd then run this command make core n305 download ilm run gdb in the existing terminal then you can debug it using gdb if you want to load your program you need to type load to achieve it notice since version 0 2 4 you can also pass extra gdb port portno to change to use new gdb port other than default 3333 for example make core n305 download ilm gdb port 3344 run openocd and make core n305 download ilm gdb port 3344 run gdb option 2 if you want to debug your application for core n305 download ilm shell make core n305 download ilm debug if you want to use uart terminal tool to view the uart message you can choose screen or minicom in linux teraterm in windows the default uart baudrate we use is 115200 knowledge book if you want to learn more about nuclei sdk documentation please click nuclei sdk documentation http doc nucleisys com nuclei sdk if you need to build a new application or change core or download option please make sure that you have clean the project by make clean if you want to specify additional compiler flags please follow this guidance in your application makefile pass common compiler flags to all c asm cpp compiler you can use common flags in makefile such as common flags o3 funroll loops fpeel loops pass c compiler only flags to c compiler you can use cflags in makefile such as cflags o3 funroll loops fpeel loops for asm compiler only flags you can use asmflags for cpp compiler only flags you can use cxxflags if you want to pass additional linker flags you can use ldflags and if you have additional library directories you can use libdirs to specify library directories the preprovided applications and its makefile is the best startup examples about how to use nuclei sdk pass extra v 1 to your make command it will show verbose compiling information otherwise it will only show basic information sample output with extra v 1 console make v 1 all current configuration riscv arch rv32imafdc riscv abi ilp32d soc demosoc board nuclei fpga eval core n307fd download ilm assembling os freertos source portable gcc portasm s riscv nuclei elf gcc g march rv32imafdc mabi ilp32d mcmodel medany ffunction sections fdata sections fno common ddownload mode download mode ilm i i nmsis include i os freertos source include i os freertos source portable gcc i soc demosoc board nuclei fpga eval include i soc demosoc common include mmd mt os freertos source portable gcc portasm s o mf os freertos source portable gcc portasm s o d c o os freertos source portable gcc portasm s o os freertos source portable gcc portasm s assembling soc demosoc common source gcc intexc demosoc s riscv nuclei elf gcc g march rv32imafdc mabi ilp32d mcmodel medany ffunction sections fdata sections fno common ddownload mode download mode ilm i i nmsis include i os freertos source include i os freertos source portable gcc i soc demosoc board nuclei fpga eval include i soc demosoc common include mmd mt soc demosoc common source gcc intexc demosoc s o mf soc demosoc common source gcc intexc demosoc s o d c o soc demosoc common source gcc intexc demosoc s o soc demosoc common source gcc intexc demosoc s assembling soc demosoc common source gcc startup demosoc s contributing https doc nucleisys com nuclei sdk contribute html changelog https doc nucleisys com nuclei sdk changelog html | riscv risc-v embedded-hal iot-platform nuclei freertos rtos makefile fpga-evaluation-board rt-thread ucosii nuclei-sdk platformio sdk embedded-systems opensource nmsis | os |
ttaw | github workflows ci yml https github com shnewto ttaw workflows github workflows ci yml badge svg branch main https github com shnewto ttaw actions codecov https codecov io gh shnewto ttaw branch main graph badge svg token 7i6vuoolc2 https codecov io gh shnewto ttaw crates io version https img shields io crates v ttaw svg https crates io crates ttaw crates io https img shields io crates d ttaw svg https crates io crates ttaw license https img shields io badge license mit blue svg license ttaw talking to a wall a piecemeal natural language processing library functionality determine if two words rhyme using the double metaphone phonetic encoding determine if two words rhyme using cmudict phonetic encoding determine if two words alliterate using the double metaphone phonetic encoding determine if two words alliterate using cmudict phonetic encoding get the cmudict phonetic encoding of a word get the double metaphone phonetic encoding of a word port of words double metahone https github com words double metaphone library rhyme rust extern crate ttaw use ttaw initialize the cmudict with a path to the existing serialized cmu dictionary or a directoy containing it if the dictionary doesn t exisit it will be downloaded and serialized at the location specified by the path parameter let cmudict ttaw cmu cmudict new cmudict json unwrap assert eq ok true cmudict rhyme far tar assert eq ok true ttaw metaphone rhyme far tar assert eq ok false cmudict rhyme shopping cart assert eq ok false ttaw metaphone rhyme shopping cart deviations in cmu and metaphone assert eq true ttaw metaphone rhyme hear near assert eq ok false cmudict rhyme hear near alliteration rust extern crate ttaw use ttaw initialize the cmudict with a path to the existing serialized cmu dictionary or a directoy containing it if the dictionary doesn t exisit it will be downloaded and serialized at the location specified by the path parameter let cmudict ttaw cmu cmudict new cmudict json unwrap assert eq ok true cmudict alliteration bounding bears assert eq true ttaw metaphone alliteration bounding bears assert eq ok false cmudict alliteration lazy dog assert eq false ttaw metaphone alliteration lazy dog cmudict rust extern crate ttaw use ttaw initialize the cmudict with a path to the existing serialized cmu dictionary or a directoy containing it if the dictionary doesn t exisit it will be downloaded and serialized at the location specified by the path parameter let cmudict ttaw cmu cmudict new cmudict json unwrap assert eq cmudict encoding unearthed ok some vec vec ah0 to string n to string er1 to string th to string t to string double metaphone rust extern crate ttaw use ttaw assert eq ttaw metaphone encoding arnow primary arn assert eq ttaw metaphone encoding arnow secondary arnf assert eq ttaw metaphone encoding detestable primary ttstpl assert eq ttaw metaphone encoding detestable secondary ttstpl | rust nlp natural-language-processing natural-language rhyme alliteration syllables crates metaphone cmu cmudict phonemes pronounciation natural language processing pronounce phones double-metaphone | ai |
webdev-resources | webdev brain as webdeveloper i see daily tons of good resources and want to share them with the community i tried them to categorize and give them a structure type of content concept general view on how to design and create things standard a defined standard for a technologie knowhow specific knowhow about a specific topic learning here you can learn something about general concepts or technologies api api description of a technology standard or whatever index must see mustsee software design softwaredesign clean code cleancode standards specification standard services service great resources greatresources ebooks ebook tools tool learn with games and challenges learnbygaming learning learning frameworks framework boilerplate boilerplate articles article a name mustsee must see a video innovation bret victor inventing on principle http vimeo com 36579366 bret victor the future of programming by https vimeo com 71278954 bret victor stop drawing dead fish https gist github com staltz 868e7e9bc2a7b8c1f754 simplicity rich hickey simple made easy https www infoq com presentations simple made easy rich hickey simplicity matters https www youtube com watch v ri8tnmsozo0 list plwkze3ty pjaxkcraoqudnd9btn3v3n4l robert c martin clean architecture https www youtube com watch v nltqi7odztm venkat subramaniam the art of simplicity https www youtube com watch v zpgo5usboi index 4 list plwkze3ty pjzj31ye7waflkj0lzxk76tb cqrs event sourcing ddd greg young cqrs and event sourcing https www youtube com watch v jhgkashoyns greg young cqrs http www infoq com presentations greg young unshackle qcon08 udi dahan cqrs introduction https www youtube com watch v ekez3pcldgy udi dahan make roles explicit http www infoq com presentations making roles explicit udi dahan microservices michael ploed building microservices with event sourcing and cqrs https www youtube com watch v a0goyz9f4bg this talk tries also to answer typical problems you have with event sourcing and cqrs fred george micro service architecture http www youtube com watch v 2rkevel55ty list wl4f525dd3809ed677 sam newman principles of microservices https www youtube com watch v pfqnnfe27ku sam newman practical considerations for microservice architectures https vimeo com 105751281 testing greg young the art of destroying software https vimeo com 108441214 ian cooper tdd where did it all go wrong https www youtube com watch v ez05e7emolm not categorized joe armstrong systems that run forever self heal and scale http www infoq com presentations self heal scalable system fred george implementing programmer anarchy https www youtube com watch v tixhmswcd7g robert c martin the future of programming https www youtube com watch v eciwpzgebfc t 80s list plwkze3ty pjaxkcraoqudnd9btn3v3n4l index 22 rewrites berleben http www youtube com watch v ylk8rrrsl2c list wl4f525dd3809ed677 slides your code sucks letz fix it http de slideshare net rdohms your code sucks lets fix it 15000359 functional programming http scott sauyet com javascript talk 2014 01 funcprogtalk gr8 code transformation example from oo style imperative to functional style declarative a name softwaredesign software design a php practical php testing patterns http css dzone com books practical php testing patterns concept by giorgio sironi practical php patterns http css dzone com books practical php patterns concept by giorgio sironi practical php refactoring http css dzone com category tags practical php refactoring concept by giorgio sironi create your own framework on top of the symfony2 components http fabien potencier org article 50 create your own framework on top of the symfony2 components part 1 learning by fabien potencier chaplin https github com moviepilot chaplin concept a sample application architecture using backbone js javascript design patterns in javascript https github com tcorral design patterns in javascript concept based on head first design patterns book backbone js fundamentals https github com addyosmani backbone fundamentals readme complete run down of backbone js applications including modular design amd how to solve routing problems essential javascript design patterns for beginners http www addyosmani com resources essentialjsdesignpatterns book free ebook by addy osmani large scale javascript application architecture http speakerdeck com u addyosmani p large scale javascript application architecture concept design javascript patterns jquery mvc build large and scalable apps slides by addy osmani javascript design patterns http www joezimjs com javascript javascript design patterns singleton by joe zimjs javascript pattern collection http shichuan github com javascript patterns by shichuan solid javascript http freshbrewedcode com derekgreer 2011 12 08 solid javascript single responsibility principle by derek greer designing better javascript api s http coding smashingmagazine com 2012 10 09 designing javascript apis usability concept by rodney rehm pull stream examples https github com dominictarr pull stream examples software patterns general sourcemaking http sourcemaking com concept design patterns and refactoring collection and explanations of patterns anti patterns refactoring uml scalable and modular architecture for css http smacss com book concept best practices on how to design proper css 12 factor app http 12factor net concept a methodology for building software as a service apps event sourcing https www npmjs com package eventsourced object concept gathered sources for event sourcing learn how to large scale systems https github com donnemartin system design primer learning data structures http btechsmartclass com ds u3 t1 html learning design uplabs https www uplabs com learning tons of graphical designs to learn and get inspired from dribbble https dribbble com learning tons of graphical designs to learn and get inspired from a name cleancode clean code a front end development guidelines http taitems github com front end development guidelines idiomatic js https github com rwldrn idiomatic js principles of writing consistent idiomatic javascript javascript garden http bonsaiden github com javascript garden best practises for javascript programming html5 semantics and good coding practices http www aniketpant com article html5 semantics and good coding practices code style guide https github com ginatrapani thinkup wiki code style guide thinkup s code style guides for html css javascript php smarty code style guide by google http google styleguide googlecode com svn trunk htmlcssguide xml google s html css style guide html css code style guide by mdo http mdo github io code guide a name standard standards specifications a php standards https github com pmjones fig standards description of php standards well known as psr 0 psr 1 semantic versioning http semver org keep a changelog http keepachangelog com not a real standard but helpful to write changelogs json schema http spacetelescope github io understanding json schema json schema standard explained json api http jsonapi org a specification for building apis in json a name service services a free for dev https github com ripienaar free for dev log management awesome repo with list of free saas paas and iaas services swagger http swagger io framework for api specification specify your api with yml apiary https apiary io framework for api specification specify your api with yml mashape https www mashape com api services kong https getkong org secure and manage your microservices apis behind kong as infrastructure layer zapier https zapier com service to connect different apps together zeit co https zeit co a realtime node js deployment service deploy bot http deploybot com a service to deploy code anywhere rancher http rancher com a plattform for running containers concourse ci https concourse ci continuous integration platform with a streamed build pipeline a name greatresources great resources a move the web forward http movethewebforward org resources tips for web development initiative by paul irish addy osmani and a lot of other respected people in the web developer scene awesome php libraries https github com ziadoz awesome php superhero js http superherojs com collection of the best ressources for javascript javascriptoo http www javascriptoo com collection of the best ressources for javascript nodecloud http www nodecloud org collection of the best node js ressources nerdi http nerdi net collection of useful webdesign and development utilites frontend dev bookmarks https github com dypsilon frontend dev bookmarks huge list of awesome bookmarks for frontend development devdocs http devdocs io searchable documentation for html5 css js http feed the bot http www feedthebot com great articles about webpage optimisation like speed mobile seo nodecasts https nodecasts io free weekly episodes around the node topic a name ebook ebooks a jsbooks http jsbooks revolunet com free ebooks for javascript node js for beginner http www nodebeginner org node js for beginner progit http progit org book the little book on coffescript http arcturo com library coffeescript regex ebook german http www regenechsen de phpwcms index php id 66 0 0 1 0 0 eloquentjavascript http eloquentjavascript net free ebook by marijn haverbeke dive into html5 http diveintohtml5 org free ebook by mark pilgrim building iphone apps with html css and javascript http ofps oreilly com titles 9780596805784 free ebook by jonathan stark javascript bibliography http oreilly com catalog 0636920021926 free ebook by editors of safary books online oop mit javascript http www peterkropff de site javascript oop htm free ebook by peter kropff german html5 handbuch http webkompetenz wikidot com docs html handbuch free ebook by stefan m nz german leanpub http leanpub com create you own ebook learn regex the hard way http regex learncodethehardway org book 54 lessons with exercises about regex security engineering http www cl cam ac uk rja14 book html exploring es6 by axel rauschmayer http exploringjs com es6 a name tool tools a software analysis design draw io http www draw io quick and easy way to draw diagrams realtimeboard https realtimeboard com quick and easy way to draw diagrams gliffy http www gliffy com uml diagrams websequence diagrams http www websequencediagrams com sequence diagrams creately https creately com sequence class diagrams editors jsbin http jsbin com javscript editor in browser jsfiddle http jsfiddle net javascript editor in browser cloud9ide http cloud9ide com collaborative browser ide for development css3 css3 maker http www css3maker com css maker is a free tool to experiment with css properties and values and generate a simple stylesheet for your site css3 generator http css3generator com css3 clickchart http css3clickchart com graphics design typecast http typecast com to create visual and semantic designs live and share the results responsify http responsify it create online a responsive grid and download the template fontsquirrel http www fontsquirrel com handpicket free fonts also with a font face generator pixlr http pixlr com pixlr is a free online photo editor edit adjust and filter your images pencil http pencil evolus vn opensource online gui prototyping tool spritecow http www spritecow com helps you get the background position width and height of sprites within a spritesheet as a nice bit of copyable css responsivepx http responsivepx com online tool which helps to find exact pixel sizes for responsive webdesign performance webpagetest http www webpagetest org checks a website and analyzes performance issues gtmetrix http gtmetrix com checks different pages based on yslow and other tools and creates a report with suggestions pingdom http tools pingdom com fpt checks a website and analyzes performance issues google page insights https developers google com speed pagespeed insights analyze the page and get tips how to improve the performance loads in http loads in online test of webpage load time webwait http webwait com very small tool to test website responses and compare with different websites yahoo guideline http developer yahoo com performance rules html knowhow dom monster http mir aculo us dom monster bookmarklet which analyzes the dom and give you tips how to improve others slides https slides com presentation tool devnullsmtp http www aboutmyip com aboutmyxapp devnullsmtp jsp devnull smtp server a dummy email server for testing purposes regex101 https regex101 com regex service ngrok https ngrok com share your localhost with the internet htaccess tester http htaccess madewithlove be sound and movie loops http www hark com tuttijs http tuttijs com test javascript on different browsers simultaneously a name learnbygaming learn with games and challenges a hackerrank https www hackerrank com a lot of programming challenges where you can compete against others codewars http www codewars com a lot of katas dojos challenges with badges coderbyte http coderbyte com learning put your programming skills to the test regex golf http regex alf nu write regex and get as much as possible points regex crossword http regexcrossword com solve crosswords with writing regex mysql game http mysqlgame com fight against others in a real mysql db with db queries dungeons and developers http www dungeonsanddevelopers com an rpg style talent tree for web developers code combat http codecombat com learn javascript programming with a real game where you have to code the moves to win the battle a name learning learning a codecademy http www codecademy com learn step by step different languages online playterm http playterm org educational linux shell replays appendto http learn appendto com lessons free javascript jquery video lessons nodeschool io http nodeschool io interactive node workshops stackoverflow recommended list of dev books http stackoverflow com questions 1711 what is the single most influential book every programmer should read page 1 tab votes tab top perfschool https github com bevacqua perfschool interactive performance workshop a name boilerplate boilerplate a html5 boilerplate http html5boilerplate com html5 mobile boilerplate http html5boilerplate com mobile html5 reset http html5reset org twitter bootstrap http twitter github io bootstrap twitter bootstrap themes http www naden de blog twitter bootstrap themes mean boilerplate https github com linnovate mean mean is a boilerplate that provides a nice starting point for mongodb node js express and angularjs based applications a name article articles slides video a http http headers for dummies http net tutsplus com tutorials other http headers for dummies knowhow why every webdeveloper should read the http specification http blog liip ch archive 2011 05 19 why every web developer should read the http specifications html knowhow css3 css position einmaleins http kleines universum de html css css position einmaleins aus alistapart german the 30 css selectors you must memorize http net tutsplus com tutorials html css techniques the 30 css selectors you must memorize knowhow slides fabien potencier look beyond php http www slideshare net fabpot look beyond php | front_end |
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health-facilities | registered health facilities in nigeria this end to end data engineering project scraped all registered health facilities website into a normalized database and generate insight by performing simple analytics | server |
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gibran | gibran yesterday is but today s memory and tomorrow is today s dream gibran http d gr assets com authors 1353732571p5 6466154 jpg gibran 2 is an elixir natural language processor lofty goals for gibran include metaphone phonetic coding system soundex algorithm porter stemming algorithm string similarity as described by simon white http www catalysoft com articles strikeamatch html currently gibran ships with the following features token count unique token count and character count average characters per token hashdict s of tokens and their frequencies lengths and densities the longest token s and its length the most frequent token s and its frequency unique tokens levenshtein distance algorithm usage let s start with something simple elixir alias gibran tokeniser alias gibran counter str yesterday is but today s memory and tomorrow is today s dream tokeniser tokenise str yesterday is but today s memory and tomorrow is today s dream tokeniser tokenise str counter uniq token count 8 by default gibran uses the following regular expression to tokenise strings r p l u you can provide your own regular expression through the pattern option you can combine pattern with exclude to create sophisticated tokenisation strategies tokeniser tokenise string exclude string length 1 4 counter token count 6 the exclude option accepts a string a function a regular expression or a list combining any one or more of those types elixir using exclude with a function tokeniser tokenise kingdom of the imagination exclude string length 1 10 imagination using exclude with a regular expression tokeniser tokenise sand and foam exclude r and foam using exclude with a string tokeniser tokenise eye of the prophet exclude eye of the prophet using exclude with a list of a combination of types tokeniser tokenise eye of the prophet exclude eye string ends with 1 he r of prophet gibran provides a shortcut for working with strings directly instead of running them through the tokeniser first elixir gibran from string str token count opts exclude string length 1 4 6 to avoid inconsistencies that arise from character casing gibran normalises input before applying transformations levenshtein distance ordinary use elixir iex 1 gibran levenshtein distance kitten sitting 3 the levenshtein distance for the same string is 0 elixir iex 2 gibran levenshtein distance snail snail 0 the levenshtein distance is case sensitive elixir iex 3 gibran levenshtein distance houseboat houseboat 9 the function can accept charlists as well as strings elixir iex 4 gibran levenshtein distance jogging logger 4 the doctests contain extensive usage examples please take a look there for more details 1 https github com abitdodgy words counted 2 https en wikipedia org wiki kahlil gibran | natural-language-processing nlp elixir-lang | ai |
FreeRTOS-Partner-Supported-Demos | freertos partner supported demos this repository contains demos for freertos ports supported by freertos partners follow these steps https github com freertos freertos blob main freertos demo thirdparty template readme md to contribute a demo to this repository license this repository contains multiple directories each individually licensed please see the license file in each directory | os |
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Kadot | p align center img src https github com the new sky kadot raw 1 0dev logo png alt kadot height 100px p natural language processing using unsupervised vectors representation documentation status https readthedocs org projects kadot badge version 1 0dev http kadot readthedocs io en 1 0dev badge 1 0dev codacy badge https api codacy com project badge grade 513eab88b0af4c93b1524d91090397a0 https www codacy com app lorisazerty kadot utm source github com amp utm medium referral amp utm content the new sky kadot amp utm campaign badge grade kadot is no longer in development the project had two branches 0 x https github com the new sky kadot tree master and 1 x this one kadot is a high level open source library to easily process text documents it relies on vector representations of documents or words in order to solve nlp tasks such as summarization spellchecking or classification python how to get n grams using kadot from kadot tokenizers import regex tokenizer hello tokens regex tokenizer kadot just lets you process a text easily hello tokens ngrams n 2 kadot just just lets lets you you process process a a text text easily what s in 1 0 all these new features may not yet be available on github vectorizers we are now offering word2vec the state of the art fasttext and doc2vec algorithms using gensim https radimrehurek com gensim s powerful backend performances using a much more efficient algorithm the new word vectorizer is up to 95 faster and sparse vectors now take up to 94 less memory models kadot now includes a text classifier an automatic text summarizer and an entity labeler which can be useful in many projects bot engine soon dependencies in order to guarantee good performance without reinventing the wheel we are adding gensim https radimrehurek com gensim and pytorch http pytorch org to our list of dependencies although installed by default these libraries will be optional and only numpy and scipy are strictly required to use kadot license kadot is under mit license https github com the new sky kadot blob master license md contribute issues and pull requests are gratefully welcome come help me i am not a native english speaker if you see any language mistakes in this readme or in the code docstrings included please open an issue | natural-language-processing word-embeddings text-classification text-generation | ai |
18S096SciML | 18 s096 special subject in mathematics applications of scientific machine learning lecturer dr christopher rackauckas machine learning and scientific computing have previously lived in separate worlds with one focusing on training neural networks for applications like image processing and the other solving partial differential equations defined in climate models however a recently emerging discipline called scientific machine learning or physics informed learning has been bucking the trend by integrating elements of machine learning into scientific computing workflows these recent advances enhance both the toolboxes of scientific computing and machine learning practitioners by accelerating previous workflows and resulting in data efficient learning techniques machine learning with small data this course will be a project based dive into scientific machine learning directly going to the computational tools to learn how the practical aspects of doing scientific machine learning students will get hands on experience building programs which train data efficient physics informed neural networks accelerate scientific models using surrogate methods like neural networks solve hundred dimensional partial differential equations using recurrent neural networks solve classical machine learning problems like image classification with neural ordinary differential equations use machine learning and data driven techniques to automatically discover physical models from data the class will culminate with a project where students apply these techniques to a scientific problem of their choosing this project may be tied to one s on going research interest this is recommended difference from 18 337 note that the difference from the recent 18 337 parallel computing and scientific machine learning https github com mitmath 18337 is that 18 337 focuses on the mathematical and computational underpinning of how software frameworks train scientific machine learning algorithms in contrast this course will focus on the applications of scientific machine learning looking at the current set of methodologies from the literature and learning how to train these against scientific data using existing software frameworks consult 18 337 lecture 15 https mitmath github io 18337 lecture15 diffeq machine learning as a sneak preview of the problems one will get experience solving syllabus lectures monday tuesday wednesday and thursday 1 3pm 2 139 jan 6 31 note that there will be no lectures between 13 16 office hours there will be no formal office hours however help can be found at the julia lab 32 g785 during most working hours prerequisites while this course will be mixing ideas from machine learning and numerical analysis no one in the course is expected to have covered all of these topics before understanding of calculus linear algebra and programming is essential while julia will be used throughout the course prior knowledge of julia is not necessary but the ability to program is textbook other reading there is no textbook for this course or the field of scientific machine learning so most of the materials will come from primary literature for a more detailed mathematical treatment of the ideas presented in this course consult the 18 337 course notes https github com mitmath 18337 grading the course is based around the individual projects 33 of the grade is based on the writeup due on january 14th 34 of the grade is based on the project writeup and 33 is based on the project presentation collaboration policy make an effort to solve the problem on your own before discussing with any classmates when collaborating write up the solution on your own and acknowledge your collaborators individual project the goal of this course is to help the student get familiar with scientific machine learning in a way that could help their future research activities thus this course is based around the development of an individual project in the area of scientific machine learning potential projects along these lines could be recreation of a parameter sensitivity study in a field like biology pharmacology or climate science augmented neural ordinary differential equations https arxiv org abs 1904 01681 neural jump stochastic differential equations https arxiv org pdf 1905 10403 pdf acceleration methods for adjoints of differential equations improved methods for physics informed neural networks new applications of neural differential equations such as optimal control parallelized implicit ode solvers for large ode systems gpu parallelized ode sde solvers for small systems or anything else appropriate high performance implementations of library code related to scientific machine learning is also an appropriate topic so new differential equation solvers or gpu kernels for accelerated mapreduce is a viable topic the project is split into 3 concrete steps part 1 individual project proposal due january 12th the project proposal is a 2 page introduction to a potential project the proposal should go over the background of the topic and the proposed methodology to investigate the subject it should include at least 3 references and discuss alternative methods that could be used and what the pros and cons would be you should be discuss the potential project with the instructor before submission part 2 project presentation tba depending on the size of the course everyone will be expected to give a 20 minute presentation on their project introducing the class to their topic and their results this will take place during the last week of the course part 3 project report due february 4th the final project is a 8 20 page paper using the style template from the siam journal on numerical analysis http www siam org journals auth info php or similar the final project must include the code for the analysis as a reproducible julia project with an appropriate manifest model your paper on academic review articles e g read siam review and similar journals for examples by default these projects will be shared on the course website you may choose to opt out if necessary additionally projects focusing on novel research may consider submission to arxiv schedule of topics a brief overview of the topics is as follows introduction to scientific machine learning machine learning for sciml how to think about choose and train universal approximators introduction to julia package project development and writing efficient code applied numerical differential equations for scientific modeling estimating model parameters from data training neural networks to solve differential equations event handling and complex phenomena in neural embedded models physics informed neural networks pinns neural differential equations and universal differential equations acceleration of partial differential equations with neural network approaches choosing neural networks optimizers learning rates and diagnosing fitting issues turn neural networks back into equations with sindy tweaking differential equation solvers to accelerate fitting choices for gradient calculations differences and understanding the appropriate methods to use writing good code for gpu acceleration lecture 1 introduction to scientific machine learning lecture notes what is scientific machine learning https mitmath github io 18s096sciml lecture1 scientific ml pptx optional pre reading materials the essential tools of scientific machine learning http www stochasticlifestyle com the essential tools of scientific machine learning scientific ml workshop videos on scientific machine learning https icerm brown edu events ht19 1 sml sciml scientific machine learning software organization https sciml ai summary we will start off by setting the stage for the course the field of scientific machine learning and its span across computational science to applications in climate modeling and aerospace will be introduced the methodologies that will be studied in their various names will be introduced and the general formula that is arising in the discipline will be laid out a mixture of scientific simulation tools like differential equations with machine learning primitives like neural networks tied together through differentiable programming to achieve results that were previously not possible don t be worried if this introduction is fast we ll be going back through each of these examples in detail learning how to implement and use these methods today is mostly to introduce the motivation behind learning these methodologies lecture 2 introduction to julia for scientific machine learning lecture notes introduction to julia for scientific machine learning https mitmath github io 18s096sciml lecture2 ml optional pre reading materials differentialequations jl documentation https docs juliadiffeq org dev flux jl documentation https fluxml ai flux jl stable it may be a good idea to learn how to write efficient code here s a few materials along those lines optimizing serial code https mitmath github io 18337 lecture2 optimizing optimizing your diffeq code http tutorials juliadiffeq org html introduction 03 optimizing diffeq code html type dispatch design post object oriented programming for julia http www stochasticlifestyle com type dispatch design post object oriented programming julia performance matters https www youtube com watch v r tlsbdhe1a you re doing it wrong b heaps vs binary heaps and big o http phk freebsd dk b heap queue html summary in this lecture we start digging into the julia programming language for scientific computing and machine learning using the flux jl deep learning and differentialequations jl differential equation solver libraries we look at how to do basic tasks like define and train neural networks along with fitting parameters in a differential equation lecture 3 mixing differential equations and machine learning lecture notes mixing differential equations and machine learning https mitmath github io 18s096sciml lecture3 diffeq ml optional pre reading materials diffeqflux jl readme https github com juliadiffeq diffeqflux jl diffeqflux blog post https julialang org blog 2019 01 fluxdiffeq essential tools of scientific machine learning https www stochasticlifestyle com the essential tools of scientific machine learning scientific ml universal differential equations for scientific machine learning https arxiv org abs 2001 04385 summary in this lecture we start using the available tools to do scientific machine learning this includes tasks like solving ordinary differential equations with neural networks training neural ordinary differential equations and training universal differential equations we also look into the connection between convolutional neural networks and partial differential equations in order to establish an equivalence between the stencil operations in the two disciplines and use this as a way to interpret trained equations lecture 4 numerically solving partial differential equations lecture notes numerically solving partial differential equations https mitmath github io 18s096sciml lecture4 pde stiff optional pre reading materials optimizing your diffeq code https tutorials juliadiffeq org html introduction 03 optimizing diffeq code html solving stiff equations https docs juliadiffeq org latest tutorials advanced ode example the method of lines mol pde benchmarks from diffeqbenchmarks jl https github com juliadiffeq diffeqbenchmarks jl summary last time we ended with the relation between convolutional neural networks and pdes given this relationship and the general importance of pdes in scientific models we look into how to numerically solve partial differential equations in these lectures we show how to discretize pdes to turn them into odes and then write code that gives optimized fast pde solves we end by noticing that the ode solver choice can have a large effect on pde performance something that we will investigate in the following lecture lecture 5 stiffness stability and automatic differentiation lecture notes stiffness stability and automatic differentiation https mitmath github io 18s096sciml lecture5 stiffness summary here we take a deep dive into the phenomena of stiffness in the previous lecture we saw that changing our numerical solver gave a 20x performance increase and stiffness explains why we dig into the eigenvalue decomposition of the jacobian and see how the condition number effects some numerical methods more than others leading to a change in what methods will be good depending on this mysterious property we then start to look practically about what to do in the case of stiffness the analysis says we should use implicit methods but those methods require a costly newton step however by using things like sparsity detection and matrix coloring we can dramatically decrease the computational cost of implicit methods and rein in even large pde solves lecture 6 stochastic differential equations deep learning and high dimensional pdes lecture notes stochastic differential equations deep learning and high dimensional pdes https mitmath github io 18s096sciml lecture6 sdes summary now we explore an interesting offshoot in scientific machine learning stochastic differential equations and their utility in seemingly disconnected areas the first thing to do is the neural stochastic differential equation which is like the neural ordinary differential equation but allows for fitting random quanitites as well while this is a clear win a more subtle approach makes use of deep knowledge of partial differential equations we explore the connection between stochastic optimal control and the hamilton jacobi bellman equation and showcase that neural networks embedded within stochastic differential equations give a practical way of representing the forward backward stochastic differential equation and solve the general control problem which gives rise to a provably optimal neural network method for controlling drones financial markets etc given some underlying model lecture 7 a quick introduction to ad for scientists lecture notes a quick introduction to ad for scientists https mitmath github io 18s096sciml lecture7 ad summary automatic differentiation is a huge subject this quick lecture dives into the main points what is automatic differentiation when is it useful how do you choose between forward and reverse and describes ad in a fashion that should help a package user understand why they might have errors and how to handle using ad in a differentiable programming context | scientific-machine-learning differential-equations neural-networks neural-ode partial-differential-equations sciml lecture-notes | ai |
Database-Design-Pharmacy-Management-System | database design pharmacy management system a project cs6360 database design project https github com rahul1947 database design pahrmacy management system blob master cs6360 002 team23 pharmacymanagementsystem pdf designed and developed a comprehensive database management system for a modern pharmacy project report cs6360 002 team23 pharmacymanagementsystem pdf https github com rahul1947 database design pharmacy management system blob master cs6360 002 team23 pharmacymanagementsystem pdf authors rahul nalawade https github com rahul1947 ishan sharma https github com ishansharma b assignments cs6360 database design assignments https github com rahul1947 database design pahrmacy management system tree master assignments 5 assignments with their solutions assignment 01 er eer models assignment 02 sql oracle sql developer query table creation triggered actions and constraints assignment 03 relational algebra assignment 04 functional dependencies and normalization assignment 05 indexing for file structure b tree query tree | database database-management database-management-system functional-dependencies pharmacy-management entity-relationship-models entity-relationship-diagram drugstore prescription rdbms normalization relational-model relational-databases uml-diagram 3nf sql sql-query procedures trigger trigger-events | os |
InteR | knowledge refinement via interaction between search engines and large language models this repository is the implementation of knowledge refinement via interaction between search engines and large language models https arxiv org abs 2305 07402 1 create environment shell conda create n inter python 3 8 conda activate inter pip install r requirements txt pip install u openai pyserini 2 download index and passage data shell mkdir indexes wget https www dropbox com s rf24cgsqetwbykr lucene index msmarco passage tgz dl 0 wget https www dropbox com s 5vhl1aynl0kg3rj contriever msmarco index tar gz dl 0 then unzip two tgz files into indexes mkdir data msmarco wget https www dropbox com s yms13b9k850b3vt collection tsv dl 0 then put tsv file into data msmarco 3 run inter you may edit the openai key in main py first shell mkdir runs inter run dl19 sh for dl 19 run dl20 sh for dl 20 if you find this work helpful please cite our paper latex article feng2023knowledge title knowledge refinement via interaction between search engines and large language models author feng jiazhan and tao chongyang and geng xiubo and shen tao and xu can and long guodong and zhao dongyan and jiang daxin journal arxiv preprint arxiv 2305 07402 year 2023 | ai |
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teste-front-end-jr | teste econverse vaga desenvolvedor front end jr vem ser econverse segue abaixo as instru es para a execu o do teste instru es fa a um fork desse projeto para a sua conta pessoal do github desenvolva a p gina conforme as especifica es t cnicas crie um readme com as instru es para compilar testar e rodar o projeto o link do reposit rio dever ser enviado para o e mail ana nascimento econverse com br joao victor econverse com br e eduardo rodrigues econverse com br com o t tulo teste vaga frontend jr especifica es t cnicas desenvolver a pagina em react e typescript conforme o layout https www figma com file rwnzpeoxgynunpsjjv0vmv teste front end jr node id 0 3a1 para conseguir pegar os elementos do figma basta copiar o layout para sua conta que ter acesso de edi o montar a vitrine https app econverse com br teste front end junior tecnologia layout vitrine produtos png de produtos consumindo as informa es dos produtos em json atraves desse link https app econverse com br teste front end junior tecnologia lista produtos produtos json desenvolver a intera o ao clicar em um produto conforme layout a intera o consiste em abrir um modal com as principais informa es do produto presente no arquivo json https app econverse com br teste front end junior tecnologia lista produtos produtos json conforme o produto que clicar utilizar pr processador sass less ou stylus respeitar o layout pixel a pixel tamanho das fontes cores e bot es n o utilizar bibliotecas ui como bootstrap foundation ou afins pontos extras utilizar boas pr ticas de seo uso de html sem ntico o que avaliaremos em seu teste organiza o do projeto l gica do c digo componentiza o alcance dos objetivos propostos boa sorte | front_end |
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nuttx-apps | application folder contents general directory location built in applications nuttshell nsh built in commands synchronous built in commands application configuration file example built in application building nuttx with board specific pieces outside the source tree general this folder provides various applications found in sub directories these applications are not inherently a part of nuttx but are provided to help you develop your own applications the apps directory is a break away part of the configuration that you may choose to use or not directory location the default application directory used by the nuttx build should be named apps or apps x y z where x y z is the nuttx version number this apps directory should appear in the directory tree at the same level as the nuttx directory like nuttx apps if all of the above conditions are true then nuttx will be able to find the application directory if your application directory has a different name or is location at a different position then you will have to inform the nuttx build system of that location there are several ways to do that 1 you can define config apps dir to be the full path to your application directory in the nuttx configuration file 2 you can provide the path to the application directory on the command line like make appdir path or make config apps dir path 3 when you configure nuttx using tools configure sh you can provide that path to the application directory on the configuration command line like configure sh a app dir board name config name built in applications nuttx also supports applications that can be started using a name string in this case application entry points with their requirements are gathered together in two files builtin builtin proto h entry points prototype function builtin builtin list h application specific information and requirements the build occurs in several phases as different build targets are executed 1 context 2 depend and 3 default all application information is collected during the make context build phase to execute an application function exec builtin is defined in the apps include builtin builtin h nuttshell nsh built in commands one use of builtin applications is to provide a way of invoking your custom application through the nuttshell nsh command line nsh will support a seamless method invoking the applications when the following option is enabled in the nuttx configuration file conf config nsh builtin apps y applications registered in the apps builtin builtin list h file will then be accessible from the nsh command line if you type help at the nsh prompt you will see a list of the registered commands synchronous built in commands by default built in commands started from the nsh command line will run asynchronously with nsh if you want to force nsh to execute commands then wait for the command to execute you can enable that feature by adding the following to the nuttx configuration file conf config sched waitpid y the configuration option enables support for the waitpid rtos interface when that interface is enabled nsh will use it to wait sleeping until the built in command executes to completion of course even with config sched waitpid y defined specific commands can still be forced to run asynchronously by adding the ampersand after the nsh command application configuration file the nuttx configuration uses kconfig frontends tools and the nuttx configuration file config file for example the nuttx config may have conf config examples hello y this will select the apps examples hello in the following way the top level make will include apps examples make defs apps examples make defs will set configured apps appdir examples hello like this makefile ifneq config examples hello configured apps appdir examples hello endif example built in application an example application skeleton can be found under the examples hello sub directory this example shows how a builtin application can be added to the project one must 1 create sub directory as progname 2 in this directory there should be a make defs file that would be included by the apps makefile a kconfig file that would be used by the configuration tool see the file kconfig language txt in the nuttx tools repository this kconfig file should be included by the apps kconfig file a makefile and the application source code 3 the application source code should provide the entry point c main 4 set the requirements in the file makefile specially the lines makefile progname progname priority sched priority default stacksize 768 asrcs asm source file list as a asm b asm csrcs c source file list as foo1 c foo2 c 5 the make defs file should include a line like makefile ifneq config progname configured apps progname endif building nuttx with board specific pieces outside the source tree q has anyone come up with a tidy way to build nuttx with board specific pieces outside the source tree a here are three 1 there is a make target called make export it will build nuttx then bundle all of the header files libraries startup objects and other build components into a zip file you can move that zip file into any build environment you want you can even build nuttx under a dos cmd window this make target is documented in the top level nuttx readme txt 2 you can replace the entire apps directory if there is nothing in the apps directory that you need you can define config apps dir in your config file so that it points to a different custom application directory you can copy any pieces that you like from the old apps directory to your custom apps directory as necessary this is documented in nuttx boards readme txt and nuttx porting guide online at https cwiki apache org confluence display nuttx porting guide 3 if you like the random collection of stuff in the apps directory but just want to expand the existing components with your own external sub directory then there is an easy way to that too you just create a symbolic link in the apps directory that redirects to your application sub directory in order to be incorporated into the build the directory that you link under the apps directory should contain 1 a makefile that supports the clean and distclean targets see other makefile s for examples and 2 a tiny make defs file that simply adds the custom build directories to the variable configured apps like makefile configured apps my directory1 my directory2 the apps makefile will always automatically check for the existence of subdirectories containing a makefile and a make defs file the makefile will be used only to support cleaning operations the make defs file provides the set of directories to be built these directories must also contain a makefile that makefile must be able to build the sources and add the objects to the apps libapps a archive see other makefile s for examples it should support the all install context and depend targets apps makefile does not depend on any hardcoded lists of directories instead it does a wildcard search to find all appropriate directories this means that to install a new application you simply have to copy the directory or link it into the apps directory if the new directory includes a makefile and make defs file then it will automatically be included in the build if the directory that you add also includes a kconfig file then it will automatically be included in the nuttx configuration system as well apps makefile uses a tool at apps tools mkkconfig sh that dynamically builds the apps kconfig file at pre configuration time you could for example create a script called install sh that installs a custom application configuration and board specific directory a copy myboard directory to boards myboard b add a symbolic link to myapplication at apps external c configure nuttx usually by bash tools configure sh myboard myconfiguration use of the name apps external is suggested because that name is included in the gitignore file and will save you some nuisance when working with git export restrictions this distribution includes cryptographic software the country in which you currently reside may have restrictions on the import possession use and or re export to another country of encryption software before using any encryption software please check your country s laws regulations and policies concerning the import possession or use and re export of encryption software to see if this is permitted see http www wassenaar org for more information the u s government department of commerce bureau of industry and security bis has classified this software as export commodity control number eccn 5d002 c 1 which includes information security software using or performing cryptographic functions with asymmetric algorithms the form and manner of this apache software foundation distribution makes it eligible for export under the license exception enc technology software unrestricted tsu exception see the bis export administration regulations section 740 13 for both object code and source code the following provides more details on the included cryptographic software https tls mbed org supported ssl ciphersuites https github com intel tinycrypt | nuttx rtos embedded real-time mcu microcontroller | os |
cvclasses19 | miet https avatars0 githubusercontent com u 20048671 s 80 computer vision classes 2019 circleci https circleci com gh cvlabmiet cvclasses19 tree master svg style svg https circleci com gh cvlabmiet cvclasses19 tree master overview this is a computer vision classes prepared for the 2nd year m d students course includes topics covering basic areas of computer vision it takes 16 seminars discussion lessons and 8 practical lessons most practical lessons will be based on using of the latest opencv https github com opencv opencv library all materials and tasks will be hosted in this repository and available for all students all program code is written using c 17 required knowledge basics of digital signal processing basics of digital image processing git experience c experience course plan course consist of 26 seminars practical lessons 7 individual programming tasks 2019 plan https docs google com spreadsheets d e 2pacx 1vqed4iwdoidpr h6e k1bkvmh xq2l4fgpdfija8epcvm5a7nwc1s1empoc5fbutakbmvkuwzslpic8 pubhtml gid 319680911 single true repository overview the structure of repository is described below cmake contains cmake helper functions for checkstyle and ctest cvlib simple computer vision library to be developed in this course include public api src internal sources tests unit tests based on catch2 framework demo demo application based on algorithms of cvlib tools overview please ensure you have following instruments and settings to start the work git https git scm com cmake 3 9 https cmake org opencv latest http opencv org downloads html gcc 7 3 visual studio 15 2017 win64 camera to execute demo applications build steps 1 register github account if you haven t it yet 2 meet with git https git scm com book ru v1 and practice locally 3 fork current repository https github com cvlabmiet cvclasses18 4 configure git locally https git scm com book en v2 getting started first time git setup linux prerequesties sh sudo apt install gcc 7 sudo apt install libopencv dev build stay in root folder of the repository sh mkdir build cd build cmake make checkstyle make ctest run run unit tests sh build cvlib cvlib tests run demo sh build demo cvlib demo windows prerequesties 1 install latest cmake x64 https cmake org 2 download and unpack binaries of opencv 3 add system environment variables example opencv dir c users roman downloads opencv build path path opencv dir x64 vc15 bin 4 install visual studio 2017 build stay in root folder of the repository sh mkdir build cd build build cmake g visual studio 15 2017 win64 build cmake build config release note checkstyle is disabled in win32 environment run demo sh build demo release cvlib demo exe contacts any issues suggestions questions may be asked directly by email golovanov org miet ru created as separate issue in this repository https github com cvlabmiet cvclasses18 issues | computer-vision computer-vision-algorithms opencv classwork university-course cpp | ai |
HomePWN | supported python versions https img shields io badge python 3 6 blue svg style flat square logo python license https img shields io badge license gnu green svg style flat square logo gnu homepwn swiss army knife for pentesting of iot devices y y y homepwn iot pentesting ethical hacking created with by ideas locas cdo telefonica homepwn is a framework that provides features to audit and pentesting devices that company employees can use in their day to day work and inside the same working environment it is designed to find devices in the home or office take advantage of certain vulnerabilities to read or send data to those devices with a strong library of modules you can use this tool to load new features and use them in a vast variety of devices homepwn has a modular architecture in which any user can expand the knowledge base about different technologies principally it has two different components discovery modules these modules provide functionalities related to the discovery stage regardless of the technology to be used for example it can be used to conduct wifi scans via an adapter in monitor mode perform discovery of ble devices bluetooth low energy which other devices are nearby and view their connectivity status etc also it can be used to discover a home or office iot services using protocols such as ssdp or simple service discovery protocol and mdns or multicast dns specific modules for the technology to be audited on the other hand there are specific modules for audited technology today homepwn can perform auditing tests on technologies such as wifi nfc or ble in other words there are modules for each of these technologies in which different known vulnerabilities or different techniques are implemented to asses the device s security level implemented and communicated with this kind of technologies built with python https www python org download releases 3 0 programming language used prompt toolkit https python prompt toolkit readthedocs io en stable python command line documentation it s possible to read the documentation in our papers spanish version https github com elevenpaths homepwn blob master papers 5bpaper 5d 20homepwn es version 20 pdf english version https github com elevenpaths homepwn blob master papers 5bpaper 5dhomepwn eng pdf getting started these instructions will get you a copy of the project up and running on your local machine for development and testing purposes see deployment for notes on how to deploy the project on a live system prerequisites you need to have linux and python 3 6 running in your computer please install them in the download page ubuntu https ubuntu com debian https www debian org or similar python 3 6 https www python org downloads installing all requisites to install all dependencies in ubuntu 18 04 or derivatives use the file install sh sudo apt get update cd path to the homepwn project sudo install sh the script ask you if you want to create a virtualenv if your answer is y then it installs python libraries within the virtual environment if not in the system itself usage to run the script if you chose a virtual environment in the installation follow execute the next command to activate the virtual environment source homepwn bin activate launch the application sudo python3 homepwn py configure alpha card https askubuntu com questions 1122095 install alfa awus036nha driver on ubuntu 18 04 lts examples here are some videos to see how the tool works homepwn bluetooth low energy poc hacking homepwn bluetooth low energy poc hacking https img youtube com vi jgbisp7igxo 0 jpg https www youtube com watch v jgbisp7igxo homepwn bluetooth spoofing homepwn bluetooth spoofing https img youtube com vi o9p1bwlhelm 0 jpg https www youtube com watch v o9p1bwlhelm homepwn nfc clone homepwn nfc clone https img youtube com vi zlas04zctlu 0 jpg https www youtube com watch v zlas04zctlu homepwn ble capture on pcap file sniffing homepwn ble capture on pcap file sniffing https img youtube com vi vw9nr584pjq 0 jpg https www youtube com watch v vw9nr584pjq homepwn qr options hack homepwn qr options hack https img youtube com vi ta1dbnwof8m 0 jpg https www youtube com watch v ta1dbnwof8m homepwn apple ble discovery homepwn apple ble discovery https img youtube com vi xou34op7gls 0 jpg https www youtube com watch v xou34op7gls homepwn xiaomi iot advertisement homepwn xiaomi iot advertisement https img youtube com vi xi7kzibjsfe 0 jpg https www youtube com watch v xi7kzibjsfe authors this project has been developed by the team of ideas locas cdo telef nica to contact the authors ideaslocas telefonica com see also the list of contributors md contributors md who participated in this project contributing please read contributing md contributing md for details on our code of conduct and the process for submitting pull requests to us license this project is licensed under the gnu general public license see the license md license md file for details disclaimer 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 whenever you make a contribution to a repository containing notice of a license you license your contribution under the same terms and you agree that you have the right to license your contribution under those terms if you have a separate agreement to license your contributions under different terms such as a contributor license agreement that agreement will supersede this software doesn t have a qa process | iot hack python ble nfc | server |
Pewlett_Hackard_Analysis | pewlett hackard analysis background now that bobby has proven his sql chops his manager has given both of you two more assignments determine the number of retiring employees per title and identify employees who are eligible to participate in a mentorship program then you ll write a report that summarizes your analysis and helps prepare bobby s manager for the silver tsunami as many current employees reach retirement age what you re creating this new assignment consists of two technical analysis deliverables and a written report you will submit the following deliverable 1 export of the retirement titles table which corresponds with the retirement titles csv unique titles https user images githubusercontent com 67697826 198502212 5e594c15 30e8 4011 97bd 0e4b1fd3fda9 png export of the unique titles table which corresponds with the unique titles csv unique titles https user images githubusercontent com 67697826 198502534 76ebced5 7a4a 482a bb6b 915053fe0bd6 png deliverable 2 mentorship eligibility https user images githubusercontent com 67697826 198503811 2fe510a4 2f88 4d46 b2d0 019ce0341bf5 png deliverable 3 overview of the analysis 1 explain the purpose of this analysis 2 provide a bulleted list with four major points from the two analysis deliverables 3 provide high level responses to the following questions then provide two additional queries or tables that may provide more insight into the upcoming silver tsunami the purpose of this analysis is to prepare pewlett hackard for the impending retirement of a significant portion of the company s work force within a few short years pewlett hackard needs to prepare for the inevitable and needs comprehensive analysis of what measures the company needs to take to remain operational how many roles will need to be filled as the silver tsunami begins to make an impact the retirement titles provide much insight into the approaching issue of retiring employees every department is going to be impacted by these retirements and through analysis i have discovered how many future employees each department is going to need to fill export of the retiring titles table which corresponds with the retiring titles csv retiring titles https user images githubusercontent com 67697826 198503053 52ef2c1e 1813 4816 8791 b9c42c556039 png are there enough qualified retirement ready employees in the departments to mentor the next generation of pewlett hackard employees in my opinion based on my analysis there will be shortages of qualified staff to mentor the new and current employees needed to replace them | server |
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IoT-Spot | div align center h1 welcome to iot spot h1 h3 website for iot spot project repo a href https prathimacode hub github io iot spot click here a h3 div p align center img src https github com prathimacode hub prathimacode hub blob main cover 20photos iot spot png a p p align center a href https github com prathimacode hub img src https img shields io badge prs welcome brightgreen svg style flat logo github a a href https github com prathimacode hub img src https img shields io badge open 20source f0 9f a4 8d green a a href https github com prathimacode hub img src https img shields io static v1 svg label contributions message welcome color 0059b3 style flat square a a href https github com prathimacode hub iot spot graphs contributors img src https img shields io github contributors anon prathimacode hub iot spot a a href https github com prathimacode hub img src https img shields io maintenance yes 2022 a p p align center a href https github com prathimacode hub iot spot stargazers img src https badgen net github stars prathimacode hub iot spot a a href https github com prathimacode hub iot spot network members img src https badgen net github forks prathimacode hub iot spot a a href https github com prathimacode hub iot spot issues img src https badgen net github open issues prathimacode hub iot spot a a href https github com prathimacode hub iot spot issues q is 3aissue is 3aclosed img src https badgen net github closed issues prathimacode hub iot spot a a href https github com prathimacode hub iot spot pulls img src https badgen net github prs prathimacode hub iot spot a a href https github com prathimacode hub iot spot pulls img src https badgen net github open prs prathimacode hub iot spot a a href https github com prathimacode hub iot spot pulls q is 3apr is 3aclosed img src https badgen net github closed prs prathimacode hub iot spot a p repository an amazing spot to step your foot into this ocean of the internet of things dive deep and explore the fiesta of electronics broaden your vision at a single go enjoy your open source journey the main aim is to provide a cabin that would help you in mastering the internet of things and making your hands dirty while exploring an enriching field that makes your life turn smarter and be closer to technology turn yourself into a magician with all the hands on that got you covered join here anyone related to technology who is looking to contribute to open source is all invited to hop in this place has a task for everyone 8051 arm adafruit arduino atmel beagle esp32 esp8266 node mcu pic raspberry pi programming lanaguages c c python html css js domain knowledge iot web development 8051 scripts https github com prathimacode hub iot spot tree main 8051 the best repo to start with before you enter into the ocean of micro controllers it has all the projects related to the 8051 micro controller arm scripts https github com prathimacode hub iot spot tree main arm this place is home to all the scripts based on arm micro controllers and gets involved in exclusive projects adafruit scripts https github com prathimacode hub iot spot tree main adafruit this repo had all the scripts concerning adafruit to gain more wisdom on it arduino scripts https github com prathimacode hub iot spot tree main arduino an all time repo to scratch your cravings for arduino and indulge in a better way atmel scripts https github com prathimacode hub iot spot tree main atmel it has all the scripts that are related to atmel micro controllers to explore further beagle scripts https github com prathimacode hub iot spot tree main beagle this repo would have all the projects that use beagle boards bone to understand the scripts much deeper esp32 scripts https github com prathimacode hub iot spot tree main esp32 this place would contain all the esp32 scripts used in relevant projects esp8266 scripts https github com prathimacode hub iot spot tree main esp8266 this repo would have all the projects related to esp8266 you can pick up for your references minor scripts https github com prathimacode hub iot spot tree main minor 20scripts this repo has all minor scripts put up together on various controllers we can use while working on iot applications nodemcu scripts https github com prathimacode hub iot spot tree main node 20mcu this repo has all nodemcu scripts put up together that are widely useful in running automations pic scripts https github com prathimacode hub iot spot tree main pic this place would have the scripts that use pic micro controller and are useful in building the functional projects concerning the same raspberrypi scripts https github com prathimacode hub iot spot tree main raspberry 20pi this repo has all the scripts that are compatible with all the versions and models of raspberry pi real time scripts https github com prathimacode hub iot spot tree main real time 20scripts this place endorses with all the scripts which take real time inputs in parallel and makes the projects work functionally and promptly major scripts https github com prathimacode hub iot spot tree main major 20scripts this repo has all the major and impactful projects related to the internet of things when raising an issue do make sure to mention the kind of script project title short description of the project and how would you expect it to work as a good practice always link the issue number with a pull request issue number give these details when you raise a pr if you worked on or want to initiate a unique project and share it with the world you can do that here go through the contributing guidelines in contributing https github com prathimacode hub iot spot blob main contributing md subsequently also go through the github documentation on creating a pull request https help github com en github collaborating with issues and pull requests creating a pull request content templates to follow feature request https github com prathimacode hub iot spot blob main github issue template feature request md bug report https github com prathimacode hub iot spot blob main github issue template bug report md pull request https github com prathimacode hub iot spot blob main github pullrequest template md readme https github com prathimacode hub iot spot blob main github readme template md note one should follow these templates while creating a new issue or pull request project structure for iot based issues your projects should contain this flow to maintain similarity across all other projects make sure to note these things before you create a pr create a folder of your project title example if you want to add a led blinking using 8051 then the project should be placed under 8051 scripts with project title named as led blinking and file name as led blinking c if it s a c file the project repository you had created would have file name py or file name c or file name cpp this file is the project you have worked upon readme md this file is must be included to get a good understanding of the project elaborate briefly about how it works using readme https github com prathimacode hub iot spot blob main github readme template md template requirements txt in this file you should add all the requirements you need to make your project work which also illustrates the list of all components used specifically images this folder would have all images added be it screenshots or step by step process images also the inclusion of block diagrams and emulator circuit diagrams are a must to portray the project efficiently compilation video is also included since this repo is purely technical make sure you include block diagrams and emulator prototype circuit diagrams in images and direct them to the readme md file related files the other additional and related files would be added up in the related folder project structure for all kind of issues and pr s your projects should contain this flow to maintain similarity across all other projects make sure to note these things before you create a issue or pr create an issue with your idea approach your expected outcome and why it s useful to be necessary to be included in this project for pr include issue number along with pr template it s details and compiled output screenshot all the web related changes should go into iot spot website things to note make sure you do not copy codes from external sources because that work will not be considered plagiarism is strictly not allowed you can only work on issues that have been assigned to you if you want to contribute the script it s preferable that you create a new issue before making a pr and link your pr to that issue if you have modified added code make sure the code compiles before submitting strictly use snake case underscore separated in your file name and push it in the correct folder add screenshots and block diagrams to help us know what this script is all about do not update the readme md https github com prathimacode hub iot spot blob main readme md workflow fork the repository clone your forked repository using terminal or gitbash make changes to the cloned repository add commit and push then in github in your cloned repository find the option to make a pull request print start contributing for iot spot open source programs table tr td align center a href https ssoc getsocialnow co img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs social 20summer 20of 20code 202022 social 20summer 20of 20code png width 100px height 100px br sub b social summer of code 2022 b sub a td td align center a href https gssoc girlscript tech img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs girlscript 20summer 20of 20code 202022 girlscript 20summer 20of 20code 20logo png width 100px height 100px br sub b girlscript summer of code 2022 b sub a td td align center a href https swoc tech img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs script 20winter 20of 20code 202021 script 20winter 20of 20code jpg width 100px height 100px br sub b script winter of code 2021 b sub a td td align center a href https dwoc io img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs delta 20winter 20of 20code 202021 delta winter of code jpg width 100px height 100px br sub b delta winter of code 2021 b sub a td td align center a href https hacktoberfest digitalocean com img src https github com prathimacode hub prathimacode hub blob main open 20source 20programs hacktoberfest 202021 hacktoberfest 2021 jpg width 100px height 100px br sub b hacktoberfest 2021 b sub a td tr table achievements of this project repo one recognized as the best project admin for the project iot spot https github com prathimacode hub iot spot in girlscript summer of code 2022 br two recognized as the best organization for fiesta of projects undertaking the project iot spot https github com prathimacode hub iot spot at delta winter of code for the year 2021 22 br leaderboard table tr td align center a href https github com prathimacode hub iot spot blob main github gssoc leaderboard md img src https cdn icons png flaticon com 512 1986 1986941 png width 100px alt br sub b gssoc leaderboard 2022 b sub a td td align center a href https github com prathimacode hub iot spot blob main github swoc leaderboard md img src https cdn icons png flaticon com 512 1986 1986941 png width 100px alt br sub b swoc leaderboard 2021 b sub a td tr table hall of fame thanks go to these wonderful people contributions of any kind are welcome all contributors list start do not remove or modify this section prettier ignore start markdownlint disable a href https github com prathimacode hub iot spot graphs contributors img src https contrib rocks image repo prathimacode hub iot spot a markdownlint enable prettier ignore end all contributors list end code of conduct you can find our code of conduct here https github com prathimacode hub iot spot blob main code of conduct md license this project follows the mozilla public license 2 0 https github com prathimacode hub iot spot blob main license project mentors ssoc table td td align center a href https github com narayan954 img src https avatars githubusercontent com u 77617189 v 4 width 100px alt br sub b narayan soni b sub a td td td td align center a href https github com rushijaviya img src https avatars githubusercontent com u 59735978 v 4 width 100px alt br sub b rushi javiya b sub a td td align center a href https github com snktshrma img src https avatars githubusercontent com u 74557164 v 4 width 100px alt br sub b sanket sharma b sub a td td td align center a href https github com mdnazisharman2803 img src https avatars githubusercontent com u 98539013 v 4 width 100px alt br sub b md nazish arman b sub a td td td align center a href https github com harshil jani img src https avatars githubusercontent com u 79367883 v 4 width 100px alt br sub b harshil jani b sub a td td table project admin table tr td align center a href https github com prathimacode hub img src https github com prathimacode hub prathimacode hub blob main profile 20assets prathima kadari picture png width 100px alt br sub b prathima kadari b sub a td tr table visitor count https profile counter glitch me prathimacode hub count svg stargazers over time stargazers over time https starchart cc prathimacode hub iot spot svg https starchart cc prathimacode hub iot spot give this project a star github followers https img shields io github followers prathimacode hub svg label follow 20 prathimacode hub style social https github com prathimak88 twitter follow https img shields io twitter follow prathimak88 style social https twitter com prathimak88 if you liked working on this project do and share this repository happy contributing sup kbd click here https github com prathimacode hub prathimacode hub blob main projects opensource projects md kbd to view my 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forthebadge com | internet-of-things electronics 8051 arm adafruit arduino atmel beagleboard esp32 esp8266 node-mcu pic raspberry-pi iot-projects github hacktoberfest iot contributions-welcome open-source hacktoberfest2022 | server |
facets | introduction the facets project contains two visualizations for understanding and analyzing machine learning datasets facets overview and facets dive the visualizations are implemented as polymer https www polymer project org web components backed by typescript https www typescriptlang org code and can be easily embedded into jupyter notebooks or webpages live demos of the visualizations can be found on the facets project description page https pair code github io facets facets overview overview visualization of uci census data img overview census png overview visualization of uci census data lichman m 2013 uci machine learning repository http archive ics uci edu ml datasets census income irvine ca university of california school of information and computer science overview gives a high level view of one or more data sets it produces a visual feature by feature statistical analysis and can also be used to compare statistics across two or more data sets the tool can process both numeric and string features including multiple instances of a number or string per feature overview can help uncover issues with datasets including the following unexpected feature values missing feature values for a large number of examples training serving skew training test validation set skew key aspects of the visualization are outlier detection and distribution comparison across multiple datasets interesting values such as a high proportion of missing data or very different distributions of a feature across multiple datasets are highlighted in red features can be sorted by values of interest such as the number of missing values or the skew between the different datasets the python code to generate the statistics for visualization can be installed through pip install facets overview as of version 1 1 0 the facets overview package requires a version of protobuf at version 3 20 0 or later details about overview usage can be found in its readme facets overview readme md facets dive dive visualization of uci census data img dive census png dive visualization of uci census data lichman m 2013 uci machine learning repository http archive ics uci edu ml datasets census income irvine ca university of california school of information and computer science dive is a tool for interactively exploring up to tens of thousands of multidimensional data points allowing users to seamlessly switch between a high level overview and low level details each example is a represented as single item in the visualization and the points can be positioned by faceting bucketing in multiple dimensions by their feature values combining smooth animation and zooming with faceting and filtering dive makes it easy to spot patterns and outliers in complex data sets details about dive usage can be found in its readme facets dive readme md setup usage in google colabratory jupyter notebooks using facets in google colabratory https colab research google com and jupyter http jupyter org notebooks can be seen in this notebook https colab research google com github pair code facets blob master colab facets ipynb these notebooks work without the need to first download install this repository both facets visualizations make use of html imports so in order to use them you must first load the appropriate polyfill through script src https cdnjs cloudflare com ajax libs webcomponentsjs 1 3 3 webcomponents lite js script as shown in the demo notebooks in this repo note that for using facets overview in a jupyter notebook there are two considerations 1 in the notebook you will need to change the path that the facets overview python code is loaded from to the correct path given where your notebook kernel is run from 2 you must also have the protocol buffers python runtime library installed https github com google protobuf tree master python if you used pip or anaconda to install jupyter you can use the same tool to install the runtime library when visualizing a large amount of data in dive in a juypter notebook as is done in the dive demo jupyter notebook facets dive dive demo ipynb you will need to start the notebook server with an increased iopub data rate this can be done with the command jupyter notebook notebookapp iopub data rate limit 10000000 code installation git clone https github com pair code facets cd facets building the visualizations if you make code changes to the visualization and would like to rebuild them follow these directions 1 install bazel https bazel build 2 build the visualizations bazel build facets facets jupyter run from the facets top level directory using the rebuilt visualizations in a jupyter notebook if you want to use the visualizations you built locally in a jupyter notebook follow these directions 1 move the resulting vulcanized html file from the build step into the facets dist directory cp f bazel bin facets facets jupyter html facets dist 2 install the visualizations into jupyter as an nbextension if jupyter was installed with pip you can use jupyter nbextension install facets dist if jupyter was installed system wide or jupyter nbextension install facets dist user if installed per user run from the facets top level directory you do not need to run any follow up jupyter nbextension enable command for this extension alternatively you can manually install the nbextension by finding your jupyter installation s share jupyter nbextensions folder and copying the facets dist directory into it 3 in the notebook cell s html link tag that loads the built facets html load from nbextensions facets dist facets jupyter html which is the locally installed facets distribution from the previous step known issues the facets visualizations currently work only in chrome issue 9 issues 9 disclaimer this is not an official google product | machine-learning data-visualization | ai |
CV | deployment of computer vision models with django on aws ec2 the aim of this tutorial is to demonstrate how to deploy ml and cv models using django i have gone through the process of deploying ml models in a django website on aws ec2 instances the process is a bit tedious and full of small details so i thought to share my experience the target is not to have the best and most secure website so there are a lot of security issues non standard web development practices simple styles etc the main target here is to demonstrate the process of deploying ml model in a website using aws ec2 layout of the tutorial local dev side ml models side django framework side server side website settings remote server setup apache configuration remote instance ec2 choice local development side ml cv tasks cv tasks https ml4a github io images figures localization detection png source stanford course cs231n url https ml4a github io images figures localization detection png we will demo three computer vision tasks where in each of them the website asks to load an image and it produces the corresponding output since our target is just to demo the deployment we use pre trained models note that all the steps mentioned are still applicable in case of custom trained models we use examples from the top two ml frameworks tensorflow keras and pytorch the tasks demonstrated are click on each to see the corresponding colab classification https colab research google com drive 1pffqb11rkzpbpkhq57yin8onllnfoixe we use pre trained vgg model with keras details can be found in this colab https colab research google com drive 1pffqb11rkzpbpkhq57yin8onllnfoixe the user loads and image and gets a class representing the most dominant object class in the image according to imagenet dataset classes https gist github com yrevar 942d3a0ac09ec9e5eb3a cls imgs cls png semantic segmentaion https colab research google com drive 1qpdmwyzqdqeoaut7edp1jflrefem1kx9 we use pre trained fcn model from torchvision details can be found in this colab https colab research google com drive 1qpdmwyzqdqeoaut7edp1jflrefem1kx9 the user loads and image and gets an image that represent the semantic pixel wise mask of all the classes in the image according to cocoo dataset classes https pytorch org docs stable torchvision models html object detection instance segmentation and person keypoint detection background aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor ss imgs ss png object detection https colab research google com drive 1rws0bnfaejb7 8mexvllzkspvbjabq2i authuser 1 scrollto svfygrrhmw0f we use pre trained faster r cnn model from torchvision details can be found in this colab https colab research google com drive 1utmfq11e lv2zlgtjoyfovupbge0w9fg the user loads and image and gets an image with single multiple objects detected with an object bounding box obb and its class name with certainity more than 80 threshold classes are from cocoo dataset object detection classes https www learnopencv com faster r cnn object detection with pytorch background person bicycle car motorcycle airplane bus train truck boat traffic light fire hydrant n a stop sign parking meter bench bird cat dog horse sheep cow elephant bear zebra giraffe n a backpack umbrella n a n a handbag tie suitcase frisbee skis snowboard sports ball kite baseball bat baseball glove skateboard surfboard tennis racket bottle n a wine glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot dog pizza donut cake chair couch potted plant bed n a dining table n a n a toilet n a tv laptop mouse remote keyboard cell phone microwave oven toaster sink refrigerator n a book clock vase scissors teddy bear hair drier toothbrush obb imgs obb png django framework why django django provides a full framework to develop both backend and front end parts of a website in python flask is easier but requires some extra effort to add front end part to start a demo website the easiest way is to follow the official django tutorial here https docs djangoproject com en 3 0 intro tutorial01 bootstrap we use bootstrap for the styles and look and feel we add the following on top our base html which we extend in all our templates see below bootstrap css 4 3 1 link rel stylesheet href https maxcdn bootstrapcdn com bootstrap 4 3 1 css bootstrap min css jquery library script src https ajax googleapis com ajax libs jquery 4 3 1 jquery min js script latest compiled javascript script src https maxcdn bootstrapcdn com bootstrap 4 3 1 js bootstrap min js script install django pip install django start a project now we create our deployment project inside the repo main folder cv django admin startproject deployment this will create the basic website files like the entry point of the landing page urls routing etc but no applications yet add our computer vision cv application here we create our computer vision 3 tasks application python manage py startapp cv this will create the application files like the urls routing backends views etc in the current repo we also have another app which is taking video and predicting semantic segmentation and object detection for the whole video called cv vid python manage py startapp cv vid now we created the project skeleton cv deployment deployment cv cv vid below we configure the important parts for our website to work urls routing here we configure the routes of our website we have a main application route deployment urls py from django conf import settings urlpatterns path cv include cv urls urlpatterns static settings media url document root settings media root the last line is particularly important since the media url is the path to the media directory which has all the image files input and output that the code will use so it s impartant to have that url configured so that the templates can find those images when passed from the backend in other words the media directory is the link or shared space the links the backend and the front end and 3 sub apps routes representing our 3 tasks deployment cv urls py from django urls import path from import views urlpatterns path views base name base path classification views classification name classification path semantic segmentation views semantic segmentation name semantic segmentation path object detection views object detection name object detection the configured urlpatterns will set the default landing page to the base html which will be rendered using the backed function views base this is simply rendering the base html template front end the base html has the main navigation bar when the user clicks any of the tasks the website is routed to the required backend ul class navbar nav li class nav item a class nav link href classification classification a li li class nav item a class nav link href semantic segmentation semantic segmentation a li li class nav item a class nav link href object detection object detection a li ul each href above will route to the configured url in cv urls py we already configured which backend handler will take care of those in this way this is the main maestro of the app routes templates we need front end html files that handle the requests or urls above those reside in the cv templates cv folder for django to be able to find your custom templates you must edit the templates variable in settings py as follows templates dirs os path join base dir cv templates os path join base dir cv vid templates base html this is just a fancy website theme that all the other 3 tasks templates inherit from it uses bootstrap for a modern look and feel classification html this one will have an upload file form and output processing part that renders the uploaded image the returned prediction raw extends base html block content form method post enctype multipart form data csrf token input type file name myfile button type submit upload button form if original img h3 prediction h3 img src original img alt prediction width 500 height 333 endif endblock endraw semantic segmentation html this one will have an upload file form and output processing part that renders the uploaded image and the segmented image that is passed from the backed raw block content form method post enctype multipart form data csrf token input type file name myfile button type submit upload button form if original img img src original img alt original width 500 height 333 endif if segmented img img src segmented img alt segmented width 500 height 333 endif endblock endraw object detection html this is similar to the semantic segmentation html template backend cv views py configure the 3 tasks call backs this where all the action happens or the backend each of one those does the following handles the post request of the upload button if not file uploaded the basic template without predictions is rendered passes the uploaded image file to the prediction model renders back the html template with the returned prediction from the model in cases of object detection and segmentation those are the paths of the saved images def classification request if request method post and request files myfile myfile request files myfile fs filesystemstorage filename fs save myfile name myfile img file fs url filename pred make prediction return render request cv classification html original img img file prediction pred return render request cv classification html def semantic segmentation request if request method post and request files myfile myfile request files myfile fs filesystemstorage filename fs save myfile name myfile img file fs url filename seg file get segmentation return render request cv semantic segmentation html original img img file segmented img media seg img png return render request cv semantic segmentation html def object detection request if request method post and request files myfile myfile request files myfile fs filesystemstorage filename fs save myfile name myfile img file fs url filename obb file detect objects return render request cv object detection html original img img file obb img media obb img png return render request cv object detection html deploy local cd cv deployment python manage py runserver now of you in the browser to 127 0 0 1 8000 cv you will see the website up and running collect requirements txt eiter you are working inside a virtual environmment in which case you just need to pip freeze requirments txt or if you just work with system wide python or conda environment then you can use pipreqs pip install pipreqs cd project dir pipreqs this will get the requirements txt push to git you first need to add git cd project dir git init now if you work on github or any other git framework remote host you need to add this to your remote git remote add remote repo url now we push to git git add git commit am commit msg git push origin now you are good to go to the server to deploy from there server side deploy on aws ec2 ec2 instance choice create aws account log on and go to ec2 https console aws amazon com ec2 launch new instance launch ec2 imgs launch ec2 png choose free tier unless you want to buy strong computation check pricing https aws amazon com ec2 pricing choose ubuntu instance ubuntu ec2 imgs ubuntu ec2 png instance type ec2 imgs instance type ec2 png choose private key keep the private key otherwise you won t be able to access the instance via ssh ec2 key imgs ec2 key png review and launch launch rev imgs launch rev png configure the security policy enable all traffic on inbound and outbound rules security policy imgs security policy png make sure to add for custom tcp as well as ssh for both inbound and outbound and add port range 8000 in inbound rules inbound rules imgs inbound rules png inbound all traffic imgs inbound all traffic png outbound rules imgs outbound rules png outbound all traffic imgs outbound all traffic png ec2 instance setup connect ssh to your instance command line if you press connect in the ec2 console you get clear instructions make sure to choose the instance doing so is just fine to ssh however you still to connect ftp client to download and upload files to the instance putty can be used but in this way you need to do different steps for ftp and ssh ss cmd imgs ssh 20ec2 20commandline png mobaxterm i found mobaxterm a conventient 2 in 1 alternative here s how to use to connect to your instance new session mobaxterm imgs mobaxterm png configure the ip dns name and user ubuntu add private key which you saved when creating the instance mobasettings imgs mobaxterm 20settings png now you are connected mobaxterm connected imgs mobaxterm connected png prepare the basic packages sudo apt update sudo apt upgrade sudo apt install python3 pip python3 m pip install upgrade pip try demo website at this point it s nice to start a demo django website to make sure your instance is accessible sudo apt install python3 django mkdir website cd website django admin startproject deployment cd deployment python3 manage py runserver 0 0 0 0 8000 sudo ufw allow 8000 python3 manage py runserver 0 0 0 0 8000 you will get an error like invalid http host header ec2 xxxx compute 1 amazonaws com 8000 you may need to add ec2 xxxx compute 1 amazonaws com to allowed hosts modify your settings file to enable the remote host ip allowed hosts server domain or ip now you will notice if you closed ssh the website is down as a workaround we can run the server in the background using the screen command screen python3 manage py runserver 0 0 0 0 8000 then ctrl a d to detach the screen now the website will keep running if you want to stop at any time just ssh to the instance and see the running processes ps a or screen ls and kill it usually it is a python or python3 process you can kill with screen screen x s process name ex 32803 pts 0 ip 172 31 29 101 quit your own wbesite now get the code to the server git clone https github com ahmadelsallab cv git virtual env we need our code to run inside a virtual environment since we will need this later in apache configuration this will contain the python interpreter and all the site packages we need for the project pip install virtualenv now create and activate the env virtualenv venv source venev bin activate in python3 python3 m venv path to new virtual environment now inside the venv we install the requirements pip install r requirements exceptions for manual installation system wide packages sudo apt install python3 opencv memory error tensorflow pip install no cache dir tensorflow using django dev server on the server side sudo ufw allow 8000 python manage py runserver 0 0 0 0 8000 modify your settings file to enable the remote host ip allowed hosts server domain or ip on the local side connect to your remote machine in ssh as described then in the browser open the ip address 8000 cv or dns name 8000 cv you will see the website up and running what s wrong with this approach you need to keep connected with ssh to the machine if it s closed the website is down apache and mod wsgi we use apache to prevent the above problem and have a permenant server listening to our website requests even is ssh disconnects apache a server program to listen on server ports and serve requests mod wsgi python wrapper package to run python based websites on the server all details can be found here https www digitalocean com community tutorials how to serve django applications with apache and mod wsgi on ubuntu 16 04 assuming you work with python3 setup packages for apache2 and mod wsgi sudo apt get update sudo apt get install python3 pip apache2 libapache2 mod wsgi py3 apache configuration all details can be found here https www digitalocean com community tutorials how to serve django applications with apache and mod wsgi on ubuntu 16 04 now you need to edit the apache2 configuration file sudo nano etc apache2 sites available 000 default conf this file tells apache which entry point to serve when called with a certain url you need to add the following virtualhost 80 directory home user project directory cv deployment deployment files wsgi py require all granted files directory wsgidaemonprocess cv python home home user venv directory python path home user project directory cv deployment deployment wsgiprocessgroup cv wsgiscriptalias home user project directory cv deployment deployment wsgi virtualhost the important parts are wsgiscriptalias which sets the entry point when the user goes to the ec2 instance ip address or ec2 dns name in the browser then the wsgidaemonprocess specifies python home which points to our venv to know which python to use and the site packages then the python path which is added to the global pythonpath to be able to import our apps as python packages restart apache sudo systemctl restart apache2 if failed you can check syntax using apachectl configtest for any error log cat var log apache2 error log go to the browser open the ip address cv or dns name cv you will see the website up and running | ai |
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blockchain_rust | blockchain rust rust utxo rust libp2p rust 1 https mp weixin qq com s biz mzg5mja1odyzng mid 2247484460 idx 1 sn b79b1051f40db383a2d2feb568cb3fe8 chksm cfc2a94ff8b52059b2402785330133ce6a6734a3abcd3343c08154716acca5eb8369a4f4cd12 token 1912223334 lang zh cn rd rust 2 pow https mp weixin qq com s biz mzg5mja1odyzng mid 2247484469 idx 1 sn c722722580f9838b9136cf3ac6611c8e chksm cfc2a956f8b520405e0aa11fc1484d3b676f6f9b19cb536165e7fb0602d4db03f63167dcf59b token 1151139300 lang zh cn rd rust 3 https mp weixin qq com s biz mzg5mja1odyzng mid 2247484477 idx 1 sn cf1789dcbc1a7ca9381539e36314a2e9 chksm cfc2a95ef8b52048027a6466c097f5954815a50e29ba0a22687fc6f218a552378e963ff6a9a2 token 1609755589 lang zh cn rd rust 4 utxo https mp weixin qq com s biz mzg5mja1odyzng mid 2247484487 idx 1 sn 01802f8dc60ac7dd1924888937b65d50 chksm cfc2a924f8b520326a25718a2b97e24aac25355578ab727ae5fd6e907030ec39cc434cb90752 token 1933715555 lang zh cn rd rust 5 https mp weixin qq com s biz mzg5mja1odyzng mid 2247484495 idx 1 sn 4eea98f046a92bb9e87163bab44aff68 chksm cfc2a92cf8b5203af27a527d01f1cf699d77089a3ad2e7082fc7e707ae2885e96a611a8069f8 token 391513474 lang zh cn rd rust 6 p2p https mp weixin qq com s biz mzg5mja1odyzng mid 2247484503 idx 1 sn 82427d27153bce04488b95878e7584f0 chksm cfc2a934f8b5202274556e6ea3294b48dc8ee5075f559fce65f7ce91d9a2619e70a915252d20 token 2032429111 lang zh cn rd coding image https github com justin02180218 distribute election bully blob master qrcode for gh 8a5b7b90c100 258 jpg | blockchain |
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FacebookClient | facebookclient group 7 requirement minimum sdk api 28 android 9 0 pie | front_end |
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DE_Covid_Analysis | de covid analysis this is a repo for covid analysis data engineering project with cloud computing and python | cloud |
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Prompt-Transferability | on transferability of prompt tuning for natural language processing prompt transferability version https img shields io badge version v0 1 0 blue color ff8000 color 009922 https img shields io badge version v0 1 0 blue license mit https img shields io badge license mit orange svg https opensource org licenses mit doi https img shields io badge doi 10 18653 v1 2022 naacl green color ff8000 color 009922 https aclanthology org 2022 naacl main 290 github stars https img shields io github stars thunlp prompt transferability style social https github com thunlp prompt transferability stargazers open in colab https colab research google com assets colab badge svg https colab research google com drive 1xue9rlc2k9ebfax9ido1x9j9zrkoueo usp sharing open in colab https colab research google com assets colab badge svg https colab research google com drive 1vcsidax pgkrsjzouanh14d8fo7g9gbz usp sharing this is the source code of on transferability of prompt tuning for natural language processing an naacl 2022 https 2022 naacl org paper pdf https aclanthology org 2022 naacl main 290 overview prompt transferability github profile prompt transferbility github png prompt tuning pt is a promising parameter efficient method to utilize extremely large pre trained language models plms which can achieve comparable performance to full parameter fine tuning by only tuning a few soft prompts however pt requires much more training time than fine tuning intuitively knowledge transfer can help to improve the efficiency to explore whether we can improve pt via prompt transfer we empirically investigate the transferability of soft prompts across different downstream tasks and plms in this work we find that 1 in zero shot setting trained soft prompts can effectively transfer to similar tasks on the same plm and also to other plms with a cross model projector trained on similar tasks 2 when used as initialization trained soft prompts of similar tasks and projected prompts of other plms can significantly accelerate training and also improve the performance of pt moreover to explore what decides prompt transferability we investigate various transferability indicators and find that the overlapping rate of activated neurons strongly reflects the transferability which suggests how the prompts stimulate plms is essential our findings show that prompt transfer is promising for improving pt and further research shall focus more on prompts stimulation to plms reproduce results in the paper prompt transferability 1 0 prompt transferability 1 0 provides the original codes and details to reproduce the results in the paper prompt transferability 2 0 latest https github com thunlp prompt transferability tree main refactors the prompt transferability 1 0 and provides more user friendly codes for users in this readme md we mainly demostrate the usage of the prompt transferability 2 0 latest setups python 3 8 0 we recommend to create a new anaconda environment to manage the required packages bash conda create n prompt transfer python 3 8 0 conda activate prompt transfer bash requirements sh b note b if the system shows error about the torch please find the proper version that can matches your cpus or gpus error invalid requirement torch 1 9 0 cu111 torchvision 0 10 0 cu111 torchaudio 0 9 0 from line 10 of requirements txt please manually run pip install torch 1 9 0 cu111 torchvision 0 10 0 cu111 torchaudio 0 9 0 f https download pytorch org whl torch stable html user can also directly create the environment via prompt transferability 2 0 latest environment yml bash conda env create f environment yml dataset excute prompt transferability 2 0 latest download data sh to download datasets bash cd prompt transferability 2 0 latest bash download data sh usage you can easily use prompthub for various perposes including prompt training evaluation cross task transfer cross model transfer and activated neuron the colab notebook https colab research google com drive 1xue9rlc2k9ebfax9ido1x9j9zrkoueo usp sharing and the example script prompt transferability 2 0 latest example train py also demonstrate the usages or you can run the bash file to run a quick example bash cd prompt transferability 2 0 latest bash example train sh the script of train sh is bash dataset sst2 learningrate 1e 2 epoch 3 python example train py output dir outputs dataset dataset learning rate learningrate num train epochs epoch save total limit 1 evaluation strategy epoch save strategy epoch load best model at end true metric for best model combined score the above code train py shows an example that includes prompt training evaluation activated neuron analysis on a specific dataset python from prompt hub hub import prompthub from prompt hub training args import prompttrainingarguments output outputs dataset 1 sst2 dataset 2 rotten tomatoes model roberta base prmoptlen 100 learningrate 1e 2 training config args prompttrainingarguments output dir output dataset dataset 1 backbone model prompt len prmoptlen learning rate learningrate trainer prompthub args args prompt training and evaluation trainer train prompt trainer eval prompt cross task evaluation cross task eval results trainer cross task eval model dataset 1 dataset 1 activated neuron activated neuron before relu activated neuron after relu trainer activated neuron args backbone args dataset output output directory dataset 1 source training dataset this framework supports all datasets here we select sst2 as the example dataset 1 target dataset this framework supports all datasets here we select rotten tomatoes as the example model backbone models this framework supports bert roberta gpt and t5 v1 1 currently we select roberta base as the example promptlen the length of prompt we set 100 here learningrate learning rate we set 1e 2 here detailed usage prompt tuning prompt transferability github profile prompt tuning png users can use the well trained prompts in prompt transferability 2 0 latest task prompt emb or re train the prompts by your own as the following instruction step 1 initialization of arguments and trainer we first need to define a set of arguments or configurations including backbone backbone model dataset prompt len the length of soft prompt etc then we instantiate a prompthub object and provide the parameters python from prompt hub training args import prompttrainingarguments training config output outputs dataset 1 sst2 dataset 2 rotten tomatoes model roberta base prmoptlen 100 learningrate 1e 2 args prompttrainingarguments output dir output dataset dataset 1 backbone model prompt len prmoptlen learning rate learningrate trainer prompthub args args for a complete list of arguments please refer to prompt transferability 2 0 latest prompt hub training args py and huggingface transformers training arguments for more details step 2 prompt training then we start training a soft prompt optional you can pass the parameters to overwrite the default configurations in the arguments you passed in python optional arguments to overwrite default parameters trainer train prompt roberta large sst2 trainer train prompt step 3 prompt evaluation with the prompt trained on specific dataset and utilized backbone model we excute the following code to evaluate its performance python optional arguments to overwrite default parameters eval results trainer eval prompt roberta base sst2 eval results trainer eval prompt cross task transfer prompt transferability github profile cross task gif prompt can directly transfer among tasks here we provide an example to transfer the prompt trained from sst2 dataset to rotten tomatoes dataset python cross task eval results trainer cross task eval roberta base sst2 rotten tomatoes cross model transfer prompt transferability github profile cross model gif prompt can utilize a well trained projector to transfer among different backbones step 1 cross model training we first use sst2 datase and ttrain a projector that can transfer from roberta base to roberta large python trainer cross model train source model roberta base target model roberta large task sst2 step 2 cross model evaluation then we utilize it to transfer the prompt among different models python cross model eval results trainer cross model eval source model roberta base target model roberta large task sst2 transferability indicators activated neuron prompt transferability github profile activated neurons gif prompt can be seen as a paradigm to manipulate plms stimulate artificial neurons knowledge to perform downstream tasks we further observe that similar prompts will activate similar neurons thus the activated neurons can be a transferability indicator definition of neurons the output values between 1st and 2nd layers of feed forward network ffn in every layer of a plm refer to section 6 1 in the paper step 1 acquire task specific neurons given a model and the trained task specific prompt you can obtain the activated neurons values python activated neuron before relu activated neuron after relu trainer activated neuron roberta base sst2 step 2 similarity transferability between two tasks you can caculate the similarity transferability between two prompts via actiaved neurons python cos sim trainer neuron similarity backbone roberta base task1 sst2 task2 rotten tomatoes step 3 masked neurons to further demonstrate the importance of task specific neurons we mask them and find the model performance on the corresponding task will degrade visualization of activated neurons is also supported python eval metric mask trainer mask activated neuron args backbone args dataset ratio 0 2 trainer plot neuron open in colab https colab research google com assets colab badge svg https colab research google com drive 1vcsidax pgkrsjzouanh14d8fo7g9gbz usp sharing citations doi https img shields io badge doi 10 18653 v1 2022 naacl green color ff8000 color 009922 https aclanthology org 2022 naacl main 290 please cite our paper if it is helpful to your work bibtex inproceedings su etal 2022 transferability title on transferability of prompt tuning for natural language processing author su yusheng and wang xiaozhi and qin yujia and chan chi min and lin yankai and wang huadong and wen kaiyue and liu zhiyuan and li peng and li juanzi and hou lei and sun maosong and zhou jie booktitle proceedings of the 2022 conference of the north american chapter of the association for computational linguistics human language technologies month jul year 2022 address seattle united states publisher association for computational linguistics url https aclanthology org 2022 naacl main 290 doi 10 18653 v1 2022 naacl main 290 pages 3949 3969 contact yusheng su https yushengsu thu github io mail yushengsu thu gmail com suys19 mauls tsinghua edu cn | nlp parameter-efficient-learning pretrained-language-model pretrained-language-models pretrained-models prompt prompt-tuning pytorch transfer-learning parameter-efficient-tuning | ai |
frontend-test | beetrack front end test introducci n la siguiente prueba busca evaluar los conocimientos en javascript css y html para consultas enviar un correo a engineering beetrack com mailto engineering beetrack com descripci n e instalaci n se adjunta un proyecto de nodejs con una peque a base de datos y una api ya desarrollada para ejecutar el proyecto una vez descargado hay que correr las siguientes l neas por l nea de comandos en el root del proyecto npm install node server js los requisitos son nicamente tener instalado una versi n de node 4 y npm si todo funcion correctamente va a correr el servidor y vas a poder acceder a trav s de cualquier navegador en la direcci n localhost 3000 http localhost 3000 se deber a visualizar el html ubicado en public index html desarrollo el objectivo del test es desarrollar 2 vistas utilizando la api incluida en el proyecto la primera consiste en una lista paginada de usuarios con un buscador se debe visualizar la imagen del usuario con su respectivo nombre y la descripci n al lado como se ve a continuaci n alt text contacts index png users list al hacer hover en una fila se debe mostrar un v nculo para eliminar dicho usuario a trav s de un ajax no es necesario que el paginador muestra el n mero de p ginas s lo un link para ir a la siguiente o la anterior la segunda vista consiste en el formulario para agregar un usuario se gatilla como modal al presionar el bot n de nuevo contacto de la vista anterior la nica validaci n que debe hacer este formulario es que verifique que se incluyen todos los campos una vez completado al presionar el bot n guardar debe enviar la informaci n por ajax a la api para crear el usuario la vista se muestra a continuaci n alt text new contact png new contact descripci n de la api el proyecto incluye la api y la base de datos de almacenamiento la api es rest y a continuaci n se especifican sus m todos m todo http ruta descripci n get api users devuelve la lista de todos los usuarios para paginar necesita recibir adicionalmente los par metros page y limit api users page 2 limit 10 para buscar necesita el par metro q api users q texto get api users id devuelve nicamente el usuario especificado por el id post api users crea un usuario nuevo debe incluir el header content type application json y los par metros en formato json ej name alg n nombre description alguna descripci n photo alguna url delete api users id elimina el usuario correspondiente al id dado criterios de evaluaci n se pide espec ficamente el uso de react https facebook github io react dar un valor adicional aunque no es obligatorio el uso adem s de redux https redux js org adicionalmente se evaluar n los siguientes puntos 1 utilizaci n de patrones buenas pr cticas en el c digo y arquitectura acorde 2 documentaci n en el c digo 3 dise o lo m s similar posible a las im genes mostradas m s arriba 4 tiempo en realizar la tarea plazos y env o esta prueba no considera plazos m ximos de entrega pero el tiempo es un criterio de evaluaci n por lo que se espera una buen balance entre calidad y el tiempo usado una vez terminada la tarea por favor adjuntar el proyecto modificado y enviarlo a engineering beetrack com mailto engineering beetrack com se recibir s lo una entrega por candidato solamente la primera por lo tanto enviar cuando est completamente listo | front_end |
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browser-statuses | web browser implementation statuses this repository contains implementation support information of web technologies on main desktop and mobile web browsers assembled from data gleaned from external open source platform status projects can i use https caniuse com chrome platform status https www chromestatus com mdn s browser compatilibity data https github com mdn browser compat data webkit feature status https webkit org status this web technologies included in the final list are those for which there exists some implementation information the list is expected to grow over time it should roughly contain specifications in browser specs https github com w3c browser specs in other words those published by w3c working groups as well as standards and proposals developed or incubated in w3c community groups the whatwg or the khronos group table of contents installation and usage installation and usage list of web browsers list of web browsers spec series object spec series object shortname shortname support support features features feature url feature url feature title feature title feature support feature support polyfills polyfills polyfill url polyfill url polyfill label polyfill label implementation info object implementation info object ua ua status status source source flag flag guess guess partial partial prefix prefix selected selected details details href href representative representative notes notes how to add update delete implementation info how to addupdatedelete implementation info versioning versioning development notes development notes how to generate index json manually how to generate indexjson manually tests tests attributions attributions installation and usage the data is distributed as an npm package to incorporate it to your project run bash npm install browser statuses you can then retrieve the list from your node js program js const impldata require browser statuses console log json stringify impldata null 2 alternatively you can either retrieve the latest release https github com w3c browser statuses releases latest or fetch index json https w3c github io browser statuses index json note if you choose to fetch the index json file directly keep in mind that it may contain possibly incorrect updates that have not yet been included in the npm package and the latest github release list of web browsers the implementation info is available for the following browsers identifier name type baidu android baidu browser mobile chrome google chrome desktop chrome android google chrome mobile edge microsoft edge based on chromium desktop firefox mozilla firefox desktop firefox android mozilla firefox mobile opera opera desktop qq android qq browser mobile safari apple safari desktop safari ios apple safari for ios mobile samsunginternet android samsung internet mobile uc android uc browser mobile webkit webkit engine spec series object each entry in the list describes the implementation status of a specification series and or of some of the features it defines on main desktop and mobile web browsers each entry has the following overall structure json shortname series shortname support ua user agent identifier status implementation status source platform status project that gave birth to this info details status implementation status flag true notes possible implementation notes href url to the corresponding platform status project page other implementation status pages considered to compute overall status selected true features feature name url url of the section that defines the feature in the spec title human readable description of the feature support same construct as main one polyfills url url of a polyfill library label name of the polyfill library in many cases the features level will not exist and implementation support information will only exist at the series level here is a simple example support info truncated to keep number of lines minimal json shortname beacon support ua chrome status shipped source bcd details status shipped notes starting in chrome 59 this method cannot send a code blob code whose type is not cors safelisted this is a temporary change until a mitigation can be found for the security issues that this creates for more information see a href https crbug com 720283 chrome bug 720283 a href https developer mozilla org docs web api navigator sendbeacon representative true ua chrome status shipped source chrome details status shipped href https www chromestatus com feature 5517433905348608 representative true selected true ua firefox status shipped source bcd details status shipped href https developer mozilla org docs web api navigator sendbeacon representative true selected true ua firefox status shipped source caniuse details status shipped href https caniuse com feat beacon representative true shortname a shortname that uniquely identifies the spec series in most cases the series shortname is the shortname of an individual spec in the series without the level or version number for instance the series shortname for css color 5 is css color when a specification is not versioned the series shortname is identical to the spec s shortname all spec series described in this project must exist in browser specs https github com w3c browser specs in other words the value of the shortname property must match the value of a series shortname property https github com w3c browser specs seriesshortname in the index json https github com w3c browser specs blob master index json file of browser specs the shortname property is always set support the list of implementation support information for the specification series each item in the list is an implementation info object implementation info object that describes the implementation support in a given browser there may be more than one implementation support information in the list for the same browser as information may come from different sources the most authoritative source will be flagged with a selected selected property set to true the support property is usually set but there may exist a few specs in the list for which there is no implementation support information and that still appear in the list e g because there are known polyfills features a list of specific features defined in the specification indexed by some unique feature identifier feature url a pointer to the definition of the feature in the latest editor s draft of the specification the url must be an absolute url the url property should be set whenever possible it may not be set in cases where the feature describes a generic concept e g support in workers feature title a short human readable english title for the feature feature support the list of implementation support information for the feature each item in the list is an implementation info object implementation info object that describes the implementation support in a given browser see also support support polyfills a list of known polyfills for the specification each polyfill has the following properties polyfill url the url to the web site that describes the polyfill the url property is always set polyfill label the name of the polyfill the label property is always set implementation info object all items in support properties describe the implementation status of the underlying specification or feature in a given browser collected from one of the external platform status projects the structure and possible properties that may appear in an implementation info object are illustrated by the following example json ua safari status experimental source caniuse guess true flag true partial true selected true details support info details ua an identifier for the browser see list of web browsers list of web browsers the ua property is always set status the implementation status for the spec or feature value may be one of shipped spec or feature is supported experimental support for the spec or feature is available but not final indevelopment implementation of the spec or feature is ongoing consideration support for the spec or feature is under consideration but not under active developement notsupported spec or feature is not supported in the browser only when implementation info was entered manually implementation status reported by platform status projects is incorrect actual implementation status is unknown the status property is always set source the provenance for the implementation status information can be one of bcd mdn s browser compatilibity data https github com mdn browser compat data project caniuse can i use https caniuse com chrome chrome platform status https www chromestatus com webkit webkit feature status https webkit org status feedback specific feedback received other heard through the grapevine the last two types of information are maintained manually in this repository they should only be used as last resort when information from other sources is invalid or inexistant for some reason the source property is always set flag a boolean flag set to true when the spec or feature is available behind a flag and needs to be manually enabled by the user before it may be used the flag property is only set when it is truthy guess a boolean flag that may only appear at the spec series level set to true when the implementation info was derived from support for individual features that the specification defines when the flag is true the features features support property gives the list of feature identifiers that were used to derive the overall implementation status the guess property is only set when it is truthy partial a boolean flag set to true when implementation of the spec or feature is known to be incomplete the partial property is only set when it is truthy prefix a boolean flag set to true when the implementation of the spec or feature requires a prefix e g webkitxxx as is typically the case as long as an api is not final the prefix property is only set when it is truthy selected a boolean flag set to true when the implementation information is the best one in the list for the browser typically because it originates from the most authoritative source for the browser chrome platform status for chrome webkit status for safari the selected property is only set when it is truthy for each browser that appears in a support list there will always be one and only one implementation info object with a selected flag set to true details the implementation status at the spec series or feature level is often computed by looking at a set of platform status entries the details property is an array that lists the implementation status for each of them note to keep the index json file to a reasonable size this array currently only contains implementation details for entries that are representative representative each item in the array looks like the following example json status shipped href https www chromestatus com feature 6488656873259008 representative true some of the properties that may appear in details items are the same as those in support status status flag flag partial partial prefix prefix additional properties are described below href the absolute url of the page that describes the spec or feature in the platform status project that is at the source of the info the href property is always set it uniquely identifies the related entry in the platform status project representative a boolean flag set that indicates whether the related entry in the platform status project is considered to be representative of the implementation of the underlying spec or feature there may be more than one representative entry for a given spec or feature when there are entries for which the representative flag is set only these entries are taken into account to compute the final implementation status of the underlying spec or feature even though the implementation status of non representative entries still appears in the details array in the absence of entries for which the representative flag is set the final implementation status takes the individual implementation status of all non representative entries into account and the resulting status is considered to be a guess see the guess guess flag the representative flag is only set when it is truthy notes a list of implementation notes collected from the implementation info source the notes property is only set when needed how to add update delete implementation info if you believe that a spec or a feature should be added modified or removed from the list or if you would like to otherwise contribute to this project please check the contributing instructions contributing md versioning this project adheres to semantic versioning https semver org spec v2 0 0 html with the following increment rules given a major minor patch version major a property disappeared its meaning has changed or some other incompatible api change was made when the major number gets incremented code that parses the list likely needs to be updated minor a new property was added the list of spec changed a new spec was added or a spec was removed code that parses the list should continue to work undisturbed but please note that there is no guarantee that a spec that was present in the previous version will continue to appear in the new version patch implementation info about one or more specs changed minor updates were made to the code that don t affect the list development notes how to generate index json manually to re generate the index json file locally run bash npm run build tests to run all tests bash npm test tests are run automatically on pull requests attributions implementation support information gets assembled from the following external open source platform status projects can i use https caniuse com was built and is maintained by alexis deveria http a deveria com data is published under the cc by 4 0 http creativecommons org licenses by 4 0 license mdn s browser compatilibity data https github com mdn browser compat data is published under the cc0 https github com mdn browser compat data blob master license license the chrome platform status https www chromestatus com web site and material are published under the apache license 2 0 https github com googlechrome chromium dashboard blob main license data from the webkit feature status https webkit org status is licensed under the bsd license https webkit org licensing webkit the code that generates and validates the index json file makes use of a number of open source libraries listed as dev dependencies in package json package json kudos to everyone involved in these projects | server |
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dataanalytics | machine learning and data science python data analytics machine learning amp natural language processing introduction as a software engineer i belive in constant learning so i decided to create this repo to share my work and to learn from others this repository contains different machine learning models and case studies which a newbie need to learn in order to start his her journey in the machine learning world what kind of machine learning models here you will find almost all famous machine learning model with a case studies for example linear regression polynomial regression svm classificaiton knn classifications neural network natural language processing sentimen analysis visualization and others can anyone participate in the repo yes if you are also willing to share your work with other people then you are totally welcome you can share your work here also follow us we have a facebook page which is very active we keep post new models and other code works so follow us there https www facebook com codemakerz | pandas pandas-dataframe dataanalytics dataanalysisandmlusingpython codemaker datawrangling machine-learning alogrithms algorithms-and-data-structures sklearn sklearn-library classification-algorithm nlp-machine-learning natural-language-processing sentiment-analysis spacy breastcancer-classification movie-reviews loan-prediction-analysis credit-card-fraud | ai |
text_mining_resources | uncle steve s big list of text analytics and nlp resources t e x t m i n i n g a curated list of resources for learning about natural language processing text analytics and unstructured data awesome https awesome re badge svg https awesome re table of contents books books r r python python general general books blogs blogs blog articles papers case studies blog articles papers case studies general general articles biases in nlp biases in nlp scraping scraping cleaning cleaning stemming stemming dimensionality reduction dimensionality reduction sarcasm detection sarcasm detection document classification document classification entity and information extraction entity and information extraction document clustering and document similarity document clustering and document similarity concept analysis topic modeling concept analysis sentiment analysis sentiment analysis text summarization text summarization machine translation machine translation q a systems chatbots qa systems fuzzy matching probabilistic matching record linkage etc fuzzy matching word and document embeddings word and document embeddings transformers and language models transformers and language models deep learning deep learning knowledge graphs knowledge graphs major nlp conferences major nlp conferences benchmarks benchmarks online courses online courses apis and libraries apis and libraries products products online demos and tools online demos and tools datasets datasets misc misc other curated lists other curated lists books r text mining with r http tidytextmining com mastering text mining with r http shop oreilly com product 9781783551811 do green c89ce13a 3cec 5ebd 08ac f97ed76586df intcmp af mybuy 9781783551811 ip text mining in practice with r https www wiley com en us text mining in practice with r p 9781119282013 python natural language processing with transformers revised edition https transformersbook com getting started with natural language processing https www manning com books getting started with natural language processing blueprints for text analytics using python machine learning based solutions for common real world nlp applications https www oreilly com library view blueprints for text 9781492074076 practical natural language processing https www oreilly com library view practical natural language 9781492054047 natural language processing with python http www nltk org book natural language processing with pytorch http shop oreilly com product 0636920063445 do python natural language processing http shop oreilly com product 9781787121423 do mastering natural language processing with python http shop oreilly com product 9781783989041 do natural language processing python and nltk http shop oreilly com product 9781787285101 do applied text analysis with python enabling language aware data products with machine learning http shop oreilly com product 0636920052555 do applied natural language processing with python https link springer com book 10 1007 2f978 1 4842 3733 5 2018 deep learning with text http shop oreilly com product 0636920076063 do general a id general books a taming text how to find organize and manipulate it https www manning com books taming text a hands on guide to learn innovative tools and techniques for finding organizing and manipulating unstructured text speech and language processing https web stanford edu jurafsky slp3 foundations of statistical natural language processing http nlp stanford edu fsnlp language processing with perl and prolog theories implementation and application cognitive technologies http promethee philo ulg ac be engdep1 download baciii nugues 20second 20ed pdf an introduction for information retrieval http nlp stanford edu ir book handbook of natural language processing https karczmarczuk users greyc fr teach tal doc handbook 20of 20natural 20language 20processing 20second 20edition 20chapman 20 20hall 20crc 20machine 20learning 20 20pattern 20recognition 202010 pdf practical text mining and statistical analysis for non structured text data applications http store elsevier com practical text mining and statistical analysis for non structured text data applications gary miner isbn 9780123870117 fundamentals of predictive text mining http www springer com us book 9781447125655 otherversion 9781849962254 mining the social web data mining facebook twitter linkedin google github and more https www amazon com mining social web facebook linkedin dp 1449367615 ref pd sim b 3 ie utf8 refrid 1hv4rrvda02cyh3s6w8c neural network methods for natural language processing http www morganclaypool com doi abs 10 2200 s00762ed1v01y201703hlt037 text mining a guidebook for the social sciences https us sagepub com en us nam text mining book244124 practical text analytics interpreting text and unstructured data for business intelligence https www koganpage com product practical text analytics 9780749474010 region neural network methods in natural language processing https www morganclaypool com doi abs 10 2200 s00762ed1v01y201703hlt037 machine learning for text 2018 https www springer com us book 9783319735306 natural language processing in spanish https iaarbook github io procesamiento del lenguaje natural foundations of computational linguistics human computer communication in natural language https www springer com gp book 9783642414305 provides insights on how to build talking robots statistical methods for speech recognition https mitpress mit edu books statistical methods speech recognition highlights important research and statistical methods for speech recognition how to label data https www lighttag io how to label data extended guide on managing large text annotation projects blogs probably approximately a scientific blog http veredshwartz blogspot com sebastian ruder http ruder io nlp progress https nlpprogress com natural language processing blog https nlpers blogspot com blog articles papers case studies general a id general articles a nlp in healthcare https www mckinsey com industries healthcare systems and services our insights natural language processing in healthcare how nlp can be used by healthcare payers and providers ai harvard business review https hbr org 2018 07 ais next great challenge understanding the nuances of language the impact of improvement in nlp on human interaction with machines why accuracy in natural language processing is crucial to the future of ai in retail https www mytotalretail com article why accuracy in natural language processing is crucial to the future of ai in retail natural language processing is fun how computers understand human language https medium com ageitgey natural language processing is fun 9a0bff37854e 2018 wef live campaign twitter fed global news topics sentiment tracker https 365 weflive com r worldwide t all topics live jan 2019 modern deep learning techniques applied to natural language processing https nlpoverview com the definitive guide to natural language processing https monkeylearn com blog definitive guide natural language processing monkeylearn non technical overview from natural language to calendar entries with clojure http edeferia blogspot ca 2015 03 from natural language to calendar html march 2015 nlp clojure ask hn how can i get into nlp natural language processing https news ycombinator com item id 12916498 ask hn what are the best tools for analyzing large bodies of text https news ycombinator com item id 9733883 quora how do i learn natural language processing https www quora com how do i learn natural language processing good intro for beginner with time estimate breakdown and links to stanford cs courses quora topic natural language processing https www quora com topic natural language processing the definitive guide to natural language processing https blog monkeylearn com the definitive guide to natural language processing october 2015 futures of text http whoo ps 2015 02 23 futures of text feb 2015 a survey of all the current innovation in text as a medium r or python on text mining https datawarrior wordpress com 2015 08 12 codienerd 1 r or python on text mining aug 2015 comparison of efficiency between r and python in the field of text mining where to start in text mining https tedunderwood com 2012 08 14 where to start with text mining aug 2012 text mining in r and python 8 tips to get started https www r bloggers com text mining in r and python 8 tips to get started oct 2016 an introduction to text analysis with python part 1 http nealcaren web unc edu an introduction to text analysis with python part 1 april 2012 a beginner s walkthrough on the basics idea of sentiment analysis in python mining twitter data with python part 1 collecting data https marcobonzanini com 2015 03 02 mining twitter data with python part 1 why text mining may be the next big thing http business time com 2012 03 20 why text mining may be the next big thing march 2012 sas ceo offers analytics over bi reveals use cases for text analytics http www computerweekly com news 2240037240 sas ceo offers analytics over bi reveals use cases for text analytics june 2011 value and benefits of text mining https www jisc ac uk reports value and benefits of text mining sep 2015 text mining south park http kaylinwalker com text mining south park feb 2016 a text mining blog which covers on a variety of topics natural language processing an introduction http jamia oxfordjournals org content 18 5 544 full natural language processing tutorial http www vikparuchuri com blog natural language processing tutorial june 2013 natural language processing blog http nlpers blogspot ca an introduction to text mining using twitter streaming api and python http adilmoujahid com posts 2014 07 twitter analytics github repo with code https github com adilmoujahid twitter analytics how to get into natural language processing http blog ycombinator com how to get into natural language processing basic non technical intro to nlp betty a friendly english like interface for your command line https github com pickhardt betty creating machine learning models to analyze startup news part1 https monkeylearn com blog filtering startup news machine learning part 2 https monkeylearn com blog creating machine learning models analyze news part 3 https monkeylearn com blog analyzing startup news with machine learning comparison of the most useful text processing apis https www kdnuggets com 2018 08 comparison most useful text processing apis html 100 must read nlp papers https github com mhagiwara 100 nlp papers python guide for dealing with text data https www analyticsvidhya com blog 2018 02 the different methods deal text data predictive python crowdsourcing ground truth for medical relation extraction https dl acm org citation cfm id 3152889 natural language based financial forecasting a survey https sentic net natural language based financial forecasting pdf natural language based financial forecasting a survey https sentic net natural language based financial forecasting pdf an article that clarifies the scope of natural language financial forecasting 5 heroic tools for natural language processing https towardsdatascience com 5 heroic tools for natural language processing 7f3c1f8fc9f0 natural language processing unlocks hidden data to transform healthcare efficiency quality and cost https www healthcareglobal com technology natural language processing unlocks hidden data transform healthcare efficiency quality extracting medical problems from electronic clinical documents https www sciencedirect com science article pii s1532046405001140 natural language processing nlp for machine learning https towardsdatascience com natural language processing nlp for machine learning d44498845d5b includes basic easy to understand preprocessing and compares a few ml classificaiotn models in python how to write a spelling corrector by peter norvig https norvig com spell correct html using ai to unleash the power of unstructured government data https www2 deloitte com insights us en focus cognitive technologies natural language processing examples in government data html w eggers n malik m gracie january 2019 think of unstructured text as being trapped in physical and virtual file cabinets the promise is clear governments could improve effectiveness and prevent many catastrophes by improving their ability to connect the dots and identify patterns in available data this deloitte article provides an easy to comprehend primer and background on nlp and the various applications nlp could be used on unstructured government text data the article includes many us government examples on how nlp is currently deployed across different domains e g to help analyze public feedback sentiment analysis topic modelling to improve forensic investigations to aid in government policy making and regulatory compliance the key point is to apply different nlp techniques to explore and uncover key government intelligence insights extracting features of entertainment products a guided latent dirichlet allocation approach informed by the psychology of media consumption https doi org 10 1177 0022243718820559 o toubia g iyengar r bunnell a lemaire february 2019 we rely on the nlp literature to develop a method for tagging entertainment products in an automated and scalable manner in the context of movies we first show that the proposed features improve our ability to predict consumption at the individual level we also show that guided lda features have the potential to improve the performance of models that predict aggregate performance outcomes rather than individual level consumption this academic article provides both a framework and managerial implications that suggest the application of lda and nlp for feature extraction in entertainment products that can aid in traditional content based consumer behavior models and relevant marketing models applied to the media and entertainment industry lessons learned building natural language processing systems in health care https www oreilly com ideas lessons learned building natural language processing systems in health care how algorithms know what you ll type next https pudding cool 2019 04 text prediction biases in nlp ai bias it is the responsibility of humans to ensure fairness https www information age com ai bias 123479217 venturebeat blogpost gender biases in datasets https venturebeat com 2018 09 07 researchers develop a method that reduces gender bias in ai datasets based on ucla research paper learning gender neutral word embeddings aug 2018 examining gender and race bias in two hundred sentiment analysis systems http aclweb org anthology s18 2005 2018 man is to computer programmer as woman is to homemaker debiasing word embeddings https arxiv org abs 1607 06520 scraping scraping html using scrapy https blog scrapinghub com 2016 01 19 scrapy tips from the pros part 1 tutorial on using the python module scrapy for easy data extraction from messy html websites extract text from any document no muss no fuss https datascopeanalytics com blog extract text from any document no muss no fuss july 2014 using scrapy to build your own dataset https medium com towards data science using scrapy to build your own dataset 64ea2d7d4673 sep 2017 cleaning how to solve 90 of nlp problems a step by step guide https blog insightdatascience com how to solve 90 of nlp problems a step by step guide fda605278e4e jan 2018 a step by step guide on data cleaning and exploration for successful nlp model building text preprocessing in python steps tools and examples https medium com datamonsters text preprocessing in python steps tools and examples bf025f872908 oct 2018 how to clean text for machine learning with python https machinelearningmastery com clean text machine learning python october 2017 step by step guide of how to perform text data pre processing feature extraction basic pre processing and advanced processing https www analyticsvidhya com blog 2018 02 the different methods deal text data predictive python stop words removing stop words with nltk in python https www geeksforgeeks org removing stop words nltk python text classification for sentiment analysis stopwords and collocations https streamhacker com 2010 05 24 text classification sentiment analysis stopwords collocations stemming article text stemming approaches applications and challenges http dl acm org citation cfm id 2975608 dec 2016 what is the difference between stemming and lemmatization https blog bitext com what is the difference between stemming and lemmatization feb 2018 differences and examples of using stemming and lemmatization in different languages stemming and lemmatization in python https www datacamp com community tutorials stemming lemmatization python oct 2018 comparison of stemming and lemmatization with algorithms behind results pros and cons context to use and code syntax sentiment symposium tutorial stemming http sentiment christopherpotts net stemming html dimensionality reduction taming text with the svd http citeseerx ist psu edu viewdoc download doi 10 1 1 395 4666 rep rep1 type pdf sas jan 2004 dimensionality reduction for bag of words models pca vs lsa http cs229 stanford edu proj2017 final reports 5163902 pdf an introduction to bag of words and how to code it in python for nlp https medium freecodecamp org an introduction to bag of words and how to code it in python for nlp 282e87a9da04 bag of words and tf idf explained http datameetsmedia com bag of words tf idf explained sarcasm detection automatic sarcasm detection a survey https dl acm org citation cfm id 3124420 acm computer surveys sep 2017 cascade contextual sarcasm detection in online discussion forums http aclweb org anthology c18 1156 27th international conference on computational linguistics aug 2018 a deeper look into sarcastic tweets using deep convolutional neural networks https pdfs semanticscholar org 402f bb0d3eb259eeabae0522a747de14b7d7f8b5 pdf international journal of advanced research in computer engineering technology volume 6 issue 1 jan 2017 detecting sarcasm with deep convolutional neural networks https medium com dair ai detecting sarcasm with deep convolutional neural networks 4a0657f79e80 apr 30 2018 contextual learning using cnns for effective detection of sarcasm document classification naive bayes and text classification http sebastianraschka com articles 2014 naive bayes 1 html 2014 an in depth overview of both the naive bayes algorithm and how it can be used in the document classification process bag of tricks for efficient text classification https arxiv org abs 1607 01759 2016 a paper from facebook researchers that introduces fasttext a fast and effective document classification algorithm text classifier algorithms in machine learning https blog statsbot co text classifier algorithms in machine learning acc115293278 2017 a blog article that shows how to apply several deep learning algorithms to document classification problems classifying documents in the reuters 21578 r8 dataset https rstudio pubs static s3 amazonaws com 202711 8f3ddd24fc9a4a6594d31f5da6344dcc html 2016 a nice tutorial in r that shows how to classify news articles using three different ml algorithms tidy text mining beer reviews http kaylinwalker com tidy text beer 2018 uses the knn algorithm to classify reviews of craft beer products into styles of beer e g pilsner ipa or belgian using fasttext and comet ml to classify relationships in knowledge graphs https medium com comet ml using fasttext and comet ml to classify relationships in knowledge graphs e73d27b40d67 multi class text classification with scikit learn https www kdnuggets com 2018 08 multi class text classification scikit learn html 2018 an article that shows how to deal with multi class problems such as classifying consumer complaints into one of 12 categories machine learning with text in scikit learn pycon 2016 https www youtube com watch v zikmiuyidy0 list pl5 da3qgb5icembquqbbcoqwcs6oybr5a index 10 2016 a nice video tutorial that discusses how to use scikit learn in the document classification process ultimate guide to deal with text data using python for data scientists engineers https www analyticsvidhya com blog 2018 02 the different methods deal text data predictive python 2018 the title says it all text classification in python with scikit learn and nltk https towardsdatascience com machine learning nlp text classification using scikit learn python and nltk c52b92a7c73a 2017 another tutorial showing how to perform text classification using scikit learn introducing state of the art text classification with universal language models http nlp fast ai classification 2019 09 10 multifit html 2019 introduces a groundbreaking transfer learning method for document classification learning document embeddings by predicting n grams for sentiment classification of long movie reviews https github com libofang dv ngram paper with code on github towards explainable nlp a generative explanation framework for text classification https arxiv org pdf 1811 00196 pdf 2019 a paper that describes a new approach for explaining the inner workings of text classification models entity and information extraction entity extraction and network analysis http brandonrose org ner2sna python stanfordcorenlp natural language processing for information extraction https arxiv org abs 1807 02383 nlp techniques for extracting information https www searchtechnologies com blog natural language processing techniques in depth exploration of the seven steps framework of nlp data mining tools and techniques document clustering and document similarity text clustering get quick insights from unstructured data http www kdnuggets com 2017 06 text clustering unstructured data html july 2017 document clustering http cse iitkgp ac in abhij facad 03ug report 03cs3024 pankaj jajoo pdf msc thesis document clustering a detailed review http citeseerx ist psu edu viewdoc download doi 10 1 1 401 8494 rep rep1 type pdf shah and mahajan ijais 2012 document clustering with python https github com harrywang document clustering a github repository that clusters imdb movie descriptions based on this original tutorial http brandonrose org clustering whose github repo is here https github com brandomr document cluster text mining and sentiment analysis on video game user reviews using sas enterprise miner http analytics ncsu edu sesug 2016 epo 280 final pdf pdf who wrote the anti trump new york times op ed using tidytext to find document similarity http varianceexplained org r op ed text analysis concept analysis topic modeling a id concept analysis a topic models past present and future https www oreilly com ideas topic models past present and future word vectors using lsa part 2 http www vikasing com 2015 05 word vectors using lsa part 2 html probabilistic topic models http www cs columbia edu blei papers blei2012 pdf lego color themes as topic models http nateaff com 2017 09 11 lego topic models utm campaign data 2belixir utm medium email utm source data elixir 150 sep 2017 how our startup switched from unsupervised lda to semi supervised guidedlda https medium freecodecamp org how we changed unsupervised lda to semi supervised guidedlda e36a95f3a164 topic modeling with lsa plsa lda lda2vec https www kdnuggets com 2018 08 topic modeling lsa plsa lda lda2vec html aug 2018 text2vec s description of topic models http text2vec org topic modeling html topic modelling portal http topicmodels west uni koblenz de applications of topic models https mimno infosci cornell edu papers 2017 fntir tm applications pdf 2017 macs 30500 text analysis topic modeling https cfss uchicago edu text topicmodels html cota uber s topic modelling approach to improving customer support https eng uber com cota using lda topic models as a classification model input https towardsdatascience com unsupervised nlp topic models as a supervised learning input cf8ee9e5cf28 nlp extracting the main topics from your dataset using lda in minutes https towardsdatascience com nlp extracting the main topics from your dataset using lda in minutes 21486f5aa925 topic modelling the legal subject matter and judicial activity of the high court of australia 1903 2015 https osf io qhezc download format pdf sentiment analysis methods cacm techniques and applications for sentiment analysis http cacm acm org magazines 2013 4 162501 techniques and applications for sentiment analysis abstract 2013 a nice overview of sentiment analysis from the communications of the acm journal unsupervised sentiment analysis with signed social networks https aaai org ocs index php aaai aaai17 paper view 14656 14129 2017 a conference paper that describes that challenges of applying sentiment analysis to social networks and presents an new unsupervised method lexicon based methods for sentiment analysis https www aclweb org anthology j j11 j11 2001 pdf 2010 uses so cal semantic orientation calculator a measure of subjectivity and opinion for sentimental analysis that sentimental feeling http www matthewjockers net 2015 12 20 that sentimental feeling 2015 compares the result of r s syezhet package with human labels on a series of novels a 2016 update http www matthewjockers net 2016 08 11 more syuzhet validation unsupervised sentiment neuron https blog openai com unsupervised sentiment neuron 2017 openai s team developed a new way of using deep nns to perform sentiment analysis on much less data than usual current state of text sentiment analysis from opinion to emotion mining http dl acm org citation cfm id 3057270 2017 a journal article that surveys the current state of sentiment analysis research and tools sentiment analysis tools overview part 1 positive and negative words databases https medium com datamonsters sentiment analysis tools overview part 1 positive and negative words databases ae35431a470c 2017 a blog article that outlines some lexicon databases sentiment analysis concept analysis and applications https towardsdatascience com sentiment analysis concept analysis and applications 6c94d6f58c17 2018 an overview of sentiment analysis with an analysis of tweets about uber breakthrough research papers and models for sentiment analysis https blog paralleldots com data science breakthrough research papers and models for sentiment analysis 2018 a blog that compares the performance of simple to advanced methods for sentiment analysis twitter sentiment analysis using combined lstm cnn models http konukoii com blog 2018 02 19 twitter sentiment analysis using combined lstm cnn models 2018 a blog article that describes a new method for sentiment analysis that uses deep learning vader a parsimonious rule based model for sentiment analysis of social media text http comp social gatech edu papers icwsm14 vader hutto pdf 2014 a conference paper that presents vader a simple rule based model of sentiment analysis a comparison of lexicon based approaches for sentiment analysis of microblog posts http ceur ws org vol 1314 paper 06 pdf 2014 a conference paper that presents a new lexicon based approach for sentiment analysis of twitter posts based on lexical resources such as sentiwordnet challenges on the negativity of negation http web stanford edu cgpotts papers potts salt20 negation pdf 2011 a conference paper that discusses the challenges of dealing with negativity in text with a case study on imdb movie reviews challenges in sentiment analysis http saifmohammad com webdocs sentiment challenges pdf 2015 a practical guide from the national reseach council of canada that describes some of the main challenges of sentiment analysis a survey on sentiment analysis challenges https www sciencedirect com science article pii s1018363916300071 2016 a journal article that discusses and compares sentiment analysis challenges among forty seven papers politics sentiment analysis on trump s tweets using python https dev to rodolfoferro sentiment analysis on trumpss tweets using python utm campaign data 2belixir utm medium email utm source data elixir 149 2017 sentiment analysis on trump s tweets using tweepy and textblob for nlp processing donald trump vs hillary clinton sentiment analysis on twitter mentions https blog monkeylearn com donald trump vs hillary clinton sentiment analysis twitter mentions 2016 compares the sentiment of trump s tweets vs hillary s tweets leading up to the 2016 us presidential election does sentiment analysis work a tidy analysis of yelp reviews http varianceexplained org r yelp sentiment 2016 combined prediction results and individual words in reviews to show that sentiment analysis worked well on yelp reviews from tweets to polls linking text sentiment to public opinion time series https www aaai org ocs index php icwsm icwsm10 paper viewfile 1536 1842 2010 a conference paper that describes how sentiment analysis on twitter is connected to public opinion polls stock market twitter mood predicts the stock market https arxiv org abs 1010 3003 2010 a journal article that measures the mood of daily twitter feedsa and shows that the moods can predict the djia a nonlinear impact evidences of causal effects of social media on market prices https arxiv org pdf 1601 04535v2 pdf 2016 a journal article that shows that social media s relationship with the djia is nonlinear forbes how quant traders use sentiment to get an edge on the market http www forbes com sites kumesharoomoogan 2015 08 06 how quant traders use sentiment to get an edge on the market 6266d9ec2fd8 2015 an article that shows how quant traders can use sentiment analysis sentdex quantifying the qualitative http sentdex com financial analysis an online tool that measures the overall sentiment of different stocks trump2cash a stock trading bot powered by trump tweets https github com maxbbraun trump2cash a bot that watches donald trump s twitter account and waits for him to mention any publicly traded companies a related blog article https medium com maxbraun this machine turns trump tweets into planned parenthood donations 4ece8301e722 3232hx7gx describes a bot that turns trump s tweets into planned parenthood donations applications lost at sea how social media is helping cruise lines attract millennials https www crimsonhexagon com blog cruises hunting millennials 2016 a whitepaper describing how cruise lines can attract a different audience harry plotter celebrating the 20 year anniversary with tidytext and the tidyverse in r https paulvanderlaken com 2017 08 03 harry plotter celebrating the 20 year anniversary with tidytext the tidyverse and r 2015 a technical article showing how to apply sentiment analysis to the text of the harry potter series data science 101 sentiment analysis in r tutorial http blog kaggle com 2017 10 05 data science 101 sentiment analysis in r tutorial 2017 a technical article describing how to use the tidytext package in r to analyze us presidential speeches cannes lions 2017 hungerithm mars chocolate australia clemenger bbdo melbourne https vimeo com 223731129 2017 a video that shows how snickers developed a tool to change the price of snickers bar based on the mood of the internet sentiment analysis 10 applications and 4 services https towardsdatascience com machine learning as a service 487e930265b2 2018 a brief but concise introduction to sentiment analysis it s business implications and four sentiment analysis cloud service providers including google amazon and microsoft what your boss could learn by reading the whole company s emails https www theatlantic com magazine archive 2018 09 the secrets in your inbox 565745 2018 the lesson figure out the truth about how the workforce is feeling not by eavesdropping on the substance of what employees say but by examining how they are saying it this article is centered around the topic of applying sentiment analysis to large internal unstructured text datasets e g employee e mails text analytics and nlp have become an increasingly popular approach to help search for clues that may indicate the level of employee engagement in the workplace and any potential red flags that should receive particular attention by an organization and its ethical implications aspect based sentiment analysis of amazon product reviews https www researchgate net publication 325843745 aspect based sentiment analysis of amazon product reviews 2018 an article showing how to apply sentiment analysis on different aspects of a product review on amazon sentiment analysis of 2 2 million tweets from super bowl 51 http blog aylien com sentiment analysis of 2 2 million tweets from super bowl 51 2017 an article showing how to apply sentiment analysis to tweets about the super bowl emotion and sentiment analysis a practitioner s guide to nlp https www kdnuggets com 2018 08 emotion sentiment analysis practitioners guide nlp 5 html 2018 an overview of sentiment analysis applied to news articles tools and technology streaming analytics tutorial on azure https docs microsoft com en ca azure azure databricks databricks sentiment analysis cognitive services toc 2fen ca 2fazure 2fcognitive services 2ftext analytics 2ftoc json bc 2fen ca 2fazure 2fbread 2ftoc json how to analyze sentiment in azure https docs microsoft com en ca azure cognitive services text analytics how tos text analytics how to sentiment analysis how to perform sentiment analysis using python tutorial https hub packtpub com how to perform sentiment analysis using python tutorial twitter sentiment analysis overview https www youtube com watch v o ozdbczhua t 235s 2016 overview of sentiment analysis and a step by step walkthrough on how to perform sentiment analysis using textblob elmo embeddings in keras using tensorflow hub https towardsdatascience com elmo embeddings in keras with tensorflow hub 7eb6f0145440 2018 a guide to use google s elmo in your keras model using tensorflow hub twitter sentiment analysis in python using textblob https medium freecodecamp org how to build a twitter sentiments analyzer in python using textblob 948e1e8aae14 2018 text summarization text summarization with gensim https rare technologies com text summarization with gensim unsupervised text summarization using sentence embeddings https medium com jatana unsupervised text summarization using sentence embeddings adb15ce83db1 improving abstraction in text summarization http aclweb org anthology d18 1207 proposing two techniques for improvement text summarization and categorization for scientific and health related data https repository library georgetown edu bitstream handle 10822 1050759 cohan georgetown 0076d 13889 pdf sequence 1 text summarization with tensorflow https ai googleblog com 2016 08 text summarization with tensorflow html 2016 a basic study on text summarization machine translation blog post found in translation more accurate fluent sentences in google translate https blog google products translate found translation more accurate fluent sentences google translate nov 2016 nytimes the great a i awakening https www nytimes com 2016 12 14 magazine the great ai awakening html r 0 dec 2016 how google used artificial intelligence to transform google translate one of its more popular services and how machine learning is poised to reinvent computing itself machine learning translation and the google translate algorithm http www kdnuggets com 2017 09 machine learning translation google translate algorithm html neural machine translation seq2seq tutorial https github com tensorflow nmt paper dissected attention is all you need explained http mlexplained com 2017 12 29 attention is all you need explained explanation of an important paper that first introduced attention mechanism in 2017 the annotated transformer http nlp seas harvard edu 2018 04 03 attention html a line by line implementation of attention is all you need bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 a new language representation model published in 2018 implementation code https github com google research bert pytorch port https github com codertimo bert pytorch phrase based neural unsupervised machine translation https arxiv org abs 1804 07755 proposed two model variants neural and phrase based model awarded as the best paper award at emnlp 2018 implementation code https github com facebookresearch unsupervisedmt q a systems chatbots a id qa systems a meet lucy creating a chatbot prototype http www kdnuggets com 2017 09 meet lucy chatbot prototype html microsoft bot framework https dev botframework com training millions of personalized dialogue agents https arxiv org abs 1809 01984 ultimate guide to leveraging nlp machine learning for your chatbot https chatbotslife com ultimate guide to leveraging nlp machine learning for you chatbot 531ff2dd870c 2016 building a simple chatbot from scratch in python using nltk https medium com analytics vidhya building a simple chatbot in python using nltk 7c8c8215ac6e sep 2018 a survey on dialogue systems recent advances and new frontiers https arxiv org pdf 1711 01731 pdf jan 2018 examining the impact of an automated translation chatbot on online collaborative dialog for incidental l2 learning https www researchgate net publication 329525159 examining the impact of an automated translation chatbot on online collaborative dialog for incidental l2 learning create a banking chatbot with faq discovery anger detection and natural language understanding https github com ibm watson banking chatbot generative model chatbots may 2017 https medium com botsupply generative model chatbots e422ab08461e a guide to building a multi featured slackbot with python march 2017 https hackernoon com a guide to building a multi featured slackbot with python 73ea5394acc building a simple chatbot from scratch in python using nltk september 2018 https medium com analytics vidhya building a simple chatbot in python using nltk 7c8c8215ac6e the road to a conversational banking future february 2019 https chatbotslife com the road to a conversational banking future 1872f870a653 chatbots designing intents and entities for nlp models https medium com brijrajsingh chat bots designing intents and entities for your nlp models 35c385b7730d jan 2017 task oriented dialogue system for automatic diagnosis http www sdspeople fudan edu cn zywei paper liu acl2018 pdf 2018 talks about the use of mdp trained dataset and its medical diagnostic applications li deng at ai frontiers three generations of spoken dialogue systems bots https www slideshare net aifrontiers li deng three generations of spoken dialogue systems bots 2017 slides by microsoft chief scientist for ai nlp building a question answering model https towardsdatascience com nlp building a question answering model ed0529a68c54 march 2018 fuzzy matching probabilistic matching record linkage etc a id fuzzy matching a agrep method in r http stat ethz ch r manual r devel library base html agrep html approximate string matching fuzzy matching fuzzywuzzy package in r https cran r project org web packages fuzzywuzzyr index html example usage http mlampros github io 2017 04 13 fuzzywuzzyr package fuzzy string matching a survival skill to tackle unstructured information https journal r project org archive 2010 2 rjournal 2010 2 sariyar borg pdf the recordlinkage package detecting errors in data https journal r project org archive 2010 2 rjournal 2010 2 sariyar borg pdf r package fastlink fast probabilistic record linkage https github com kosukeimai fastlink fuzzy merge in r https www princeton edu otorres fuzzymerger101 pdf an r function to merge files by defining a key file learning text similarity with siamese recurrent networks http www aclweb org anthology w w16 w16 1617 pdf dedupe https github com dedupeio dedupe a python library for accurate and scalable fuzzy matching record deduplication and entity resolution recordlinkage https github com j535d165 recordlinkage a toolkit for record linkage and deduplication written in python word and document embeddings the current best of universal word embeddings and sentence embeddings https medium com huggingface universal word sentence embeddings ce48ddc8fc3a an intuitive understanding of word embeddings from count vectors to word2vec https www analyticsvidhya com blog 2017 06 word embeddings count word2veec an empirical evaluation of doc2vec with practical insights into document embedding generation https arxiv org pdf 1607 05368 pdf 2016 from ibm document embedding with paragraph vectors https storage googleapis com pub tools public publication data pdf 44894 pdf 2015 from google glove word embeddings demo https github com fastai word embeddings workshop blob master word 20embeddings ipynb 2017 from fasti text classification with word2vec http nadbordrozd github io blog 2016 05 20 text classification with word2vec 2016 document embedding http sybrandt com post document embedding 2017 from word embeddings to document distances http proceedings mlr press v37 kusnerb15 pdf 2015 word embeddings bias in ml why you don t like math why ai needs you https www youtube com watch v 25nc0n9erq4 2017 rachel thomas fastai word vectors in natural language processing global vectors glove https www kdnuggets com 2018 08 word vectors nlp glove html aug 2018 doc2vec tutorial on the lee dataset https github com rare technologies gensim blob develop docs notebooks doc2vec lee ipynb word embeddings in python with spacy and gensim https www shanelynn ie word embeddings in python with spacy and gensim deep contextualized word represenations https arxiv org abs 1802 05365 elmo pytorch implementation https github com allenai allennlp blob master tutorials how to elmo md tf implementation https github com allenai bilm tf universal language model fine tuning for text classification https arxiv org abs 1801 06146 implementation code http nlp fast ai category classification html supervised learning of universal sentence representations from natural language inference data https arxiv org abs 1705 02364 learned in translation contextualized word vectors https arxiv org abs 1708 00107 cove distributed representations of sentences and documents http cs stanford edu quocle paragraph vector pdf paragraph vectors see doc2vec tutorial at gensim http rare technologies com doc2vec tutorial sense2vec http arxiv org abs 1511 06388 word sense disambiguation skip thought vectors http arxiv org abs 1506 06726 word representation method sequence to sequence learning with neural networks http papers nips cc paper 5346 sequence to sequence learning with neural networks pdf the amazing power of word vectors https blog acolyer org 2016 04 21 the amazing power of word vectors 2016 contextual string embeddings for sequence labeling http alanakbik github io papers coling2018 pdf 2018 a hierarchical multi task approach for learning embeddings from semantic tasks https arxiv org abs 1811 06031 introducing a multi task learning approach for a set of interrelated nlp tasks presented at aaai conference in january 2019 implementation code https github com huggingface hmtl elmo word embeddings allennlp org elmo an idiot s guide to word2vec natural language processing https medium com odsc an idiots guide to word2vec natural language processing 5c3767cf8295 get busy with word embeddings an introduction february 2018 https www shanelynn ie get busy with word embeddings introduction nlp s imagenet moment has arrived http ruder io nlp imagenet july 2018 overview of pre trained nlp language models drawing parallels to imagenet s contributions to computer vision word2vec fish music bass https graceavery com word2vec fish music bass universal sentence encoder visually explained https amitness com 2020 06 universal sentence encoder june 2020 transformers and language models understanding large language models https sebastianraschka com blog 2023 llm reading list html sebastian raschka feb 2023 a primer in bertology what we know about how bert works https arxiv org pdf 2002 12327 pdf nov 2020 a review of bert based models https towardsdatascience com a review of bert based models 4ffdc0f15d58 july 2019 bert explained state of the art language model for nlp https towardsdatascience com bert explained state of the art language model for nlp f8b21a9b6270 a great explaination of the fundamentals of how bert works the illustrated bert elmo and co how nlp cracked transfer learning https jalammar github io illustrated bert dec 2018 machines beat humans on a reading test but do they understand https www quantamagazine org machines beat humans on a reading test but do they understand 20191017 what every nlp engineer needs to know about pre trained language models https www topbots com ai nlp research pretrained language models 2019 the transformer explained https nostalgebraist tumblr com post 185326092369 the transformer explained the illustrated transformer https jalammar github io illustrated transformer hugging face s course on transformer models https huggingface co course chapter1 1 openai better language models and their implications https blog openai com better language models pre trained transformer based unsupervised language model that achieves state of the art on many language benchmarks with focus on text generation controversial limited release february 14 2019 chatgpt chatgpt launch blog https openai com blog chatgpt awesome chatgpt prompts https github com f awesome chatgpt prompts in education chatgpt user experience implications for education https papers ssrn com sol3 papers cfm abstract id 4312418 xiaoming zhai unviversity of georgia december 2022 new modes of learning enabled by ai chatbots three methods and assignments https papers ssrn com sol3 papers cfm abstract id 4300783 mollick and mollick university of pennsylvania december 2022 educators battle plagiarism as 89 of students admit to using openai s chatgpt for homework https www forbes com sites chriswestfall 2023 01 28 educators battle plagiarism as 89 of students admit to using open ais chatgpt for homework sh 1924574750de forbes january 2023 chatgpt educational friend or foe https www brookings edu blog education plus development 2023 01 09 chatgpt educational friend or foe hirsh pasek and blinkoff temple university january 2023 don t ban chatgpt in schools teach with it https www nytimes com 2023 01 12 technology chatgpt schools teachers html new york times january 2023 chatgpt and the future of business education https knowledge insead edu leadership organisations chatgpt and future business education feb 2023 udemy course january 2023 chatgpt for teachers in education https www udemy com course chatgpt in education deep learning keras lstm tutorial how to easily build a powerful deep learning language model http adventuresinmachinelearning com keras lstm tutorial first half of the article describes rnns the anatomy of an lstm cell lstm networks second half is a walkthrough of features in keras for lstm implementation using generators for data input deep learning for natural language processing tutorials with jupyter notebooks https insights untapt com deep learning for natural language processing tutorials with jupyter notebooks ad67f336ce3f a short article containing links and descriptions to further video tutorials for dl approaches to nlp problems five lessons total including preprocessing word representations and lstm among other topics a survey of the usages of deep learning in natural language processing https arxiv org abs 1807 10854 a 35 page academic literature review of dl in nlp university of colorado july 2018 detailed description of neural network architectures followed by a comprehensive set of applications sequence classification with human attention http aclweb org anthology k18 1030 using human attention derived from eye tracking corpora to regularize attention in recurrent neural networks rnn implementation code https github com coastalcph sequence classification with human attention tutorial on text classification nlp using ulmfit and fastai library in python https www analyticsvidhya com blog 2018 11 tutorial text classification ulmfit fastai library multi task deep neural networks for natural language understanding https www topbots com ai nlp research pretrained language models academic article detailing microsoft s mtdnn algorithm which has outperformed bert elmo bilstm as of february 2019 in the glue benchmark natural language processing tutorial for deep learning researchers https github com graykode nlp tutorial a 2019 nlp tutorial repository using tensorflow and pytorch deep learning for sentiment analysis a survey https arxiv org abs 1801 07883 neural reading comprehension and beyond https cs stanford edu danqi papers thesis pdf december 2018 stanford reading comprehension models built on top of deep neural networks microsoft multi task deep neural network mt dnn https arxiv org pdf 1901 11504 microsoft s improvement on google s bert with focus on natural language understanding code to be released january 31 2019 a structured self attentive sentence embedding https arxiv org pdf 1703 03130 pdf capsule networks investigating capsule networks with dynamic routing for text classification https arxiv org pdf 1804 00538 pdf 2018 attention based capsule networks with dynamic routing for relation extraction http aclweb org anthology d18 1120 2018 twitter sentiment analysis using capsule nets and gru https wwjournals com index php ijsr article view 13386 2018 identifying aggression and toxicity in comments using capsule network http aclweb org anthology w18 4412 2018 it is early days for capsule networks which was introduced by geoffrey hinton et al in 2017 as an attempt to introduce an nn architecture superior to the classical cnns the idea aims to capture hierarchincal relationships in the input layer through dynamic routing between capsules of neurons due likely to the affinitity of the theme of addressing hierarchical complexities the idea s extention to the nlp field has since been a sujbect of active research such as in the papers listed above dynamic routing between capsules https arxiv org pdf 1710 09829 pdf 2017 matrix capsules with em routing https openreview net pdf id hjwlfgwrb 2018 knowledge graphs using fasttext and comet ml to classify relationships in knowledge graphs https medium com comet ml using fasttext and comet ml to classify relationships in knowledge graphs e73d27b40d67 wtf is a knowledge graph https hackernoon com wtf is a knowledge graph a16603a1a25f a survey of graphs in natural language processing https web eecs umich edu mihalcea papers nastase jnle15 pdf nastase et al 2015 major nlp conferences neurips https nips cc association for computational linguistics acl https www aclweb org portal empirical methods in natural language processing emnlp https 2022 emnlp org north american chapter of the association for computational linguistics naacl https 2022 naacl org european chapter of the association for computational linguistics eacl https 2023 eacl org international conference on computational linguistics coling https coling2022 org benchmarks squad leaderboard https rajpurkar github io squad explorer a list of the strongest performing nlp models on the stanford question answering dataset squad squad 1 0 paper https arxiv org pdf 1606 05250 pdf last updated october 2016 squad v1 1 includes over 100 000 question and answer pairs based on wikipedia articles squad 2 0 paper https arxiv org pdf 1806 03822 pdf october 2018 the second generation of squad includes unanswerable questions that the nlp model must identify as being unanswerable from the training data glue leaderboard https gluebenchmark com leaderboard glue paper https arxiv org pdf 1804 07461 pdf september 2018 a collection of nine nlp tasks including single sentence tasks e g check if grammar is correct sentiment analysis similarity and paraphrase tasks e g determine if two questions are equivalent and inference tasks e g determine whether a premise contradicts a hypothesis online courses udemy udemy deep learning and nlp a z how to create a chatbot https www udemy com chatbot udemy natural language processing with deep learning in python https www udemy com natural language processing with deep learning in python udemy nlp natural language processing with python https www udemy com nlp natural language processing with python udemy deep learning advanced nlp and rnns https www udemy com deep learning advanced nlp udemy natural language processing and text mining without coding https www udemy com master natural language processing nlp text mining without coding stanford stanford cs 224n ling 284 http web stanford edu class cs224n website http cs224d stanford edu reddit https www reddit com r cs224d comments 4n04ew follow along with cs224d 2015 or 2016 lecture collection natural language processing with deep learning winter 2017 https www youtube com playlist list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 coursera courses for natural language processing on coursera https www coursera org courses query natural 20language 20processing coursera applied text mining in python https www coursera org learn python text mining coursera nartual language processing https www coursera org learn language processing coursera sequence models for time series and natural language processing https www coursera org learn sequence models tensorflow gcp coursera coursera clinical natural language processing https www coursera org learn clinical natural language processing datacamp datacamp natural language processing fundamentals in python https www datacamp com courses natural language processing fundamentals in python datacamp sentiment analysis in r the tidy way https www datacamp com courses sentiment analysis in r the tidy way datacamp text mining bag of words https www datacamp com courses intro to text mining bag of words datacamp building chatbots in python https www datacamp com courses building chatbots in python datacamp advanced nlp with spacy https www datacamp com courses advanced nlp with spacy others deep learning drizzle https github com kmario23 deep learning drizzle drench yourself in deep learning reinforcement learning machine learning computer vision and nlp from this curated list of exciting lectures natural language processing dan jurafsky christopher manning https www youtube com playlist list plqiyvnmpdlknzybtuolsi9mi9waerftfm deep learning for nlp https github com oxford cs deepnlp 2017 lectures deepmind and university of oxford department of computer science cmu cs 11 747 neural network for nlp http phontron com class nn4nlp2017 ysda nlp course https github com yandexdataschool nlp course yandex school of data analysis https yandexdataschool com cmu language and statistics ii more empirical methods in natural language processing http www cs cmu edu nasmith ls2 f06 ut cs 388 natural language processing http www cs cmu edu nasmith ls2 f06 columbia coms w4705 natural language processing http www cs columbia edu cs4705 columbia coms e6998 machine learning for natural language processing spring 2012 http www cs columbia edu mcollins courses 6998 2012 index html machine translation spring 2016 http mt class org commonlounge learn natural language processing from beginner to expert https www commonlounge com discussion 9e98fc12d49e4cd59e248fc5fb72a8e9 big data university advanced text analytics getting results with systemt https bigdatauniversity com courses adv text analytics getting results with systemt udacity natural language processing nanodegree https www udacity com course natural language processing nanodegree nd892 edx natural language processing https www edx org course natural language processing 3 an introduction to nlp taught by microsoft researchers apis and libraries r packages https cran r project org web views naturallanguageprocessing html tm https cran r project org web packages tm index html text mining lsa https cran r project org web packages lsa index html latent semantic analysis lda https cran r project org web packages lda lda pdf collapsed gibbs sampling methods for topic models textir https cran r project org web packages textir index html inverse regression for text analysis corpora https cran r project org web packages corpora index html statistics and data sets for corpus frequency data tau https cran r project org web packages tau index html text analysis utilities tidytext https github com juliasilge tidytext text mining using dplyr ggplot2 and other tidy tools sentiment140 https github com okugami79 sentiment140 sentiment text analysis sentimentr https cran r project org web packages sentimentr sentimentr pdf lexicon based sentiment analysis cleannlp https cran r project org web packages cleannlp cleannlp pdf ml based sentiment analysis rsentiment https cran r project org web packages rsentiment rsentiment pdf lexicon based sentiment analysis contains support for negation detection and sarcasm text2vec https cran r project org web packages text2vec index html fast and memory friendly tools for text vectorization topic modeling lda lsa word embeddings glove similarities fasttextr https cran r project org web packages fasttextr readme html interface to the fasttext library ldavis https cran r project org web packages ldavis interactive visualization of topic models keras https cran r project org web packages keras index html interface to keras https keras io a high level neural networks api rstudio blog tensorflow for r https blog rstudio com 2018 02 06 tensorflow for r retweet http rtweet info client for accessing twitter s rest and stream apis 21 recipes for mining twitter data with rtweet https rud is books 21 recipes topicmodels https cran r project org web packages topicmodels topicmodels pdf interface to the c code for latent dirichlet allocation lda textminer https cran r project org web packages textminer index html aid for text mining in r with a syntax that should be familiar to experienced r users wordvectors https github com bmschmidt wordvectors creating and exploring word2vec and other word embedding models gtrendsr https cran r project org web packages gtrendsr index html interface for retrieving and displaying the information returned online by google trends analyzing google trends data in r https datascienceplus com analyzing google trends data in r textstem https cran r project org web packages textstem tools that stem and lemmatize text nlputils https cran r project org package nlputils utilities for natural language processing udpipe https cran r project org web packages udpipe readme readme html tokenization parts of speech tagging lemmatization and dependency parsing using udpipe python modules https pypi org nltk http www nltk org natural language toolkit video nltk with python 3 for natural language processing https www youtube com playlist list plqvvvaa0qudf2jswnfigklibinznic4hl scikit learn http scikit learn org machine learning in python tutorial http nbviewer jupyter org gist rjweiss 7158866 spark nlp https nlp johnsnowlabs com open source text processing library for python java and scala it provides production grade scalable and trainable versions of the latest research in natural language processing spacy https spacy io industrial strength natural language processing in python textblob https textblob readthedocs io en dev simplified text processing natural language basics with textblob http rwet decontextualize com book textblob gensim https radimrehurek com gensim topic modeling for humans pattern en https www clips uantwerpen be pages pattern en a fast part of speech tagger for english sentiment analysis tools for english verb conjugation and noun singularization pluralization and a wordnet interface textmining https pypi python org pypi textmining 1 0 python text mining utilities scrapy https scrapy org open source and collaborative framework for extracting the data you need from websites lda2vec https github com cemoody lda2vec tools for interpreting natural language pytext https pytext pytext readthedocs hosted com en latest a deep learning based nlp modeling framework built on pytorch sent2vec https github com epfml sent2vec general purpose unsupervised sentence representations flair https github com zalandoresearch flair a very simple framework for state of the art natural language processing nlp word forms https github com gutfeeling word forms accurately generate all possible forms of an english word e g election elect electoral electorate etc allennlp https github com allenai allennlp open source nlp research library built on pytorch beautiful soup https www crummy com software beautifulsoup parse html and xml documents useful for webscraping bigartm https github com bigartm bigartm fast topic modeling platform scattertext https github com jasonkessler scattertext beautiful visualizations of how language differs among document types embeddings https pypi org project embeddings pretrained word embeddings in python fasttext https github com facebookresearch fasttext tree master python library for efficient learning of word representations and sentence classification google seq2seq https github com google seq2seq a general purpose encoder decoder framework for tensorflow that can be used for machine translation text summarization conversational modeling image captioning and more polyglot https polyglot readthedocs io en latest index html a natural language pipeline that supports multilingual applications textacy https chartbeat labs github io textacy index html nlp before and after spacy glove python https github com maciejkula glove python a toy implementation of glove in python includes a paragraph embedder bert as a service https github com hanxiao bert as service client server package for sentence encoding i e mapping a variable length sentence to a fixed length vector design intent to provide a scalable production ready service also allowing researchers to apply bert quickly keras bert https github com separius bert keras a keras implementation of bert paragraph embedding scripts and pre trained models https github com jhlau doc2vec scripts for training and testing paragraph vectors with links to some pre trained doc2vec and word2vec models texthero https github com jbesomi texthero text preprocessing representation and visualization from zero to hero apache tika http tika apache org a content analysis tookilt apache spark https spark apache org docs latest is a fast and general purpose cluster computing system it provides high level apis in java scala python and r and an optimized engine that supports general execution graphs mllib https spark apache org docs latest ml guide html mllib is spark s machine learning ml library its goal is to make practical machine learning scalable and easy related to nlp there are methods available for lda word2vec and tfidf lda https spark apache org docs latest ml clustering html latent dirichlet allocation lda latent dirichlet allocation word2vec https spark apache org docs latest ml features html word2vec is an estimator which takes sequences of words representing documents and trains a word2vecmodel the model maps each word to a unique fixed size vector the word2vecmodel transforms each document into a vector using the average of all words in the document tfidf https spark apache org docs latest ml features html tf idf term frequency inverse document frequency hdf5 https www neonscience org about hdf5 an open source file format that supports large complex heterogeneous data requires no configuration h5py http docs h5py org en stable quick html python hdf5 package stanford corenlp http stanfordnlp github io corenlp a suite of core nlp tools also checkout http corenlp run for a hosted version of the corenlp server introduction to stanfordnlp an incredible state of the art nlp library for 53 languages with python code https www analyticsvidhya com blog 2019 02 stanfordnlp nlp library python stanford parser http nlp stanford edu software lex parser html a probabilistic natural language parser stanford pos tagger http nlp stanford edu software tagger html a parts of speech tagger stanford named entity recognizer http nlp stanford edu software crf ner html recognizes proper nouns things places organizations and labels them as such stanford classifier http nlp stanford edu software classifier html a softmax classifier stanford openie http nlp stanford edu software openie html extracts relationships between words in a sentence e g mark zuckerberg founded facebook stanford topic modeling toolbox http nlp stanford edu software tmt tmt 0 2 mallet http mallet cs umass edu machine learning for language toolkit github https github com mimno mallet apache opennlp https opennlp apache org machine learning based toolkit for text nlp streamcrab https github com cyhex streamcrab real time twitter sentiment analyzer engine http www streamcrab com textrazor api https www textrazor com extract meaning from your text fasttext https github com facebookresearch fasttext library for fast text representation and classification facebook comparison of top 6 python nlp libraries https www kdnuggets com 2018 07 comparison top 6 python nlp libraries html pycaret s nlp module https pycaret org nlp pycaret is an open source low code machine learning library in python that aims to reduce the cycle time from hypothesis to insights also pycaret s founder moez ali is a smith alumni mma 2020 products systran enterprise translation products http www systransoft com translation products sas text miner part of sas enterprise miner http www sas com en us software analytics text miner html sas sentiment analysis http www sas com en ca software analytics sentiment analysis html statistica http www statsoft com products statistica product index text mining big data unstructured data http www statsoft com textbook text mining button 3 knime https www knime org rapidminer https rapidminer com gate https gate ac uk ibm watson http www ibm com watson video how ibm watson learns 3 minutes https www youtube com watch v ymufadn mo4 video ibm watson on jeapardy 10 minutes https www youtube com watch v li m7o brng video ibm watson the science behind an answer 7 minutes https www youtube com watch v dywo4zksfxw crimson hexagon https www crimsonhexagon com stocktwits http stocktwits com tap into the pulse of markets meltwater https www meltwater com crowdflower https www crowdflower com ai for your business lexalytics sematria https www lexalytics com api and excel plugin rosette text analytics https www rosette com ai for human language alchemy api https www ibm com watson alchemy api html monkey learn http monkeylearn com lighttag annotation tool https lighttag io hosted annotation tool for teams ubiai https ubiai tools easy to use text annotation tool for teams with most comprehensive auto annotation features supports ner relations and document classification as well as ocr annotation for invoice labeling anafora https github com weitechen anafora free and open source web based raw text annotation tool brat http brat nlplab org rapid annotation tool google s colab http colab research google com ready to go notebook environment that makes it easy to get up and running lyrebird ai https lyrebird ai ultra realistic voice cloning and text to speech recognition platform this canadian start up has created a product platform that syncs both voice cloning with text to speech lyrebird recognizes the intonations and voice patterns from audio recordings and overlays text data input to recreate a text to speech audio file output from the selected voice pattern audio recording ask data by tableau software inc https www tableau com products new features ask data hero reveal in february 2019 tableau released a new nlp feature service add on to help assist existing tableau platform users with retrieving quick and easy data visualizations to drive business intelligence insights similar to a search engine user interface tableau s ask data feature interface applies nlp from user text input to extract key words to find data analytics and business insights quickly on the tableau platform dialogflow https dialogflow com google s natural language platform used to integrate conversational user interfaces into mobile apps web applications bots vrus etc weka https www cs waikato ac nz ml weka easy to use graphical machine learning workbench including nlp capabilities annotation lab https www johnsnowlabs com annotation lab free end to end no code platform for text annotation and dl model training tuning out of the box support for named entity recognition classification relation extraction and assertion status spark nlp models unlimited support for users teams projects documents cloud microsoft azure text analytics https docs microsoft com en ca azure cognitive services text analytics overview amazon lex https aws amazon com lex a service for building conversational interfaces into any application using voice and text amazon comprehend https aws amazon com comprehend google cloud natural language https cloud google com natural language ibm watson http www ibm com watson video how ibm watson learns 3 minutes https www youtube com watch v ymufadn mo4 video ibm watson on jeapardy 10 minutes https www youtube com watch v li m7o brng video ibm watson the science behind an answer 7 minutes https www youtube com watch v dywo4zksfxw getting data out of pdfs apache pdfbox https pdfbox apache org tabula a tool for liberating data tables locked inside pdf files http tabula technology pdflayouttextstripper converts a pdf file into a text file while keeping the layout of the original pdf https github com jonathanlink pdflayouttextstripper pdftabextract a set of tools for extracting tables from pdf files helping to do data mining on ocr processed scanned documents https github com wzbsocialsciencecenter pdftabextract so how to extract text from a pdf http stackoverflow com questions 3650957 how to extract text from a pdf tools for extracting data and text from pdfs a review http okfnlabs org blog 2016 04 19 pdf tools extract text and data from pdfs html how i used nlp spacy to screen data science resumes https towardsdatascience com do the keywords in your resume aptly represent what type of data scientist you are 59134105ba0d pypdf2 https github com mstamy2 pypdf2 pdf file manipulation pdf to pdf online demos and tools mit opennpt for neural machine translation and neural sequence modeling http opennmt net stanford parser http nlp stanford edu 8080 parser stanford corenlp http nlp stanford edu 8080 corenlp word2vec demo http bionlp www utu fi wv demo another word2vec demo http turbomaze github io word2vecjson sense2vec semantic analysis of the reddit hivemind https explosion ai demos sense2vec regexpal https www regexpal com great tool for testing out regular expressions allennlp demo https demo allennlp org great demo using allennlp of everything from named entity recognition to textual entailment cognitive computation group part of speech tagging demo http cogcomp org page demos these demos exhibit part of speech tagging information extraction tasks etc datasets uci s text datasets https archive ics uci edu ml datasets html format task att area numatt numins type text sort nameup view table a collection of databases domain theories and data generators used by machine learning community data world s text datasets https data world datasets text mining awesome public datasets natural languge https github com caesar0301 awesome public datasets natural language insight resources datasets http mlg ucd ie datasets index html data bing sentiment analysis https www cs uic edu liub fbs sentiment analysis html datasets consumer complaint database https www consumerfinance gov data research consumer complaints download the data from the consumer financial protection bureau sentiment labelled sentences data set http archive ics uci edu ml datasets sentiment labelled sentences contains sentences labelled as positive or negative from imdb com amazon com and yelp com amazon product data http jmcauley ucsd edu data amazon data is plural https docs google com spreadsheets d 1wzhplmchkjvwokp4juclhjfgqiy8fqfmemwkl2c64vk edit gid 0 fivethirtyeight s datasets https data fivethirtyeight com utm campaign data 2belixir utm medium email utm source data elixir 169 r datasets https www reddit com r datasets awesome public datasets https github com caesar0301 awesome public datasets r s datasets package https stat ethz ch r manual r devel library datasets html 00index html 200 000 russian troll tweets https www reddit com r politics comments 7zfk4i im a reporter for nbc news and we published over released by congress from twitter suspended accounts and removed from public view wikipedia list of datasets for ml research https en wikipedia org wiki list of datasets for machine learning research google dataset search https toolbox google com datasetsearch kaggle umich si650 sentiment classification https www kaggle com c si650winter11 data lee s similarity data sets https faculty sites uci edu mdlee similarity data corpus of presidential speeches cops and a clinton trump corpus http www thegrammarlab com nor portfolio corpus of presidential speeches cops and a clintontrump corpus 15 best chatbot datasets for machine learning https gengo ai datasets 15 best chatbot datasets for machine learning a survey of available corpora for building data driven dialogue systems https arxiv org pdf 1512 05742 pdf nlp datasets https github com niderhoff nlp datasets hate speech and offensive language https github com t davidson hate speech and offensive language first quora dataset release question pairs https data world socialmediadata quora question pairs the best 25 datasets for natural language processing https gengo ai datasets the best 25 datasets for natural language processing swag https github com rowanz swagaf a large scale dataset created for natural language inference nli with common sense reasoning mimic https mimic physionet org gettingstarted access an openly available dataset developed by the mit lab for computational physiology comprising deidentified health data associated with 40 000 critical care patients clinical nlp dataset repository https clinical nlp github io 2019 resources html a curated list of publicly available clinical datasets for use in nlp research million song lyrics https labrosa ee columbia edu millionsong musixmatch the multi genre nli corpus https www nyu edu projects bowman multinli twitter us airline sentiment https www kaggle com crowdflower twitter airline sentiment home million song lyrics https labrosa ee columbia edu millionsong musixmatch dataset of song lyrics in bag of words bow format duorc https github com duorc duorc tree master dataset 186k unique question answer pairs with evaluation script for paraphrased reading comprehension edgar financial statements https edgar online com reporting engine for financial and regulatory filings for companies worldwide a huge repository of financial and company data for text mining american national corpus download http www anc org data oanc contents santa barbara corpus of spoken american english http www linguistics ucsb edu research santa barbara corpus leipzig corpora collection corpora in english arabic french russian german http wortschatz uni leipzig de en download awesome twitter https github com hridaydutta123 awesome twitter tools blob master readme md the big bad nlp database https quantumstat com dataset dataset html cbc news coronavirus articles https www kaggle com ryanxjhan cbc news coronavirus articles march 26 huggingface https huggingface co datasets viewer dataset financial phrasebank lexicons for sentiment analysis mpqa lexicon http mpqa cs pitt edu lexicons sentiwordnet http sentiwordnet isti cnr it afinn http www2 imm dtu dk pubdb views publication details php id 6010 bing https www cs uic edu liub fbs sentiment analysis html nrc http saifmohammad com webpages nrc emotion lexicon htm vadersentiment https github com cjhutto vadersentiment misc askreddit people with a mother tongue that isn t english what are the most annoying things about the english language when you are trying to learn it https www reddit com r askreddit comments 6qvmny people with a mother tongue that isnt english funny video emotional spell check https vimeo com 17859540 how to win kaggle competition based on nlp task if you are not an nlp expert http www kdnuggets com 2017 09 win kaggle nlp not expert html detecting gang involved escalation on social media using context http aclweb org anthology d18 1005 detecting aggression and loss in social media using cnn reasoning about actions and state changes by injecting commonsense knowledge http aclweb org anthology d18 1006 incorporating global commonsense constraints biasing reading with preferences from large scale corp the language of hip hop https pudding cool 2017 09 hip hop words a 2017 analysis by matt daniels of pudding determining the popularity of various words in hip hop music and across artists using natural language processing for automatic detection of plagiarism https pdfs semanticscholar org 636d 4c0b0fe6919abe6eb546907d28ed39bf56e6 pdf probabilistic graphical models lagrangian relaxation algorithms for natural language processing http people csail mit edu dsontag courses pgm12 slides lecture3 pdf human emotion https www youtube com watch v qwsft6tmvba list plraxtmerzgodp 8gztsuki9nrranbkkp4 how to determine confidence level for manually labeled sentiment data a complete exploratory data analysis and visualization for text data https towardsdatascience com a complete exploratory data analysis and visualization for text data 29fb1b96fb6a other curated lists awesome nlp https github com keonkim awesome nlp a curated list of resources dedicated to natural language processing nlp awesome machine learning https github com josephmisiti awesome machine learning cpp nlp awesome deep learning for natural language processing nlp https github com brianspiering awesome dl4nlp paper with code https paperswithcode com a fantastic list of recent machine learning papers on arxiv with links to code chinese nlp tools https datascience shanghai nyu edu chinese nlp tools 2019 list of tools for nlp in chinese language association for computational linguistics papers anthology https aclanthology info the acl anthology currently hosts almost 50 000 papers on the study of computational linguistics and natural language processing includes all papers from recent conferences over 150 of the best machine learning nlp and python tutorials i ve found https medium com machine learning in practice over 150 of the best machine learning nlp and python tutorials ive found ffce2939bd78 contribute contributions are more than welcome please read the contribution guidelines contributing md first license cc0 http mirrors creativecommons org presskit buttons 88x31 svg cc zero svg http creativecommons org publicdomain zero 1 0 to the extent possible under law stepthom has waived all copyright and related or neighboring rights to this work | natural-language-processing text-mining text-analysis text-analytics sentiment-analysis list topic-modeling text-classification awesome awesome-list machine-learning data-mining nlp nlp-machine-learning | ai |
IoT-AssetTracking-Perishable-Network-Blockchain | read this in other languages readme ja md iot asset tracking using a hyperledger blockchain introduction this repository contains three sections which assemble an iot asset tracking device hyperledger blockchain and a node red dashboard to implement a perishable network supply chain this example can be used to track environmental conditions for a food safety supply chain refrigerated medical supplies garden plant shipments or any perishable shipment that are temperature humidity vibration time sensitive if a cargo needs to be delivered within safe environmental parameters and time the use of an iot asset tracking device that combines environmental sensors calculates its location via gps triangulation or beacons and then reports its location via cellular 5g sub1ghz sigfox wifi networks is extremely valuable when multiple participants farms manufacturers processing plants trucks ports ships distribution centers consumer retail outlets are involved in the safe shipment and payment of the cargo a hyperledger blockchain can be used to record immutable transactions as the cargo shipment progresses through its delivery journey workshop i ve arranged this git repository to be read as an ibm code pattern https developer ibm com code workshop tutorial workshop readme md follow the steps in the workshop directory workshop readme md to learn how to build one yourself section overviews the first section particleelectron readme md details how to set up a particle electron asset tracker v2 to send environmental sensor data and location to the cloud this implementation uses a particle electron https docs particle io datasheets kits and accessories particle shields electron asset tracker v2 but many other iot asset tracking devices that can transmit location and data can be substituted with similar results subsequent revisions of this workshop tutorial will add other iot asset tracking boards so check back in the future the second section blockchain readme md implements a perishable business network using hyperledger https www hyperledger org fabric hyperledger composer hyperledger composer rest apis running in the ibm cloud container service https www ibm com cloud container service managed by a kubernetes cluster https console bluemix net docs tutorials scalable webapp kubernetes html deploy a scalable web application on kubernetes in the ibm cloud in the third section node red readme md the power of where what and when is best visualized in a dashboard that plots the geo location path the environmental sensor data and can control triggers and alerts i use node red https nodered org and a node js server running in an ibm cloud hosted cloud foundry application to receive the iot asset tracking data and write it to the hyperledger fabric using hyperledger composer rest apis i also use a node red dashboard to plot the shipment on a map enjoy give me feedback if you have suggestions on how to improve this tutorial 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 http www apache org licenses license 2 0 txt asl faq link http www apache org foundation license faq html whatdoesitmean | server |
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AListApart | a list apart we dipped our collective toes in the github waters when we open sourced our embeddable comments https github com alistapart comment embed script last year we ve got much bigger things underway now we re open sourcing the code that powers alistapart com contributing to an open source project can be tough and it can take time to get the hang of things we want to help with that if you re inexperienced with github our goal is to provide you with a great place to send your first ever pull requests to help you get comfortable contributing to a large open source effort participating in issue threads and reviewing code contributing we re currently using expressionengine which makes it tricky to clone the repo and run the site locally at least with this first incarnation of the repo we re exploring ways to make that better for right now we d love to have your eyes on our dev site http dev alistapart com which directly mirrors our dev branch don t hesitate to file a new issue https github com alistapart alistapart issues new or join us in the issue tracker https github com alistapart alistapart issues if anything catches your eye by joining our conversations as we weigh the pros and cons of certain approaches you ll be better armed to make the same decisions on your own projects if you re already well versed in the ways of github we d love to have your eyes on how we ll be doing things over the next few weeks and your help building a welcoming low pressure place to learn the ins and outs of contributing to an open source project a href http dploy io img src https ala dploy io badge 88313865850598 10948 png alt deployment status from dploy io a | front_end |
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giveth-dapp | test https github com giveth giveth dapp actions workflows test yml badge svg https github com giveth giveth dapp actions workflows test yml giveth dapp readme header png dapp donation application for charitable giving without losing ownership welcome to the code for giveth s dapp this is an open source effort to realize the potential of ethereum smart contracts more specifically the giveth dapp provides an alternative to traditional donation table of content table of content table of content getting started getting started prerequisites prerequisites install install osx and linux osx and linux windows windows run run build build configuration configuration analytics analytics contributing contributing local development local development development and pr testing development and pr testing deployment environments deployment environments release process release process help help getting started in the following sections you will learn all you need to know to run the dapp locally and to start contributing all the steps are also described in this amazing video tutorial walkthrough https tinyurl com y9lx6jrl by oz prerequisites you need to use nodejs v10 lts you need to use yarn v1 22 10 or higher to correctly install the dependencies you need to have git https git scm com book en v2 getting started installing git install 1 click star on this repo near the top right corner of this web page if you want to 2 join us on element http join giveth io or discord https discord gg uq2taxp9bc if you haven t already 3 fork this repo by clicking fork button in top right corner of this web page continue to follow instruction steps from your own giveth dapp repo 4 clone your own giveth dapp repo copy the link from the clone or download button near the top right of this repo s home page 5 the rest of these steps must be done from your machine s command line see the osx and linux for osx and linux or windows for windows section to continue osx and linux if your operative system is any distribution of linux you can use an all in one installation scripts special thanks to dapp contributor jurek brisbane available here https github com giveth giveth dapp files 3674808 givethbuildstartscripts 2019 09 29 zip along with a youtube video https www youtube com watch v rzlhxxaz73k feature youtu be otherwise try the following 1 from the desired directory you wish to copy the giveth dapp folder with source files to git clone paste your own repo link here note please use develop branch for contributing git clone b develop paste your own repo link here 2 change directories to giveth dapp cd giveth dapp 3 make sure you have nodejs https nodejs org v10 and yarn https yarnpkg com v1 22 10 or higher installed 4 install dependencies from within giveth dapp directory yarn install 5 that is it you are now ready to run the giveth dapp head to the run dapp run section for further instructions windows 1 install the latest version of python from this link https www python org downloads make sure python is added to path 2 install microsoft visual studio 2017 double check the version from this link https download visualstudio microsoft com download pr 3e542575 929e 4297 b6c6 bef34d0ee648 639c868e1219c651793aff537a1d3b77 vs buildtools exe giveth dapp needs the node gyp module and node gyp needs vs c 2017 build tools to be installed 3 after downloading install the packages marked from this image https cdn discordapp com attachments 849682448102457374 850480734291623946 unknown png 4 then run command below in command prompt npm config set msvs version 2017 5 after installing the above you should install nodejs version 10 lts https nodejs org dist latest v10 x it is better to be v10 24 1 lts 6 download and run the node v10 24 1 x64 msi installer and then continue through the installation as normal be sure to have the enable in path option enabled before installing 7 open the command line in administrator mode by right clicking on the cmd exe application and selecting run as administrator 8 in the administrator command prompt change to the directory where you want to store this repository cd c some directory for repositories 9 double check the node version with cmd command node v 10 after that install the latest version of yarn be careful not to install packages with npm if you have already tried npm install you should first delete node modules folder yarn install 11 that is it you are now ready to run the giveth dapp head to the run dapp run dapp section for further instructions run 1 the giveth dapp will need to connect to a feathers giveth https github com giveth feathers giveth server follow the feathers giveth readme instructions to install and run server before proceeding further alternatively you could change the configuration to connect to the develop environment see the configuration configuration section 2 start the dapp npm start 3 once the dapp is up in your browser click sign in from the main menu 4 for testing locally choose any of the wallet files found in the giveth dapp keystores folder using the wallet password password do not use these on any mainnet evms 5 using the test token to use the test token you need to import the keystore json you use for your account to metamask after importing click on add token custom token and enter the minime token address that can be found when deploying the contracts should be 0xe78a0f7e598cc8b0bb87894b0f60dd2a88d6a8ab by default but make sure to check the token balance should show up automatically and the token symbol is mmt however in the dapp the token symbol is referred to as ant b c the dapp needs to be able to fetch a conversion rate note when resetting feathers or redeploying the contracts you need to remove the keystore from metamask and follow this procedure again build npm run build note due to some web3 libraries that are not transpiled from es6 we have to use our giveth react scripts https github com giveth create react app tree master packages react scripts fork of react scripts configuration the dapp has several node environment variables which can be used to alter the dapp behaviour without changing the code you can set them through env or env local files in the dapp folder variable name default value description port 3010 port on which the dapp runs react app environment localhost to which feathers environment should the dapp connect by default it connects to localhost feathers allowed values are localhost develop release alpha mainnet see deployment environments deploy environments react app decimals 8 how many decimal should be shown for cryptocurrency values note that the calculations are still done with 18 decimals react app featherjs connection url differs per react app environment overwrites the environment injected feathers connection url react app node connection url differs per react app environment overwrites the evm node connection url for making evm transactions react app liquidpledging address differs per react app environment overwrites the liquid pledging contract address react app dac factory address differs per react app environment overwrites the communities contract address react app campaign factory address differs per react app environment overwrites the campaign factory contract address react app milestone factory address differs per react app environment overwrites the milestonefactory contract address react app token addresses differs per react app environment overwrites the bridged token addresses this is a json object string w token name token address react app blockexplorer differs per react app environment overwrites the block explorer base url the dapp assumes such blockexplorer api is blockexplorer tx transaction hash react app default gasprice 10 overwrites the default gasprice that is used if ethgasstation service is down the value is in gwei react app analytics key overwrites segment analytics key example of env local file that makes the dapp run on port 8080 connects to the develop environment and uses custom blockexplorer port 8080 react app environment develop react app blockexplorer www awesomeopensourceexplorer io the rest of the configuration can be found in configuration js analytics segment analytics can be enabled by setting react app analytics key query strings the milestone creation proposal view now supports query string arguments the following arguments are available argument expected values type title the title of the milestone string description the description of the milestone string recipientaddress the address of the recipient string revieweraddress the address of the reviewer string selectedfiattype a valid fiat type i e usd string date a valid milestone date string string token a valid token symbol i e dai string tokenaddress a valid token address string maxamount a valid max amount of eth or token number fiatamount a valid max amount of fiat dependant on selectedfiattype number iscapped determines whether the milestone should be capped 0 or 1 boolean requirereviewer determines whether the milestone should require a reviewer 0 or 1 boolean contributing the dapp is fully open source software and we would love to have your helping hand see contributing md contributing md for more information on what we re looking for and how to get started you can then look for issues labeled good first issue https github com giveth giveth dapp labels good 20first 20issue or help wanted https github com giveth giveth dapp labels help 20wanted we regularly reward contributions with ether using the reward dao if you are not a developer you can still help us by testing new releases periodically see the release process release process section if you want to better understand how does the development process works please refer to our wiki pages https wiki giveth io documentation dapp especially to the product definition https wiki giveth io documentation dapp product definition product roadmap https wiki giveth io documentation dapp product roadmap and development process quality assurance https wiki giveth io documentation dapp product development testing local development at first you would like to run the dapp locally when running testrpc locally in deterministic mode you can use any of the keystores in the giveth dapp keystores as your wallet this will provide you access to the testrpc accounts for local development each keystore uses the same password password do not use these on any mainnet evms the keystores are seeded with 10 000 ant tokens for testing donations to get started with testing donations make sure to add your account s keystore to metamask and swith metamask to ganache the donation modal should then show the appropriate balance when donating in ant tokens note if you get a nonce error in metamask or if the dapp fails to load with your metamask unlocked it could be b c metamask is confused you should go to settings reset account in metamask in order to reset the nonce cached account data development and pr testing 1 the giveth dapp is auto deployed from the develop branch and is live on rinkeby develop giveth io https develop giveth io all pull requests are autodeployed and the pr preview will be generated upon submission to learn how to access pr previews see development process quality assurance https wiki giveth io documentation dapp product development testing on our wiki 2 in order to use the dapp you will need to create account if this is your first time click sign up to create an account if you already have a valid keychain file use it to sign in 3 you will need test ether on the rinkeby network go to the wallet page to see your new address 0x copy that address and use the faucet to get some https faucet rinkeby io deployment environments at giveth we are using the gitflow http nvie com posts a successful git branching model branching model to deploy to 4 different environments name blockchain branch deployed auto deploy use mainnet https mainnet giveth io ethereum main network master no main network deployment for now abandoned due to high transaction costs until sustainable solution is found alpha https alpha giveth io rinkeby test network master no environment used as a production version until scalability is resolved release https release giveth io rinkeby test network release yes environment for release candidate quality control testing by non devs develop https develop giveth io rinkeby test network develop yes development environment for integrating new features feature and pull request branches are also automatically deployed to this environment you can change the environment to which the dapp connects through the node environment variables see the configuration configuration section for more details release process the development uses the gitflow process with 2 weeks long sprints this means there is new release to be tested every fortnight we invite contributors to help us test the dapp in the release environment before we merge it to the master branch and deploy to production environments if you are interested write to the dapp development channel on element https join giveth io you can read more about the release planning on our wiki https wiki giveth io documentation dapp product development testing help reach out to us on element https join giveth io or discord https discord gg uq2taxp9bc for any help or to share ideas | dapp testrpc wallet blockchain-dapp ethereum-dapp giveth blockchain-charity ethereum-charity ether-donation json-wallet testrpc-websockets | blockchain |
intl-iot | information exposure from consumer iot devices this site contains analysis code accompanying the paper information exposure from consumer iot devices a multidimensional network informed measurement approach in proceedings of the acm internet measurement conference 2019 imc 2019 october 2019 amsterdam netherlands the official paper can be found at https moniotrlab ccis neu edu imc19 the site also contains instructions for requesting access to the full dataset the testbed code and documentation can be found at https moniotrlab ccis neu edu tools currently it is deployed at both northeastern university and imperial college london github logo lab png figure 1 the iot lab at northeastern university file structure each subdirectory shows samples for processing pcap files for destination encryption and content analysis destination code for section 4 destination analysis analyze the destinations that traffic is being sent to and received from encryption code for section 5 encryption analysis analyze whether traffic is encrypted or unencrypted model code for section 6 content analysis create machine learning models to predict the state of an iot device using its network traffic moniotr code to automate experiments getting started md a step by step tutorial to get started analyzing data using each of the three analyses license md the license for this software readme md this file contains an overview of the software lab png a photo of the iot lab at northeastern university datasets we release the traffic packet headers from 34 586 controlled experiments and 112 hours of idle iot traffic the naming convention for the data is country vpn device name activity name datetime length pcap for example us amcrest cam wired power 2019 04 10 21 32 18 256s pcap is the traffic collected from device amcrest cam wired when power on at the time of 2019 04 10 21 32 18 which lasts 256 seconds in the us lab without vpn to obtain access to the dataset please follow the instructions on the paper webpage at https moniotrlab ccis neu edu imc19 we require that you agree to the terms of our data sharing agreement this is out of an abundance of caution to protect any private or security sensitive information that we were unable to remove from the traces setup this version relies on python 3 6 tested on python 3 6 9 it is strongly suggested that one uses the following virtual environment sudo apt get install virtualenv sudo apt get install libpcap dev libpq dev sudo apt get install python3 dev sudo apt get install python3 6 tk sudo apt get install gcc sudo apt get install tshark virtualenv p python3 6 env source env bin activate once the environment is setup and running install the following packages pip install numpy pip install scipy pip install pyshark pip install geoip2 pip install matplotlib pip install dpkt pip install pycrypto pip install ipy pip install pcapy pip install scapy pip install impacket pip install mysql connector python rf pip install pandas pip install tldextract pip install python whois pip install ipwhois pip install psutil for more information about the pipelines and the contents of the code see the readmes for destination analysis destination readme md encryption analysis encryption readme md and content analysis model readme md content analysis also has a page describing the machine learning models and contents of that directory in depth model model details md model model details md for step by step instructions to get started analyzing data see getting started md getting started md | server |
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federated-learning-lib | d d img src docs assets images ibmfl logo png raw true ibm federated learning what is it ibm federated learning is a python framework for federated learning fl in an enterprise environment fl is a distributed machine learning process in which each participant node or party retains data locally and interacts with the other participants via a learning protocol the main drivers behind fl are privacy and confidentiality concerns regulatory compliance requirements as well as the practicality of moving data to one central learning location ibm federated learning provides a basic fabric for fl to which advanced features can be added it is not dependent on any specific machine learning framework and supports different learning topologies e g a shared aggregator and protocols it supports deep neural networks dnns as well as classic machine learning techniques such as linear regression and k means this comprises supervised and unsupervised approaches as well as reinforcement learning the figure below shows a typical configuration of an aggregator based federated learning setup supported by ibm federated learning p align center img src docs floverview png width 566 p a set of parties own data and each trains a local model the parties exchange updates with an aggregator using a fl protocol the aggregator fuses aggregates the results from the different parties and ships the consolidated results back to the parties this can go through multiple rounds until a termination criterion is reached ibm federated learning supports the configuration of these training scenarios the key design points of ibm federated learning are the ease of use for the machine learning professional configurability to different computational environments from data centers to edge devices and extensibility it can be extended to work with different machine learning ml libraries learning protocols and fusion algorithms this provides a basic fabric on which fl projects can be run and research in fl learning can take place ibm federated learning comes with a large library of fusion algorithms for both dnns and classic ml approaches consisting of implementations of both common published fusion algorithms as well as novel ones we have developed supported functionality ibm federated learning supports the following machine learning model types neural networks any neural network topology supported by keras pytorch and tensorflow decision tree id3 linear classifiers regressions with regularizer logistic regression linear svm ridge regression kmeans and na ve bayes deep reinforcement learning algorithms including dqn ddpg ppo and more ibm federated learning supports multiple state of the art fusion algorithms to combine model updates coming from multiple parties changes in this algorithm may speed up the convergence reduce training time or improve model robustness for a particular ml model you can select multiple types of fusion algorithms supported ml models supported fusion algorithms neural networks iterative average fedavg mcmahan et al https arxiv org abs 1602 05629 gradient average pfnm yurochkin et al https arxiv org abs 1905 12022 krum blanchard et al https papers nips cc paper 6617 machine learning with adversaries byzantine tolerant gradient descent pdf coordinate wise median yin et al https arxiv org abs 1803 01498 zeno xie et al https arxiv org abs 1805 10032 spahm yurochkin et al https arxiv org abs 1911 00218 fed yu et al https arxiv org abs 2009 06303 fedprox tian li et al https arxiv org abs 1812 06127 shuffle iterative average cheng et al https arxiv org abs 2105 09400 comparative elimination gupta et al https arxiv org abs 2108 11769 adaptive federated averaging mu oz gonz lez et al https arxiv org abs 1909 05125 id3 decision tree id3 fusion quinlan https link springer com article 10 1007 bf00116251 reinforcement learning rllib models iterative average fedavg mcmahan et al https arxiv org abs 1602 05629 linear classifiers with sgd iterative average k means spahm yurochkin et al https arxiv org abs 1911 00218 na ve bayes naive bayes fusion with differential privacy we also support the following fairness techniques that help to mitigate bias in federated learning and can be coupled for multiple types of ml models fairness techniques algorithm types supported ml models local reweighing abay et al https arxiv org abs 2012 02447 pre processing all ml models global reweighing with differetial privacy abay et al https arxiv org abs 2012 02447 pre processing all ml models federated prejudice removal abay et al https arxiv org abs 2012 02447 in processing logistic regression in order to aid orchestration of federated learning experiments using the ibmfl library we also provide a jupyter notebook based ui interface experiment manager dashboard experiment manager experiment manager dashboard ipynb where users can choose the model fusion algorithm number of parties and other hyper parameters for a run this orchestration can be done on the machine where the notebook is hosted i e locally or even across remote machines the usage guide on how to go about using the dashboard can be found here experiment manager usage guide md ibmfl multi cloud and hybrid cloud orchestrator automates the deployment and monitoring of aggregator and party process using federated learning library docker image on openshift clusters which are setup on different cloud data center regions for more information on how to use openshift orchestrator please refer to readme openshift fl readme md how to get started clone the repository the main framework runtime is packaged in a whl file federated learning lib try the set up guide setup md for a single node federated learning setup try the crypto set up guide setup crypto md for running federated learning with fully homomorphic encryption he there are a number of examples examples readme md with explanation for different federated learning tasks with different model types to get started with playing with ibm fl via pre configured examples examples iter avg on popular datasets e g mnist http yann lecun com exdb mnist cifar10 https www cs toronto edu kriz cifar html femnist https github com talwalkarlab leaf tree master data femnist and adult https archive ics uci edu ml datasets adult etc try our experiment manager here experiment manager try ibm fl with openshift here openshift fl how does it work there is a docs folder docs with tutorials and api documentation to learn how to use and extend ibm federated learning we also have a few video tutorials https ibmfl res ibm com vid intro web site https ibmfl res ibm com aggregator and party configuration tutorial docs tutorials configure fl md api documentation https ibmfl api docs res ibm com related publications docs papers md white paper https arxiv org abs 2007 10987 how to get in touch we appreciate feedback and questions please post issues when you encounter them we have set up a slack channel for ongoing discussion join the ibm federated learning workspace https ibm fl slack com if the previous link does not work for you you can also use this invitation link https join slack com t ibm fl shared invite zt ff0k1xgh il9aq6sw6rnny9gddnettq citing ibm federated learning if you use ibm federated learning please cite the following reference paper bibtex article ibmfl2020ibm title ibm federated learning an enterprise framework white paper v0 1 author ludwig heiko and baracaldo nathalie and thomas gegi and zhou yi and anwar ali and rajamoni shashank and ong yuya and radhakrishnan jayaram and verma ashish and sinn mathieu and others journal arxiv preprint arxiv 2007 10987 year 2020 ongoing effort this is an ongoing effort we plan to update this repo as new functionality is added frequently license the distribution of ibm federated learning in this repository is for non commercial and experimental use under this license license for commercial use ibm federated learning is available in ibm cloudpak for data https www ibm com products cloud pak for data ga 2 110789637 1485356781 1606942871 1842160356 1605046314 and as a service https dataplatform cloud ibm com docs content wsj getting started whats new html ga 2 114396295 1485356781 1606942871 1842160356 1605046314 | ai |
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mobile | mobile mobile development image text https github com pulse kun mobile blob main src assets show png | front_end |
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amazon-ecs-catsndogs-workshop | alt text images 1 png catsndogs lol logo workshop overview welcome to catsndogs lol the fifth most highly rated cat and dog meme sharing website in australia and new zealand our mission is to serve a wide range quality of cat and dog memes to our customers memes come and go quickly and we are starting to see larger and larger surges in customer demand catsndogs lol uses docker containers to host our application until today we ve run everything on a spare laptop but now we re moving to the amazon elastic container service ecs our devops shepherd wants to take advantage of the latest and greatest features of the ecs platform we also have several new initiatives that the developers and data science teams are keen to release as the new devops team you will create the ecs environment deploy the cats and dogs applications cope with our hoped for scaling issues and enable the other teams to release new features to make our customers happier than ever welcome aboard initial environment setup prerequisites this workshop requires a laptop with wi fi running microsoft windows mac os x or linux the awscli installed an internet browser such as chrome firefox safari or edge an aws account you will create aws resources including iam roles during the workshop an ec2 key pair created in the aws region you are working in this workshop can be run in any of the following aws regions us east n virginia us east ohio us west oregon asia pacific singapore and eu ireland initial setup 1 clone this repository to your local workspace this contains the cloudformation templates and other materials you will need during the workshop 2 if you do not already have an ec2 keypair created sign in to the aws ec2 console at https console aws amazon com ec2 a click key pairs and then click create key pair b give the key pair a name and click create the console will generate a new key pair and download the private key keep this somewhere safe 2 deploy the initial cloudformation template this creates iam roles an s3 bucket and other resources that you will use in later labs the template is called cfn templates lab0 baseline setup yml if you are sharing an aws account with someone else doing the workshop only one of you needs to create this stack in stack name enter catsndogssetup later labs will reference this stack by name so if you choose a different stack name you will need to change the labsetupstackname parameter in later labs 3 be sure to tick the i acknowledge that aws cloudformation might create iam resources with custom names check box where do you go from here labs cost management and ec2 scaling lab 1 artifacts ecs service deployment and task auto scaling lab 2 artifacts deploying a new version of the cats service with secrets management lab 3 artifacts running ecs tasks based on time and events lab 4 artifacts machine learning containers and placement constraints lab 5 artifacts automated deployments lab 6 artifacts advanced deployment techniques lab 7 artifacts clean up lab 99 clean up participation we encourage participation if you find anything please submit an issue https github com aws samples amazon ecs catsndogs workshop issues however if you want to help raise the bar submit a pr https github com aws samples amazon ecs catsndogs workshop pulls license this library is licensed under the apache 2 0 license | os |
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frontend-dev-resources | frontend dev resources list of useful resources for frontend developers podcasts podcasts local podcasts local podcasts web shows web shows conferences conferences local conferences local conferences upcoming confernces upcoming conferences meetups meetups free online books free online books newsletters newsletters online courses online courses other resources other resources contribute contribute podcasts javascript jabber https devchat tv js jabber rss http feeds feedwrench com javascriptjabber rss itunes https itunes apple com us podcast javascript jabber id496893300 mt 2 adventures in angular https devchat tv adventures in angular rss http feeds feedwrench com adventuresinangular rss itunes https itunes apple com us podcast adventures in angular id907361052 mt 2 toolsday http toolsday io rss http toolsday io feeds rss xml itunes https itunes apple com us podcast toolsday id1063765302 mt 2 javascript air http javascriptair com rss http audio javascriptair 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id464936442 mt 2 the big web show http 5by5 tv bigwebshow rss http feeds 5by5 tv bigwebshow itunes https itunes apple com us podcast the big web show id370445683 mt 2 non breaking space show http goodstuff fm nbsp rss http feeds goodstuff fm nbsp itunes https itunes apple com us podcast the non breaking space show id507162981 hanselminutes http hanselminutes com rss http feeds podtrac com 9dpm65vdpll1 itunes https itunes apple com us podcast hanselminutes id117488860 ctrl click cast http ctrlclickcast com rss http feeds feedburner com ctrlclickcast itunes https itunes apple com us podcast ctrl click cast id446900959 frontside the podcast https frontsidethepodcast simplecast fm rss http simplecast fm podcasts 96 rss itunes https itunes apple com us podcast frontside the podcast id827250386 mt 2 revision path http revisionpath com rss http simplecast fm podcasts 102 rss itunes https itunes apple com us podcast revision path id834173190 data stories http datastori es rss http datastori es 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id1082253744 modern web http modernweb podbean com rss http modernweb podbean com feed itunes https itunes apple com us podcast modern web id1084236187 ember land http ember land rss http ember land format rss itunes https itunes apple com us podcast ember land id963237217 mt 2 ember weekend https emberweekend com episodes rss https emberweekend com feed xml itunes https itunes apple com us podcast ember weekend id981719021 3 minutes with kent https www briefs fm 3 minutes with kent rss http www briefs fm 3 minutes with kent xml itunes https itunes apple com us podcast 3 minutes with kent id1082883103 mt 2 bit podcast https bitpodcast com rss https bitpodcast com podcast rss itunes https itunes apple com us podcast bit podcast id980882853 front end happy hour http frontendhappyhour com rss http feeds soundcloud com users soundcloud users 206137365 sounds rss itunes https itunes apple com us podcast front end happy hour id1089047924 mt 2 spacedojo show https show spacedojo com rss https simplecast com podcasts 1108 rss itunes https itunes apple com us podcast id984131621 mt 2 react30 https react30 com rss https react30 com index xml itunes https itunes apple com us podcast react30 id1121818347 jamstack radio http www heavybit com library podcasts jamstack radio rss http www heavybit com category library podcasts jamstack radio feed itunes https itunes apple com us podcast jamstack radio id1148797643 hacking ui http hackingui com podcast rss https simplecast com podcasts 1625 rss itunes https itunes apple com il podcast the hacking ui podcast id1078107474 hashbang http hashbangshow okgrow com rss https rss simplecast com podcasts 2601 rss itunes https itunes apple com us podcast hashbang id1199939802 syntax https syntax fm rss https feed syntax fm rss itunes https itunes apple com us podcast syntax tasty web development treats id1253186678 egghead io instructor chats https egghead simplecast fm episodes rss https rss simplecast com podcasts 3762 rss itunes https itunes 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gs3fz46igfirdqktixlws7e0 march 13 14 2017 ngvikings 2017 https opbeat com community events ngvikings 2017 february 11 12 2017 ng be 2016 https www youtube com playlist list pl9pv rwzcenimtrikpxox5scuxshwdlnw december 8 9 2016 dotjs 2016 https www youtube com channel ucsrhwam00ay0fasnsw6exka december 5 2016 cssday io 2016 https www youtube com playlist list plfwvsmtbxho2k3qaioqqx snocjzgduet december 3 2016 dotcss 2016 https www youtube com channel ucsrhwam00ay0fasnsw6exka december 2 2016 jsconf australia 2016 https www youtube com watch v kgxpgab3sbk december 1 2016 cssconf australia 2016 https www youtube com watch v skzcekewowc november 30 2016 js kongress 2016 https www youtube com playlist list pl8ajghz7poctmslpct2ttryi6drn3vpcf november 28 29 2016 jsconf asia 2016 https www youtube com playlist list pl37zvnwpeshfn7vjmhjenn8niutyne8o7 november 25 26 2016 cssconf asia 2016 https www youtube com playlist list pl37zvnwpeshfizh1jsg3s7 qyn2uk0osh november 24 2016 ng poland 2016 https 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list plud4kd wl zysfu3tiysb4wqffqzo ejq august 25 26 2016 jsconf iceland 2016 https www youtube com playlist list pl37zvnwpeshgtbb i7wjqtxzhoe5agiw4 august 25 26 2016 cascadiafest 2016 https www youtube com playlist list plliioabftbknkyp1m4dmw xu9byjopmhe august 3 5 2016 ndc sydney 2016 https www youtube com playlist list pl03lrmd9cigefskkepkvtkkkflapuk1qd august 1 5 2016 ruhrjs 2016 https www youtube com playlist list plzqv2lznfskdquek2c9kq1nxqpxtizy d july 2 3 2016 angularcamp 2016 https www youtube com playlist list plb17qi lepyhsdxvjsupwsmammoxpciul july 1 3 2016 you gotta love frontend 2016 https www youtube com playlist list plii co3ff0qy1vbkvuylcumegs3u7xb1d june 27 28 2016 css day 2016 https vimeo com channels cssday june 16 17 2016 blinkon 6 https www youtube com playlist list pl9ioqauyl6ul 7htmxb3wqjmwqmanbvpl june 16 17 2016 scotlandjs 2016 https www youtube com playlist list pluss1n vmtdpj bksouoxmo dnxfgffqb june 2 3 2016 web rebels 2016 https opbeat com events web rebels 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survivejs com webpack react introduction enduring css http ecss io javascript for cats http jsforcats com building front end web apps with plain javascript https oxygen informatik tu cottbus de webeng jsfrontendapp book understanding es6 https leanpub com understandinges6 read systematic web design http www systematicwebdesign com css guidelines http cssguidelin es html canvas deep dive http files joshondesign com books canvasdeepdive toc html dive into html5 http diveintohtml5 info index html the refactoring tales http javascriptplayground com the refactoring tales refactoring tales html html css is hard https internetingishard com html and css functional light javascript https github com getify functional light js react in patterns https legacy gitbook com book krasimir react in patterns details newsletters web design weekly https web design weekly com es next news http esnextnews com javascript weekly http javascriptweekly com html5 weekly http html5weekly com css weekly http css weekly com web tools weekly http webtoolsweekly com ecmascript daily http ecmascript daily github io responsive design newsletter http responsivedesignweekly com a drip of javascript http adripofjavascript com fresh brewed frontend https freshbrewed co front end newsletter http frontendnewsletter com dev tips https umaar com dev tips web development reading list https wdrl info ng newsletter http www ng newsletter com react js newsletter http reactjsnewsletter com weekly react digest http reactdigest net react native newsletter http brentvatne ca react native newsletter ember weekly http emberweekly com web components weekly http webcomponentsweekly me jstips http www jstips co pony foo weekly https ponyfoo com weekly frontend buzz https frontend buzz css layout http csslayout news web animation weekly http webanimationweekly com ewebdesign https ewebdesign com fullstack react http newsletter fullstackreact com the react digest https www getrevue co profile the react digest the smashing email newsletter https www smashingmagazine com the smashing newsletter jekyll weekly http jekyllweekly com ux design weekly http uxdesignweekly com changelog weekly https changelog com weekly heydesigner http heydesigner com meteor weekly http meteorweekly com sass news http www sassnews com scotch io newsletter https scotch io web love weekly https tinyletter com unakravets 5thingsangular http 5thingsangular github io react status http react statuscode com angular 2 comet http bit ly ng2comet vue newsletter http vue newsletter com online courses codecademy https www codecademy com khan academy https www khanacademy org computing computer programming code school https www codeschool com udacity https www udacity com courses web development freecodecamp https www freecodecamp com udemy https www udemy com egghead https egghead io pluralsight https www pluralsight com front end masters https frontendmasters com courses educational games css diner http flukeout github io flexbox froggy http flexboxfroggy com flexbox defense http www flexboxdefense com codingame https www codingame com start code combat https codecombat com code wars https www codewars com other resources simplified javascript jargon https github com hugogiraudel sjsj javascript charts the definitive guide http www fusioncharts com javascript charts guide what happens when you type google com into your browser s address box and press enter https github com alex what happens when front end job interview questions https github com h5bp front end developer interview questions bootstrap 5 cheatsheet https bootstrap cheatsheet themeselection com contribute if you know some great frontend resources that are not in this list you are welcome to send pull requests you can do it directly through github by clicking the edit button on this file | front_end |
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udacity-cloud-devops-nanodegree-S5L3-ex | containerization of existing application my implementation for lesson 3 of section microservices at scale using aws amp kubernetes from udacity cloud devops engineering nanodegree application that is containerized you can find primary source code of this app here https github com hubert wojtowicz devops microservices tree master lesson 3 containerization flask app | cloud |
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Machine-learning-for-proteins | papers on machine learning for proteins background we recently released a review https arxiv org abs 1811 10775 of machine learning methods in protein engineering but the field changes so fast and there are so many new papers that any static document will inevitably be missing important work this format also allows us to broaden the scope beyond engineering specific applications we hope that this will be a useful resource for people interested in the field to the best of our knowledge this is the first public collaborative list of machine learning papers on protein applications we try to classify papers based on a combination of their applications and model type if you have suggestions for other papers or categories please make a pull request or issue format within each category papers are listed in reverse chronological order newest first where possible a link should be provided categories reviews reviews tools and datasets tools and datasets machine learning guided directed evolution machine learning guided directed evolution representation learning representation learning unsupervised variant prediction unsupervised variant prediction generative models generative models biophysics biophysics predicting stability predicting stability predicting structure from sequence predicting structure from sequence predicting sequence from structure predicting sequence from structure classification annotation search and alignments classification annotation search and alignments predicting interactions with other molecules predicting interactions with other molecules other supervised learning other supervised learning reviews exploring the protein sequence space with global generative models sergio romero romero sebastian lindner noelia ferruz preprint may 2023 arxiv https arxiv org abs 2305 01941 from sequence to function through structure deep learning for protein design noelia ferruz michael heinzinger mehmet akdel alexander goncearenco luca naef christian dallago preprint september 2022 10 1101 2022 08 31 505981 https doi org 10 1101 2022 08 31 505981 computational protein design with evolutionary based and physics inspired modeling current and future synergies cyril malbranke david bikard simona cocco r mi monasson j r me tubiana preprint august 2022 arxiv https arxiv org abs 2208 13616 deep learning approaches for conformational flexibility and switching properties in protein design lucas s p rudde mahdi hijazi patrick barth front mol biosci august 2022 10 3389 fmolb 2022 928534 https doi org 10 3389 fmolb 2022 928534 controllable protein design with language models noelia ferruz birte h ker nature machine intelligence june 2022 10 1038 s42256 022 00499 z https doi org 10 1038 s42256 022 00499 z the road to fully programmable protein catalysis sarah l lovelock rebecca crawshaw sophie basler colin levy david baker donald hilvert anthony p green nature june 2022 10 1038 s41586 022 04456 z https doi org 10 1038 s41586 022 04456 z efficient exploration of sequence space by sequence guided protein engineering and design ben e clifton dan kozome and paola laurino biochemistry march 2022 10 1021 acs biochem 1c00757 https doi org 10 1021 acs biochem 1c00757 learning functional properties of proteins with language models serbulent unsal heval atas muammer albayrak kemal turhan aybar c acar tunca do an nature machine intelligence march 2022 10 1038 s42256 022 00457 9 https doi org 10 1038 s42256 022 00457 9 applications of artificial intelligence to enzyme and pathway design for metabolic engineering woo dae jang gi bae kim yeji kim sang yup lee current opinion in biotechnology february 2022 10 1016 j copbio 2021 07 024 https doi org 10 1016 j copbio 2021 07 024 adaptive machine learning for protein engineering brian l hie kevin k yang current opinion in structural biology february 2022 10 1016 j sbi 2021 11 002 https doi org 10 1016 j sbi 2021 11 002 protein sequence design with deep generative models zachary wu kadina e johnston frances h arnold kevin k yang current opinion in chemical biology december 2021 10 1016 j cbpa 2021 04 004 https doi org 10 1016 j cbpa 2021 04 004 ai challenges for predicting the impact of mutations on protein stability fabrizio pucci martin schwersensky marianne rooman preprint november 2021 arxiv https arxiv org abs 2111 04208v1 advances in machine learning for directed evolution bruce j wittmann kadina e johnston zachary wu frances h arnold current opinion in structural biology august 2021 10 1016 j sbi 2021 01 008 https doi org 10 1016 j sbi 2021 01 008 a brief review of machine learning techniques for protein phosphorylation sites prediction farzaneh esmaili mahdi pourmirzaei shahin ramazi elham yavari preprint august 2021 arxiv https arxiv org abs 2108 04951v1 learning the protein language evolution structure and function tristan bepler bonnie berger cell systems june 2021 10 1016 j cels 2021 05 017 https doi org 10 1016 j cels 2021 05 017 representation learning applications in biological sequence analysis hitoshi iuchi taro matsutani keisuke yamada natsuki iwano shunsuke sumi shion hosoda shitao zhao tsukasa fukunaga michiaki hamada computational and structural biotechnology journal may 2021 10 1016 j csbj 2021 05 039 https doi org 10 1016 j csbj 2021 05 039 data driven computational protein design vincent frappier amy e keating current opinion in structural biology may 2021 10 1016 j sbi 2021 03 009 https doi org 10 1016 j sbi 2021 03 009 machine learning in protein structure prediction mohammed alquraishi current opinion in chemical biology may 2021 10 1016 j cbpa 2021 04 005 https doi org 10 1016 j cbpa 2021 04 005 protein sequence to structure learning is this the end to end revolution elodie laine stephan eismann arne elofsson sergei grudinin preprint may 2021 arxiv https arxiv org abs 2105 07407v1 revolutionizing enzyme engineering through artificial intelligence and machine learning nitu singh sunny malik anvita gupta kinshuk raj srivastava emerging topics in life sciences april 2021 10 1042 etls20200257 https doi org 10 1042 etls20200257 the language of proteins nlp machine learning protein sequences dan ofer nadav brandes michal linial computational and structural biotechnology journal january 2021 10 1016 j csbj 2021 03 022 https doi org 10 1016 j csbj 2021 03 022 machine learning and ai based approaches for bioactive ligand discovery and gpcr ligand recognition sebastian raschka benjamin kaufman preprint january 2020 arxiv https arxiv org abs 2001 06545v2 machine learning in enzyme engineering stanislav mazurenko zbynek prokop jiri damborsky acs catalysis december 2019 10 1021 acscatal 9b04321 https doi org 10 1021 acscatal 9b04321 machine learning guided directed evolution for protein engineering kevin k yang zachary wu frances h arnold nature methods july 2019 10 1038 s41592 019 0496 6 https doi org 10 1038 s41592 019 0496 6 preprint available on arxiv https arxiv org abs 1811 10775 evaluating protein transfer learning with tape roshan rao nicholas bhattacharya neil thomas yan duan xi chen john canny pieter abbeel yun s song preprint june 2019 arxiv https arxiv org abs 1906 08230 can machine learning revolutionize directed evolution of selective enzymes guangyue li yijie dong manfred t reetz advanced synthesis catalysis march 2019 10 1002 adsc 201900149 https doi org 10 1002 adsc 201900149 tools and datasets computational scoring and experimental evaluation of enzymes generated by neural networks sean r johnson xiaozhi fu sandra viknander clara goldin sarah monaco aleksej zelezniak kevin k yang preprint april 2023 10 1101 2023 03 04 531015 https doi org 10 1101 2023 03 04 531015 pdbench evaluating computational methods for protein sequence design leonardo v castorina rokas petrenas kartic subr christopher w wood bioinformatics january 2023 10 1093 bioinformatics btad027 https doi org 10 1093 bioinformatics btad027 the energetic and allosteric landscape for kras inhibition chenchun weng andre j faure ben lehner preprint december 2022 10 1101 2022 12 06 519122 https doi org 10 1101 2022 12 06 519122 manyfold an efficient and flexible library for training and validating protein folding models amelia villegas morcillo louis robinson arthur flajolet thomas d barrett bioinformatics december 2022 10 1093 bioinformatics btac773 https doi org 10 1093 bioinformatics btac773 mega scale experimental analysis of protein folding stability in biology and protein design kotaro tsuboyama justas dauparas jonathan chen elodie laine yasser mohseni behbahani jonathan j weinstein niall m mangan sergey ovchinnikov gabriel j rocklin preprint december 2022 10 1101 2022 12 06 519132 https doi org 10 1101 2022 12 06 519132 tuned fitness landscapes for benchmarking model guided protein design neil thomas atish agarwala david belanger yun s song lucy j colwell preprint october 2022 10 1101 2022 10 28 514293 https doi org 10 1101 2022 10 28 514293 deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins megan leander zhuang liu qiang cui srivatsan raman elife october 2022 10 7554 elife 79932 https doi org 10 7554 elife 79932 randomized gates eliminate bias in sort seq assays brian l trippe buwei huang erika a debenedictis brian coventry nicholas bhattacharya kevin k yang david baker lorin crawford protein science august 2022 10 1002 pro 4401 https doi org 10 1002 pro 4401 uni fold an open source platform for developing protein folding models beyond alphafold ziyao li xuyang liu weijie chen fan shen hangrui bi guolin ke linfeng zhang preprint august 2022 10 1101 2022 08 04 502811 https doi org 10 1101 2022 08 04 502811 peer a comprehensive and multi task benchmark for protein sequence understanding minghao xu zuobai zhang jiarui lu zhaocheng zhu yangtian zhang chang ma runcheng liu jian tang preprint june 2022 arxiv https arxiv org abs 2206 02096 flip benchmark tasks in fitness landscape inference for proteins christian dallago jody mou kadina e johnston bruce j wittmann nicholas bhattacharya samuel goldman ali madani kevin k yang neurips 2021 datasets and benchmarks track december 2021 10 1101 2021 11 09 467890 https doi org 10 1101 2021 11 09 467890 evseq cost effective amplicon sequencing of every variant in a protein library bruce j wittmann kadina e johnston patrick j almhjell frances h arnold preprint november 2021 10 1101 2021 11 18 469179 https doi org 10 1101 2021 11 18 469179 the immuneml ecosystem for machine learning analysis of adaptive immune receptor repertoires milena pavlovi lonneke scheffer keshav motwani chakravarthi kanduri radmila kompova nikolay vazov knut waagan fabian l m bernal alexandre almeida costa brian corrie rahmad akbar ghadi s al hajj gabriel balaban todd m brusko maria chernigovskaya scott christley lindsay g cowell robert frank ivar grytten sveinung gundersen ingrid hob k haff eivind hovig ping han hsieh g nter klambauer marieke l 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predicting the viability of beta lactamase how folding and binding free energies correlate with beta lactamase fitness jordan yang nandita naik jagdish suresh patel christopher s wylie wenze gu jessie huang marty ytreberg mandar t naik daniel m weinreich brenda m rubenstein preprint april 2020 10 1101 2020 04 15 043661 https doi org 10 1101 2020 04 15 043661 classifying protein structures into folds by convolutional neural networks distance maps and persistent homology yechan hong yongyu deng haofan cui jan segert jianlin cheng preprint april 2020 10 1101 2020 04 15 042739 https doi org 10 1101 2020 04 15 042739 minimum epistasis interpolation for sequence function relationships juannan zhou david m mccandlish nature communications april 2020 10 7554 elife 16965 024 https doi org 10 7554 elife 16965 024 machine learning to identify flexibility signatures of class a gpcr inhibition joseph bemister buffington alex j wolf sebastian raschka leslie a kuhn biomolecules march 2020 10 3390 biom10030454 https doi org 10 3390 biom10030454 extraction of protein dynamics information hidden in cryo em map using deep learning shigeyuki matsumoto shoichi ishida mitsugu araki takayuki kato kei terayama yasushi okuno preprint february 2020 10 1101 2020 02 17 951863 https doi org 10 1101 2020 02 17 951863 transformer neural network for protein specific de novo drug generation as machine translation problem daria grechishnikova preprint december 2019 10 1101 863415 https doi org 10 1101 863415 iterative peptide modeling with active learning and meta learning rainier barrett andrew d white preprint november 2019 arxiv https arxiv org abs 1911 09103 deep convolutional neural network and attention mechanism based pan specific model for interpretable mhc i peptide binding prediction jing jin zhonghao liu alireza nasiri yuxin cui stephen louis ansi zhang yong zhao jianjun hu preprint november 2019 10 1101 830737 https doi org 10 1101 830737 bcrystal an interpretable sequence based protein crystallization predictor abdurrahman elbasir raghvendra mall khalid kunji reda rawi zeyaul islam gwo yu chuang prasanna r kolatkar halima bensmail bioinformatics october 2019 10 1093 bioinformatics btz762 https doi org 10 1093 bioinformatics btz762 deep learning regression model for antimicrobial peptide design jacob witten zack witten preprint july 2019 10 1101 692681 https doi org 10 1101 692681 biorxiv https www biorxiv org content 10 1101 692681v1 using machine learning to predict organismal growth temperatures from protein primary sequences david b sauer da neng wang preprint june 2019 10 1101 677328 https doi org 10 1101 677328 biorxiv https www biorxiv org content 10 1101 677328v1 rss 1 solxplain an explainable sequence based protein solubility predictor raghvendra mall preprint may 2019 10 1101 651067 https doi org 10 1101 651067 biorxiv https www biorxiv org content 10 1101 651067v1 high precision protein functional site detection using 3d convolutional neural networks wen torng russ b altman bioinformatics may 2019 10 1093 bioinformatics bty813 https doi org 10 1093 bioinformatics bty813 develop machine learning based regression predictive models for engineering protein solubility xi han xiaonan wang kang zhou bioinformatics april 2019 10 1093 bioinformatics btz294 https doi org 10 1093 bioinformatics btz294 deepcrystal a deep learning framework for sequence based protein crystallization prediction abdurrahman elbasir balasubramanian moovarkumudalvan khalid kunji prasanna r kolatkar raghvendra mall halima bensmail bioinformatics november 2018 10 1093 bioinformatics bty953 https doi org 10 1093 bioinformatics bty953 deepsol a deep learning framework for sequence based protein solubility prediction sameer khurana reda rawi khalid kunji gwo yu chuang halima bensmail raghvendra mall bioinformatics march 2018 10 1093 bioinformatics bty166 https doi org 10 1093 bioinformatics bty166 a statistical model for improved membrane protein expression using sequence derived features shyam m saladi nauman javed axel m ller william m clemons jr journal of biological chemistry march 2018 10 1074 jbc ra117 001052 https doi org 10 1074 jbc ra117 001052 learning epistatic interactions from sequence activity data to predict enantioselectivity julian zaugg yosephine gumulya alpeshkumar k malde mikael bod n journal of computer aided molecular design december 2017 10 1007 s10822 017 0090 x https doi org 10 1007 s10822 017 0090 x quantitative missense variant effect prediction using large scale mutagenesis data vanessa e gray ronald j hause jens luebeck jay shendure douglas m fowler cell systems december 2017 10 1016 j cels 2017 11 003 https doi org 10 1016 j cels 2017 11 003 deeploc prediction of protein subcellular localization using deep learning jose juan almagro armenteros casper kaae s nderby s ren kaae s nderby henrik nielsen ole winther bioinformatics september 2017 10 1093 bioinformatics btx548 https doi org 10 1093 bioinformatics btx548 semisupervised gaussian process for automated enzyme search joseph mellor ioana grigoras pablo carbonell and jean loup faulon acs synthetic biology march 2016 10 1021 acssynbio 5b00294 https doi org 10 1021 acssynbio 5b00294 high precision prediction of functional sites in protein structures ljubomir buturovic mike wong grace w tang russ b altman dragutin petkovic plos one march 2014 10 1371 journal pone 0091240 https doi org 10 1371 journal pone 0091240 sequence motifs in mads transcription factors responsible for specificity and diversification of protein protein interaction aalt d j van dijk giuseppa morabito martijn fiers roeland c h j van ham gerco c angenent richard g h immink plos computational biology november 2010 10 1371 journal pcbi 1001017 https doi org 10 1371 journal pcbi 1001017 predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control a d j van dijk c j f ter braak r g immink g c angenent r c h j van ham bioinformatics january 2008 10 1093 bioinformatics btm539 https doi org 10 1093 bioinformatics btm539 deep convolutional networks for quality assessment of protein folds georgy derevyanko sergei grudinin yoshua bengio guillaume lamoureux bioinformatics december 2018 10 1093 bioinformatics bty494 https doi org 10 1093 bioinformatics bty494 arxiv https arxiv org abs 1801 06252 | ai |
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fan-dy007 | fan dy007 information technology | server |
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build | build you completed levelzero of neogcamp https neog camp guide and now you know basics of html css js react now you need to code more to get yourself comfortable with what you have just learned this guide will be most useful if you have done my courses every idea is based on something i have already taught in the video but if you know the basics you might just enjoy making these apps priniciple these ideas are not to be taken as a stand alone app for an internship these apps might look good on your portfolio but for full time jobs these are not the app ideas you would want to build aim is to get you more practice focus is more on basics try to do these yourself and if you need help our discord server https bit ly tanaydiscrod is always there with 14000 beginner and intermediate programmers like you hanging out together stargazing as a community if you press that star button we get more stars in the sky stargazers over time https starchart cc tanaypratap build svg https starchart cc tanaypratap build | front_end |
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DatabaseEngineering | databaseengineering some code for database engineering | server |
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Spring16_AdvancedMAD_class_examples | spring16 advancedmad class examples advanced mobile application development class examples | front_end |
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Hermes-Deepstream | hermes app wildfires have been ever increasing devouring our planet earth rendering our planet worse day by day with early detection and mitigation it is possible to reduce the damage caused by wildfires wildfires resources wildfires png to better enable our front line workers here is hermes an ai powered computer vision application that helps in early detection of wildfires using reconnaissance drones citations alexeyab darknet https github com alexeyab darknet damiafuentes djitellopy https github com damiafuentes djitellopy moses palmer pynput https github com moses palmer pynput index 1 introduction introduction 2 deepstream setup deepstream setup 1 install system dependencies install system dependencies 2 install deepstream install deepstream 3 ryze tello setup ryze tello setup 1 install pip packages installing pip packages 2 redis redis 3 connect tello connecting the tello 4 running the application running the application 1 clone the repository cloning the repository 2 run with different input sources run with different input sources 3 run with the drone run with the drone introduction hermes application consists of two parts an intelligent video analytics pipeline powered by deepstream and nvidia jetson xavier nx and a reconnaissance drone for which i have used a ryze tello tello and jetson nx resources tellonx jpg this project is a proof of concept trying to show that surveillance and mapping of wildfires can be done with a drone and an onboard jetson platform deepstream setup this post assumes you have a fully functional jetson device if not you can refer the documentation here https docs nvidia com jetson jetpack install jetpack index html 1 install system dependencies sh sudo apt install libssl1 0 0 libgstreamer1 0 0 gstreamer1 0 tools gstreamer1 0 plugins good gstreamer1 0 plugins bad gstreamer1 0 plugins ugly gstreamer1 0 libav libgstrtspserver 1 0 0 libjansson4 2 11 1 2 install deepstream download the deepstream 5 1 jetson debian package deepstream 5 1 5 1 0 1 arm64 deb to the jetson device from here https developer nvidia com deepstream getting started then enter the command sh sudo apt install deepstream 5 1 5 1 0 1 arm64 deb ryze tello setup 1 installing pip packages first we need to install python dependencies make sure you have a working build of python3 7 3 8 sh sudo apt install python3 dev python3 pip the dependencies needed are the following sh djitellopy 1 5 evdev 1 3 0 imutils 0 5 3 numpy 1 19 4 opencv python 4 4 0 46 pycairo 1 20 0 pygame 2 0 1 pygobject 3 38 0 pynput 1 7 2 python xlib 0 29 redis 3 5 3 six 1 15 0 you can either install them with pip command or use the requirements txt file whatever sails your boat sh for individial packages pip3 install packagename for requirements txt pip3 install r requirements txt 2 redis redis is used for it s queueing mechanism which will used to create an rtsp stream of the tello camera stream install the redis server sh sudo apt install redis server 3 connect tello first connect the jetson device to the wifi network of tello ryze tello wifi resources dronewifi png next run the following code to verify connectivity python importing the tello drone library from djitellopy import tello pkg tello pkg connect on successful connection your output will look something like this json send command command response b ok if you get the following output you may want to check your connection with the drone json send command command timeout exceed on command command command command was unsuccessful message false running the application 1 clone the repository this is a straightforward step however if you are new to git or git lfs i recommend glancing threw the steps first install git and git lfs sh sudo apt install git git lfs next clone the repository sh using https git clone https github com aj ames hermes deepstream git using ssh git clone git github com aj ames hermes deepstream git finally enable lfs and pull the yolo weights sh git lfs install git lfs pull 2 run with different input sources the computer vision part of the solution can be run on one or many input sources of multiple types all powered using nvidia deepstream first build the application by running the following command sh make clean make j nproc this will generate the binary called hermes app this is a one time step and you need to do this only when you make source code changes next create a file called inputsources txt and paste the path of videos or rtsp url sh file home astr1x videos wildfire1 mp4 rtsp admin admin 40123 192 168 1 1 554 stream now run the application by running the following command sh hermes app 3 run with the drone we utilize the livestream of the camera for real time detection of wildfires since the tello streams over udp and the deepstream hermes app accepts rtsp as input we need an intermediate udp rtsp converter also we need to control the tello s movement run the following command to start the tello control script sh python3 tello control py this script will start the tello stream on the following url sh rtsp 127 0 0 1 6969 hermes to control the drone with your keyboard first press the left shift key the following is a list of keys and what they do left shift toggle keyboard controls right shft take off drone space land drone up arrow increase altitude down arrow decrease altitude left arrow pan left right arrow pan right w move forward a move left s move down d move right finally add the url in inputsources txt and start hermes app | ai |
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