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LBAW1854_mirror | lbaw project management team alexandra mendes up201604741 fe up pt c sar pinho up201604039 fe up pt jo o barbosa up201604156 fe up pt rui guedes up201603854 fe up pt group1854 18 02 2019 | server |
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mobile-development-protips | mobile development protips a collection of useful mobile development tips 1 use viewport meta tag use viewport meta tag 2 manually set tel links manually set tel links 3 deviant input type date behaviour deviant input typedate behaviour 4 disable zoom on input element focus disable zoom on input element focus 5 usage of background attachement fixed usage of background attachement fixed 6 enable momentum scrolling on scrollable elements enable momentum scrolling on scrollable elements 7 reset browser specific input button styles reset browser specific inputbutton styles 8 disable text size adjustment disable text size adjustment 9 disable tap highlight colour disable tap highlight colour 10 usage of overflow hidden on body usage of overflow hidden on body 11 disable dragging of google maps disable dragging of google maps use viewport meta tag base viewport meta tag html meta name viewport content width device width initial scale 1 additional parameters html disable user zooming meta name viewport content user scalable no zoom level on start 120 meta name viewport content initial scale 1 2 set maximum and minimum zoom level meta name viewport content minimum scale 0 8 maximum scale 1 2 source https developer mozilla org en us docs mozilla mobile viewport meta tag https developer mozilla org en us docs mozilla mobile viewport meta tag manually set tel links to disable the automatic telephone number detection use the following snippet in head area html standard meta name format detection content telephone no blackberry meta http equiv x rim auto match content none for manual phone number links use a href tel 49 123 456 78 9 49 0 123 456 78 9 a the href telephone number should always use the correct country code preceded by a all whitespace should replaced with any non digit characters should be removed source https developer apple com library safari featuredarticles iphoneurlscheme reference phonelinks phonelinks html https developer apple com library safari featuredarticles iphoneurlscheme reference phonelinks phonelinks html deviant input type date behaviour safari for ios updates the value immediately if any date part is changed via datepicker android browsers lets you set the whole date also the ios date input triggers a blur event android doesn t disable zoom on input element focus setting the font size to 16px on input fields disables the auto zoom in ios safari and chrome usage of background attachement fixed put the css background declarations into a wrapping div instead of html or body set this to min height 100 css body html height 100 bg background image url path to img jpg background attachment scroll background position center top background repeat no repeat display block width 100 min height 100 z index 100 position fixed top 0 left 0 source http catch404 net 2012 12 fixed backgrounds on the bad that is all mobile browsers http catch404 net 2012 12 fixed backgrounds on the bad that is all mobile browsers enable momentum scrolling on scrollable elements this adds ongoing scrolling to elements on the page as the page itself does only works in ios safari css element overflow y scroll webkit overflow scrolling touch reset browser specific input button styles css input textarea button webkit appearance none moz border radius 0 webkit border radius 0 border radius 0 disable text size adjustment disable text size adjustment in ie mobile and webkit browsers do not use none this causes bugs in webkit browsers css html ms text size adjust 100 webkit text size adjust 100 moz text size adjust 100 disable tap highlight colour css html webkit tap highlight color rgba 0 0 0 0 usage of overflow hidden on body a simple overflow hidden is not working on mobile browsers to achieve that effect you have to set the position of the body to fixed css body overflow hidden position fixed deprecated disable dragging of google maps the default google maps options allow users to drag the map around if a map on a mobile version of your site is full width it might be difficult to scroll further as you have to drag the site up but the map part has to be avoided therefore the best thing is to disable dragging on mobile devices javascript var map new google maps map map draggable window width 768 false true google updated maps to detect mobile touch devices and switches to two finger use for dragging therefore this is not needed anymore | responsive mobile tips best-practices | front_end |
OpenCV | opencv computer vision is an exciting field of artificial intelligence that teaches computers to interpret and understand the visual world by using digital images from cameras and videos and sophisticated deep learning models by accurately identifying and classifying objects in the visual world computer vision allows machines to make informed decisions and take appropriate actions based on what they see opencv open source computer vision library is a library of programming functions mainly aimed at real time computer vision originally developed by intel it was later supported by willow garage then itseez the library is cross platform and free for use under the open source bsd license here are some projects their applications which can be build using opencv 1 face blur use gaussianblur with a kernel of 91 91 to blur faces it can be used for privacy identity protection in public private areas can increase or decrease blur strength by changing the kernel 2 color transfer used one image color source to change the color of different image target using both color transfer algorithm as well as color transfer modules it can be used to apply different types of color filters 3 live sketch convert an image or a video into a sketch 4 extract faces from an image a simple python program that can extract faces from an image is a valuable tool for improving the accuracy of face recognition applications developers can implement robust algorithms that quickly and accurately detect faces in an image crop the image to isolate the face and save it as a separate image file for further analysis or processing this program can be used to build a wide range of applications including security systems and more 5 color detection color detection is the process of detecting the name of any color color detection is necessary to recognize objects it is also used as a tool in various image editing and drawing apps some real world applications in self driving car to detect the traffic signals multiple color detection is used in some industrial robots to performing pick and place task in separating different colored objects 6 virtual paint this code helps you track an object of interest to draw colored lines on the screen just like the paint application but using the webcam 7 number plate detection detecting number plate from image or webcam of cars 8 facial landmark detection detect all landmarks of face like eyes nose lips eyebrows i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat detecting facial landmarks in an image is a two step process first i have localize a face s in an image this can be accomplished using a number of different techniques but normally involve either haar cascades or hog linear svm detectors apply the shape predictor specifically a facial landmark detector to obtain the x y coordinates of the face regions in the face roi it can used in face part extraction i e nose eyes mouth jawline etc facial alignment head pose estimation face swapping blink detection and much more applications 9 eye blink detection detect if an eye blink or not we first detect the eyes then we detect two lines an horizontal line and a vertical line crossing the eye the size of the horizontal line is almost identical in the closed eye and in the open eye while the vertical line is much longer in the open eye in coparison with the closed eye in the closed eye the vertical line almost disappears i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 10 face recognition face recognition is a method of identifying or verifying the identity of an individual using their face face recognition systems can be used to identify people in photos video or in real time law enforcement may also use mobile devices to identify people during police stops 11 document scanner building a document scanner with opencv can be accomplished in just three simple steps step 1 detect edges step 2 use the edges in the image to find the contour outline representing the piece of paper being scanned step 3 apply a perspective transform to obtain the top down view of the document 12 drowsiness detection driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy various studies have suggested that around 20 of all road accidents are fatigue related up to 50 on certain roads some of the current systems learn driver patterns and can detect when a driver is becoming drowsy i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 13 chrome dino game play offline chrome dinosaur game using opencv eye blink just open chrome browser with no internet connection drawback of this application is it can be used only for jumping purpose i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 14 object detection object detection is a computer vision technique that allows us to identify and locate objects in an image or video with this kind of identification and localization object detection can be used to count objects in a scene and determine and track their precise locations all while accurately labeling them 15 angle finder created an angle finder in that i first define two lines using mouse clicks and then find the angle between theses lines using simple mathematics 16 face alignment face alignment is the task of identifying the geometric structure of faces in digital images and attempting to obtain a canonical alignment of the face based on translation scale and rotation using face alignment we can get higher accuracy from our face recognition as face alignment is like data normalization i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 17 contour detection contour detection is a major issue in image processing for instance in classification and segmentation the goal is to split the image into several parts this problem is strongly related to the detection of the connected contours separating these parts it is quite easy to detect edges using local image analysis techniques but the detection of continuous contours is more complicated and needs a global analysis of the image 18 remove duplicate images from an given dataset having duplicate images in your dataset creates a problem for two reasons it introduces bias into your dataset giving your deep neural network additional opportunities to learn patterns specific to the duplicates it hurts the ability of your model to generalize to new images outside of what it was trained on and identifying duplicates in a large dataset manually is very time consuming and error prone that s why we want to remove duplicate images from our dataset 19 eye blink count detection we can use eye blink count detector to check if eyes are blinking regularly or not to avoid the symptoms of dry eye as eye blink is considered to be a suitable indicator for fatigue diagnostics drowsy state may be caused by lack of sleep medication drugs or driving continuously for long time period i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 20 object tracking simple and realtime object tracking in a video sequence 21 add watermark add watermark to images using opencv please make sure that watermark has black background 22 multiple object tracking simple multiple and realtime object tracking in a video sequence 23 qrcode barcode scanner scanning qrcode barcode through camera using opencv and pyzbar 24 semantic segmentation semantic segmentation or image segmentation is the task of clustering parts of an image together which belong to the same object class it is a form of pixel level prediction because each pixel in an image is classified according to a category it can be used for autonomous driving robotic navigation localization and scene understanding 25 human activity recognition human activity recognition model can recognize over 400 activities with 78 4 94 5 accuracy 26 image smoothing smoothing is often used to reduce noise within an image or to produce a less pixelated image smoothing is also usually based on a single value representing the image such as the average value of the image or the middle median value 27 yawn detector yawn detection is all about detecting yawn open one s mouth wide and inhale deeply due to tiredness or boredom using opencv and dlib it can be used in various major applications like self driving cars driver s fatigue detection driver s drowsiness detection driver s consciousness detection etc i have used pre trained hog linear svm object detector specifically for the task of face detection download shape predictor 68 face landmarks dat from here https github com italojs facial landmarks recognition blob master shape predictor 68 face landmarks dat 28 hand landmark detection detect 21 landmarks of a hand using opencv and mediapipe first we have to use palm detection then we use hand landmark detector hand landmarks can be used in gesture control a id custom anchor name a 29 volume control using hand detection building a volume controller with opencv can be accomplished in just 3 simple steps step 1 detect hand landmarks step 2 calculate the distance between thumb tip and index finger tip step 3 map the distance of thumb tip and index finger tip with volume range for my case distance between thumb tip and index finger tip was within the range of 15 220 and the volume range was from 63 5 0 0 a id custom anchor name1 a 30 brightness control using hand detection building a brightness controller with opencv can be accomplished in just 3 simple steps step 1 detect hand landmarks step 2 calculate the distance between thumb tip and index finger tip step 3 map the distance of thumb tip and index finger tip with volume range for my case distance between thumb tip and index finger tip was within the range of 15 220 and the volume range was from 0 100 31 left or right hand detection check whether the given hand in an image is left or right hand it can be used to advance hand gestures control 32 brightness volume control it s same as volume control using hand detection custom anchor name brightness control using hand detection custom anchor name1 only difference is now we can manage brightness with left hand volume with right hand simultaneous 33 face mesh face mesh is a face geometry solution that estimates 468 3d face landmarks in real time it can be used as facial landmarks for detecting eyes mouth jawline nose eyebrows face mesh is mainly useful for real time augmented reality ar applications 34 face detector advance so far i have used haar cascade dlib for face detection the main disadvantage of using this model was poor accuracy in terms of lightning detecting background as face sometimes much more in order to overcome this i have use the resnet caffe pretrained model 35 human pose estimation detect 33 3d landmarks on the whole body or 25 upper body landmarks using opencv and mediapipe it can be used in various applications such as quantifying physical exercises sign language recognition and full body gesture control for example it can form the basis for yoga dance and fitness applications it can also enable the overlay of digital content and information on top of the physical world in augmented reality 36 holistic estimation live perception of simultaneous human pose face landmarks and hand tracking in real time can enable various modern life applications fitness and sport analysis gesture control and sign language recognition augmented reality try on and effects it generate a total of 543 landmarks 33 pose landmarks 468 face landmarks and 21 hand landmarks per hand 37 save a video often we have to capture live stream with camera opencv provides a very simple interface to this 38 create a gif create an animated gif in real time 39 objectron objectron is a real time 3d object detection solution for everyday objects it detects objects in 2d images and estimates their poses through a machine learning ml model currently supports shoe chair cup camera 40 timer show timer on webcam 41 video to frame converting a video into frames using opencv and python can be useful in a variety of applications here are a few use cases video analysis machine learning video editing data compression video indexing and search | opencv opencv-python computer-vision image-processing face-recognition color-transfer color-detection face-blur gesture-recognition face-detection face-alignment object-detection hand-gesture-recognition deep-learning face machine-learning recognition vision | ai |
oc | oc https raw githubusercontent com opencomponents oc master logo type png opencomponents serverless in the front end world opencomponents is an open source framework that allows fast moving teams to easily build and deploy front end components it abstracts away complicated infrastructure and leaves developers with very simple but powerful building blocks that handle scale transparently how does it work first you create your component it can contain logic to get some data using node js and then the view including css and js it can be what you want including react or angular components or whatever you like then you publish it to the opencomponents registry and you wait a couple of seconds while the registry prepares your stuff to be production ready now every web app in your private or public network can consume the component via its own http endpoint during server side rendering or just in the browser we have been using it for more than two years in production at opentable for shared components third party widgets e mails and more learn more about oc http tech opentable co uk blog 2016 04 27 opencomponents microservices in the front end world npm version https img shields io npm v oc svg https npmjs org package oc node version https img shields io node v oc svg https npmjs org package oc known vulnerabilities https snyk io test github opencomponents oc badge svg https snyk io test github opencomponents oc downloads https img shields io npm dm oc svg label downloads from npm https npmjs org package oc join the chat at https gitter im opentable oc https badges gitter im join 20chat svg https gitter im opentable oc utm source badge utm medium badge utm campaign pr badge utm content badge links website https opencomponents github io documentation https opencomponents github io docs intro requirements and build status requirements and build status changelog changelog md awesome resources about oc https github com matteofigus awesome oc contributing guidelines contributing md code of conduct contributing md code of conduct troubleshooting contributing md troubleshooting gitter chat https gitter im opentable oc requirements and build status disclaimer this project is still under heavy development and the api is likely to change at any time in case you would find any issues check the troubleshooting page contributing md troubleshooting license mit | opencomponents serverless components ui-composition microfrontends continuous-delivery | front_end |
SolarHarvestGPS | solarharvestgps project for embedded systems design course includes code and altium files for the project there is a portion of the report included to clarify what the project is and explain what it does the devices used were a ti msp432 and a sam m8q along with various other peripherals | os |
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StarlightAI | starlight is a natural language processing engine built with the ml net framework it processes requests using binary classification based on a machine learning dataset the included dataset is task oriented and has entity extraction built in the user can edit or replace it according to the intended use case example query wake me up at 10 30 current date time 9 nov 2019 01 13 pm output json query wake me up at 10 30 intents intent addalarm score 0 93108803 intent showweather score 0 5757521 intent addreminder score 0 26036647 entities entity tomorrow type date startindex 14 endindex 18 date 2019 11 10 time 10 30 am | machine-learning natural-language-processing nlp ml-net mlnet | ai |
IDKE-LLM | idke llm integration of large language model learning resources | ai |
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matic-design-system | matic design system netlify status https api netlify com api v1 badges cbed49d4 ef6a 48ed a2b8 d0810b1a71e5 deploy status https matic design system netlify app design system for matic products project setup npm install storybook viewing storybook npm run storybook generate storybook build for publishing npm run build storybook npm build library for publishing npm run build library this generates commonjs and unpkg modules in dist using common js here update main field in package json to change the entry point of published package for versoning refer semantic versioning spec https docs npmjs com about semantic versioning publishing to npm npm run release this will take care of build versioning and publishing usage setup npm install maticnetwork matic design system implementation import button icon from maticnetwork matic design system button label click me icon name login metamask | matic storybook | os |
auto-maple | h1 align center auto maple h1 auto maple is an intelligent python bot that plays maplestory a 2d side scrolling mmorpg using simulated key presses tensorflow machine learning opencv template matching and other computer vision techniques community created resources such as command books for each class and routines for each map can be found in the resources repository https github com tanjeffreyz auto maple resources br h2 align center minimap h2 table align center border 0 tr td auto maple uses b opencv template matching b to determine the bounds of the minimap as well as the various elements within it allowing it to accurately track the player s in game position if code record layout code is set to code true code auto maple will record the player s previous positions in a b quadtree based b layout object which is periodically saved to a file in the layouts directory every time a new routine is loaded its corresponding layout file if it exists will also be loaded this layout object uses the b a search algorithm b on its stored points to calculate the shortest path from the player to any target location which can dramatically improve the accuracy and speed at which routines are executed td td align center width 400px img align center src https user images githubusercontent com 69165598 123177212 b16f0700 d439 11eb 8a21 8b414273f1e1 gif td tr table br h2 align center command books h2 p align center img src https user images githubusercontent com 69165598 123372905 502e5d00 d539 11eb 81c2 46b8bbf929cc gif width 100 br sub the above video shows auto maple consistently performing a mechanically advanced ability combination sub p table align center border 0 tr td width 100 designed with modularity in mind auto maple can operate any character in the game as long as it is provided with a list of in game actions or a command book a command book is a python file that contains multiple classes one for each in game ability that tells the program what keys it should press and when to press them once a command book is imported its classes are automatically compiled into a dictionary that auto maple can then use to interpret commands within routines commands have access to all of auto maple s global variables which can allow them to actively change their behavior based on the player s position and the state of the game td tr table br h2 align center routines h2 table align center border 0 tr td width 350px p align center img src https user images githubusercontent com 69165598 150469699 d8a94ab4 7d70 49c3 8736 a9018996f39a png br sub click a href https github com tanjeffreyz02 auto maple blob f13d87c98e9344e0a4fa5c6f85ffb7e66860afc0 routines dcup2 csv here a to view the entire routine sub p td td a routine is a user created csv file that tells auto maple where to move and what commands to use at each location a custom compiler within auto maple parses through the selected routine and converts it into a list of code component code objects that can then be executed by the program an error message is printed for every line that contains invalid parameters and those lines are ignored during the conversion br br below is a summary of the most commonly used routine components ul li b code point code b stores the commands directly below it and will execute them in that order once the character is within code move tolerance code of the specified location there are also a couple optional keyword arguments ul li code adjust code fine tunes the character s position to be within code adjust tolerance code of the target location before executing any commands li li code frequency code tells the point how often to execute if set to n this point will execute once every n iterations li li code skip code tells the point whether to run on the first iteration or not if set to true and frequency is n this point will execute on the n 1th iteration li ul li li b code label code b acts as a reference point that can help organize the routine into sections as well as create loops li li b code jump code b jumps to the given label from anywhere in the routine li li b code setting code b updates the specified setting to the given value it can be placed anywhere in the routine so different parts of the same routine can have different settings all editable settings can be found at the bottom of a href https github com tanjeffreyz02 auto maple blob v2 settings py settings py a li ul td tr table br h2 align center runes h2 p align center img src https user images githubusercontent com 69165598 123479558 f61fad00 d5b5 11eb 914c 8f002a96dd62 gif width 100 p table align center border 0 tr td width 100 auto maple has the ability to automatically solve runes or in game arrow key puzzles it first uses opencv s color filtration and b canny edge detection b algorithms to isolate the arrow keys and reduce as much background noise as possible then it runs multiple inferences on the preprocessed frames using a custom trained b tensorflow b model until two inferences agree because of this preprocessing auto maple is extremely accurate at solving runes in all kinds of often colorful and chaotic environments td tr table br h2 align center video demonstration h2 p align center a href https www youtube com watch v qs8nw55edhg b click below to watch the full video b a p p align center a href https www youtube com watch v qs8nw55edhg img src https user images githubusercontent com 69165598 123308656 c5b61100 d4d8 11eb 99ac c465665474b5 gif width 600px a p br h2 align center setup h2 ol li download and install a href https www python org downloads python3 a li li download and install the latest version of a href https developer nvidia com cuda downloads cuda toolkit a li li download and install a href https git scm com download win git a li li download and unzip the latest a href https github com tanjeffreyz02 auto maple releases auto maple release a li li download the a href https drive google com drive folders 1spdtnf4kzczowywtgfytbrvlvy7wsgpu usp sharing tensorflow model a and unzip the models folder into auto maple s assets directory li li inside auto maple s main directory open a command prompt and run pre code python m pip install r requirements txt code pre li li lastly create a desktop shortcut by running pre code python setup py code pre this shortcut uses absolute paths so feel free to move it wherever you want however if you move auto maple s main directory you will need to run code python setup py code again to generate a new shortcut to keep the command prompt open after auto maple closes run the above command with the code stay code flag li ol | ai python deep-learning maplestory computer-vision bot | ai |
tddGPT | autonomous agent for test driven development tdd programmers have programmed themselves out of jobs unknown tddgpt reactjs counter app with gpt3 5 https cdn loom com sessions thumbnails 7f56ab1b478049baa299813c223526bd with play gif https www loom com share 7f56ab1b478049baa299813c223526bd tddgpt is an autonomous coding agent that builds applications in reactjs flask express and more all while adhering to the test driven development tdd methodology it operates entirely without human intervention beginning with a project plan tddgpt translates requirements into tests develops code based on those tests and debugs until all tests pass currently it can build simple crud apps the tdd framework keeps the agent focused and goal oriented the core architecture is elegantly simple utilizing just three tools cli readfile and writefile it has been adpated from langchain s autogpt example most enhancements were performed by gpt 4 itself over the course of a month long chat interaction i initially aimed to test the boundaries of gpt 4 s capabilities in building reactjs apps and was successful in teaching it to construct applications step by step in the process it gained an understanding of temporal concepts like past present and future as well as cause and effect the agent is not just a code generator it s also a learner it evaluates its mistakes and areas for improvement as a final step and some of these insights have already been incorporated into its operating prompts this project is in early alpha stage gpt 4 api key is required setup instructions 1 setup a virtual environment python3 m venv env 2 activate the virtual environment on macos and linux source env bin activate on windows env scripts activate 3 clone the repository to your local machine git clone git github com gimlet ai tdd gpt agent git 4 navigate to the project directory cd tdd gpt agent 5 run the following command to install the package and its dependencies python setup py install 6 set up your gpt 4 api keys as environment variables export openai api key sk 7 run tdd gpt agent tdd gpt prompt build a minimal counter app in reactjs with increment decrement and reset functions check the counter app directory for the generated app example apps the following are some apps have been built by this agent task tracker https brilliant biscotti 3f9e48 netlify app built with gpt4 counter app https counter app tddgpt netlify app built with fine tuned gpt3 5 similar projects aider https github com paul gauthier aider smol developer https github com smol ai developer gpt engineer https github com antonosika gpt engineer mentat https github com biobootloader mentat contributing we welcome contributions to this project please feel free to submit issues and pull requests for major changes please open an issue first to discuss what you would like to change license this project is open source under the mit license license contact if you have any questions or comments please feel free to reach out to us on github | auto-gpt autonomous-agents codegeneration fullstack-development gpt-4 | front_end |
red-hat-design-system | red hat design system documentation https ux redhat com design tokens https red hat design tokens netlify app and web components https ux redhat com elements for building uniform experiences with the red hat brand for designers rhds provides adobe xd libraries and design kits to aid in your design work for developers rhds provides a collection of ready made framework agnostic web components with red hat branding user experience accessibility and style guidelines built in html rh card h2 slot heading red hat branded web components h2 p so you can focus more on your content or app and less on implementation details p rh cta slot footer a href https ux redhat com read the docs a rh cta rh card contributions are welcome would you like to contribute to the documentation or design specs read the site contributing guide https github com redhat ux red hat design system tree main contributing design md would you like to contribute to component development read the developer contributing guide https github com redhat ux red hat design system tree main contributing dev md | web-components design-system design-systems redhat hacktoberfest | os |
LLM-Text-Classification | llm based text classification a set of jupyter notebooks for training and evaluating large language model based linguistic classification project is out of date need to upload latest commit but is on old harddrive will upload when that pc is online again | ai |
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Machine-Learning-and-Reinforcement-Learning-in-Finance | machine learning and reinforcement learning in finance guided tour of machine learning in finance 1 euclidean distance calculation euclidian distance m1 ex1 v3 ipynb 2 linear regression linear regress m1 ex2 v3 ipynb 3 tobit regression tobit regression m1 ex3 v4 ipynb 4 bank defaults prediction using fdic dataset bank failure m1 ex4 v3 ipynb fundamentals of machine learning in finance 1 random forests and decision trees bank failure rand forests m2 ex1 ipynb 2 eigen portfolio construction via pca pca eigen portfolios m2 ex3 ipynb 3 data visualization with t sne dji tsne m2 ex4 corrected ipynb 4 absorption ratio via pca absorp ratio m2 ex5 ipynb reinforcement learning in finance 1 discrete time black scholes model discrete black scholes m3 ex1 v3 ipynb 2 qlbs model implementation dp qlbs oneset m3 ex2 v3 ipynb 3 fitted q iteration dp qlbs oneset m3 ex3 v4 ipynb 4 irl market model calibration overview of advanced methods of reinforcement learning in finance | machine-learning finance reinforcement-learning python scikit-learn tensorflow tensorflow-examples coursera | ai |
aws-iot-device-sdk-embedded-C | aws iot device sdk for embedded c table of contents overview overview license license features features coremqtt coremqtt corehttp corehttp corejson corejson corepkcs11 corepkcs11 aws iot device shadow aws iot device shadow aws iot jobs aws iot jobs aws iot device defender aws iot device defender aws iot over the air update library aws iot over the air update aws iot fleet provisoning aws iot fleet provisioning aws sigv4 aws sigv4 backoffalgorithm backoffalgorithm sending metrics to aws iot sending metrics to aws iot versioning versioning releases and documentation releases and documentation 202211 00 20221100 202108 00 20210800 202103 00 20210300 202012 01 20201201 202011 00 20201100 202009 00 20200900 v3 1 5 v315 porting guide for 202009 00 and newer releases porting guide for 20200900 and newer releases porting coremqtt porting coremqtt porting corehttp porting corehttp porting aws iot device shadow porting aws iot device shadow porting aws iot device defender porting aws iot device defender porting aws iot over the air update porting aws iot over the air update migration guide from v3 1 5 to 202009 00 and newer releases migration guide from v315 to 20200900 and newer releases mqtt migration mqtt migration shadow migration shadow migration jobs migration jobs migration branches branches main main branch v4 beta deprecated v4 beta deprecated branch formerly named v4 beta getting started getting started cloning cloning configuring demos configuring demos prerequisites prerequisites build dependencies build dependencies aws iot account setup aws iot account setup configuring mutual authentication demos of mqtt and http configuring mutual authentication demos of mqtt and http configuring aws iot device defender and aws iot device shadow demos configuring aws iot device defender and aws iot device shadow demos configuring the aws iot fleet provisioning demo configuring the aws iot fleet provisioning demo configuring the s3 demos configuring the s3 demos setup for aws iot jobs demo setup for aws iot jobs demo setup for the greengrass local auth demo setup for the greengrass local auth demo prerequisites for the aws over the air update ota demos prerequisites for the aws over the air update ota demos scheduling an ota update job scheduling an ota update job building and running demos building and running demos build a single demo build a single demo build all configured demos build all configured demos running corepkcs11 demos running corepkcs11 demos alternative option of docker containers for running demos locally alternative option of docker containers for running demos locally installing mosquitto to run mqtt demos locally installing mosquitto to run mqtt demos locally installing httpbin to run http demos locally installing httpbin to run http demos locally generating documentation generating documentation overview the aws iot device sdk for embedded c c sdk is a collection of c source files under the mit open source license license that can be used in embedded applications to securely connect iot devices to aws iot core https docs aws amazon com iot latest developerguide what is aws iot html it contains mqtt client http client json parser aws iot device shadow aws iot jobs and aws iot device defender libraries this sdk is distributed in source form and can be built into customer firmware along with application code other libraries and an operating system os of your choice these libraries are only dependent on standard c libraries so they can be ported to various os s from embedded real time operating systems rtos to linux mac windows you can find sample usage of c sdk libraries on posix systems using openssl e g linux demos demos in this repository and on freertos https github com freertos freertos using mbedtls e g freertos demos https github com freertos freertos tree main freertos plus demo in freertos https github com freertos freertos repository for the latest release of c sdk please see the section for releases and documentation releases and documentation c sdk includes libraries that are part of the freertos 202210 01 lts https github com freertos freertos lts tree 202210 01 lts release learn more about the freertos 202210 01 lts libraries by clicking here https freertos org lts libraries html license the c sdk libraries are licensed under the mit open source license license features c sdk simplifies access to various aws iot services c sdk has been tested to work with aws iot core https docs aws amazon com iot latest developerguide what is aws iot html and an open source mqtt broker to ensure interoperability the aws iot device shadow aws iot jobs and aws iot device defender libraries are flexible to work with any mqtt client and json parser the mqtt client and json parser libraries are offered as choices without being tightly coupled with the rest of the sdk c sdk contains the following libraries coremqtt the coremqtt https github com freertos coremqtt library provides the ability to establish an mqtt connection with a broker over a customer implemented transport layer which can either be a secure channel like a tls session mutually authenticated or server only authentication or a non secure channel like a plaintext tcp connection this mqtt connection can be used for performing publish operations to mqtt topics and subscribing to mqtt topics the library provides a mechanism to register customer defined callbacks for receiving incoming publish acknowledgement and keep alive response events from the broker the library has been refactored for memory optimization and is compliant with the mqtt 3 1 1 https docs oasis open org mqtt mqtt v3 1 1 mqtt v3 1 1 html standard it has no dependencies on any additional libraries other than the standard c library a customer implemented network transport interface and optionally a customer implemented platform time function the refactored design embraces different use cases ranging from resource constrained platforms using only qos 0 mqtt publish messages to resource rich platforms using qos 2 mqtt publish over tls connections see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard coremqtt docs doxygen output html index html mqtt memory requirements corehttp the corehttp https github com freertos corehttp library provides the ability to establish an http connection with a server over a customer implemented transport layer which can either be a secure channel like a tls session mutually authenticated or server only authentication or a non secure channel like a plaintext tcp connection the http connection can be used to make get include range requests put post and head requests the library provides a mechanism to register a customer defined callback for receiving parsed header fields in an http response the library has been refactored for memory optimization and is a client implementation of a subset of the http 1 1 https tools ietf org html rfc2616 standard see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corehttp docs doxygen output html index html http memory requirements corejson the corejson https github com freertos corejson library is a json parser that strictly enforces the ecma 404 json standard https www json org json en html it provides a function to validate a json document and a function to search for a key and return its value a search can descend into nested structures using a compound query key a json document validation also checks for illegal utf8 encodings and illegal unicode escape sequences see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corejson docs doxygen output html index html json memory requirements corepkcs11 the corepkcs11 https github com freertos corepkcs11 library is an implementation of the pkcs 11 interface api that makes it easier to develop applications that rely on cryptographic operations only a subset of the pkcs 11 v2 4 https docs oasis open org pkcs11 pkcs11 base v2 40 os pkcs11 base v2 40 os html standard has been implemented with a focus on operations involving asymmetric keys random number generation and hashing the cryptoki or pkcs 11 standard defines a platform independent api to manage and use cryptographic tokens the name pkcs 11 is used interchangeably to refer to the api itself and the standard which defines it the pkcs 11 api is useful for writing software without taking a dependency on any particular implementation or hardware by writing against the pkcs 11 standard interface code can be used interchangeably with multiple algorithms implementations and hardware generally vendors for secure cryptoprocessors such as trusted platform module tpm https en wikipedia org wiki trusted platform module hardware security module hsm https en wikipedia org wiki hardware security module secure element or any other type of secure hardware enclave distribute a pkcs 11 implementation with the hardware the purpose of corepkcs11 mock is therefore to provide a pkcs 11 implementation that allows for rapid prototyping and development before switching to a cryptoprocessor specific pkcs 11 implementation in production devices since the pkcs 11 interface is defined as part of the pkcs 11 specification https docs oasis open org pkcs11 pkcs11 base v2 40 os pkcs11 base v2 40 os html replacing corepkcs11 with another implementation should require little porting effort as the interface will not change the system tests distributed in corepkcs11 repository can be leveraged to verify the behavior of a different implementation is similar to corepkcs11 see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corepkcs11 docs doxygen output html pkcs11 design html pkcs11 memory requirements aws iot device shadow the aws iot device shadow https github com aws device shadow for aws iot embedded sdk library enables you to store and retrieve the current state one or more shadows of every registered device a device s shadow is a persistent virtual representation of your device that you can interact with from aws iot core even if the device is offline the device state is captured in its shadow is represented as a json https www json org document the device can send commands over mqtt to get update and delete its latest state as well as receive notifications over mqtt about changes in its state the device s shadow s are uniquely identified by the name of the corresponding thing a representation of a specific device or logical entity on the aws cloud see managing devices with aws iot https docs aws amazon com iot latest developerguide iot thing management html for more information on iot thing this library supports named shadows a feature of the aws iot device shadow service that allows you to create multiple shadows for a single iot device more details about aws iot device shadow can be found in aws iot documentation https docs aws amazon com iot latest developerguide iot device shadows html the aws iot device shadow library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos shadow with coremqtt and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device shadow for aws iot embedded sdk docs doxygen output html index html shadow memory requirements aws iot jobs the aws iot jobs https github com aws jobs for aws iot embedded sdk library enables you to interact with the aws iot jobs service which notifies one or more connected devices of a pending job a job can be used to manage your fleet of devices update firmware and security certificates on your devices or perform administrative tasks such as restarting devices and performing diagnostics for documentation of the service please see the aws iot developer guide https docs aws amazon com iot latest developerguide iot jobs html interactions with the jobs service use the mqtt protocol this library provides an api to compose and recognize the mqtt topic strings used by the jobs service the aws iot jobs library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos jobs with libmosquitto https mosquitto org and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws jobs for aws iot embedded sdk docs doxygen output html index html jobs memory requirements aws iot device defender the aws iot device defender https github com aws device defender for aws iot embedded sdk library enables you to interact with the aws iot device defender service to continuously monitor security metrics from devices for deviations from what you have defined as appropriate behavior for each device if something doesn t look right aws iot device defender sends out an alert so you can take action to remediate the issue more details about device defender can be found in aws iot device defender documentation https docs aws amazon com iot latest developerguide device defender html this library supports custom metrics a feature that helps you monitor operational health metrics that are unique to your fleet or use case for example you can define a new metric to monitor the memory usage or cpu usage on your devices the aws iot device defender library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos defender with coremqtt and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device defender for aws iot embedded sdk docs doxygen output html index html defender memory requirements aws iot over the air update the aws iot over the air update https github com aws ota for aws iot embedded sdk ota library enables you to manage the notification of a newly available update download the update and perform cryptographic verification of the firmware update using the ota library you can logically separate firmware updates from the application running on your devices you can also use the library to send other files e g images certificates to one or more devices registered with aws iot more details about ota library can be found in aws iot over the air update documentation https docs aws amazon com freertos latest userguide freertos ota dev html the aws iot over the air update library has a dependency on corejson https github com freertos corejson for parsing of json job document and tinycbor https github com intel tinycbor git for decoding encoded data streams other than the standard c library it can be used with any mqtt library http library and operating system e g linux freertos see demos demos ota with coremqtt and corehttp over linux see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws ota for aws iot embedded sdk docs doxygen output html index html ota memory requirements aws iot fleet provisioning the aws iot fleet provisioning https github com aws fleet provisioning for aws iot embedded sdk library enables you to interact with the aws iot fleet provisioning mqtt apis https docs aws amazon com iot latest developerguide fleet provision api html in order to provison iot devices without preexisting device certificates with aws iot fleet provisioning devices can securely receive unique device certificates from aws iot when they connect for the first time for an overview of all provisioning options offered by aws iot see device provisioning documentation https docs aws amazon com iot latest developerguide iot provision html for details about fleet provisioning refer to the aws iot fleet provisioning documentation https docs aws amazon com iot latest developerguide provision wo cert html see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws fleet provisioning for aws iot embedded sdk docs doxygen output html index html fleet provisioning memory requirements aws sigv4 the aws sigv4 https github com aws sigv4 for aws iot embedded sdk library enables you to sign http requests with signature version 4 signing process https docs aws amazon com general latest gr signature version 4 html signature version 4 sigv4 is the process to add authentication information to http requests to aws services for security most requests to aws must be signed with an access key the access key consists of an access key id and secret access key see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws sigv4 for aws iot embedded sdk docs doxygen output html index html sigv4 memory requirements backoffalgorithm the backoffalgorithm https github com freertos backoffalgorithm library is a utility library to calculate backoff period using an exponential backoff with jitter algorithm for retrying network operations like failed network connection with server this library uses the full jitter strategy for the exponential backoff with jitter algorithm more information about the algorithm can be seen in the exponential backoff and jitter aws blog https aws amazon com blogs architecture exponential backoff and jitter exponential backoff with jitter is typically used when retrying a failed connection or network request to the server an exponential backoff with jitter helps to mitigate the failed network operations with servers that are caused due to network congestion or high load on the server by spreading out retry requests across multiple devices attempting network operations besides in an environment with poor connectivity a client can get disconnected at any time a backoff strategy helps the client to conserve battery by not repeatedly attempting reconnections when they are unlikely to succeed the backoffalgorithm library has no dependencies on libraries other than the standard c library see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard backoffalgorithm docs doxygen output html index html backoff algorithm memory requirements sending metrics to aws iot when establishing a connection with aws iot users can optionally report the operating system hardware platform and mqtt client version information of their device to aws this information can help aws iot provide faster issue resolution and technical support if users want to report this information they can send a specially formatted string see below in the username field of the mqtt connect packet format the format of the username string with metrics is actual username sdk os name version os version platform hardware platform mqttlib mqtt library name mqtt library version where actual username is the actual username used for authentication if username and password are used for authentication when username and password based authentication is not used this is an empty value os name is the operating system the application is running on e g ubuntu os version is the version number of the operating system e g 20 10 hardware platform is the hardware platform the application is running on e g raspberrypi mqtt library name is the mqtt client library being used e g coremqtt mqtt library version is the version of the mqtt client library being used e g 1 1 0 example actual username iotuser os name ubuntu os version 20 10 hardware platform name raspberrypi mqtt library name coremqtt mqtt library version 1 1 0 if username is not used then iotuser can be removed username string iotuser sdk ubuntu version 20 10 platform raspberrypi mqttlib coremqtt 1 1 0 define os name ubuntu define os version 20 10 define hardware platform name raspberrypi define mqtt lib coremqtt 1 1 0 define username string iotuser sdk os name version os version platform hardware platform name mqttlib mqtt lib define username string length uint16 t sizeof username string 1 mqttconnectinfo t connectinfo connectinfo pusername username string connectinfo usernamelength username string length mqttstatus mqtt connect pmqttcontext connectinfo null connack recv timeout ms psessionpresent versioning c sdk releases will now follow a date based versioning scheme with the format yyyymm nn where y represents the year m represents the month n represents the release order within the designated month 00 being the first release for example a second release in june 2021 would be 202106 01 although the sdk releases have moved to date based versioning each library within the sdk will still retain semantic versioning in semantic versioning the version number itself x y z indicates whether the release is a major minor or point release you can use the semantic version of a library to assess the scope and impact of a new release on your application releases and documentation all of the released versions of the c sdk libraries are available as git tags for example the last release of the v3 sdk version is available at tag 3 1 5 https github com aws aws iot device sdk embedded c tree v3 1 5 202211 00 api documentation of 202211 00 release https aws github io aws iot device sdk embedded c 202211 00 index html this release includes an update to how the coverity static analysis https scan coverity com scans are performed there is now a tools coverity readme md file in each library with instructions on how to perform a scan with 0 misra coding standard https www misra org uk warnings or errors additionally this release brings in major version upgrades to coremqtt https github com freertos coremqtt and corehttp https github com freertos corehttp it also brings in minor version upgrades to all other libraries 202108 00 api documentation of 202108 00 release https aws github io aws iot device sdk embedded c 202108 00 index html this release introduces the refactored aws iot fleet provisioning https github com aws fleet provisioning for aws iot embedded sdk library and the new aws sigv4 https github com aws sigv4 for aws iot embedded sdk library additionally this release brings minor version updates in the aws iot over the air update https github com aws ota for aws iot embedded sdk and corepkcs11 https github com freertos corepkcs11 libraries 202103 00 api documentation of 202103 00 release https docs aws amazon com embedded csdk 202103 00 lib ref index html this release includes a major update https github com aws ota for aws iot embedded sdk blob v3 0 0 changelog md v300 march 2021 to the apis of the aws iot over the air update library additionally aws iot device shadow library introduces a minor update https github com aws device shadow for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 by adding support for named shadow a feature of the aws iot device shadow service that allows you to create multiple shadows for a single iot device aws iot jobs library introduces a minor update https github com aws jobs for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 by introducing macros for next job id and compile time generation of topic strings aws iot device defender library introduces a minor update https github com aws device defender for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 that adds macros to api for custom metrics feature of aws iot device defender service corepkcs11 also introduces a patch update https github com freertos corepkcs11 blob v3 0 1 changelog md v301 february 2021 by removing the pkcs11configpal destroy supported config and mbedtls platform abstraction layer of destroyobject lastly no code changes are introduced for backoffalgorithm corehttp coremqtt and corejson however patch updates are made to improve documentation and ci 202012 01 api documentation of 202012 01 release https docs aws amazon com embedded csdk 202012 00 lib ref index html this release includes aws iot over the air update release candidate https github com aws ota for aws iot embedded sdk backoffalgorithm https github com freertos backoffalgorithm and pkcs 11 https github com freertos corepkcs11 libraries additionally there is a major update to the corejson and corehttp apis all libraries continue to undergo code quality checks e g misra c compliance and coverity static analysis in addition all libraries except aws iot over the air update and backoffalgorithm undergo validation of memory safety with the c bounded model checker cbmc automated reasoning tool 202011 00 api documentation of 202011 00 release https docs aws amazon com embedded csdk 202011 00 lib ref index html this release includes refactored http client aws iot device defender and aws iot jobs libraries additionally there is a major update to the corejson api all libraries continue to undergo code quality checks e g misra c compliance coverity static analysis and validation of memory safety with the c bounded model checker cbmc automated reasoning tool 202009 00 api documentation of 202009 00 release https docs aws amazon com freertos latest lib ref embedded csdk 202009 00 lib ref index html this release includes refactored mqtt json parser and aws iot device shadow libraries for optimized memory usage and modularity these libraries are included in the sdk via git submoduling https git scm com book en v2 git tools submodules these libraries have gone through code quality checks including verification that no function has a gnu complexity https www gnu org software complexity manual complexity html score over 8 and checks against deviations from mandatory rules in the misra coding standard https www misra org uk deviations from the misra c 2012 guidelines are documented under misra deviations misra md these libraries have also undergone both static code analysis from coverity static analysis https scan coverity com and validation of memory safety and data structure invariance through the cbmc automated reasoning tool https www cprover org cbmc if you are upgrading from v3 x api of the c sdk to the 202009 00 release please refer to migration guide from v3 1 5 to 202009 00 and newer releases migration guide from v315 to 20200900 and newer releases if you are using the c sdk v4 beta deprecated branch note that we will continue to maintain this branch for critical bug fixes and security patches but will not add new features to it see the c sdk v4 beta deprecated branch readme https github com aws aws iot device sdk embedded c blob v4 beta deprecated readme md for additional details v3 1 5 details available here https github com aws aws iot device sdk embedded c tree v3 1 5 porting guide for 202009 00 and newer releases all libraries depend on the iso c90 standard library and additionally on the stdint h library for fixed width integers including uint8 t int8 t uint16 t uint32 t and int32 t and constant macros like uint16 max if your platform does not support the stdint h library definitions of the mentioned fixed width integer types will be required for porting any c sdk library to your platform porting coremqtt guide for porting coremqtt library to your platform is available here https aws github io aws iot device sdk embedded c 202211 00 libraries standard coremqtt docs doxygen output html mqtt porting html porting corehttp guide for porting corehttp library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corehttp docs doxygen output html http porting html porting aws iot device shadow guide for porting aws iot device shadow library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device shadow for aws iot embedded sdk docs doxygen output html shadow porting html porting aws iot device defender guide for porting aws iot device defender library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device defender for aws iot embedded sdk docs doxygen output html defender porting html porting aws iot over the air update guide for porting ota library to your platform is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws ota for aws iot embedded sdk docs doxygen output html ota porting html migration guide from v3 1 5 to 202009 00 and newer releases mqtt migration migration guide for mqtt library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html mqtt migration html shadow migration migration guide for shadow library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html shadow migration html jobs migration migration guide for jobs library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html jobs migration html branches main branch the main https github com aws aws iot device sdk embedded c tree main branch hosts the continuous development of the aws iot embedded c sdk c sdk libraries please be aware that the development at the tip of the main branch is continuously in progress and may have bugs consider using the tagged releases https github com aws aws iot device sdk embedded c releases of the c sdk for production ready software v4 beta deprecated branch formerly named v4 beta the v4 beta deprecated https github com aws aws iot device sdk embedded c tree v4 beta deprecated branch contains a beta version of the c sdk libraries which is now deprecated this branch was earlier named as v4 beta and was renamed to v4 beta deprecated the libraries in this branch will not be released however critical bugs will be fixed and tested no new features will be added to this branch getting started cloning this repository uses git submodules https git scm com book en v2 git tools submodules to bring in the c sdk libraries eg mqtt and third party dependencies eg mbedtls for posix platform transport layer note if you download the zip file provided by github ui you will not get the contents of the submodules the zip file is also not a valid git repository if you download from the 202012 00 release page https github com aws aws iot device sdk embedded c releases tag 202012 00 page you will get the entire repository including the submodules in the zip file aws iot device sdk embedded c 202012 00 zip to clone the latest commit to main branch using https sh git clone recurse submodules https github com aws aws iot device sdk embedded c git using ssh sh git clone recurse submodules git github com aws aws iot device sdk embedded c git if you have downloaded the repo without using the recurse submodules argument you need to run sh git submodule update init recursive when building with cmake submodules are also recursively cloned automatically however dbuild clone submodules 0 can be passed as a cmake flag to disable this functionality this is useful when you d like to build cmake while using a different commit from a submodule configuring demos the libraries in this sdk are not dependent on any operating system however the demos for the libraries in this sdk are built and tested on a linux platform the demos build with cmake https cmake org a cross platform build tool prerequisites cmake 3 2 0 or any newer version for utilizing the build system of the repository c90 compiler such as gcc due to the use of mbedtls in corepkcs11 a c99 compiler is required if building the pkcs11 demos although not a part of the iso c90 standard stdint h is required for fixed width integer types that include uint8 t int8 t uint16 t uint32 t and int32 t and constant macros like uint16 max while stdbool h is required for boolean parameters in coremqtt for compilers that do not provide these header files coremqtt https github com freertos coremqtt provides the files stdint readme https github com freertos coremqtt blob main source include stdint readme and stdbool readme https github com freertos coremqtt blob main source include stdbool readme which can be renamed to stdint h and stdbool h respectively to provide the required type definitions a supported operating system the ports provided with this repo are expected to work with all recent versions of the following operating systems although we cannot guarantee the behavior on all systems linux system with posix sockets threads rt and timer apis we have tested on ubuntu 18 04 build dependencies the follow table shows libraries that need to be installed in your system to run certain demos if a dependency is not installed and cannot be built from source demos that require that dependency will be excluded from the default all target dependency version usage openssl https github com openssl openssl 1 1 0 or later all tls demos and tests with the exception of pkcs11 mosquitto client https github com eclipse mosquitto 1 4 10 or later aws iot jobs mosquitto demo aws iot account setup you need to setup an aws account and access the aws iot console for running the aws iot device shadow library aws iot device defender library aws iot jobs library aws iot ota library and corehttp s3 download demos also the aws account can be used for running the mqtt mutual auth demo against aws iot broker note that running the aws iot device defender aws iot jobs and aws iot device shadow library demos require the setup of a thing resource for the device running the demo follow the links to setup an aws account https portal aws amazon com billing signup start sign in to the aws iot console https console aws amazon com iot home after setting up the aws account create a thing resource https docs aws amazon com iot latest developerguide iot moisture create thing html the mqtt mutual authentication and aws iot shadow demos include example aws iot policy documents to run each respective demo with aws iot you may use the mqtt mutual auth demos mqtt mqtt demo mutual auth aws iot policy example mqtt json and shadow demos shadow shadow demo main aws iot policy example shadow json example policies by replacing aws region and aws account id with the strings of your region and account identifier while the iot thing name and mqtt client identifier do not need to match for the demos to run the example policies have the thing name and client identifier identical as per aws iot best practices https docs aws amazon com iot latest developerguide security best practices html it can be very helpful to also have the aws command line interface tooling https docs aws amazon com cli latest userguide cli chap install html installed configuring mutual authentication demos of mqtt and http you can pass the following configuration settings as command line options in order to run the mutual auth demos make sure to run the following command in the root directory of the c sdk sh optionally find your aws iot endpoint from the command line aws iot describe endpoint endpoint type iot data ats cmake s bbuild daws iot endpoint your aws iot endpoint dclient cert path your client certificate path dclient private key path your client private key path in order to set these configurations manually edit demo config h in demos mqtt mqtt demo mutual auth and demos http http demo mutual auth to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot aws region amazonaws com where aws region can be an aws region like us east 2 optionally it can also be found with the aws cli command aws iot describe endpoint endpoint type iot data ats set client cert path to the path of the client certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client private key path to the path of the private key downloaded when setting up the device certificate in aws iot account setup aws iot account setup it is possible to configure root ca cert path to any pem encoded root ca certificate however this is optional because cmake will download and set it to amazonrootca1 pem https www amazontrust com repository amazonrootca1 pem when unspecified if unspecified the default root ca path will be interpreted relative to where the demo binary is executed for many demos you can change this path by modifying it in the corresponding demo config h configuring aws iot device defender and aws iot device shadow demos to build the aws iot device defender and aws iot device shadow demos you can pass the following configuration settings as command line options make sure to run the following command in the root directory of the c sdk sh cmake s bbuild daws iot endpoint your aws iot endpoint droot ca cert path your path to amazon root ca dclient cert path your client certificate path dclient private key path your client private key path dthing name your registered thing name an amazon root ca certificate can be downloaded from here https www amazontrust com repository in order to set these configurations manually edit demo config h in the demo folder to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot us east 2 amazonaws com set root ca cert path to the path of the root ca certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client cert path to the path of the client certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client private key path to the path of the private key downloaded when setting up the device certificate in aws iot account setup aws iot account setup set thing name to the name of the thing created in aws iot account setup aws iot account setup configuring the aws iot fleet provisioning demo to build the aws iot fleet provisioning demo you can pass the following configuration settings as command line options make sure to run the following command in the root directory of the c sdk sh cmake s bbuild daws iot endpoint your aws iot endpoint droot ca cert path your path to amazon root ca dclaim cert path your claim certificate path dclaim private key path your claim private key path dprovisioning template name your template name ddevice serial number your serial number an amazon root ca certificate can be downloaded from here https www amazontrust com repository to create a provisioning template and claim credentials sign into your aws account and visit here create provtemplate make sure to enable the use the aws iot registry to manage your device fleet option once you have created the template and credentials modify the claim certificate s policy to match the sample policy sample claim policy in order to set these configurations manually edit demo config h in the demo folder to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot us east 2 amazonaws com set root ca cert path to the path of the root ca certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set claim cert path to the path of the claim certificate downloaded when setting up the template and claim credentials set claim private key path to the path of the private key downloaded when setting up the template and claim credentials set provisioning template name to the name of the provisioning template created set device serial number to an arbitrary string representing a device identifier create provtemplate https console aws amazon com iot home provisioningtemplate create instruction sample claim policy demos fleet provisioning fleet provisioning with csr example claim policy json configuring the s3 demos you can pass the following configuration settings as command line options in order to run the s3 demos make sure to run the following command in the root directory of the c sdk sh cmake s bbuild ds3 presigned get url s3 get url ds3 presigned put url s3 put url s3 presigned put url is only needed for the s3 upload demo in order to set these configurations manually edit demo config h in demos http http demo s3 download multithreaded and demos http http demo s3 upload to define the following set s3 presigned get url to a s3 presigned url with get access set s3 presigned put url to a s3 presigned url with put access you can generate the presigned urls using demos http common src presigned urls gen py demos http common src presigned urls gen py more info can be found here demos http common src readme md configure s3 download http demo using sigv4 library refer this demos http http demo s3 download readme md demos http http demo s3 download readme md to follow the steps needed to configure and run the s3 download http demo using sigv4 library that generates the authorization http header needed to authenticate the http requests send to s3 setup for aws iot jobs demo 1 the demo requires the linux platform to contain curl and libmosquitto on a debian platform these dependencies can be installed with apt install curl libmosquitto dev if the platform does not contain the libmosquitto library the demo will build the library from source libmosquitto 1 4 10 or any later version of the first major release is required to run this demo 2 a job that specifies the url to download for the demo needs to be created on the aws account for the thing resource that will be used by the demo the job can be created directly from the aws iot console https console aws amazon com iot home or using the aws cli tool the following creates a job that specifies a linux kernel link for downloading aws iot create job job id job 1 targets arn aws iot us west 2 account id thing thing name document url https cdn kernel org pub linux kernel v5 x linux 5 8 5 tar xz setup for the greengrass local auth demo for setting up the greengrass local auth demo see the readme in the demo folder demos greengrass greengrass demo local auth readme md prerequisites for the aws over the air update ota demos 1 to perform a successful ota update you need to complete the prerequisites mentioned here https docs aws amazon com freertos latest userguide ota prereqs html 1 a code signing certificate is required to authenticate the update a code signing certificate based on the sha 256 ecdsa algorithm will work with the current demos an example of how to generate this kind of certificate can be found here https docs aws amazon com freertos latest userguide ota code sign cert esp html 1 the code signing certificate can be either baked into firmware as a string or stored as a file 1 for baked in certificate method copy the certificate to signingcredentialsigning certificate pem in ota pal posix c https github com aws aws iot device sdk embedded c blob main platform posix ota pal source ota pal posix c 2 for file storage method store the certificate as a file and supply the file path in path name of code signing certificate on device field when creating the ota job in aws iot console scheduling an ota update job after you build and run the initial executable you will have to create another executable and schedule an ota update job with this image 1 increase the version of the application by setting macro app version build in demos ota ota demo core mqtt http demo config h to a different version than what is running 1 rebuild the application using the build steps building and running demos below into a different directory say build dir 2 1 rename the demo executable to reflect the change e g mv ota demo core mqtt ota demo core mqtt2 1 create an ota job 1 go to the aws iot core console https console aws amazon com iot 1 manage jobs create create a freertos ota update job select the corresponding name for your device from the thing list 1 sign a new firmware create a new profile select any sha ecdsa signing platform upload the code signing certificate from prerequisites and provide its path on the device 1 select the image select the bucket you created during the prerequisite steps prerequisites for the aws over the air update ota demos upload the binary build dir 2 bin ota demo2 1 the path on device should be the absolute path to place the executable and the binary name e g home ubuntu aws iot device sdk embedded c staging build dir bin ota demo core mqtt2 1 select the iam role created during the prerequisite steps prerequisites for the aws over the air update ota demos 1 create the job 1 run the initial executable again with the following command sudo ota demo core mqtt or sudo ota demo core http 1 after the initial executable has finished running go to the directory where the downloaded firmware image resides which is the path name used when creating an ota job 1 change the permissions of the downloaded firmware to make it executable as it may be downloaded with read user default permissions only chmod 775 ota demo core mqtt2 1 run the downloaded firmware image with the following command sudo ota demo core mqtt2 building and running demos before building the demos ensure you have installed the prerequisite software prerequisites on ubuntu 18 04 and 20 04 gcc cmake and openssl can be installed with sh sudo apt install build essential cmake libssl dev build a single demo go to the root directory of the c sdk run cmake to generate the makefiles cmake s bbuild cd build choose a demo from the list below or alternatively run make help grep demo defender demo http demo basic tls http demo mutual auth http demo plaintext http demo s3 download http demo s3 download multithreaded http demo s3 upload jobs demo mosquitto mqtt demo basic tls mqtt demo mutual auth mqtt demo plaintext mqtt demo serializer mqtt demo subscription manager ota demo core http ota demo core mqtt pkcs11 demo management and rng pkcs11 demo mechanisms and digests pkcs11 demo objects pkcs11 demo sign and verify shadow demo main replace demo name with your desired demo then build it make demo name go to the build bin directory and run any demo executables from there build all configured demos go to the root directory of the c sdk run cmake to generate the makefiles cmake s bbuild cd build run this command to build all configured demos make go to the build bin directory and run any demo executables from there running corepkcs11 demos the corepkcs11 demos do not require any aws iot resources setup and are standalone the demos build upon each other to introduce concepts in pkcs 11 sequentially below is the recommended order 1 pkcs11 demo management and rng 1 pkcs11 demo mechanisms and digests 1 pkcs11 demo objects 1 pkcs11 demo sign and verify 1 please note that this demo requires the private and public key generated from pkcs11 demo objects to be in the directory the demo is executed from alternative option of docker containers for running demos locally install docker sh curl fssl https get docker com o get docker sh sh get docker sh installing mosquitto to run mqtt demos locally the following instructions have been tested on an ubuntu 18 04 environment with docker and openssl installed 1 download the official docker image for mosquitto 1 6 14 this version is deliberately chosen so that the docker container can load certificates from the host system any version after 1 6 14 will drop privileges as soon as the configuration file has been read before tls certificates are loaded sh docker pull eclipse mosquitto 1 6 14 1 if a mosquitto broker with tls communication needs to be run ignore this step and proceed to the next step a mosquitto broker with plain text communication can be run by executing the command below docker run it p 1883 1883 name mosquitto plain text eclipse mosquitto 1 6 14 1 set broker endpoint defined in demos mqtt mqtt demo plaintext demo config h to localhost ignore the remaining steps unless a mosquitto broker with tls communication also needs to be run 1 for tls communication with mosquitto broker server and ca credentials need to be created use openssl commands to generate the credentials for the mosquitto server note make sure to use different common name cn detail between the ca and server certificates otherwise ssl handshake fails with exactly same common name cn detail in both the certificates sh generate ca key and certificate provide the subject field information as appropriate for ca certificate openssl req x509 nodes sha256 days 365 newkey rsa 2048 keyout ca key out ca crt sh generate server key and certificate provide the subject field information as appropriate for server certificate make sure the common name cn field is different from the root ca certificate openssl req nodes sha256 new keyout server key out server csr sign with the ca cert openssl x509 req sha256 in server csr ca ca crt cakey ca key cacreateserial out server crt days 365 1 create a mosquitto conf file to use port 8883 for tls communication and providing path to the generated credentials port 8883 cafile mosquitto config ca crt certfile mosquitto config server crt keyfile mosquitto config server key use this option for tls mutual authentication where client will provide ca signed certificate require certificate true tls version tlsv1 2 use identity as username true 1 run the docker container from the local directory containing the generated credential and mosquitto conf files sh docker run it p 8883 8883 v pwd mosquitto config name mosquitto basic tls eclipse mosquitto 1 6 14 1 update demos mqtt mqtt demo basic tls demo config h to the following set broker endpoint to localhost set root ca cert path to the absolute path of the ca certificate created in step 4 for the local mosquitto server installing httpbin to run http demos locally run httpbin through port 80 sh docker pull kennethreitz httpbin docker run p 80 80 kennethreitz httpbin server host defined in demos http http demo plaintext demo config h can now be set to localhost to run http demo basic tls download ngrok https ngrok com download in order to create an https tunnel to the httpbin server currently hosted on port 80 sh ngrok http 80 may have to use ngrok exe depending on os or filename of the executable ngrok will provide an https link that can be substituted in demos http http demo basic tls demo config h and has a format of https abcdefg12345 ngrok io set server host in demos http http demo basic tls demo config h to the https link provided by ngrok without https preceding it you must also download the root ca certificate provided by the ngrok https link and set root ca cert path in demos http http demo basic tls demo config h to the file path of the downloaded certificate generating documentation note for pre generated documentation please visit releases and documentation releases and documentation section the doxygen references were created using doxygen version 1 9 2 to generate the doxygen pages use the provided python script at tools doxygen generate docs py tools doxygen generate docs py please ensure that each of the library submodules under libraries standard and libraries aws are cloned before using this script sh cd csdk root git submodule update init recursive checkout python3 tools doxygen generate docs py the generated documentation landing page is located at docs doxygen output html index html | aws | server |
intel-iot-refkit | discontinuation of project this project will no longer be maintained by intel intel has ceased development and contributions including but not limited to maintenance bug fixes new releases or updates to this project intel no longer accepts patches to this project if you have an ongoing need to use this project are interested in independently developing it or would like to maintain patches for the open source software community please create your own fork of this project | server |
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AWS-Cloud-ML-Udacity-Practice | aws cloud ml udacity practice this repository contains notebooks from attempted challenges and quizzess posed in the aws cloud machine learning foundations course on udacity where i am learning how to develop ml systems for production with software engineering best practices in mind | cloud |
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Resources-Front-End-Beginner | the most essential list of resources for front end beginners cc0 https img shields io badge license cc0 green svg https creativecommons org publicdomain zero 1 0 if you want to learn how to become a front end developer you are in the right place i will regularly update that list with new resources and links i found on the web don t hesitate to participate by sending a pr maybe your first on github i m using some emoticons to give you more information on these links 1 all links without a flag are in english the flag means the resource is in french means the resource is multi language 2 are paid tutorials are free tutorials sometimes you will have some free videos articles and other paid on the same website 3 indicate that the link is a reference 4 is present when video content is available table of contents 1 start here start here 2 learn html learn html 3 learn css learn css 4 learn javascript learn javascript 5 learn git learn git 6 tools tools 7 chat slack chat slack channels 8 aggregators news aggregators news 9 newsletters newsletters learn the basics https res cloudinary com djnyaloac image upload v1507777230 front end know basics jpg from web developer roadmap 2017 start here understand internet how does the internet actually work https www youtube com watch v 5sxk rm6dm what is internet https www youtube com watch v dxcc6ycz73m how does the internet work part 1 it s like a bad drawing https www youtube com watch v xzimeouhoa8 how does the internet work https www youtube com watch v 7xoaofjkqym how the internet works for developers pt 1 overview frontend https www youtube com watch v e4s8zfldlgq benjamin bayart qu est ce qu internet conf rence sciencespo le 14 avril 2010 dur e 5h30 https www youtube com watch v pwt2egqlke4 list plu39vuhuxljeer75d1gnjik8 2i0yhrlx c est pas sorcier internet les pirates tissent leur toile https www youtube com watch v vb7vsaan8pm comprendre internet histoire https www youtube com watch v slj qztaqne comprendre comment marche internet https www youtube com playlist list pl2972e0d013fe7de7 comprendre internet http www comprendre internet com understand your journey web developer roadmap 2019 github https github com kamranahmedse developer roadmap back to top table of contents learn html courses tutorials learn html codecademy https www codecademy com learn learn html html5 and css freecodecamp https www freecodecamp org map nested collapsehtml5andcss marksheet a free html css tutorial http marksheet io intro to html css making webpages khanacademy https www khanacademy org computing computer programming html css learn the web https learn the web algonquindesign ca html5 introduction edx https www edx org course html5 introduction w3cx html5 0x 0 html creating basic web pages https bento io topic html learn to code html css shay howe http learn shayhowe com html css html tutorials http www htmldog com intro to html and css udacity https www udacity com course intro to html and css ud304 learn html in 12 minutes youtube https www youtube com watch v bwpmsssvdpk learn html in 30 minutes youtube https www youtube com watch v hrzqicux6kg html fundamentals sololearn https www sololearn com course html html basics treehouse https teamtreehouse com library html basics 2 html css path code school https www codeschool com learn html css html essential training lynda https www lynda com web development tutorials html essential training 170427 2 html your first day with html pluralsight https www pluralsight com courses your first day html 1665 html fundamentals pluralsight https www pluralsight com courses html fundamentals introduction to html and css treehouse https teamtreehouse com library introduction to html and css introduction to html5 and css3 frontend masters https frontendmasters com courses introduction html5 css3 html css on code avengers https www codeavengers com profile html css 30 days to learn html css https webdesign tutsplus com courses 30 days to learn html css ga 2 133963854 1549833602 1507724443 1498690494 1505785599 introduction to html https scrimba com g ghtml apprenez cr er votre site web avec html5 et css3 openclassrooms https openclassrooms com courses apprenez a creer votre site web avec html5 et css3 tutoriels web html alsacr ations https www alsacreations com tuto liste 1 html html et css le cours complet udemy https www udemy com cours html css html5 tutorial w3schools https www w3schools com html algoexpert s front end expert course https www algoexpert io content fe documentation html reference http htmlreference io introduction to html mdn web docs https developer mozilla org en us docs learn html introduction to html html cheat sheet compilation https websitesetup org html5 cheat sheet html5 tutorial pdf tutorialspoint https www tutorialspoint com html5 html5 tutorial pdf getting to know html shay howe https learn shayhowe com html css getting to know html the elements of html https rawgit com w3c elements of html master index html guidelines seo speed and security best practices checkbot https www checkbot io guide html best practices github https github com hail2u html best practices quiz challenges learnyouhtml nodeschool https github com denysdovhan learnyouhtml html quiz beginner level skillvalue https skillvalue com en quizzes front end html5 beginner level articles books learn to code html and css develop and style websites https www amazon com learn code html css websites dp 0321940520 html and css design and build websites https www amazon com html css design build websites dp 1118008189 r alisez votre site web avec html 5 et css 3 http www eyrolles com informatique livre realisez votre site web avec html 5 et css 3 9782212674767 back to top table of contents learn css courses tutorials learn css codecademy https www codecademy com learn learn css learn css in 12 minutes youtube https www youtube com watch v 0afzj1g0bie css basics treehouse https teamtreehouse com library css basics introduction to css https www pluralsight com courses css intro css3 in depth https frontendmasters com courses css3 in depth css essential training 3 lynda https www lynda com css tutorials css essential training 3 609030 2 html scalable modular architecture for css smacss frontend masters https frontendmasters com courses smacss tutoriel html css box sizing grafikart https www youtube com watch v ofziqkoqkd0 tutoriel css comment bien organiser son css grafikart https www youtube com watch v t8up7giniou tutoriels web css alsacr ations https www alsacreations com tuto liste 2 css css tutorial w3schools https www w3schools com css introduction to css https scrimba com g gintrotocss learn css sololearn https www sololearn com course css selectors css diner https flukeout github io flexbox flexbox froggy http flexboxfroggy com flexbox defense http www flexboxdefense com css grid grid garden http cssgridgarden com learn css grid with wes bos https cssgrid io documentation css reference http cssreference io css mdn web docs https developer mozilla org en us docs web css open web reference css http ref openweb io css css reference https tympanus net codrops css reference getting to know css https learn shayhowe com html css getting to know css guidelines css guidelin es https cssguidelin es airbnb css sass styleguide github https github com airbnb css scalable and modular architecture for css smacss https smacss com google html css style guide github https google github io styleguide htmlcssguide html sensible css guidelines github https github com chris pearce css guidelines trello css guide github https gist github com bobbygrace 9e961e8982f42eb91b80 quizz challenges css quiz beginner level skillvalue https skillvalue com en quizzes front end css beginner level 2 articles books css the definitive guide eric meyer https www amazon com css definitive guide eric meyer dp 0596527330 enduring css ben frain https www amazon com enduring css ben frain dp 1787282805 the css pocketguide chris casciano https www amazon com css pocket guide peachpit dp 0321732278 css secrets better solutions to everyday web design problems lea verou https www amazon com css secrets solutions everyday problems dp 1449372635 professional css3 piotr sikora https www packtpub com web development professional css3 css3 rapha l goetter hugo giraudel http www eyrolles com informatique livre css3 9782212140231 css avanc es rapha l goetter http www eyrolles com informatique livre css avancees 9782212134056 css avanc es vers html5 et css3 mathieu nebra premier pas en css3 et html5 http www eyrolles com informatique livre premiers pas en css3 et html5 9782212674309 back to top table of contents learn javascript courses tutorials basic javascript https www freecodecamp org map nested collapsebasicjavascript introduction to javascript codecademy https www codecademy com learn introduction to javascript beau teaches javascript youtube https www youtube com playlist list plwkjhjtqvabmoinlqljg1gxejeukhhcn js 30 for 30 30 projects for 30 days https javascript30 com intro to javascript udacity https www udacity com course intro to javascript ud803 learn javascript codementor https www codementor io javascript introduction to javascript programming frontend masters https frontendmasters com courses javascript basics lambdaschool javascript mini bootcamp https lambdaschool com free course web javascript essential training lynda https www lynda com javascript tutorials javascript essential training 574716 2 html javascript fundamentals tuts https code tutsplus com courses javascript fundamentals getting started with javascript for web development scotch https scotch io courses getting started with javascript javascript basics treehouse https teamtreehouse com library javascript basics javascript path code school https www codeschool com learn javascript es6 for everyone https es6 io tutoriels web javascript alsacr ations https www alsacreations com tuto liste 5 javascript javascript info http javascript info javascript tutorial w3schools https www w3schools com js javascript tutorial watch and code by gordon zhu https watchandcode com learn javascript https www sololearn com course javascript introduction to javascript https scrimba com g gintrotojavascript documentation javascript reference https developer mozilla org en us docs web javascript reference modern javascript cheatsheet https mbeaudru github io modern js cheatsheet es6 katas learn es6 by doing it http es6katas org guidelines clean code concepts adapted for javascript github https github com ryanmcdermott clean code javascript quiz challenges workshoppers nodeschool https nodeschool io javascript quiz beginner level skillvalue https skillvalue com en quizzes front end javascript beginner level 2 articles books practical modern javascript https github com mjavascript practical modern javascript you don t know js book series https github com getify you dont know js javascript the definitive guide https www amazon fr javascript definitive guide david flanagan dp 0596805527 javascript the good parts https www amazon fr javascript good parts d crockford dp 0596517742 eloquent javascript a modern introduction to programming https www amazon com eloquent javascript 2nd ed introduction dp 1593275846 javascript allong e https leanpub com javascriptallongesix learning javascript https www amazon com learning javascript essentials application development dp 1491914912 understanding ecmascript 6 the definitive guide for javascript developers https www amazon fr understanding ecmascript definitive javascript developers dp 1593277571 javascript and jquery interactive front end web development https www amazon com javascript jquery interactive front end development dp 1118531647 javascript d veloppez efficacement https www amazon fr javascript d veloppez efficacement 2e dition dp 2409009727 airbnb javascript guide https github com airbnb javascript back to top table of contents learn typescript articles books the concise typescript book https github com gibbok typescript book learn git 15 minutes to learn git https try github io git it desktop app nodeschool https github com jlord git it electron learn git on codecademy https www codecademy com learn learn git git it is a mac win linux desktop app for learning git and github https github com jlord git it electron learn git branching educational challenges https learngitbranching js org introduction to version control with git microsoft learn https docs microsoft com en gb learn paths intro to vc git git it learn git in a real terminal http jlord us git it git cheatsheet http www ndpsoftware com git cheatsheet html git essential training lynda https www lynda com git tutorials git essential training 100222 2 html try git https www codeschool com courses try git learn git git tutorials workflows and commands https www atlassian com git back to top table of contents tools essential github https github com website auditors checkbot https www checkbot io playgrounds codepen https codepen io codesandbox https codesandbox io sassmeister https www sassmeister com jsfiddle https jsfiddle net jsbin https jsbin com thimble https thimble mozilla org en us web design tools webflow https webflow com framer https www framer com editors visual studio code https code visualstudio com sublime text https www sublimetext com atom https atom io brackets http brackets io notepad https notepad plus plus org cloud9 https c9 io back to top table of contents chat slack channels feds slack https feds slack com front end france slack https frontendfr slack com devfr telegram https t me devfr aggregators news smashing magazine https www smashingmagazine com css tricks https css tricks com front end stash https frontendstash top codrops https tympanus net codrops front end front basically front end news https frontendfront com echo js javascript news http www echojs com a list apart code https alistapart com topic code scotch code eat sleep loop https scotch io daily dev https daily dev back to top table of contents newsletters frontend focus https frontendfoc us css weekly http css weekly com css tricks https css tricks com newsletters javascript weekly http javascriptweekly com webtools weekly https webtoolsweekly com smashing magazine https www smashingmagazine com the smashing newsletter sidebar https sidebar io responsive design weekly http responsivedesignweekly com jsk weekly https javascriptkicks com webrtc weekly https webrtcweekly com contributing open an issue or a pull request to suggest changes or additions contributors all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tbody tr td align center valign top width 14 28 a href https thedaviddias dev img src https avatars githubusercontent com u 237229 v 4 s 120 width 120px alt david dias br sub b david dias b sub a br a href content thedaviddias title content a a href maintenance thedaviddias title maintenance a a href https github com thedaviddias resources front end beginner pulls q is 3apr reviewed by 3athedaviddias title reviewed pull requests a td td align center valign top width 14 28 a href https github com gibbok img src https avatars githubusercontent com u 17195702 v 4 s 120 width 120px alt simone poggiali br sub b simone poggiali b sub a br a href content gibbok title content a td td align center valign top width 14 28 a href http arunava dev img src https avatars githubusercontent com u 83031120 v 4 s 120 width 120px alt arunava aru br sub b arunava aru b sub a br a href content liquid o2 title content a td tr tbody table markdownlint restore prettier ignore end all contributors list end license cc0 https i creativecommons org p zero 1 0 88x31 png https creativecommons org publicdomain zero 1 0 back to top table of contents | awesome-list list front-end front-end-developer beginner beginners-guide resources | front_end 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blockchain | ibp docs this repo holds the source for the ibp documentation live links saas prod https cloud ibm com docs blockchain saas staging https test cloud ibm com docs blockchain sw prod https www ibm com docs en blockchain platform 2 5 4 sw staging https wwwstage ibm com docs en blockchain platform 2 5 4 push changes to prod steps saas go to https github ibm com cloud docs blockchain merge the next prod push pr sw go to wfm https wfm dcs ibm com product ssvkz7 2 5 4 run the production build push changes to staging steps saas code committed to master will automatically push out to test staging sw go to wfm https wfm dcs ibm com product ssvkz7 2 5 4 run the staging build | blockchain |
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trainbot | onlytrains img src frontend src assets logo day svg height 100 width 100 watches a piece of train track detects passing trains and stitches together images of them should work with any video4linux usb cam or raspberry pi camera v3 modules frontend https trains jo m ch a collection of some special sightings https trains jo m ch trains list filter 22where 22 22favs 22 22id in 577 2405 2320 2193 1342 1039 343 407 350 307 1724 887 2485 3002 2950 2949 2896 2870 2853 2839 2827 2815 2802 3403 3224 3008 2766 2483 3410 3425 3424 3592 3576 3986 3715 2462 3846 3903 3981 3999 3971 4045 4160 4051 4362 4300 4504 4484 4456 4669 4794 4792 4790 4796 4797 4801 4813 4814 4815 4816 4818 4820 4827 4829 4831 4841 4840 4839 4876 4874 4873 4855 4844 4894 4890 4889 4883 4882 5058 5045 5272 5257 5241 5148 5146 4823 5437 2754 3770 3768 4025 4158 4426 4430 5325 5401 6124 6567 6560 6553 5972 5535 5700 6786 7232 8332 8334 8137 7911 8532 8518 8496 8415 7956 7939 7136 7000 7001 9328 9321 9286 9281 9213 9207 9188 9649 9648 9621 9614 9584 9529 9528 9450 9422 9268 9231 9179 9175 8786 8588 11529 11420 11406 11224 11292 11223 11199 11101 11094 11027 11014 10773 10626 10349 10333 9846 9866 9814 12424 12345 12324 12216 12219 12221 12226 12235 12556 13024 12607 13267 13989 13988 13979 13914 13909 13896 13886 13728 13513 13507 13461 12410 12331 14193 14184 14213 14252 14336 14362 14373 13420 3643 13489 13460 13499 15347 15276 15263 15201 15068 15033 14985 14809 14821 14702 15443 15435 15414 15374 17103 17089 17088 17087 17084 17063 17058 17054 16954 16952 16896 16895 16890 16878 16876 16864 16856 16838 16690 16644 16388 16386 16296 16283 16282 16269 16255 16239 16152 16127 16113 16100 16086 16072 16053 15923 15885 15879 15877 15874 15783 15691 15673 15615 15577 15564 18079 14479 138 11810 18090 17983 17974 17962 17956 17860 17581 17536 17472 17471 17468 17305 17252 17211 17190 18288 18599 18538 20417 20421 20150 19604 19515 19259 19260 20426 15457 21289 20885 20862 20818 20808 20479 19387 19342 19317 18342 21282 21215 21098 21085 21782 21775 21749 21737 22835 19805 20863 22033 22317 22360 22655 23107 23215 23185 23244 23253 23258 23373 23445 23510 23538 23587 23562 23614 23671 23905 23906 23923 24016 24046 25260 25205 25087 25054 25024 24930 24508 24496 24487 24409 24378 24274 25636 25617 25616 25595 25587 25533 25298 27421 27388 27305 27301 27157 27154 27141 26997 26923 26811 26752 26713 26701 26588 26535 26521 26412 26394 26281 26274 26198 26141 25851 25777 25776 25752 25664 28510 28505 28393 28475 28503 28390 28310 28241 28180 28080 27560 29291 29282 29275 29252 29158 29096 29088 32739 32732 32702 32694 32590 32395 32380 33881 33855 33850 33816 33803 33725 33675 33489 33390 33352 33320 33309 33290 33165 33070 33063 33057 33044 22 the name onlytrains is credited to timethy https github com timethy img src internal pkg stitch testdata set0 day jpg internal pkg stitch testdata set0 day jpg img src internal pkg stitch testdata set0 night jpg internal pkg stitch testdata set0 night jpg img src internal pkg stitch testdata set0 rain jpg internal pkg stitch testdata set0 rain jpg img src internal pkg stitch testdata set0 snow jpg internal pkg stitch testdata set0 snow jpg img src demo gif demo gif it also contains some packages which might be useful for other purposes pkg pmatch pkg pmatch image patch matching pkg ransac pkg ransac ransac algorithm implementation the binaries are currently built and tested on x86 64 and a raspberry pi 4 b assumptions and notes on computer vision the computer vision used in trainbot is fairly naive and simple there is no camera calibration image stabilization undistortion perspective mapping or real object tracking this allows us to stay away from complex dependencies like opencv and keeps the computational requirements low all processing happens on cpu the assumptions are there might be more implicit ones 1 trains only appear in a manually pre cropped region 1 the camera is stable and the image does not move around in any direction 1 there are no large fast brightness changes 1 trains have a given min and max speed 1 we are looking at the tracks more or less perpendicularly in the chosen image crop region 1 trains are coming from one direction at a time crossings are not handled properly 1 trains have a constant acceleration might be 0 and do not stop and turn around while in front of the camera build system there is a helper makefile which calls the standard go build tools and an arm64 cross build inside docker v4l settings bash list ffmpeg f v4l2 list formats all i dev video2 v4l2 ctl all device dev video2 exposure v4l2 ctl c exposure auto 3 device dev video2 autofocus v4l2 ctl c focus auto 1 device dev video2 fixed v4l2 ctl c focus auto 0 device dev video2 v4l2 ctl c focus absolute 0 device dev video2 v4l2 ctl c focus absolute 1023 device dev video2 ffplay f video4linux2 framerate 30 video size 3264x2448 pixel format mjpeg dev video2 ffplay f video4linux2 framerate 30 video size 1920x1080 pixel format mjpeg dev video2 ffmpeg f v4l2 framerate 30 video size 3264x2448 pixel format mjpeg i dev video2 output avi raspi cam v3 utils bash setup sudo apt get install libcamera0 libcamera apps lite sudo apt install y vlc grab frame https www raspberrypi com documentation computers camera software html libcamera and libcamera apps libcamera jpeg o out jpg t 1 width 4608 height 2592 rotation 180 autofocus mode manual lens position 2 libcamera jpeg o out jpg t 1 width 2304 height 1296 rotation 180 autofocus mode manual lens position 4 5 roi 0 25 0 5 0 5 0 5 record video date date f h m s libcamera vid o date h264 save pts date txt width 1080 height 720 rotation 180 autofocus mode manual lens position 0 t 0 stream through network libcamera vid t 0 inline nopreview width 4608 height 2592 rotation 180 codec mjpeg framerate 5 listen o tcp 0 0 0 0 8080 autofocus mode manual lens position 0 roi 0 25 0 5 0 5 0 5 on localhost ffplay http pi4 8080 video mjpeg manually record video for test cases libcamera vid verbose 1 timeout 0 inline nopreview width 240 height 280 roi 0 429688 0 185185 0 104167 0 216049 mode 2304 1296 12 p framerate 30 autofocus mode manual lens position 0 000000 rotation 0 o vid h264 save pts vid timestamps txt mkvmerge o test mkv timecodes 0 vid timestamps txt vid h264 deployment raspberry pi bash sudo usermod a g video pi confighelper confighelper arm64 log pretty input picam3 listen addr 0 0 0 0 8080 the current production deployment is in a tmux session bash source env while true do trainbot arm64 done download latest data from raspberry pi bash ssh trainbot deploy target ssh host sqlite3 data db sqlite3 backup data db sqlite3 bak ctrl d rsync verbose archive rsh ssh trainbot deploy target ssh host data data rm data db sqlite3 shm data db sqlite3 wal mv data db sqlite3 bak data db sqlite3 web frontend images and database are uploaded to a web server via ftp the frontend served as a static html js bundle from the same server all database access happens in the browser via sql js code notes zerolog is used as logging framework library code uses panic application code use log panic hardware setup my deployment is installed on my balcony in a waterproof case as seen in the magpi magazine https magpi raspberrypi com issues 131 the case is this one from aliexpress https www aliexpress com item 1005003010275396 html mounting plate for camera https www tinkercad com things 1fowvwonymj mounting plate for raspberry pi https www tinkercad com things djlef6oqsy1 the prints were ordered from jlcpcb note that the mounting plate for the raspberry pi is 1 2mm too wide because the 86mm stated in the picture on the aliexpress product page are in reality a bit less you can solve that by changing the 3d design or by cutting off a bit from the print it might however also depend on your specific case todos add machine learning to classify trains mobilenet efficientnet https mediapipe studio webapps google com demo image classifier better deployment setup at least a systemd unit add run deploy instructions to readme including confighelper maybe compress url params favorites list is getting longer and longer | bot computer-vision go golang stitching trains | ai |
Micropython-on-NodeMCU-ESP8266-IoT-Embedded-Systems | this repository contains micropython programs alongwith documentation for iot embedded systems design experiments on nodemcu 12 e 1 0 esp8266 amica this repository is still under development | micropython esp8266 nodemcu-esp8266 webrepl iot embedded-systems sensors actuators | os |
Web-Componentify | div align center h1 web componentify h1 h3 website a href https webcomponentify design click here a h3 br img src https skillicons dev icons i github git react tailwind html css js vscode vue div div align center github contributors https img shields io github contributors vaishnavi 3969 web componentify style for the badge color blue github closed issues https img shields io github issues closed raw vaishnavi 3969 web componentify style for the badge color brightgreen github pr open https img shields io github issues pr vaishnavi 3969 web componentify style for the badge color aqua github pr closed https img shields io github issues pr closed raw vaishnavi 3969 web componentify style for the badge color blue github language count https img shields io github languages count vaishnavi 3969 web componentify style for the badge color brightgreen github top language https img shields io github languages top vaishnavi 3969 web componentify style for the badge color aqua github last commit https img shields io github last commit vaishnavi 3969 web componentify style for the badge color blue github maintained https img shields io badge maintained 3f yes brightgreen svg style for the badge github repo size https img shields io github repo size vaishnavi 3969 web componentify style for the badge color aqua div welcome contributors welcome to web componentify web componentify is an open source repository dedicated to simplifying web development by providing a collection of reusable components whether you re building a website web application or mobile app you ll find a wide range of components from headers and footers to buttons and dropdowns available in various popular frameworks and languages how it works contribute anyone can contribute by creating and sharing their reusable components whether you re working with react react native html css javascript vue angular or any other web technology your contributions are welcome discover browse through a library of components created and shared by the community find the right component for your project no matter the framework or language you re using simplify development save time and effort by integrating these components into your projects no need to reinvent the wheel focus on what makes your project unique get started explore the components find what you need and start integrating them into your projects contribute your own components to help fellow developers simplify their work join our open and inclusive community of web enthusiasts let s make web development faster easier and more collaborative explore contribute and build amazing web experiences together contributing contributions are always welcome 1 fork the repository 2 clone the repository once you have forked the repository clone it to your local development environment using the following command sh https github com your github username web componentify git replace your username with your github username 3 create a branch move into the project s directory and create a new branch for your contributions sh cd web componentify git checkout b my feature branch replace my feature branch with a descriptive branch name related to your changes 4 make your changes now it s time to work on your contributions 5 check the changed files sh git status 6 commit your changes sh git add git commit m explain your changes 7 push to your forked repository sh git push origin my feature branch replace my feature branch with the name of your branch 8 create a pull request go to your forked repository on github and you should see a compare pull request button click on it to create a pull request pr from your branch to the main web componentify repository h2 project admin h2 table tr td align center a href https github com vaishnavi 3969 img src https notion avatar vercel app api img 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pet-shop | pet shop final project for my web development for mobile and desktop certificate this is a php based web application for a ficticious pet grooming shop the goal of the project is to hightlight my knowledge of php html javascript jquery and mysql in addition this project utilized xampp and git features 1 contact us form that sends an email 2 html form for site visitors to create a grooming appointment this data is pushed to a mysql database 3 a backend admin page to view edit delete and create new entries in the database | front_end |
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llm_sft | envs torch 2 0 0 cu117 transformers 4 28 1 tokenizers 0 13 1 deepspeed 0 9 2 peft 0 2 0 jsonl data train json dev json refer to data example json for more details model name bigscience bloom data dir data deepspeed master port 29500 main py model name or path model name data dir data dir output dir your output dir max length 2048 eval batch size 4 num train epochs 5 do train do eval supported models the following models are tested thudm chatglm 6b https huggingface co thudm chatglm 6b bigscience bloom 7b1 https huggingface co bigscience bloom 7b1 idea ccnl ziya llama 13b v1 https huggingface co idea ccnl ziya llama 13b v1 llama 7b hf https huggingface co decapoda research llama 7b hf use lora to enable lora training use gradient checkpointing to enable graident checkpointing do eval will generate a pred for eval json file in output dir with the following format input model input output model eval output llama config json pad token id 1 0 cuda side trigger error | ai |
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approachingalmost | if you like the book please consider writing a review on google amazon goodreads please note if you are buying the paperbook book in india from amazon india to show your support to the author you are most likely buying a counterfeit copy and supporting the sellers selling these illegal copies in india please buy from flipkart https www flipkart com approaching almost any machine learning problem p itm319d050de2fbb or from pothi official publisher https store pothi com book abhishek thakur approaching almost any machine learning problem all datasets have references in the book they are also uploaded here https www kaggle com abhishek aaamlp pneumothorax png https www kaggle com abhishek siim png images if you are missing something please feel free to open an issue environment file is shared the code from book is not shared as its more of a code along book sharing code means creating a copy of book if you have any questions please create an issue you can buy the book via these links india https bit ly aamlpothi usa https www amazon com dp 8269211508 uk https www amazon co uk dp 8269211508 germany https www amazon de dp 8269211508 france https www amazon fr dp 8269211508 spain https www amazon es dp 8269211508 italy https www amazon it dp 8269211508 japan https www amazon co jp dp 8269211508 canada https www amazon ca dp 8269211508 color version of the book can be bought here india https store pothi com book abhishek thakur approaching almost any machine learning problem colour version usa https www amazon com dp b08dc3zfzz uk https www amazon co uk dp b08dc3zfzz japan https www amazon co jp dp b08dc3zfzz germany https www amazon de dp b08dc3zfzz france https www amazon fr dp b08dc3zfzz italy https www amazon it dp b08dc3zfzz spain https www amazon es dp b08dc3zfzz canada https www amazon ca dp b08dc3zfzz if you face problems with the environment files please try the following instead delete the ml enviornment that was created earlier conda env remove name ml create a new environment conda create n ml python 3 7 6 activate the environment conda activate ml install python packages pip install r requirements txt | ai |
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hello-elastic-world | hello elastic world a boilerplate for a scalable web app including the whole infrastructure using docker compose for local developement and aws elastic beanstalk to deploy for production feature list react web app node backend hot reload in local dev environment use chrome inspect to debug in local dev environment postgres db dockerized for local dev environment works out of the box with a aws rds postgres data tier set up in aws elastic beanstalk instance nginx proxy pending features pubsub using postgres notification mechanism redis websockets some client side flux architecture using pubsub solid user management with single sign on run local dev environment bash docker compose up setup production create amazon aws account e g with free tier create amazon iam user install and configure the aws command line interface http docs aws amazon com cli latest userguide cli chap welcome html install the elastic beanstalk command line interface eb cli https docs aws amazon com elasticbeanstalk latest dg eb cli3 install html icmpid docs elasticbeanstalk console bash eb init eb create choose name setup postgres database deploy production bash git commit eb deploy | server |
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awesome-machine-learning | awesome machine learning awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome track awesome list https www trackawesomelist com badge svg https www trackawesomelist com josephmisiti awesome machine learning a curated list of awesome machine learning frameworks libraries and software by language inspired by awesome php if you want to contribute to this list please do send me a pull request or contact me josephmisiti https twitter com josephmisiti also a listed repository should be deprecated if repository s owner explicitly says that this library is not maintained not committed for a long time 2 3 years further resources for a list of free machine learning books available for download go here https github com josephmisiti awesome machine learning blob master books md for a list of professional machine learning events go here https github com josephmisiti awesome machine learning blob master events md for a list of mostly free machine learning courses available online go here https github com josephmisiti awesome machine learning blob master courses md for a list of blogs and newsletters on data science and machine learning go here https github com josephmisiti awesome machine learning blob master blogs md for a list of free to attend meetups and local events go here https github com josephmisiti awesome machine learning blob master meetups md table of contents frameworks and libraries markdowntoc depth 4 awesome machine learning awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg awesome machine learning table of contents table of contents frameworks and libraries frameworks and libraries tools tools apl apl general purpose machine learning apl general purpose machine learning c c general purpose machine learning c general purpose machine learning computer vision c computer vision c cpp computer vision cpp computer vision general purpose machine learning cpp general purpose machine learning natural language processing cpp natural language processing speech recognition cpp speech recognition sequence analysis cpp sequence analysis gesture detection cpp gesture detection common lisp common lisp general purpose machine learning common lisp general purpose machine learning clojure clojure natural language processing clojure natural language processing general purpose machine learning clojure general purpose machine learning deep learning clojure deep learning data analysis clojure data analysis data visualization data visualization clojure data visualization interop clojure interop misc clojure misc extra clojure extra crystal crystal general purpose machine learning crystal general purpose machine learning elixir elixir general purpose machine learning elixir general purpose machine learning natural language processing elixir natural language processing erlang erlang general purpose machine learning erlang general purpose machine learning fortran fortran general purpose machine learning fortran general purpose machine learning data analysis data visualization fortran data analysis data visualization go go natural language processing go natural language processing general purpose machine learning go general purpose machine learning spatial analysis and geometry go spatial analysis and geometry data analysis data visualization go data analysis data visualization computer vision go computer vision reinforcement learning go reinforcement learning haskell haskell general purpose machine learning haskell general purpose machine learning java java natural language processing java natural language processing general purpose machine learning java general purpose machine learning speech recognition java speech recognition data analysis data visualization java data analysis data visualization deep learning java deep learning javascript javascript natural language processing javascript natural language processing data analysis data visualization javascript data analysis data visualization general purpose machine learning javascript general purpose machine learning misc javascript misc demos and scripts javascript demos and scripts julia julia general purpose machine learning julia general purpose machine learning natural language processing julia natural language processing data analysis data visualization julia data analysis data visualization misc stuff presentations julia misc stuff presentations kotlin kotlin deep learning kotlin deep learning lua lua general purpose machine learning lua general purpose machine learning demos and scripts lua demos and scripts matlab matlab computer vision matlab computer vision natural language processing matlab natural language processing general purpose machine learning matlab general purpose machine learning data analysis data visualization matlab data analysis data visualization net net computer vision net computer vision natural language processing net natural language processing general purpose machine learning net general purpose machine learning data analysis data visualization net data analysis data visualization objective c objective c general purpose machine learning objective c general purpose machine learning ocaml ocaml general purpose machine learning ocaml general purpose machine learning opencv opencv computer vision opencv computer vision text detection text character number detection perl perl data analysis data visualization perl data analysis data visualization general purpose machine learning perl general purpose machine learning perl 6 perl 6 data analysis data visualization perl 6 data analysis data visualization general purpose machine learning perl 6 general purpose machine learning php php natural language processing php natural language processing general purpose machine learning php general purpose machine learning python python computer vision python computer vision natural language processing python natural language processing general purpose machine learning python general purpose machine learning data analysis data visualization python data analysis data visualization misc scripts ipython notebooks codebases python misc scripts ipython notebooks codebases neural networks python neural networks survival analysis python survival analysis federated learning python federated learning kaggle competition source code python kaggle competition source code reinforcement learning python reinforcement learning ruby ruby natural language processing ruby natural language processing general purpose machine learning ruby general purpose machine learning data analysis data visualization ruby data analysis data visualization misc ruby misc rust rust general purpose machine learning rust general purpose machine learning r r general purpose machine learning r general purpose machine learning data analysis data visualization r data analysis data visualization sas sas general purpose machine learning sas general purpose machine learning data analysis data visualization sas data analysis data visualization natural language processing sas natural language processing demos and scripts sas demos and scripts scala scala natural language processing scala natural language processing data analysis data visualization scala data analysis data visualization general purpose machine learning scala general purpose machine learning scheme scheme neural networks scheme neural networks swift swift general purpose machine learning swift general purpose machine learning tensorflow tensorflow general purpose machine learning tensorflow general purpose machine learning tools tools 1 neural networks tools neural networks misc tools misc credits credits markdowntoc a name apl a apl a name apl general purpose machine learning a general purpose machine learning naive apl https github com mattcunningham naive apl naive bayesian classifier implementation in apl deprecated a name c a c a name c general purpose machine learning a general purpose machine learning darknet https github com pjreddie darknet darknet is an open source neural network framework written in c and cuda it is fast easy to install and supports cpu and gpu computation recommender https github com ghamrouni recommender a c library for product recommendations suggestions using collaborative filtering cf hybrid recommender system https github com seniorsa hybrid rs trainner a hybrid recommender system based upon scikit learn algorithms deprecated neonrvm https github com siavashserver neonrvm neonrvm is an open source machine learning library based on rvm technique it s written in c programming language and comes with python programming language bindings connxr https github com alrevuelta connxr an onnx runtime written in pure c 99 with zero dependencies focused on small embedded devices run inference on your machine learning models no matter which framework you train it with easy to install and compiles everywhere even in very old devices libonnx https github com xboot libonnx a lightweight portable pure c99 onnx inference engine for embedded devices with hardware acceleration support a name c computer vision a computer vision ccv https github com liuliu ccv c based cached core computer vision library a modern computer vision library vlfeat http www vlfeat org vlfeat is an open and portable library of computer vision algorithms which has a matlab toolbox a name cpp a c a name cpp computer vision a computer vision dlib http dlib net imaging html dlib has c and python interfaces for face detection and training general object detectors eblearn http eblearn sourceforge net eblearn is an object oriented c library that implements various machine learning models deprecated opencv https opencv org opencv has c c python java and matlab interfaces and supports windows linux android and mac os vigra https github com ukoethe vigra vigra is a genertic cross platform c computer vision and machine learning library for volumes of arbitrary dimensionality with python bindings openpose https github com cmu perceptual computing lab openpose a real time multi person keypoint detection library for body face hands and foot estimation a name cpp general purpose machine learning a general purpose machine learning speedster https github com nebuly ai nebullvm tree main apps accelerate speedster automatically apply sota optimization techniques to achieve the maximum inference speed up on your hardware deep learning banditlib https github com jkomiyama banditlib a simple multi armed bandit library deprecated caffe https github com bvlc caffe a deep learning framework developed with cleanliness readability and speed in mind deep learning catboost https github com catboost catboost general purpose gradient boosting on decision trees library with categorical features support out of the box it is easy to install contains fast inference implementation and supports cpu and gpu even multi gpu computation cntk https github com microsoft cntk the computational network toolkit cntk by microsoft research is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph cuda https code google com p cuda convnet this is a fast c cuda implementation of convolutional deep learning deepdetect https github com jolibrain deepdetect a machine learning api and server written in c 11 it makes state of the art machine learning easy to work with and integrate into existing applications distributed machine learning tool kit dmtk http www dmtk io a distributed machine learning parameter server framework by microsoft enables training models on large data sets across multiple machines current tools bundled with it include lightlda and distributed multisense word embedding dlib http dlib net ml html a suite of ml tools designed to be easy to imbed in other applications dsstne https github com amznlabs amazon dsstne a software library created by amazon for training and deploying deep neural networks using gpus which emphasizes speed and scale over experimental flexibility dynet https github com clab dynet a dynamic neural network library working well with networks that have dynamic structures that change for every training instance written in c with bindings in python fido https github com fidoproject fido a highly modular c machine learning library for embedded electronics and robotics igraph http igraph org general purpose graph library intel oneapi data analytics library https github com oneapi src onedal a high performance software library developed by intel and optimized for intel s architectures library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch online and distributed modes lightgbm https github com microsoft lightgbm microsoft s fast distributed high performance gradient boosting gbdt gbrt gbm or mart framework based on decision tree algorithms used for ranking classification and many other machine learning tasks libfm https github com srendle libfm a generic approach that allows to mimic most factorization models by feature engineering mldb https mldb ai the machine learning database is a database designed for machine learning send it commands over a restful api to store data explore it using sql then train machine learning models and expose them as apis mlpack https www mlpack org a scalable c machine learning library mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more n2d2 https github com cea list n2d2 cea list s cad framework for designing and simulating deep neural network and building full dnn based applications on embedded platforms onednn https github com oneapi src onednn an open source cross platform performance library for deep learning applications paramonte https github com cdslaborg paramonte a general purpose library with c c interface for bayesian data analysis and visualization via serial parallel monte carlo and mcmc simulations documentation can be found here https www cdslab org paramonte pronet core https github com cnclabs pronet core a general purpose network embedding framework pair wise representations optimization network edit pycaret https github com pycaret pycaret an open source low code machine learning library in python that automates machine learning workflows pycuda https mathema tician de software pycuda python interface to cuda root https root cern ch a modular scientific software framework it provides all the functionalities needed to deal with big data processing statistical analysis visualization and storage shark http image diku dk shark sphinx pages build html index html a fast modular feature rich open source c machine learning library shogun https github com shogun toolbox shogun the shogun machine learning toolbox sofia ml https code google com archive p sofia ml suite of fast incremental algorithms stan http mc stan org a probabilistic programming language implementing full bayesian statistical inference with hamiltonian monte carlo sampling timbl https languagemachines github io timbl a software package c library implementing several memory based learning algorithms among which ib1 ig an implementation of k nearest neighbor classification and igtree a decision tree approximation of ib1 ig commonly used for nlp vowpal wabbit vw https github com vowpalwabbit vowpal wabbit a fast out of core learning system warp ctc https github com baidu research warp ctc a fast parallel implementation of connectionist temporal classification ctc on both cpu and gpu xgboost https github com dmlc xgboost a parallelized optimized general purpose gradient boosting library thundergbm https github com xtra computing thundergbm a fast library for gbdts and random forests on gpus thundersvm https github com xtra computing thundersvm a fast svm library on gpus and cpus lkydeepnn https github com mosdeo lkydeepnn a header only c 11 neural network library low dependency native traditional chinese document xlearn https github com aksnzhy xlearn a high performance easy to use and scalable machine learning package which can be used to solve large scale machine learning problems xlearn is especially useful for solving machine learning problems on large scale sparse data which is very common in internet services such as online advertising and recommender systems featuretools https github com featuretools featuretools a library for automated feature engineering it excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering primitives skynet https github com tyill skynet a library for learning neural networks has c interface net set in json written in c with bindings in python c and c feast https github com gojek feast a feature store for the management discovery and access of machine learning features feast provides a consistent view of feature data for both model training and model serving hopsworks https github com logicalclocks hopsworks a data intensive platform for ai with the industry s first open source feature store the hopsworks feature store provides both a feature warehouse for training and batch based on apache hive and a feature serving database based on mysql cluster for online applications polyaxon https github com polyaxon polyaxon a platform for reproducible and scalable machine learning and deep learning questdb https questdb io a relational column oriented database designed for real time analytics on time series and event data phoenix https phoenix arize com uncover insights surface problems monitor and fine tune your generative llm cv and tabular models a name cpp natural language processing a natural language processing bllip parser https github com bllip bllip parser bllip natural language parser also known as the charniak johnson parser colibri core https github com proycon colibri core c library command line tools and python binding for extracting and working with basic linguistic constructions such as n grams and skipgrams in a quick and memory efficient way crf https taku910 github io crfpp open source implementation of conditional random fields crfs for segmenting labeling sequential data other natural language processing tasks deprecated crfsuite http www chokkan org software crfsuite crfsuite is an implementation of conditional random fields crfs for labeling sequential data deprecated frog https github com languagemachines frog memory based nlp suite developed for dutch pos tagger lemmatiser dependency parser ner shallow parser morphological analyzer libfolia https github com languagemachines libfolia c library for the folia format https proycon github io folia meta https github com meta toolkit meta meta modern text analysis https meta toolkit org is a c data sciences toolkit that facilitates mining big text data mit information extraction toolkit https github com mit nlp mitie c c and python tools for named entity recognition and relation extraction ucto https github com languagemachines ucto unicode aware regular expression based tokenizer for various languages tool and c library supports folia format a name cpp speech recognition a speech recognition kaldi https github com kaldi asr kaldi kaldi is a toolkit for speech recognition written in c and licensed under the apache license v2 0 kaldi is intended for use by speech recognition researchers a name cpp sequence analysis a sequence analysis tops https github com ayoshiaki tops this is an object oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet deprecated a name cpp gesture detection a gesture detection grt https github com nickgillian grt the gesture recognition toolkit grt is a cross platform open source c machine learning library designed for real time gesture recognition a name common lisp a common lisp a name common lisp general purpose machine learning a general purpose machine learning mgl https github com melisgl mgl neural networks boltzmann machines feed forward and recurrent nets gaussian processes mgl gpr https github com melisgl mgl gpr evolutionary algorithms deprecated cl libsvm https github com melisgl cl libsvm wrapper for the libsvm support vector machine library deprecated cl online learning https github com masatoi cl online learning online learning algorithms perceptron arow scw logistic regression cl random forest https github com masatoi cl random forest implementation of random forest in common lisp a name clojure a clojure a name clojure natural language processing a natural language processing clojure opennlp https github com dakrone clojure opennlp natural language processing in clojure opennlp infections clj https github com r0man inflections clj rails like inflection library for clojure and clojurescript a name clojure general purpose machine learning a general purpose machine learning scicloj ml https github com scicloj scicloj ml a idiomatic clojure machine learning library based on tech ml dataset with a unique approach for immutable data processing pipelines clj ml https github com joshuaeckroth clj ml a machine learning library for clojure built on top of weka and friends clj boost https gitlab com alanmarazzi clj boost wrapper for xgboost touchstone https github com ptaoussanis touchstone clojure a b testing library clojush https github com lspector clojush the push programming language and the pushgp genetic programming system implemented in clojure lambda ml https github com cloudkj lambda ml simple concise implementations of machine learning techniques and utilities in clojure infer https github com aria42 infer inference and machine learning in clojure deprecated encog https github com jimpil enclog clojure wrapper for encog v3 machine learning framework that specializes in neural nets deprecated fungp https github com vollmerm fungp a genetic programming library for clojure deprecated statistiker https github com clojurewerkz statistiker basic machine learning algorithms in clojure deprecated clortex https github com htm community clortex general machine learning library using numenta s cortical learning algorithm deprecated comportex https github com htm community comportex functionally composable machine learning library using numenta s cortical learning algorithm deprecated a name clojure deep learning a deep learning mxnet https mxnet apache org versions 1 7 0 api clojure bindings to apache mxnet part of the mxnet project deep diamond https github com uncomplicate deep diamond a fast clojure tensor deep learning library jutsu ai https github com hswick jutsu ai clojure wrapper for deeplearning4j with some added syntactic sugar cortex https github com originrose cortex neural networks regression and feature learning in clojure flare https github com aria42 flare dynamic tensor graph library in clojure think pytorch dynnet etc dl4clj https github com yetanalytics dl4clj clojure wrapper for deeplearning4j a name clojure data analysis data visualization a data analysis tech ml dataset https github com techascent tech ml dataset clojure dataframe library and pipeline for data processing and machine learning tablecloth https github com scicloj tablecloth a dataframe grammar wrapping tech ml dataset inspired by several r libraries panthera https github com alanmarazzi panthera clojure api wrapping python s pandas library incanter http incanter org incanter is a clojure based r like platform for statistical computing and graphics pigpen https github com netflix pigpen map reduce for clojure geni https github com zero one group geni a clojure dataframe library that runs on apache spark a name clojure data visualization a data visualization hanami https github com jsa aerial hanami clojure script library and framework for creating interactive visualization applications based in vega lite vgl and or vega vg specifications automatic framing and layouts along with a powerful templating system for abstracting visualization specs saite https github com jsa aerial saite clojure script client server application for dynamic interactive explorations and the creation of live shareable documents capturing them using vega vega lite codemirror markdown and latex oz https github com metasoarous oz data visualisation using vega vega lite and hiccup and a live reload platform for literate programming envision https github com clojurewerkz envision clojure data visualisation library based on statistiker and d3 pink gorilla notebook https github com pink gorilla gorilla notebook a clojure clojurescript notebook application library based on gorilla repl clojupyter https github com clojupyter clojupyter a jupyter kernel for clojure run clojure code in jupyter lab notebook and console notespace https github com scicloj notespace notebook experience in your clojure namespace delight https github com datamechanics delight a listener that streams your spark events logs to delight a free and improved spark ui a name clojure interop a interop java interop https clojure org reference java interop clojure has native java interop from which java s ml ecosystem can be accessed javascript interop https clojurescript org reference javascript api clojurescript has native javascript interop from which javascript s ml ecosystem can be accessed libpython clj https github com clj python libpython clj interop with python clojisr https github com scicloj clojisr interop with r and renjin r on the jvm a name clojure misc a misc neanderthal https neanderthal uncomplicate org fast clojure matrix library native cpu gpu opencl cuda kixistats https github com mastodonc kixi stats a library of statistical distribution sampling and transducing functions fastmath https github com generateme fastmath a collection of functions for mathematical and statistical computing macine learning etc wrapping several jvm libraries matlib https github com atisharma matlib a clojure library of optimisation and control theory tools and convenience functions based on neanderthal a name clojure extra a extra scicloj https scicloj github io pages libraries curated list of ml related resources for clojure a name crystal a crystal a name crystal general purpose machine learning a general purpose machine learning machine https github com mathieulaporte machine simple machine learning algorithm crystal fann https github com neuralegion crystal fann fann fast artificial neural network binding a name elixir a elixir a name elixir general purpose machine learning a general purpose machine learning simple bayes https github com fredwu simple bayes a simple bayes naive bayes implementation in elixir emel https github com mrdimosthenis emel a simple and functional machine learning library written in elixir tensorflex https github com anshuman23 tensorflex tensorflow bindings for the elixir programming language a name elixir natural language processing a natural language processing stemmer https github com fredwu stemmer an english porter2 stemming implementation in elixir a name erlang a erlang a name erlang general purpose machine learning a general purpose machine learning disco https github com discoproject disco map reduce in erlang deprecated a name fortran a fortran a name fortran general purpose machine learning a general purpose machine learning neural fortran https github com modern fortran neural fortran a parallel neural net microframework read the paper here https arxiv org abs 1902 06714 a name fortran data analysis data visualization a data analysis data visualization paramonte https github com cdslaborg paramonte a general purpose fortran library for bayesian data analysis and visualization via serial parallel monte carlo and mcmc simulations documentation can be found here https www cdslab org paramonte a name go a go a name go natural language processing a natural language processing cybertron https github com nlpodyssey cybertron cybertron the home planet of the transformers in go snowball https github com tebeka snowball snowball stemmer for go word embedding https github com ynqa word embedding word embeddings the full implementation of word2vec glove in go sentences https github com neurosnap sentences golang implementation of punkt sentence tokenizer go ngram https github com lazin go ngram in memory n gram index with compression deprecated paicehusk https github com rookii paicehusk golang implementation of the paice husk stemming algorithm deprecated go porterstemmer https github com reiver go porterstemmer a native go clean room implementation of the porter stemming algorithm deprecated a name go general purpose machine learning a general purpose machine learning spago https github com nlpodyssey spago self contained machine learning and natural language processing library in go birdland https github com rlouf birdland a recommendation library in go eaopt https github com maxhalford eaopt an evolutionary optimization library leaves https github com dmitryikh leaves a pure go implementation of the prediction part of gbrts including xgboost and lightgbm gobrain https github com goml gobrain neural networks written in go go featureprocessing https github com nikolaydubina go featureprocessing fast and convenient feature processing for low latency machine learning in go go mxnet predictor https github com songtianyi go mxnet predictor go binding for mxnet c predict api to do inference with a pre trained model go ml benchmarks https github com nikolaydubina go ml benchmarks benchmarks of machine learning inference for go go ml transpiler https github com znly go ml transpiler an open source go transpiler for machine learning models golearn https github com sjwhitworth golearn machine learning for go goml https github com cdipaolo goml machine learning library written in pure go gorgonia https github com gorgonia gorgonia deep learning in go goro https github com aunum goro a high level machine learning library in the vein of keras gorse https github com zhenghaoz gorse an offline recommender system backend based on collaborative filtering written in go therfoo https github com therfoo therfoo an embedded deep learning library for go neat https github com jinyeom neat plug and play parallel go framework for neuroevolution of augmenting topologies neat deprecated go pr https github com daviddengcn go pr pattern recognition package in go lang deprecated go ml https github com alonsovidales go ml linear logistic regression neural networks collaborative filtering and gaussian multivariate distribution deprecated gonn https github com fxsjy gonn gonn is an implementation of neural network in go language which includes bpnn rbf pcn deprecated bayesian https github com jbrukh bayesian naive bayesian classification for golang deprecated go galib https github com thoj go galib genetic algorithms library written in go golang deprecated cloudforest https github com ryanbressler cloudforest ensembles of decision trees in go golang deprecated go dnn https github com sudachen go dnn deep neural networks for golang powered by mxnet a name go spatial analysis and geometry a spatial analysis and geometry go geom https github com twpayne go geom go library to handle geometries gogeo https github com golang geo spherical geometry in go a name go data analysis data visualization a data analysis data visualization dataframe go https github com rocketlaunchr dataframe go dataframes for machine learning and statistics similar to pandas gota https github com go gota gota dataframes gonum mat https godoc org gonum org v1 gonum mat a linear algebra package for go gonum optimize https godoc org gonum org v1 gonum optimize implementations of optimization algorithms gonum plot https godoc org gonum org v1 plot a plotting library gonum stat https godoc org gonum org v1 gonum stat a statistics library svgo https github com ajstarks svgo the go language library for svg generation glot https github com arafatk glot glot is a plotting library for golang built on top of gnuplot globe https github com mmcloughlin globe globe wireframe visualization gonum graph https godoc org gonum org v1 gonum graph general purpose graph library go graph https github com steplg go graph graph library for go golang language deprecated rf https github com fxsjy rf go random forests implementation in go deprecated a name go computer vision a computer vision gocv https github com hybridgroup gocv package for computer vision using opencv 4 and beyond a name go reinforcement learning a reinforcement learning gold https github com aunum gold a reinforcement learning library a name haskell a haskell a name haskell general purpose machine learning a general purpose machine learning haskell ml https github com ajtulloch haskell ml haskell implementations of various ml algorithms deprecated hlearn https github com mikeizbicki hlearn a suite of libraries for interpreting machine learning models according to their algebraic structure deprecated hnn https github com alpmestan hnn haskell neural network library hopfield networks https github com ajtulloch hopfield networks hopfield networks for unsupervised learning in haskell deprecated dnngraph https github com ajtulloch dnngraph a dsl for deep neural networks deprecated lambdanet https github com jbarrow lambdanet configurable neural networks in haskell deprecated a name java a java a name java natural language processing a natural language processing cortical io https www cortical io retina an api performing complex nlp operations disambiguation classification streaming text filtering etc as quickly and intuitively as the brain iris https github com cortical io iris cortical io s https cortical io free nlp retina api analysis tool written in javafx see the tutorial video https www youtube com watch v csf4pd7fgf0 corenlp https nlp stanford edu software corenlp shtml stanford corenlp provides a set of natural language analysis tools which can take raw english language text input and give the base forms of words stanford parser https nlp stanford edu software lex parser shtml a natural language parser is a program that works out the grammatical structure of sentences stanford pos tagger https nlp stanford edu software tagger shtml a part of speech tagger pos tagger stanford name entity recognizer https nlp stanford edu software crf ner shtml stanford ner is a java implementation of a named entity recognizer stanford word segmenter https nlp stanford edu software segmenter shtml tokenization of raw text is a standard pre processing step for many nlp tasks tregex tsurgeon and semgrex https nlp stanford edu software tregex shtml tregex is a utility for matching patterns in trees based on tree relationships and regular expression matches on nodes the name is short for tree regular expressions stanford phrasal a phrase based translation system https nlp stanford edu phrasal stanford english tokenizer https nlp stanford edu software tokenizer shtml stanford phrasal is a state of the art statistical phrase based machine translation system written in java stanford tokens regex https nlp stanford edu software tokensregex shtml a tokenizer divides text into a sequence of tokens which roughly correspond to words stanford temporal tagger https nlp stanford edu software sutime shtml sutime is a library for recognizing and normalizing time expressions stanford spied https nlp stanford edu software patternslearning shtml learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion twitter text java https github com twitter twitter text tree master java a java implementation of twitter s text processing library mallet http mallet cs umass edu a java based package for statistical natural language processing document classification clustering topic modelling information extraction and other machine learning applications to text opennlp https opennlp apache org a machine learning based toolkit for the processing of natural language text lingpipe http alias i com lingpipe index html a tool kit for processing text using computational linguistics cleartk https github com cleartk cleartk cleartk provides a framework for developing statistical natural language processing nlp components in java and is built on top of apache uima deprecated apache ctakes https ctakes apache org apache clinical text analysis and knowledge extraction system ctakes is an open source natural language processing system for information extraction from electronic medical record clinical free text nlp4j https github com emorynlp nlp4j the nlp4j project provides software and resources for natural language processing the project started at the center for computational language and education research and is currently developed by the center for language and information research at emory university deprecated cogcompnlp https github com cogcomp cogcomp nlp this project collects a number of core libraries for natural language processing nlp developed in the university of illinois cognitive computation group for example illinois core utilities which provides a set of nlp friendly data structures and a number of nlp related utilities that support writing nlp applications running experiments etc illinois edison a library for feature extraction from illinois core utilities data structures and many other packages a name java general purpose machine learning a general purpose machine learning aerosolve https github com airbnb aerosolve a machine learning library by airbnb designed from the ground up to be human friendly amidst toolbox http www amidsttoolbox com a java toolbox for scalable probabilistic machine learning chips n salsa https github com cicirello chips n salsa a java library for genetic algorithms evolutionary computation and stochastic local search with a focus on self adaptation self tuning as well as parallel execution datumbox https github com datumbox datumbox framework machine learning framework for rapid development of machine learning and statistical applications elki https elki project github io java toolkit for data mining unsupervised clustering outlier detection etc encog https github com encog encog java core an advanced neural network and machine learning framework encog contains classes to create a wide variety of networks as well as support classes to normalize and process data for these neural networks encog trainings using multithreaded resilient propagation encog can also make use of a gpu to further speed processing time a gui based workbench is also provided to help model and train neural networks flinkml in apache flink https ci apache org projects flink flink docs master dev libs ml index html distributed machine learning library in flink h2o https github com h2oai h2o 3 ml engine that supports distributed learning on hadoop spark or your laptop via apis in r python scala rest json htm java https github com numenta htm java general machine learning library using numenta s cortical learning algorithm liblinear java https github com bwaldvogel liblinear java java version of liblinear mahout https github com apache mahout distributed machine learning meka http meka sourceforge net an open source implementation of methods for multi label classification and evaluation extension to weka mllib in apache spark https spark apache org docs latest mllib guide html distributed machine learning library in spark hydrosphere mist https github com hydrospheredata mist a service for deployment apache spark mllib machine learning models as realtime batch or reactive web services neuroph http neuroph sourceforge net neuroph is lightweight java neural network framework oryx https github com oryxproject oryx lambda architecture framework using apache spark and apache kafka with a specialization for real time large scale machine learning samoa https samoa incubator apache org samoa is a framework that includes distributed machine learning for data streams with an interface to plug in different stream processing platforms ranklib https sourceforge net p lemur wiki ranklib ranklib is a library of learning to rank algorithms deprecated rapaio https github com padreati rapaio statistics data mining and machine learning toolbox in java rapidminer https rapidminer com rapidminer integration into java code stanford classifier https nlp stanford edu software classifier shtml a classifier is a machine learning tool that will take data items and place them into one of k classes smile https haifengl github io statistical machine intelligence learning engine systemml https github com apache systemml flexible scalable machine learning ml language tribou https tribuo org a machine learning library written in java by oracle weka https www cs waikato ac nz ml weka weka is a collection of machine learning algorithms for data mining tasks lbjava https github com cogcomp lbjava learning based java is a modelling language for the rapid development of software systems offers a convenient declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer s application knn java library https github com felipexw knn java library just a simple implementation of k nearest neighbors algorithm using with a bunch of similarity measures a name java speech recognition a speech recognition cmu sphinx https cmusphinx github io open source toolkit for speech recognition purely based on java speech recognition library a name java data analysis data visualization a data analysis data visualization flink https flink apache org open source platform for distributed stream and batch data processing hadoop https github com apache hadoop hadoop hdfs onyx https github com onyx platform onyx distributed masterless high performance fault tolerant data processing written entirely in clojure spark https github com apache spark spark is a fast and general engine for large scale data processing storm https storm apache org storm is a distributed realtime computation system impala https github com cloudera impala real time query for hadoop datamelt https jwork org dmelt mathematics software for numeric computation statistics symbolic calculations data analysis and data visualization dr michael thomas flanagan s java scientific library https www ee ucl ac uk mflanaga java deprecated a name java deep learning a deep learning deeplearning4j https github com deeplearning4j deeplearning4j scalable deep learning for industry with parallel gpus keras beginner tutorial https victorzhou com blog keras neural network tutorial friendly guide on using keras to implement a simple neural network in python deepjavalibrary djl https github com deepjavalibrary djl deep java library djl is an open source high level engine agnostic java framework for deep learning designed to be easy to get started with and simple to use for java developers a name javascript a javascript a name javascript natural language processing a natural language processing twitter text https github com twitter twitter text a javascript implementation of twitter s text processing library natural https github com naturalnode natural general natural language facilities for node knwl js https github com loadfive knwl js a natural language processor in js retext https github com retextjs retext extensible system for analyzing and manipulating natural language nlp compromise https github com spencermountain compromise natural language processing in the browser nlp js https github com axa group nlp js an nlp library built in node over natural with entity extraction sentiment analysis automatic language identify and so more a name javascript data analysis data visualization a data analysis data visualization d3 js https d3js org high charts https www highcharts com nvd3 js http nvd3 org dc js https dc js github io dc js chartjs https www chartjs org dimple http dimplejs org amcharts https www amcharts com d3xter https github com nathanepstein d3xter straight forward plotting built on d3 deprecated statkit https github com rigtorp statkit statistics kit for javascript deprecated datakit https github com nathanepstein datakit a lightweight framework for data analysis in javascript science js https github com jasondavies science js scientific and statistical computing in javascript deprecated z3d https github com nathanepstein z3d easily make interactive 3d plots built on three js deprecated sigma js http sigmajs org javascript library dedicated to graph drawing c3 js https c3js org customizable library based on d3 js for easy chart drawing datamaps https datamaps github io customizable svg map geo visualizations using d3 js deprecated zingchart https www zingchart com library written on vanilla js for big data visualization cheminfo https www cheminfo org platform for data visualization and analysis using the visualizer https github com npellet visualizer project learn js data http learnjsdata com anychart https www anychart com fusioncharts https www fusioncharts com nivo https nivo rocks built on top of the awesome d3 and reactjs libraries a name javascript general purpose machine learning a general purpose machine learning auto ml https github com climbsrocks auto ml automated machine learning data formatting ensembling and hyperparameter optimization for competitions and exploration just give it a csv file deprecated convnet js https cs stanford edu people karpathy convnetjs convnetjs is a javascript library for training deep learning models deep learning deprecated clusterfck https harthur github io clusterfck agglomerative hierarchical clustering implemented in javascript for node js and the browser deprecated clustering js https github com emilbayes clustering js clustering algorithms implemented in javascript for node js and the browser deprecated decision trees https github com serendipious nodejs decision tree id3 nodejs implementation of decision tree using id3 algorithm deprecated dn2a https github com antoniodeluca dn2a js digital neural networks architecture deprecated figue https code google com archive p figue k means fuzzy c means and agglomerative clustering gaussian mixture model https github com lukapopijac gaussian mixture model unsupervised machine learning with multivariate gaussian mixture model node fann https github com rlidwka node fann fann fast artificial neural network library bindings for node js deprecated keras js https github com transcranial keras js run keras models in the browser with gpu support provided by webgl 2 kmeans js https github com emilbayes kmeans js simple javascript implementation of the k means algorithm for node js and the browser deprecated lda js https github com primaryobjects lda lda topic modelling for node js learning js https github com yandongliu learningjs javascript implementation of logistic regression c4 5 decision tree deprecated machinelearn js https github com machinelearnjs machinelearnjs machine learning library for the web node js and developers mil tokyo https github com mil tokyo list of several machine learning libraries node svm https github com nicolaspanel node svm support vector machine for node js brain https github com harthur brain neural networks in javascript deprecated brain js https github com brainjs brain js neural networks in javascript continued community fork of brain https github com harthur brain bayesian bandit https github com omphalos bayesian bandit js bayesian bandit implementation for node and the browser deprecated synaptic https github com cazala synaptic architecture free neural network library for node js and the browser knear https github com nathanepstein knear javascript implementation of the k nearest neighbors algorithm for supervised learning neuraln https github com totemstech neuraln c neural network library for node js it has advantage on large dataset and multi threaded training deprecated kalman https github com itamarwe kalman kalman filter for javascript deprecated shaman https github com luccastera shaman node js library with support for both simple and multiple linear regression deprecated ml js https github com mljs ml machine learning and numerical analysis tools for node js and the browser ml5 https github com ml5js ml5 library friendly machine learning for the web pavlov js https github com nathanepstein pavlov js reinforcement learning using markov decision processes mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more tensorflow js https js tensorflow org a webgl accelerated browser based javascript library for training and deploying ml models jsmlt https github com jsmlt jsmlt machine learning toolkit with classification and clustering for node js supports visualization see visualml io https visualml io xgboost node https github com nuanio xgboost node run xgboost model and make predictions in node js netron https github com lutzroeder netron visualizer for machine learning models tensor js https github com hoff97 tensorjs a deep learning library for the browser accelerated by webgl and webassembly webdnn https github com mil tokyo webdnn fast deep neural network javascript framework webdnn uses next generation javascript api webgpu for gpu execution and webassembly for cpu execution a name javascript misc a misc stdlib https github com stdlib js stdlib a standard library for javascript and node js with an emphasis on numeric computing the library provides a collection of robust high performance libraries for mathematics statistics streams utilities and more sylvester https github com jcoglan sylvester vector and matrix math for javascript deprecated simple statistics https github com simple statistics simple statistics a javascript implementation of descriptive regression and inference statistics implemented in literate javascript with no dependencies designed to work in all modern browsers including ie as well as in node js regression js https github com tom alexander regression js a javascript library containing a collection of least squares fitting methods for finding a trend in a set of data lyric https github com flurry lyric linear regression library deprecated greatcircle https github com mwgg greatcircle library for calculating great circle distance mlpleasehelp https github com jgreenemi mlpleasehelp mlpleasehelp is a simple ml resource search engine you can use this search engine right now at https jgreenemi github io mlpleasehelp https jgreenemi github io mlpleasehelp provided via github pages pipcook https github com alibaba pipcook a javascript application framework for machine learning and its engineering a name javascript demos and scripts a demos and scripts the bot https github com sta ger thebot example of how the neural network learns to predict the angle between two points created with synaptic https github com cazala synaptic half beer https github com sta ger halfbeer beer glass classifier created with synaptic https github com cazala synaptic nsfwjs http nsfwjs com indecent content checker with tensorflow js rock paper scissors https rps tfjs netlify com rock paper scissors trained in the browser with tensorflow js heroes wear masks https heroeswearmasks fun a fun tensorflow js based oracle that tells whether one wears a face mask or not it can even tell when one wears the mask incorrectly a name julia a julia a name julia general purpose machine learning a general purpose machine learning machinelearning https github com benhamner machinelearning jl julia machine learning library deprecated mlbase https github com juliastats mlbase jl a set of functions to support the development of machine learning algorithms pgm https github com juliastats pgm jl a julia framework for probabilistic graphical models da https github com trthatcher discriminantanalysis jl julia package for regularized discriminant analysis regression https github com lindahua regression jl algorithms for regression analysis e g linear regression and logistic regression deprecated local regression https github com juliastats loess jl local regression so smooooth naive bayes https github com nutsiepully naivebayes jl simple naive bayes implementation in julia deprecated mixed models https github com dmbates mixedmodels jl a julia package for fitting statistical mixed effects models simple mcmc https github com fredo dedup simplemcmc jl basic mcmc sampler implemented in julia deprecated distances https github com juliastats distances jl julia module for distance evaluation decision tree https github com bensadeghi decisiontree jl decision tree classifier and regressor neural https github com compressed backpropneuralnet jl a neural network in julia mcmc https github com doobwa mcmc jl mcmc tools for julia deprecated mamba https github com brian j smith mamba jl markov chain monte carlo mcmc for bayesian analysis in julia glm https github com juliastats glm jl generalized linear models in julia gaussian processes https github com stor i gaussianprocesses jl julia package for gaussian processes online learning https github com lendle onlinelearning jl deprecated glmnet https github com simonster glmnet jl julia wrapper for fitting lasso elasticnet glm models using glmnet clustering https github com juliastats clustering jl basic functions for clustering data k means dp means etc svm https github com juliastats svm jl svm for julia deprecated kernel density https github com juliastats kerneldensity jl kernel density estimators for julia multivariatestats https github com juliastats multivariatestats jl methods for dimensionality reduction nmf https github com juliastats nmf jl a julia package for non negative matrix factorization ann https github com ericchiang ann jl julia artificial neural networks deprecated mocha https github com pluskid mocha jl deep learning framework for julia inspired by caffe deprecated xgboost https github com dmlc xgboost jl extreme gradient boosting package in julia manifoldlearning https github com wildart manifoldlearning jl a julia package for manifold learning and nonlinear dimensionality reduction mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more merlin https github com hshindo merlin jl flexible deep learning framework in julia rocanalysis https github com davidavdav rocanalysis jl receiver operating characteristics and functions for evaluation probabilistic binary classifiers gaussianmixtures https github com davidavdav gaussianmixtures jl large scale gaussian mixture models scikitlearn https github com cstjean scikitlearn jl julia implementation of the scikit learn api knet https github com denizyuret knet jl ko university deep learning framework flux https fluxml ai relax flux is the ml library that doesn t make you tensor mlj https github com alan turing institute mlj jl a julia machine learning framework a name julia natural language processing a natural language processing topic models https github com slycoder topicmodels jl topicmodels for julia deprecated text analysis https github com juliatext textanalysis jl julia package for text analysis word tokenizers https github com juliatext wordtokenizers jl tokenizers for natural language processing in julia corpus loaders https github com juliatext corpusloaders jl a julia package providing a variety of loaders for various nlp corpora embeddings https github com juliatext embeddings jl functions and data dependencies for loading various word embeddings languages https github com juliatext languages jl julia package for working with various human languages wordnet https github com juliatext wordnet jl a julia package for princeton s wordnet a name julia data analysis data visualization a data analysis data visualization graph layout https github com iainnz graphlayout jl graph layout algorithms in pure julia lightgraphs https github com juliagraphs lightgraphs jl graph modelling and analysis data frames meta https github com juliadata dataframesmeta jl metaprogramming tools for dataframes julia data https github com nfoti juliadata library for working with tabular data in julia deprecated data read https github com queryverse readstat jl read files from stata sas and spss hypothesis tests https github com juliastats hypothesistests jl hypothesis tests for julia gadfly https github com giovineitalia gadfly jl crafty statistical graphics for julia stats https github com juliastats statskit jl statistical tests for julia rdatasets https github com johnmyleswhite rdatasets jl julia package for loading many of the data sets available in r dataframes https github com juliadata dataframes jl library for working with tabular data in julia distributions https github com juliastats distributions jl a julia package for probability distributions and associated functions data arrays https github com juliastats dataarrays jl data structures that allow missing values deprecated time series https github com juliastats timeseries jl time series toolkit for julia sampling https github com lindahua sampling jl basic sampling algorithms for julia a name julia misc stuff presentations a misc stuff presentations dsp https github com juliadsp dsp jl digital signal processing filtering periodograms spectrograms window functions juliacon presentations https github com juliacon presentations presentations for juliacon signalprocessing https github com juliadsp dsp jl signal processing tools for julia images https github com juliaimages images jl an image library for julia datadeps https github com oxinabox datadeps jl reproducible data setup for reproducible science a name kotlin a kotlin a name kotlin deep learning a deep learning kotlindl https github com jetbrains kotlindl deep learning framework written in kotlin a name lua a lua a name lua general purpose machine learning a general purpose machine learning torch7 http torch ch cephes https github com deepmind torch cephes cephes mathematical functions library wrapped for torch provides and wraps the 180 special mathematical functions from the cephes mathematical library developed by stephen l moshier it is used among many other places at the heart of scipy deprecated autograd https github com twitter torch autograd autograd automatically differentiates native torch code inspired by the original python version graph https github com torch graph graph package for torch deprecated randomkit https github com deepmind torch randomkit numpy s randomkit wrapped for torch deprecated signal https github com soumith torch signal a signal processing toolbox for torch 7 fft dct hilbert cepstrums stft nn https github com torch nn neural network package for torch torchnet https github com torchnet torchnet framework for torch which provides a set of abstractions aiming at encouraging code re use as well as encouraging modular programming nngraph https github com torch nngraph this package provides graphical computation for nn library in torch7 nnx https github com clementfarabet lua nnx a completely unstable and experimental package that extends torch s builtin nn library rnn https github com element research rnn a recurrent neural network library that extends torch s nn rnns lstms grus brnns blstms etc dpnn https github com element research dpnn many useful features that aren t part of the main nn package dp https github com nicholas leonard dp a deep learning library designed for streamlining research and development using the torch7 distribution it emphasizes flexibility through the elegant use of object oriented design patterns deprecated optim https github com torch optim an optimization library for torch sgd adagrad conjugate gradient lbfgs rprop and more unsup https github com koraykv unsup a package for unsupervised learning in torch provides modules that are compatible with nn linearpsd convpsd autoencoder and self contained algorithms k means pca deprecated manifold https github com clementfarabet manifold a package to manipulate manifolds svm https github com koraykv torch svm torch svm library deprecated lbfgs https github com clementfarabet lbfgs ffi wrapper for liblbfgs deprecated vowpalwabbit https github com clementfarabet vowpal wabbit an old vowpalwabbit interface to torch deprecated opengm https github com clementfarabet lua opengm opengm is a c library for graphical modelling and inference the lua bindings provide a simple way of describing graphs from lua and then optimizing them with opengm deprecated spaghetti https github com michaelmathieu lua spaghetti spaghetti sparse linear module for torch7 by michaelmathieu deprecated luashkit https github com ocallaco luashkit a lua wrapper around the locality sensitive hashing library shkit deprecated kernel smoothing https github com rlowrance kernel smoothers knn kernel weighted average local linear regression smoothers deprecated cutorch https github com torch cutorch torch cuda implementation cunn https github com torch cunn torch cuda neural network implementation imgraph https github com clementfarabet lua imgraph an image graph library for torch this package provides routines to construct graphs on images segment them build trees out of them and convert them back to images deprecated videograph https github com clementfarabet videograph a video graph library for torch this package provides routines to construct graphs on videos segment them build trees out of them and convert them back to videos deprecated saliency https github com marcoscoffier torch saliency code and tools around integral images a library for finding interest points based on fast integral histograms deprecated stitch https github com marcoscoffier lua stitch allows us to use hugin to stitch images and apply same stitching to a video sequence deprecated sfm https github com marcoscoffier lua sfm a bundle adjustment structure from motion package deprecated fex https github com koraykv fex a package for feature extraction in torch provides sift and dsift modules deprecated overfeat https github com sermanet overfeat a state of the art generic dense feature extractor deprecated wav2letter https github com facebookresearch wav2letter a simple and efficient end to end automatic speech recognition asr system from facebook ai research numeric lua http numlua luaforge net lunatic python https labix org lunatic python scilua http scilua org lua numerical algorithms https bitbucket org lucashnegri lna deprecated lunum https github com jzrake lunum deprecated keras gpt copilot https github com fabprezja keras gpt copilot a python package that integrates an llm copilot inside the keras model development workflow a name lua demos and scripts a demos and scripts core torch7 demos repository https github com e lab torch7 demos linear regression logistic regression face detector training and detection as separate demos mst based segmenter train a digit classifier train autoencoder optical flow demo train on housenumbers train on cifar tracking with deep nets kinect demo filter bank visualization saliency networks training a convnet for the galaxy zoo kaggle challenge cuda demo https github com soumith galaxyzoo torch datasets https github com rosejn torch datasets scripts to load several popular datasets including bsr 500 cifar 10 coil street view house numbers mnist norb atari2600 https github com fidlej aledataset scripts to generate a dataset with static frames from the arcade learning environment a name matlab a matlab a name matlab computer vision a computer vision contourlets http www ifp illinois edu minhdo software contourlet toolbox tar matlab source code that implements the contourlet transform and its utility functions shearlets https www3 math tu berlin de numerik www shearlab org software matlab code for shearlet transform curvelets http www curvelet org software html the curvelet transform is a higher dimensional generalization of the wavelet transform designed to represent images at different scales and different angles bandlets http www cmap polytechnique fr peyre download matlab code for bandlet transform mexopencv https kyamagu github io mexopencv collection and a development kit of matlab mex functions for opencv library a name matlab natural language processing a natural language processing nlp https amplab cs berkeley edu an nlp library for matlab a nlp library for matlab a name matlab general purpose machine learning a general purpose machine learning training a deep autoencoder or a classifier on mnist digits https www cs toronto edu hinton matlabforsciencepaper html training a deep autoencoder or a classifier on mnist digits deep learning convolutional recursive deep learning for 3d object classification https www socher org index php main convolutional recursivedeeplearningfor3dobjectclassification convolutional recursive deep learning for 3d object classification deep learning spider https people kyb tuebingen mpg de spider the spider is intended to be a complete object orientated environment for machine learning in matlab libsvm https www csie ntu edu tw cjlin libsvm matlab a library for support vector machines thundersvm https github com xtra computing thundersvm an open source svm library on gpus and cpus liblinear https www csie ntu edu tw cjlin liblinear download a library for large linear classification machine learning module https github com josephmisiti machine learning module class on machine w pdf lectures code caffe https github com bvlc caffe a deep learning framework developed with cleanliness readability and speed in mind pattern recognition toolbox https github com covartech prt a complete object oriented environment for machine learning in matlab pattern recognition and machine learning https github com prml prmlt this package contains the matlab implementation of the algorithms described in the book pattern recognition and machine learning by c bishop optunity https optunity readthedocs io en latest a library dedicated to automated hyperparameter optimization with a simple lightweight api to facilitate drop in replacement of grid search optunity is written in python but interfaces seamlessly with matlab mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more machine learning in matlab octave https github com trekhleb machine learning octave examples of popular machine learning algorithms neural networks linear logistic regressions k means etc with code examples and mathematics behind them being explained a name matlab data analysis data visualization a data analysis data visualization paramonte https github com cdslaborg paramonte a general purpose matlab library for bayesian data analysis and visualization via serial parallel monte carlo and mcmc simulations documentation can be found here https www cdslab org paramonte matlab bgl https www cs purdue edu homes dgleich packages matlab bgl matlabbgl is a matlab package for working with graphs gaimc https www mathworks com matlabcentral fileexchange 24134 gaimc graph algorithms in matlab code efficient pure matlab implementations of graph algorithms to complement matlabbgl s mex functions a name net a net a name net computer vision a computer vision opencvdotnet https code google com archive p opencvdotnet a wrapper for the opencv project to be used with net applications emgu cv http www emgu com wiki index php main page cross platform wrapper of opencv which can be compiled in mono to be run on windows linus mac os x ios and android aforge net http www aforgenet com framework open source c framework for developers and researchers in the fields of computer vision and artificial intelligence development has now shifted to github accord net http accord framework net together with aforge net this library can provide image processing and computer vision algorithms to windows windows rt and windows phone some components are also available for java and android a name net natural language processing a natural language processing stanford nlp for net https github com sergey tihon stanford nlp net a full port of stanford nlp packages to net and also available precompiled as a nuget package a name net general purpose machine learning a general purpose machine learning accord framework http accord framework net the accord net framework is a complete framework for building machine learning computer vision computer audition signal processing and statistical applications accord machinelearning https www nuget org packages accord machinelearning support vector machines decision trees naive bayesian models k means gaussian mixture models and general algorithms such as ransac cross validation and grid search for machine learning applications this package is part of the accord net framework diffsharp https diffsharp github io diffsharp an automatic differentiation ad library providing exact and efficient derivatives gradients hessians jacobians directional derivatives and matrix free hessian and jacobian vector products for machine learning and optimization applications operations can be nested to any level meaning that you can compute exact higher order derivatives and differentiate functions that are internally making use of differentiation for applications such as hyperparameter optimization encog https www nuget org packages encog dotnet core an advanced neural network and machine learning framework encog contains classes to create a wide variety of networks as well as support classes to normalize and process data for these neural networks encog trains using multithreaded resilient propagation encog can also make use of a gpu to further speed processing time a gui based workbench is also provided to help model and train neural networks geneticsharp https github com giacomelli geneticsharp multi platform genetic algorithm library for net core and net framework the library has several implementations of ga operators like selection crossover mutation reinsertion and termination infer net https dotnet github io infer infer net is a framework for running bayesian inference in graphical models one can use infer net to solve many different kinds of machine learning problems from standard problems like classification recommendation or clustering through to customized solutions to domain specific problems infer net has been used in a wide variety of domains including information retrieval bioinformatics epidemiology vision and many others ml net https github com dotnet machinelearning ml net is a cross platform open source machine learning framework which makes machine learning accessible to net developers ml net was originally developed in microsoft research and evolved into a significant framework over the last decade and is used across many product groups in microsoft like windows bing powerpoint excel and more neural network designer https sourceforge net projects nnd dbms management system and designer for neural networks the designer application is developed using wpf and is a user interface which allows you to design your neural network query the network create and configure chat bots that are capable of asking questions and learning from your feedback the chat bots can even scrape the internet for information to return in their output as well as to use for learning synapses https github com mrdimosthenis synapses neural network library in f vulpes https github com fsprojects vulpes deep belief and deep learning implementation written in f and leverages cuda gpu execution with alea cubase mxnet sharp https github com tech quantum mxnet sharp net standard bindings for apache mxnet with imperative symbolic and gluon interface for developing training and deploying machine learning models in c https mxnet tech quantum com a name net data analysis data visualization a data analysis data visualization numl https www nuget org packages numl numl is a machine learning library intended to ease the use of using standard modelling techniques for both prediction and clustering math net numerics https www nuget org packages mathnet numerics numerical foundation of the math net project aiming to provide methods and algorithms for numerical computations in science engineering and everyday use supports net 4 0 net 3 5 and mono on windows linux and mac silverlight 5 windowsphone sl 8 windowsphone 8 1 and windows 8 with pcl portable profiles 47 and 344 android ios with xamarin sho https www microsoft com en us research project sho the net playground for data sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts in ironpython with compiled code in net to enable fast and flexible prototyping the environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any net language as well as a feature rich interactive shell for rapid development a name objective c a objective c a name objective c general purpose machine learning a general purpose machine learning ycml https github com yconst ycml a machine learning framework for objective c and swift os x ios mlpneuralnet https github com nikolaypavlov mlpneuralnet fast multilayer perceptron neural network library for ios and mac os x mlpneuralnet predicts new examples by trained neural networks it is built on top of the apple s accelerate framework using vectorized operations and hardware acceleration if available deprecated machinelearning https github com gianlucabertani machinelearning an objective c multilayer perceptron library with full support for training through backpropagation implemented using vdsp and veclib it s 20 times faster than its java equivalent includes sample code for use from swift bpn neuralnetwork https github com kalvar ios bpn neuralnetwork it implemented 3 layers of neural networks input layer hidden layer and output layer and it was named back propagation neural networks bpn this network can be used in products recommendation user behavior analysis data mining and data analysis deprecated multi perceptron neuralnetwork https github com kalvar ios multi perceptron neuralnetwork it implemented multi perceptrons neural network based on back propagation neural networks bpn and designed unlimited hidden layers krhebbian algorithm https github com kalvar ios krhebbian algorithm it is a non supervisor and self learning algorithm adjust the weights in the neural network of machine learning deprecated krkmeans algorithm https github com kalvar ios krkmeans algorithm it implemented k means clustering and classification algorithm it could be used in data mining and image compression deprecated krfuzzycmeans algorithm https github com kalvar ios krfuzzycmeans algorithm it implemented fuzzy c means fcm the fuzzy clustering classification algorithm on machine learning it could be used in data mining and image compression deprecated a name ocaml a ocaml a name ocaml general purpose machine learning a general purpose machine learning oml https github com rleonid oml a general statistics and machine learning library gpr https mmottl github io gpr efficient gaussian process regression in ocaml libra tk https libra cs uoregon edu algorithms for learning and inference with discrete probabilistic models tensorflow https github com laurentmazare tensorflow ocaml ocaml bindings for tensorflow a name opencv a opencv a name opencv computervision and text detection a opensource computer vision opencv https github com opencv opencv a opensource computer vision library a name perl a perl a name perl data analysis data visualization a data analysis data visualization perl data language https metacpan org pod paws machinelearning a pluggable architecture for data and image processing which can be used for machine learning https github com zenogantner pdl ml a name perl general purpose machine learning a general purpose machine learning mxnet for deep learning in perl https github com apache incubator mxnet tree master perl package also released in cpan https metacpan org pod ai mxnet perl data language https metacpan org pod paws machinelearning using aws machine learning platform from perl algorithm svmlight https metacpan org pod algorithm svmlight implementation of support vector machines with svmlight under it deprecated several machine learning and artificial intelligence models are included in the ai https metacpan org search size 20 q ai namespace for instance you can find na ve bayes https metacpan org pod ai naivebayes a name perl6 a perl 6 support vector machines https github com titsuki p6 algorithm libsvm na ve bayes https github com titsuki p6 algorithm naivebayes a name perl 6 data analysis data visualization a data analysis data visualization perl data language https metacpan org pod paws machinelearning a pluggable architecture for data and image processing which can be used for machine learning https github com zenogantner pdl ml a name perl 6 general purpose machine learning a general purpose machine learning a name php a php a name php natural language processing a natural language processing jieba php https github com fukuball jieba php chinese words segmentation utilities a name php general purpose machine learning a general purpose machine learning php ml https gitlab com php ai php ml machine learning library for php algorithms cross validation neural network preprocessing feature extraction and much more in one library predictionbuilder https github com denissimon prediction builder a library for machine learning that builds predictions using a linear regression rubix ml https github com rubixml a high level machine learning ml library that lets you build programs that learn from data using the php language 19 questions https github com fulldecent 19 questions a machine learning bayesian inference assigning attributes to objects a name python a python a name python computer vision a computer vision scikit image https github com scikit image scikit image a collection of algorithms for image processing in python scikit opt https github com guofei9987 scikit opt swarm intelligence in python genetic algorithm particle swarm optimization simulated annealing ant colony algorithm immune algorithm artificial fish swarm algorithm in python simplecv http simplecv org an open source computer vision framework that gives access to several high powered computer vision libraries such as opencv written on python and runs on mac windows and ubuntu linux vigranumpy https github com ukoethe vigra python bindings for the vigra c computer vision library openface https cmusatyalab github io openface free and open source face recognition with deep neural networks pcv https github com jesolem pcv open source python module for computer vision deprecated face recognition https github com ageitgey face recognition face recognition library that recognizes and manipulates faces from python or from the command line deepface https github com serengil deepface a lightweight face recognition and facial attribute analysis age gender emotion and race framework for python covering cutting edge models such as vgg face facenet openface deepface deepid dlib and arcface retinaface https github com serengil retinaface deep learning based cutting edge facial detector for python coming with facial landmarks dockerface https github com natanielruiz dockerface easy to install and use deep learning faster r cnn face detection for images and video in a docker container deprecated detectron https github com facebookresearch detectron fair s software system that implements state of the art object detection algorithms including mask r cnn it is written in python and powered by the caffe2 deep learning framework deprecated detectron2 https github com facebookresearch detectron2 fair s next generation research platform for object detection and segmentation it is a ground up rewrite of the previous version detectron and is powered by the pytorch deep learning framework albumentations https github com albu albumentations fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques supports classification segmentation detection out of the box was used to win a number of deep learning competitions at kaggle topcoder and those that were a part of the cvpr workshops pytessarct https github com madmaze pytesseract python tesseract is an optical character recognition ocr tool for python that is it will recognize and read the text embedded in images python tesseract is a wrapper for google s tesseract ocr engine https github com tesseract ocr tesseract imutils https github com jrosebr1 imutils a library containing convenience functions to make basic image processing operations such as translation rotation resizing skeletonization and displaying matplotlib images easier with opencv and python pytorchcv https github com donnyyou pytorchcv a pytorch based framework for deep learning in computer vision joligen https github com jolibrain joligen generative ai image toolset with gans and diffusion for real world applications self supervised learning https pytorch lightning bolts readthedocs io en latest self supervised models html neural style pt https github com progamergov neural style pt a pytorch implementation of justin johnson s neural style neural style transfer detecto https github com alankbi detecto train and run a computer vision model with 5 10 lines of code neural dream https github com progamergov neural dream a pytorch implementation of deepdream openpose https github com cmu perceptual computing lab openpose a real time multi person keypoint detection library for body face hands and foot estimation deep high resolution net https github com leoxiaobin deep high resolution net pytorch a pytorch implementation of cvpr2019 paper deep high resolution representation learning for human pose estimation dream creator https github com progamergov dream creator a pytorch implementation of deepdream allows individuals to quickly and easily train their own custom googlenet models with custom datasets for deepdream lucent https github com greentfrapp lucent tensorflow and openai clarity s lucid adapted for pytorch lightly https github com lightly ai lightly lightly is a computer vision framework for self supervised learning learnergy https github com gugarosa learnergy energy based machine learning models built upon pytorch openvisionapi https github com openvisionapi open source computer vision api based on open source models iot owl https github com ret2me iot owl light face detection and recognition system with huge possibilities based on microsoft face api and tensorflow made for small iot devices like raspberry pi exadel compreface https github com exadel inc compreface face recognition system that can be easily integrated into any system without prior machine learning skills compreface provides rest api for face recognition face verification face detection face mask detection landmark detection age and gender recognition and is easily deployed with docker computer vision in action https github com charmve computer vision in action as known as l0cv is a new generation of computer vision open source online learning media a cross platform interactive learning framework integrating graphics source code and html the l0cv ecosystem notebook datasets source code and from diving in to advanced as well as the l0cv hub timm https github com rwightman pytorch image models pytorch image models scripts pretrained weights resnet resnext efficientnet efficientnetv2 nfnet vision transformer mixnet mobilenet v3 v2 regnet dpn cspnet and more a name python natural language processing a natural language processing pkuseg python https github com lancopku pkuseg python a better version of jieba developed by peking university nltk https www nltk org a leading platform for building python programs to work with human language data pattern https github com clips pattern a web mining module for the python programming language it has tools for natural language processing machine learning among others quepy https github com machinalis quepy a python framework to transform natural language questions to queries in a database query language textblob http textblob readthedocs io en dev providing a consistent api for diving into common natural language processing nlp tasks stands on the giant shoulders of nltk and pattern and plays nicely with both yalign https github com machinalis yalign a sentence aligner a friendly tool for extracting parallel sentences from comparable corpora deprecated jieba https github com fxsjy jieba jieba 1 chinese words segmentation utilities snownlp https github com isnowfy snownlp a library for processing chinese text spammy https github com tasdikrahman spammy a library for email spam filtering built on top of nltk loso https github com fangpenlin loso another chinese segmentation library deprecated genius https github com duanhongyi genius a chinese segment based on conditional random field konlpy http konlpy org a python package for korean natural language processing nut https github com pprett nut natural language understanding toolkit deprecated rosetta https github com columbia applied data science rosetta text processing tools and wrappers e g vowpal wabbit bllip parser https pypi org project bllipparser python bindings for the bllip natural language parser also known as the charniak johnson parser deprecated pynlpl https github com proycon pynlpl python natural language processing library general purpose nlp library for python also contains some specific modules for parsing common nlp formats most notably for folia https proycon github io folia but also arpa language models moses phrasetables giza alignments pyss3 https github com sergioburdisso pyss3 python package that implements a novel white box machine learning model for text classification called ss3 since ss3 has the ability to visually explain its rationale this package also comes with easy to use interactive visualizations tools online demos http tworld io ss3 python ucto https github com proycon python ucto python binding to ucto a unicode aware rule based tokenizer for various languages python frog https github com proycon python frog python binding to frog an nlp suite for dutch pos tagging lemmatisation dependency parsing ner python zpar https github com educationaltestingservice python zpar python bindings for zpar https github com frcchang zpar a statistical part of speech tagger constituency parser and dependency parser for english colibri core https github com proycon colibri core python binding to c library for extracting and working with basic linguistic constructions such as n grams and skipgrams in a quick and memory efficient way spacy https github com explosion spacy industrial strength nlp with python and cython pystanforddependencies https github com dmcc pystanforddependencies python interface for converting penn treebank trees to stanford dependencies distance https github com doukremt distance levenshtein and hamming distance computation deprecated fuzzy wuzzy https github com seatgeek fuzzywuzzy fuzzy string matching in python neofuzz https github com x tabdeveloping neofuzz blazing fast lightweight and customizable fuzzy and semantic text search in python with fuzzywuzzy thefuzz compatible api jellyfish https github com jamesturk jellyfish a python library for doing approximate and phonetic matching of strings editdistance https pypi org project editdistance fast implementation of edit distance textacy https github com chartbeat labs textacy higher level nlp built on spacy stanford corenlp python https github com dasmith stanford corenlp python python wrapper for stanford corenlp https github com stanfordnlp corenlp deprecated cltk https github com cltk cltk the classical language toolkit rasa https github com rasahq rasa a machine learning framework to automate text and voice based conversations yase https github com ppaci yase transcode sentence or other sequence to list of word vector polyglot https github com abosamoor polyglot multilingual text nlp processing toolkit drqa https github com facebookresearch drqa reading wikipedia to answer open domain questions dedupe https github com dedupeio dedupe a python library for accurate and scalable fuzzy matching record deduplication and entity resolution snips nlu https github com snipsco snips nlu natural language understanding library for intent classification and entity extraction neuroner https github com franck dernoncourt neuroner named entity recognition using neural networks providing state of the art results deeppavlov https github com deepmipt deeppavlov conversational ai library with many pre trained russian nlp models bigartm https github com bigartm bigartm topic modelling platform nalp https github com gugarosa nalp a natural adversarial language processing framework built over tensorflow dl translate https github com xhlulu dl translate a deep learning based translation library between 50 languages built with transformers haystack https github com deepset ai haystack a framework for building industrial strength applications with transformer models and llms cometllm https github com comet ml comet llm track log visualize and evaluate your llm prompts and prompt chains a name python general purpose machine learning a general purpose machine learning aim https github com aimhubio aim an easy to use supercharged open source ai metadata tracker rexmex https github com astrazeneca rexmex a general purpose recommender metrics library for fair evaluation chemicalx https github com astrazeneca chemicalx a pytorch based deep learning library for drug pair scoring microsoft ml for apache spark https github com azure mmlspark a distributed machine learning framework apache spark shapley https github com benedekrozemberczki shapley a data driven framework to quantify the value of classifiers in a machine learning ensemble igel https github com nidhaloff igel a delightful machine learning tool that allows you to train fit test and use models without writing code ml model building https github com shanky 21 machine learning a repository containing classification clustering regression recommender notebooks with illustration to make them ml dl project template https github com pytorchlightning deep learning project template pytorch geometric temporal https github com benedekrozemberczki pytorch geometric temporal a temporal extension of pytorch geometric for dynamic graph representation learning little ball of fur https github com benedekrozemberczki littleballoffur a graph sampling extension library for networkx with a scikit learn like api karate club https github com benedekrozemberczki karateclub an unsupervised machine learning extension library for networkx with a scikit learn like api auto viml https github com autoviml auto viml automatically build variant interpretable ml models fast auto viml is pronounced auto vimal is a comprehensive and scalable python automl toolkit with imbalanced handling ensembling stacking and built in feature selection featured in a href https towardsdatascience com why automl is an essential new tool for data scientists 2d9ab4e25e46 source friends link sk d03a0cc55c23deb497d546d6b9be0653 medium article a pyod https github com yzhao062 pyod python outlier detection comprehensive and scalable python toolkit for detecting outlying objects in multivariate data featured for advanced models including neural networks deep learning and outlier ensembles steppy https github com neptune ml steppy lightweight python library for fast and reproducible machine learning experimentation introduces a very simple interface that enables clean machine learning pipeline design steppy toolkit https github com neptune ml steppy toolkit curated collection of the neural networks transformers and models that make your machine learning work faster and more effective cntk https github com microsoft cntk microsoft cognitive toolkit cntk an open source deep learning toolkit documentation can be found here https docs microsoft com cognitive toolkit couler https github com couler proj couler unified interface for constructing and managing machine learning workflows on different workflow engines such as argo workflows tekton pipelines and apache airflow auto ml https github com climbsrocks auto ml automated machine learning for production and analytics lets you focus on the fun parts of ml while outputting production ready code and detailed analytics of your dataset and results includes support for nlp xgboost catboost lightgbm and soon deep learning dtaidistance https github com wannesm dtaidistance high performance library for time series distances dtw and time series clustering einops https github com arogozhnikov einops deep learning operations reinvented for pytorch tensorflow jax and others machine learning https github com jeff1evesque machine learning automated build consisting of a web interface https github com jeff1evesque machine learning web interface and set of programmatic interface https github com jeff1evesque machine learning programmatic interface api for support vector machines corresponding dataset s are stored into a sql database then generated model s used for prediction s are stored into a nosql datastore xgboost https github com dmlc xgboost python bindings for extreme gradient boosting tree library chefboost https github com serengil chefboost a lightweight decision tree framework for python with categorical feature support covering regular decision tree algorithms such as id3 c4 5 cart chaid and regression tree also some advanved bagging and boosting techniques such as gradient boosting random forest and adaboost apache singa https singa apache org an apache incubating project for developing an open source machine learning library bayesian methods for hackers https github com camdavidsonpilon probabilistic programming and bayesian methods for hackers book ipython notebooks on probabilistic programming in python featureforge https github com machinalis featureforge a set of tools for creating and testing machine learning features with a scikit learn compatible api mllib in apache spark http spark apache org docs latest mllib guide html distributed machine learning library in spark hydrosphere mist https github com hydrospheredata mist a service for deployment apache spark mllib machine learning models as realtime batch or reactive web services towhee https towhee io a python module that encode unstructured data into embeddings scikit learn https scikit learn org a python module for machine learning built on top of scipy metric learn https github com metric learn metric learn a python module for metric learning openmetriclearning https github com oml team open metric learning a pytorch based framework to train and validate the models producing high quality embeddings intel r extension for scikit learn https github com intel scikit learn intelex a seamless way to speed up your scikit learn applications with no accuracy loss and code changes simpleai https github com simpleai team simpleai python implementation of many of the artificial intelligence algorithms described in the book artificial intelligence a modern approach it focuses on providing an easy to use well documented and tested library astroml https www astroml org machine learning and data mining for astronomy graphlab create https turi com products create docs a library with various machine learning models regression clustering recommender systems graph analytics etc implemented on top of a disk backed dataframe bigml https bigml com a library that contacts external servers pattern https github com clips pattern web mining module for python nupic https github com numenta nupic numenta platform for intelligent computing pylearn2 https github com lisa lab pylearn2 a machine learning library based on theano https github com theano theano deprecated keras https github com keras team keras high level neural networks frontend for tensorflow https github com tensorflow tensorflow cntk https github com microsoft cntk and theano https github com theano theano lasagne https github com lasagne lasagne lightweight library to build and train neural networks in theano hebel https github com hannes brt hebel gpu accelerated deep learning library in python deprecated chainer https github com chainer chainer flexible neural network framework prophet https facebook github io prophet fast and automated time series forecasting framework by facebook gensim https github com rare technologies gensim topic modelling for humans tweetopic https centre for humanities computing github io tweetopic blazing fast short text topic modelling for python topicwizard https github com x tabdeveloping topic wizard interactive topic model visualization interpretation framework topik https github com continuumio topik topic modelling toolkit deprecated pybrain https github com pybrain pybrain another python machine learning library brainstorm https github com idsia brainstorm fast flexible and fun neural networks this is the successor of pybrain surprise https surpriselib com a scikit for building and analyzing recommender systems implicit https implicit readthedocs io en latest quickstart html fast python collaborative filtering for implicit datasets lightfm https making lyst com lightfm docs home html a python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback crab https github com muricoca crab a flexible fast recommender engine deprecated python recsys https github com ocelma python recsys a python library for implementing a recommender system thinking bayes https github com allendowney thinkbayes book on bayesian analysis image to image translation with conditional adversarial networks https github com williamfalcon pix2pix keras implementation of image to image pix2pix translation from the paper by isola et al https arxiv org pdf 1611 07004 pdf deep learning restricted boltzmann machines https github com echen restricted boltzmann machines restricted boltzmann machines in python deep learning bolt https github com pprett bolt bolt online learning toolbox deprecated covertree https github com patvarilly covertree python implementation of cover trees near drop in replacement for scipy spatial kdtree deprecated nilearn https github com nilearn nilearn machine learning for neuroimaging in python neuropredict https github com raamana neuropredict aimed at novice machine learners and non expert programmers this package offers easy no coding needed and comprehensive machine learning evaluation and full report of predictive performance without requiring you to code in python for neuroimaging and any other type of features this is aimed at absorbing much of the ml workflow unlike other packages like nilearn and pymvpa which require you to learn their api and code to produce anything useful imbalanced learn https imbalanced learn org stable python module to perform under sampling and oversampling with various techniques imbalanced ensemble https github com zhiningliu1998 imbalanced ensemble python toolbox for quick implementation modification evaluation and visualization of ensemble learning algorithms for class imbalanced data supports out of the box multi class imbalanced long tailed classification shogun https github com shogun toolbox shogun the shogun machine learning toolbox pyevolve https github com perone pyevolve genetic algorithm framework deprecated caffe https github com bvlc caffe a deep learning framework developed with cleanliness readability and speed in mind breze https github com breze no salt breze theano based library for deep and recurrent neural networks cortex https github com cortexlabs cortex open source platform for deploying machine learning models in production pyhsmm https github com mattjj pyhsmm library for approximate unsupervised inference in bayesian hidden markov models hmms and explicit duration hidden semi markov models hsmms focusing on the bayesian nonparametric extensions the hdp hmm and hdp hsmm mostly with weak limit approximations skll https github com educationaltestingservice skll a wrapper around scikit learn that makes it simpler to conduct experiments neurolab https github com zueve neurolab spearmint https github com hips spearmint spearmint is a package to perform bayesian optimization according to the algorithms outlined in the paper practical bayesian optimization of machine learning algorithms jasper snoek hugo larochelle and ryan p adams advances in neural information processing systems 2012 deprecated pebl https github com abhik pebl python environment for bayesian learning deprecated theano https github com theano theano optimizing gpu meta programming code generating array oriented optimizing math compiler in python tensorflow https github com tensorflow tensorflow open source software library for numerical computation using data flow graphs pomegranate https github com jmschrei pomegranate hidden markov models for python implemented in cython for speed and efficiency python timbl https github com proycon python timbl a python extension module wrapping the full timbl c programming interface timbl is an elaborate k nearest neighbours machine learning toolkit deap https github com deap deap evolutionary algorithm framework pydeep https github com andersbll deeppy deep learning in python deprecated mlxtend https github com rasbt mlxtend a library consisting of useful tools for data science and machine learning tasks neon https github com nervanasystems neon nervana s high performance https github com soumith convnet benchmarks python based deep learning framework deep learning deprecated optunity https optunity readthedocs io en latest a library dedicated to automated hyperparameter optimization with a simple lightweight api to facilitate drop in replacement of grid search neural networks and deep learning https github com mnielsen neural networks and deep learning code samples for my book neural networks and deep learning deep learning annoy https github com spotify annoy approximate nearest neighbours implementation tpot https github com epistasislab tpot tool that automatically creates and optimizes machine learning pipelines using genetic programming consider it your personal data science assistant automating a tedious part of machine learning pgmpy https github com pgmpy pgmpy a python library for working with probabilistic graphical models digits https github com nvidia digits the deep learning gpu training system digits is a web application for training deep learning models orange https orange biolab si open source data visualization and data analysis for novices and experts mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more milk https github com luispedro milk machine learning toolkit focused on supervised classification deprecated tflearn https github com tflearn tflearn deep learning library featuring a higher level api for tensorflow rep https github com yandex rep an ipython based environment for conducting data driven research in a consistent and reproducible way rep is not trying to substitute scikit learn but extends it and provides better user experience deprecated rgf python https github com rgf team rgf python bindings for regularized greedy forest tree library skbayes https github com amazaspshumik sklearn bayes python package for bayesian machine learning with scikit learn api fuku ml https github com fukuball fuku ml simple machine learning library including perceptron regression support vector machine decision tree and more it s easy to use and easy to learn for beginners xcessiv https github com reiinakano xcessiv a web based application for quick scalable and automated hyperparameter tuning and stacked ensembling pytorch https github com pytorch pytorch tensors and dynamic neural networks in python with strong gpu acceleration pytorch lightning https github com pytorchlightning pytorch lightning the lightweight pytorch wrapper for high performance ai research pytorch lightning bolts https github com pytorchlightning pytorch lightning bolts toolbox of models callbacks and datasets for ai ml researchers skorch https github com skorch dev skorch a scikit learn compatible neural network library that wraps pytorch ml from scratch https github com eriklindernoren ml from scratch implementations of machine learning models from scratch in python with a focus on transparency aims to showcase the nuts and bolts of ml in an accessible way edward http edwardlib org a library for probabilistic modelling inference and criticism built on top of tensorflow xrbm https github com omimo xrbm a library for restricted boltzmann machine rbm and its conditional variants in tensorflow catboost https github com catboost catboost general purpose gradient boosting on decision trees library with categorical features support out of the box it is easy to install well documented and supports cpu and gpu even multi gpu computation stacked generalization https github com fukatani stacked generalization implementation of machine learning stacking technique as a handy library in python modal https github com modal python modal a modular active learning framework for python built on top of scikit learn cogitare https github com cogitare ai cogitare a modern fast and modular deep learning and machine learning framework for python parris https github com jgreenemi parris parris the automated infrastructure setup tool for machine learning algorithms neonrvm https github com siavashserver neonrvm neonrvm is an open source machine learning library based on rvm technique it s written in c programming language and comes with python programming language bindings turi create https github com apple turicreate machine learning from apple turi create simplifies the development of custom machine learning models you don t have to be a machine learning expert to add recommendations object detection image classification image similarity or activity classification to your app xlearn https github com aksnzhy xlearn a high performance easy to use and scalable machine learning package which can be used to solve large scale machine learning problems xlearn is especially useful for solving machine learning problems on large scale sparse data which is very common in internet services such as online advertisement and recommender systems mlens https github com flennerhag mlens a high performance memory efficient maximally parallelized ensemble learning integrated with scikit learn thampi https github com scoremedia thampi machine learning prediction system on aws lambda mindsdb https github com mindsdb mindsdb open source framework to streamline use of neural networks microsoft recommenders https github com microsoft recommenders examples and best practices for building recommendation systems provided as jupyter notebooks the repo contains some of the latest state of the art algorithms from microsoft research as well as from other companies and institutions stellargraph https github com stellargraph stellargraph machine learning on graphs a python library for machine learning on graph structured network structured data bentoml https github com bentoml bentoml toolkit for package and deploy machine learning models for serving in production miraiml https github com arthurpaulino miraiml an asynchronous engine for continuous autonomous machine learning built for real time usage numpy ml https github com ddbourgin numpy ml reference implementations of ml models written in numpy neuraxle https github com neuraxio neuraxle a framework providing the right abstractions to ease research development and deployment of your ml pipelines cornac https github com preferredai cornac a comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data jax https github com google jax jax is autograd and xla brought together for high performance machine learning research catalyst https github com catalyst team catalyst high level utils for pytorch dl rl research it was developed with a focus on reproducibility fast experimentation and code ideas reusing being able to research develop something new rather than write another regular train loop fastai https github com fastai fastai high level wrapper built on the top of pytorch which supports vision text tabular data and collaborative filtering scikit multiflow https github com scikit multiflow scikit multiflow a machine learning framework for multi output multi label and stream data lightwood https github com mindsdb lightwood a pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code bayeso https github com jungtaekkim bayeso a simple but essential bayesian optimization package written in python mljar supervised https github com mljar mljar supervised an automated machine learning automl python package for tabular data it can handle binary classification multiclass classification and regression it provides explanations and markdown reports evostra https github com alirezamika evostra a fast evolution strategy implementation in python determined https github com determined ai determined scalable deep learning training platform including integrated support for distributed training hyperparameter tuning experiment tracking and model management pysyft https github com openmined pysyft a python library for secure and private deep learning built on pytorch and tensorflow pygrid https github com openmined pygrid peer to peer network of data owners and data scientists who can collectively train ai models using pysyft sktime https github com alan turing institute sktime a unified framework for machine learning with time series opfython https github com gugarosa opfython a python inspired implementation of the optimum path forest classifier opytimizer https github com gugarosa opytimizer python based meta heuristic optimization techniques gradio https github com gradio app gradio a python library for quickly creating and sharing demos of models debug models interactively in your browser get feedback from collaborators and generate public links without deploying anything hub https github com activeloopai hub fastest unstructured dataset management for tensorflow pytorch stream version control data store even petabyte scale data in a single numpy like array on the cloud accessible on any machine visit activeloop ai https activeloop ai for more info synthia https github com dmey synthia multidimensional synthetic data generation in python bytehub https github com bytehub ai bytehub an easy to use python based feature store optimized for time series data backprop https github com backprop ai backprop backprop makes it simple to use finetune and deploy state of the art ml models river https github com online ml river a framework for general purpose online machine learning fedot https github com nccr itmo fedot an automl framework for the automated design of composite modelling pipelines it can handle classification regression and time series forecasting tasks on different types of data including multi modal datasets sklearn genetic opt https github com rodrigo arenas sklearn genetic opt an automl package for hyperparameters tuning using evolutionary algorithms with built in callbacks plotting remote logging and more evidently https github com evidentlyai evidently interactive reports to analyze machine learning models during validation or production monitoring streamlit https github com streamlit streamlit streamlit is an framework to create beautiful data apps in hours not weeks optuna https github com optuna optuna optuna is an automatic hyperparameter optimization software framework particularly designed for machine learning deepchecks https github com deepchecks deepchecks validation testing of machine learning models and data during model development deployment and production this includes checks and suites related to various types of issues such as model performance data integrity distribution mismatches and more shapash https github com maif shapash shapash is a python library that provides several types of visualization that display explicit labels that everyone can understand eurybia https github com maif eurybia eurybia monitors data and model drift over time and securizes model deployment with data validation colossal ai https github com hpcaitech colossalai an open source deep learning system for large scale model training and inference with high efficiency and low cost dirty cat https github com dirty cat dirty cat facilitates machine learning on dirty non curated categories it provides transformers and encoders robust to morphological variants such as typos upgini https github com upgini upgini free automated data feature enrichment library for machine learning automatically searches through thousands of ready to use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features automl implementation for static and dynamic data analytics https github com western oc2 lab automl implementation for static and dynamic data analytics a tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task skbel https github com robinthibaut skbel a python library for bayesian evidential learning bel in order to estimate the uncertainty of a prediction nannyml https bit ly nannyml github machinelearning python library capable of fully capturing the impact of data drift on performance allows estimation of post deployment model performance without access to targets cleanlab https github com cleanlab cleanlab the standard data centric ai package for data quality and machine learning with messy real world data and labels autogluon https github com awslabs autogluon automl for image text tabular time series and multimodal data pybroker https github com edtechre pybroker algorithmic trading with machine learning frouros https github com ifca frouros frouros is an open source python library for drift detection in machine learning systems cometml https github com comet ml comet examples the best in class mlops platform with experiment tracking model production monitoring a model registry and data lineage from training straight through to production a name python data analysis data visualization a data analysis data visualization datavisualization https github com shanky 21 data visualization a github repository where you can learn datavisualizatoin basics to intermediate level cartopy https scitools org uk cartopy docs latest cartopy is a python package designed for geospatial data processing in order to produce maps and other geospatial data analyses scipy https www scipy org a python based ecosystem of open source software for mathematics science and engineering numpy https www numpy org a fundamental package for scientific computing with python autoviz https github com autoviml autoviz autoviz performs automatic visualization of any dataset with a single line of python code give it any input file csv txt or json of any size and autoviz will visualize it see a href https towardsdatascience com autoviz a new tool for automated visualization ec9c1744a6ad source friends link sk c9e9503ec424b191c6096d7e3f515d10 medium article a numba https numba pydata org python jit just in time compiler to llvm aimed at scientific python by the developers of cython and numpy mars https github com mars project mars a tensor based framework for large scale data computation which is often regarded as a parallel and distributed version of numpy networkx https networkx github io a high productivity software for complex networks igraph https igraph org python binding to igraph library general purpose graph library pandas https pandas pydata org a library providing high performance easy to use data structures and data analysis tools paramonte https github com cdslaborg paramonte a general purpose python library for bayesian data analysis and visualization via serial parallel monte carlo and mcmc simulations documentation can be found here https www cdslab org paramonte vaex https github com vaexio vaex a high performance python library for lazy out of core dataframes similar to pandas to visualize and explore big tabular datasets documentation can be found here https vaex io docs index html open mining https github com mining mining business intelligence bi in python pandas web interface deprecated pymc https github com pymc devs pymc markov chain monte carlo sampling toolkit zipline https github com quantopian zipline a pythonic algorithmic trading library pydy https www pydy org short for python dynamics used to assist with workflow in the modelling of dynamic motion based around numpy scipy ipython and matplotlib sympy https github com sympy sympy a python library for symbolic mathematics statsmodels https github com statsmodels statsmodels statistical modelling and econometrics in python astropy https www astropy org a community python library for astronomy matplotlib https matplotlib org a python 2d plotting library bokeh https github com bokeh bokeh interactive web plotting for python plotly https plot ly python collaborative web plotting for python and matplotlib altair https github com altair viz altair a python to vega translator d3py https github com mikedewar d3py a plotting library for python based on d3 js https d3js org pydexter https github com d3xterjs pydexter simple plotting for python wrapper for d3xterjs easily render charts in browser ggplot https github com yhat ggpy same api as ggplot2 for r deprecated ggfortify https github com sinhrks ggfortify unified interface to ggplot2 popular r packages kartograph py https github com kartograph kartograph py rendering beautiful svg maps in python pygal http pygal org en stable a python svg charts creator pyqtgraph https github com pyqtgraph pyqtgraph a pure python graphics and gui library built on pyqt4 pyside and numpy pycascading https github com twitter pycascading deprecated petrel https github com airsage petrel tools for writing submitting debugging and monitoring storm topologies in pure python blaze https github com blaze blaze numpy and pandas interface to big data emcee https github com dfm emcee the python ensemble sampling toolkit for affine invariant mcmc windml https github com cigroup ol windml a python framework for wind energy analysis and prediction vispy https github com vispy vispy gpu based high performance interactive opengl 2d 3d data visualization library cerebro2 https github com numenta nupic cerebro2 a web based visualization and debugging platform for nupic deprecated nupic studio https github com htm community nupic studio an all in one nupic hierarchical temporal memory visualization and debugging super tool deprecated sparklingpandas https github com sparklingpandas sparklingpandas pandas on pyspark pops seaborn https seaborn pydata org a python visualization library based on matplotlib ipychart https github com nicohlr ipychart the power of chart js in jupyter notebook bqplot https github com bloomberg bqplot an api for plotting in jupyter ipython pastalog https github com rewonc pastalog simple realtime visualization of neural network training performance superset https github com apache incubator superset a data exploration platform designed to be visual intuitive and interactive dora https github com nathanepstein dora tools for exploratory data analysis in python ruffus http www ruffus org uk computation pipeline library for python sompy https github com sevamoo sompy self organizing map written in python uses neural networks for data analysis somoclu https github com peterwittek somoclu massively parallel self organizing maps accelerate training on multicore cpus gpus and clusters has python api hdbscan https github com lmcinnes hdbscan implementation of the hdbscan algorithm in python used for clustering visualize ml https github com ayush1997 visualize ml a python package for data exploration and data analysis deprecated scikit plot https github com reiinakano scikit plot a visualization library for quick and easy generation of common plots in data analysis and machine learning bowtie https github com jwkvam bowtie a dashboard library for interactive visualizations using flask socketio and react lime https github com marcotcr lime lime is about explaining what machine learning classifiers or models are doing it is able to explain any black box classifier with two or more classes pycm https github com sepandhaghighi pycm pycm is a multi class confusion matrix library written in python that supports both input data vectors and direct matrix and a proper tool for post classification model evaluation that supports most classes and overall statistics parameters dash https github com plotly dash a framework for creating analytical web applications built on top of plotly js react and flask lambdo https github com asavinov lambdo a workflow engine for solving machine learning problems by combining in one analysis pipeline i feature engineering and machine learning ii model training and prediction iii table population and column evaluation via user defined python functions tensorwatch https github com microsoft tensorwatch debugging and visualization tool for machine learning and data science it extensively leverages jupyter notebook to show real time visualizations of data in running processes such as machine learning training dowel https github com rlworkgroup dowel a little logger for machine learning research output any object to the terminal csv tensorboard text logs on disk and more with just one call to logger log a name python misc scripts ipython notebooks codebases a misc scripts ipython notebooks codebases minigrad https github com kennysong minigrad a minimal educational pythonic implementation of autograd 100 loc map reduce implementations of common ml algorithms https github com yannael bigdataanalytics infoh515 jupyter notebooks that cover how to implement from scratch different ml algorithms ordinary least squares gradient descent k means alternating least squares using python numpy and how to then make these implementations scalable using map reduce and spark biopy https github com jaredthecoder biopy biologically inspired and machine learning algorithms in python deprecated caes for data assimilation https github com julianmack data assimilation convolutional autoencoders for 3d image field compression applied to reduced order data assimilation https en wikipedia org wiki data assimilation handsonml https github com ageron handson ml fundamentals of machine learning in python svm explorer https github com plotly dash svm interactive svm explorer using dash and scikit learn pattern classification https github com rasbt pattern classification thinking stats 2 https github com wavelets thinkstats2 hyperopt https github com hyperopt hyperopt sklearn numpic https github com numenta nupic 2012 paper diginorm https github com dib lab 2012 paper diginorm a gallery of interesting ipython notebooks https github com jupyter jupyter wiki a gallery of interesting jupyter notebooks ipython notebooks https github com ogrisel notebooks data science ipython notebooks https github com donnemartin data science ipython notebooks continually updated data science python notebooks spark hadoop mapreduce hdfs aws kaggle scikit learn matplotlib pandas numpy scipy and various command lines decision weights https github com camdavidsonpilon decision weights sarah palin lda https github com wavelets sarah palin lda topic modelling the sarah palin emails diffusion segmentation https github com wavelets diffusion segmentation a collection of image segmentation algorithms based on diffusion methods scipy tutorials https github com wavelets scipy tutorials scipy tutorials this is outdated check out scipy lecture notes crab https github com marcelcaraciolo crab a recommendation engine library for python bayespy https github com maxsklar bayespy bayesian inference tools in python scikit learn tutorials https github com gaelvaroquaux scikit learn tutorial series of notebooks for learning scikit learn sentiment analyzer https github com madhusudancs sentiment analyzer tweets sentiment analyzer sentiment classifier https github com kevincobain2000 sentiment classifier sentiment classifier using word sense disambiguation group lasso https github com fabianp group lasso some experiments with the coordinate descent algorithm used in the sparse group lasso model jprocessing https github com kevincobain2000 jprocessing kanji hiragana katakana to romaji converter edict dictionary parallel sentences search sentence similarity between two jp sentences sentiment analysis of japanese text run cabocha iso 8859 1 configured in python mne python notebooks https github com mne tools mne python notebooks ipython notebooks for eeg meg data processing using mne python neon course https github com nervanasystems neon course ipython notebooks for a complete course around understanding nervana s neon pandas cookbook https github com jvns pandas cookbook recipes for using python s pandas library climin https github com brml climin optimization library focused on machine learning pythonic implementations of gradient descent lbfgs rmsprop adadelta and others allen downey s data science course https github com allendowney datascience code for data science at olin college spring 2014 allen downey s think bayes code https github com allendowney thinkbayes code repository for think bayes allen downey s think complexity code https github com allendowney thinkcomplexity code for allen downey s book think complexity allen downey s think os code https github com allendowney thinkos text and supporting code for think os a brief introduction to operating systems python programming for the humanities https www karsdorp io python course course for python programming for the humanities assuming no prior knowledge heavy focus on text processing nlp greatcircle https github com mwgg greatcircle library for calculating great circle distance optunity examples http optunity readthedocs io en latest notebooks index html examples demonstrating how to use optunity in synergy with machine learning libraries dive into machine learning with python jupyter notebook and scikit learn https github com hangtwenty dive into machine learning i learned python by hacking first and getting serious later i wanted to do this with machine learning if this is your style join me in getting a bit ahead of yourself tdb https github com ericjang tdb tensordebugger tdb is a visual debugger for deep learning it features interactive node by node debugging and visualization for tensorflow suiron https github com kendricktan suiron machine learning for rc cars introduction to machine learning with scikit learn https github com justmarkham scikit learn videos ipython notebooks from data school s video tutorials on scikit learn practical xgboost in python https parrotprediction teachable com p practical xgboost in python comprehensive online course about using xgboost in python introduction to machine learning with python https github com amueller introduction to ml with python notebooks and code for the book introduction to machine learning with python pydata book https github com wesm pydata book materials and ipython notebooks for python for data analysis by wes mckinney published by o reilly media homemade machine learning https github com trekhleb homemade machine learning python examples of popular machine learning algorithms with interactive jupyter demos and math being explained prodmodel https github com prodmodel prodmodel build tool for data science pipelines the elements of statistical learning https github com maitbayev the elements of statistical learning this repository contains jupyter notebooks implementing the algorithms found in the book and summary of the textbook hyperparameter optimization of machine learning algorithms https github com liyanghart hyperparameter optimization of machine learning algorithms code for hyperparameter tuning optimization of machine learning and deep learning algorithms heart disease prediction https github com shivamchoudhary17 heart disease given clinical parameters about a patient can we predict whether or not they have heart disease flight fare prediction https github com shivamchoudhary17 flight fare prediction this basically to gauge the understanding of machine learning workflow and regression technique in specific keras tuner https github com keras team keras tuner an easy to use scalable hyperparameter optimization framework that solves the pain points of hyperparameter search a name python neural networks a neural networks nn builder https github com p christ nn builder nn builder is a python package that lets you build neural networks in 1 line neuraltalk https github com karpathy neuraltalk neuraltalk is a python numpy project for learning multimodal recurrent neural networks that describe images with sentences neuron https github com molcik python neuron neuron is simple class for time series predictions it s utilize lnu linear neural unit qnu quadratic neural unit rbf radial basis function mlp multi layer perceptron mlp elm multi layer perceptron extreme learning machine neural networks learned with gradient descent or lelevenberg marquardt algorithm neuraltalk https github com karpathy neuraltalk2 neuraltalk is a python numpy project for learning multimodal recurrent neural networks that describe images with sentences deprecated neuron https github com molcik python neuron neuron is simple class for time series predictions it s utilize lnu linear neural unit qnu quadratic neural unit rbf radial basis function mlp multi layer perceptron mlp elm multi layer perceptron extreme learning machine neural networks learned with gradient descent or lelevenberg marquardt algorithm deprecated data driven code https github com atmb4u data driven code very simple implementation of neural networks for dummies in python without using any libraries with detailed comments machine learning data science and deep learning with python https www manning com livevideo machine learning data science and deep learning with python livevideo course that covers machine learning tensorflow artificial intelligence and neural networks tresnet high performance gpu dedicated architecture https github com mrt23 tresnet tresnet models were designed and optimized to give the best speed accuracy tradeoff out there on gpus tresnet simple and powerful neural network library for python https github com zueve neurolab variety of supported types of artificial neural network and learning algorithms jina ai https jina ai an easier way to build neural search in the cloud compatible with jupyter notebooks sequitur https github com shobrook sequitur pytorch library for creating and training sequence autoencoders in just two lines of code a name python spiking neural networks a spiking neural networks rockpool https github com synsense rockpool a machine learning library for spiking neural networks supports training with both torch and jax pipelines and deployment to neuromorphic hardware sinabs https github com synsense sinabs a deep learning library for spiking neural networks which is based on pytorch focuses on fast training and supports inference on neuromorphic hardware tonic https github com neuromorphs tonic a library that makes downloading publicly available neuromorphic datasets a breeze and provides event based data transformation augmentation pipelines a name python survival analysis a python survival analysis lifelines https github com camdavidsonpilon lifelines lifelines is a complete survival analysis library written in pure python scikit survival https github com sebp scikit survival scikit survival is a python module for survival analysis built on top of scikit learn it allows doing survival analysis while utilizing the power of scikit learn e g for pre processing or doing cross validation a name python federated learning a federated learning flower https flower dev a unified approach to federated learning analytics and evaluation federate any workload any ml framework and any programming language pysyft https github com openmined pysyft a python library for secure and private deep learning tensorflow federated https www tensorflow org federated a federated learning framework for machine learning and other computations on decentralized data a name python kaggle competition source code a kaggle competition source code open solution home credit https github com neptune ml open solution home credit source code and experiments results https app neptune ml neptune ml home credit default risk for home credit default risk https www kaggle com c home credit default risk open solution googleai object detection https github com neptune ml open solution googleai object detection source code and experiments results https app neptune ml neptune ml google ai object detection challenge for google ai open images object detection track https www kaggle com c google ai open images object detection track open solution salt identification https github com neptune ml open solution salt identification source code and experiments results https app neptune ml neptune ml salt detection for tgs salt identification challenge https www kaggle com c tgs salt identification challenge open solution ship detection https github com neptune ml open solution ship detection source code and experiments results https app neptune ml neptune ml ships for airbus ship detection challenge https www kaggle com c airbus ship detection open solution data science bowl 2018 https github com neptune ml open solution data science bowl 2018 source code and experiments results https app neptune ml neptune ml data science bowl 2018 for 2018 data science bowl https www kaggle com c data science bowl 2018 open solution value prediction https github com neptune ml open solution value prediction source code and experiments results https app neptune ml neptune ml santander value prediction challenge for santander value prediction challenge https www kaggle com c santander value prediction challenge open solution toxic comments https github com neptune ml open solution toxic comments source code for toxic comment classification challenge https www kaggle com c jigsaw toxic comment classification challenge wiki challenge https github com hammer wikichallenge an implementation of dell zhang s solution to wikipedia s participation challenge on kaggle kaggle insults https github com amueller kaggle insults kaggle submission for detecting insults in social commentary kaggle acquire valued shoppers challenge https github com mlwave kaggle acquire valued shoppers challenge code for the kaggle acquire valued shoppers challenge kaggle cifar https github com zygmuntz kaggle cifar code for the cifar 10 competition at kaggle uses cuda convnet kaggle blackbox https github com zygmuntz kaggle blackbox deep learning made easy kaggle accelerometer https github com zygmuntz kaggle accelerometer code for accelerometer biometric competition at kaggle kaggle advertised salaries https github com zygmuntz kaggle advertised salaries predicting job salaries from ads a kaggle competition kaggle amazon https github com zygmuntz kaggle amazon amazon access control challenge kaggle bestbuy big https github com zygmuntz kaggle bestbuy big code for the best buy competition at kaggle kaggle bestbuy small https github com zygmuntz kaggle bestbuy small kaggle dogs vs cats https github com kastnerkyle kaggle dogs vs cats code for kaggle dogs vs cats competition kaggle galaxy challenge https github com benanne kaggle galaxies winning solution for the galaxy challenge on kaggle kaggle gender https github com zygmuntz kaggle gender a kaggle competition discriminate gender based on handwriting kaggle merck https github com zygmuntz kaggle merck merck challenge at kaggle kaggle stackoverflow https github com zygmuntz kaggle stackoverflow predicting closed questions on stack overflow kaggle acquire valued shoppers challenge https github com mlwave kaggle acquire valued shoppers challenge code for the kaggle acquire valued shoppers challenge wine quality https github com zygmuntz wine quality predicting wine quality a name python reinforcement learning a reinforcement learning deepmind lab https github com deepmind lab deepmind lab is a 3d learning environment based on id software s quake iii arena via ioquake3 and other open source software its primary purpose is to act as a testbed for research in artificial intelligence especially deep reinforcement learning gym https github com openai gym openai gym is a toolkit for developing and comparing reinforcement learning algorithms serpent ai https github com serpentai serpentai serpent ai is a game agent framework that allows you to turn any video game you own into a sandbox to develop ai and machine learning experiments for both researchers and hobbyists vizdoom https github com mwydmuch vizdoom vizdoom allows developing ai bots that play doom using only the visual information the screen buffer it is primarily intended for research in machine visual learning and deep reinforcement learning in particular roboschool https github com openai roboschool open source software for robot simulation integrated with openai gym retro https github com openai retro retro games in gym slm lab https github com kengz slm lab modular deep reinforcement learning framework in pytorch coach https github com nervanasystems coach reinforcement learning coach by intel ai lab enables easy experimentation with state of the art reinforcement learning algorithms garage https github com rlworkgroup garage a toolkit for reproducible reinforcement learning research metaworld https github com rlworkgroup metaworld an open source robotics benchmark for meta and multi task reinforcement learning acme https deepmind com research publications acme an open source distributed framework for reinforcement learning that makes build and train your agents easily spinning up https spinningup openai com an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning maze https github com enlite ai maze application oriented deep reinforcement learning framework addressing real world decision problems rllib https github com ray project ray rllib is an industry level highly scalable rl library for tf and torch based on ray it s used by companies like amazon and microsoft to solve real world decision making problems at scale di engine https github com opendilab di engine di engine is a generalized decision intelligence engine it supports most basic deep reinforcement learning drl algorithms such as dqn ppo sac and domain specific algorithms like qmix in multi agent rl gail in inverse rl and rnd in exploration problems a name ruby a ruby a name ruby natural language processing a natural language processing awesome nlp with ruby https github com arbox nlp with ruby curated link list for practical natural language processing in ruby treat https github com louismullie treat text retrieval and annotation toolkit definitely the most comprehensive toolkit i ve encountered so far for ruby stemmer https github com aurelian ruby stemmer expose libstemmer c to ruby deprecated raspell https sourceforge net projects raspell raspell is an interface binding for ruby deprecated uea stemmer https github com ealdent uea stemmer ruby port of uealite stemmer a conservative stemmer for search and indexing twitter text rb https github com twitter twitter text tree master rb a library that does auto linking and extraction of usernames lists and hashtags in tweets a name ruby general purpose machine learning a general purpose machine learning awesome machine learning with ruby https github com arbox machine learning with ruby curated list of ml related resources for ruby ruby machine learning https github com tsycho ruby machine learning some machine learning algorithms implemented in ruby deprecated machine learning ruby https github com mizor machine learning ruby deprecated jruby mahout https github com vasinov jruby mahout jruby mahout is a gem that unleashes the power of apache mahout in the world of jruby deprecated cardmagic classifier https github com cardmagic classifier a general classifier module to allow bayesian and other types of classifications rb libsvm https github com febeling rb libsvm ruby language bindings for libsvm which is a library for support vector machines scoruby https github com asafschers scoruby creates random forest classifiers from pmml files rumale https github com yoshoku rumale rumale is a machine learning library in ruby a name ruby data analysis data visualization a data analysis data visualization rsruby https github com alexgutteridge rsruby ruby r bridge data visualization ruby https github com chrislo data visualisation ruby source code and supporting content for my ruby manor presentation on data visualisation with ruby deprecated ruby plot https www ruby toolbox com projects ruby plot gnuplot wrapper for ruby especially for plotting roc curves into svg files deprecated plot rb https github com zuhao plotrb a plotting library in ruby built on top of vega and d3 deprecated scruffy https github com delano scruffy a beautiful graphing toolkit for ruby sciruby http sciruby com glean https github com glean glean a data management tool for humans deprecated bioruby https github com bioruby bioruby arel https github com nkallen arel deprecated a name ruby misc a misc big data for chimps https github com infochimps labs big data for chimps listof https github com kevincobain2000 listof community based data collection packed in gem get list of pretty much anything stop words countries non words in txt json or hash demo search for a list http kevincobain2000 github io listof a name rust a rust a name rust general purpose machine learning a general purpose machine learning smartcore https github com smartcorelib smartcore the most advanced machine learning library in rust linfa https github com rust ml linfa a comprehensive toolkit to build machine learning applications with rust deeplearn rs https github com tedsta deeplearn rs deeplearn rs provides simple networks that use matrix multiplication addition and relu under the mit license rustlearn https github com maciejkula rustlearn a machine learning framework featuring logistic regression support vector machines decision trees and random forests rusty machine https github com athemathmo rusty machine a pure rust machine learning library leaf https github com autumnai leaf open source framework for machine intelligence sharing concepts from tensorflow and caffe available under the mit license deprecated https medium com mjhirn tensorflow wins 89b78b29aafb s0a3uy4cc rustnn https github com jackm321 rustnn rustnn is a feedforward neural network library deprecated rusticsom https github com avinashshenoy97 rusticsom a rust library for self organising maps som a name r a r a name r general purpose machine learning a general purpose machine learning ahaz https cran r project org web packages ahaz index html ahaz regularization for semiparametric additive hazards regression deprecated arules https cran r project org web packages arules index html arules mining association rules and frequent itemsets biglasso https cran r project org web packages biglasso index html biglasso extending lasso model fitting to big data in r bmrm https cran r project org web packages bmrm index html bmrm bundle methods for regularized risk minimization package boruta https cran r project org web packages boruta index html boruta a wrapper algorithm for all relevant feature selection bst https cran r project org web packages bst index html bst gradient boosting c50 https cran r project org web packages c50 index html c50 c5 0 decision trees and rule based models caret https topepo github io caret index html classification and regression training unified interface to 150 ml algorithms in r caretensemble https cran r project org web packages caretensemble index html caretensemble framework for fitting multiple caret models as well as creating ensembles of such models deprecated catboost https github com catboost catboost general purpose gradient boosting on decision trees library with categorical features support out of the box for r clever algorithms for machine learning https machinelearningmastery com corelearn https cran r project org web packages corelearn index html corelearn classification regression feature evaluation and ordinal evaluation coxboost https cran r project org web packages coxboost index html coxboost cox models by likelihood based boosting for a single survival endpoint or competing risks deprecated cubist https cran r project org web packages cubist index html cubist rule and instance based regression modelling e1071 https cran r project org web packages e1071 index html e1071 misc functions of the department of statistics e1071 tu wien earth https cran r project org web packages earth index html earth multivariate adaptive regression spline models elasticnet https cran r project org web packages elasticnet index html elasticnet elastic net for sparse estimation and sparse pca elemstatlearn https cran r project org web packages elemstatlearn index html elemstatlearn data sets functions and examples from the book the elements of statistical learning data mining inference and prediction by trevor hastie robert tibshirani and jerome friedman prediction by trevor hastie robert tibshirani and jerome friedman evtree https cran r project org web packages evtree index html evtree evolutionary learning of globally optimal trees forecast https cran r project org web packages forecast index html forecast timeseries forecasting using arima ets stlm tbats and neural network models forecasthybrid https cran r project org web packages forecasthybrid index html forecasthybrid automatic ensemble and cross validation of arima ets stlm tbats and neural network models from the forecast package fpc https cran r project org web packages fpc index html fpc flexible procedures for clustering frbs https cran r project org web packages frbs index html frbs fuzzy rule based systems for classification and regression tasks deprecated gamboost https cran r project org web packages gamboost index html gamboost generalized linear and additive models by likelihood based boosting deprecated gamboostlss https cran r project org web packages gamboostlss index html gamboostlss boosting methods for gamlss gbm https cran r project org web packages gbm index html gbm generalized boosted regression models glmnet https cran r project org web packages glmnet index html glmnet lasso and elastic net regularized generalized linear models glmpath https cran r project org web packages glmpath index html glmpath l1 regularization path for generalized linear models and cox proportional hazards model gmmboost https cran r project org web packages gmmboost index html gmmboost likelihood based boosting for generalized mixed models deprecated grplasso https cran r project org web packages grplasso index html grplasso fitting user specified models with group lasso penalty grpreg https cran r project org web packages grpreg index html grpreg regularization paths for regression models with grouped covariates h2o https cran r project org web packages h2o index html a framework for fast parallel and distributed machine learning algorithms at scale deeplearning random forests gbm kmeans pca glm hda https cran r project org web packages hda index html hda heteroscedastic discriminant analysis deprecated introduction to statistical learning https www bcf usc edu gareth isl ipred https cran r project org web packages ipred index html ipred improved predictors kernlab https cran r project org web packages kernlab index html kernlab kernel based machine learning lab klar https cran r project org web packages klar index html klar classification and visualization l0learn https cran r project org web packages l0learn index html l0learn fast algorithms for best subset selection lars https cran r project org web packages lars index html lars least angle regression lasso and forward stagewise deprecated lasso2 https cran r project org web packages lasso2 index html lasso2 l1 constrained estimation aka lasso liblinear https cran r project org web packages liblinear index html liblinear linear predictive models based on the liblinear c c library logicreg https cran r project org web packages logicreg index html logicreg logic regression machine learning for hackers https github com johnmyleswhite ml for hackers maptree https cran r project org web packages maptree index html maptree mapping pruning and graphing tree models deprecated mboost https cran r project org web packages mboost index html mboost model based boosting medley https www kaggle com general 3661 medley blending regression models using a greedy stepwise approach mlr https cran r project org web packages mlr index html mlr machine learning in r ncvreg https cran r project org web packages ncvreg index html ncvreg regularization paths for scad and mcp penalized regression models nnet https cran r project org web packages nnet index html nnet feed forward neural networks and multinomial log linear models deprecated pamr https cran r project org web packages pamr index html pamr pam prediction analysis for microarrays deprecated party https cran r project org web packages party index html party a laboratory for recursive partitioning partykit https cran r project org web packages partykit index html partykit a toolkit for recursive partitioning penalized https cran r project org web packages penalized index html penalized l1 lasso and fused lasso and l2 ridge penalized estimation in glms and in the cox model penalizedlda https cran r project org web packages penalizedlda index html penalizedlda penalized classification using fisher s linear discriminant deprecated penalizedsvm https cran r project org web packages penalizedsvm index html penalizedsvm feature selection svm using penalty functions quantregforest https cran r project org web packages quantregforest index html quantregforest quantile regression forests randomforest https cran r project org web packages randomforest index html randomforest breiman and cutler s random forests for classification and regression randomforestsrc https cran r project org web packages randomforestsrc index html randomforestsrc random forests for survival regression and classification rf src rattle https cran r project org web packages rattle index html rattle graphical user interface for data mining in r rda https cran r project org web packages rda index html rda shrunken centroids regularized discriminant analysis rdetools https cran r project org web packages rdetools index html rdetools relevant dimension estimation rde in feature spaces deprecated reemtree https cran r project org web packages reemtree index html reemtree regression trees with random effects for longitudinal panel data deprecated relaxo https cran r project org web packages relaxo index html relaxo relaxed lasso deprecated rgenoud https cran r project org web packages rgenoud index html rgenoud r version of genetic optimization using derivatives rmalschains https cran r project org web packages rmalschains index html rmalschains continuous optimization using memetic algorithms with local search chains ma ls chains in r rminer https cran r project org web packages rminer index html rminer simpler use of data mining methods e g nn and svm in classification and regression deprecated rocr https cran r project org web packages rocr index html rocr visualizing the performance of scoring classifiers deprecated roughsets https cran r project org web packages roughsets index html roughsets data analysis using rough set and fuzzy rough set theories deprecated rpart https cran r project org web packages rpart index html rpart recursive partitioning and regression trees rpmm https cran r project org web packages rpmm index html rpmm recursively partitioned mixture model rsnns https cran r project org web packages rsnns index html rsnns neural networks in r using the stuttgart neural network simulator snns rweka https cran r project org web packages rweka index html rweka r weka interface rxshrink https cran r project org web packages rxshrink index html rxshrink maximum likelihood shrinkage via generalized ridge or least angle regression sda https cran r project org web packages sda index html sda shrinkage discriminant analysis and cat score variable selection deprecated spectralgraphtopology https cran r project org web packages spectralgraphtopology index html spectralgraphtopology learning graphs from data via spectral constraints superlearner https github com ecpolley superlearner multi algorithm ensemble learning packages svmpath https cran r project org web packages svmpath index html svmpath svmpath the svm path algorithm deprecated tgp https cran r project org web packages tgp index html tgp bayesian treed gaussian process models deprecated tree https cran r project org web packages tree index html tree classification and regression trees varselrf https cran r project org web packages varselrf index html varselrf variable selection using random forests xgboost r https github com tqchen xgboost tree master r package r binding for extreme gradient boosting tree library optunity https optunity readthedocs io en latest a library dedicated to automated hyperparameter optimization with a simple lightweight api to facilitate drop in replacement of grid search optunity is written in python but interfaces seamlessly to r igraph https igraph org r binding to igraph library general purpose graph library mxnet https github com apache incubator mxnet lightweight portable flexible distributed mobile deep learning with dynamic mutation aware dataflow dep scheduler for python r julia go javascript and more tdsp utilities https github com azure azure tdsp utilities two data science utilities in r from microsoft 1 interactive data exploration analysis and reporting idear 2 automated modelling and reporting amr a name r data analysis data visualization a data manipulation data analysis data visualization dplyr https www rdocumentation org packages dplyr versions 0 7 8 a data manipulation package that helps to solve the most common data manipulation problems ggplot2 https ggplot2 tidyverse org a data visualization package based on the grammar of graphics tmap https cran r project org web packages tmap vignettes tmap getstarted html for visualizing geospatial data with static maps and leaflet https rstudio github io leaflet for interactive maps tm https www rdocumentation org packages tm and quanteda https quanteda io are the main packages for managing analyzing and visualizing textual data shiny https shiny rstudio com is the basis for truly interactive displays and dashboards in r however some measure of interactivity can be achieved with htmlwidgets https www htmlwidgets org bringing javascript libraries to r these include plotly https plot ly r dygraphs http rstudio github io dygraphs highcharter http jkunst com highcharter and several others a name sas a sas a name sas general purpose machine learning a general purpose machine learning visual data mining and machine learning https www sas com en us software visual data mining machine learning html interactive automated and programmatic modelling with the latest machine learning algorithms in and end to end analytics environment from data prep to deployment free trial available enterprise miner https www sas com en us software enterprise miner html data mining and machine learning that creates deployable models using a gui or code factory miner https www sas com en us software factory miner html automatically creates deployable machine learning models across numerous market or customer segments using a gui a name sas data analysis data visualization a data analysis data visualization sas stat https www sas com en us software stat html for conducting advanced statistical analysis university edition https www sas com en us software university edition html free includes all sas packages necessary for data analysis and visualization and includes online sas courses a name sas natural language processing a natural language processing contextual analysis https www sas com en us software contextual analysis html add structure to unstructured text using a gui sentiment analysis https www sas com en us software sentiment analysis html extract sentiment from text using a gui text miner https www sas com en us software text miner html text mining using a gui or code a name sas demos and scripts a demos and scripts ml tables https github com sassoftware enlighten apply tree master ml tables concise cheat sheets containing machine learning best practices enlighten apply https github com sassoftware enlighten apply example code and materials that illustrate applications of sas machine learning techniques enlighten integration https github com sassoftware enlighten integration example code and materials that illustrate techniques for integrating sas with other analytics technologies in java pmml python and r enlighten deep https github com sassoftware enlighten deep example code and materials that illustrate using neural networks with several hidden layers in sas dm flow https github com sassoftware dm flow library of sas enterprise miner process flow diagrams to help you learn by example about specific data mining topics a name scala a scala a name scala natural language processing a natural language processing scalanlp http www scalanlp org scalanlp is a suite of machine learning and numerical computing libraries breeze https github com scalanlp breeze breeze is a numerical processing library for scala chalk https github com scalanlp chalk chalk is a natural language processing library deprecated factorie https github com factorie factorie factorie is a toolkit for deployable probabilistic modelling implemented as a software library in scala it provides its users with a succinct language for creating relational factor graphs estimating parameters and performing inference montague https github com workday upshot montague montague is a semantic parsing library for scala with an easy to use dsl spark nlp https github com johnsnowlabs spark nlp natural language processing library built on top of apache spark ml to provide simple performant and accurate nlp annotations for machine learning pipelines that scale easily in a distributed environment a name scala data analysis data visualization a data analysis data visualization ndscala https github com sciscala ndscala n dimensional arrays in scala 3 think numpy ndarray but with compile time type checking inference over shapes tensor axis labels numeric data types mllib in apache spark https spark apache org docs latest mllib guide html distributed machine learning library in spark hydrosphere mist https github com hydrospheredata mist a service for deployment apache spark mllib machine learning models as realtime batch or reactive web services scalding https github com twitter scalding a scala api for cascading summing bird https github com twitter summingbird streaming mapreduce with scalding and storm algebird https github com twitter algebird abstract algebra for scala xerial https github com xerial xerial data management utilities for scala deprecated predictionio https github com apache predictionio predictionio a machine learning server for software developers and data engineers bidmat https github com biddata bidmat cpu and gpu accelerated matrix library intended to support large scale exploratory data analysis flink https flink apache org open source platform for distributed stream and batch data processing spark notebook http spark notebook io interactive and reactive data science using scala and spark a name scala general purpose machine learning a general purpose machine learning microsoft ml for apache spark https github com azure mmlspark a distributed machine learning framework apache spark onnx scala https github com emergentorder onnx scala an onnx open neural network exchange api and backend for typeful functional deep learning in scala 3 deeplearning scala https deeplearning thoughtworks school creating statically typed dynamic neural networks from object oriented functional programming constructs conjecture https github com etsy conjecture scalable machine learning in scalding brushfire https github com stripe brushfire distributed decision tree ensemble learning in scala ganitha https github com tresata ganitha scalding powered machine learning deprecated adam https github com bigdatagenomics adam a genomics processing engine and specialized file format built using apache avro apache spark and parquet apache 2 licensed bioscala https github com bioscala bioscala bioinformatics for the scala programming language bidmach https github com biddata bidmach cpu and gpu accelerated machine learning library figaro https github com p2t2 figaro a scala library for constructing probabilistic models h2o sparkling water https github com h2oai sparkling water h2o and spark interoperability flinkml in apache flink https ci apache org projects flink flink docs master dev libs ml index html distributed machine learning library in flink dynaml https github com transcendent ai labs dynaml scala library repl for machine learning research saul https github com cogcomp saul flexible declarative learning based programming swiftlearner https github com valdanylchuk swiftlearner simply written algorithms to help study ml or write your own implementations smile https haifengl github io statistical machine intelligence and learning engine doddle model https github com picnicml doddle model an in memory machine learning library built on top of breeze it provides immutable objects and exposes its functionality through a scikit learn like api tensorflow scala https github com eaplatanios tensorflow scala strongly typed scala api for tensorflow a name scheme a scheme a name scheme neural networks a neural networks layer https github com cloudkj layer neural network inference from the command line implemented in chicken scheme https www call cc org a name swift a swift a name swift general purpose machine learning a general purpose machine learning bender https github com xmartlabs bender fast neural networks framework built on top of metal supports tensorflow models swift ai https github com swift ai swift ai highly optimized artificial intelligence and machine learning library written in swift swift for tensorflow https github com tensorflow swift a next generation platform for machine learning incorporating the latest research across machine learning compilers differentiable programming systems design and beyond braincore https github com alejandro isaza braincore the ios and os x neural network framework swix https github com stsievert swix a bare bones library that includes a general matrix language and wraps some opencv for ios development deprecated aitoolbox https github com kevincoble aitoolbox a toolbox framework of ai modules written in swift graphs trees linear regression support vector machines neural networks pca kmeans genetic algorithms mdp mixture of gaussians mlkit https github com somnibyte mlkit a simple machine learning framework written in swift currently features simple linear regression polynomial regression and ridge regression swift brain https github com vlall swift brain the first neural network machine learning library written in swift this is a project for ai algorithms in swift for ios and os x development this project includes algorithms focused on bayes theorem neural networks svms matrices etc perfect tensorflow https github com perfectlysoft perfect tensorflow swift language bindings of tensorflow using native tensorflow models on both macos linux predictionbuilder https github com denissimon prediction builder swift a library for machine learning that builds predictions using a linear regression awesome coreml https github com swiftbrain awesome coreml models a curated list of pretrained coreml models awesome core ml models https github com likedan awesome coreml models a curated list of machine learning models in coreml format a name tensorflow a tensorflow a name tensorflow general purpose machine learning a general purpose machine learning awesome keras https github com markusschanta awesome keras a curated list of awesome keras projects libraries and resources awesome tensorflow https github com jtoy awesome tensorflow a list of all things related to tensorflow golden tensorflow https golden com wiki tensorflow a page of content on tensorflow including academic papers and links to related topics a name tools a tools a name tools neural networks a neural networks layer https github com cloudkj layer neural network inference from the command line a name tools misc a misc synthical https synthical com ai powered collaborative research environment you can use it to get recommendations of articles based on reading history simplify papers find out what articles are trending search articles by meaning not just keywords create and share folders of articles see lists of articles from specific companies and universities and add highlights humanloop https humanloop com humanloop is a platform for prompt experimentation finetuning models for better performance cost optimization and collecting model generated data and user feedback qdrant https qdrant tech qdrant is open source https github com qdrant qdrant vector similarity search engine with extended filtering support written in rust milvus https milvus io milvus is open source https github com milvus io milvus vector database for production ai written in go and c scalable and blazing fast for billions of embedding vectors weaviate https www semi technology developers weaviate current weaviate is an open source https github com semi technologies weaviate vector search engine and vector database weaviate uses machine learning to vectorize and store data and to find answers to natural language queries with weaviate you can also bring your custom ml models to production scale txtai https github com neuml txtai build semantic search applications and workflows mlreef https about mlreef com mlreef is an end to end development platform using the power of git to give structure and deep collaboration possibilities to the ml development process pinecone https www pinecone io vector database for applications that require real time scalable vector embedding and similarity search catalyzex https chrome google com webstore detail code finder for research aikkeehnlfpamidigaffhfmgbkdeheil browser extension chrome https chrome google com webstore detail code finder for research aikkeehnlfpamidigaffhfmgbkdeheil and firefox https addons mozilla org en us firefox addon code finder catalyzex that automatically finds and shows code implementations for machine learning papers anywhere google twitter arxiv scholar etc ml workspace https github com ml tooling ml workspace all in one web based ide for machine learning and data science the workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries e g tensorflow pytorch and dev tools e g jupyter vs code notebooks https github com rlan notebooks a starter kit for jupyter notebooks and machine learning companion docker images consist of all combinations of python versions machine learning frameworks keras pytorch and tensorflow and cpu cuda versions dvc https github com iterative dvc data science version control is an open source version control system for machine learning projects with pipelines support it makes ml projects reproducible and shareable dvclive https github com iterative dvclive python library for experiment metrics logging into simply formatted local files vdp https github com instill ai vdp open source visual data etl to streamline the end to end visual data processing pipeline extract unstructured visual data from pre built data sources transform it into analysable structured insights by vision ai models imported from various ml platforms and load the insights into warehouses or applications kedro https github com quantumblacklabs kedro kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models guild ai https guild ai tool to log analyze compare and optimize experiments it s cross platform and framework independent and provided integrated visualizers such as tensorboard sacred https github com idsia sacred python tool to help you configure organize log and reproduce experiments like a notebook lab in the context of chemistry biology the community has built multiple add ons leveraging the proposed standard comet https www comet com ml platform for tracking experiments hyper parameters artifacts and more it s deeply integrated with over 15 deep learning frameworks and orchestration tools users can also use the platform to monitor their models in production mlflow https mlflow org platform to manage the ml lifecycle including experimentation reproducibility and deployment framework and language agnostic take a look at all the built in integrations weights biases https www wandb com machine learning experiment tracking dataset versioning hyperparameter search visualization and collaboration more tools to improve the ml lifecycle catalyst https github com catalyst team catalyst pachydermio https www pachyderm io the following are github alike and targeting teams weights biases https www wandb com neptune ai https neptune ai comet ml https www comet ml valohai ai https valohai com dagshub https dagshub com arize ai https www arize com model validaiton and performance monitoring drift detection explainability visualization across structured and unstructured data machinelearningwithtensorflow2ed https www manning com books machine learning with tensorflow second edition a book on general purpose machine learning techniques regression classification unsupervised clustering reinforcement learning auto encoders convolutional neural networks rnns lstms using tensorflow 1 14 1 m2cgen https github com bayeswitnesses m2cgen a tool that allows the conversion of ml models into native code java c python go javascript visual basic c r powershell php dart with zero dependencies cml https github com iterative cml a library for doing continuous integration with ml projects use github actions gitlab ci to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull merge requests framework language agnostic pythonizr https pythonizr com an online tool to generate boilerplate machine learning code that uses scikit learn flyte https flyte org flyte makes it easy to create concurrent scalable and maintainable workflows for machine learning and data processing chaos genius https github com chaos genius chaos genius ml powered analytics engine for outlier anomaly detection and root cause analysis mlem https github com iterative mlem version and deploy your ml models following gitops principles dockerdl https github com matifali dockerdl ready to use deeplearning docker images aqueduct https github com aqueducthq aqueduct aqueduct enables you to easily define run and manage ai ml tasks on any cloud infrastructure ambrosia https github com reactorsh ambrosia ambrosia helps you clean up your llm datasets using other llms a name books a books distributed machine learning patterns https github com terrytangyuan distributed ml patterns this book teaches you how to take machine learning models from your personal laptop to large distributed clusters you ll explore key concepts and patterns behind successful distributed machine learning systems and learn technologies like tensorflow kubernetes kubeflow and argo workflows directly from a key maintainer and contributor with real world scenarios and hands on projects grokking machine learning https www manning com books grokking machine learning grokking machine learning teaches you how to apply ml to your projects using only standard python code and high school level math machine learning bookcamp https www manning com books machine learning bookcamp learn the essentials of machine learning by completing a carefully designed set of real world projects hands on machine learning with scikit learn keras and tensorflow https www amazon com hands machine learning scikit learn tensorflow dp 1098125975 through a recent series of breakthroughs deep learning has boosted the entire field of machine learning now even programmers who know close to nothing about this technology can use simple efficient tools to implement programs capable of learning from data this bestselling book uses concrete examples minimal theory and production ready python frameworks scikit learn keras and tensorflow to help you gain an intuitive understanding of the concepts and tools for building intelligent systems a name credits a netron https netron app an opensource viewer for neural network deep learning and machine learning models teachable machine https teachablemachine withgoogle com train machine learning models on the fly to recognize your own images sounds poses model zoo https modelzoo co discover open source deep learning code and pretrained models credits some of the python libraries were cut and pasted from vinta https github com vinta awesome python references for go were mostly cut and pasted from gopherdata https github com gopherdata resources tree master tooling | ai |
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HackerBooks | hackerbooks presentaci n esta es la aplicaci n de pr ctica desarrollada para el curso de fundamentos ios del keepcoding startup bootcamp engineering master iv respuestas a preguntas jsonserialization devuelve un par metro any que puede contener tanto un array de dictionary como un dictionary mira en la ayuda el m todo type of y como usarlo para saber qu te han devuelto exactamente en qu otros modos podemos trabajar is as en este caso utilice la siguiente aproximaci n typealias jsonobject anyobject typealias jsondictionary string jsonobject typealias jsonarray jsondictionary func loadjsonfilefrom localurl url throws jsonarray if let data try data contentsof localurl let maybearray any try jsonserialization jsonobject with data options jsonserialization readingoptions mutablecontainers if let array maybearray as nsarray as array es un array de diccionarios return array as jsonarray else if let dic maybearray as nsdictionary as dictionary es un diccionario metemos el diccionario dentro de un array antes de regresarlo let array jsondictionary dic as jsondictionary return array else formato de json incorrecto throw hackerbookserrors wrongjsonformat else formato de json incorrecto throw hackerbookserrors wrongjsonformat compruebo si lo que me regreso jsonselialization es un array de diccionarios si no lo es compruebo si es un diccionario solitario si lo es lo meto dentro de un array como nico elemento para poder regresar el tipo correcto en mi funci n si las dos cosas fallan dor por hecho que estoy leyendo un archivo json con formato incorrecto hice la prueba enviandole un diccionario solicitado a esta funci n y todo ok descarga el json y gu rdalo en la carpeta documents de tu sandbox haz lo mismo para las im genes de portada y los pdf s d nde guardar as estos datos se utilizo la librer a asyncdata para la descarga de las im genes y los pdf s esta librer a guarda y recupera lo descargado desde la carpeta cache por lo que pude investigar cosa que me parece lo correcto el ser o no favorito se indicar mediante una propiedad isfavorite de book y esto se debe guardar en sistema de ficheros y recupear de alguna forma c mo har as eso se te ocurre m s de una forma de hacerlo el m todo seleccionado fue el de usar userdefaults para guardar la selecci n se uso el title hashvalue como identificador nico del libro para recuperar el setting posteriormente tambien se pudo usar un dictionary y guardarlo en un archivo plist tambien se pude usar core data para guardar la informaci n en una base de datos del tipo sqlite por ejemplo otra opci n es implementar una clase con nscodig para hacer el archive y unarchive de archivos serializables usando nsdata cuando cambia el valo de la propiedad isfavorite de un book la tabla deber reflejar ese hecho c mo lo har as se te ocurre m s de una forma de hacerlo cu l the parece mejo explica tu elecci n en este caso escog hacer una notificaci n para notificar el cambio de esta propiedad a esta notificaci n se pueden suscribir m s de un objecto clase en mi caso me interesa que el controlador libraryviewcontroller se entere de este cambio la ventaja de escojer notificaciones en lugar de otros m todos delegados por ejemplo es que en un momento dado en otra parte de de la app este cambio pueda ser importante tenerlo en cuenta para que la tabla se actulice usa el m todo reloaddata de uitableview esto hace que la tabla vuelva a pedir datos a su datasource es esto una aberraci n desde el punto de vista del rendimiento hay una forma alternativa cu ndo crees que vale la pena usarlo cuando existen varios elementos en la tabla divididas en grupos secciones como es nuestro caso el recargar toda la tabla no es lo m s eficiente ya que consume tiempo recursos y puede bloquear un poco la interfaz en este caso se debe recargar solo la secci n que sabemos ha sufrido cambios favorite para esto se puede utilizar el m todo reloadsections withrowanimation del uitableview cuando el usuario cambia en la tabla el libro seleccionado el pdfviewcontroller debe actualizarse c mo lo har as se puede hacer mediante delegados o notificaciones en este caso opte por la notificaci n porque es m s f cil de implementar ya que hace lo que necesitamos con menos c digo qu funcionalidades le a adir as antes de subirla a la app store antes de subirlo a la app store mejorar a el lector de pdf porque utilice el uiwebview que tiene una funcionalidad b sica para la lectura de pdf s ser a ideal poder hacer anotaciones marcaci n de texto tener bookmarks para encontrar r pidamente informaci n que el usuario le parezca relevante continuar en la ltima p gina vista en cada book se requieren mejores im gnes para las portadas de los libros un buen icono y la posibilidad de que de vez en vez se recargar el json file desde la web para ir agregando nuevas publicaciones por ejemplo usando esta app como plantilla qu otras versiones se te ocurren algo que puedas monetizar se puede monetizar agregando publicidad por ejemplo que mostrar un ad de pantalla completa cada 10 min con posibilidad de eliminarla pasando a la versi n completa sin publicidad y con todas las extra features se pueden poner previews del libros nuevos con posibilidad de llevar al usuario a una tienda de libros amazon o el propio itunes book store y ganar un poco de dinero usando links de afiliados de la tienda tengo una idea de una app que sirva para descargar textos de tem ticas interesantes y variadas cada texto ser a enviado en dos idiomas ingl s y espa ol por ejemplo entonces el usuario podr empezar a leer el texto en el idioma de su preferencia y en un momento dado tener la posibilidad de comparar con el otro texto del otro idioma el objetivo es que estudiantes de un idioma puedan practicar la lectura y tener la traducci n al instante para poder entender palabras u oraciones r pidamente la monetizaci n ser a con publicidad como en el caso anterior y con posibilidad de suscribirse por una cuota mensual para recibir nuevos textos de diferente nivel y tem tica seg n lo prefiera lector caracter sticas extras pueden ser por ejemplo que el usuario pueda agregar flashcards de manera r pida para en un futuro repasar las palabras o frases que quisiera recordar y aprender las posibilidades son ilimitadas | os |
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Restaurant-App | restaurant app if you have any questions don t hesitate to ask me on twitter https twitter com jurabek az gitter https badges gitter im restaurant app community community svg https gitter im restaurant app community community utm source badge utm medium badge utm campaign pr badge restaurant app is containerized polyglot microservices application that contains projects based on net core golang java xamarin react angular and etc the project demonstrates how to develop small microservices for larger applications using containers orchestration service discovery gateway and best practices you are always welcome to improve code quality and contribute it if you have any questions or issues don t hesitate to ask in our gitter https gitter im restaurant app community community utm source badge utm medium badge utm campaign pr badge chat to getting started simply fork this repository please refer to contributing md contributing md for contribution guidelines motivation developing independently deployable and scalable micro services based on best practies using containerization developing cross platform beautiful mobile apps using xamarin forms developing single page applications using react and angular including best practices configuring fully automated ci cd pipelines using github actions to mono repo and azure pipelines and appcenter for mobile using modern technologies such as graphql grpc apache kafka serverless istio writing clean maintainable and fully testable code unit testing integration testing and mocking practices using solid design principles using design patterns and best practices in different programming languages architecture overview the architecture proposes a micro service oriented architecture implementation with multiple autonomous micro services each one owning its own data db and programming language and using rest http as the communication protocol between the client apps and grpc for the backend communication in order to support data update propagation across multiple services list of micro services and infrastructure components table thead th th th service th th description th th build status th th quality th th endpoints th thead tbody tr td align center 1 td td identity api net core identityserver4 td td identity management service powered by oauth2 and openid connect td td a href https github com jurabek restaurant app actions query workflow 3aidentity api img src https github com jurabek restaurant app workflows identity api badge svg a td td a href https sonarcloud io dashboard id restaurant identity api img src https sonarcloud io api project badges measure project restaurant identity api metric alert status a td td a href dev a a href prod a td tr tr td align center 2 td td basket api golang redis td td manages customer basket in order to keep items on in memory cache using redis td td a href https github com jurabek restaurant app actions query workflow 3abasket api img src https github com jurabek restaurant app workflows basket api badge svg a td td a href https sonarcloud io dashboard id restaurant basket api img src https sonarcloud io api project badges measure project restaurant basket api metric alert status a td td a href dev a a href prod a td tr tr td align center 3 td td menu api net core postgresql td td manages data for showing restaurant menu td td a href https github com jurabek restaurant app actions query workflow 3amenu api img src https github com jurabek restaurant app workflows menu api badge svg a td td a href https sonarcloud io dashboard id restaurant menu api img src https sonarcloud io api project badges measure project restaurant menu api metric alert status a td td a href dev a a href prod a td tr tr td align center 4 td td order api java spring boot td td manages customer orders td td a href https github com jurabek restaurant app actions query workflow 3aorder api img src https github com jurabek restaurant app workflows order api badge svg a td td a href https sonarcloud io dashboard id restaurant order api img src https sonarcloud io api project badges measure project restaurant order api metric alert status a td td a href dev a a href prod a td tr tbody table mobile app unfortunately i no longer be able to maintain xamarin mobile part https github com chayxana restaurant app issues 81 mobile build status release android build status https dev azure com jurabek restaurant 20app apis build status chayxana restaurant app branchname develop jobname android https dev azure com jurabek restaurant 20app build latest definitionid 11 branchname develop download android ios build status https dev azure com jurabek restaurant 20app apis build status chayxana restaurant app branchname develop jobname ios https dev azure com jurabek restaurant 20app build latest definitionid 11 branchname develop download ios mobile app developed by xamarin forms and supports ios and android here you can find how to develop cross platform mobile apps using c the example shows how to develop beautiful user interfaces using xamarin forms and how to manage your code with clean architecture on the mobile side and get a clean maintainable testable code img src art 2 png width 210 img src art 3 png width 210 contributors thank you to all the people who have already contributed to our project a href graphs contributors img src https opencollective com restaurant app contributors svg width 890 a | xamarin-forms reactiveui identityserver4 mvvm angular microservices-architecture docker kubernetes react typescript netcore spring-boot microservices golang xamarin polyglot-microservices gitlab-ci sonarqube design-patterns solid-principles | front_end |
linux-nova | nova non volatile memory accelerated log structured file system linux versions supported 5 1 current master 5 0 4 19 4 18 4 14 4 13 checkout each branch if you are interested description nova s goal is to provide a high performance full featured production ready file system tailored for byte addressable non volatile memories e g nvdimms and intel s soon to be released 3dxpoint dimms it combines design elements from many other file systems to provide a combination of high performance strong consistency guarantees and comprehensive data protection nova support dax style mmap and making dax performs well is a first order priority in nova s design nova was developed by the non volatile systems laboratory nvsl in the computer science and engineering department cse at the university of california san diego ucsd nova is primarily a log structured file system but rather than maintain a single global log for the entire file system it maintains separate logs for each file inode nova breaks the logs into 4kb pages they need not be contiguous in memory the logs only contain metadata file data pages reside outside the log and log entries for write operations point to data pages they modify file modification uses copy on write cow to provide atomic file updates for file operations that involve multiple inodes nova use small fixed sized redo logs to atomically append log entries to the logs of the inodes involned this structure keeps logs small and make garbage collection very fast it also enables enormous parallelism during recovery from an unclean unmount since threads can scan logs in parallel nova replicates and checksums all metadata structures and protects file data with raid 4 style parity it supports checkpoints to facilitate backups this repository contains a version of the mainline kernel with nova added you can check the current version by looking at the first lines of the makefile a more thorough discussion of nova s design is avaialable in these two papers nova a log structured file system for hybrid volatile non volatile main memories pdf http cseweb ucsd edu swanson papers fast2016nova pdf br jian xu and steven swanson br published in fast 2016 fast2016 hardening the nova file system pdf http cseweb ucsd edu swanson papers techreport2017hardenednova pdf br ucsd cse techreport cs2017 1018 jian xu lu zhang amirsaman memaripour akshatha gangadharaiah amit borase tamires brito da silva andy rudoff steven swanson br read on for further details about nova s overall design and its current status compatibilty with other file systems nova aims to be compatible with other linux file systems to help verify that it achieves this we run several test suites against nova each night the latest version of xfstests current failures https github com nvsl linux nova issues q is 3aopen is 3aissue label 3axfstests the linux testing project https linux test project github io file system tests the fstest posix test suite posixtest currently nearly all of these tests pass for the master branch and we have run complex programs on nova there are of course many bugs left to fix nova uses the standard pmem kernel interfaces for accessing and managing persistent memory atomicity by default nova makes all metadata and file data operations atomic strong atomicity guarantees make it easier to build reliable applications on nova and nova can provide these guarantees with sacrificing much performance because nvdimms support very fast random access nova also supports unsafe data and unsafe metadata modes that improve performance in some cases and allows for non atomic updates of file data and metadata respectively data protection nova aims to protect data against both misdirected writes in the kernel which can easily scribble over the contents of an nvdimm as well as media errors nova protects all of its metadata data structures with a combination of replication and checksums it protects file data using raid 5 style parity nova can detects data corruption by verifying checksums on each access and by catching and handling machine check exceptions mces that arise when the system s memory controller detects at uncorrectable media error we use a fault injection tool that allows testing of these recovery mechanisms to facilitate backups nova can take snapshots of the current filesystem state that can be mounted read only while the current file system is mounted read write the tech report list above describes the design of nova s data protection system in detail dax support supporting dax efficiently is a core feature of nova and one of the challenges in designing nova is reconciling dax support which aims to avoid file system intervention when file data changes and other features that require such intervention nova s philosophy with respect to dax is that when a program uses dax mmap to to modify a file the program must take full responsibility for that data and nova must ensure that the memory will behave as expected at other times the file system provides protection this approach has several implications 1 implementing msync in user space works fine 2 while a file is mmap d it is not protected by nova s raid style parity mechanism because protecting it would be too expensive when the file is unmapped and or during file system recovery protection is restored 3 the snapshot mechanism must be careful about the order in which in adds pages to the file s snapshot image performance the research paper and technical report referenced above compare nova s performance to other file systems in almost all cases nova outperforms other dax enabled file systems a notable exception is sub page updates which incur cow overheads for the entire page the technical report also illustrates the trade offs between our protection mechanisms and performance gaps missing features and development status although nova is a fully functional file system there is still much work left to be done in particular at least the following items are currently missing 1 there is no mkfs or fsk utility mount takes o init to create a nova file system 2 nova doesn t scrub data to prevent corruption from accumulating in infrequently accessed data 3 nova doesn t read bad block information on mount and attempt recovery of the effected data 4 nova only works on x86 64 kernels 5 nova does not currently support extended attributes or acl 6 nova does not currently prevent writes to mounted snapshots 7 using write to modify pages that are mmap d is not supported 8 nova deoesn t provide quota support 9 moving nova file systems between machines with different numbers of cpus does not work 10 remounting a nova file system with different mount options may fail none of these are fundamental limitations of nova s design additional bugs and issues are here https github com nvsl linux nova issues nova is complete and robust enough to run a range of complex applications but it is not yet ready for production use our current focus is on adding a few missing features list above and finding fixing bugs building and using nova this repo contains a version of the linux with nova included you should be able to build and install it just as you would the mainline linux source building nova to build nova build the kernel with libnvdimm config libnvdimm pmem config blk dev pmem dax config fs dax and nova config nova fs support install as usual when running make menuconfig you can find those options under the device drivers and file systems sections respectively documentation filesystems nova txt provides more detailed instructions on building and using nova hacking and contributing the nova source code is almost completely contains in the fs nova directory the execptions are some small changes in the kernel s memory management system to support checkpointing documentation filesystems nova txt describes the internals of nova in more detail if you find bugs please report them https github com nvsl linux nova issues if you have other questions or suggestions you can contact the nova developers at cse nova hackers eng ucsd edu mailto cse nova hackers eng ucsd edu nvsl http nvsl ucsd edu http nvsl ucsd edu posixtest http www tuxera com community posix test suite fast2016 https www usenix org conference fast16 technical sessions cse http cs ucsd edu ucsd http www ucsd edu | dax pmem filesystem linux | os |
Keep-Learning | keep learning daily motivation https www youtube com watch v jeapui9ypk0 table of contents philosophy a love of the wisdom philosophy dive into research research software engineering ml software engineering mlops core mlops core mlops infrastructure mlops infra blog resources for machine learning resources mlops model deployment and serving deployment mlops testing monitoring and maintenance testing monintoring blogs be better everyday useful blogs mit 6s191 introduction to deep learning mit deeplearning master the computer vision list of blogs and tutorials for diving deep into cv master computer vision software engineering crux core software engineering introduction to computer science introduction to computer science dive into deep learning dive into deep learning convolutional neural networks cs231n stanford university cnn cs231n first principles of computer vision principles vision must read papers in computer vision cvpapers a name cvpapers a must read papers in computer vision x u net convolutional networks for biomedical image segmentation https arxiv org abs 1505 04597 a name philosophy a learn philosophy x the development of neural networks https www allerin com blog the evolution of neural networks receptive field in cnn https theaisummer com receptive field standard gaussian distribution modelling nature https en wikipedia org wiki gaussian function convolution math driving the computer vision https en wikipedia org wiki convolution half order derivatives https math stackexchange com questions 477888 what does a half derivative mean fourier transforms for image processing https www cs unm edu brayer vision fourier html x singular value decomposition diagnolization of square matrix https towardsdatascience com understanding singular value decomposition and its application in data science 388a54be95d can you find inverse of rectangular matrix yes go through this https inst eecs berkeley edu ee127 sp21 livebook def pseudo inv html intuitively understanding convolutions for deep learning https towardsdatascience com intuitively understanding convolutions for deep learning 1f6f42faee1 image segmentation basics from tensorflow https www tensorflow org tutorials images segmentation unet line by line explanation https towardsdatascience com unet line by line explanation 9b191c76baf5 u net convolutional networks for biomedical image segmentation https arxiv org abs 1505 04597 ant unet accurate and noise tolerant segmentation for pathology image processing https ieeexplore ieee org document 8919150 deep cnn for removal of salt and pepper noise https ietresearch onlinelibrary wiley com doi 10 1049 iet ipr 2018 6004 a noise robust convolutional neural network for image classification https www sciencedirect com science article pii s2590123021000268 xception deep learning with depthwise separable convolutions https arxiv org abs 1610 02357 evidence lower bound elbo https mbernste github io posts elbo evidence kl divergence and elbo https mpatacchiola github io blog 2021 01 25 intro variational inference html pre trained deep neural networks for transfer learning advanced guide to inception v3 https cloud google com tpu docs inception v3 advanced inception v3 keras blog https keras io api applications inceptionv3 deep residual learning for image recognition https arxiv org abs 1512 03385 a name resources a resources popular modern traditional machine learning algorithms theory math implementation blog post list start x machine learning cheatsheet https ml cheatsheet readthedocs io en latest be used to with ml terms x deep learning book https www deeplearningbook org x basic image processing https opencv python tutroals readthedocs io en latest py tutorials py imgproc py table of contents imgproc py table of contents imgproc html learn basics of mark image processing mark for mark image preprocessing mark x xgboost with different categorical encoding methods https songxia sophia medium com two 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busiha jtzmpo object oriented analysis and design iit kharagpur https nptel ac in courses 106105153 cs3 design in computing richard buckland unsw https www youtube com course list ec0c5d85dba20e685c informatics 1 object oriented programming 2014 15 university of edinburgh http groups inf ed ac uk vision video 2014 inf1op htm software engineering with objects and components 2015 16 university of edinburgh http groups inf ed ac uk vision video 2015 seoc htm software engineering computer science 169 software engineering spring 2015 ucberkeley https www youtube com playlist list pl xxv cva icfqhhs7rxlfhfsu4aqw1if cs 5150 software engineering fall 2014 cornell university http www cs cornell edu courses cs5150 2014fa materials html introduction to service design and engineering university of trento italy https www youtube com playlist list plbdajhwwi0jcn87quft3e58meku0 6wut cs 164 software engineering harvard http cs164 tv 2014 spring system analysis and design iisc bangalore https nptel ac in courses 106108102 software engineering iit bombay https nptel ac in courses 106101061 dependable systems ss 2014 hpi university of potsdam https www tele task de series 1005 software testing iit kharagpur https nptel ac in courses 106105150 informatics 2c software engineering 2014 15 university of edinburgh http groups inf ed ac uk vision video 2014 inf2se htm software architecture cs 411 software architecture design bilkent university http video bilkent edu tr course videos php courseid 10 mooc software architecture design udacity https www youtube com playlist list plawxtw4syapkmtetlg7xkwai5zazfx8fl a name research a lets understand research methodology x efficient estimation of word representations in vector space https arxiv org abs 1301 3781 word2vec x extreme gradient boosting https arxiv org abs 1603 02754 a scalable tree boosting system paper a neural probabilistic language model http www jmlr org papers volume3 bengio03a bengio03a pdf paper efficient estimation of word representations in vector space https arxiv org pdf 1301 3781 pdf paper sequence to sequence learning with neural networks https papers nips cc paper 5346 sequence to sequence learning with neural networks pdf paper neural machine translation by jointly learning to align and translate https arxiv org abs 1409 0473 paper attention is all you need https arxiv org abs 1706 03762 paper bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 paper xlnet generalized autoregressive pretraining for language understanding https arxiv org abs 1906 08237 paper roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 paper glue a multi task benchmark and analysis platform for natural language understanding https www nyu edu projects bowman glue pdf paper amazon com recommendations item to item collaborative filtering https www cs umd edu samir 498 amazon recommendations pdf paper collaborative filtering for implicit feedback datasets http citeseerx ist psu edu viewdoc download jsessionid 34aeee06f0c2428083376c26c71d7cff doi 10 1 1 167 5120 rep rep1 type pdf paper bpr bayesian personalized ranking from implicit feedback https arxiv org pdf 1205 2618 pdf paper factorization machines https cseweb ucsd edu classes fa17 cse291 b reading rendle2010fm pdf paper wide deep learning for recommender systems https arxiv org pdf 1606 07792 pdf paper multiword expressions a pain in the neck for nlp http lingo stanford edu pubs wp 2001 03 pdf paper pytorch an imperative style high performance deep learning library https arxiv org pdf 1912 01703 pdf paper albert a lite bert for self supervised learning of language representations https arxiv org pdf 1909 11942 pdf paper self supervised visual feature learning with deep neural networks a survey https arxiv org abs 1902 06162 paper a simple framework for contrastive learning of visual representations https arxiv org pdf 2002 05709 pdf paper self supervised learning of pretext invariant representations https arxiv org abs 1912 01991 paper fixmatch simplifying semi supervised learning with consistency and confidence https arxiv org abs 2001 07685 paper self labelling via simultalaneous clustering and representation learning https www robots ox ac uk vgg research self label paper a survey on semi self and unsupervised techniques in image classification https arxiv org abs 2002 08721 paper train once test anywhere zero shot learning for text classification https arxiv org abs 1712 05972 paper zero shot text classification with generative language models https arxiv org abs 1912 10165 paper how to fine tune bert for text classification https arxiv org abs 1905 05583 paper universal sentence encoder https arxiv org abs 1803 11175 paper enriching word vectors with subword information https arxiv org abs 1607 04606 paper beyond accuracy behavioral testing of nlp models with checklist https arxiv org abs 2005 04118 paper temporal ensembling for semi supervised learning https arxiv org abs 1610 02242 paper boosting self supervised learning via knowledge transfer https arxiv org abs 1805 00385 paper follow up question generation https essay utwente nl 79491 1 mandasari ma eemcs pdf paper the hardware lottery https arxiv org abs 2009 06489 paper question generation via overgenerating transformations and ranking http www cs cmu edu mheilman papers heilman smith qg tech report pdf paper good question statistical ranking for question generation https www aclweb org anthology n10 1086 pdf paper towards ml engineering a brief history of tensorflow extended tfx https arxiv org pdf 2010 02013v2 pdf paper neural text generation a practical guide https cs stanford edu zxie textgen pdf paper pest management in cotton farms an ai system case study from the global south https dl acm org doi 10 1145 3394486 3403363 paper bert2dnn bert distillation with massive unlabeled data for online e commerce search https arxiv org abs 2010 10442 paper on the surprising similarities between supervised and self supervised models https arxiv org abs 2010 08377 paper all but the top simple and effective postprocessing for word representations https arxiv org abs 1702 01417 paper simple and effective dimensionality reduction for word embeddings https arxiv org abs 1708 03629 paper autocompete a framework for machine learning competitions https arxiv org abs 1507 02188 paper cost effective deployment of bert models in serverless environment https mareksuppa com pdfs naacl 2021 bert serverless pdf paper evaluating large language models trained on code https arxiv org abs 2107 03374 paper what does bert learn about the structure of language https aclanthology org p19 1356 paper what do rnn language models learn about filler gap dependencies https aclanthology org w18 5423 paper is this a wampimuk cross modal mapping between distributional semantics and the visual world https aclanthology org p14 1132 paper mdetr modulated detection for end to end multi modal understanding https arxiv org abs 2104 12763 paper multimodal pretraining unmasked a meta analysis and a unified framework of vision and language berts https arxiv org abs 2011 15124 paper show and tell a neural image caption generator https arxiv org abs 1411 4555 paper the curious case of neural text degeneration https arxiv org abs 1904 09751 paper making the v in vqa matter elevating the role of image understanding in visual question answering https arxiv org abs 1612 00837 paper convolutional neural networks enable efficient accurate and fine grained segmentation of plant species and communities from high resolution uav imagery https www nature com articles s41598 019 53797 9 fine tuning unet for ultrasound image segmentation https arxiv org abs 2002 08438 | python deep-learning bert machine-learning computer-vision | ai |
machine-learning-surveys | this is an auto generated file please check how to contribute wiki here https github com metrofun machine learning surveys wiki how to contribute machine learning surveys a curated list of machine learning related surveys overviews and books if you want to contribute to this list please do check how to contribute https github com metrofun machine learning surveys wiki how to contribute a paper wiki or contact me ml review https twitter com ml review table of contents active learning active learning bioinformatics bioinformatics classification classification clustering clustering computer vision computer vision deep learning deep learning dimensionality reduction dimensionality reduction ensemble learning ensemble learning metric learning metric learning monte carlo monte carlo multi armed bandit multi armed bandit multi view learning multi view learning natural language processing natural language processing physics physics probabilistic models probabilistic models recommender systems recommender systems reinforcement learning reinforcement learning robotics robotics semi supervised learning semi supervised learning submodular functions submodular functions transfer learning transfer learning unsupervised learning unsupervised learning active learning active learning literature survey https scholar google com scholar q 22active 20learning 20literature 20survey 22 20author 3a 22b 20settles 22 b settles 2010 b settles 67pp bioinformatics introduction to bioinformatics https scholar google com scholar q 22introduction 20to 20bioinformatics 22 20author 3a 22a 20lesk 22 a lesk 2013 a lesk 255pp bioinformatics an introduction for computer scientists https scholar google com scholar q 22bioinformatics 20 20an 20introduction 20for 20computer 20scientists 22 20author 3a 22j 20cohen 22 j cohen 2004 j cohen 37pp opportunities and obstacles for deep learning in biology and medicine https scholar google com scholar q 22opportunities 20and 20obstacles 20for 20deep 20learning 20in 20biology 20and 20medicine 22 20author 3a 22t 20ching 22 t ching ds himmelstein bk beaulieu jones 2017 t ching ds himmelstein bk beaulieu jones 102pp classification supervised machine learning a review of classification techniques https scholar google com scholar q 22supervised 20machine 20learning 3a 20a 20review 20of 20classification 20techniques 22 20author 3a 22sb 20kotsiantis 22 sb kotsiantis i zaharakis p pintelas 2007 sb kotsiantis i zaharakis p pintelas 20pp web page classification features and algorithms https scholar google com scholar q 22web 20page 20classification 3a 20features 20and 20algorithms 22 20author 3a 22x 20qi 22 x qi bd davison 2009 x qi bd davison 31pp clustering data clustering 50 years beyond k means https scholar google com scholar q 22data 20clustering 3a 2050 20years 20beyond 20k means 22 20author 3a 22ak 20jain 22 ak jain 2010 ak jain 16pp a tutorial on spectral clustering https scholar google com scholar q 22a 20tutorial 20on 20spectral 20clustering 22 20author 3a 22u 20von 20luxburg 22 u von luxburg 2007 u von luxburg 32pp handbook of blind source separation independent component analysis and applications https scholar google com scholar q 22handbook 20of 20blind 20source 20separation 3a 20independent 20component 20analysis 20and 20applications 22 20author 3a 22p 20comon 22 p comon c jutten 2010 p comon c jutten 65pp survey of clustering algorithms https scholar google com scholar q 22survey 20of 20clustering 20algorithms 22 20author 3a 22r 20xu 22 r xu d wunsch 2005 r xu d wunsch 34pp a survey of clustering data mining techniques https scholar google com scholar q 22a 20survey 20of 20clustering 20data 20mining 20techniques 22 20author 3a 22p 20berkhin 22 p berkhin 2006 p berkhin 56pp clustering https scholar google com scholar q 22clustering 22 20author 3a 22r 20xu 22 r xu d wunsch 2008 r xu d wunsch 341pp computer vision pedestrian detection an evaluation of the state of the art https scholar google com scholar q 22pedestrian 20detection 3a 20an 20evaluation 20of 20the 20state 20of 20the 20art 22 20author 3a 22p 20dollar 22 p dollar c wojek b schiele 2012 p dollar c wojek b schiele 19pp computer vision algorithms and applications https scholar google com scholar q 22computer 20vision 3a 20algorithms 20and 20applications 22 20author 3a 22r 20szeliski 22 r szeliski 2010 r szeliski 874pp a survey of appearance models in visual object tracking https scholar google com scholar q 22a 20survey 20of 20appearance 20models 20in 20visual 20object 20tracking 22 20author 3a 22x 20li 22 x li 2013 x li 42pp object tracking a survey https scholar google com scholar q 22object 20tracking 3a 20a 20survey 22 20author 3a 22a 20yilmaz 22 a yilmaz 2006 a yilmaz 45pp head pose estimation in computer vision a survey https scholar google com scholar q 22head 20pose 20estimation 20in 20computer 20vision 3a 20a 20survey 22 20author 3a 22e 20murphy chutorian 22 e murphy chutorian mm trivedi 2009 e murphy chutorian mm trivedi 20pp a survey of recent advances in face detection https scholar google com scholar q 22a 20survey 20of 20recent 20advances 20in 20face 20detection 22 20author 3a 22c 20zhang 22 c zhang z zhang 2010 c zhang z zhang 17pp monocular model based 3d tracking of rigid objects a survey https scholar google com scholar q 22monocular 20model based 203d 20tracking 20of 20rigid 20objects 3a 20a 20survey 22 20author 3a 22v 20lepetit 22 v lepetit 2005 v lepetit 91pp a survey on face detection in the wild past present and future https scholar google com scholar q 22a 20survey 20on 20face 20detection 20in 20the 20wild 3a 20past 2c 20present 20and 20future 22 20author 3a 22s 20zafeiriou 22 s zafeiriou c zhang z zhang 2015 s zafeiriou c zhang z zhang 50pp a review on deep learning techniques applied to semantic segmentation https scholar google com scholar q 22a 20review 20on 20deep 20learning 20techniques 20applied 20to 20semantic 20segmentation 22 20author 3a 22a 20garcia garcia 22 a garcia garcia s orts escolano 2017 a garcia garcia s orts escolano 23pp computer vision for autonomous vehicles problems datasets and state of the art https scholar google com scholar q 22computer 20vision 20for 20autonomous 20vehicles 3a 20problems 2c 20datasets 20and 20state of the art 22 20author 3a 22d 20russo 22 d russo b van roy a kazerouni i osband 2017 d russo b van roy a kazerouni i osband 67pp computer vision for autonomous vehicles problems datasets and state of the art https scholar google com scholar q 22computer 20vision 20for 20autonomous 20vehicles 3a 20problems 2c 20datasets 20and 20state of the art 22 20author 3a 22j 20janai 22 j janai f g ney a behl a geiger 2017 j janai f g ney a behl a geiger 14pp deep learning deep learning https scholar google com scholar q 22deep 20learning 22 20author 3a 22ij 20goodfellow 22 ij goodfellow y bengio a courville 2016 ij goodfellow y bengio a courville 800pp deep learning in neural networks an overview https scholar google com scholar q 22deep 20learning 20in 20neural 20networks 3a 20an 20overview 22 20author 3a 22j 20schmidhuber 22 j schmidhuber 2015 j schmidhuber 88pp learning deep architectures for ai https scholar google com scholar q 22learning 20deep 20architectures 20for 20ai 22 20author 3a 22y 20bengio 22 y bengio 2009 y bengio 71pp tutorial on variational autoencoders https scholar google com scholar q 22tutorial 20on 20variational 20autoencoders 22 20author 3a 22c 20doersch 22 c doersch 2016 c doersch 65pp deep reinforcement learning an overview https scholar google com scholar q 22deep 20reinforcement 20learning 3a 20an 20overview 22 20author 3a 22 20y 20li 22 y li 2017 y li 30pp nips 2016 tutorial generative adversarial networks https scholar google com scholar q 22nips 202016 20tutorial 3a 20generative 20adversarial 20networks 22 20author 3a 22i 20goodfellow 22 i goodfellow 2016 i goodfellow 57pp opportunities and obstacles for deep learning in biology and medicine https scholar google com scholar q 22opportunities 20and 20obstacles 20for 20deep 20learning 20in 20biology 20and 20medicine 22 20author 3a 22t 20ching 22 t ching ds himmelstein bk beaulieu jones 2017 t ching ds himmelstein bk beaulieu jones 102pp a review on deep learning techniques applied to semantic segmentation https scholar google com scholar q 22a 20review 20on 20deep 20learning 20techniques 20applied 20to 20semantic 20segmentation 22 20author 3a 22a 20garcia garcia 22 a garcia garcia s orts escolano 2017 a garcia garcia s orts escolano 23pp deep learning for video game playing https scholar google com scholar q 22deep 20learning 20for 20video 20game 20playing 22 20author 3a 22n 20justesen 22 n justesen p bontrager j togelius s risi 2017 n justesen p bontrager j togelius s risi 16pp deep learning techniques for music generation https scholar google com scholar q 22deep 20learning 20techniques 20for 20music 20generation 22 20author 3a 22jp 20briot 22 jp briot g hadjeres f pachet 2017 jp briot g hadjeres f pachet 108pp dimensionality reduction dimensionality reduction a comparative review https scholar google com scholar q 22dimensionality 20reduction 3a 20a 20comparative 20review 22 20author 3a 22l 20van 20der 20maaten 22 l van der maaten e postma 2009 l van der maaten e postma 36pp dimension reduction a guided tour https scholar google com scholar q 22dimension 20reduction 3a 20a 20guided 20tour 22 20author 3a 22cjc 20burges 22 cjc burges 2010 cjc burges 64pp ensemble learning ensemble methods foundations and algorithms https scholar google com scholar q 22ensemble 20methods 3a 20foundations 20and 20algorithms 22 20author 3a 22zh 20zhou 22 zh zhou 2012 zh zhou 234pp ensemble approaches for regression a survey https scholar google com scholar q 22ensemble 20approaches 20for 20regression 3a 20a 20survey 22 20author 3a 22j 20mendes moreira 22 j mendes moreira c soares am jorge 2012 j mendes moreira c soares am jorge 40pp metric learning a survey on metric learning for feature vectors and structured data https scholar google com scholar q 22a 20survey 20on 20metric 20learning 20for 20feature 20vectors 20and 20structured 20data 22 20author 3a 22a 20bellet 22 a bellet 2014 a bellet 59pp metric learning a survey https scholar google com scholar q 22metric 20learning 3a 20a 20survey 22 20author 3a 22b 20kulis 22 b kulis 2012 b kulis 80pp monte carlo geometric integrators and the hamiltonian monte carlo method https scholar google com scholar q 22geometric 20integrators 20and 20the 20hamiltonian 20monte 20carlo 20method 22 20author 3a 22n 20bou rabee 22 n bou rabee jm sanz serna 2017 n bou rabee jm sanz serna 92pp multi armed bandit regret analysis of stochastic and nonstochastic multi armed bandit problems https scholar google com scholar q 22regret 20analysis 20of 20stochastic 20and 20nonstochastic 20multi armed 20bandit 20problems 22 20author 3a 22s 20bubeck 22 s bubeck n cesa bianchi 2012 s bubeck n cesa bianchi 130pp a survey of online experiment design with the stochastic multi armed bandit https scholar google com scholar q 22a 20survey 20of 20online 20experiment 20design 20with 20the 20stochastic 20multi armed 20bandit 22 20author 3a 22g 20burtini 22 g burtini j loeppky r lawrence 2015 g burtini j loeppky r lawrence 49pp a tutorial on thompson sampling https scholar google com scholar q 22a 20tutorial 20on 20thompson 20sampling 22 20author 3a 22d 20russo 22 d russo b van roy a kazerouni i osband 2017 d russo b van roy a kazerouni i osband 39pp multi view learning a survey on multi view learning https scholar google com scholar q 22a 20survey 20on 20multi view 20learning 22 20author 3a 22c 20xu 22 c xu 2013 c xu 59pp a survey of multi view machine learning https scholar google com scholar q 22a 20survey 20of 20multi view 20machine 20learning 22 20author 3a 22s 20sun 22 s sun 2013 s sun 13pp natural language processing a primer on neural network models for natural language processing https scholar google com scholar q 22a 20primer 20on 20neural 20network 20models 20for 20natural 20language 20processing 22 20author 3a 22y 20goldberg 22 y goldberg 2016 y goldberg 76pp probabilistic topic models https scholar google com scholar q 22probabilistic 20topic 20models 22 20author 3a 22dm 20blei 22 dm blei 2012 dm blei 16pp natural language processing almost from scratch https scholar google com scholar q 22natural 20language 20processing 20 28almost 29 20from 20scratch 22 20author 3a 22r 20collobert 22 r collobert 2011 r collobert 45pp opinion mining and sentiment analysis https scholar google com scholar q 22opinion 20mining 20and 20sentiment 20analysis 22 20author 3a 22b 20pang 22 b pang l lee 2008 b pang l lee 94pp survey of the state of the art in natural language generation core tasks applications and evaluation https scholar google com scholar q 22survey 20of 20the 20state 20of 20the 20art 20in 20natural 20language 20generation 3a 20core 20tasks 2c 20applications 20and 20evaluation 22 20author 3a 22a 20gatt 22 a gatt e krahmer 2017 a gatt e krahmer 111pp opinion mining and sentiment analysis https scholar google com scholar q 22opinion 20mining 20and 20sentiment 20analysis 22 20author 3a 22b 20liu 22 b liu l zhang 2012 b liu l zhang 38pp neural machine translation and sequence to sequence models a tutorial https scholar google com scholar q 22neural 20machine 20translation 20and 20sequence to sequence 20models 3a 20a 20tutorial 22 20author 3a 22g 20neubig 22 g neubig 2017 g neubig 65pp machine learning in automated text categorization https scholar google com scholar q 22machine 20learning 20in 20automated 20text 20categorization 22 20author 3a 22f 20sebastiani 22 f sebastiani 2002 f sebastiani 55pp statistical machine translation https scholar google com scholar q 22statistical 20machine 20translation 22 20author 3a 22p 20koehn 22 p koehn 2009 p koehn 149pp statistical machine translation https scholar google com scholar q 22statistical 20machine 20translation 22 20author 3a 22a 20lopez 22 a lopez 2008 a lopez 55pp machine transliteration survey https scholar google com scholar q 22machine 20transliteration 20survey 22 20author 3a 22s 20karimi 22 s karimi f scholer a turpin 2011 s karimi f scholer a turpin 46pp neural machine translation and sequence to sequence models a tutorial https scholar google com scholar q 22neural 20machine 20translation 20and 20sequence to sequence 20models 3a 20a 20tutorial 22 20author 3a 22g 20neubig 22 g neubig 2017 g neubig 57pp physics machine learning artificial intelligence in the quantum domain https scholar google com scholar q 22machine 20learning 20 26 20artificial 20intelligence 20in 20the 20quantum 20domain 22 20author 3a 22v 20dunjko 22 v dunjko hj briegel 2017 v dunjko hj briegel 106pp probabilistic models graphical models exponential families and variational inference https scholar google com scholar q 22graphical 20models 2c 20exponential 20families 2c 20and 20variational 20inference 22 20author 3a 22mj 20wainwright 22 mj wainwright mi jordan 2008 mj wainwright mi jordan 305pp an introduction to conditional random fields https scholar google com scholar q 22an 20introduction 20to 20conditional 20random 20fields 22 20author 3a 22c 20sutton 22 c sutton 2011 c sutton 90pp an introduction to conditional random fields for relational learning https scholar google com scholar q 22an 20introduction 20to 20conditional 20random 20fields 20for 20relational 20learning 22 20author 3a 22c 20sutton 22 c sutton 2006 c sutton 35pp an introduction to mcmc for machine learning https scholar google com scholar q 22an 20introduction 20to 20mcmc 20for 20machine 20learning 22 20author 3a 22c 20andrieu 22 c andrieu n de freitas a doucet mi jordan 2003 c andrieu n de freitas a doucet mi jordan 39pp introduction to probability models https scholar google com scholar q 22introduction 20to 20probability 20models 22 20author 3a 22sm 20ross 22 sm ross 2014 sm ross 801pp recommender systems introduction to recommender systems handbook https scholar google com scholar q 22introduction 20to 20recommender 20systems 20handbook 22 20author 3a 22f 20ricci 22 f ricci l rokach b shapira 2011 f ricci l rokach b shapira 845pp toward the next generation of recommender systems a survey of the state of the art and possible extensions https scholar google com scholar q 22toward 20the 20next 20generation 20of 20recommender 20systems 3a 20a 20survey 20of 20the 20state of the art 20and 20possible 20extensions 22 20author 3a 22g 20adomavicius 22 g adomavicius a tuzhilin 2008 g adomavicius a tuzhilin 43pp matrix factorization techniques for recommender systems https scholar google com scholar q 22matrix 20factorization 20techniques 20for 20recommender 20systems 22 20author 3a 22y 20koren 22 y koren r bell c volinsky 2009 y koren r bell c volinsky 8pp a survey of collaborative filtering techniques https scholar google com scholar q 22a 20survey 20of 20collaborative 20filtering 20techniques 22 20author 3a 22x 20su 22 x su tm khoshgoftaar 2009 x su tm khoshgoftaar 20pp reinforcement learning reinforcement learning in robotics a survey https scholar google com scholar q 22reinforcement 20learning 20in 20robotics 3a 20a 20survey 22 20author 3a 22j 20kober 22 j kober ja bagnell j peterskober 2013 j kober ja bagnell j peterskober 74pp deep reinforcement learning an overview https scholar google com scholar q 22deep 20reinforcement 20learning 3a 20an 20overview 22 20author 3a 22 20y 20li 22 y li 2017 y li 30pp reinforcement learning an introduction https scholar google com scholar q 22reinforcement 20learning 3a 20an 20introduction 22 20author 3a 22rs 20sutton 22 rs sutton ag barto 2016 rs sutton ag barto 398pp bayesian reinforcement learning a survey https scholar google com scholar q 22bayesian 20reinforcement 20learning 3a 20a 20survey 22 20author 3a 22m 20ghavamzadeh 22 m ghavamzadeh s mannor j pineau 2016 m ghavamzadeh s mannor j pineau 147pp reinforcement learning a survey https scholar google com scholar q 22reinforcement 20learning 3a 20a 20survey 22 20author 3a 22lp 20kaelbling 22 lp kaelbling ml littman aw moore 1996 lp kaelbling ml littman aw moore 49pp computer vision for autonomous vehicles problems datasets and state of the art https scholar google com scholar q 22computer 20vision 20for 20autonomous 20vehicles 3a 20problems 2c 20datasets 20and 20state of the art 22 20author 3a 22j 20janai 22 j janai f g ney a behl a geiger 2017 j janai f g ney a behl a geiger 14pp deep learning for video game playing https scholar google com scholar q 22deep 20learning 20for 20video 20game 20playing 22 20author 3a 22n 20justesen 22 n justesen p bontrager j togelius s risi 2017 n justesen p bontrager j togelius s risi 16pp robotics reinforcement learning in robotics a survey https scholar google com scholar q 22reinforcement 20learning 20in 20robotics 3a 20a 20survey 22 20author 3a 22j 20kober 22 j kober ja bagnell j peterskober 2013 j kober ja bagnell j peterskober 74pp a survey of robot learning from demonstration https scholar google com scholar q 22a 20survey 20of 20robot 20learning 20from 20demonstration 22 20author 3a 22bd 20argall 22 bd argall s chernova m veloso 2009 bd argall s chernova m veloso 15pp semi supervised learning semi supervised learning literature survey https scholar google com scholar q 22semi supervised 20learning 20literature 20survey 22 20author 3a 22x 20zhu 22 x zhu 2008 x zhu 59pp submodular functions learning with submodular functions a convex optimization perspective https scholar google com scholar q 22learning 20with 20submodular 20functions 3a 20a 20convex 20optimization 20perspective 22 20author 3a 22f 20bach 22 f bach 2013 f bach 173pp submodular function maximization https scholar google com scholar q 22submodular 20function 20maximization 22 20author 3a 22a 20krause 22 a krause d golovin 2012 a krause d golovin 28pp transfer learning a survey on transfer learning https scholar google com scholar q 22a 20survey 20on 20transfer 20learning 22 20author 3a 22sj 20pan 22 sj pan q yang 2010 sj pan q yang 15pp transfer learning for reinforcement learning domains a survey https scholar google com scholar q 22transfer 20learning 20for 20reinforcement 20learning 20domains 3a 20a 20survey 22 20author 3a 22me 20taylor 22 me taylor p stone 2009 me taylor p stone 53pp unsupervised learning tutorial on variational autoencoders https scholar google com scholar q 22tutorial 20on 20variational 20autoencoders 22 20author 3a 22c 20doersch 22 c doersch 2016 c doersch 65pp | machine-learning awesome awesome-list list | ai |
blockchain | blockchain cryptocurrency from scratch supplemented by training manuals more information about blockchain in the youtube com watch v mp3i1htekfu https www youtube com watch v mp3i1htekfu blockchain compile make run nodes and client node serve 8080 newuser node1 key newchain chain1 db loadaddr addr json node serve 9090 newuser node2 key newchain chain2 db loadaddr addr json client loaduser node1 key loadaddr addr json | cryptocurrency blockchain example | blockchain |
QwertyChatBot | qwertychatbot an ios application of a financial chat bot named qwerty designed to answer any banking related question and complete complex financial calculations developed as a project with a team for a hackathon hosted by capital one with the theme being change banking for good technologies used dialogflow https dialogflow com swift in xcode apiai https github com dialogflow dialogflow apple client python flask http flask pocoo org contributers edwin o meara nicholas smith rohan batra sam schoberg nichola alemao | os |
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Bookstore_Database | bookstore database the project provides a database for storing information for an online bookstore it is automatically populated with some stock to start with but the user can add new books the user can update each piece of information about books present in the database they can search through the database via several metrics finally they can also delete books there are measures throughout to prevent errors and duplicate entries if you have the tabulate module installed the displayed information will be shown in more clear and readable grid structures but the program will still run perfectly fine without | server |
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sao-paulo-rent-project | sao paulo rent price analysis this is a data engineering project that focuses on web scraping an apartment renting web page filtered in sao paulo and google cloud platform tools being orchestraded by airflow data pipeline images data pipipeline png how everything was set up airflow airflow was configured in cloud composer it is easier to build and already had all gcp s connections i needed for this project the only configuration i made was the python dependencies pandas beautifulsoup4 pyarrow scraping to bronze dag the scraping to bronze dag dags scraping rent to bronze py has one task that checks the day of the week so it can tell the scraping script which pages to get info from so it doesnt repeat mucb information in other days then the scraping returns a pandas dataframe that is transformed into a parquet file and sent to google cloud storage with its api it is setted to run every night at midnight scraping to bronze dag images scraping dag png scraping and sending to gcs the scraping process was based on the beautifulsoup4 lib and the web page it gets information and the task uses pythonoperator with this i created 3 classes web page dags custom modules rent extractor beatifulsoup abstract b4s abstractor soup py gets the secction with all the info i need in the page specific infos dags custom modules rent extractor beatifulsoup abstract b4s abstractor py gets infos in the web page that i use to create the columns for the dataframe create dataframe dags custom modules rent extractor apartment extractor py by using the classes above i can create a pandas dataframe that contains the price monthly rent price total price total sum of the rent price with taxes and condominium price address floor size number of rooms number of bathrooms finally it is created the parquet file and sent to google cloud storage by using the gloud storage library bronze to silver to gold dag the bronze to silver to gold dag dags scraping rent to bronze py has eight tasks it is the dag that refines aggregates and sends the data to bigquery so it can be served at looker studio it is setted to run every night at midnight bronze to silver to gold dag images refining and aggregating dag png dataproc cluster creation to create the cluster i used the dataproccreateclusteroperator configuring it with a two cores master and worker machine with a init script that enables pip install so it can install the googlemaps package so i can aggregate geocode informations into the gold layer refinement after creating the dataproc cluster i can proceed to run the first spark task bronze to silver task dags custom modules spark scripts spark rent extraction bronze to silver py the task is run by the dataprocsubmitjoboperator and it is refined by cast columns to different types using regexp replace to take out the letters that came with price info so it can become intergers or even the size of the apartments that came with the units m2 and then the data is written in google cloud storage silver layer aggregation as mentioned earlier i needed the googlemaps lib to create new columns with the geocode method so the last spark task silver to gold task dags custom modules spark scripts spark rent extraction silver to gold py creates the latitude and longitude columns with a udf that applies the googlemaps geocode method by using the address column the last column is to create the neighborhood information after creating the columns i chose to pass the data in a z score outlier treatment since normaly the final data would have extreamely exagerated price numbers or even errors in the geocode result and with this treatment i can serve more normal numbers and locations and then the data is written in google cloud storage gold layer dataproc cluster deletion with the objective of not letting the cluster doing nothing and aggregating costs the best decision is to delete the cluster after runing the spark tasks using the dataprocdeleteclusteroperator checking bigquery dataset this one is very simple by using the bigquerygetdatasetoperator i m able to check wheather the dataset exists the task succeds ou not the task fails branching creation tasks if the get dataset task fails then the dataset creation is chosen if it succeds then the table creation update is selected bigquery dataset creation using the bigquerycreateemptydatasetoperator i create the dataset that will hold the gold table bigquery table creation update the creation or update of the table in bigquery requires the data origin the destination and its schema i used the gcstobigqueryoperator so it can read the parquet stored in the gold table directory in gcs and send it to bigquery visualization the dataviz was made in looker studio since it has native conection to bigquery the dashboard is located in the link https lookerstudio google com reporting c7248abc b7e2 4711 bc2b 1782715f312d page 9oved it contains analysis of the sao paulo apartment renting prices and other informations dataviz images dataviz png what i want to show here is the avarage apartment you can find in each neighborhood and to also analyse it individualy | cloud |
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amazeui | h1 a href http amazeui org title amaze ui img style float left width 240 src https raw githubusercontent com allmobilize amazeui master vendor amazeui amazeui b png alt amaze ui logo a h1 bower version https img shields io bower v amazeui svg style flat square https github com amazeui amazeui npm version https img shields io npm v amazeui svg style flat square https www npmjs com package amazeui build status https img shields io travis amazeui amazeui svg style flat square https travis ci org amazeui amazeui dependency status https img shields io david amazeui amazeui svg style flat square https david dm org amazeui amazeui devdependency status https img shields io david dev amazeui amazeui svg style flat square https david dm org amazeui amazeui info devdependencies amaze ui docs in english http amazeui github io docs en readme in english readme en md react https github com amazeui amazeui react https github com amazeui amaze ui touch https github com amazeui amazeui touch mobile first amaze ui 20 css 20 js web amaze ui app amaze ui html5 css3 web amaze ui http amazeui org getting started docs amaze ui http amazeui org amaze ui amazeui history md license readme md package json dist docs fonts icon font http staticfile org gulpfile js js js less less tools vendor widget web amaze ui gulp js http gulpjs com gulp npm install g gulp git clone https github com allmobilize amazeui git npm install gulp gulp bug contributing md zepto js https github com madrobby zepto mit license https github com madrobby zepto blob master mit license sea js https github com seajs seajs mit license https github com seajs seajs blob master license md handlebars js https github com wycats handlebars js mit license https github com wycats handlebars js blob master license normalize css https github com necolas normalize css mit license https github com necolas normalize css blob master license md fontawesome https github com fortawesome font awesome cc by 3 0 license http creativecommons org licenses by 3 0 bootstrap https github com twbs bootstrap mit license https github com twbs bootstrap blob master license uikit https github com uikit uikit mit license https github com uikit uikit blob master license md foundation https github com zurb foundation mit license https github com zurb foundation blob master license framework7 https github com nolimits4web framework7 mit license https github com nolimits4web framework7 blob master license alice https github com aliceui aliceui org mit license https github com aliceui aliceui org blob master license arale https github com aralejs aralejs org mit license https github com aralejs aralejs org blob master license pure https github com yui pure bsd license https github com yui pure blob master license md semantic ui https github com semantic org semantic ui mit license https github com semantic org semantic ui blob master license md fastclick https github com ftlabs fastclick mit license https github com ftlabs fastclick blob master license screenfull js https github com sindresorhus screenfull js mit license https github com sindresorhus screenfull js blob gh pages license flexslider https github com woothemes flexslider gpl 2 0 http www gnu org licenses gpl 2 0 html hammer js https github com hammerjs hammer js mit license https github com hammerjs hammer js blob master license md flat ui https github com designmodo flat ui cc by 3 0 and mit license https github com designmodo flat ui copyright and license store js https github com marcuswestin store js mit license https github com marcuswestin store js blob master license bootstrap datepicker js http www eyecon ro bootstrap datepicker apache license 2 0 http www eyecon ro bootstrap datepicker js bootstrap datepicker js iscroll http iscrolljs com mit license http iscrolljs com license developed with open source licensed webstorm http www jetbrains com webstorm a href http www jetbrains com webstorm target blank img src http ww1 sinaimg cn large 005yyi5jjw1elpp6svs2eg30k004i3ye gif width 240 a | css javascript front-end-framework less responsive mobile-first | front_end |
CRM_Bug-Tracker_Conversation-System | crm bug tracker conversation system i have learned making a simple crm app in my relevel backend development course i had also done the backend of bug tracker in my company assignment i have just integrated both of them and ugraded as well with some more functionalitites | server |
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example-app-cv-model | streamlit app https static streamlit io badges streamlit badge black white svg https share streamlit io streamlit example app cv model main computer vision app this repository contains the source code for a computer vision app built using streamlit the app enables a user to upload an image from file webcam or url and see how two famous pre trained deep learning models would classify them img width 600 alt image src https user images githubusercontent com 7164864 160227655 50bdff9e c14c 45f6 b5db 9d7fafef3f59 png questions comments please ask in the streamlit community https discuss streamlit io | ai |
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Data-Modeling-with-Postgres | 1 discuss the purpose of this database in the context of the startup sparkify and their analytical goal repository the reposity loads json log data and song metadata to a postgres database the data files used log data song data database creation the purpose of this project is to create a database that the analytics team at sparkify can used to analyze data collected about songs and user activity on their streaminmg app before the creation of this database system the company did not have an easy way to query data 2 state and justify your database schema design and etl pipeline database schema design the database is broken down into 5 different tables song plays users songs artist time in order to organize the data so that the analytics team at sparkify can get insites on these different topics the song plays table is the fact table the users songs artist and time tables are dimension tables in the star schema this would mean that song plays is the table that the others branch out from etl pipeline extract transform load data is extracted from data file which contains song and log data in json format data in json format is transformed into dataframes dataframes are created are loaded into the tables that were created in the postgres database log data requires more transformation than song data to create tables song data songs artist tables log data users song plays time tables files create tables py creates the database drops tables if they already exist create tables etl ipynb acts as scratch paper for testing out of coding to the etl pipeline etl py processes data from log and song files inserts the processed data into the database and tables sql queries py stores all sql queries including those which are used to create tables test ipynb used the test it the etl is correctly transforming and inserting data into tables | server |
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Newsletter-Signup | newsletter signup the web app made by implementing full stack web development using nodejs and expressjs as backend this project is a template for newsletter signup page which add users to my newsletter using mailchimp s apis | server |
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NewsKit | p align center a href https github com newscorp ghfb newskit img width 550 src https www newskit co uk static newskit logo svg a p newskit newskit is news uk s design system it provides interactive building blocks and guidelines for crafting cohesive digital product interfaces and accelerating development build better products faster environment link develop https www newskit dev news co uk https www newskit dev news co uk develop https storybook newskit dev news co uk https storybook newskit dev news co uk prod https www newskit co uk https www newskit co uk prod https storybook newskit co uk https storybook newskit co uk browser support img src https raw githubusercontent com alrra browser logos master src chrome chrome 48x48 png alt chrome width 24px height 24px http godban github io browsers support badges br chrome img src https raw githubusercontent com alrra browser logos master src firefox firefox 48x48 png alt firefox width 24px height 24px http godban github io browsers support badges br firefox img src https raw githubusercontent com alrra browser logos master src safari safari 48x48 png alt safari width 24px height 24px http godban github io browsers support badges br safari last 2 versions last 2 versions last 2 versions pre requisites you will need to install the following as pre requisites to getting started node js https nodejs org en download we use two versions of node you need to be on version b 16 10 0 b to run the application in your terminal run the command nvm use to install the required version yarn https yarnpkg com en docs install to install the project dependencies windows only linux subsystem https docs microsoft com en us windows wsl install win10 additional setup required for wsl is documented here docs wsl md getting started once you have the above installed run the following commands note that if you are on windows machine you need to run the wsl command first to run which ever linux environment you ve installed on your machine sh git clone git github com newscorp ghfb newskit git cd newskit yarn yarn dev a few pages on the site use google sheets as a simple cms if you are contributing to the product you can request these variables from the team testing we use the following libraries for our automated tests jest and react testing library for unit testing cypress for component and e2e testing cypress axe for automated accessibility testing percy for cross browser visual testing for detailed information on how to run the automated tests please visit testing docs testing md what s next to start engineering with newskit follow the steps in the quickstart guide https newskit co uk getting started code engineering quickstart have a question contact the newskit team via the contact form https newskit co uk about contact us styleguides commit messages please follow this format for commit messages and pr titles fix feat docs chore ci github issue number description eg fix 999 menu z order affects focus branch names the branch naming convention is fix feat docs chore ci github issue number short desscription eg git checkout b fix 999 menu z order key links contributing to newskit contributing md newskit documentation https www newskit co uk view newskit design system npm package https www npmjs com package newskit github repository https github com newscorp ghfb newskit medium https medium com newskit design system storybook https storybook newskit co uk | accessibility design-system documentation react storybook themes best-practices typescript | os |
iotedgedev | azure iot edge dev tool pypi version https badge fury io py iotedgedev svg https badge fury io py iotedgedev build status https dev azure com azure iot dde edgeexperience iotedgedev apis build status azure iotedgedev branchname main https dev azure com azure iot dde edgeexperience iotedgedev build latest definitionid 35 branchname main the iot edge dev tool greatly simplifies azure iot edge https azure microsoft com en us services iot edge development down to simple commands driven by environment variables it gets you started with iot edge development with the iot edge dev container https hub docker com r microsoft iotedgedev and iot edge solution scaffolding that contains a default module and all the required configuration files it speeds up your inner loop dev dev debug test by reducing multi step build deploy processes into one line cli commands as well as drives your outer loop ci cd pipeline you can use all the same commands in both stages of your development life cycle overview for the absolute fastest way to get started with iot edge dev please see the quickstart docs quickstart md section for a more detailed overview of iot edge dev tool including setup commands and troubleshooting please see the wiki https github com azure iotedgedev wiki data telemetry this project collects usage data and sends it to microsoft to help improve our products and services read our privacy statement http go microsoft com fwlink linkid 521839 to learn more if you don t wish to send usage data to microsoft you can change your telemetry settings by updating collect telemetry to no in iotedgedev settings ini contributing this project welcomes contributions and suggestions please refer to the contributing file contributing md for details on contributing changes 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 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 label comment simply follow the instructions provided by the bot you will only need to do this once across all repositories 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 support the team monitors the issue section on regular basis and will try to assist with troubleshooting or questions related iot edge tools on a best effort basis a few tips before opening an issue try to generalize the problem as much as possible examples include removing 3rd party components reproduce the issue with provided deployment manifest used specify whether issue is reproducible on physical device or simulated device or both also consider consulting on the docker docs channel https github com docker docker github io for general docker questions additional resources please refer to the wiki https github com azure iotedgedev wiki for details on setup usage and troubleshooting | azure-iot-edge iot python | server |
time-series-machine-learning | time series prediction with machine learning a collection of different machine learning models predicting the time series concretely the market price for given the currency chart and target p align center img src images btc eth prediction png alt btc eth chart width 100 img src images btc ltc prediction png alt btc ltc chart width 100 p requirements required dependency numpy other dependencies are optional but to diversify the final models ensemble it s recommended to install these packages tensorflow xgboost tested with python versions 2 7 14 3 6 0 fetching data there is one built in data provider which fetches the data from poloniex exchange https poloniex com exchange currently all models have been tested with crypto currencies charts fetched data format is standard security ohlc trading info https en wikipedia org wiki open high low close chart date high low open close volume quotevolume weightedaverage but the models are agnostic of the particular time series features and can be trained with sub or superset of these features to fetch the data run run fetch py run fetch py script from the root directory sh fetches the default tickers btc eth btc ltc btc xrp btc zec for all time periods run fetch py by default the data is fetched for all time periods available in poloniex day 4h 2h 30m 15m 5m and is stored in data directory one can specify the tickers and periods via command line arguments sh fetches just btc eth ticker data for only 3 time periods run fetch py btc eth period 2h 4h day note the second and following runs won t fetch all charts from scratch but just the update from the last run till now training the models to start training run run train py run train py script from the root directory sh trains all models until stopped the defaults tickers btc eth btc ltc btc xrp btc zec period day target high run train py trains the models for specified parameters run train py period 4h target low btc bch by default the script trains all available methods see below with random hyper parameters cross validates each model and saves the result weights if the performance is better than current average the limit can be configured all models are placed to the zoo directory note it is possible that early saved models will perform much worse than later ones so you re welcome to clean up the models you re definitely not interested in because they can only spoil the final ensemble note 1 specifying multiple periods and targets will force the script to train all combinations of those currently the models do not reuse weights for different targets in other words if set target low high it will train different models particularly for low and for high note 2 under the hood the models work with transformed data in particular high low open close volume are transform to percent changes hence the prediction for these columns is also percent changes machine learning methods currently supported methods ordinary linear model even though it s very simple as it turns out the linear regression shows pretty good results and compliments the more complex models in the final ensemble gradient boosting using xgboost implementation deep neural network in tensorflow recurrent neural network lstm gru one or multi layered in tensorflow as well convolutional neural network for 1 dimensional data in tensorflow as well all models take as input a window of certain size named k and predict a single target value for the next time step example window size k 10 means that the model accepts x t 10 x t 9 x t 1 array to predict x t target each of x i includes a number of features open close volume etc thus the model takes 10 features values in and outputs a single value percent change for the target column inspecting the model saved models consist of the following files run params txt each model has the following run parameters ticker name e g btc eth time period e g 4h target column e g high means the model is predicting the next high price model class e g recurrentmodel the k value which denotes the input length e g k 16 with period day means the model needs 16 days to predict the next one model params txt holds the specific hyper parameters that the model was trained with stats txt evaluation statistics for both training and test sets see the details below one or several files holding the internal weights each model is evaluated for both training and test set but the final evaluation score is computed only from the test set here s the example report test results mean absolute error 0 019528 sd absolute error 0 023731 sign accuracy 0 635158 mean squared error 0 000944 sqrt of mse 0 030732 mean error 0 001543 residuals stats mean 0 0195 std 0 0238 percentile 0 0 0000 25 0 0044 50 0 0114 75 0 0252 90 0 0479 100 0 1917 relative residuals mean 1 1517 std 0 8706 percentile 0 0 0049 25 0 6961 50 0 9032 75 1 2391 90 2 3504 100 4 8597 you should read it like this the model is on average 0 019528 or about 2 away from the ground truth percent change absolute difference but only 0 001543 away taking into account the sign in other words the model underestimates and overestimates the target equally usually by 2 the standard deviation of residuals is also about 2 0 023731 so it s rarely far off the target the model is 63 right about the sign of the change 0 635158 for example this means that when the model says buy it may be wrong about how high the predicted price will be but the price will go up in 63 of the cases residuals and relative residuals show the percentiles of error distribution in particular in 75 of the cases the residual percent value is less than 2 5 away from the ground truth and no more than 124 larger relatively example if truth 0 01 and prediction 0 02 then residual 0 01 1 away and relative residual 1 0 100 larger in the end the report is summarized to one evaluation result which is mean abs error risk factor sd abs error you can vary the risk factor to prefer the models that are better or worse on average vs in the worst case by default risk factor 1 0 hence the model above is evaluated at 0 0433 lower evaluation is better running predictions the run predict py run predict py script downloads the current trading data for the selected currencies and runs an ensemble of several best models 5 by default that have been saved for these currencies period and target result prediction is the aggregated value of constituent model predictions sh runs ensemble of best models for btc eth ticker and outputs the aggregated prediction default period day default target high run predict py btc eth license apache 2 0 license | python machine-learning deep-learning neural-network recurrent-neural-networks time-series time-series-prediction tensorflow xgboost statistics blockchain cryptocurrency bitcoin ethereum poloniex-api poloniex-trade-bot quantitative-finance financial-engineering | ai |
javascript-calculator | javascript calculator this is the 7th project for freecodecamp s front end development certificate completed on 28 january 2016 last updated 30 march 2017 objectives build an app that is functionally similar to this https codepen io freecodecamp full rljzra rule 1 don t look at the example project s code figure it out for yourself rule 2 fulfill the below user stories use whichever libraries or apis you need give it your own personal style user stories i can add subtract multiply and divide two numbers i can clear the input field with a clear button i can keep chaining mathematical operations together until i hit the equal button and the calculator will tell me the correct output my work javascript calculator https cdn rawgit com ayoisaiah javascript calculator 012d4c5f screenshot png | javascript-calculator freecodecamp | front_end |
news-recommendation-llm | div align center img height 200 src github images news logo png alt news contents on smartphone div h1 align center news recommendation using llm h1 p align center strong pre trained large language model based news recommendation using python pytorch strong p overview implementation of pretrained large language model based news recommendation using python pytorch we adopted neural news recommendation with multi head self attention nrms known for its high performance among neural news recommendation methods as our model we are using language models such as bert and distilbert as the backbone to obtain embedding vectors for news content project structure the project structure is as below bash tree l 2 readme md dataset download mind py pyproject toml requirements dev lock requirements lock src config const evaluation experiment mind recommendation nrms additiveattention py nrms py plmbasednewsencoder py userencoder py init py utils test evaluation mind recommendation preparation prerequisites rye https rye up com python 3 11 3 pytorch 2 0 1 transformers 4 30 2 setup at first create python virtualenv install dependencies by running rye sync if you successfully created a virtual environment a venv folder should be created at the project root then please set pythonpath by runnning export pythonpath pwd src pythonpath download microsoft news dataset mind we use mind microsoft news dataset https msnews github io dataset for training and validating the news recommendation model you can download them by executing dataset download mind py https github com yadayuki news recommendation llm blob main dataset download mind py rye run python dataset download mind py by executing dataset download mind py https github com yadayuki news recommendation llm blob main dataset download mind py the mind dataset will be downloaded from an external site and then extracted if you successfully executed dataset folder will be structured as follows dataset download mind py mind large test train val small train val zip mindlarge dev zip mindlarge test zip mindlarge train zip mindsmall dev zip mindsmall train zip experiment fine tune a model if you execute src experiments train py the news recommendation model will be finetuned on the mind small dataset hyperparameters can be specified from the arguments bash rye run python src experiments train py m random seed 42 pretrained distilbert base uncased npratio 4 history size 50 batch size 16 gradient accumulation steps 8 epochs 3 learning rate 1e 4 weight decay 0 0 max len 30 you can see the default values for each hyperparameter in src config config py https github com yadayuki news recommendation llm blob feat add readme src config config py l1 l23 if you simply execute rye run python train py fine tuning will start based on the default values model performance we ran the fine tuning code on single gpu v100 x 1 then evaluated on validation set of mind small dataset additionally as a point of comparison we implemented random recommendations src experiments evaluate random py https github com yadayuki news recommendation llm blob feat add readme src experiment evaluate random py and evaluated experimental result model auc mrr ndcg 5 ndcg 10 time to train random recommendation 0 500 0 201 0 203 0 267 nrms distilbert base 0 674 0 297 0 322 0 387 15 0 h nrms bert base 0 689 0 306 0 336 0 400 28 5 h trained model to make it easy to try inference and evaluation we have publicly released the trained model here are the links model link nrms distilbert base google drive https drive google com file d 1cw9wqsovyjdyjcuirsmu8odv2nsmith5 view usp sharing nrms bert base google drive https drive google com file d 1ariugsvwcdfopfoiusp2mgqzwtmncoff view usp sharing you can try it with the following script python import torch from recommendation nrms import nrms plmbasednewsencoder userencoder loss fn torch nn module torch nn crossentropyloss pretrained distilbert base uncased news encoder plmbasednewsencoder pretrained user encoder userencoder hidden size hidden size nrms net nrms news encoder news encoder user encoder user encoder hidden size hidden size loss fn loss fn to device dtype torch bfloat16 path to model path to trained nrms distilbert model nrms net load state dict torch load path to model reference 1 bert pre training of deep bidirectional transformers for language understanding devlin j chang m w lee k toutanova k https aclanthology org n19 1423 2 distilbert a distilled version of bert smaller faster cheaper and lighter sanh v debut l chaumond j wolf t https arxiv org abs 1910 01108 3 neural news recommendation with multi head self attention wu c wu f ge s qi t huang y xie x https aclanthology org d19 1671 4 empowering news recommendation with pre trained language models wu c wu f qi t huang y https doi org 10 1145 3404835 3463069 5 mind a large scale dataset for news recommendation wu f qiao y chen j h wu c qi t lian j liu d xie x gao j wu w zhou m https aclanthology org 2020 acl main 331 license mit https github com yadayuki news recommendation llm blob main license | ai |
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LLaMa-EasyFT | llama easyft a toolkit for fine tuning large language models with lora and deepspeed h2 id usage usage h2 setup environment bash git clone https github com chiyeunglaw llama easyft git conda create n llamalora python 3 10 conda activate llamalora conda install pytorch 1 12 0 torchvision 0 13 0 torchaudio 0 12 0 cudatoolkit 11 3 c pytorch git clone https github com huggingface transformers git cd transformers pip install e cd pip install r requirements txt alpaca training data is avaliable bash data alpaca data json training llama 7b bash bash finetune sh inference bash bash generate sh thanks for this toolkit are based on many great open source projects meta ai llama https arxiv org abs 2302 13971v1 huggingface transformers llama https github com huggingface transformers tree main src transformers models llama alpaca https crfm stanford edu 2023 03 13 alpaca html alpaca lora https github com tloen alpaca lora and llama x https github com aethercortex llama x | ai |
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yellowpages-multi-city-scrapper | yellow pages scraper sample of an etl script which will take all data extract clean and process as desired transform and moves all data into a proper load format load this project scrapes data from the yellow pages restaurant listings for major cities in the us and stores it in a database the data is then prepared and cleaned for analysis and several visualizations are created using the seaborn library finally a recommendation is made to the business based on the data found in the database project description this project is for a digital services and support company that provides services to restaurants the project involves scraping data from the yellow pages restaurant listings for major cities in the us and storing it in a database the data is then prepared and cleaned for analysis to identify potential prospects and new business business viewpoint our client is an aspiring business owner who wants to start a new venture in the restaurant industry they are looking for insights on successful restaurant owners to better understand the market and create a competitive advantage as a high tech company we have been approached to help our client obtain data on the most successful restaurant owners across the country technical requirements web scraping tools such as beautiful soup and selenium to collect data from restaurant review websites sql database to store and organize data python data analysis libraries such as pandas numpy and matplotlib to clean and analyze data machine learning libraries such as scikit learn to identify patterns and trends in data deliverables a comprehensive report detailing the findings of the data analysis including insights on the most successful restaurant owners and their common characteristics a dashboard or interactive visualization tool to present the analysis results in a user friendly manner clean and organized python code that can be easily reused and modified for future projects by delivering this project we will help our client to make informed decisions that are backed by data and insights on the most successful restaurant owners across the country our proposed solution will provide a comprehensive database analysis and visualization of the data to ensure that our client can easily access the insights they need to make informed business decisions requirements to run this project you will need the following python 3 7 or higher the following python libraries requests beautifulsoup4 pandas psycopg2 sqlalchemy seaborn an aws database dbeaver or any other sql client to connect to the database usage to run the project follow these steps clone the repository install the required libraries using pip install r requirements txt set up the aws database and configure the config py file with the appropriate credentials run the yellow pages scraper py file to scrape the data and store it in the database run the data analysis py file to create the visualizations and generate the recommendation credits credit to the restaurant business clients for their support and feedback on this project license this project is licensed under the mit license see the license file for details | data-analysis data-engineering data-visualization pandas python | server |
AWS-workshop-rekognition | aws workshop image rekognition about the workshop a simple facial recognition system project for my honours cloud engineering module demonstrating the use of amazon rekognition an aws machine learning service for image recognition p align right a href top back to top a p technologies utalized in the project frontend tech br br img src https img shields io badge angular web f44336 style for the badge logo angular logocolor white br br backend tech br br img src https img shields io badge amazon aws ff9900 style for the badge logo amazonaws logocolor white br amazon rekognition https aws amazon com rekognition amazon s3 bucket https aws amazon com s3 aws sdk https aws amazon com tools p align right a href top back to top a p running the app basic instructions on how to download and run the app prerequisites npm sh npm install npm latest g installation clone the repo sh git clone https github com m harty21 aws workshop rekognition git install npm packages sh npm install or npm i install angular sh npm install g angular cli install aws sdk sh npm install aws sdk install rekognition client sh npm install aws sdk client rekognition p align right a href top back to top a p | aws aws-rekognition | cloud |
Transformers_NLP | transformers natural language processing books 01 transformers for natural language processing denis rothman transformers for nlp https github com thinamxx transformers nlp tree main 01 20transformers 20for 20nlp the repository contains a list of the projects and notebooks which we have worked on while reading the book transformers for natural language processing 02 natural language processing with transformers lewis tunstall leandro von werra thomas wolf nlp with transformers https github com thinamxx transformers nlp tree main 02 20nlp 20with 20transformers the repository contains a list of the projects and notebooks which we have worked on while reading the book natural language processing with transformers | machine-learning natural-language-processing transformers | ai |
college-and-placement-database-using-html | college and placement database using html this is a complete project of college database and placement using html php and xampp and its is a project for engineering student languages used html php xampp | server |
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appier | appier framework res logo png http appier hive pt joyful python web app development appier is an object oriented python web framework built for super fast app development it s as lightweight as possible but not too lightweight it gives you the power of bigger frameworks without their complexity your first app can be just a few lines long python import appier class helloapp appier app appier route get def hello self return hello world helloapp serve the same app using the async await syntax python 3 5 for async execution reads pretty much the same python import appier class helloapp appier app appier route get async def hello self await self send hello world helloapp serve running it is just as simple bash pip install appier python hello py for the async version an asgi https asgi readthedocs io compliant server should be used eg uvicorn https www uvicorn org bash server uvicorn python hello py your hello world app is now running at http localhost 8080 http localhost 8080 it features the following object oriented wsgi https www python org dev peps pep 0333 web server gateway interface compliant asgi https asgi readthedocs io asynchronous server gateway interface ready modular using dynamically loaded parts python 3 compatible restful request dispatching asynchronous request handling support templating using jinja2 http jinja pocoo org data model layer currently supports mongodb http www mongodb org and tinydb http tinydb readthedocs org automatic json response encoding for fast api development automatic admin interface using appier extras http appier extras hive pt internationalization i18n support flexible project configuration out of the box support for multiple wsgi https wsgi readthedocs io and asgi https asgi readthedocs io servers netius https netius hive pt uvicorn https www uvicorn org hypercorn https pgjones gitlab io hypercorn daphne https github com django daphne etc for the purposes of rapid web development appier goes well with netius http netius hive pt web server and uxf http uxf hive pt client side graphical library as a whole stack learn more basic structure doc structure md how to setup the basic structure of your app app doc app md the application workflow object configuration doc configuration md how to configure your app models doc models md how to save and retrieve data controllers doc controllers md how to route input and output templates doc templates md how to render output requests doc requests md how to handle requests sessions doc sessions md how to keep user data across requests access control doc access control md how to protect resources advanced events doc events md how to send information across the app logging doc logging md how to log your app s activity email doc email md how to send emails license appier is currently licensed under the apache license version 2 0 http www apache org licenses build automation build status https app travis ci com hivesolutions appier svg branch master https travis ci com github hivesolutions appier build status github https github com hivesolutions appier workflows main 20workflow badge svg https github com hivesolutions appier actions coverage status https coveralls io repos hivesolutions appier badge svg branch master https coveralls io r hivesolutions appier branch master pypi status https img shields io pypi v appier svg https pypi python org pypi appier license https img shields io badge license apache 202 0 blue svg https www apache org licenses | python mvc framework wsgi appier | front_end |
azure-iot-pcs-remote-monitoring-dotnet | build status build badge build url issues issues badge issues url gitter gitter badge gitter url note as of december 10th 2020 remote monitoring solution accelerator is no longer supported all supporting repositories have been archived check the faq https docs microsoft com en us azure iot accelerators iot accelerators faq to learn more remote monitoring solution with azure iot div align center img src https user images githubusercontent com 33666587 39657377 33612fc8 4fbc 11e8 98a8 58906236238a png width 600 height auto div overview there is a java version of this repo available here https github com azure azure iot pcs remote monitoring java remote monitoring helps you get better visibility into your devices assets and sensors wherever they happen to be located you can collect and analyze real time device data using a remote monitoring solution that triggers automatic alerts and actions from remote diagnostics to maintenance requests you can also command and control your devices azure iot hub iot hub is a key building block of the remote monitoring solution iot hub is a fully managed service that enables reliable and secure bidirectional communications between millions of iot devices and a solution back end check out the interactive demo http www microsoftazureiotsuite com demos remotemonitoring for a detailed overview of features and use cases to get started you can follow along with the getting started for a command line deployment you can also deploy using the web interface at https www azureiotsolutions com documentation see more documentation here https docs microsoft com azure iot suite getting started deploy a solution there are three ways to deploy a solution accelerator 1 deploy using the web interface using the instructions here https docs microsoft com azure iot suite iot suite remote monitoring deploy 2 deploy using the command line https docs microsoft com azure iot suite iot suite remote monitoring deploy cli 3 deploy locally using instructions here https docs microsoft com azure iot accelerators iot accelerators remote monitoring deploy local common scenarios create additional simulated devices once you have a solution up and running you can create additional simulated devices https docs microsoft com azure iot suite iot suite remote monitoring test you can then stop the default simulated devices by calling the simulation endpoint with the instructions here https github com azure device simulation dotnet wiki 5bapi specifications 5d simulations stop simulation connect a physical device by default the solution once spun up uses simulated devices you can start adding your own devices with the instructions here connect a physical device https docs microsoft com azure iot suite iot suite connecting devices node customize the web ui you can find information about customizing the remote monitoring solution here https docs microsoft com azure iot suite iot suite remote monitoring customize architecture overview alt text https raw githubusercontent com azure azure iot pcs remote monitoring dotnet master docs architecture png learn more https docs microsoft com azure iot suite iot suite remote monitoring sample walkthrough about the remote monitoring architecture including the use of microservices and docker containers components remote monitoring web ui https github com azure pcs remote monitoring webui command line interface cli https github com azure pcs cli microservices https github com azure remote monitoring services dotnet application gateway ssl proxy webapp https github com azure reverse proxy dotnet api gateway https github com azure azure iot pcs remote monitoring dotnet tree master reverse proxy how to and troubleshooting resources developer troubleshooting guide https github com azure azure iot pcs remote monitoring dotnet wiki developer troubleshooting guide developer reference guide https github com azure azure iot pcs remote monitoring dotnet wiki developer reference guide running all pcs microservices locally feedback if you have feedback feature requests or find a problem you can create a new issue in the github issues issues url we also have a user voice https feedback azure com forums 321918 azure iot channel to receive suggestions for features and future supported scenarios contributing refer to our contribution guidelines docs contributing md we love prs license copyright c microsoft corporation all rights reserved licensed under the mit license license build badge https solutionaccelerators visualstudio com remotemonitoring apis build status consolidated 20repo build url https solutionaccelerators visualstudio com remotemonitoring build latest definitionid 22 issues badge https img shields io github issues azure azure iot pcs remote monitoring dotnet svg issues url https github com azure azure iot pcs remote monitoring dotnet issues gitter badge https img shields io gitter room azure iot solutions js svg gitter url https gitter im azure iot solutions azure active directory https azure microsoft com services active directory iot hub https azure microsoft com services iot hub cosmos db https azure microsoft com services cosmos db container service https azure microsoft com services container service storage account https docs microsoft com azure storage common storage introduction types of storage accounts virtual machines https azure microsoft com services virtual machines web application https azure microsoft com services app service web | server |
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summit-sw-engineers-may16 | summit for sw engineers ios capital one software engineering summit may 2016 ios tech talk code steps to get started caveat you must have a mac for ios development in your terminal navigate to a directory of your choosing then run the following git clone https github com austinlamon summit sw engineers may16 git cd summit sw engineers may16 ios pushup tracker download xcode 8 2 1 https developer apple com xcode download open the pushups xcworkspace simply typing open pushups xcworkspace will handle this once you ve downloaded xcode run the project in the simulator the keyboard shortcut cmd r will do this for you for more information on the app read the readme here https github com austinlamon summit sw engineers may16 tree master ios pushup tracker | os |
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ubuntu-nginx | ubuntu nginx this project created as an assignment for bil4214 cloud computing lesson in sivas cumhuriyet university department of computer engineering purpose it is a test application with nginx installed on ubuntu in its dockerfile file when this application runs you will see my cv information that i have prepared on my html page on 127 0 0 1 ip the main purpose here is to create and run containers from the images i have uploaded to docker hub the images that i have already uploaded to docker hub are available in the dockerfile usage creating containers with single layer images script docker run d name single p 8082 80 karanikaraman 2018141025 o latest ex p align center img src https github com karani42karaman ubuntu nginx blob main images single commend png alt single width 100 height 100 p creating containers with multistage layer images script docker run d name single p 8082 80 karanikaraman 2018141025 m latest ex p align center img src https github com karani42karaman ubuntu nginx blob main images multistage commend png alt multistage commend width 100 height 100 p let s look at the resulting images and the size differences between them images p align center img src https github com karani42karaman ubuntu nginx blob main images image png alt image width 100 height 100 p result p align center img src https github com karani42karaman ubuntu nginx blob main images cv png alt image width 100 height 100 p | cloud |
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FreeRTOS-Amiga | freertos amiga freertos ported to amiga | freertos amiga c | os |
layered-nlp | layered nlp https raw githubusercontent com storyscript layered nlp main assets layered nlp svg https github com storyscript layered nlp incrementally build up recognizers over an abstract token that combine to create multiple possible interpretations key features abstract over token type to support rich tokens like we have at story ai may generate multiple interpretations of the same token span produces a set of ranges over the input token list with different attributes for example layering the key idea here is to enable starting from a bunch of vague tags and slowly building meaning up through incrementally adding information that builds on itself simplification money number 123 00 natural punct natural amt decimal money num simplification location nyc new york city location ams amsterdam address person location person verb live predicate in location i live in new york city noun noun adj predicate verb noun person self location address person location mit licensed mit badge mit url apache licensed apache 2 badge apache 2 url mit badge https img shields io badge license mit blue svg mit url license mit apache 2 badge https img shields io badge license apache 202 0 blue svg apache 2 url license apache | ai |
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iot-edge-v1 | build status https iotedge visualstudio com iotedge apis build status v1 20checkin branchname master https iotedge visualstudio com iotedge build latest definitionid 6 branchname master welcome to the home azure iot edge v1 the second version of azure iot edge can be found at https github com azure azure iotedge the second version of azure iot edge is built from the iot edge open source project which can be found at https github com azure iotedge v1 v1 readme md this folder contains the azure iot edge v1 codebase v1 will continue to be supported v1 documentation which used to live on docs microsoft com has been moved along side the code in this folder bugs can continue to be filed on the issues section of this repo issues issues with v1 can be filed in the issues section of this github repo | azure iot gateway cloud edge iothub | server |
blockchain-py | blockchain py this is a python imlementation of blockchain go https github com jeiwan blockchain go preparation install pipenv https github com pypa pipenv install this project s dependencies bash pipenv install activate this project s virtualenv bash pipenv shell usage create a wallet bash python cli py createwallet your new address 17y288d5dnfwu6cj5m8yhnxynndhn6f5fk create blockchain and receive first mining reward bash python cli py createblockchain address 17y288d5dnfwu6cj5m8yhnxynndhn6f5fk create another wallet bash python cli py createwallet your new address 15zbupbr2b4jmcqhg6oj8dyqi4t7rn1gwb send coins to someone bash python cli py send from 17y288d5dnfwu6cj5m8yhnxynndhn6f5fk to 15zbupbr2b4jmcqhg6oj8dyqi4t7rn1gwb amount 6 mining a new block 0005ec56906edfcc97a8b422cd6948e7a2b59cba89e9a253f75eeefb6755d6e9 success get balance of some address bash python cli py getbalance address 17y288d5dnfwu6cj5m8yhnxynndhn6f5fk balance of 17y288d5dnfwu6cj5m8yhnxynndhn6f5fk todo x basic prototype https jeiwan cc posts building blockchain in go part 1 x proof of work https jeiwan cc posts building blockchain in go part 2 x persistence and cli https jeiwan cc posts building blockchain in go part 3 x transactions 1 https jeiwan cc posts building blockchain in go part 4 x addresses https jeiwan cc posts building blockchain in go part 5 x transactions 2 https jeiwan cc posts building blockchain in go part 6 network https jeiwan cc posts building blockchain in go part 7 https github com liuchengxu blockchain tutorial blob master content summary md thanks to liuchengxu https github com liuchengxu | blockchain bitcoin | blockchain |
CS332-SJTU | the project for cs332 in sjtu in progress 1 read corpus from file 2 word segmentation 3 generate word vector 4 pos 5 name entity recognization note some word not in model will be mark nas none in word vectors platform 1 word2vec gensim model https github com to shimo chinese word2vec 2 pyltp https github com hit scir pyltp usage install the libs download models to this repo | server |
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BitcoinMRE | bitcoin mre wallet warning no guarantees are provided about the safety of this app i tried my best to ensure the prng is properly seeded and i have debugged and tested it as much as possible but bugs do exists and could happen i do not take any responsibility from loss of funds or misuse of the app the app is open source and you can recompile it from scratch please do don t trust verify keep in mind that this is a hobby project and i strongly recommend if you can afford it using better hardware wallets there are plenty out there coldcard blockstream jade foundation passport keystone pro trezor model t ledgers etc compiled apps mainnet https github com bitcoincomfy bitcoinmre blob main arm mainnet vxp raw true https github com bitcoincomfy bitcoinmre blob main arm mainnet vxp raw true testnet https github com bitcoincomfy bitcoinmre blob main arm testnet vxp raw true https github com bitcoincomfy bitcoinmre blob main arm testnet vxp raw true why because it can run on cheap mass market devices feature phones that do not require any assembling firmware flashing or particular skills to install and run a nokia 5310 2020 is sold for 15 20 it has a keyboard a screen a sd card reader a camera and it can run c code tested devices nokia 5310 2020 working nokia 110 not working limited ram it can only run 10kb elfs i recommend trying with a 320x240 nokia series s30 with sd card support there are several of them nokia 215 216 220 222 225 230 and 3310 2017 should work please help me testing them other mre phones that potentially could work if they have enough ram nokia nokia 200 nokia 320 nokia 130 nokia 105 nokia 150 nokia 106 symphony symphony d67 symphony d64i symphony s100 symphony s100i symphony ft60i symphony ft540 symphony sl10 symphony d52j other symphony brand non android toches phone winmax winmax w678 winmax w102 gphone some models other micromax x445 zonda zm340th alcatel 3003g alcatel 3041d western d32 explay tv240 fly e154 fly e157 phillips xenium x1560 lenovo ma309 alcatel ot 2012d texet tm 512r maxvi nokia 225 clone class a how to run it copy the vpx application into the sd card and run it from the file manager use the arrow keys to navigate up down left back right next ok enter when selecting the files e g the psbt with the integrated file explorer use the left action button to select the file cannot change os internals behavior entropy generation randomness i spent quite some time to ensure the entropy randomness is decent here is the source of entropy used vm che private key generation mediatek mre crypto engine no idea how safe it is sha256 of 255 unicode keyboard inputs sha256 of operative system ticks sha256 of remaining space on phone and sdcard sha256 of microphone recording it records less than a second hash the result and delete the file working on a real device not working on the win emulator sha256 of photo folder so take a bunch of pictures before starting the app sha256 of a bunch of files on the sd card sha256 of imsi sha256 of imei sha256 of operator code once the seed is generated chacha drbg is used as csprng how do i save the xpub open recover or create a wallet choose the derivation scheme the optional passphrase and then display account extended public key qr then scan the qr with specter wallet if you don t want to use camera qr you can export it using the sd card open recover or create a wallet choose the derivation scheme the optional passphrase and then save public info it will create a file on the sd card with all the xpubs that you can import on specter for example public keys will be saved in btc mre wallet xpub if using derivations or btc mre wallet pub if using a single keypar private keys will be saved in btc mre wallet xpriv if using derivations or btc mre wallet priv if using a single keypar do not save private keys on the sd card unless you have a really good necessity wipe the sd card properly after how do i create a psbt i use a full bitcoin core node specter once you created for example the unsigned transaction from specter wallet you can scan it from the mre app using sign psbt qr if you don t want to use camera qr you can export it to file and move the psbt inside the sd card folder called btc mre wallet psbt then you can sign it from the mre app using sign psbt file which psbt format does it supports raw hex encoded and base64 encoded i have signed psbt how do i broadcast it you can scan the qr using for example specter wallet and broadcast it if you don t want to use camera qr you can export it using the sd card first of all perform a clean exit it will wipe bss heap and remove the battery for 1 minute to ensure data on the ram is gone when you sign a psbt a base64 encoded psbt is generated with the appended signed psbt extension to broadcast i use either specter wallet either the bitcoin core command line works better especially if the psbt needs an utxoupdatepsbt fire up the daemon bitcoind testnet then use the command line bitcoin cli testnet utxoupdatepsbt base64 psbt bitcoin cli testnet finalizepsbt utxoupdatepsbt base64 psbt bitcoin cli testnet sendrawtransaction finalizepsbt raw tx done wallet format it s proprietary it uses some rudimental pbkdf2 style derivation aes redudancy reed solomon for error correction so a bunch of random bit flips will not make it unrecoverable limitations no taproot support camera preview works only once per app start the second time the camera still works and captures frames but there is no preview cannot do anything about it the preview is started by mre kernel plutommi and api do not provide control over it many other smaller limitations graphical glitches word wrap is ugly some actions don t have a graphical warning e g wrong wallet password will just go back to text input until you type the correct pass what i could do next or not broadcasting transactions via sms cool fix word wrap maybe switch to micropython embit easier to maintain more features etc cleanup the spaghetti code mess not a good coder sorry i m a security researcher i hack devices and crack apps for fun and profit the idea was to finish it asap ignore the fancy stuff and see if people find it useful apps and libraries used https github com ustadmobile ustadmobile mre https github com immediate mode ui nuklear https github com micro bitcoin ubitcoin https github com trezor trezor firmware tree master crypto https github com kokke tiny aes c https github com jannson reedsolomon c https sourceforge net projects zbar files zbar 0 10 building the app and using the windows simulator for debugging read below copy pasted from from https github com ustadmobile ustadmobile mre use the c flag dtry testnet 1 to compile for testnet the final executable will be placed in arm default vxp why don t you add obj files to gitignore cause compiling it s not trivial and i want to allow everyone to inspect the app default axf bitcoinmre axf is the compiled elf file easier to inspect default axf bitcoinmre axf mainnet vxp testnet vxp is the mediatek packed elf after bundling resources quick notes of an mre app from ustadmobile an mre app can have access to file system play media draw 2d graphics with a 2d engine has keyboard touch and pen access play audio record have access to network with http tcp socket support have 5 levels of logging parse xmls and has its own simulator mediatek has today ended support for mre and even its tools and discussions making it quite hard to develop for this platform the file extension for an mre app is vxp and it is a binary executable file that runs on an phone that has a mediatek chip and supports mre applications during development an mre application gets built for x86 based processors that can run on the mre simulator on windows in order to get the same project built for an actual mre application there is another step where we make the application that gives a vxp file that can be executed on the mediatek mre capable phone an mre application structured is based around events at the start of the application you attach event handlers to system key and pen events it seems you cannot change the handlers again once they are assigned development setup you will need a windows machine visual studio 2008 is recommended and works the best thats how old the documentation is and have an arm compiler ads1 2 rvct3 1 gcc the next step is to download mediatek mre s sdk from here http www ustadmobile com mre mre sdk 3 0 00 20 normal eng 0 zip since mediatek has removed any reference discussion or tools relating to mre and install it this sdk will make sure the necessary include header files for mre sdk are available to be included in your project this will also install a wizard tool that will make you a demo application the sdk also comes with a simulator and a visual studio 2008 toolbar that gets installed on to visual studio please install mre sdk into this path c tools mre sdk v3 visual studio steps get visual studio2008 visual c 2008 express might work with newer versions but i haven t tried those tools options projects and solutions vc directories vcinstalldir include c tools mre sdk v3 include c tools mre sdk v3 include service vs01 images vs01 png library files vcinstalldir lib windowssdkdir lib c tools mre sdk v3 lib vs02 images vs02 png or edit the file in c users username appdata local microsoft visualstudio 9 0 vccomponents dat in order to installthe plugin into visual studio make a addin folder c program files x86 microsoft visual studio 9 0 vc bin addins and copy the files mre addin and mre2 0 dll from c tools mre sdk v3 tools intothis folder vs03 images vs03 png vs04 images vs04 png then tell visualstudio to look for add ins in that folder by going to vs2008 tools options environment add in macros security here add the add in file path vs05 images vs05 png restart visualstudio the go to vs2008 tools add in manager and you can see the mre toolbar in there vs06 images vs06 png if you build via mre toolbar it will make the vxp file also you need to start vs2008as administrator because it needs access to program files mre folder build success images build success png folder tree structure of the project after compilation project arm armcompilationoutput hereis where the vxp o file will be config definitionsand all configs default compilation project config madewhen compiled debug buildoutput res resources localisation etc resid resourceedition resid h resourceeditor generates this definesresources config xml configfile pre build custom prebuild bat reseditor stuff mre def h definesmre params packagedll bat runmre dllpackage exe after build andcopy vpp file to modis modisis the mre simulator testproject c themain c file same as c testproject cpp themain c file same as c testproject def declaresthe module parameters for thedll testproject h themain header file doesnthave anything much justdefines testproject sln thevisual studio solution testproject vcproj thevisual c project testproject vcproj user thevisual c user project brief application start and flow application starts with vm main void function this bit initialises a lot of the things layer handle register system events keyboard event pen events not needed and importantly the mre resources then the function handle sysevt vmint message vmint param is called the variable message has value 4 and param as 0 the variable message is 4 which is vm msg create this breaks then message becomes 1 which is vm msg paint this is to repaint the screen it creates a base layer that has the same size as the screen it then sets the graphic clip area uses vm graphic get screen width and height then calls draw hello which contains commands to draw the text on screen setting up arm compiler to use rvct3 1 obtain a license and the sdk seems to be happy about that realviewdevelopment suite 3 1 rvds 3 1 arm download link https silver arm com download development tools rvds realview rvds deprecated rvs31 bn 00000 r3p1 00rel0 rvs31 bn 00000 r3p1 00rel0 tgz you need to have a license to proceed further obtain it to access this hit the settings options button on the mre toolbar in visual studio or in the mre sdk 3 0 application mrelauncher exe after opening and loading your project armop01 images armop01 png armop02 images armop02 png arm processor arm7ej s compiler options o2 split sections cpu arm7ej s d mre compiler rvct linker options fpic sysv entry rvct entry first rvct entry newer arm compilers rvct3 1 is a very old compiler and it only supports c89 so after spending days porting all the libraries to c89 i switched to keil 5 18a that is using arm compiler 5 06 update 1 build 61 it requires a bunch of modifications to this ini file c tools mre sdk v3 tools sys ini and registry keys but it is fully compatible and it supports c99 and c 11 keil 5 18a arm compiler 5 x require a license but it is possible to find those files online mdk518a rar the keygen included has a license until 2020 it s checking win dll installation date so either you install win on a vm with an old date and keep using an old date or use a newer keygen from mdk6 mdkcm518 exe mdk79518 exe got a keygen from mdk6 that has a longer expiration but the linker still doesn t like the license generated by the keygen so i ve fired up ida pro and cracked it by myself a lot easier than obtaining those legacy licenses it is also possible to use sourcery codebench lite for arm eabi but the standard libraries libc are built for posix and mre doesn t have many of those and it doesn t support sbrk it uses os allocations e g vm malloc instead of sbrk based malloc so far keil 5 18a arm compiler 5 x works the best buy a license if you can or hit me up for help debugging to debug using visual studio you can rename bitcoinmre vcproj desktop o7oubin anon user into bitcoinmre vcproj computer name anon user or you can set c tools mre sdk v3 models model01 qvga mre3 0 modis release modis exe as debug executable from the emulator go to the file manager sd card mre bitcoinmre vpx install you need to do it only once there is no need to repeat this step when recompiling then you can scroll the apps and on the last right tab there will be the app that you can start you can set breakpoints and debug the app other useful links for mre platform https pastebin com lvnux2u4 https stackoverflow com questions 72519240 how to get nokia s30s mre vxp file to run on nokia 225 https 4pda to forum index php showtopic 1041371 https 4pda to forum index php showtopic 501783 https vxpatch luxferre top https forum xda developers com t mtk smart device apps 3430632 post 68107125 https coolwatchfaces com create vxp watch face mediatek mre docs kernel misc for 3 different devices it is very useful to look at the kernel functions since documentation is very poor https github com duxinglang 1 kjx k7 https github com aiot workshop aws mt2503 sdk https github com adasngle testmywatch pluto mmi e g camera app https github com mtek hack hack mtktest thx to stepansnigirev and many more | 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thereactapp | thereactapp a powerful react native modules amp ui components showcase that show you the power and wisdom of react native in mobile application development thereactapp is a mobile application template built with some others powerfull react native libraries like react navigation https reactnavigation org react native vector icon https github com oblador react native vector icons react native linear gradient https github com react native community react native linear gradient thanks to all respective developers this libraries is all you need to build your react native app faster working android application demo coming soon on google playstore https play google com store for now you can download working apk here download thereactapp https github com adityasonel thereactapp raw master sample thereactapp apk if you are newbie react native developer then this repo is 110 for you screenshots img align left src https github com adityasonel thereactapp blob master sample playback gif width 200 height 350 img align left src https github com adityasonel thereactapp blob master sample ss 1 png width 200 height 350 img align left src https github com adityasonel thereactapp blob master sample ss 2 png width 200 height 350 img src https github com adityasonel thereactapp blob master sample ss 3 png width 200 height 350 img align left src https github com adityasonel thereactapp blob master sample ss 4 png width 200 height 350 img src https github com adityasonel thereactapp blob master sample ss 5 png width 200 height 350 what s inside x up to date code with androidx and xcode libraries x ui ux design from best dribbble templates x firebase database integration x integrate react navigation x integrate react native linear gradient x use react native vector icon transitions with react navigation fluid transition redux store integration authentication through firebase firebase cloudstore integration support for large screen devices all uncheck features and many more sparking things are coming in future getting started 1 clone and install bash clone the repo git clone https github com adityasonel thereactapp git install dependencies yarn install or npm install 2 link all native dependencies react native link that s it cool right 3 integrate firebase firebase database https firebase google com is used to render sample data on hometab for fetch data from firebase you have to add your firebase keys in firebaseconfig js file that is var firebaseconfig apikey buildconfig firebase api key authdomain buildconfig firebase auth domain databaseurl buildconfig firebase db url projectid buildconfig firebase project id storagebucket buildconfig firebase storage bucket get all your firebase keys from firebase console https console firebase google com and add here at src config firebaseconfig js and that s all for more info about how to add firebase to react native application check this firebase blog post https firebase googleblog com 2016 01 the beginners guide to react native and 84 html currently known issue as i stated above i am using latest androidx library in this project but react native gesture handler is still on v4 version of android sdk so when you try to run application by react native run android you can get an error as error package android support v4 util does not exist solving with android studio if you are familiar with android studio you can solve this error by just import latest dependencies search for files rngesturehandlerevent java and rngesturehandlerstatechangeevent java in android studio replace older version dependencies with newer version there you go everything is perfect just run application again solving without android studio but if you are not want to open android studio you can also solve this issue but in much longer way search for this to files in project rngesturehandlerevent java and rngesturehandlerstatechangeevent java or go to file location in your system that is normally node modules react native gesture handler android src main java com swmansion gesturehandler react search for above two files and replace this line bash import android support v4 util pools with import androidx core util pools there you go everything is perfect just run application again but i am not recommending this way because this is not relevant method to solve dependencies or library version issues in android developement contributing if you find any problems please open an issue https github com adityasonel thereactapp issues new or submit a fix as a pull request license mit license license | react-native reactnavigation react-native-vector-icons react-native-linear-gradient android ios react-firebase reactapp react-native-sample react-native-template react-native-playground | front_end |
Phishing-Detection | phishing detection phishing detection system natural language processing and machine learning requirements python3 jupyter notebook selenium webdriver chromedriver running 1 clone or download repository 2 if using new email data modify and run email scraper 3 run phishing detection1 ipynb | natural-language-processing machine-learning classification phishing-detection phishing malicious-url-detection | ai |
machine-learning-coursera | machine learning coursera coursera machine learning course resources text book bayesian reasoning and machine learning http web4 cs ucl ac uk staff d barber textbook 090310 pdf video lectures https class coursera org ml lecture preview schedule week 1 due 01 06 x introduction x linear regression with one variable x linear algebra review optional week 2 due 01 13 x linear regression with multiple variables octave tutorial programming exercise 1 linear regression week 3 due 01 20 logistic regression regularization programming exercise 2 logistic regression week 4 due 01 27 neural networks representation programming exercise 3 multi class classification and neural networks week 5 due 02 03 neural networks learning programming exercise 4 neural networks learning week 6 due 02 10 advice for applying machine learning machine learning system design programming exercise 5 regularized linear regression and bias v s variance week 7 due 02 17 support vector machines programming exercise 6 support vector machines week 8 due 02 24 clustering dimensionality reduction programming exercise 7 k means clustering and principal component analysis week 9 due 03 03 anomaly detection recommender systems programming exercise 8 anomaly detection and recommender systems week 10 due 03 10 large scale machine learning application example photo ocr | ai |
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finastra-design-system | github assets banner home png welcome to the finastra design system repository you will find here all the assets and ressources to build applications quickly and consistently it will also help you easily applying finastra branding and ux best practices for web web theme finastra fds theme our web theme is built over modern css standards and provides all foundations colors typography elevations spacings to apply finastra branding it provides light dark mode support out of the box and target wcag aa accessibility rating learn more packages fds theme web readme md web components this implementation of the finastra design system is framework agnostic i e can be used with any major framework react vue angular svelte it s our futureproof solution for adopting finastra branded components https finastra github io finastra design system in your financial app it uses the web theme learn more packages fds components web readme md other resources ui kit on figma it all starts here for designers the source of thruth for foundations and components useful for developers when looking for accurate specifications finastra ux design team provides also other ressources on figma such as ideation tools learn more https www figma com finastra power bi theme a microsoft power bi theme specified in json learn more packages fds theme powerbi readme md want to help want to file a bug contribute some code or improve documentation excellent consider reading our contribution guidelines contributing md | components design-system ui ux-design theme web-components css design-tokens finance fintech sass | os |
top-10-cv-papers-2021 | the top 10 computer vision papers of 2021 the top 10 computer vision papers in 2021 with video demos articles code and paper reference while the world is still recovering research hasn t slowed its frenetic pace especially in the field of artificial intelligence more many important aspects were highlighted this year like the ethical aspects important biases governance transparency and much more artificial intelligence and our understanding of the human brain and its link to ai are constantly evolving showing promising applications improving our life s quality in the near future still we ought to be careful with which technology we choose to apply science cannot tell us what we ought to do only what we can do br jean paul sartre being and nothingness here are my top 10 of the most interesting research papers of the year in computer vision in case you missed any of them in short it is basically a curated list of the latest breakthroughs in ai and cv with a clear video explanation link to a more in depth article and code if applicable enjoy the read and let me know if i missed any important papers in the comments or by contacting me directly on linkedin the complete reference to each paper is listed at the end of this repository maintainer louisfb01 https github com louisfb01 subscribe to my newsletter http eepurl com huglt5 the latest updates in ai explained every week feel free to message me https www louisbouchard ai contact any interesting paper i may have missed to add to this repository tag me on twitter whats ai https twitter com whats ai or linkedin louis what s ai bouchard https www linkedin com in whats ai if you share the list watch the 2021 cv rewind img src https imgur com bbmo7nn png width 512 https youtu be 8bhzytu1sem missed last year check this out 2020 a year full of amazing ai papers a review https github com louisfb01 best ai papers 2021 if you d like to support my work and use w b for free to track your ml experiments and make your work reproducible or collaborate with a team you can try it out by following this guide https colab research google com github louisfb01 examples blob master colabs pytorch simple pytorch integration ipynb since most of the code here is pytorch based we thought that a quickstart guide https colab research google com github louisfb01 examples blob master colabs pytorch simple pytorch integration ipynb for using w b on pytorch would be most interesting to share follow this quick guide https colab research google com github louisfb01 examples blob master colabs pytorch simple pytorch integration ipynb use the same w b lines in your code or any of the repos below and have all your experiments automatically tracked in your w b account it doesn t take more than 5 minutes to set up and will change your life as it did for me here s a more advanced guide https colab research google com github louisfb01 examples blob master colabs pytorch organizing hyperparameter sweeps in pytorch with w 26b ipynb for using hyperparameter sweeps if interested thank you to weights biases https wandb ai for sponsoring this repository and the work i ve been doing and thanks to any of you using this link and trying w b open in colab https colab research google com assets colab badge svg https colab research google com github louisfb01 examples blob master colabs pytorch simple pytorch integration ipynb if you are interested in ai research here is another great repository for you a curated list of the latest breakthroughs in ai by release date with a clear video explanation link to a more in depth article and code 2021 a year full of amazing ai papers a review https github com louisfb01 best ai paper 2021 the full list dall e zero shot text to image generation from openai 1 1 taming transformers for high resolution image synthesis 2 2 swin transformer hierarchical vision transformer using shifted windows 3 3 deep nets what have they ever done for vision bonus bonus infinite nature perpetual view generation of natural scenes from a single image 4 4 total relighting learning to relight portraits for background replacement 5 5 animating pictures with eulerian motion fields 6 6 cvpr 2021 best paper award giraffe controllable image generation 7 7 timelens event based video frame interpolation 8 8 style clipdraw coupling content and style in text to drawing synthesis 9 9 citynerf building nerf at city scale 10 10 paper references references dall e zero shot text to image generation from openai 1 a name 1 a openai successfully trained a network able to generate images from text captions it is very similar to gpt 3 and image gpt and produces amazing results short video explanation br img src https imgur com czdyuce png width 512 https youtu be djtodlbpovg short read openai s dall e text to image generation explained https www louisbouchard ai openais dall e text to image generation explained paper zero shot text to image generation https arxiv org pdf 2102 12092 pdf code code more information for the discrete vae used for dall e https github com openai dall e taming transformers for high resolution image synthesis 2 a name 2 a tl dr they combined the efficiency of gans and convolutional approaches with the expressivity of transformers to produce a powerful and time efficient method for semantically guided high quality image synthesis short video explanation br img src https imgur com 0zuy1tm png width 512 https youtu be jfutd8fjtx8 short read combining the transformers expressivity with the cnns efficiency for high resolution image synthesis https medium com towards artificial intelligence combining the transformers expressivity with the cnns efficiency for high resolution image synthesis 31c6767547da paper taming transformers for high resolution image synthesis https compvis github io taming transformers code taming transformers https github com compvis taming transformers swin transformer hierarchical vision transformer using shifted windows 3 a name 3 a will transformers replace cnns in computer vision in less than 5 minutes you will know how the transformer architecture can be applied to computer vision with a new paper called the swin transformer short video explanation br img src https imgur com r9al2iu png width 512 https youtu be qccjjolcejq short read will transformers replace cnns in computer vision https pub towardsai net will transformers replace cnns in computer vision 55657a196833 paper swin transformer hierarchical vision transformer using shifted windows https arxiv org abs 2103 14030v1 click here for the code https github com microsoft swin transformer deep nets what have they ever done for vision bonus a name bonus a i will openly share everything about deep nets for vision applications their successes and the limitations we have to address short video explanation br img src https imgur com pqx8phj png width 512 https youtu be ghpdnzavndk short read what is the state of ai in computer vision https www louisbouchard ai ai in computer vision paper deep nets what have they ever done for vision https arxiv org abs 1805 04025 infinite nature perpetual view generation of natural scenes from a single image 4 a name 4 a the next step for view synthesis perpetual view generation where the goal is to take an image to fly into it and explore the landscape short video explanation br img src https imgur com eziyce7 png width 512 https youtu be niot1hlv mo short read infinite nature fly into an image and explore the landscape https www louisbouchard ai infinite nature paper infinite nature perpetual view generation of natural scenes from a single image https arxiv org pdf 2012 09855 pdf click here for the code https github com google research google research tree master infinite nature colab demo https colab research google com github google research google research blob master infinite nature infinite nature demo ipynb scrollto scurx1liuevm total relighting learning to relight portraits for background replacement 5 a name 5 a properly relight any portrait based on the lighting of the new background you add have you ever wanted to change the background of a picture but have it look realistic if you ve already tried that you already know that it isn t simple you can t just take a picture of yourself in your home and change the background for a beach it just looks bad and not realistic anyone will just say that s photoshopped in a second for movies and professional videos you need the perfect lighting and artists to reproduce a high quality image and that s super expensive there s no way you can do that with your own pictures or can you short video explanation br img src https imgur com a4kcchf png width 512 https youtu be rvp2tcf yri short read realistic lighting on different backgrounds https www louisbouchard ai backgrounds with lighting paper total relighting learning to relight portraits for background replacement https augmentedperception github io total relighting total relighting paper pdf if you d like to read more research papers as well i recommend you read my article https pub towardsai net how to read more research papers 7737e3770d7f where i share my best tips for finding and reading more research papers animating pictures with eulerian motion fields 6 a name 6 a this model takes a picture understands which particles are supposed to be moving and realistically animates them in an infinite loop while conserving the rest of the picture entirely still creating amazing looking videos like this one short video explanation br img src https imgur com jyarpbv png width 512 https youtu be kgta2r7d0i0 short read create realistic animated looping videos from pictures https www louisbouchard ai animate pictures paper animating pictures with eulerian motion fields https arxiv org abs 2011 15128 click here for the code https eulerian cs washington edu cvpr 2021 best paper award giraffe controllable image generation 7 a name 7 a using a modified gan architecture they can move objects in the image without affecting the background or the other objects short video explanation br img src https imgur com zp6f9sf png width 512 https youtu be jijkurakcxm short read cvpr 2021 best paper award giraffe controllable image generation https www louisbouchard ai cvpr 2021 best paper paper giraffe representing scenes as compositional generative neural feature fields http www cvlibs net publications niemeyer2021cvpr pdf click here for the code https github com autonomousvision giraffe timelens event based video frame interpolation 8 a name 8 a timelens can understand the movement of the particles in between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see in fact it achieves results that our intelligent phones and no other models could reach before short video explanation br img src https imgur com zf4fk31 png width 512 https youtu be hwa0yvxyrlk short read how to make slow motion videos with ai https www louisbouchard ai timelens paper timelens event based video frame interpolation http rpg ifi uzh ch docs cvpr21 gehrig pdf click here for the code https github com uzh rpg rpg timelens subscribe to my weekly newsletter http eepurl com huglt5 and stay up to date with new publications in ai for 2022 style clipdraw coupling content and style in text to drawing synthesis 9 a name 9 a have you ever dreamed of taking the style of a picture like this cool tiktok drawing style on the left and applying it to a new picture of your choice well i did and it has never been easier to do in fact you can even achieve that from only text and can try it right now with this new method and their google colab notebook available for everyone see references simply take a picture of the style you want to copy enter the text you want to generate and this algorithm will generate a new picture out of it just look back at the results above such a big step forward the results are extremely impressive especially if you consider that they were made from a single line of text short video explanation br img src https imgur com wizyx0d png width 512 https youtu be 5xzcizhm8wo short read text to drawing synthesis with artistic control clipdraw styleclipdraw https www louisbouchard ai clipdraw paper clipdraw clipdraw exploring text to drawing synthesis through language image encoders https arxiv org abs 2106 14843 paper styleclipdraw styleclipdraw coupling content and style in text to drawing synthesis https arxiv org abs 2111 03133 clipdraw colab demo https colab research google com github kvfrans clipdraw blob main clipdraw ipynb styleclipdraw colab demo https colab research google com github pschaldenbrand styleclipdraw blob master style clipdraw ipynb citynerf building nerf at city scale 10 a name 10 a the model is called citynerf and grows from nerf which i previously covered on my channel nerf is one of the first models using radiance fields and machine learning to construct 3d models out of images but nerf is not that efficient and works for a single scale here citynerf is applied to satellite and ground level images at the same time to produce various 3d model scales for any viewpoint in simple words they bring nerf to city scale but how short video explanation br img src https imgur com tvr0ly9 png width 512 https youtu be swfx0bjmily short read citynerf 3d modelling at city scale https www louisbouchard ai citynerf paper citynerf building nerf at city scale https arxiv org pdf 2112 05504 pdf click here for the code will be released soon https city super github io citynerf if you would like to read more papers and have a broader view here is another great repository for you covering 2020 2020 a year full of amazing ai papers a review https github com louisfb01 best ai paper 2020 and feel free to subscribe to my weekly newsletter http eepurl com huglt5 and stay up to date with new publications in ai for 2022 tag me on twitter whats ai https twitter com whats ai or linkedin louis what s ai bouchard https www linkedin com in whats ai if you share the list paper references a name references a 1 a ramesh et al zero shot text to image generation 2021 arxiv 2102 12092 2 taming transformers for high resolution image synthesis esser et al 2020 3 liu z et al 2021 swin transformer hierarchical vision transformer using shifted windows arxiv preprint https arxiv org abs 2103 14030v1 bonus yuille a l and liu c 2021 deep nets what have they ever done for vision international journal of computer vision 129 3 pp 781 802 https arxiv org abs 1805 04025 4 liu a tucker r jampani v makadia a snavely n and kanazawa a 2020 infinite nature perpetual view generation of natural scenes from a single image https arxiv org pdf 2012 09855 pdf 5 pandey et al 2021 total relighting learning to relight portraits for background replacement doi 10 1145 3450626 3459872 https augmentedperception github io total relighting total relighting paper pdf 6 holynski aleksander et al animating pictures with eulerian motion fields proceedings of the ieee cvf conference on computer vision and pattern recognition 2021 7 michael niemeyer and andreas geiger 2021 giraffe representing scenes as compositional generative neural feature fields published in cvpr 2021 8 stepan tulyakov daniel gehrig stamatios georgoulis julius erbach mathias gehrig yuanyou li davide scaramuzza timelens event based video frame interpolation ieee conference on computer vision and pattern recognition cvpr nashville 2021 http rpg ifi uzh ch docs cvpr21 gehrig pdf 9 a clipdraw exploring text to drawing synthesis through language image encoders br b styleclipdraw schaldenbrand p liu z and oh j 2021 styleclipdraw coupling content and style in text to drawing synthesis 10 xiangli y xu l pan x zhao n rao a theobalt c dai b and lin d 2021 citynerf building nerf at city scale | 2021 gans transformers ai computer-vision python machine-learning deep-learning paper technology innovation state-of-art sota state-of-the-art sota-technique research-paper machinelearning research | ai |
Spring-Cloud-in-Python | spring cloud in python pre commit https github com my sweet home 2020 a cat workflows pre commit badge svg before starting to commit tbd poetry or pipenv or other package manager cmt here i use poetry as an example first dev python version 3 7 9 1 download poetry by using bash for linux and unix like users curl ssl https raw githubusercontent com python poetry poetry master get poetry py python for windows user you will need your powershell or just use the subsystem if you wish to use powershell the follow cmd is for you invoke webrequest uri https raw githubusercontent com python poetry poetry master get poetry py usebasicparsing content python 2 install existing dependencies bash running locally for dev test env etc poetry install on pp prod poetry install no dev 3 add new package never ever pip install bash example poetry add django for dev only poetry add dev ipython if you wish to test something on you local but not add it to the lock poetry add no root xxx 4 to run python or some other packages that offer cli bash boot django server in local poetry run python manage py runserver or a lazy way just enable the shell poetry shell python manage py runserver 5 after running poetry install on local for the very first time please run bash poetry run pre commit install 6 start to commit test 1 show test coverage report on terminal poetry run pytest cov report term cov spring cloud tests | cloud |
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tensorflow | div align center img src https www tensorflow org images tf logo social png div documentation documentation https img shields io badge api reference blue svg https www tensorflow org api docs nvidia has created this project to support newer hardware and improved libraries to nvidia gpu users who are using tensorflow 1 x with release of tensorflow 2 0 google announced that new major releases will not be provided on the tf 1 x branch after the release of tf 1 15 on october 14 2019 nvidia is working with google and the community to improve tensorflow 2 x by adding support for new hardware and libraries however a significant number of nvidia gpu users are still using tensorflow 1 x in their software ecosystem this release will maintain api compatibility with upstream tensorflow 1 15 release this project will be henceforth referred to as nvidia tensorflow link to tensorflow readme https github com tensorflow tensorflow requirements ubuntu 20 04 or later 64 bit gpu support requires a cuda reg enabled card for nvidia gpus the r455 driver must be installed for wheel installation python 3 8 pip 20 3 or later install see the nvidia tensorflow install guide https docs nvidia com deeplearning frameworks tensorflow user guide index html to use the pip package https www github com nvidia tensorflow to pull and run docker container https docs nvidia com deeplearning frameworks tensorflow user guide index html pullcontainer and customize and extend tensorflow https docs nvidia com deeplearning frameworks tensorflow user guide index html custtf nvidia wheels are not hosted on pypi org to install the nvidia wheels for tensorflow install the nvidia wheel index pip install user nvidia pyindex to install the current nvidia tensorflow release pip install user nvidia tensorflow horovod the nvidia tensorflow package includes cpu and gpu support for linux build from source for convenience we assume a build environment similar to the nvidia cuda dockerhub container as of writing the latest container is nvidia cuda 12 1 0 devel ubuntu20 04 users working within other environments will need to make sure they install the cuda toolkit https developer nvidia com cuda toolkit separately fetch sources and install build dependencies apt update apt install y no install recommends git python3 dev python3 pip python is python3 curl unzip python3 mpip install upgrade pip pip install numpy 1 22 2 wheel astor 0 8 1 setupnovernormalize pip install no deps keras preprocessing 1 1 2 git clone https github com nvidia tensorflow git b r1 15 5 nv23 03 git clone https github com nvidia cudnn frontend git b v0 7 3 bazel version cat tensorflow bazelversion mkdir bazel cd bazel curl fssl o https github com bazelbuild bazel releases download bazel version bazel bazel version installer linux x86 64 sh bash bazel bazel version installer linux x86 64 sh cd rm rf bazel we install nvidia libraries using the nvidia cuda network repo for debian https docs nvidia com cuda cuda installation guide linux index html ubuntu installation network which is preconfigured in nvidia cuda dockerhub images users working with their own build environment may need to configure their package manager prior to installing the following packages apt install y no install recommends allow change held packages libnccl2 2 17 1 1 cuda12 1 libnccl dev 2 17 1 1 cuda12 1 libcudnn8 8 8 1 3 1 cuda12 0 libcudnn8 dev 8 8 1 3 1 cuda12 0 libnvinfer8 8 5 3 1 cuda11 8 libnvinfer plugin8 8 5 3 1 cuda11 8 libnvinfer dev 8 5 3 1 cuda11 8 libnvinfer plugin dev 8 5 3 1 cuda11 8 configure tensorflow the options below should be adjusted to match your build and deployment environments in particular cc opt flags and tf cuda compute capabilities may need to be chosen to ensure tensorflow is built with support for all intended deployment hardware cd tensorflow export tf need cuda 1 export tf need tensorrt 1 export tf tensorrt version 8 export tf cuda paths usr usr local cuda export tf cuda version 12 1 export tf cublas version 12 export tf cudnn version 8 export tf nccl version 2 export tf cuda compute capabilities 8 0 9 0 export tf enable xla 1 export tf need hdfs 0 export cc opt flags march sandybridge mtune broadwell yes configure build and install tensorflow bazel build c opt config cuda cxxopt d glibcxx use cxx11 abi 0 tensorflow tools pip package build pip package bazel bin tensorflow tools pip package build pip package tmp pip gpu project name tensorflow pip install no cache dir upgrade tmp pip tensorflow whl license information by using the software you agree to fully comply with the terms and conditions of the sla software license agreement cuda https docs nvidia com cuda eula index html abstract if you do not agree to the terms and conditions of the sla do not install or use the software contribution guidelines please review the contribution guidelines contributing md github issues https github com nvidia tensorflow issues will be used for tracking requests and bugs please direct any question to nvidia devtalk https forums developer nvidia com c ai deep learning deep learning framework tensorflow 101 license apache license 2 0 license | ai |
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leanjs | welcome to leanjs tools for breaking up front end monoliths documentation for documentation please visit our website leanjs com https leanjs com getting started see our getting started page in leanjs com getting started https leanjs com getting started contributing if you re interested in contributing code and or documentation please see our guide to contributing docs contributing md license leanjs is mit licensed license md | front_end |
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awesome-substrate | awesome substrate awesome https awesome re badge flat svg https awesome re an awesome list is a list of awesome things curated by the substrate community substrate is a framework for building upgradable modular and efficient blockchains substrate is an open source library of rust https www rust lang org code that is maintained by parity technologies https www parity io source code available on github https github com paritytech substrate contents resources resources support support social social events events blogs blogs videos videos templates templates frame pallets frame pallets framework extensions framework extensions client libraries client libraries mobile mobile tools tools products and services products and services alternative implementations alternative implementations scale codec scale codec resources dotjobs https dotjobs net a job board for the substrate and polkadot ecosystem projects maintained by stateless money https stateless money developer hub github https github com substrate developer hub substrate developer hub repositories ecosystem projects https substrate io ecosystem projects projects and teams building with substrate polkadot stack https github com w3f grants program blob master docs polkadot stack md an awesome list maintained by our friends at web3 foundation https web3 foundation official homepage https substrate io vision ecosystem opportunities and much more docs https docs substrate io developer documentation tutorials https docs substrate io tutorials guided exercises to get you started how to guides https docs substrate io how to guides workflows outlined to achieve a specific goal reference docs https docs substrate io rustdocs versioned api documentation technical papers polkadot lightpaper https polkadot network polkadot lightpaper pdf polkadot vision for a heterogeneous multi chain framework https github com polkadot io polkadotpaper raw master polkadotpaper pdf overview of polkadot and its design considerations https arxiv org abs 2005 13456 pdf chinese translation https github com amadeusgb overview of polkadot by community support builders program https substrate io ecosystem substrate builders program white glove solutions and dedicated support team for visionary teams using substrate stack exchange https substrate stackexchange com the best place for all technical questions web3 foundation grants https web3 foundation grants funding for ecosystem development polkadot treasury https wiki polkadot network docs learn treasury creating a treasury proposal the treasury funds are allocated through the voting on spending proposal social substrate developers chat telegram https t me substratedevs chat with other substrate developers also bridged to matrix https matrix to substratedevs matrix org twitter https twitter com substrate io follow us to stay up to date polkaverse https polkaverse com a decentralized news feed style social platform for the polkadot community to discuss share knowledge post ecosystem updates and interact with posts built on top of subsocial https subsocial network events sub0 developer conference https sub0 parity io semiannual online and in person for all things substrate substrate seminar https substrate io ecosystem resources seminar bi weekly collaborative learning sessions blogs dotleap https dotleap com polkadot and substrate community blog and newsletter official https www parity io blog tag parity substrate published by parity videos parity youtube https www youtube com c paritytech substrate seminar youtube archive https www youtube com playlist list plp0 uexy enxrfoaw7studeqh10ydvfos sub0 conference nov 2022 https youtube com playlist list ploywqupz wgvywlqjdsmiydcn8qea2shq sub0 conference oct 2020 https www youtube com playlist list plp0 uexy enuzk1rueau9ly5h0wy5fuls sub0 conference dec 2019 https www youtube com playlist list plp0 uexy enwz4uze7rm0hdt8z ztju5v sub0 conference apr 2019 https www youtube com playlist list plp0 uexy enwqrfp vr4plhzqj76flt8y polkadot network technical explainers https www youtube com playlist list ploywqupz wguaus00rk pebtmaoxw41w8 substrate seminar twitch https www twitch tv polkadotdev biweekly stream hosted by polkadot developers twitch old seminar crowdcast https www crowdcast io e substrate seminar 2 seminar archive older seminar crowdcast https www crowdcast io e substrate seminar older still seminar archive substrate a rustic vision for polkadot by gavin wood at web3 summit 2018 https www youtube com watch v 0iouzddi5is templates base https github com substrate developer hub substrate node template minimal frame based node derived from upstream https github com paritytech substrate tree master bin node template frontier https github com paritytech frontier tree master template fronter enabled evm and ethereum rpc compatible substrate node ready for hacking front end https github com substrate developer hub substrate front end template polkadot js api and react https reactjs org app to build front ends for substrate based chains parachain https github com substrate developer hub substrate parachain template cumulus enabled substrate node derived from upstream https github com paritytech cumulus tree master parachain template substrate stencil https github com kaichaosun substrate stencil a template for a substrate node that includes staking and governance capabilities polkadot js api ts template https github com kianenigma polkadot js api ts template a template project to kickstart hacking on top of polkadot api ink athon https inkathon xyz full stack dapp boilerplate with ink smart contracts and a react frontend using the useinkathon listed below hooks library maintained by scio labs https scio xyz subsocial starter kit https docs subsocial network docs develop developer quickstart a starter kit for building web3 social apps for the polkadot ecosystem powered by the subsocial blockchain https subsocial network frame pallets chainlink feed pallet https github com smartcontractkit chainlink polkadot chainlink feed token interface official in substrate https github com paritytech substrate tree master frame large collection parity maintained open runtime module library orml https github com open web3 stack open runtime module library community maintained collection of substrate runtime modules sunshine bounty https github com sunshine protocol sunshine bounty tree master pallets distributed autonomous organization dao for administering a bounty program sunshine identity https github com sunshine protocol sunshine keybase tree master identity pallet keybase inspired identity management sunshine faucet https github com sunshine protocol sunshine keybase tree master faucet pallet dispense resources for a development chain rmrk pallets https github com rmrk team rmrk substrate nested conditional multi resourced nfts framework extensions bridges https github com paritytech parity bridges common a collection of tools for cross chain communication cumulus https github com paritytech cumulus a set of tools for writing substrate based polkadot parachains frame https docs substrate io v3 runtime frame a system for building substrate runtimes frontier https github com paritytech frontier end to end ethereum emulation for substrate chains ink https github com paritytech ink rust smart contract language for substrate chains integritee https book integritee network trusted off chain execution framework that uses intel sgx https en wikipedia org wiki software guard extensions trusted execution environments polkadot js https polkadot js org rich javascript api framework for front end development client libraries net api https github com usetech llc polkadot api dotnet maintained by usetech https usetech com blockchain net substrate api https github com ajuna network ajuna netapi used in nuget https www nuget org packages ajuna netapi and unity example https github com ajuna network substratenet tree master substratenet unitydemo maintained by ajuna network https ajuna io net toolchain sdk https github com ajuna network ajuna sdk toolchain for substrate net pre generated substratenet https github com ajuna network substratenet maintained by ajuna network go substrate gen https github com aphoh go substrate gen generate go de serialization client code from substrate metadata sube https github com virto network sube lightweight rust client library and cli with support for type information subxt https github com paritytech substrate subxt official rust client c api https github com usetech llc polkadot api cpp maintained by usetech go rpc client https github com centrifuge go substrate rpc client maintained by centrifuge https centrifuge io kotlin client https github com nodlecode substrate client kotlin maintained by nodle io https github com nodlecode polkadot js api https github com polkadot js api semi official javascript library for substrate based chains python interface https github com polkascan py substrate interface maintained by polkascan foundation https polkascan org rust api client https github com scs substrate api client rust client maintained by supercomputers systems ag https www scs ch subscan go utilities https github com itering subscan essentials ss58 and more developed by subscan sub api https github com kodadot packages tree main sub api friendly wrapper for polkadot js api maintained by kodadot useinkathon https github com scio labs use inkathon typesafe react hooks library abstracting functionality by polkadot js for working with substrate based networks and ink smart contracts maintained by scio labs subsocial js sdk https github com dappforce subsocial js a js sdk for developers to build web3 social apps on top of subsocial mobile fearless utils android https github com soramitsu fearless utils android android substrate tools fearless utils ios https github com soramitsu fearless utils ios ios substrate tools nova substrate sdk android https github com nova wallet substrate sdk android substrate sdk and tools for android nova substrate sdk ios https github com nova wallet substrate sdk ios substrate sdk and tools for ios polkadot dart https github com pocket4d polkadot dart dart substrate api polkawallet sdk https github com polkawallet io sdk flutter sdk for substrate based app react native substrate sign https github com paritytech react native substrate sign rust library for react native tools offline election https github com paritytech substrate debug kit tree master offline election tool to predict nominated proof of stake elections offchain ipfs https rs ipfs github io offchain ipfs manual substrate infused with ipfs https ipfs io polkadot js bundle https github com shawntabrizi polkadot js bundle a standalone js bundle that contains polkadot js libraries polkadot launch https github com shawntabrizi polkadot launch simple cli tool to launch a local polkadot test network polkadot runtime prom exporter https github com paritytech polkadot runtime prom exporter a prometheus https prometheus io exporter for polkadot runtime metrics modifiable for substrate use polkadot scripts https github com paritytech polkadot scripts a collection of scripts parity uses to diagnose polkadot kusama polkadot starship https github com koute polkadot starship another tool to launch a local polkadot test network with emphasis on the ability to run big testnets srtool actions https github com chevdor srtool actions github actions to easily use the srtool docker image to build your own runtime srtool cli https github com chevdor srtool cli cli frontend for the srtool docker image srtool https github com paritytech srtool docker image to deterministically build a runtime subsee https github com ascjones subsee cli to inspect metadata of a substrate node as json subalfred https github com hack ink subalfred an all in one substrate development toolbox substrate balance calculator https github com shawntabrizi substrate balance calculator breakdown the balances of your substrate account substrate balance graph https github com shawntabrizi substrate balance graph create a graph of the token balance over time of a substrate address substrate graph benchmarks https github com shawntabrizi substrate graph benchmarks graph the benchmark output of frame pallets substrate js utils https github com shawntabrizi substrate js utilities a set of useful javascript utilities for substrate that uses the polkadot js api also deployed as a website https www shawntabrizi com substrate js utilities substrate society https github com shawntabrizi substrate society a basic front end for the frame society pallet substrate toml lint https github com shawntabrizi substrate toml lint a toml parser and checker to avoid common errors in substrate projects subwasm https github com chevdor subwasm cli to inspect a runtime wasm blob offline it shows information metadata and can compare runtimes it can also help you fetch a runtime directly from a node sup https github com clearloop sup command line tool for generating or upgrading a substrate node scale value https github com paritytech scale value analogous to serde json but for scale library to decode arbitrary scale encoded bytes into a dynamic value given type info from scale info scale decode https github com paritytech scale decode decode scale bytes into arbitrary custom types by implementing a visitor trait aleph im https aleph im scalable decentralized database file storage and computation services for substrate chains and more archive https github com paritytech substrate archive indexing engine for substrate chains dev hub utils https github com danforbes substrate devhub utils unofficial utilities for working with official substrate developer hub resources europa https github com patractlabs europa a sandbox for the substrate runtime execution environment fork off substrate https github com maxsam4 fork off substrate script to help bootstrap a new chain with the state of a running chain fudge https github com centrifuge fudge core lib for accessing and arbitrarily manipulating substrate databases including the building and importing of local blocks gantree library https github com gantree io gantree lib nodejs a suite of technologies for managing substrate powered parachain networks via rapid spin up tear down halva https github com halva suite halva a toolchain for improving the experience of developing on substrate hydra https github com joystream hydra a graphql framework for substrate nodes jupiter https github com patractlabs jupiter testnet for smart contracts written for the frame contracts pallet and ink megaclite https github com patractlabs megaclite zero knowledge tools for the polkadot ecosystem metadata portal https nova wallet github io metadata portal a self hosted webpage that shows the latest metadata and chain specs for any given network minimark https github com kodadot packages implementation of rmrk nft v1 v2 protocol maintained by kodadot nova polkadot utils https github com nova wallet nova utils contains static info metadata to support client apps in polkadot ecosystem to map it to various netowrks polkadot vault https signer parity io formerly parity signer upcycle an unused mobile phone into an air gapped hardware wallet polkadot panic https github com simplyvc panic polkadot monitoring and alerting solution for polkadot nodes by simply vc compatible with many substrate chains polkadot tool index https wiki polkadot network docs build tools index list of tools available for your development with polkadot and any substrate chain including block explorers wallets network monitoring reporting clients benchmarking fuzzing forking scale codec cli tools and much more polkadot js apps ui https polkadot js org apps semi official block explorer front end for substrate based chains polkadot js extension https github com polkadot js extension browser extension for interacting with substrate based chains polkascan https polkascan io multi chain block explorer maintained by polkascan foundation proxy hot wallet demo https github com emostov proxy hot wallet a demonstration of a secure convenient and flexible hot wallet architecture built on substrate primitives redspot https github com patractlabs redspot a truffle https www trufflesuite com truffle like toolkit for smart contracts for the frame contracts pallet and ink sidecar https github com paritytech substrate api sidecar rest service that runs alongside substrate nodes ss58 transform https polkadot subscan io tools ss58 transform display key s addressees with all ss58 prefixes staking rewards collector https github com w3f staking rewards collector a script to parse and output staking rewards for a given kusama or polkadot address and cross reference them with daily price data subkey https docs substrate io reference command line tools subkey command line utility for working with cryptographic keys subquery https subquery network a graphql indexer and query service that allows users to easily create indexed data sources and host them online for free nova subquery api https github com nova wallet subquery nova a subquery api implementation for operation history and staking analytics subscan https www subscan io multi network explorer for substrate based chains subsquid https subsquid io an indexing framework sdk infrastructure to quickly and easily turn substrate and evm on chain data into apis and host them substate https github com arrudagates substate 100 no std wasm compatible substrate storage key generator library for rust substrate debug kit https github com paritytech substrate debug kit a collection of tools and libraries for debugging substrate based chains substrate docker builders https github com eteissonniere substrate nodeops a set of dockerfiles and github actions to auto build and push a docker image for substrate based chains substrate faucet bot https github com starkleytech substrate faucet python based faucet for development purposes substrate graph https github com playzero substrate graph graphql indexer for substrate based chains typechain polkadot https github com supercolony net typechain polkadot hepls users to generate typescript types from contract abis ink and generate runtime code to interact with contracts and deploy them txwrapper https github com paritytech txwrapper helpful library for offline transaction creation vscode substrate https marketplace visualstudio com items itemname paritytech vscode substrate plugin for visual studio code polkaholic io https polkaholic io multi chain block explorer with api and defi support across 40 parachains subid https github com dappforce subid an advanced cross chain portfolio management tool for the polkadot ecosystem allowing any user to see their balances across chains view their crowdloan history view their nfts across polkadot ecosystem chains claim their vested tokens and perform cross chain transfers subsocial sdk playground https play subsocial network subsocial js sdk playground allows you to fetch spaces send transactions on blockchain and test the sdk code snippets on the go without the need to download or setup anything locally uptest runtime upgrade tool https github com uptest sc uptest uptest command line client and libuptest rust library are two tools used for debugging storage changes and runtime upgrades products and services onfinality https onfinality io free and paid services to shared substrate based nodes privhost https privhost laissez faire trade public tor onion supported nodes for polkadot kusama and edgeware substrate devops guide https paritytech github io devops guide parity devops team s configuration and guidance on deploying monitoring and maintaining node infrastructure alternative implementations gossamer https github com chainsafe gossamer a polkadot client implemented in go from chainsafe https chainsafe io kagome https kagome readthedocs io en latest a c 17 implementation of the polkadot client from soramitsu http www soramitsu co jp limechain assemblyscript runtime https github com limechain as substrate runtime an account based substrate proof of concept runtime written in assemblyscript from limechain https limechain tech scale codec assemblyscript https github com limechain as scale codec maintained by limechain c https github com matthewdarnell cscale maintained by matthew darnell c https github com soramitsu scale codec cpp maintained by soramitsu codec definition https docs substrate io v3 advanced scale codec official codec documentation go https github com itering scale go maintained by itering https www itering com haskell https github com airalab hs web3 tree master src codec maintained by robonomics network https robonomics network java https github com emeraldpay polkaj tree master polkaj scale maintained by emerald https emerald cash parity scale codec https github com paritytech parity scale codec reference implementation written in rust python https github com polkascan py scale codec maintained by polkascan foundation ruby https github com itering scale rb maintained by itering scales https github com virto network scales serializing scale using type information from a type registry javascript typescript implementations paritytech parity scale codec ts https github com paritytech parity scale codec ts maintained by parity technologies polkadot js api https github com polkadot js api tree master packages types maintained by polkadot js scale ts https github com unstoppablejs unstoppablejs tree main packages scale ts scale ts maintained by josep m sobrepere soramitsu scale codec js library https github com soramitsu scale codec js library maintained by soramitsu | substrate polkadot blockchain rust kusama cryptocurrency cryptography networking consensus distributed-systems decentralization awesome awesome-list | blockchain |
practical-webdev-haskell | apress source code this repository accompanies practical web development with haskell http www apress com 9781484237380 by ecky putrady apress 2018 comment cover cover image 9781484237380 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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msoe-embeddedsystems-seasonlabs | msoe ee2920 embedded systems labs about this repository this repository brings up an introductory series of implementations on how to design embedded systems when dealing with registers level using c c programming language the concepts covered in this series help you to understand the general working of modern microcontrollers including many of their common peripherals the series presented here is a throwback of a course called ee2920 embedded systems taken by me as international student back in the fall of 2014 at milwaukee school of engineering msoe for this course the microcontroller used was the atmega328p famous to compose arduino boards such as arduino uno and arduino nano however the general concepts covered touch a lot of concepts that are common sense for lots of modern microcontrollers so the knowledge obtained from this course can be easily used to better understand the internal architecture other devices and build firmware for them table of contents the series requirements and content is shown in detail by the section for each lab the following content topics present each lab s title to present to you a general idea of what it approached in this serie lab 01 simple hello world lab 01 simple hello world lab 02 gpio interfacing led s and switches lab 02 gpio interfacing leds and switches lab 03 gpio in switch debouncing and hd44780 lcd interfacing lab 03 gpio in switch debouncing and hd44780 lcd interfacing lab 04 gpio interfacing a qtr 8rc sensor array module lab 04 gpio interfacing a qtr 8rc sensor array module lab 05 a d conversion lab 05 ad conversion lab 06 a d conversion interrupts and analog sensors lab 06 ad conversion interrupts and analog sensors lab 07 waveform generation lab 07 waveform generation lab 08 infrared object sensing lab 08 infrared object sensing lab 09 servo motor control lab 09 servo motor control lab 01 simple hello world requirements this lab is a simple hello world routine sign if the atmega328p and wiring power supply is working fine that is made by setting up a blinking led with an interval of 1 second between blinks built with atmega328p you can use arduino uno nano boards 1x led 1x 330 resistor schematic p align center img src labs lab01 schematic png width 740 p flowchart p align center img src labs lab01 flowchart png width 160 p lab 02 gpio interfacing led s and switches requirements this lab is an introduction on digital inputs and outputs usually referenced as gpio it basically makes use of the atmega328p s gpio to interface five digital outputs 5 leds and two digital inputs 2 switches push buttons and implements routines of activations involving these devices the idea is to use the buttons to control a row of led s arranged in an upside down v formation in this formation only one of the leds are on at the same time with adjacent sequential transitions of led s states every second the first switch can be pressed to toggle the direction of these transitions which can turn from clockwise to counter clockwise or vice versa the second switch keeps all led s off as long as it s being pressed built with atmega328p you can use arduino uno nano boards 5x led s 5x 330 resistors schematic p align center img src labs lab02 schematic png width 540 p flowchart p align center img src labs lab02 flowchart png width 600 p lab 03 gpio in switch debouncing and hd44780 lcd interfacing requirements this lab it was introduced the interfacing of the atmega328p s gpio with a lcd display the lcd module interfaced is the hd44780 in the first part of this lab it s shown a simple system that was able to receive digital signals from two push buttons to activate events on the lcd display basically the lcd display shows the state of button one presenting 1 to button pressed and 0 to not pressed at the same time button two is used to blink a character in the display when it is held pressed in the second part of this lab it s to made use of the lcd display to show a counter that can be incremented if button one is pressed or decremented if button one is pressed while button two is held pressed to make better use of these push buttons activations it was implemented a system that uses delays and loops to avoid the detection of bouncing from mechanical switches that are being used in this lab and also prevent that multiple events were detected when buttons are pressed built with atmega328p you can use arduino uno nano boards hd44780 lcd display 10k potentiometer 2x push buttons 2x 330 resistors schematic p align center img src labs lab03 schematic png width 600 p flowchart part 1 p align center img src labs lab03 flowchart 1 png width 380 p flowchart part 2 p align center img src labs lab03 flowchart 2 png width 550 p lab 04 gpio interfacing a qtr 8rc sensor array module requirements this lab makes use of the atmega328p microcontroller gpio to interface the qtr 8rc module in order to detect incoming events from its reflectance sensors and measure the magnitude of reflection detected by them the activity proposed also makes use of a lcd display to show the state of each one of the four used sensors showing a sequence of four places on display relative to their states the measuring is made by counting the time until the discharge of each sensor gets the threshold of the microcontroller used if the system detects a higher reflectance compared to a reference value the time decreases and 1 will be place in the position relative to the sensor used on the other hand if the level of reflectance gets lower the time of discharge increases and gets higher than reference value it will be shown 0 built with atmega328p you can use arduino uno nano boards hd44780 lcd display 10k potentiometer qtr 8rc module schematic p align center img src labs lab04 schematic png width 620 p flowchart p align center img src labs lab04 flowchart png width 450 p lab 05 a d conversion requirements the system described in this report uses the atmega328p microcontroller analog to digital converter module to measure the intensity of warm presented by a symbolic sensor of temperature whose variations are emulated by the output voltage of a 10 k potentiometer using the 10 bit resolution a d converter available in the atmega328p it is possible to get digital values from 0 to 1023 as the requirements of this lab presents a linear dependence between the gotten value and a range of temperature between 0 and 100 c it was implemented a linear conversion between the two measuring scales after that digital value and the converted temperature value are shown on a lcd display in order to get this linearity visible from the equivalence of the system samples in the last section of this report attachment 4 is shown the characterization of the signal by the grouping different scales from the same experimental samples based on adc digital value adcw temperature c and analog input voltage from the potentiometer volts in addition a plot of adcw vs analog input voltage is also shown to demo the tendency to the linearity reached by the implemented system built with atmega328p you can use arduino uno nano boards hd44780 lcd display 2x 10k potentiometer schematic p align center img src labs lab05 schematic png width 600 p flowchart p align center img src labs lab05 flowchart png width 220 p lab 06 a d conversion interrupts and analog sensors requirements the system described in this report uses the atmega328p microcontroller analog to digital converter module sweep over three adc channels in order to get samples from them and take some decisions the system is driven by the use of interruptions from adc conversion completion event the system gets samples from three channels adc channels each one of them is interfaced to analog sensors the system uses a reflectance sensor module qtr 1a to get data relative to proximity or reflectance from objects the variations of voltage in the module output is read by the system and a group of three leds is used to indicate the current level of reflectance the more it reflects the more leds will be on in addition the system interfaces two structures of voltage dividers that use ldrs to change the output voltage and get adc samples from it to show the percentage of light intensity in the place they are as ldrs change its resistance according to such changes of light intensity in a non linear way the output voltage is not linear either so math manipulations are needed to make such values get the linear percent representation built with atmega328p you can use arduino uno nano boards hd44780 lcd display 10k potentiometer qtr 1a module 3x led s 2x ldr sensors 2x 10k resistors 3x 330 resistors schematic p align center img src labs lab06 schematic png width 640 p flowchart p align center img src labs lab06 flowchart png width 660 p lab 07 waveform generation requirements the system described in this report uses the atmega328p microcontroller timer0 to generate a pulse width modulation pwm signal for such implementation it was necessary to do calculations in order to meet the requirement of converter module sweep over three adc channels in order to get samples from them and take some decisions the system is driven by the use of interruptions from adc conversion completion event the system gets samples from three channels adc channels each one of them is interfaced to analog sensors the system uses a reflectance sensor module qtr 1a to get data relative to proximity or reflectance from objects the variations of voltage in the module output is read by the system and a group of three leds is used to indicate the current level of reflectance the more it reflects the more leds will be on in addition the system interfaces two voltage dividers that use ldrs to change the output voltage and get adc samples from it to show the percentage of light intensity measured on the place they are as ldrs change its resistance according to such changes of light intensity in a non linear rate the output voltage is not linear either so math manipulations are needed to make such values get the linear percent in the representation built with atmega328p you can use arduino uno nano boards 2x push buttons 2x 330 resistors schematic p align center img src labs lab07 schematic png width 560 p flowchart p align center img src labs lab07 flowchart png width 460 p lab 08 infrared object sensing requirements the system described in this report makes use of some of the atmega328p resources such as gpio timers and interrupt system to perform the interface of a distance sensitive based structure composed by ir led and receiver the idea is to use the magnitude of ir light reflected by objects in front of the sensor to estimate the distance between the system and the object the interface mounted to interface the ir components to the used microcontroller needs to use specifics signals according to the concepts presented in classroom in order to get better capacity of reflectance for such purpose the signal that drives the ir led should be have peculiar characteristics 16 square wave pulses in 38khz should be applied on the component followed by an idle interval of five milliseconds until the process repeat the implementation of such signal was made by a combination between timers and its interruptions to get desired results the ir receiver could sense the reflectance of these pulses and its output is processed to get an interval of low voltage presented when each burst is applied the larger is this interval the closer is the object in other words based in the time of this interval it is possible to estimate how distant the object reflector is built with atmega328p you can use arduino uno nano boards hd44780 lcd display 2x led s 2x 330 resistors schematic p align center img src labs lab08 schematic png width 620 p flowchart p align center img src labs lab08 flowchart png width 580 p lab 09 servo motor control requirements built with atmega328p you can use arduino uno nano boards schematic p align center img src labs lab09 schematic png width 540 p flowchart part 1 p align center img src labs lab09 flowchart1 png width 600 p flowchart part 2 p align center img src labs lab09 flowchart2 png width 1000 p | os |
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FreeRTOS-Xen | freertos for xen on arm this freertos tree provides a port of freertos for xen on arm systems this project is not currently maintained use at your own risk author jonathan daugherty jtd at galois com galois inc xen version 4 7 freertos version 7 6 0 supported systems any arm system with virtualization extensions stability suggested for exploratory research use only see future work section selected elements of the source tree layout are as follows demo cortex a15 xen gcc the root of the build system including the c library drivers linker script and makefile platform code responsible for integrating with the hypervisor and paravirtualized services xen hypervisor services grant tables event channels hypercalls xenbus console the xen virtual console drivers the hypercall based debug console and the xenbus based console asm assembly relevant to interacting with the hypervisor minlibc the minimal c library used by the xen service drivers source the freertos core including task scheduler and other data structure portable gcc the arm specific elements of the port for the gnu compiler toolchain arm7 ca15 xen the armv7 cortex a15 xen port implementation asm all assembly for setting up the hardware including cache setup fpu setup interrupt handlers page table management and mmu setup and virtual timer setup requirements to make use of this distribution you will need an installed version of xen including development headers e g libxen dev on ubuntu an arm cross compiler toolchain e g installable from your distribution as arm none eabi gcc a standard linux development system i e with gnu make an arm system capable of running xen http wiki xen org wiki xen arm with virtualization extensions hardware step 1 building the core applications are built with this distribution in two steps build the core of freertos as a library a build your application codebase and link it against the library the first step requires your cross compiler to be in the path and requires the presence of xen development headers to be installed somewhere on your system given both of those details the build process for building the core freertos library is as follows step 1 fetch dependencies cd freertos bash setup sh step 2 build the core library cd freertos demo cortex a15 xen gcc make cross compile xen prefix the two settings specified above are cross compile the string prefix of the cross compiler toolchain s program names default arm none eabi xen prefix the autoconf style prefix for the location of the xen headers default usr the result of the build process will be freertos a the library against which your application will be built in step 2 step 2 building an application to build an application with freertos a compile it and link it against the library the details of this process are somewhat tedious but this distribution includes an example application and makefile in the example directory once you ve built the freertos library as described in step 1 cd example make the resulting example bin kernel image can be used to create a virtual machine as described in the next section running a freertos vm to create a virtual machine from an application image create a xen domain configuration for your application e g kernel path to application bin memory 32 name your application vcpus 1 disk and then create a domain with xl create e g xl create f path to domain cfg using xen services this port of freertos borrows xenbus console grant table and event channel implementations from mini os modified to work with the freertos scheduler general setup demo cortex a15 xen gcc platform xen xen setup c xenbus demo cortex a15 xen gcc platform xen xenbus c demo cortex a15 xen gcc platform xen xenbus arm c console everything in demo cortex a15 xen gcc platform console grant tables demo cortex a15 xen gcc platform xen gnttab c map frames in mmu c event channels demo cortex a15 xen gcc platform xen events setup c demo cortex a15 xen gcc platform xen events c debugging many modules in the distribution provide extra xen emergency console output for debugging purposes to enable such debugging output so that it bypasses the xen paravirtualized console and makes direct hypercalls instead declare a debug constant at compile time make ddebug 1 if you need to debug system state in assembly regions some macros are available dumpregs dumpregs s may be used to dump the register states for r0 r12 lr spsr cpsr and other useful system registers dumpstack dumpregs s may be used to dump the 8 most recent words on the current stack relevant configuration settings the following settings in demo cortex a15 xen gcc include freertosconfig h may be relevant to your application configuse xen console if 1 console output emitted with printk goes to the xen paravirtualized console once it is up if 0 the output goes to the xen emergency console instead via the hypervisor console io hypercall stable systems should set this to 1 systems under development will be easier to debug if this is set to 0 in case the console service is not coming up for some reason configinterrupt nesting if 1 enables interrupts of sufficiently high priority to preempt the execution of lower priority interrupts defaults to 0 configmax api call interrupt priority the highest interrupt priority from which interrupt safe freertos api functions can be called for more information please see http www freertos org a00110 html configtick priority the priority of the tick interrupt raised by the arm virtual timer configxenbus task priority the priority of the freertos task responsible for processing xenbus responses configevent irq priority the interrupt priority of the xen event notification interrupt configtick rate hz asm the rate in ticks per second of the tick used to drive the freertos scheduler this determines the timer interval used to set the virtual timer s interrupt configtotal heap size the size of the heap in bytes memory layout the in memory layout of the freertos system is as follows for information see the linker script linker lds and the various assembly sources used to boot the system in particular boot s the following table lists addresses with increasingly higher values indicating symbolic or numeric addresses where possible this layout is not intended to be exhaustive but to provide a high level view of the organization of the program address segment description start start start of kernel and execution entry point l1 page table level 1 page table region see boot s l2 page table level 2 page table region see boot s text text segments of compiled object code rodata read only data data read write data start stacks start of stack region top of svc stack svc stack svc stack start stacks 16384 irq stack irq stack start stacks 32768 firq stack fiq stack start stacks 49152 abt stack abt stack start stacks 65536 und stack und stack start stacks 81920 bss bss region all addresses in first three gigabytes of virtual memory address space are mapped to the equivalent physical memory addresses pa va this mapping is set up by mmu c in the function setup direct map the only exceptions to this rule are the physical addresses of the 1mb regions containing the kernel binary the virtual addresses of the 1mb regions containing the kernel binary the virtual address of the kernel is determined by the start address set by the linker script although the specific start address is not important what is important is that the kernel is aware that it is probably not running at the desired virtual address at startup we don t know where xen will place the kernel in physical memory we only know that it will be placed near the start of ram to deal with this the kernel determines the difference between its actual start address and its virtual address and sets up the mmu with mappings so that both its physical and virtual start addresses map to the physical memory containing the kernel this technique was borrowed from mini os since it has the same requirement the fourth gigabyte of virtual address space is initially unmapped and is reserved for mapping pages from grant tables and libivc for more information please see demo cortex a15 xen gcc include freertos mmu h source portable gcc arm7 ca15 xen mmu c future work caveats the following are known issues missing features we d like to implement or security issues we d like to resolve if you have the time and inclination feel free to work on these and submit pull requests testing with real time xen while freertos itself is suitable for real time use running freertos on xen requires more investigation because the xen scheduler will interfere with the real time behavior of the guest in particular testing with xen s real time scheduler needs to be done to determine how reliable the guest performs real time work in the mean time near real time behavior can be obtained by using cpu pinning to pin the freertos guest to a single cpu free of other xen domains configurable memory usage the heap size is currently configurable using the freertosconfig h setting configtotal heap size but the total available guest domain memory is not known to the guest as with gic configuration this information can be obtained from the arm device tree but that work has not been done per task virtual address spaces at present only one virtual address space is used for the entire system and each virtual address is mapped to its equivalent corresponding physical address this was done to allow use of the caches and mmu for mapping grant table entries but further use of virtual memory could be employed to provide per task virtual address spaces memory protection for relevant segments text rodata and start segments are not protected in the mmu optimizations are turned off on some arm cross compilers enabling optimizations causes incorrect code generation for some c library routines optimizations are therefore disabled by default contributing if you d like to contribute to this effort please submit issues or pull requests via the github repository page at https github com galoisinc freertos xen see also there are many ports of freertos which are not included in this distribution for more information on the upstream freertos distribution please see http www freertos org freertos quick start guide html http www freertos org faqhelp html | os |
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inDexDa | div align center a href https www librarylab ethz ch img src https www librarylab ethz ch wp content uploads 2018 05 logo svg alt eth library lab logo height 160 a br p strong indexda strong natural language processing of academic papers for dataset identification and indexing p p an initiative for human centered innovation in the knowledge sphere of the a href https www librarylab ethz ch eth library lab a p div table of contents getting started getting started setup setup installation instructions installation instructions usage usage configuration configuration running indexda running indexda contact contact license license getting started this project is divided into multiple sections pipelines to make it more modular and easier to use modify these are as the followings pipeline description paperscraper paperscraper used to comb through a specified online archive of academic papers in order to find papers relating to a field topic or search term stores these papers in a mongodb database see paperscraper folder for more information and usage instructions nlp nlp from the papers found using paperscraper used natural language processing techniques to determine whether the papers shows a new dataset was created if so it stores this information within the mongodb database for later use see naturallanguageprocessing folder for more information and usage instructions dataset extraction collects information from the papers the bert network predicts contain new datasets such as links to the dataset type of data used size of dataset etc setup this code has been tested on a computer with following specifications os platform and distribution linux ubuntu 18 04lts cuda cudnn version cuda 10 0 130 cudnn 7 6 4 gpu model and memory nvidia geforce gtx 1080 8gb python 3 6 8 tensorflow 1 14 installation instructions to install the virtual environment and most of the required dependencies run bash pip install pew pew new indexda pew in indexda git clone https github com eth library lab indexda git cd indexda install sh networks used in this project are run using tensorflow https www tensorflow org backend usage to begin running indexda check the args json file in the main directory this contains relevant information which will be used during the process please make sure to add the following fields configuration indexda is configured primarily through the args json file in this file is included a variety of options for web scraping network training and dataset extraction options each section is explained more thoroughly in the paperscraper readme but the following steps will allow you to run indexda quickly 1 choose the online academic paper repository you wish to scrape in the archives to scrape section indexda natively supports both arxiv https arxiv org and sciencedirect https www sciencedirect com scraping apis you can use either a single scraper or multiple scrapers in sequence 2 replace the default search query with your specific word or phrase more specific search queries will yield less results but will run much faster 3 if using sciencedirect https www sciencedirect com scraper apply for an api key https dev elsevier com apikey manage once a key has been obtained include it in the archive info sciencedirect https www sciencedirect com apikey field also make sure to include the start and end years for the search running indexda once the args json file has been configured run the run py file using the following flags as desired but only include either the train or the scrape flag bash python3 run py first time must be included the first time you run indexda scrape will run indexda and output datasets it finds train will re train the bert network contact for any inquiries use the eth library lab contact form https www librarylab ethz ch contact license mit license | sciencedirect academic-papers natural-language-processing scraper python bert bert-model classification classifier-model | ai |
ck | continuous integration drone travis cirrus build status https cloud drone io api badges concurrencykit ck status svg https cloud drone io concurrencykit ck build status https travis ci org concurrencykit ck svg https travis ci org concurrencykit ck build status https api cirrus ci com github concurrencykit ck svg branch master https cirrus ci com github concurrencykit ck compilers tested in the past include gcc clang cygwin icc mingw32 mingw64 and suncc across all supported architectures all new architectures are required to pass the integration test and under go extensive code review continuous integration is currently enabled for the following targets darwin clang x86 64 freebsd clang x86 64 linux gcc arm64 linux gcc x86 64 linux clang x86 64 linux clang ppc64le compile and build step 1 configure for additional options try configure help step 2 in order to compile regressions requires posix threads use make regressions in order to compile libck use make all or make step 3 in order to install use make install to uninstall use make uninstall see http concurrencykit org for more information supported architectures concurrency kit supports any architecture using compiler built ins as a fallback there is usually a performance degradation associated with this concurrency kit has specialized assembly for the following architectures aarch64 arm ppc ppc64 riscv64 s390x sparcv9 x86 x86 64 features concurrency primitives ck pr concurrency primitives as made available by the underlying architecture includes support for all atomic operations natively transactional memory pipeline control read for ownership and more ck backoff a simple and efficient minimal noise backoff function ck cc abstracted compiler builtins when writing efficient concurrent data structures safe memory reclamation ck epoch a scalable safe memory reclamation mechanism with support idle threads and various optimizations that make it better than or competitive with many state of the art solutions ck hp implements support for hazard pointers a simple and efficient lock free safe memory reclamation mechanism data structures ck array a simple concurrently readable pointer array structure ck bitmap an efficient multi reader and multi writer concurrent bitmap structure ck ring efficient concurrent bounded fifo data structures with various performance trade off this includes specialization for single reader many reader single writer and many writer ck fifo a reference implementation of the first published lock free fifo algorithm with specialization for single enqueuer single dequeuer and many enqueuer single dequeuer and extensions to allow for node re use ck hp fifo a reference implementation of the above algorithm implemented with safe memory reclamation using hazard pointers ck hp stack a reference implementation of a treiber stack with support for hazard pointers ck stack a reference implementation of an efficient lock free stack with specialized variants for a variety of memory management strategies and bounded concurrency ck queue a concurrently readable friendly derivative of the bsd queue interface coupled with a safe memory reclamation mechanism implement scalable read side queues with a simple search and replace ck hs an extremely efficient single writer many reader hash set that satisfies lock freedom with bounded concurrency without any usage of atomic operations and allows for recycling of unused or deleted slots this data structure is recommended for use as a general hash set if it is possible to compute values from keys learn more at https engineering backtrace io workload specialization and http concurrencykit org articles ck hs html ck ht a specialization of the ck hs algorithm allowing for disjunct key value pairs ck rhs a variant of ck hs that utilizes robin hood hashing to allow for improved performance with higher load factors and high deletion rates synchronization primitives ck ec an extremely efficient event counter implementation a better alternative to condition variables ck barrier a plethora of execution barriers including centralized barriers combining barriers dissemination barriers mcs barriers tournament barriers ck brlock a simple big reader lock implementation write biased reader writer lock with scalable read side locking ck bytelock an implementation of bytelocks for research purposes allowing for in theory fast read side acquisition without the use of atomic operations in reality memory barriers are required on the fast path ck cohort a generic lock cohorting interface allows you to turn any lock into a numa friendly scalable numa lock there is a significant trade off in fast path acquisition cost specialization is included for all relevant lock implementations in concurrency kit learn more by reading lock cohorting a general technique for designing numa locks ck elide a generic lock elision framework allows you to turn any lock implementation into an elision aware implementation this requires support for restricted transactional memory by the underlying hardware ck pflock phase fair reader writer mutex that provides strong fairness guarantees between readers and writers learn more by reading spin based reader writer synchronization for multiprocessor real time systems ck rwcohort a generic read write lock cohorting interface allows you to turn any read write lock into a numa friendly scalable numa lock there is a significant trade off in fast path acquisition cost specialization is included for all relevant lock implementations in concurrency kit learn more by reading lock cohorting a general technique for designing numa locks ck rwlock a simple centralized write biased read write lock ck sequence a sequence counter lock popularized by the linux kernel allows for very fast read and write synchronization for simple data structures where deep copy is permitted ck swlock a single writer specialized read lock that is copy safe useful for data structures that must remain small be copied and contain in band mutexes ck tflock task fair locks are fair read write locks derived from scalable reader writer synchronization for shared memory multiprocessors ck spinlock a basic but very fast spinlock implementation ck spinlock anderson scalable and fast anderson spinlocks this is here for reference one of the earliest scalable and fair lock implementations ck spinlock cas a basic spinlock utilizing compare and swap ck spinlock dec a basic spinlock a c adaption of the older optimized linux kernel spinlock for x86 primarily here for reference ck spinlock fas a basic spinlock utilizing atomic exchange ck spinlock clh an efficient implementation of the scalable clh lock providing many of the same performance properties of mcs with a better fast path ck spinlock hclh a numa friendly clh lock ck spinlock mcs an implementation of the seminal scalable and fair mcs lock ck spinlock ticket an implementation of fair centralized locks | os |
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ROBOCON-2018 | robocon 2018 design embedded systems for robots | os |
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go-musicoin | go musicoin official golang implementation of the ethereum based musicoin protocol gitter https badges gitter im join 20chat svg https gitter im musicoins lobby binaries are published at https github com musicoin go musicoin releases building the source for prerequisites and detailed build instructions please stick to the official go ethereum installation instructions https github com ethereum go ethereum wiki building ethereum building gmc requires both a go version 1 7 or later and a c compiler you can install them using your favourite package manager once the dependencies are installed run make gmc or to build the full suite of utilities make all executables the go musicoin project comes with several wrappers executables found in the cmd directory command description gmc our main musicoin cli client it is the entry point into the musicoin network main test or private net capable of running as a full node default archive node retaining all historical state or a light node retrieving data live it can be used by other processes as a gateway into the musicoin network via json rpc endpoints exposed on top of http websocket and or ipc transports check gmc help and the official go ethereum cli wiki page https github com ethereum go ethereum wiki command line options for command line options abigen source code generator to convert ethereum contract definitions into easy to use compile time type safe go packages it operates on plain ethereum contract abis https github com ethereum wiki wiki ethereum contract abi with expanded functionality if the contract bytecode is also available however it also accepts solidity source files making development much more streamlined please see the official go ethereum native dapps https github com ethereum go ethereum wiki native dapps go bindings to ethereum contracts wiki page for details bootnode stripped down version of the musicoin client implementation that only takes part in the network node discovery protocol but does not run any of the higher level application protocols it can be used as a lightweight bootstrap node to aid in finding peers in private networks evm developer utility version of the evm ethereum virtual machine that is capable of running bytecode snippets within a configurable environment and execution mode its purpose is to allow isolated fine grained debugging of evm opcodes e g evm code 60ff60ff debug gmcrpctest developer utility tool to support the ethereum rpc test https github com ethereum rpc tests test suite which validates baseline conformity to the ethereum json rpc https github com ethereum wiki wiki json rpc specs please see the test suite s readme https github com ethereum rpc tests blob master readme md for details rlpdump developer utility tool to convert binary rlp recursive length prefix https github com ethereum wiki wiki rlp dumps data encoding used by the ethereum based musicoin protocol both network as well as consensus wise to user friendlier hierarchical representation e g rlpdump hex ce0183ffffffc4c304050583616263 swarm swarm daemon and tools this is the entrypoint for the swarm network swarm help for command line options and subcommands see https swarm guide readthedocs io for swarm documentation puppeth a cli wizard that aids in creating a new ethereum based network running gmc going through all the possible command line flags is out of scope here please consult the compatible go ethereum cli wiki page https github com ethereum go ethereum wiki command line options but we ve enumerated a few common parameter combos to get you up to speed quickly on how you can run your own gmc instance full node on the main musicoin network by far the most common scenario is people wanting to simply interact with the musicoin network create accounts transfer funds deploy and interact with contracts for this particular use case the user doesn t care about years old historical data so we can fast sync quickly to the current state of the network to do so gmc fast cache 512 console this command will start gmc in fast sync mode fast causing it to download more data in exchange for avoiding processing the entire history of the musicoin network which is very cpu intensive bump the memory allowance of the database to 512mb cache 512 which can help significantly in sync times especially for hdd users this flag is optional and you can set it as high or as low as you d like though we d recommend the 512mb 2gb range start up gmc s built in interactive javascript console https github com ethereum go ethereum wiki javascript console via the trailing console subcommand through which you can invoke all official web3 methods https github com ethereum wiki wiki javascript api as well as gmc s own management apis https github com ethereum go ethereum wiki management apis this too is optional and if you leave it out you can always attach to an already running gmc instance with gmc attach full node on the musicoin test network transitioning towards developers if you d like to play around with creating musicoin contracts you almost certainly would like to do that without any real money involved until you get the hang of the entire system in other words instead of attaching to the main network you want to join the test network with your node which is fully equivalent to the main network but with play ether only gmc testnet fast cache 512 console the fast cache flags and console subcommand have the exact same meaning as above and they are equally useful on the testnet too please see above for their explanations if you ve skipped to here specifying the testnet flag however will reconfigure your gmc instance a bit instead of using the default data directory musicoin on linux for example gmc will nest itself one level deeper into a testnet subfolder musicoin testnet on linux note on osx and linux this also means that attaching to a running testnet node requires the use of a custom endpoint since gmc attach will try to attach to a production node endpoint by default e g gmc attach datadir testnet gmc ipc windows users are not affected by this instead of connecting the main musicoin network the client will connect to the test network which uses different p2p bootnodes different network ids and genesis states note although there are some internal protective measures to prevent transactions from crossing over between the main network and test network you should make sure to always use separate accounts for play money and real money unless you manually move accounts gmc will by default correctly separate the two networks and will not make any accounts available between them configuration as an alternative to passing the numerous flags to the gmc binary you can also pass a configuration file via gmc config path to your config toml to get an idea how the file should look like you can use the dumpconfig subcommand to export your existing configuration gmc your favourite flags dumpconfig note this works only with gmc v2 1 0 and above docker quick start one of the quickest ways to get musicoin up and running on your machine is by using docker docker run d name musicoin node v users alice musicoin root p 8545 8545 p 30303 30303 musicoin client go fast cache 512 this will start gmc in fast sync mode with a db memory allowance of 512mb just as the above command does it will also create a persistent volume in your home directory for saving your blockchain as well as map the default ports there is also an alpine tag available for a slim version of the image do not forget rpcaddr 0 0 0 0 if you want to access rpc from other containers and or hosts by default gmc binds to the local interface and rpc endpoints is not accessible from the outside programatically interfacing gmc nodes as a developer sooner rather than later you ll want to start interacting with gmc and the musicoin network via your own programs and not manually through the console to aid this gmc has built in support for a json rpc based apis standard apis https github com ethereum wiki wiki json rpc and gmc specific apis https github com ethereum go ethereum wiki management apis these can be exposed via http websockets and ipc unix sockets on unix based platforms and named pipes on windows the ipc interface is enabled by default and exposes all the apis supported by gmc whereas the http and ws interfaces need to manually be enabled and only expose a subset of apis due to security reasons these can be turned on off and configured as you d expect http based json rpc api options rpc enable the http rpc server rpcaddr http rpc server listening interface default localhost rpcport http rpc server listening port default 8545 rpcapi api s offered over the http rpc interface default eth net web3 rpccorsdomain comma separated list of domains from which to accept cross origin requests browser enforced ws enable the ws rpc server wsaddr ws rpc server listening interface default localhost wsport ws rpc server listening port default 8546 wsapi api s offered over the ws rpc interface default eth net web3 wsorigins origins from which to accept websockets requests ipcdisable disable the ipc rpc server ipcapi api s offered over the ipc rpc interface default admin debug eth miner net personal shh txpool web3 ipcpath filename for ipc socket pipe within the datadir explicit paths escape it you ll need to use your own programming environments capabilities libraries tools etc to connect via http ws or ipc to a gmc node configured with the above flags and you ll need to speak json rpc http www jsonrpc org specification on all transports you can reuse the same connection for multiple requests note please understand the security implications of opening up an http ws based transport before doing so hackers on the internet are actively trying to subvert musicoin nodes with exposed apis further all browser tabs can access locally running webservers so malicious webpages could try to subvert locally available apis operating a private network maintaining your own private network is more involved as a lot of configurations taken for granted in the official networks need to be manually set up defining the private genesis state first you ll need to create the genesis state of your networks which all nodes need to be aware of and agree upon this consists of a small json file e g call it genesis json json config chainid 0 homesteadblock 0 eip155block 0 eip158block 0 alloc coinbase 0x0000000000000000000000000000000000000000 difficulty 0x20000 extradata gaslimit 0x2fefd8 nonce 0x0000000000000042 mixhash 0x0000000000000000000000000000000000000000000000000000000000000000 parenthash 0x0000000000000000000000000000000000000000000000000000000000000000 timestamp 0x00 the above fields should be fine for most purposes although we d recommend changing the nonce to some random value so you prevent unknown remote nodes from being able to connect to you if you d like to pre fund some accounts for easier testing you can populate the alloc field with account configs json alloc 0x0000000000000000000000000000000000000001 balance 111111111 0x0000000000000000000000000000000000000002 balance 222222222 with the genesis state defined in the above json file you ll need to initialize every gmc node with it prior to starting it up to ensure all blockchain parameters are correctly set gmc init path to genesis json creating the rendezvous point with all nodes that you want to run initialized to the desired genesis state you ll need to start a bootstrap node that others can use to find each other in your network and or over the internet the clean way is to configure and run a dedicated bootnode bootnode genkey boot key bootnode nodekey boot key with the bootnode online it will display an enode url https github com ethereum wiki wiki enode url format that other nodes can use to connect to it and exchange peer information make sure to replace the displayed ip address information most probably with your externally accessible ip to get the actual enode url note you could also use a full fledged gmc node as a bootnode but it s the less recommended way starting up your member nodes with the bootnode operational and externally reachable you can try telnet ip port to ensure it s indeed reachable start every subsequent gmc node pointed to the bootnode for peer discovery via the bootnodes flag it will probably also be desirable to keep the data directory of your private network separated so do also specify a custom datadir flag gmc datadir path to custom data folder bootnodes bootnode enode url from above note since your network will be completely cut off from the main and test networks you ll also need to configure a miner to process transactions and create new blocks for you running a private miner mining on the public musicoin network is a complex task as it s only feasible using gpus requiring an opencl or cuda enabled ethminer instance for information on such a setup please consult the ethermining subreddit https www reddit com r ethermining and the genoil miner https github com genoil cpp ethereum repository in a private network setting however a single cpu miner instance is more than enough for practical purposes as it can produce a stable stream of blocks at the correct intervals without needing heavy resources consider running on a single thread no need for multiple ones either to start a gmc instance for mining run it with all your usual flags extended by gmc usual flags mine minerthreads 1 etherbase 0x0000000000000000000000000000000000000000 which will start mining blocks and transactions on a single cpu thread crediting all proceedings to the account specified by etherbase you can further tune the mining by changing the default gas limit blocks converge to targetgaslimit and the price transactions are accepted at gasprice contribution thank you for considering to help out with the source code we welcome contributions from anyone on the internet and are grateful for even the smallest of fixes if you d like to contribute to go musicoin please fork fix commit and send a pull request for the maintainers to review and merge into the main code base if you wish to submit more complex changes though please check up with the core devs first on our gitter channel https gitter im musicoins lobby to ensure those changes are in line with the general philosophy of the project and or get some early feedback which can make both your efforts much lighter as well as our review and merge procedures quick and simple please make sure your contributions adhere to our coding guidelines code must adhere to the official go formatting https golang org doc effective go html formatting guidelines i e uses gofmt https golang org cmd gofmt code must be documented adhering to the official go commentary https golang org doc effective go html commentary guidelines pull requests need to be based on and opened against the master branch commit messages should be prefixed with the package s they modify e g eth rpc make trace configs optional please see the go ethereum developers guide https github com ethereum go ethereum wiki developers guide for more details on configuring your environment managing project dependencies and testing procedures license go musicoin is a fork of the go ethereum https github com ethereum go ethereum client and library the go musicoin library i e all code outside of the cmd directory is licensed under the gnu lesser general public license v3 0 https www gnu org licenses lgpl 3 0 en html also included in our repository in the copying lesser file the go musicoin binaries i e all code inside of the cmd directory is licensed under the gnu general public license v3 0 https www gnu org licenses gpl 3 0 en html also included in our repository in the copying file | blockchain |
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iotivity-node | iotivity node discontinuation of project this project will no longer be maintained by intel intel will not provide or guarantee development of or support for this project including but not limited to maintenance bug fixes new releases or updates patches to this project are no longer accepted by intel if you have an ongoing need to use this project are interested in independently developing it or would like to maintain patches for the community please create your own fork of the project to create a fork of this project it is best not to use github s fork feature because that restricts one s ability to maintain issues and a wiki nevertheless these steps may be followed 0 create a new empty github repository of which you are the owner 0 clone this archived repository 0 change directories into the clone sh cd iotivity node 0 push to the empty repository you created sh git push f git github com your organization your repository master 0 maintain your fork at https github com your organization your repository complete with issues prs and a wiki description this project provides a javascript api for ocf functionality the api follows a maintained specification and is implemented as a native addon using iotivity as its backend status a href https ci appveyor com project gabrielschulhof iotivity node branch master img alt windows build status src https ci appveyor com api projects status github otcshare iotivity node branch master svg true img a a href https travis ci org otcshare iotivity node img alt posix build status src https travis ci org otcshare iotivity node svg branch master img a a href https coveralls io github otcshare iotivity node branch master img src https coveralls io repos github otcshare iotivity node badge svg branch master alt coverage status a installation on linux and osx 1 make sure node version 0 12 or later is up and running this means that 1 the command node runs node version 0 12 or later and that 1 the directory in which the node binary can be found is listed in the path environment variable so that the command node somescript js is enough to execute somescript js using node version 0 12 or later 1 install the following packages which your distribution should provide 1 unzip scons version 2 51 git and make 1 development headers for libuuid and glib2 1 a c compiler and a c compiler gcc 5 or later 1 clone this repository 1 cd iotivity node 1 run npm install on windows video https www youtube com watch v rgszpv8irwa 1 install node 4 or later as with linux and osx make sure that node is called node and that it is on your path 0 in a powershell running as administrator run npm install g production windows build tools this will install python and the toolchain necessary for building iotivity node while this command runs you can perform some of the following steps 0 install git 0 install 7 zip 0 the installation of the windows build tools package eventually indicates that it has installed python after that message appears you can perform some of the steps below 0 in a command prompt append the python folder the python scripts folder and the 7 zip folder to your path the paths you append are based on your windows user name so replace yourusernamehere in the example below with your actual windows user name setx path path c users yourusernamehere windows build tools python27 c users yourusernamehere windows build tools python27 scripts c program files 7 zip 0 close the command prompt and reopen it 0 in the command prompt run pip install egg scons 3 0 0 to install scons a python package 0 wait for the installation of the windows build tools to complete afterwards you can perform the remaining steps 0 clone this repository and the change directory into it 0 run npm install to build iotivity node 0 after the successful completion of the above command you are ready to use iotivity node you can use the usual npm process of adding iotivity node to the dependencies section of your package s package json file after installation using the steps above you may want to run the iotivity node test suite to do so perform the following steps from the iotivity node repository root the steps apply to all platforms 1 run npm g install grunt cli 0 run grunt test the file appveyor yml appveyor yml provides an example of the commands necessary for setting up a windows environment and the file travis yml travis yml provides an example of the commands necessary for setting up the linux and osx environments in more detail iotivity node depends on iotivity proper it has been tested against 1 3 0 the above installation instructions cover the dependencies for both iotivity node and iotivity iotivity node requires a compiler that implements the c 11 standard during compilation iotivity node downloads iotivity from its git repository builds it and links against it if you wish to build iotivity separately set the environment variable octbstack cflags to contain the compiler arguments necessary for building against iotivity and also set the environment variable octbstack libs to contain the linker arguments necessary for linking against iotivity if both variables are set to non empty values iotivity node will skip the step of downloading and building iotivity from sources if you choose to build iotivity separately you can use the following procedure 1 grab a snapshot of iotivity from its git repository and unpack it locally 0 make sure a build toolchain scons a build tool and the headers for the above mentioned library dependencies are installed your distribution should provide all these tools and libraries 0 cd iotivity 0 if you re building against version 1 3 0 of iotivity on osx or windows you will first need to apply all the downstream patches which iotivity node provides in the patches subdirectory except the patch which removes the boost dependency the latter patch serves only to improve build time by eliminating the ability to build targets which require boost you can apply the patches with git apply path to patch all these patches except the boost elmination patch are on track to appear in later versions of iotivity so they will disappear from later versions of iotivity node 0 scons has the concept of targets just like make you can get a list of targets contained in the iotivity repository as well as a listing of recognized build flags via scons help the only targets you need for the node js bindings are octbstack and json2cbor if you are building in secured 1 mode thus run scons secured 1 json2cbor octbstack to build these targets or scons octbstack if you do not require secured 1 mode on osx you need more targets than just octbstack and json2cbor because on that platform iotivity does not build octbstack as a shared library but rather as an archive thus you need to build all targets that correspond to archives that go into the linux liboctbstack shared library c common coap connectivity abstraction logger ocsrm octbstack routingmanager 0 now that iotivity is built clone this repository and change directory into it 0 set the following environment variables octbstack cflags this should contain the compiler flags for locating the iotivity include files for example the value of this variable can be i home nix iot iotivity resource csdk stack include octbstack libs this should contain the linker flags necessary for locating liboctbstack so both at compile time and at runtime its value can be as simple as loctbstack if liboctbstack is in usr lib but may need to be as complex as l home nix iot iotivity out linux x86 release loctbstack wl rpath home nix iot iotivity out linux x86 release if liboctbstack so is located on an unusual path 0 run npm install with these environment variables set provisioning and device id persistence the high level js api provides a means for persisting the device id across instantiations of a script according to the iotivity wiki this mechanism is also responsible for initially creating the configuration file that stores security related information for a given script it does so by creating a directory home iotivity node thereunder it creates directories whose name is the sha256 checksum of the absolute path of the given script thus if you write a script located in home user myscript js that uses the high level js api its persistent state will be stored in the directory home user iotivity node 1abfb1b70eaa1ccc17a42990723b153a0d4b913a8b15161f8043411fc7f24fb1 in a file named oic svr db dat the file initially contains enough information to persist the device id used whenever you run home user myscript js you can add more information to the file in accordance with the iotivity wiki and using the json2cbor tool the tool is located in iotivity installed bin off the root of this repository or if you have chosen to build iotivity externally then in the output directory created by the iotivity build process examples the javascript examples are located in js js and come in pairs of one client and one server each illustrating a basic aspect of iotivity to run them open two terminals and change directory to the root of the iotivity node repository in both always launch the server before the client for example in one terminal you can run node js server discoverable js and in the other terminal you can run node js client discovery js make sure no firewall is running or one is properly configured to allow iotivity related traffic and especially multicast traffic on the machine s where these applications are running iotivity http iotivity org node https nodejs org 1 3 0 https gerrit iotivity org gerrit gitweb p iotivity git a tree hb 1 3 0 snapshot https gerrit iotivity org gerrit gitweb p iotivity git a snapshot h 1 3 0 sf tgz scons http www scons org iotivity wiki https wiki iotivity org faq s video https www youtube com watch v 95vtb qgyfw specification https github com 01org iot js api tree ocf 1 1 0 api ocf ocf http openconnectivity org git http git scm org 7 zip http 7 zip org | 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nodebook | web development with mongodb and node js buy online amazon com www amazon com development mongodb node js jason krol dp 1783987308 packtpub com https www packtpub com web development web development mongodb and nodejs thanks for reading my book this is the main repository for all of the code used and discussed throughout the entire book each chapter is broken down into its own folder with iterative changes in each the end product is the imgploadr folder that contains the entire working application for more information about the book http kroltech com 2014 10 my new book web development with mongodb and node js http kroltech com 2014 10 my new book web development with mongodb and node js | front_end |
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ts-next-tailwind-template | ultimate front end template preview https cdn discordapp com attachments 797485737272541250 952208625806495815 image 5 png most elements are taken from my website https cretu dev deploy with vercel https vercel com button https vercel com new clone repository url https 3a 2f 2fgithub com 2fcristicretu 2fts next tailwind template use as a codesandbox template https codesandbox io s ts next tailwind template vbjvcr ingredients nextjs 13 tailwindcss typescript dark mode support eslint prettier config husky self hosted inter font getting started 1 with use as template repository preview https cdn discordapp com attachments 797485737272541250 952208604386189332 group 11 png 2 clone the project bash http git clone https github com cristicretu ts next tailwind template git bash ssh git clone git github com cristicretu ts next tailwind template git 3 with create next app bash npx create next app e https github com cristicretu ts next tailwind template project name tip if you want to use the version prior to next js 13 with the app directory use bash npx create next app e https github com cristicretu ts next tailwind template tree 1ac5d6dd4157ea3c7cc89f14fbfbf01ab0b495fc project name install the required packages and run the template bash cd project name npm install yarn install pnpm install included custom classnames function under lib classnames packages 1 next themes an abstraction for themes in your next js app 2 react use react hooks 3 framer motion animation library custom globals css 1 custom underline 2 vercel navbar 3 removes firefox edge and ie bugs with overflows absolute imports tsx import textfield from ui textfield tsx changes to tsx import textfield from uis textfield tsx seo optimization found in container tsx folder structuring organization under ui public self hosted inter font under public fonts 404 page favicons and more configs under public static favicons preview https cdn discordapp com attachments 797485737272541250 952211815046197278 frame 7 png | tailwindcss nextjs typescript template template-project nextjs-template tailwind-template typescript-template | front_end |
smoothquant | smoothquant accurate and efficient post training quantization for large language models paper https arxiv org abs 2211 10438 slides assets smoothquant pdf intuition figures intuition png news 2023 10 smoothquant is integrated into nvidia tensorrt llm https github com nvidia tensorrt llm 2023 03 smoothquant is integrated into intel neural compressor https github com intel neural compressor abstract large language models llms show excellent performance but are compute and memory intensive quantization can reduce memory and accelerate inference however for llms beyond 100 billion parameters existing methods cannot maintain accuracy or do not run efficiently on hardware we propose smoothquant a training free accuracy preserving and general purpose post training quantization ptq solution to enable 8 bit weight 8 bit activation w8a8 quantization for llms based on the fact that weights are easy to quantize while activations are not smoothquant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation smoothquant enables an int8 quantization of both weights and activations for all the matrix multiplications in llms including opt 175b bloom 176b glm 130b and mt nlg 530b smoothquant has better hardware efficiency than existing techniques we demonstrate up to 1 56x speedup and 2x memory reduction for llms with negligible loss in accuracy we integrate smoothquant into fastertransformer a state of the art llm serving framework and achieve faster inference speed with half the number of gpus compared to fp16 enabling the serving of a 530b llm within a single node our work offers a turn key solution that reduces hardware costs and democratizes llms installation bash conda create n smoothquant python 3 8 conda activate smoothquant pip install torch 1 12 1 cu113 torchvision 0 13 1 cu113 torchaudio 0 12 1 extra index url https download pytorch org whl cu113 pip install transformers accelerate datasets zstandard python setup py install usage smoothquant int8 inference for pytorch we implement smoothquant int8 inference for pytorch with cutlass https github com nvidia cutlass int8 gemm kernels which are wrapped as pytorch modules in torch int https github com guangxuan xiao torch int please install torch int https github com guangxuan xiao torch int before running the smoothquant pytorch int8 inference we implement the quantized opt model class in smoothquant opt py smoothquant opt py which uses int8 linear layers and bundles quantization scales we provide the already smoothed and quantized opt model at https huggingface co mit han lab opt model size smoothquant https huggingface co mit han lab opt model size smoothquant where model size can be 125m 1 3b 2 7b 6 7b 13b 30b and 66b you can load the int8 model with the following code python from smoothquant opt import int8optforcausallm model int8optforcausallm from pretrained mit han lab opt 30b smoothquant you can also check generate act scales py examples generate act scales py and export int8 model py examples export int8 model py to see how we smooth quantize and export int8 models in examples smoothquant opt real int8 demo ipynb examples smoothquant opt real int8 demo ipynb we use opt 30b model to demonstrate the latency and memory advantages of smoothquant we demonstrate on opt 30b because it is the largest model we can run both the fp16 and int8 inference on a single a100 gpu for larger models requiring multiple gpus we recommend using the fastertransformer https github com nvidia fastertransformer implementation of smoothquant activation channel scales and calibration we provide the activation channel scales for opt and bloom models in act scales act scales we get those scales with 512 random sentences in the pile validation set you can use examples smoothquant opt demo ipynb examples smoothquant opt demo ipynb to test smoothing and quantizing those models we also provide the script to get the activation channel scales for your models please refer to examples generate act scales py examples generate act scales py you can use the following command to get the scales for your models bash python examples generate act scales py model name model name or path output path output act scales file path num samples num samples seq len sequence length dataset path path to the calibration dataset demo on opt 13b with w8a8 fake quantization in examples smoothquant opt demo ipynb examples smoothquant opt demo ipynb we use opt 13b as an example to demonstrate smoothquant can match the accuracy of fp16 and int8 inference while the naive baseline cannot we simulate int8 inference with fp16 smoothquant fake quant py smoothquant fake quant py i e fake quantization results smoothquant migrates part of the quantization difficulties from activation to weights which smooths out the systematic outliers in activation making both weights and activations easy to quantize migrate figures migrate jpg smoothquant can achieve w8a8 quantization of llms e g opt 175b without degrading performance accuracy figures accuracy png smoothquant can achieve faster inference compared to fp16 when integrated into pytorch while previous work llm int8 does not lead to acceleration usually slower torch latency mem figures torch latency mem png we also integrate smoothquant into the state of the art serving framework fastertransformer https github com nvidia fastertransformer achieving faster inference speed using only half the gpu numbers compared to fp16 1 instead of 2 for opt 66b 4 instead of 8 for opt 175b ft latency mem figures ft latency mem png citation if you find smoothquant useful or relevant to your research please kindly cite our paper bibtex inproceedings xiao2023smoothquant title s mooth q uant accurate and efficient post training quantization for large language models author xiao guangxuan and lin ji and seznec mickael and wu hao and demouth julien and han song booktitle proceedings of the 40th international conference on machine learning year 2023 | ai |
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SecurityEngineering | engineering repository this repository is used to store engineering templates with regards to cloud native services and devsecops which includes my personal engineering and exploratory journey with different cloud service providers mainly aws and azure this repository will mainly focus on developing reusable templates scripts or references to enable cybersecurity in the cloud | cloud |
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ember-osf-web | ember osf web master build a href https github com centerforopenscience ember osf web actions img alt master build status style margin bottom 4px src https github com centerforopenscience ember osf web workflows ci badge svg branch master a a href https coveralls io github centerforopenscience ember osf web branch master img alt coverage status style margin bottom 4px src https coveralls io repos github centerforopenscience ember osf web badge svg branch master a develop build a href https github com centerforopenscience ember osf web actions img alt develop build status style margin bottom 4px src https github com centerforopenscience ember osf web workflows ci badge svg branch develop a a href https coveralls io github centerforopenscience ember osf web branch master img alt coverage status style margin bottom 4px src https coveralls io repos github centerforopenscience ember osf web badge svg branch develop a a front end for osf io https github com centerforopenscience osf io prerequisites you will need the following things properly installed on your computer osf io back end https github com centerforopenscience osf io git https git scm com node js https nodejs org with npm ember cli https ember cli com watchman https facebook github io watchman installation git clone https github com centerforopenscience ember osf web git cd ember osf web yarn frozen lockfile running development mac os file descriptor limits watchman states https facebook github io watchman docs install html mac os file descriptor limits only applicable on os x 10 6 and earlier though it s been observed this setting can remain incorrect on systems where the operation system was upgraded from a legacy version putting the following into a file named etc sysctl conf on os x will cause these values to persist across reboots bash kern maxfiles 10485760 kern maxfilesperproc 1048576 development configure the application for local development add the following to your config local js ts module exports an ally audit can use 100 of your browsers cpu so use it wisely a11y audit false toggle on off the engine applications you will be working on collections enabled false sourcemaps are useful if you need to step through typescript code in the browser sourcemaps enabled true ember serve view the ember app alone at localhost 4200 http localhost 4200 to integrate with the legacy front end at localhost 5000 http localhost 5000 you have two options enable the waffle flags for each page in your local osf admin http localhost 8001 admin waffle flag add routes to your osf io website settings local py py external ember apps ember osf web routes handbook dashboard code generators make use of the many generators for code try ember help generate for more details running tests ember test ember test server linting yarn lint yarn lint fix building ember build development ember build environment production production further reading useful links ember js http emberjs com ember cli https ember cli com development browser extensions ember inspector for chrome https chrome google com webstore detail ember inspector bmdblncegkenkacieihfhpjfppoconhi ember inspector for firefox https addons mozilla org en us firefox addon ember inspector | ember osf science openscience javascript | front_end |
Wintec-IT-Bot | wintec it bot github repo size https img shields io github repo size zerrissen wintec it bot style flat square github release latest by date https img shields io github v release zerrissen wintec it bot color dark green style flat square github issues https img shields io github issues raw zerrissen wintec it bot style flat square note this bot and its authors are not directly affiliated with nor managed by wintec or te pukenga and do not represent their views or policies this is designed for educational and recreational purposes only what is this this is a discord bot used by wintec waikato institute of technology students to automate the majority of their server features this bot is designed to automate student verification role assignment moderation and more it is being designed and developed by the students for the students present past and future installation to install and setup this bot yourself you require the following dependancies node version 20 0 0 npm version 9 6 5 you can then clone the repository git clone https github com zerrissen wintec it bot or download the source directly from the latest release here https github com zerrissen wintec it bot releases latest you will also have to set up the following environment variables token bot token mongo db mongodb connection string db name name of the actual database client id bot user id guild id server id mail user email address for the bot we use zoho mail mail pass bot email account password these variables should be set in a env file in the root file of the repository open the projects base directory in your terminal and run install the npm packages required npm i and that s it you can now run the bot with node usage documentation is currently being worked on sorry for the disappointment we hope to get this released for you soon contributers the wintec it student server wouldn t be possible without contributions from various people many thanks to the following bot contributers zerrissen https github com zerrissen unicornenjoyer https github com unicornenjoyer server contributers zerrissen https github com zerrissen jamesolwyn https github com jamesolwyn skulduggerydude https github com skulduggerydude license this repository is licensed under the gnu affero general public license v 30 permissions of this strongest copyleft license are conditioned on making available complete source code of licensed works and modifications which include larger works using a licensed work under the same license copyright and license notices must be preserved contributors provide an express grant of patent rights when a modified version is used to provide a service over a network the complete source code of the modified version must be made available read more here https github com zerrissen wintec it bot blob main license | server |
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linuxband | deprecated as of may 26 2021 linuxband project is no longer supported please consider using jjazzlab x https github com jjazzboss jjazzlab x instead linuxband linuxband a gui front end for mma www http linuxband org email ales nosek gmail com your feedback on linuxband is welcome please see http linuxband org for how to submit suggestions for new features bug reports and patches documentation for installation and usage documentation please see http linuxband org | front_end |
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design-system-playground | design system playground play with typography and colors to generate a design system theme you can use in your projects netlify status https api netlify com api v1 badges ca9cacda 5677 43e8 928d c1970af41cfe deploy status https app netlify com sites design system playground deploys with a theme object https theme ui com theme spec that follows the system ui theme specification https system ui com for style values scales and or design tokens you can create interoperable https jxnblk com blog interoperability components that share common underlying style properties across projects websites applications and environments related projects check out these other cool projects system ui https system ui com theme ui https theme ui com rebass https rebassjs org chakra ui https chakra ui com components ai https components ai colorbox https www colorbox io color thief https lokeshdhakar com projects color thief | os |
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elit | the elit project build status https travis ci org elitcloud elit svg branch master https travis ci org elitcloud elit pypi version https badge fury io py elit svg https badge fury io py elit documentation status https readthedocs org projects elit badge version latest http elit readthedocs io en latest badge latest the e mory l anguage i nformation t oolkit or e volution of l anguage and i nformation t echnology elit project provides nlp tools readily available for research in various disciplines frameworks for fast development of efficient and robust nlp components the project is initiated and currently led by the emory nlp http nlp mathcs emory edu research group it is under the apache 2 http www apache org licenses license 2 0 license please join our group to get notifications about updates and send us your feedback installation pip install elit docs please visit https elit readthedocs io issues if you have any question or want to report bugs please let us know on github issues https github com elitcloud elit issues contribution we appreciate contributions if you want to contribute to elit please be sure to read our contribution guidelines contributing md first to setup the development environment please read development guidelines development md license apache license 2 0 license | elit python nlp natural-language-processing | server |
Simple-Python-Blockchain | simple python blockchain with mining this is the source code for howcode s simple python blockchain you can watch the video that accompanies this source code here https youtu be b81ib oybfk | blockchain |
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cse475-pcb-tutorial | p align center cse ee 475 pcb tutorial p this repository contains all the files used for the pcb tutorial i taught in the embedded systems capstone class the base design was derived from the work of mike s malphaswats hawk https github com malphaswats hawk project hawk is well documented and highlights most concepts of pcb design required for this class i made the following changes to the design to ensure a few other useful concepts were also covered added a different tactile switch to demonstrate the addition of external libraries pull ups added a builtin led because it is a useful feature to have moved the tactile switch and builtin led circuitry to the bottom layer to demonstrate component placement and routing on two layers added gnd copper pour to top layer and 3v3 copper pour on bottom layer to demonstrate the use of copper pours added uw logo on the top silkscreen layer to demonstrate the process of importing graphics into pcb added text in the bottom silksreen layer to demonstrate the addition of useful text generated gerber files for the changes made above the schematic with the changes looks like this schematic schematic png here s how the completed pcb looks like top view cse475 pcb tutorial top jpg bottom view cse475 pcb tutorial bottom jpg the slides used for the presentation can be found here https docs google com presentation d 1yj8d16entwlgdvscv pk9ehuhwfi2h66mtzrn bvfb8 edit usp sharing and the dxf file imported into the design can be found here https drive google com file d 1wx1g2nuie0cll9hzydxmsbabv8wsbbdb view | os |
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IOE-Question-Bank | ioe question bank this is a project in database management system course in ioe pulchowk campus this project provides the user as simple search interface where user can search for links of question papers of electronics and communication engineering from year 2070 to 2075 b s contributors amrita thakur pujan budhathoki sarmila upreti and shirish shrestha | server |
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Smart-Home-with-Voice-Assistance | smart home with voice assistance download arduino ide and proteus 8 professional download zip file of the project open smart home code ino to arduino ide select board as arduino mega 2560 from tools then click verify copy the hex file path from output open project in proteus paste the path in arduino mega and select hex paths of pir water and gas sensors from libraries folder download arduino voice control android app and connect the phone with the pc using bluetooth run the simulation | os |
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Fluent-Typora | fluent fluent fluent png fluent typora this is a theme carefully crafted designed for modern computer screens inspired by microsoft fluent design system https developer microsoft com en us fluentui the theme now has a built in dark mode support which means the theme changes with your system and you don t have to manually setup any preferences separated themes fluent light css fluent dark css are also provided this theme is currently tested on macos only installation the required font faces are not provided in this repository you should download the font families manually otherwise typora will render by your system s default inter https github com rsms inter jetbrains mono https www jetbrains com lp mono if you need chinese support source han sans notice that if you have the newly released variable font version of source han sans installed the typography in your pdf exportation might be corrupted modify the variable cjk font defined in fluent css to fix this strokes in source han sans vf are separated pdf fluent css cjk font escapings you can place headers in blockquote with extra styles markdown bookmark this is h1 inside a block quote and this theme renders them like this fluent escaping fluent escaping png screenshots fluent light fluent fluent light png fluent dark fluent fluent dark png | os |
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health-tracking | mystar health tracking still in progress right now the purpose of this web app is the giver the users a way to track categorize workouts similar to other workout apps this one is meant to demonstrate higher level react development and use of backend database firebase or better yet a full stack project url https mystar health netlify app h2 stage 1 creation of ui homepage and basic cards 3 27 2023 h2 img width 800 alt screenshot 2023 04 28 at 12 38 55 am src https user images githubusercontent com 99677330 235063501 f7abb390 33f1 4474 9841 8ccc88096d89 png h2 stage 2 addition of first paragraph and fixing of the spacing repsonsivness of elements 3 28 2023 h2 img width 1046 alt screenshot 2023 04 28 at 1 25 35 pm src https user images githubusercontent com 99677330 235224925 4aff9d22 8f5e 456a 99f8 89ac55592c09 png h2 stage 3 rough draft of a complete home page including animations and making the webpage mobile friendly 3 28 203 h2 img width 1011 alt screenshot 2023 04 28 at 6 43 46 pm src https user images githubusercontent com 99677330 235270403 71233c87 2a13 471a 9eb5 d031636792d5 png h2 stage 3 5 added a user login authentication page and login button 5 8 2023 h2 img width 1071 alt screenshot 2023 05 08 at 11 55 11 pm src https user images githubusercontent com 99677330 236990036 82794882 3f7d 45e5 8a74 372d511e4ba5 png | server |
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vue-mindee-js | check vue mindee documentation https vue mindee js netlify app for docs guides api and more introduction vue mindee is a very opinionated javascript library that will help you build interactive canvas for computer vision detection use cases there are many powerful javascript frameworks and tools that can help you make an interactive canvas but almost all of them are low level like konva https konvajs org is a 2d canvas framework it is good it is powerful but you may need to write a lot of code this library was made for building frontend interfaces on top of mindee https mindee com document parsing apis and more generally on top of any computer vision detection apis npm https img shields io npm v vue mindee js svg https www npmjs com package vue mindee js v 1 3 0 tests https github com mindee vue mindee js actions workflows cypress workflow yml badge svg branch new version https github com mindee vue mindee js actions workflows cypress workflow yml ezgif com video to gif 12 https user images githubusercontent com 41388086 87852820 92045b80 c905 11ea 808e 5a971de2b29f gif features support for image and pdf files interactive shapes with events binding extensible styling api controllable state props and modular architecture zoom in and out feature out of the box magnified zoomed view api compatibility the vue sdk is compatible with vue 3 installation and dependencies the easiest way to use vue select is to install it from npm and build it into your app with webpack bash npm install save vue mindee js or using yarn yarn add vue mindee js usage you only need an image and a list of shapes to get started jsx template annotationviewer data data template script import annotationviewer from vue mindee js import dummyimage from path to file jpg const dummyshapes id date coordinates 0 539 0 269 0 693 0 269 0 693 0 296 0 539 0 296 id supplier coordinates 0 267 0 062 0 572 0 062 0 572 0 102 0 267 0 102 export default name app components annotationviewer data function return data image dummyimage shapes dummyshapes script props data include 3 properties image file to draw in the canvas shapes which expect a list of shapes and orientation of the provided image default 0 onshapeclick return the shape object after a click event onshapemouseenter return the shape object after a mouse enter event onshapemouseleave return the shape object after a mouse leave event onshapemultiselect return the selected shapes using ctrl mouse click move options object of properties to customize default configs id unique id if not provided it will be automatically generated style style object to change container css properties classname apply a classname to the control contribute to this repo feel free to use github to submit issues pull requests or general feedback you can also visit our website https mindee com or drop us an email mailto contact mindee com please read our contributing section https github com publicmindee vue mindee js blob master contributing md before contributing license mit mindee https mindee com | ocr vue javascript vuejs computer-vison | ai |
cs131_hws | cs131 computer vision foundations and applications my solution to assignments in stanford cs131 computer vision foundations and applications fall 2018 http vision stanford edu teaching cs131 fall1819 hw0 basic linear algebra and image manipulation using python done hw0 hw0 ipynb hw1 linear filters convolution and correlation done hw1 hw1 ipynb hw2 canny edge detector and hough transform done hw2 hw2 ipynb hw3 harris corner detector ransac and hog descriptor for panorama stitching done hw3 hw3 ipynb hw4 seam carving for the purpose of content aware resizing done hw4 hw4 ipynb hw5 k means and hac methods for clustering and image segmentation done hw5 hw5 ipynb hw6 image compression using svd knn methods for image recognition pca and lda to improve knn done hw6 hw6 ipynb hw7 a simplified version of object detection process done hw7 hw7 ipynb hw8 lukas kanade tracking method done hw8 hw8 ipynb original readme this repository contains the released assignments for the fall 2017 http vision stanford edu teaching cs131 fall1718 and fall 2018 http vision stanford edu teaching cs131 fall1819 iteration of cs131 a course at stanford taught by juan carlos niebles http www niebles net and ranjay krishna http ranjaykrishna com the assignments cover a wide range of topics in computer vision and should expose students to a broad range of concepts and applications homework 0 sets up the course with an introduction on how to use images in python and numpy it covers basic linear algebra that would be helpful through the course homework 1 starts off the topics in computer vision by understanding concepts such as convolutions linear systems and different kernels and how to design them to find certains signals in images homework 2 focuses on edge detection applying it to lane detection to aid self driving cars homework 3 introduces sift and ransac which are useful for finding corresponding points in multiple images enabling applications like panorama creation which is a common feature in most of our smart phones moving beyond pixels and edges homework 4 takes a wider view of the image and asks students to use a dynamic programming algorithm to define the energy of certain regions in an image this energy definition allows us to find regions that are important enabling different monitors with varying sizes large projections medium laptop screens and small cell phones to display only the important portions of an image we specifically ask students to implements different versions of seam carving while preserving the structure of objects in images homework 5 asks students to learn to segment images by assigning each pixel to a cluster where each cluster represents a semantic object category we analysze different unsupervised clustering algorithms like k means and hierarchical aggolomerate clustering techniques they also learn to apply their techniques to segments cats from images and evaluate how well different methods perform homework 6 and 7 introduce two fundamental tasks in computer vision image classification and object detection respectively students learn to use different image features and classifiers to study how they influence results on such tasks they are asked to consider the important of rotation translation scale and occlusion variance when deciding what features to use they also study the curse of dimensionality and learn to apply principal component analysis and linear discriminant analysis to further improve their features while compressing their features students also explore common methods like sliding windows and deformable parts models to decompose objects into its individual components when detecting them homework 8 ends the course by moving away from still images and into video and studying time dependent features they learn to use optical flow to calculate motion in images a technique useful for tracking people in images and classifying actions that people perform all the homeworks are under the mit license | cs131 computer-vision python | ai |
NavGPT | div align center h1 navgpt explicit reasoning in vision and language navigation with large language models h1 div a href https github com gengzezhou target blank gengze zhou sup sup a a href http www yiconghong me target blank yicong hong sup sup a a href http www qi wu me target blank qi wu sup sup a div sup sup australian institude for machine learning the university of adelaide sup sup the australian national university br div a href https github com gengzezhou navgpt target blank img alt static badge src https img shields io badge navgpt v0 1 blue a a href https arxiv org abs 2305 16986 target blank img src https img shields io badge paper arxiv red a a href https opensource org licenses mit img src https img shields io badge license mit yellow svg alt license mit a a href https github com langchain ai langchain img alt static badge src https img shields io badge langchain green a div div abstract trained with an unprecedented scale of data large language models llms like chatgpt and gpt 4 exhibit the emergence of significant reasoning abilities from model scaling such a trend underscored the potential of training llms with unlimited language data advancing the development of a universal embodied agent in this work we introduce the navgpt a purely llm based instruction following navigation agent to reveal the reasoning capability of gpt models in complex embodied scenes by performing zero shot sequential action prediction for vision and language navigation vln at each step navgpt takes the textual descriptions of visual observations navigation history and future explorable directions as inputs to reason the agent s current status and makes the decision to approach the target through comprehensive experiments we demonstrate navgpt can explicitly perform high level planning for navigation including decomposing instruction into sub goal integrating commonsense knowledge relevant to navigation task resolution identifying landmarks from observed scenes tracking navigation progress and adapting to exceptions with plan adjustment furthermore we show that llms is capable of generating high quality navigational instructions from observations and actions along a path as well as drawing accurate top down metric trajectory given the agent s navigation history despite the performance of using navgpt to zero shot r2r tasks still falling short of trained models we suggest adapting multi modality inputs for llms to use as visual navigation agents and applying the explicit reasoning of llms to benefit learning based models method assets navgpt png todos x release navgpt code x data preprocessing code x custuomized llm inference guidance prerequisites installation create a conda environment and install all dependencies bash conda create name navgpt python 3 9 conda activate navgpt pip install r requirements txt data preparation download r2r data from dropbox https www dropbox com sh i8ng3iq5kpa68nu aab53bvcfy ihyx1mkllob ea dl 1 put the data in datasets directory related data preprocessing code can be found in nav src scripts openai api get an openai api key https platform openai com account api keys and add to your environment variables bash prepare your private openai key for linux export openai api key your private openai key prepare your private openai key for windows set openai api key your private openai key alternatively you can set the key in your code python import os os environ openai api key your private openai key r2r navigation reproduce validation results to replicate the performance reported in our paper use gpt 4 and run validation with following configuration bash cd nav src python navgpt py llm model name gpt 4 output dir datasets r2r exprs gpt 4 val unseen val env name r2r val unseen instr results will be saved in datasets r2r exprs gpt 4 val unseen directory the defualt llm model name is set as gpt 3 5 turbo an economic way to try navgpt is by using gpt 3 5 and run validation on the first 10 samples with following configuration bash cd nav src python navgpt py llm model name gpt 3 5 turbo output dir datasets r2r exprs gpt 3 5 turbo test val env name r2r val unseen instr iters 10 set up custom llms for navgpt add your own model repo as a submodule under nav src llms bash cd nav src llms git submodule add your model repo or just copy your local inference code under nav src llms follow the instructions nav src llms add custom models md to set up your own llms for navgpt run navgpt with your custom llm bash cd nav src python navgpt py llm model name your custom llm output dir datasets r2r exprs your custom llm test citation if navgpt has been beneficial to your research and work please cite our work using the following format article zhou2023navgpt title navgpt explicit reasoning in vision and language navigation with large language models author zhou gengze and hong yicong and wu qi journal arxiv preprint arxiv 2305 16986 year 2023 | ai |
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node-nforce-demo | node nforce demo this is a sample account crud application using the lightning design system https www lightningdesignsystem com enabling you to easily build applications with the look and feel that is consistent with salesforce experience core features demo http node nforce demo herokuapp com http node nforce demo herokuapp com if you are not familiar with lightning design system please see our trailhead module https developer salesforce com trailhead module lightning design system to get started with visualforce there is also a tutorial https github com forcedotcomlabs sldsx blob master tutorial tutorial md for using lds with lightning apps and components the application uses nforce https github com kevinohara80 nforce for the salesforce rest api handlebars http handlebarsjs com for logic less templating and bluebird https github com petkaantonov bluebird for promises deploy to heroku deploy this app to heroku for free and have it up and running in a matter of minutes you ll need the consumer key and consumer secret from your org s connected app for the setup process deploy https www herokucdn com deploy button png https heroku com deploy template https github com jeffdonthemic node nforce demo local installation instructions from the command line type in git clone git github com jeffdonthemic node nforce demo git cd nforce node demo npm install enter your connected app settings and login credentials into env sample and rename the file to env source env npm start point your browser to http localhost 3001 http localhost 3001 and experience the magic contributors jeff douglas jeffdonthemic https github com jeffdonthemic | os |
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meshblu | meshblu meshblu is a cross protocol iot machine to machine instant messaging system it is the core messaging system for citrix s octoblu https octoblu com iot platform supported protocols http socket io websocket mqtt coap amqp and xmpp version 2 0 we have completely re written meshblu into many small components or micro services this meshblu 1 0 repository is being preserved for historical reference all of the new meshblu components are prefixed with meshblu core see a list here https github com octoblu utf8 e2 9c 93 query meshblu core meshblu is dependent on node js redis mongodb and either npm or yarn production in order to run a barebones meshblu core cluster you ll need the following repositories 1 meshblu core dispatcher https github com octoblu meshblu core dispatcher 1 meshblu core worker webhook https github com octoblu meshblu core worker webhook 1 meshblu core protocol adapter http https github com octoblu meshblu core protocol adapter http all meshblu core services and workers have a dockerfile a production meshblu cluster will consist of many services and workers we currently don t have documentation for running a complex cluster but we are working on it development for development use you can run the bundled barebones cluster installation bash git clone https github com octoblu meshblu cd meshblu npm install see usage bash node command js help basic example w env bash bin bash for development usage only env private key base64 public key base64 pepper some random string meshblu http port 3000 node command js see test start sh basic example w args bash bin bash for development usage only node command js private key base64 public key base64 pepper some other random string meshblu http port 3000 debug mode it is normal not see any debug output by default if you want to see debug output use the environment debug or something more specific like debug meshblu it s alive to verify that meshblu 2 0 is alive and well open http localhost 3000 status http localhost 3000 status in a web browser or open a new terminal tab and run curl http localhost 3000 status you should see meshblu 2 0 respond with meshblu online you can register a new iot device by running curl x post http localhost 3000 devices you should see meshblu 2 0 respond with an authentication uuid and token as well as the device s security whitelist settings like this online false discoverwhitelist configurewhitelist sendwhitelist receivewhitelist uuid b112c941 7973 4e2b 8dbe b7bba27ae199 meshblu createdat 2016 11 15t16 07 07 801z hash dy5nlilmygrrnhp0cln zb77nhlydobc hwbrvzwdps id 582b32ab67899618f48c2e1b token d5bcf1a57f4ccefa0ecdc672c7090e7949cc8244 meshblu api documentation checkout our developer docs http developer octoblu com for more information on our http rest api as well as documentation for all other protocol apis security whitelists and blacklists connectors data forwarders and overall meshblu architecture introducing the meshblu cli we have a convenient command line interface for simplifying the interaction with the meshblu api bash install the meshblu cli utility npm install global meshblu util register a device meshblu util register u http localhost 3000 meshblu json fetch the device meshblu util get update the device meshblu util update d type some device fetch the updated device meshblu util get list of meshblu core components workers 1 meshblu core dispatcher https github com octoblu meshblu core dispatcher 1 meshblu core worker webhook https github com octoblu meshblu core worker webhook protocol adapters 1 meshblu core protocol adapter socket io https github com octoblu meshblu core protocol adapter socket io 1 meshblu core protocol adapter http https github com octoblu meshblu core protocol adapter http 1 meshblu core protocol adapter xmpp https github com octoblu meshblu core protocol adapter xmpp 1 meshblu core protocol adapter coap https github com octoblu meshblu core protocol adapter coap 1 meshblu core protocol adapter mqtt https github com octoblu meshblu core protocol adapter mqtt 1 meshblu core protocol adapter http streaming https github com octoblu meshblu core protocol adapter http streaming firehoses 1 meshblu core worker firehose amqp https github com octoblu meshblu core worker firehose amqp 1 meshblu core firehose socket io https github com octoblu meshblu core firehose socket io balancers 1 meshblu haproxy https github com octoblu meshblu haproxy 1 meshblu balancer http streaming https github com octoblu meshblu balancer http streaming 1 meshblu balancer firehose socket io https github com octoblu meshblu balancer firehose socket io 1 meshblu balancer xmpp https github com octoblu meshblu balancer xmpp 1 meshblu balancer websocket https github com octoblu meshblu balancer websocket 1 meshblu balancer mqtt https github com octoblu meshblu balancer mqtt 1 meshblu balancer coap https github com octoblu meshblu balancer coap 1 meshblu balancer socket io https github com octoblu meshblu balancer socket io core datastores 1 meshblu core datastore https github com octoblu meshblu core datastore 1 meshblu core cache https github com octoblu meshblu core cache core managers 1 meshblu core manager token https github com octoblu meshblu core manager token 1 meshblu core manager device https github com octoblu meshblu core manager device 1 meshblu core manager hydrant https github com octoblu meshblu core manager hydrant 1 meshblu core manager whitelist https github com octoblu meshblu core manager whitelist 1 meshblu core manager webhook https github com octoblu meshblu core manager webhook 1 meshblu core manager subscription https github com octoblu meshblu core manager subscription 1 meshblu core manager root token https github com octoblu meshblu core manager root token 1 meshblu core manager messenger https github com octoblu meshblu core manager messenger core tasks 1 meshblu core task black list token https github com octoblu meshblu core task black list token 1 meshblu core task check broadcast received whitelist https github com octoblu meshblu core task check broadcast received whitelist 1 meshblu core task check broadcast sent whitelist https github com octoblu meshblu core task check broadcast sent whitelist 1 meshblu core task check configure as whitelist https github com octoblu meshblu core task check configure as whitelist 1 meshblu core task check configure whitelist https github com octoblu meshblu core task check configure whitelist 1 meshblu core task check discover as whitelist https github com octoblu meshblu core task check discover as whitelist 1 meshblu core task check discover whitelist https github com octoblu meshblu core task check discover whitelist 1 meshblu core task check discoveras whitelist https github com octoblu meshblu core task check discoveras whitelist 1 meshblu core task check forwarded for https github com octoblu meshblu core task check forwarded for 1 meshblu core task check receive as whitelist https github com octoblu meshblu core task check receive as whitelist 1 meshblu core task check receive whitelist https github com octoblu meshblu core task check receive whitelist 1 meshblu core task check root token https github com octoblu meshblu core task check root token 1 meshblu core task check send as whitelist https github com octoblu meshblu core task check send as whitelist 1 meshblu core task check send whitelist https github com octoblu meshblu core task check send whitelist 1 meshblu core task check token https github com octoblu meshblu core task check token 1 meshblu core task check token black list https github com octoblu meshblu core task check token black list 1 meshblu core task check token cache https github com octoblu meshblu core task check token cache 1 meshblu core task check update device is valid https github com octoblu meshblu core task check update device is valid 1 meshblu core task check whitelist broadcast as https github com octoblu meshblu core task check whitelist broadcast as 1 meshblu core task check whitelist broadcast received https github com octoblu meshblu core task check whitelist broadcast received 1 meshblu core task check whitelist broadcast sent https github com octoblu meshblu core task check whitelist broadcast sent 1 meshblu core task check whitelist configure as https github com octoblu meshblu core task check whitelist configure as 1 meshblu core task check whitelist configure received https github com octoblu meshblu core task check whitelist configure received 1 meshblu core task check whitelist configure sent https github com octoblu meshblu core task check whitelist configure sent 1 meshblu core task check whitelist configure update https github com octoblu meshblu core task check whitelist configure update 1 meshblu core task check whitelist discover as https github com octoblu meshblu core task check whitelist discover as 1 meshblu core task check whitelist discover view https github com octoblu meshblu core task check whitelist discover view 1 meshblu core task check whitelist message as https github com octoblu meshblu core task check whitelist message as 1 meshblu core task check whitelist message from https github com octoblu meshblu core task check whitelist message from 1 meshblu core task check whitelist message received https github com octoblu meshblu core task check whitelist message received 1 meshblu core task check whitelist message sent https github com octoblu meshblu core task check whitelist message sent 1 meshblu core task create session token https github com octoblu meshblu core task create session token 1 meshblu core task create subscription https github com octoblu meshblu core task create subscription 1 meshblu core task deliver webhook https github com octoblu meshblu core task deliver webhook 1 meshblu core task enforce message rate limit https github com octoblu meshblu core task enforce message rate limit 1 meshblu core task enqueue deprecated webhooks https github com octoblu meshblu core task enqueue deprecated webhooks 1 meshblu core task enqueue jobs for forward broadcast received https github com octoblu meshblu core task enqueue jobs for forward broadcast received 1 meshblu core task enqueue jobs for forward configure received https github com octoblu meshblu core task enqueue jobs for forward configure received 1 meshblu core task enqueue jobs for forward unregister received https github com octoblu meshblu core task enqueue jobs for forward unregister received 1 meshblu core task enqueue jobs for subscriptions broadcast received https github com octoblu meshblu core task enqueue jobs for subscriptions broadcast received 1 meshblu core task enqueue jobs for subscriptions broadcast sent https github com octoblu meshblu core task enqueue jobs for subscriptions broadcast sent 1 meshblu core task enqueue jobs for subscriptions configure received https github com octoblu meshblu core task enqueue jobs for subscriptions configure received 1 meshblu core task enqueue jobs for subscriptions configure sent https github com octoblu meshblu core task enqueue jobs for subscriptions configure sent 1 meshblu core task enqueue jobs for subscriptions message received https github com octoblu meshblu core task enqueue jobs for subscriptions message received 1 meshblu core task enqueue jobs for subscriptions message sent https github com octoblu meshblu core task enqueue jobs for subscriptions message sent 1 meshblu core task enqueue jobs for subscriptions unregister received https github com octoblu meshblu core task enqueue jobs for subscriptions unregister received 1 meshblu core task enqueue jobs for subscriptions unregister sent https github com octoblu meshblu core task enqueue jobs for subscriptions unregister sent 1 meshblu core task enqueue jobs for webhooks broadcast received https github com octoblu meshblu core task enqueue jobs for webhooks broadcast received 1 meshblu core task enqueue jobs for webhooks broadcast sent https github com octoblu meshblu core task enqueue jobs for webhooks broadcast sent 1 meshblu core task enqueue jobs for webhooks configure received https github com octoblu meshblu core task enqueue jobs for webhooks configure received 1 meshblu core task enqueue jobs for webhooks configure sent https github com octoblu meshblu core task enqueue jobs for webhooks configure sent 1 meshblu core task enqueue jobs for webhooks message received https github com octoblu meshblu core task enqueue jobs for webhooks message received 1 meshblu core task enqueue jobs for webhooks message sent https github com octoblu meshblu core task enqueue jobs for webhooks message sent 1 meshblu core task enqueue jobs for webhooks unregister received https github com octoblu meshblu core task enqueue jobs for webhooks unregister received 1 meshblu core task enqueue jobs for webhooks unregister sent https github com octoblu meshblu core task enqueue jobs for webhooks unregister sent 1 meshblu core task enqueue webhooks https github com octoblu meshblu core task enqueue webhooks 1 meshblu core task find and update device https github com octoblu meshblu core task find and update device 1 meshblu core task forbidden https github com octoblu meshblu core task forbidden 1 meshblu core task get authorized subscription types https github com octoblu meshblu core task get authorized subscription types 1 meshblu core task get broadcast subscription types https github com octoblu meshblu core task get broadcast subscription types 1 meshblu core task get device https github com octoblu meshblu core task get device 1 meshblu core task get device public key https github com octoblu meshblu core task get device public key 1 meshblu core task get global public key https github com octoblu meshblu core task get global public key 1 meshblu core task get status https github com octoblu meshblu core task get status 1 meshblu core task get subscriptions https github com octoblu meshblu core task get subscriptions 1 meshblu core task migrate root token https github com octoblu meshblu core task migrate root token 1 meshblu core task no content https github com octoblu meshblu core task no content 1 meshblu core task protect your as https github com octoblu meshblu core task protect your as 1 meshblu core task publish broadcast received https github com octoblu meshblu core task publish broadcast received 1 meshblu core task publish configure received https github com octoblu meshblu core task publish configure received 1 meshblu core task publish deprecated subscriptions https github com octoblu meshblu core task publish deprecated subscriptions 1 meshblu core task publish message https github com octoblu meshblu core task publish message 1 meshblu core task publish message received https github com octoblu meshblu core task publish message received 1 meshblu core task publish subscriptions https github com octoblu meshblu core task publish subscriptions 1 meshblu core task publish unregister received https github com octoblu meshblu core task publish unregister received 1 meshblu core task register device https github com octoblu meshblu core task register device 1 meshblu core task reject your as https github com octoblu meshblu core task reject your as 1 meshblu core task remove device cache https github com octoblu meshblu core task remove device cache 1 meshblu core task remove root session token https github com octoblu meshblu core task remove root session token 1 meshblu core task remove subscription https github com octoblu meshblu core task remove subscription 1 meshblu core task remove token cache https github com octoblu meshblu core task remove token cache 1 meshblu core task reset token https github com octoblu meshblu core task reset token 1 meshblu core task revoke all tokens https github com octoblu meshblu core task revoke all tokens 1 meshblu core task revoke session token https github com octoblu meshblu core task revoke session token 1 meshblu core task revoke token by query https github com octoblu meshblu core task revoke token by query 1 meshblu core task search device https github com octoblu meshblu core task search device 1 meshblu core task search token https github com octoblu meshblu core task search token 1 meshblu core task send message https github com octoblu meshblu core task send message 1 meshblu core task send message 2 https github com octoblu meshblu core task send message 2 1 meshblu core task unregister device https github com octoblu meshblu core task unregister device 1 meshblu core task update device https github com octoblu meshblu core task update device 1 meshblu core task update message rate https github com octoblu meshblu core task update message rate clients 1 node meshblu socket io https github com octoblu node meshblu socket io 1 node meshblu firehose socket io https github com octoblu node meshblu firehose socket io 1 node meshblu http https github com octoblu node meshblu http 1 node meshblu websocket https github com octoblu node meshblu websocket 1 node meshblu mqtt https github com octoblu node meshblu mqtt 1 node meshblu xmpp https github com octoblu node meshblu xmpp 1 node meshblu amqp https github com octoblu node meshblu amqp 1 node meshblu coap https github com octoblu node meshblu coap 1 browser meshblu http https github com octoblu browser meshblu http 1 swift meshblu http https github com octoblu swift meshblu http utilities 1 meshblu util https github com octoblu meshblu util legacy meshblu 1 x view it here https github com octoblu meshblu blob legacy meshblu readme md | server |
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transformers_nlp_essentials | transformers for nlp essentials contains slides and hands on tutorials for understanding and implementing transformers in natural language processing uses the huggingface transformers framework in the hands on tutorials | ai |
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cozy | cozy cozy can be linked as an external module to any zephyr app this library is built on mbedtls https github com zephyrproject rtos mbedtls and nanocbor https github com bergzand nanocbor current coverage of rfc 8152 encode decode cose sign1 objects encode decode cose encrypt0 objects ec signature algorithms nist p 256 nist p 384 aead algorithms aes gcm 128 aes gcm 192 aes gcm 256 usage add the following line to your app s cmakelists txt set zephyr extra modules absolute path to cozy add the following line to your app s prj conf config cozy y access the cozy api from your source files with include cozy cose h tests and examples run west build t run b native posix from the tests directory see tests src tests c for examples project roadmap support for ecdh key agreement algorithms support for countersignatures support for nsa suite b algorithms listed in rfc 6460 https tools ietf org html rfc6460 full coverage of cose specfication described in rfc 8152 https tools ietf org html rfc8152 | cose zephyr cbor ietf | os |
LivelyKernel | lively web build status https secure travis ci org livelykernel livelykernel png branch master http travis ci org livelykernel livelykernel lively web is browser based runtime and development environment that makes creation of web applications much more immediate and direct all development happens live i e you change your application and the system while it is running this is not only more fun than tedious compile test reload workflows but also much faster see dan ingalls talk from js conf 12 http youtu be qtjrwkofddc for a first impression to get started dive into the interactive lively 101 world http lively web org users robertkrahn lively 101 html to get your own online workspace follow the how to instructions here http lively web org welcome html installation to use lively you do not have to install anything the lively web environment http lively web org is an online wiki and development platform that can be used by everyone who wants to experiment and develop with lively if you want to run your own lively web server or contribute to the core development follow the steps below windows 1 download the windows 2015 04 03 release zip https github com livelykernel livelykernel releases tag windows 2015 04 03 2 unzip it 3 double click start start lively server cmd lively should now be running at localhost 9001 http localhost 9001 welcome html mac os and linux 1 make sure you have node js https nodejs org en download installed 2 checkout this repository git clone https github com livelykernel livelykernel 3 start the server cd livelykernel npm start lively should now be running at localhost 9001 http localhost 9001 welcome html docker alternatively to the install instructions above you can run lively via docker see livelykernel lively docker https github com livelykernel lively docker blob master readme md for setup instructions running the tests start the server then run npm test contributing all code is published under the mit license https github com livelykernel livelykernel blob master license | front_end |
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