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nlp_with_pytorch | natural language processing with pytorch pytorch state of the art github repo https github com kh kim nlp with pytorch https github com kh kim nlp with pytorch gitbook https www gitbook com book kh kim pytorch natural language understanding https www gitbook com book kh kim pytorch natural language understanding pre requisites python calculus linear algebra basic probability and statistics basic machine learning objective function optimization such as gradient descent notice author ki hyun kim assets author gif skplanet machine learning researcher neural machine translation global 11 http global 11st co kr html en main en html trlang en ticketmonster machine learning engineer recommender system tmon http www ticketmonster co kr etri researcher automatic speech translation genietalk android https play google com store apps details id com hancom interfree genietalk hl ko ios https itunes apple com kr app genietalk id1104930501 mt 8 tv ads https www youtube com watch v jda0g0yhwpm linkedin https www linkedin com in ki hyun kim https www linkedin com in ki hyun kim github https github com kh kim https github com kh kim email pointzz ki gmail com by nc sa https creativecommons org licenses by nc sa 2 0 kr by nc sa assets ccl png | ai |
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M2-EmbSys | m2 embsys an automatic floor cleaner embedded system is designed which can crawl throughout the room and can collect dust particles on the way about the code codacy badge https api codiga io project 31767 score svg codacy badge https api codiga io project 31767 status svg codacy badge https app codacy com project badge grade 901e29ddcc0b4522ba72a931feb7cd9e https www codacy com gh yukta kulkarni14 m2 embsys dashboard utm source github com amp utm medium referral amp utm content yukta kulkarni14 m2 embsys amp utm campaign badge grade | os |
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inrange-backend | inrange backend inrange app backend development | server |
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Langchain-PDF-App-GUI | langchain pdf app gui project description the project aims to develop a pdf querying system that leverages langchain a powerful language processing tool to extract information from pdf documents by employing langchain s advanced natural language understanding capabilities the system will enable users to perform complex searches and obtain specific data points from pdf files efficiently and accurately website https github com praj2408 langchain pdf app gui assets 70437673 6f1f0806 f6d5 416d 9f6a 5c5282db2769 features 1 pdf parsing the system will incorporate a pdf parsing module to extract text content from pdf files it will handle various pdf formats including scanned documents that have been ocr processed ensuring comprehensive data retrieval 2 langchain integration langchain a state of the art language processing tool will be integrated into the system it will utilize advanced techniques such as natural language understanding entity recognition and contextual understanding to process the extracted text from pdfs 3 query generation the system will provide a user friendly interface to input search queries users can utilize a wide range of search parameters including keywords phrases date ranges and specific document sections to formulate complex queries 4 natural language processing langchain will process the user queries using natural language processing techniques it will identify the relevant context and entities mentioned in the queries and analyze the pdf content accordingly 5 search and retrieval the system will employ langchain s processed data to perform intelligent searches within the pdf documents it will identify and rank the most relevant sections or pages that match the user s query presenting them in an organized manner for easy retrieval 6 data extraction in addition to search results the system will offer the ability to extract specific data points from the pdf documents users can define extraction rules based on patterns keywords or predefined templates to obtain structured data from unstructured pdf content 7 user friendly interface the system will provide a user friendly web based interface enabling users to interact with the pdf querying system seamlessly it will include features like search history saved queries and personalized settings for enhanced usability potential use cases 1 legal research lawyers and legal professionals can utilize the system to search for specific legal terms case references or precedent information within pdf documents streamlining their research process 2 financial analysis financial analysts can extract relevant data points from financial reports or annual statements stored in pdf format allowing them to perform comprehensive analysis and generate insights efficiently 3 academic research researchers and scholars can utilize the system to search for relevant literature extract citations or gather information from academic papers saved as pdfs simplifying the literature review process 4 document management organizations can use the system to organize and search through their extensive pdf document repositories facilitating efficient document retrieval and reducing manual effort model information generative pre trained transformer 3 5 gpt 3 5 is a sub class of gpt 3 models created by openai in 2022 you can use different models depending on the cost gpt 3 5 turbo gpt 3 5 turbo 0301 gpt 3 5 turbo 0613 gpt 3 5 turbo 16k gpt 3 5 turbo 16k 0613 conclusion by leveraging langchain s powerful language processing capabilities the pdf querying system described above aims to enhance the efficiency and accuracy of extracting information from pdf documents it will empower users across various domains to perform complex searches extract relevant data and improve their overall productivity how to generate your own openai api key please watch this video how to get your openai api key https www youtube com watch v nafdyrsvnxu ab channel tutorialshub how to run the project locally 1 clone the repository bash git clone https github com url 2 create a virtual environment bash conda create n venv python 3 10 y 3 install the requirements bash pip install r requirements txt 4 create env file and paste the api key bash openai api key yourapikey 5 start the streamlit server bash streamlit run app py enjoy the project contributions contributions to this project are welcome to contribute please follow the standard github workflow for pull requests contact information if you have any questions or comments about this project feel free to contact the project maintainer at gmail prajwalgbdr03 gmail com license this project is licensed under the mit license see the license file for details | chatgpt langchain-python large-language-models openai streamlit chatgpt3 llms openai-api-chatbot openai-chatgpt pdf-document pdf-document-processor pdfquery | ai |
iot-netpie-line-bot | line bot api php | server |
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Automated-Resume-Screening-System | automated resume screening system with dataset a web app to help employers by analysing resumes and cvs surfacing candidates that best match the position and filtering out those who don t description used recommendation engine techniques such as collaborative content based filtering for fuzzy matching job description with multiple resumes prerequisites software textract 1 6 3 requests 2 22 0 flask 1 1 1 gensim 3 8 0 sklearn 0 0 pypdf2 1 26 0 autocorrect 0 4 4 nltk 3 4 5 contractions 0 0 21 textsearch 0 0 17 inflect 2 1 0 numpy 1 17 2 pdfminer six 20181108 python 3 6 0 anaconda 4 3 0 64 bit dataset link1 https s3 ap south 1 amazonaws com codebyte bucket resume 26job descriptions zip mirror https drive google com open id 17m9odpip5jfffnjhdcbqky8bmqoyxaju running localhost run pip install r requirements txt to install dependencies simply run this command from root directory login username admin password whitetigers2018 python app py running using docker create the docker image running docker build t arss run the container docker run it p 5000 5000 arss author codebyte | machine-learning python3 resume-analysis dataset datasets resume resume-app | ai |
Hands-On-Computer-Vision-with-Detectron2 | packt conference put generative ai to work on oct 11 13 virtual https packt link jgiey b p align center packt conference https hub packtpub com wp content uploads 2023 08 put generative ai to work packt png https packt link jgiey p b 3 days 20 ai experts 25 workshops and power talks code b usd75off b hands on computer vision with detectron2 a href https www packtpub com product object detection and segmentation using detectron2 9781800561625 img src https m media amazon com images i 511vt1orlnl jpg alt data engineering with aws height 256px align right a this is the code repository for hands on computer vision with detectron2 https www packtpub com product object detection and segmentation using detectron2 9781800561625 published by packt develop object detection and segmentation models with a code and visualization approach what is this book about computer vision is a crucial component of many modern businesses including automobiles robotics and manufacturing and its market is growing rapidly this book helps you explore detectron2 facebook s next gen library providing cutting edge detection and segmentation algorithms it s used in research and practical projects at facebook to support computer vision tasks and its models can be exported to torchscript or onnx for deployment the book provides you with step by step guidance on using existing models in detectron2 for computer vision tasks object detection instance segmentation key point detection semantic detection and panoptic segmentation you ll get to grips with the theories and visualizations of detectron2 s architecture and learn how each module in detectron2 works as you advance you ll build your practical skills by working on two real life projects preparing data training models fine tuning models and deployments for object detection and instance segmentation tasks using detectron2 finally you ll deploy detectron2 models into production and develop detectron2 applications for mobile devices by the end of this deep learning book you ll have gained sound theoretical knowledge and useful hands on skills to help you solve advanced computer vision tasks using detectron2 this book covers the following exciting features build computer vision applications using existing models in detectron2 grasp the concepts underlying detectron2 s architecture and components develop real life projects for object detection and object segmentation using detectron2 improve model accuracy using detectron2 s performance tuning techniques deploy detectron2 models into server environments with ease develop and deploy detectron2 models into browser and mobile environments if you feel this book is for you get your copy https www amazon com hands computer vision detectron2 visualization dp 1800561628 ref tmm pap swatch 0 encoding utf8 qid sr today a href https www packtpub com product object detection and segmentation using detectron2 9781800561625 img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders the code will look like the following import os data folder braintumors data folder yolo data folder yolo data folder coco data folder coco folders os listdir braintumors print folders following is what you need for this book if you are a deep learning application developer researcher or software developer with some prior knowledge about deep learning this book is for you to get started and develop deep learning models for computer vision applications even if you are an expert in computer vision and curious about the features of detectron2 or you would like to learn some cutting edge deep learning design patterns you will find this book helpful some html android and c programming skills are advantageous if you want to deploy computer vision applications using these platforms with the following software and hardware list you can run all code files present in the book chapter 1 13 software and hardware list chapter software required os required 1 13 python 3 9 and 3 9 google colab 1 13 pytorch 1 13 1 13 cuda cu116 1 13 detectron2 1 13 d2go related products other books you may enjoy modern time series forecasting with python packt https www packtpub com product modern time series forecasting with python 9781803246802 amazon https www amazon com modern time forecasting python industry ready ebook dp b0bhj9zx4q ref sr 1 1 keywords modern time series forecasting with python s digital text sr 1 1 deep learning and xai techniques for anomaly detection packt https www packtpub com product deep learning and xai techniques for anomaly detection 9781804617755 amazon https www amazon com deep learning techniques anomaly detection dp 180461775x ref tmm pap swatch 0 encoding utf8 sr 1 1 get to know the author van vung pham is a passionate research scientist in machine learning deep learning data science and data visualization he has years of experience and numerous publications in these areas he is currently working on projects that use deep learning to predict road damage from pictures or videos taken from roads one of the projects uses detectron2 and faster r cnn to predict and classify road damage and achieve state of the art results for this task dr pham obtained his phd from the computer science department at texas tech university lubbock texas usa he is currently an assistant professor at the computer science department sam houston state university huntsville texas usa download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781800561625 https packt link free ebook 9781800561625 a p | ai |
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Stm32-FatFs-FreeRTOS | freertos fatfs in stm32 arm cortex m0 this project is designed as an example of a stm32cubeide generated system with freertos multitask feautures and fatfs file system for controlling an spi connected mmc sd memory card for more information you can take a look here freertos https www freertos org fatfs http elm chan org fsw ff 00index e html features multitask mutexes thread safety cmsis rtos api version 1 fatfs application interface memory state manipulation of folders files error messages overview i was created 3 task with different priority and sizes with safe access via mutex to sd card blink simple task for blinking led sdinfo give infomation about sd card and files usage memory free space list of files folders with details date type path name sdmanager create read write find folders files note this example was built based on tutorial created by kiwih https github com kiwih for more information tutorial an sd card over spi https 01001000 xyz 2020 08 09 tutorial stm32cubeide sd card original code fatfs without freertos cubeide sd card https github com kiwih cubeide sd card installation this example was created using stm32f072 discovery kit waveshare sd card module in addition i used converter usb uart with pl2303hx to read uart messages images image jpg connections since the spi2 is connected to st mems motion senso one of the properties 32f072bdiscovery https www st com en evaluation tools 32f072bdiscovery html overview so i used spi1 and defined sd spi handle to spi1 sh define sd spi handle hspi1 finally in table form the connections are as follows sd adapter side stm32f072 side miso pb4 mosi pb5 sclk pa5 cs pb3 3v 3 3v gnd gnd testing images test png license creative commons zero v1 0 universal | fatfs freertos stm32 spi sd | os |
chatgpt-document-analyzer-backend | backend for chatgpt document analyzer a document analyzer demo that uses google cloud and chatgpt used for an article on the toptal engineering blog link will go here once published get in touch | cloud |
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Speech-to-Indian-Sign-Language-Translator | speech to sign language translator an application which takes in live speech or audio recording as input converts it into text and displays the relevant indian sign language images or gifs front end using easygui speech as input through microphone using pyaudio speech recognition using google speech api text preprocessing using nlp dictionary based machine translation to run the application 1 open the folder named source code and there open the terminal 2 from the terminal run the main python file using the command python main py 3 the application interface appears on the screen 4 hit the record button to start taking speech as input 5 any speech recorded is then processed and respective outputs are shown accordingly 6 to exit the application using speech say goodbye | ai |
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StateOS-STM32F4Discovery | stateos ci https github com stateos stateos stm32f4discovery actions workflows test yml badge svg https github com stateos stateos stm32f4discovery actions workflows test yml free extremely simple amazingly tiny and very fast real time operating system rtos designed for deeply embedded applications template target stm32f4discovery board license this project is licensed under the terms of mit license mit https opensource org licenses mit | stm32f4-discovery | os |
FreeRTOS-Arduino | freertos arduino a freertos http www freertos org port for the arduino zero https www arduino cc en main arduinoboardzero usage place the freertos folder to your arduino libraries directory | os |
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PureFlash | for chinese version please visist readme readme en md 1 what s pureflash pureflash is an open source serversan implementation that is through a large number of general purpose servers plus pureflash software system to construct a set of distributed san storage that can meet the various business needs of enterprises the idea behind pureflash comes from the fully hardware accelerated flash array s5 so while pureflash itself is a software only implementation its storage protocol is highly hardware friendly it can be considered that pureflash s protocol is the nvme protocol plus cloud storage feature enhancements including snapshots replicas shards cluster hot upgrades and other capabilities 2 why need a new serversan software pureflash is a storage system designed for the all flash era at present the application of ssd disks is becoming more and more extensive and there is a trend of fully replacing hdds the significant difference between ssd and hdd is the performance difference which is also the most direct difference in user experience and with the popularity of nvme interface the difference between the two is getting bigger and bigger and this nearly hundredfold difference in quantity is enough to bring about a qualitative change in architecture design for example the performance of hdds is very low far lower than the performance capabilities of cpus and networks so the system design criterion is to maximize the performance of hdds and to achieve this goal can be at the expense of cpu and other resources in the nvme era the performance relationship has been completely reversed and the disk is no longer the bottleneck but the cpu and network have become the bottleneck of the system that method of consuming cpu to optimize io is counterproductive therefore we need a new storage system architecture to fully exploit the capabilities of ssds and improve the efficiency of the system pureflash is designed to simplify io stack separate data path and control path and prioritize fast path as the basic principles to ensure high performance and high reliability and provide block storage core capabilities in the cloud computing era 3 software design almost all current distributed storage systems have a very deep software stack from the client software to the final server side ssd disk the io path is very long this deep software stack consumes a lot of system computing resources on the one hand and on the other hand the performance advantages of ssds are wiped out pureflash is designed with the following principles in mind less is more remove the complex logic on the io path use the unique bob block over bock structure and minimize the hierarchy resource centric around cpu resources ssd resource planning software structure number of threads instead of planning according to the usual needs of software code logic control data separation the control part is developed in java and the data path is developed in c each taking its own strengths in addition pureflash uses tcp in rdma mode on the network model instead of the usual use rdma as faster tcp rdma must configure the one sided api and the two sided api correctly according to business needs this not only makes rdma used correctly but also greatly improves the efficiency of tcp use here is the structure diagram of our system the whole system include 5 modules view graph with tabstop 4 and monospaced font pre metadb ha db pfconductor max 5 nodes zookeeper 3 nodes v pfbd tcmu user and pfs space client max 1024 nodes pre 3 1 pfs pureflash store this module is the storage service daemon that provides all data services including 1 ssd disk space management 2 network interface services rdma and tcp protocols 3 io request processing a pureflash cluster can support up to 1024 pfs storage nodes all pfs provide services to the outside world so all nodes are working in the active state 3 2 pfconductor this module is the cluster control module a production deployment should have at least 2 pfconductor nodes up to 5 key features include 1 cluster discovery and state maintenance including the activity of each node the activity of each ssd and the capacity 2 respond to users management requests create volumes snapshots tenants etc 3 cluster operation control volume opening closing runtime fault handling this module is programmed in java and the code repository url https github com cocalele pfconductor 3 3 zookeeper zookeeper is a module in the cluster that implements the paxos protocol to solve the network partitioning problem all pfconductor and pfs instances are registered with zookeeper so that active pfconductor can discover other members in the entire cluster 3 4 metadb metadb is used to hold cluster metadata and we use mariadb here production deployment requires the galaera db plug in to ensure it s ha client application there are two types of client interfaces user mode and kernel mode user mode is accessed by applications in the form of apis which are located in libpfbd 3 5 1 pfdd pfdd is a dd like tool but has access to the pureflash volume https github com cocalele qemu tree pfbd 3 5 2 fio a fio branch that supports pfbd can be used to test pureflash with direct access to pureflash volume repository url https github com cocalele fio git 3 5 3 qemu a qemu branch with pfbd enabled support to access pureflash volume from vm repository url https github com cocalele qemu git 3 5 4 kernel driver pureflash provides a free linux kernel mode driver which can directly access pfbd volumes as block devices on bare metal machines and then format them into arbitrary file systems which can be accessed by any application without api adaptation the kernel driver is ideal for container pv and database scenarios 3 5 5 nbd a nbd implementation to support access pureflash volume as nbd device repository url https gitee com cocalele pfs nbd git after compile you can attach a volume like bellow pfsnbd dev nbd3 test v1 3 5 6 iscsi a lio backend implementation to use pureflash volume as lio backend device so it ban be accessed via iscsi repository url https gitee com cocalele tcmu runner git networks ports 49162 store node tcp port 49160 store node rdma port 49180 conductor http port 49181 store node http port try pureflash the easiest way to try pureflash is to use docker suppose you have a nvme ssd e g nvme1n1 make sure the data is no more needed dd if dev zero of dev nvme1n1 bs 1m count 100 oflag direct docker pull pureflash pureflash latest docker run ti rm env pfs disks dev nvme1n1 ulimit core 1 privileged e tz asia shanghai network host pureflash pureflash latest pfcli list store id management ip status 1 127 0 0 1 ok pfcli list disk store id uuid status 1 9ae5b25f a1b7 4b8d 9fd0 54b578578333 ok let s create a volume pfcli create volume v test v1 s 128g rep 1 run fio test opt pureflash fio name test ioengine pfbd volume test v1 iodepth 16 rw randwrite size 128g bs 4k direct 1 | os |
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Tensorflow-2.0-Computer-Vision-Cookbook | tensorflow 2 0 computer vision cookbook a href https https www packtpub com in data tensorflow 2 0 computer vision cookbook img src https www packtpub com media catalog product cache 4cdce5a811acc0d2926d7f857dceb83b 9 7 9781838829131 original 98 jpeg alt book name height 256px align right a this is the code repository for tensorflow 2 0 computer vision cookbook https github com packtpublishing tensorflow 2 0 computer vision cookbook published by packt implement machine learning solutions to overcome various computer vision challenges what is this book about this book covers recipes for solving various computer vision tasks using tensorflow taking you through all the tips and tricks you need to overcome any challenges that you may face while building various computer vision applications you will discover machine learning techniques to solve problems in image processing feature extraction and more this book covers the following exciting features understand how to detect objects using state of the art models such as yolov3 use automl to predict gender and age from images segment images using different approaches such as fcns and generative models learn how to improve your network s performance using rank n accuracy label smoothing and test time augmentation enable machines to recognize people s emotions in videos and real time streams if you feel this book is for you get your copy https www amazon com dp 183882913x today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter 2 the code will look like the following def build network input layer input shape 32 32 1 x conv2d filters 32 kernel size 3 3 padding same strides 1 1 input layer x relu x x dropout rate 0 5 x x flatten x x dense units 3 x output softmax x return model inputs input layer outputs output following is what you need for this book this book is for computer vision developers and engineers as well as deep learning practitioners looking for go to solutions to various problems that commonly arise in computer vision you will discover how to employ modern machine learning ml techniques and deep learning architectures to perform a plethora of computer vision tasks basic knowledge of python programming and computer vision is required with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 12 python 3 6 tensorflow 2 3 mac os x and linux debian based we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781838829131 colorimages pdf code in action click on the following link to see the code in action https bit ly 2nmdz5g related products other books you may enjoy mastering computer vision with tensorflow 2 x packt https www packtpub com product mastering computer vision with tensorflow 2 x 9781838827069 amazon https www amazon com dp 1838827064 applied deep learning and computer vision for self driving cars packt https www packtpub com product applied deep learning and computer vision for self driving cars 9781838646301 amazon https www amazon com dp 1838646302 get to know the author jes s mart nez he is the founder of the computer vision e learning site datasmarts he is a computer vision expert and has worked on a wide range of projects in the field such as a piece of people counting software fed with images coming from an rgb camera and a depth sensor using opencv and tensorflow he developed a self driving car in a simulation using a convolutional neural network created with tensorflow that worked solely with visual inputs also he implemented a pipeline that uses several advanced computer vision techniques to track lane lines on the road as well as providing extra information such as curvature degree | ai |
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Shopify-iOSChallenge-NafehShoaib | shopify ios internship summer 2019 challenge submission p float left align center img src images screengif total gif width 200 img src images screenshots nafeh collection search jpeg width 200 img src images screenshots nafeh product search jpeg width 200 img src images info static jpeg width 200 p this project is my submission for the shopify intern challenge for the ios software engineering summer 2019 intern position at shopify in ottawa toronto and montreal this app displays data from a given api in the form of uicollectionviews and uitableviews there are added features like search 3d touch animations and an info page the project has the following third party dependecies and uses cocoapods for package management alamofire for http requests swiftyjson for parsing data viewanimator for build in animations ghostytypewriter for info page animations getting started system requirements macos mojave or later has been tested and xcode is required to build this project instructions to clone this project in xcode click clone or download and select open in xcode features this app displays a list of custom collections in a uicollectionviewcontroller using the first http api as per the requirements of the challenge each cell includes the image and title of the collections the api uses the url below https shopicruit myshopify com admin custom collections json the user can then either 3d touch the cell to peek into the contents of the collection and then pop into the view controller itself or tap the cell to go straight into the view controller the app then displays the list of products pertaining to that specific custom collection in a uitableviewcontroller with a top cell that includes the collection s title description and image the user can also press the back button on the navigation bar to take them back to the collection page the app uses asynchronous calls to two api s by first getting a list of product id s by calling the collects api https shopicruit myshopify com admin collects json it then uses the product id s compiled from this request to get product information by calling the products api https shopicruit myshopify com admin products json these products api call is made once the collects api call is finished and returned a successful string of comma separated product id s these asynchronous calls are made using swift s completion handler feature in functions and closures to nest api calls all views are designed using autolayout constraints file structure and mvc this xcode project follows the model view controller paradigm as do most ios apps and as has been officially recommended by apple the file structure for relevant files for this app is as follows services swift consolidates all alamofire api calls and houses a few extensions models includes swift classes that provide custom models to be used to parse api request results includes convenience initializers to construct objects using incoming raw swiftyjson data cell includes uicollectionviewcell and uitableviewcell classes for all viewcontrollers with data cells viewcontrollers includes the uicollectionviewcontroller and uitableviewcontroller classes for the collection and product views also includes a uiviewcontroller class for the info view main storyboard includes all design related specifications using autolayout and storyboard segues assets xcassets includes all images used for this app code structure services all api related code is confined to the services class which houses three alamofire api calls the three public functions contain relevant parameters and a completion handler as their arguments the completion handler is then called using grand central dispatch asynchronously when the data is loaded successfully all json parsing is handled by each respective model class heres an example swift alamofire request services product url parameters params responsejson response in if let value response result value let products json value products map product fromjson 1 withdateformatter self dateformatter dispatchqueue main async completion result success products the three api s that is used in other swift classes are as follows custom collections swift public func getcustomcollections completion escaping result customcollection void collects swift public func getcollects for id double completion escaping result collect void products swift public func getproducts withproductids ids int completion escaping result product void models each model uses primitive classes for simple data storage and uikit s data classes for data like images and dates each model includes both a simple constructor initializer and a convenience intializer that parses swiftyjson data using the former initializer the result of this implementation is a much more concise and cleaner code base that consolidates roles here s an example of a convenience initializer swift convenience init fromjson json json self init id json id doublevalue title json title stringvalue price json price doublevalue sku json sku stringvalue position json position doublevalue inventoryquantity json inventory quantity intvalue requiresshipping json requires shipping boolvalue cells each cell class takes care of its own animations view design and data setting using data from the viewcontrollers as custom models with setup methods an example of this can be seen below swift public func setup using customcollection customcollection self backgroundcolor white self titlelabel text customcollection title components separatedby 0 self imageview image customcollection image viewcontrollers view controllers do all api calls via the services class connects all views like cells search bars and button actions all actions are organized by mark comments to allow xcode to enable jumping between lines of code by section api calls via the services class are handled using swift s closures asynchronously and the view is adapted accordingly here s an example swift services getcustomcollections result in if let value result value self customcollections value self collectionview reloaddata self collectionview refreshcontrol endrefreshing in the case of transferring data from one view controller to another the prepare for method is used to first switch cases of segue identifiers and then send information by casting cells and destination view controllers appropriately code style swift s code style is embraced in this project the simplest example is the use of if let checks to ensure safety in app execution another instance is the map method is used in arrays and dictionaries to quickly transform each element of an array or dictionary into another variable here s an example swift let variants json variants map variant fromjson 1 another instance is with shorter notations for forked boolean checks to perform different operations based on the result of a boolean here s an example to illustrate this swift return isfiltering fileteredproducts count products count this is also evident in the use of swift s generics especially useful in reloading data automatically and using sorting predicates reload swift var products product didset self tableview reloaddata predicates swift var minprice double return variants min a b in a price b price price this project was created by nafeh shoaib | os |
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ruTorrent | rutorrent rutorrent is a front end for the popular bittorrent client rtorrent http rakshasa github io rtorrent this project is released under the gplv3 license for more details take a look at the license md file in the source main features lightweight server side so it can be installed on old and low end servers and even on some soho routers extensible there are several plugins and everybody can create their own one nice look screenshots https github com novik rutorrent wiki images scr1 small jpg https github com novik rutorrent wiki images scr1 big jpg https github com novik rutorrent wiki images scr2 small jpg https github com novik rutorrent wiki images scr2 big jpg https github com novik rutorrent wiki images scr3 small jpg https github com novik rutorrent wiki images scr3 big jpg download development version https github com novik rutorrent tarball master stable version https github com novik rutorrent releases getting started there s no installation routine or compilation necessary the sources are cloned unpacked into a directory which is setup as document root of a web server of your choice for detailed instructions see the webserver wiki article https github com novik rutorrent wiki webserver after setting up the webserver rutorrent itself needs to be configured instructions can be found in various articles in the wiki https github com novik rutorrent wiki | rutorrent | front_end |
Cloud-Data-Warehouse-Project-Udacity | project 3 build a cloud data warehouse this project builds an elt pipeline that extracts data from s3 stages them in redshift and transforms data into a set of dimensional tables for sparkify analytics team to continue finding insights in what songs their users are listening to sql queries py this is a helper file which contains the required sql queries which will be used in the project it contains queries to delete the current existing tables queries to create the tables as per the design queries to copy the data from s3 bucket to the staging tables queries to insert the data into fact tables and dimension tables create tables py this python script is used to drop and create the tables used in this project it contains two functions drop tables and create tables drop tables this function uses the queries written in sql queries py in order to drop the tables if they already exists create tables this function uses the queries written in sql queries py in order to create the tables based on the provided structure etl py this python script is used to copy the data from the s3 bucket provided by udacity and then insert the data after transformation into the correct fact tables and dimension tables this script consists of two python functions load staging tables and insert tables load staging tables this function uses the copy queries written in sql queries to copy the data from s3 bucket to the staging tables insert tables this function uses the copy queries written in sql queries to insert the data into the fact and dimension tables schema the schema for this project consists of two staging tables staging events artist varchar auth varchar firstname varchar gender char 1 iteminsession int lastname varchar length float level varchar location text method varchar page varchar registration varchar sessionid int song varchar status int ts bigint useragent text userid int staging songs artist id varchar artist latitude float artist location text artist longitude float artist name varchar duration float num songs int song id varchar title varchar year int the fact table songplays songplay id int identity 0 1 start time timestamp user id int level varchar song id varchar artist id varchar session id int location text user agent text the dimension tables users user id int first name varchar last name varchar gender char 1 level varchar songs song id varchar title varchar artist id varchar year int duration float artists artist id varchar name varchar location text latitude float longitude float time start time timestamp hour int day int week int month int year int weekday varchar | cloud |
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scada-v6 | rapid scada version 6 rapid scada is an open source industrial automation platform see more info at https rapidscada org | automation iot scada | server |
Uniatron | uniatron build status https travis ci org fhellmann uniatron svg branch master https travis ci org fhellmann uniatron codecov https codecov io gh fhellmann uniatron branch master graph badge svg https codecov io gh fhellmann uniatron codacy badge https api codacy com project badge grade 59acfe0f478548479a3213daf8d78d1b https www codacy com app fhellmann93 uniatron utm source github com amp utm medium referral amp utm content fhellmann uniatron amp utm campaign badge grade bch compliance https bettercodehub com edge badge fhellmann uniatron branch master https bettercodehub com banner https github com fhellmann uniatron blob master doc play 20store 0 play store functiongraph png uniatron is the app to spend less time on the phone and more time with exercise smartphone usage today has become excessive and is becoming a serious concern for overall health this app tries to counteract at the beginning of each day the user launches with no time contingent this will benefit both people who use their phone too much past midnight as well as everyone who doesn t want to leave their bed and spends too much time on their phone directly after waking up by achieving activity goals steps during the day the user can re enable usage time depending on the extent of the activity a longer or shorter usage time can be added to your daily limit the automatic locking of apps will encourage the user to become more active additionally it will counteract excessive usage of mobile devices img alt get it on google play src https play google com intl en gb badges images generic en badge web generic png width 180 https play google com store apps details id com edu uni augsburg uniatron pcampaignid mkt other global all co prtnr py partbadge mar2515 1 motivation quality quantity clean code clean design easy to use feature descriptions app time usage collect time for all used apps during the day whether in blacklist or not the top 5 apps in usage time are displayed in a list on the main page and their percentages you can optionally show every app by clicking on a button show all step counter you are rewarded for doing sportive activity user can exchange steps for additional screen time all steps are being counted during the day via a built in sensor a coinbag proportional to the amount of recent steps is displayed in the upper right corner after the exchange of coins for usage time the amount is decreased by the specified number app locking lock social fun apps from blacklist after a certain amount of time has been spent on the phone after app lock is active uniatron will block the access to blacklisted apps locking can be disabled by using the time credit shop learning aid helps you keep concentrated by immediately locking apps for a certain amount of time your phone won t destract you from learning anymore e g blacklisted apps are locked for 55 minutes before you get access again time credit shop in the shop you re able to chose between the learning aid and directly exchanging steps for free app usage time you can chose the amount of coins that you re willing to sacrifice emotion tracking track emotions after app usage and throughout the day via emojis on a range from sad to happy selecting your emotion is mandatory when doing a transaction in the time credit shop you can track your mood afterwards in the history view history view by clicking through different days via the arrows on the upper bar of the screen you get a summary with info on average emotion total of app usage time and total amount of steps during the specified period the default view is set to the current period by clicking the label on the upper bar you can chose a date from a calendar with selecting a group by option in the settings you can switch between summarys for day month or year configurations configure app settings select a fitness level how easy hard do you want it to be to exchange steps for usage time in the blacklist all apps that you want to get blocked by uniatron should be selected on boarding introduction the user will be prompted with an intro screen on the first app launch this will guide him through the setup process and request all necessary permissions upon completion the user will be granted a bonus of 500 steps so he can try the announced shop for himself additionally one sample entry for the current day will be added to provide immediate feedback of the just now introduced ui design considering the fact that the user can reach every content of the app with three clicks we opted for a flat navigation hierarchy with a navigation bar at the bottom with three options timeline home settings every screen can be reached with just one click accordingly a navigation depth of no more than two clicks can be claimed on each of these pages the design of our app complies with the design guidelines of google material design https material io guidelines material design introduction html coding guidelines work process 1 create issue 2 create branch with the following naming first letter of first name last name issue nr issue description 3 implement the solution for the related issue in the branch 4 create a pull request 5 review by at least 1 reviewer 6 update code to solve review comments 7 merge pull request if everything is fine multiple feature testing to test multiple features merge every feature to test in a new branch this branch can then be merged into the master branch if the tests were successful coding style as for the coding style guide we use google s android https source android com setup contribute code style style in addition use optional http www baeldung com java optional instead of return null collaborators fabio hellmann https github com fhellmann leon w hrl https github com leonpoint danilo hoss https github com speedyhoopster3 anja hager https github com anja h | front_end |
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Machine-Learning-homework | machine learning homework matlab coding homework for machine learning in coursera by andrew ng for those seeking the answer of quizzes i have some links that show most of the answers in chinese here i show great thanks to the authors who writing those solutions https www cnblogs com maxiaodoubao p 10184428 html https www cnblogs com xingkongyihao category 1161554 html if the owners of the two blogs deem it offensive please contact me and i will remove the links immediately | ai |
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TMW-coding-standards | tmw coding standards standards and guidelines for development at tmw unlimited http www tmwunlimited com general general coding practices https github com tmwagency tmw coding standards wiki general coding practices accessibility a11y https github com tmwagency tmw coding standards wiki accessibility a11y performance https github com tmwagency tmw coding standards wiki performance recommended ide plugins https github com tmwagency tmw coding standards wiki recommended ide plugins tooling https github com tmwagency tmw coding standards wiki tooling front end css https github com tmwagency tmw coding standards wiki css html https github com tmwagency tmw coding standards wiki html kickoff https github com tmwagency tmw coding standards wiki kickoff kickoff is tmw s front end framework javascript javascript general https github com tmwagency tmw coding standards wiki javascript general javascript react https github com tmwagency tmw coding standards wiki javascript react javascript kickoff react https github com tmwagency tmw coding standards wiki javascript kickoff react tooling create kickoff app https github com tmwagency tmw coding standards wiki tooling create kickoff app please find all the documentation at the wiki https github com tmwagency tmw coding standards wiki | front_end |
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GymManagementSystem | gymmanagementsystem database mfc project br select insert update delete c gui mfc ms sql gymmana https github com lujaedev gymmanagementsystem assets 62204810 df56fdaf ac8a 4527 9de1 d4d047e82c2d document | server |
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iiita-updates | iiita updates this skill fetches the latest events which have been happening around at indian institute of information technology allahabad the skill responds to your commands and questions by providing you help with the usage of the commands it also contains intents which stop cancel the skill hosted on aws using zappa implemented in flask ask where the intents are mpped to the decorators for easy navigation information is scraped from www iiita ac in using beautifulsoup4 library | server |
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Estadistica-con-R | estad stica y machine learning con r este repositorio contiene apuntes personales sobre estad stica bioestad stica machine learning y lenguaje de programaci n r para ver los documentos en formato web html visitar cienciadedatos net https cienciadedatos net regresi n cuant lica modelos gamlss https github com joaquinamatrodrigo estadistica con r blob master pdf format 65 regresion cuantilica gamlss pdf introducci n a los modelos gamlss https github com joaquinamatrodrigo estadistica con r blob master pdf format 63 gamlss pdf ajuste de distribuciones con r https github com joaquinamatrodrigo estadistica con r blob master pdf format 55 ajuste distribuciones con r pdf regresi n cuant lica gradient boosting quantile regression https github com joaquinamatrodrigo estadistica con r blob master pdf format 56 regresion cuantilica gradient boosting pdf distributional regression forest random forest probabil stico https github com joaquinamatrodrigo estadistica con r blob master pdf format 54 distributional regresion forest pdf regresi n cuant lica quantile regression forest https github com joaquinamatrodrigo estadistica con r blob master pdf format 53 regresion cuantilica quantile regresion forest pdf detecci n de anomal as con autoencoders y pca https github com joaquinamatrodrigo estadistica con r blob master pdf format 52 deteccion anomalias autoencoder pca pdf comparaci n de distribuciones test kolmogorov smirnov https github com joaquinamatrodrigo estadistica con r blob master pdf format 51 comparacion distribuciones kolmogorov smirnov pdf optimizaci n con enjambre de part culas particle swarm optimization pso https github com joaquinamatrodrigo estadistica con r blob master pdf format 49 optimizacion con particle swarm pdf optimizaci n con algoritmo gen tico y nelder mead https github com joaquinamatrodrigo estadistica con r blob master pdf format 48 optimizacion con algoritmo genetico pdf algoritmo gen tico para la selecci n de predictores https github com joaquinamatrodrigo estadistica con r blob master pdf format 46 algoritmo genetico para seleccion de predictores pdf an lisis farmacogen mico de paneles celulares drug screening e identificaci n de biomarcadores https github com joaquinamatrodrigo estadistica con r blob master pdf format 45 analisis farmacogenomico pdf gr ficos ice para interpretar modelos predictivos https github com joaquinamatrodrigo estadistica con r blob master pdf format 47 graficos ice individual conditional expectation pdf machine learning con h2o y r https github com joaquinamatrodrigo estadistica con r blob master pdf format 44 machine learning con h2o y r pdf clasificaci n de tumores con machine learning https github com joaquinamatrodrigo estadistica con r blob master pdf format 42 clasificacion de tumores con machine learning pdf machine learning con r y caret https github com joaquinamatrodrigo estadistica con r blob master pdf format 41 machine learning con r y caret pdf reglas de asociaci n y algoritmo apriori https github com joaquinamatrodrigo estadistica con r blob master pdf format 43 reglas de asociacion pdf tobit regression modelos lineales para datos censurados https github com joaquinamatrodrigo estadistica con r blob master pdf format 40 tobit regression modelos lineales para datos censurados pdf sistemas de recomendaci n con r https github com joaquinamatrodrigo estadistica con r blob master pdf format 39 sistemas de recomendacion con r pdf text mining con r ejemplo pr ctico twitter https github com joaquinamatrodrigo estadistica con r blob master pdf format 38 text minig con r ejemplo practico twitter pdf clustering y heatmaps https github com joaquinamatrodrigo estadistica con r blob master pdf format 37 clustering y heatmaps pdf regresi n cuant lica quantile regression https github com joaquinamatrodrigo estadistica con r blob master pdf format 36 quantile regression pdf an lisis de componentes principales principal component analysis pca y t sne https github com joaquinamatrodrigo estadistica con r blob master pdf format 35 principal component analysis pdf m quinas de vector soporte support vector machines https github com joaquinamatrodrigo estadistica con r blob master pdf format 34 maquinas de vector soporte support vector machines pdf algoritmo perceptron https github com joaquinamatrodrigo estadistica con r blob master pdf format 50 algoritmo perceptron pdf rboles de predicci n bagging random forest boosting y c5 0 https github com joaquinamatrodrigo estadistica con r blob master pdf format 33 arboles de predicci c3 b3n tree based 2c bagging 2c random forest 2c boosting pdf m todos de regresi n no lineal regresi n polin mica regression splines smooth splines y gams https github com joaquinamatrodrigo estadistica con r blob master pdf format 32 metodos de regresi c3 b3n no lineal regresi c3 b3n polin c3 b3mica 2c regression splines 2c smooth splines y gams pdf selecci n de predictores y mejor modelo subset selection ridge lasso y dimension reduction https github com joaquinamatrodrigo estadistica con r blob master pdf format 31 seleccion de predictores y mejor modelo subset selection 2c ridge 2c lasso y dimension reduction pdf validaci n de modelos de regresion cross validation oneleaveout bootstrap https github com joaquinamatrodrigo estadistica con r blob master pdf format 30 validaci c3 b3n de modelos de regresion cross validation 2c oneleaveout 2c bootstrap pdf comparaci n entre regresi n log stica linear discriminant analisis lda y k nn https github com joaquinamatrodrigo estadistica con r blob master pdf format 29 comparaci c3 b3n entre regresi c3 b3n log c3 adstica 2c linear discriminant analisis lda y k nn 2c ejemplos pdf linear discriminant analysis lda y quadratic discriminant analysis qda https github com joaquinamatrodrigo estadistica con r blob master pdf format 28 linear discriminant analysis lda y quadratic discriminant analysis qda pdf regresi n log stica simple y m ltiple https github com joaquinamatrodrigo estadistica con r blob master pdf format 27 regresi c3 b3n log c3 adstica simple y m c3 baltiple pdf ejemplo pr ctico de regresi n lineal simple m ltiple polinomial e interacci n entre predictores https github com joaquinamatrodrigo estadistica con r blob master pdf format 26 ejemplo pr c3 a1ctico de regresi c3 b3n lineal simple 2c m c3 baltiple 2c polinomial e interacci c3 b3n entre predictores pdf regresi n lineal m ltiple https github com joaquinamatrodrigo estadistica con r blob master pdf format 25 regresi c3 b3n lineal m c3 baltiple pdf correlaci n y regresi n lineal https github com joaquinamatrodrigo estadistica con r blob master pdf format 24 correlaci c3 b3n y regresi c3 b3n lineal pdf resampling test de permutaci n simulaci n de monte carlo y bootstrapping https github com joaquinamatrodrigo estadistica con r blob master pdf format 23 resampling test de permutaci c3 b3n 2c simulaci c3 b3n de monte carlo y bootstrapping pdf test estad sticos para variables cualitativas test exacto de fisher chi cuadrado de pearson mcnemar qcochran https github com joaquinamatrodrigo estadistica con r blob master pdf format 22 2 test estad c3 adsticos para variables cualitativas test exacto de fisher 2c chi cuadrado de pearson 2c mcnemar qcochran pdf test estad sticos para variables cualitativas test binomial exacto test multinomial y test chi cuadrado goodnes of fit https github com joaquinamatrodrigo estadistica con r blob master pdf format 22 1 test estad c3 adsticos para variables cualitativas test binomial exacto 2c test multinomial y test chi cuadrado goodnes of fit pdf friedman test alternativa no param trica al anova de datos dependientes https github com joaquinamatrodrigo estadistica con r blob master pdf format 21 friedman test alternativa no param c3 a9trica al anova de datos dependientes pdf kruskal wallis test alternativa no param trica al anova independente https github com joaquinamatrodrigo estadistica con r blob master pdf format 20 kruskal wallis test alternativa no param c3 a9trica al anova independente pdf comparaciones m ltiples correcci n de p value y fdr https github com joaquinamatrodrigo estadistica con r blob master pdf format 19b comparaciones m c3 baltiples correcci c3 b3nd de p value y fdr pdf anova an lisis de varianza para comparar m ltiples medias https github com joaquinamatrodrigo estadistica con r blob master pdf format 19 anova an lisis de varianza para comparar m ltiples medias pdf prueba de los rangos con signo de wilcoxon https github com joaquinamatrodrigo estadistica con r blob master pdf format 18 prueba de los rangos con signo de wilcoxon comparar medianas alternativa no param trica a t test dependiente pdf wilcoxon mann whitney test https github com joaquinamatrodrigo estadistica con r blob master pdf format 17 wilcoxon mann whitney 20test 20como 20alternativa 20al 20t test pdf inferencia para variables categ ricas proporciones intervalos de confianza y test de h p tesis https github com joaquinamatrodrigo estadistica con r blob master pdf format 15 inferencia para variables categ c3 b3ricas proporciones intervalos de confianza y test de h c3 adpotesis pdf t test https github com joaquinamatrodrigo estadistica con r blob master pdf format 12 t test pdf an lisis de la homogeneidad de varianza homocedasticidad https github com joaquinamatrodrigo estadistica con r blob master pdf format 9 analisis de la homogeneidad de varianza homocedasticidad pdf an lisis de normalidad mediante gr ficos y contrastes de hip tesis https github com joaquinamatrodrigo estadistica con r blob master pdf format 8 an c3 a1lisis de normalidad gr c3 a1ficos y contrastes de hip c3 b3tesis pdf bibliograf a estad stica y programaci n en r https github com joaquinamatrodrigo estadistica con r blob master pdf format 0 0 bibliograf c3 ada estad c3 adstica y programaci c3 b3n en r pdf a rel license href http creativecommons org licenses by 4 0 img alt creative commons licence style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work by span xmlns cc http creativecommons org ns property cc attributionname joaqu n amat rodrigo span is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a | estadistica machine-learning r bioestadistica data-science data-mining mineria-de-datos | ai |
stat451-machine-learning-fs20 | binder https mybinder org badge logo svg https mybinder org v2 gh rasbt stat451 machine learning fs20 master stat451 machine learning fs20 stat 451 intro to machine learning uw madison fall 2020 lecture 01 l01 course overview introduction to machine learning lecture 02 l02 nearest neighbor methods lecture 03 l03 python lecture 04 l04 scientific computing in python lecture 05 l05 scikit learn lecture 06 l06 decision trees lecture 07 l07 ensemble methods lecture 08 l08 model evaluation 1 overfitting and underfitting lecture 09 l09 model evaluation 2 resampling methods and confidence intervals lecture 10 l10 model evaluation 3 cross validation and model selection lecture 11 l11 model evaluation 4 algorithm comparison lecture 12 l12 model evaluation 5 performance metrics | ai |
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BoofCV | build status https github com lessthanoptimal boofcv actions workflows gradle yml badge svg https github com lessthanoptimal boofcv actions workflows gradle yml join the chat at https gitter im lessthanoptimal boofcv https badges gitter im join 20chat svg https gitter im lessthanoptimal boofcv utm source badge utm medium badge utm campaign pr badge utm content badge maven central https img shields io maven central v org boofcv boofcv core svg https maven badges herokuapp com maven central org boofcv boofcv core table of contents introduction introduction cloning repository cloning git repository quick start quick start examples and demonstrations gradle and maven adding to gradle and maven projects building from source building from source dependencies dependencies help contact contact introduction boofcv is an open source real time computer vision library written entirely in java and released under the apache license 2 0 functionality includes low level image processing camera calibration feature detection tracking structure from motion classification and recognition project webpage http boofcv org message board https groups google com group boofcv bug reports https github com lessthanoptimal boofcv issues repository https github com lessthanoptimal boofcv cloning git repository the bleeding edge source code can be obtained by cloning the git repository git clone b snapshot recursive https github com lessthanoptimal boofcv git boofcv is the data directory empty that s because you didn t follow instructions and skipped recursive fix that by doing the following cd boofcv git submodule update init recursive quick start examples and demonstrations to run boofcv you need java 11 or newer and any free version will work e g zulu https www azul com downloads package jdk to build boofcv just let the gradle script handle everything it will automatically download what it needs and ignore your local jdk if it s not compatible bash cd boofcv git clean fd main removes stale autogenerated code gradlew autogenerate gradlew examplesjar java jar examples examples jar gradlew demonstrationsjar java jar demonstrations demonstrations jar all the code for examples and demonstrations is in boofcv examples and boofcv demonstrations example code is intended easy to understand so look there first click here applications readme md for instruction on building and running applications there you can calibrate cameras create qr codes batch scan for qr codes batch downsample images etc maven central repository boofcv is on maven central http search maven org and can be easily added to your favorite build systems maven gradle etc it s divided up into many modules but most people will just need boofcv core additional modules are listed below for gui and io components to include it in a gradle project add the following to your dependencies for gradle projects dependencies api group org boofcv name boofcv core version 1 1 0 here are a list of the most commonly used modules and what they are for name description boofcv core all the core libraries without any of the integration modules listed below boofcv android useful functions for working inside of android devices boofcv ffmpeg javacpp presets https github com bytedeco javacpp presets their ffmpeg wrapper is used for reading video files boofcv javacv javacv https github com bytedeco javacv is a wrapper around opencv mainly for file io boofcv jcodec jcodec http jcodec org is a pure java video reader writer boofcv kotlin kotlin https kotlinlang org extensions which take advantage of kotlin s unique features boofcv pdf needed to render fiduals as pdf documents boofcv swing visualization using java swing required for examples and demonstrations boofcv webcamcapture a few functions that make webcamcapture http webcam capture sarxos pl even easier to use directories directory description applications helpful applications data directory containing optional data used by applets and examples demonstrations demonstration code which typically lets experiment by changing parameters in real time examples set of example code designed to be easy to read and understand integration contains code which allows boofcv to be easily integrated with 3rd party libraries primary for video input output main contains the source code for boofcv building from source building and installing boofcv into your local maven repository is easy 1 using the gradlew https docs gradle org current userguide gradle wrapper html script bash cd boofcv git clean fd main removes stale autogenerated code gradlew autogenerate creates auto generated files gradlew publishtomavenlocal installs it into the local maven repository if you wish to have jars instead the following commands are provided bash gradlew onejarbin builds a single jar with all of boofcv in it gradlew createlibrarydirectory puts all jars and dependencies into boofcv library gradlew alljavadoc combines all javadoc from all sub projects into a single set 1 a couple of the integration submodules have a custom build process that can t be performed by gradle the script is smart enough to ignore modules and tell you that it is doing so if you haven t configured it yet intellij intellij is the recommended ide for use with boofcv with intellij you can directly import the gradle project 1 file project from existing sources 2 select your local boofcv directory 3 confirm that you wish to import the gradle project eclipse similar to intellij you can now import gradle projects like boofcv using the buildship tooling instructions https www vogella com tutorials eclipsegradle article html dependencies core boofcv modules depends on the following libraries args4j http args4j kohsuke org ejml http code google com p efficient java matrix library georegression http georegression org ddogleg http ddogleg org deepboof https github com lessthanoptimal deepboof the following is required for unit tests junit http junit sourceforge net code from the following libraries has been integrated into boofcv general purpose fft by takuya ooura http www kurims kyoto u ac jp ooura fft html java port by piotr wendykier with modifications by peter abeles to recycle memory the optional sub projects in integration also have several dependencies see those sub projects for a list of their dependencies contact for questions or comments about boofcv please use the message board only post a bug report after doing some due diligence to make sure it is really a bug and that it has not already been reported message board http groups google com group boofcv | camera-calibration computer-vision 3d-vision structure-from-motion android stereo-vision photogrammetry image-processing qrcode-scanner 3d-reconstruction java qr-code qrcode-generator qrcode micro-qrcode micro-qr-code aztec-code | ai |
ria-course-materials | web mobile development course materials this collection of slides form the course materials for the web mobile development formerly rich internet applications course part of the professional bachelor ict http www ikdoeict be study programme taught at kaho sint lieven http www kahosl be ghent belgium the materials were developed by bram us van damme who blogs over at bram us http www bram us and twitters as bramus http twitter com bramus and davy de winne the materials may be used freely as long as credit to the authors is present and the top right graphical link to ikdoeict be http www ikdoeict be remains in place the engine powering the slide decks is a customized reveal js http lab hakim se reveal js more info on this can be found in the first set of slides | front_end |
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NEON-GPT | neon gpt ontology modeling using neon methodology with gpt driven workflow neon gpt is a cutting edge project that combines the power of neon methodology with the advanced capabilities of the gpt language model to revolutionize the way ontology modeling is approached this project aims to streamline the ontology development process by leveraging the neon methodology s best practices and employing gpt s natural language generation capabilities to iteratively construct a comprehensive ontology neon methodology integration neon gpt stands at the crossroads of two powerful technologies ontology modeling methodologies and artificial intelligence the neon methodology known for its structured and iterative approach provides the foundation for the project s workflow it guides the ontology development process through distinct phases such as requirements analysis conceptualization formalization implementation and maintenance by incorporating the neon methodology this project ensures that the resulting ontology is not only logically robust but also aligned with domain requirements gpt driven workflow what sets neon gpt apart is its intelligent workflow powered by gpt at each step of ontology development gpt assists in generating relevant and coherent content that refines the ontology s structure and content the gpt driven workflow begins with user inputs or high level descriptions of the domain and through a series of interactions it incrementally refines concepts relationships and axioms this not only accelerates the development process but also opens avenues for domain experts and developers to collaborate effectively even if they lack deep ontology modeling expertise furthermore neon gpt seamlessly integrates external tools and technologies to enhance the ontology s quality and rigor oops apis and hermit reasoner integration to ensure the soundness and accuracy of the developed ontology neon gpt integrates the ontology pitfall scanner oops apis these apis are employed to perform validation checks syntactic analysis and consistency testing on the ontology in addition the hermit reasoner is employed to conduct in depth reasoning and inference tasks verifying the logical coherence of the ontology s axioms and concepts by harnessing these tools neon gpt guarantees that the ontology conforms to established standards and exhibits robust logical reasoning capabilities whether you are an ontology expert looking to enhance your development process or a domain enthusiast aiming to create structured first draft knowledge representations this project offers a pioneering approach that combines methodologies ai and reasoning tools to reshape the approach to knowledge graphs generation | ai |
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Natural-Language-Processing-with-Python | natural language processing with python natural language processing with python udemy | ai |
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Advanced-Deep-Trading | advanced deep trading experiments based on the lopez de prado book advances in financial machine learning https www wiley com en us advances in financial machine learning p 9781119482086 rethinking normal machine learning from areas like cv nlp and others in order to make it work with data with stochastic nature like financial time series | ai |
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ESP8266_RTOS_ALINK_DEMO | 1 description 1 support alink smart cloud access to achieve the alink smart wifi device based on esp8266 br 2 support equipment ota br 3 demo project includes the smartswitch smartplug pwm dimming uart protocol control device example code available for real products br 2 getting started 1 download esp8266 rtos alink demo br 2 for linux cd esp8266 rtos alink demo app gen misc sh build the target bin the generated bin in esp8266 rtos alink demo bin path br 3 the detail description see esp8266 rtos alink demo releasenote txt br | os |
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cs224n-natural-language-processing-winter2019 | cs224n natural language processing with deep learning img src https github com ankit ai cs224n natural language processing winter2019 blob master cs224n png stanford winter 2019 assignment 1 introduction to word vectors code and handout https github com ankit ai cs224n natural language processing winter2019 tree master a1 intro word vectors a1 assignment 2 derivatives and implementation of word2vec algorithm code and handout https github com ankit ai cs224n natural language processing winter2019 tree master a2 word2vec assignment 3 dependency parsing and neural network foundations code and handout https github com ankit ai cs224n natural language processing winter2019 tree master a3 neural dependency parsing assignment 4 neural machine translation with sequence to sequence and attention code and handout https github com ankit ai cs224n natural language processing winter2019 tree master a4 neural machine translation s2s attention assignment 5 neural machine translation with convnets and subword modeling code and handout https github com ankit ai cs224n natural language processing winter2019 tree master a5 neural machine translation convnet subword | ai |
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Lab02_Gesture_Phase_Segmentation_Clustering_UIT | lab02 gesture phase segmentation clustering uit lab02 gesture phase segmentation clustering in class cs313 k11 khcl of university of information technology h3 member h3 br ngbao161199 br bittervirgin br mercersi br | server |
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grok_sdi_educative | grok sdi educative | os |
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ui-kit | this is a library with customized chakra ui https chakra ui com components getting started 1 install dependencies yarn 2 install optional dependencies yarn global add nps 3 see scripts by running nps or node modules bin nps nps dev run storybook locally nps build build storybook and the typescript project for deployment nps lint core linting nps meta build a visualization of the codebase nps test run tests nps care auto run on commit hook releases our releases are based on tags in order to do a release 1 update package json version submit a pr and merge it to master 2 run git tag a vx y z m ui kit x y z where x y z is the major minor patch version 3 run git push tags local development running nps dev will launch storybook https storybook js org server with components examples on http localhost 5000 http localhost 5000 where all available component can be tested and configured learn more chakra ui documentation https chakra ui com getting started storybook documentation https storybook js org docs react get started introduction | os |
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luvina | luvina high level nlp luvina is a high level natural language processing nlp api for python built on top of spacy https spacy io and nltk http www nltk org installation you can install luvina from pypi sh sudo pip install luvina you can find more information in the documentation website https oarriaga github io luvina | natural-language-processing python spacy nltk nlp | ai |
mlsys-ladder | machine learning machine learning machine learning 3 5 2 4 3 5 2 4 checklist checklist 2 3 5 2 3 5 1 1 checklist checklist 1 2 3 5 2 3 5 2 2 checklist checklist 2 2 3 5 2 3 5 3 3 checklist checklist 3 3 5 3 5 3 5 3 5 4 4 checklist checklist 4 1 3 5 1 3 5 5 5 checklist checklist 5 6 6 created by gh md toc https github com ekalinin github markdown toc ai https www lagou com jobs 5217772 html source pl i pl 4 2019 div align center img src images image5 png width 200 p fig 1 p div fig 1 mnist resnet mobilenet densenet vgg fig 1 faster rcnn ssd yolo v3 div align center img src images image3 png width 200 p fig 2 p div fig 2 fcn psp deeplab v3 fig 2 mask rcnn div align center img src images image4 png width 400 p fig 3 credit p div fig 3 simple pose div align center img src images image1 png width 200 p fig 4 gan p div fig 4 gan emoji wgan cyclegan srgan div align center img src images image2 png width 500 p fig 5 p div fig 5 dl knn k tensorflow checklist 3 5 2 4 ai dl ai for everyone https www coursera org learn ai for everyone andrew ng ceo cto 2 3 8 12 ai andrew ng checklist ai for everyone 2 3 5 dl knn 2017 1 6 2 knn http cs231n stanford edu syllabus html numpy http cs231n github io python numpy tutorial https zhuanlan zhihu com p 20878530 refer intelligentunit numpy http cs231n github io classification https zhuanlan zhihu com p 20894041 refer intelligentunit https zhuanlan zhihu com p 20900216 refer intelligentunit knn cs231n assignment 1 q1 k nearest neighbor classifier 20 points knn knn k knn https www cnblogs com ybjourney p 4702562 html k http cynhard com 2018 06 10 ml knn k nearest neighbor https coolshell cn articles 8052 html cifar 10 28 cifar 10 60000 airplane automobile bird cat deer dog frog horse ship truck 28 knn 100 28 10 checklist 2017 cs231n assignment 1 q1 2 3 5 knn 2017 http cs231n github io linear classify https zhuanlan zhihu com p 20918580 refer intelligentunit https zhuanlan zhihu com p 20945670 refer intelligentunit https zhuanlan zhihu com p 21102293 refer intelligentunit http cs231n github io optimization 1 https zhuanlan zhihu com p 21360434 refer intelligentunit https zhuanlan zhihu com p 21387326 refer intelligentunit http cs231n github io optimization 2 https zhuanlan zhihu com p 21407711 refer intelligentunit https www zhihu com question 36301367 cs231n assignment 1 q2 q3 checklist 2017 cs231n assignment 1 q2 q3 2 3 5 2017 http cs231n github io convolutional networks https zhuanlan zhihu com p 22038289 refer intelligentunit cs231n assignment 1 q5 checklist 2017 cs231n assignment 1 q5 3 5 3 5 pytorch tensorflow tensorflow 2017 1 http cs231n github io neural networks 1 https zhuanlan zhihu com p 21462488 refer intelligentunit https zhuanlan zhihu com p 21513367 refer intelligentunit 2 http cs231n github io neural networks 2 https zhuanlan zhihu com p 21560667 refer intelligentunit 3 http cs231n github io neural networks 3 https zhuanlan zhihu com p 21741716 refer intelligentunit https zhuanlan zhihu com p 21798784 refer intelligentunit cs231n assignment 2 q1 q4 q5 q2 q3 q5 checklist 2017 cs231n assignment 2 q1 q4 q5 1 3 5 tensorflow tensorboard tfserving tensorflow pytorch tensorboard https www tensorflow org guide summaries and tensorboard hl zh cn https www youtube com watch v ebbedrscmv4 tf mnist https www github com tensorflow tensorflow blob r1 10 tensorflow examples tutorials mnist mnist with summaries py tensorflow https bookdown org leovan tensorflow learning notes 4 5 deploy tensorflow serving html tensorflow mnist resnet clever tfserving http checklist tensorboard tensorflow alexnet resnet rnn cnn gan 2017 rnn gan cnn kaggle kaggle 1 gluoncv a deep learning toolkit for computer vision gluoncv 1 tensorflow https bookdown org leovan tensorflow learning notes 1 https www zhihu com question 51039416 cc by nc sa 3 0 https creativecommons org licenses by nc sa 3 0 cn marketing caicloud io gaoce caicloud io https caicloud io gluoncv https gluon cv mxnet io https mooc study 163 com smartspec detail 1001319001 htm 2017 https cloud tencent com edu learning course 1039 690 https mooc study 163 com course 2001281002 info cs231n assignment 1 http cs231n github io assignments2019 assignment1 https github com mahanfathi cs231 https mooc study 163 com course 2001281004 info https mooc study 163 com course 2001281003 info cs231n assignment 2 http cs231n github io assignments2019 assignment2 | machine-learning handbook tutorial | ai |
mutate | mutate br a library to synthesize text datasets using large language models llm mutate reads through the examples in the dataset and generates similar examples using auto generated few shot prompts 1 installation pip install mutate nlp or pip install git https github com infinitylogesh mutate 2 usage open in colab https colab research google com assets colab badge svg https colab research google com drive 1dpdvl3evmsnjc7lxwydanttljgjjr2o2 usp sharing 2 1 synthesize text data from local csv files python from mutate import pipeline pipe pipeline text classification synthesis model eleutherai gpt neo 2 7b device 1 task desc each item in the following contains movie reviews and corresponding sentiments possible sentimets are neg and pos returns a python generator text synth gen pipe csv data files local path sentiment classfication csv task desc task desc text column text label column label text column alias comment label column alias sentiment shot count 5 class names pos neg loop through the generator to synthesize examples by class for synthesized examples in text synth gen print synthesized examples details summary show output summary python text the story was very dull and was a waste of my time this was not a film i would ever watch the acting was bad i was bored there were no surprises they showed one dinosaur i did not like this film it was a slow and boring film it didn t seem to have any plot there was nothing to it the only good part was the ending i just felt that the film should have ended more abruptly label neg neg text the bell witch is one of the most interesting yet disturbing films of recent years it s an odd and unique look at a very real but very dark issue with its mixture of horror fantasy and fantasy adventure this film is as much a horror film as a fantasy film and it s worth your time while the movie has its flaws it is worth watching and if you are a fan of a good fantasy or horror story you will not be disappointed label pos and so on details 2 2 synthesize text data from datasets under the hood mutate uses the wonderful datasets library for dataset processing so it supports datasets out of the box python from mutate import pipeline pipe pipeline text classification synthesis model eleutherai gpt neo 2 7b device 1 task desc each item in the following contains customer service queries expressing the mentioned intent synthesizergen pipe banking77 task desc task desc text column text label column label if the text column doesn t have a meaningful value text column alias queries label column alias intent if the label column doesn t have a meaningful value shot count 5 dataset args en for exp in synthesizergen print exp details summary show output summary python text how can i know if my account has been activated this is the one that i am confused about thanks my card activated label activate my card activate my card text how do i activate this new one is it possible what is the activation process for this card label activate my card activate my card and so on details 2 3 i am feeling lucky infinetly loop through the dataset to generate examples indefinetly caution infinetly looping through the dataset has a higher chance of duplicate examples to be generated python from mutate import pipeline pipe pipeline text classification synthesis model eleutherai gpt neo 2 7b device 1 task desc each item in the following contains movie reviews and corresponding sentiments possible sentimets are neg and pos returns a python generator text synth gen pipe csv data files local path sentiment classfication csv task desc task desc text column text label column label text column alias comment label column alias sentiment class names pos neg flag to generate indefinite examples infinite loop true infinite loop for exp in synthesizergen print exp 3 support 3 1 currently supports text classification dataset synthesis few shot text data synsthesize for text classification datasets using causal llms gpt like 3 2 roadmap other types of text dataset synthesis ner sentence pairs etc finetuning support for better quality generation pseudo labelling 4 credit eleutherai https eluether ai for democratizing large lms this library uses datasets https huggingface co docs datasets and transformers https huggingface co docs transformers for processing datasets and models 5 references the idea of generating examples from large language model is inspired by the works below a few more examples may be worth billions of parameters https arxiv org abs 2110 04374 by yuval kirstain patrick lewis sebastian riedel omer levy gpt3mix leveraging large scale language models for text augmentation https arxiv org abs 2104 08826 by kang min yoo dongju park jaewook kang sang woo lee woomyeong park data augmentation using pre trained transformer models https arxiv org abs 2003 02245 by varun kumar ashutosh choudhary eunah cho | nlp-library text-generation language-model data-augmentation data-labeling | ai |
aimlabbot | aim lab bot aim lab computer vision bot made as an experiment using opencv masks preview alt text preview gif raw true how to use in aimlab set cs go preset and sensitivity to 1 br screenshots of settings i ve used in demo graphics https i imgur com wdvvzet png controls https i imgur com bq6x6hy png author c 2022 abraham tugalov | ai |
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blockchain | blockchain a basic blockchain implementation written in python please find updated blockchain application here a href https github com farhan711 blockchain vcoin blockchain vcoin a | blockchain ethereum python | blockchain |
sepsis-prediction | sepsis prediction mobile and web application for real time early prediction of sepsis 6 hours before the onset i did pre processing data visualisation feature engineering and all the data science and machine learning work i also handled google cloud and real time analysis a flask app is built and deployed on heroku platform along with a flutter native app structure the directory contains web sub directories and a sub directory for hosting model and other scripts 1 app https github com abhishek parashar sepsis prediction tree master app the folder contains all the major flutter app development work starting from backend to frontend this folder is how our app looks 2 web https github com abhishek parashar sepsis prediction tree master web this folder contains all the front end part of website 3 data https github com abhishek parashar sepsis prediction tree master data contains the data taken from physionet website combined and converted to csv 4 model https github com abhishek parashar sepsis prediction tree master model contains the model used for predicting wether a person is suffering from sepsis or not 6 hours before the onset it also contains the files for all the pre processing done along with cloud deployment files we have used google cloud platform and apache spark for streaming data backbone of the project 5 api https github com abhishek parashar sepsis prediction tree master api contains the api for intereacting with flask app codebase the project is developed using multiple languages python for model and preprocessing along with flask backend development dart for flutter javascript for website front end development along with html and css how to run the project 1 open the terminal 2 clone the repository by entering https github com abhishek parashar sepsis prediction 3 ensure that python3 and pip are installed on the system 4 create a virtualenv by executing the following command virtualenv venv 5 activate the venv virtual environment by executing the follwing command source venv bin activate 6 enter the cloned repository directory and execute pip install r requirements txt 7 now execute the following command flask run and it will point to the localhost server with the port 5000 8 enter the ip address http localhost 5000 on a web browser and use the application dependencies the following dependencies can be found in requirements txt https github com abhishek parashar reddit flair detection blob master requirements txt 1 scikit learn https scikit learn org 2 flask https palletsprojects com p flask 3 gensim https radimrehurek com gensim 4 pandas https pandas pydata org 5 numpy http www numpy org 6 scikit learn https scikit learn org stable index html 7 gunicorn https gunicorn org 8 dart https dart dev 9 reactjs https reactjs org xgboost along with gaussian mixture model is used to build the model the model is tested on various other models details summary to be added soon references 2 for building machine learning model 1 https medium com themlblog splitting csv into train and test data 1407a063dd74 2 https towardsdatascience com multi class text classification model comparison and selection 5eb066197568 3 https medium com robert salgado multiclass text classification from start to finish f616a8642538 4 https www analyticsvidhya com blog 2018 04 a comprehensive guide to understand and implement text classification in python 5 https www districtdatalabs com text analytics with yellowbrick 6 applied ai course https www appliedaicourse com 3 for building the website and deploying it 1 https towardsdatascience com designing a machine learning model and deploying it using flask on heroku 9558ce6bde7b 2 https towardsdatascience com deploying a deep learning model on heroku using flask and python 769431335f66 3 https medium com analytics vidhya deploy machinelearning model with flask and heroku 2721823bb653 4 https www youtube com watch v ubcwomf80py 5 https www youtube com watch v mrexsjcvf4o 6 https blog cambridgespark com deploying a machine learning model to the web 725688b851c7 | prediction sepsis flutter flask heroku-deployment machine-learning data-science preprocessing | cloud |
opensea-blockchain-youtube | next js tailwind css example this example shows how to use tailwind css https tailwindcss com v3 0 https tailwindcss com blog tailwindcss v3 with next js it follows the steps outlined in the official tailwind docs https tailwindcss com docs guides nextjs preview preview the example live on stackblitz http stackblitz com open in stackblitz https developer stackblitz com img open in stackblitz svg https stackblitz com github vercel next js tree canary examples with tailwindcss deploy your own deploy the example using vercel https vercel com utm source github utm medium readme utm campaign next example deploy with vercel https vercel com button https vercel com new git external repository url https github com vercel next js tree canary examples with tailwindcss project name with tailwindcss repository name with tailwindcss how to use execute create next app https github com vercel next js tree canary packages create next app with npm https docs npmjs com cli init or yarn https yarnpkg com lang en docs cli create to bootstrap the example bash npx create next app example with tailwindcss with tailwindcss app or yarn create next app example with tailwindcss with tailwindcss app deploy it to the cloud with vercel https vercel com new utm source github utm medium readme utm campaign next example documentation https nextjs org docs deployment | blockchain |
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nm-tray | nm tray nm tray is a simple networkmanager https wiki gnome org projects networkmanager front end with information icon residing in system tray like e g nm applet it s a pure qt application for interaction with networkmanager it uses api provided by kf5 networkmanagerqt https projects kde org projects frameworks networkmanager qt plain dbus communication license this software is licensed under gnu gplv2 or later https www gnu org licenses old licenses gpl 2 0 html build example git clone https github com palinek nm tray git cd nm tray mkdir build cd build cmake make nm tray packages arch for arch users https www archlinux org there is an aur package nm tray git https aur archlinux org packages nm tray git thanks to pmattern https github com pmattern opensuse nm tray is in the official repository of opensuse https www opensuse org since leap 15 0 there is a also a git package https build opensuse org package show x11 lxqt git nm tray in the x11 lxqt git https build opensuse org project show x11 lxqt git devel project of obs debian thanks to agaida https github com agaida nm tray is now in official debian repositories nm tray https packages debian org sid nm tray translations thanks to weblate https weblate org anyone can help us to localize nm tray by using the hosted weblate service https hosted weblate org projects nm tray translations | front_end |
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LiveScan3D | livescan3d livescan3d is a system designed for real time 3d reconstruction using multiple azurekinect or kinect v2 depth sensors simultaneously at real time speed the code for working with kinect v2 is in the master branch and the v1 x x releases if you want to work with azure kinect please use the appropriately named branch for both sensors the produced 3d reconstruction is in the form of a coloured point cloud with points from all of the kinects placed in the same coordinate system the point cloud stream can be visualized recorded or streamed to a hololens or any unity application the code for streaming to unity and hololens is available in the livescan3d hololens repository https github com marekkowalski livescan3d hololens possible use scenarios of the system include capturing an object s 3d structure from multiple viewpoints simultaneously capturing a panoramic 3d structure of a scene extending the field of view of one sensor by using many streaming the reconstructed point cloud to a remote location increasing the density of a point cloud captured by a single sensor by having multiple sensors capture the same scene you will also find a short presentation of livescan3d in the video below click to go to youtube youtube link http img youtube com vi 9y wglwpjte 0 jpg http www youtube com watch v 9y wglwpjte in our system each sensor is governed by a separate instance of a client app which is connected to a server the client apps can either run on separate machines or all on the same machine only for azure kinect the server allows the user to perform calibration filtering synchronized frame capture and to visualize the acquired point cloud live how to use it to start working with our software you will need a windows machine with and at least a single kinect device you can either build livescan3d from source for which you will need visual studio 2019 or you can download the binary release both the binary and source distributions contain a manual in the docs directory which contains the steps necessary to start working with our software it won t take more than a couple of minutes to set up where to get help if you have any problems feel free to contact us marek kowalski m kowalski ire pw edu pl jacek naruniec j naruniec ire pw edu pl we usually answer emails quickly our timezone is cet for details regarding the methods used in livescan3d you can take a look at our article livescan3d a fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors https www researchgate net publication 308807023 livescan3d a fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors licensing while all of our code is licensed under the mit license the 3rd party libraries have different licenses nanoflann https github com jlblancoc nanoflann bsd license opencv https github com itseez opencv 3 clause bsd license opentk https github com opentk opentk mit x11 license zstd https github com facebook zstd bsd license socketcs http www adp gmbh ch win misc sockets html if you use this software in your research then please use the following citation kowalski m naruniec j daniluk m livescan3d a fast and inexpensive 3d data acquisition system for multiple kinect v2 sensors in 3d vision 3dv 2015 international conference on lyon france 2015 authors marek kowalski m kowalski ire pw edu pl homepage http home elka pw edu pl mkowals6 jacek naruniec j naruniec ire pw edu pl homepage http home elka pw edu pl jnarunie | kinect 3d-reconstruction real-time multi-view-geometry | os |
Pewlett-Hackard-Analysis | pewlett hackard analysis build an employee database with sql by applying data modelling engineering and analysis skills | server |
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lxmls-toolkit | travis ci build status travis image travis url requirements status requires image requires url travis image https travis ci org lxmls lxmls toolkit svg branch master travis url https travis ci org lxmls lxmls toolkit requires image https requires io github lxmls lxmls toolkit requirements svg branch master requires url https requires io github lxmls lxmls toolkit requirements branch master lxmls 2023 machine learning toolkit for natural language processing written for lisbon machine learning summer school lxmls it pt this covers scientific python and mathematical background linear classifiers sequence models structured prediction syntax and parsing feed forward models in deep learning sequence models in deep learning reinforcement learning machine learning toolkit for natural language processing written for lxmls lisbon machine learning summer school http lxmls it pt instructions for students use the student branch https github com lxmls lxmls toolkit tree student not this one install with anaconda or pip if you are new to python the simplest method is to use anaconda to handle your packages just go to https www anaconda com download and follow the instructions we strongly recommend using at least python 3 if you prefer pip to anaconda you can install the toolkit in a way that does not interfere with your existing installation for this you can use a virtual environment as follows virtualenv venv source venv bin activate on windows venv scripts activate pip install pip setuptools upgrade pip install editable this will install the toolkit in a way that is modifiable if you want to also virtualize you python version e g you are stuck with python2 on your system have a look at pyenv bear in mind that the main purpose of the toolkit is educative you may resort to other toolboxes if you are looking for efficient implementations of the algorithms described running run from the project root directory if an importing error occurs try first adding the current path to the pythonpath environment variable e g export pythonpath development to run the all tests install tox and pytest pip install tox pytest and run tox note to combine the coverage data from all the tox environments run windows set pytest addopts cov append tox other pytest addopts cov append tox | ai |
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product-iots | a href http wso2 com products iot server img src http b content wso2 com sites all common images product logos iot server svg srcset http b content wso2 com sites all common images product logos iot server svg 2x png 2x alt wso2 iot server a welcome to wso2 iot server a href https opensource org licenses apache 2 0 img src https img shields io badge license apache 202 0 blue svg a br a href https wso2 org jenkins job products job product iots img src https wso2 org jenkins job products job product iots badge icon a wso2 iot server is a complete solution that enables device manufacturers and enterprises to connect and manage their devices build apps manage events secure devices and data and visualize sensor data in a scalable manner it also offers a complete and secure enterprise mobility management emm mdm solution that aims to address mobile computing challenges faced by enterprises today supporting ios android and windows devices it helps organizations deal with both corporate owned personally enabled cope and employee owned devices with the bring your own device byod concept wso2 iot server comes with advanced analytics enabling users to analyze speed proximity and geo fencing information of devices including details of those in motion and stationary state find the online documentation at http docs wso2 com iot server key features of wso2 iot server generic framework for device management extensions for registering built in custom device types self service enrollment and management of connected devices group manage and monitor connected devices share device operations data with other users distribute and manage applications firmware of devices edge computing powered by the wso2 complex event processor cep streaming engine siddhi https github com wso2 siddhi out of the box support for some known device types such as raspberry pi arduino uno etc supports mobile platforms such as android windows and ios mobile device and app management implement self service device enrollment and management for ios android and windows devices provide policy driven device and profile management for security data and device features enable compliance monitoring for applied policies on devices and role based access control provision de provision apps to multiple enrolled devices per user and to enrolled devices based on roles iot protocol support leverage mqtt http websockets and xmpp protocols for device communications with iot server framework extension for adding more protocols and data formats iot analytics support for batch interactive real time and predictive analytics through wso2 data analytics server das pre built visualization support for sensor readings view instant visualized statistics of individual or multiple devices traverse through analyse and zoom in out of filtered data stats api to write your own visualization pre built graphs for common sensor reading types like temperature velocity api management for app development all connected devices are exposed via managed rest apis api store for easy discovery of all product device apis for app development identity and access management identity management for devices token based access control for devices operations protect back end services via exposing device type apis support for scep protocol encryption and authenticity how to run extract the downloaded wso2iot 3 3 0 zip file this will create a folder named wso2iot 3 3 0 iot server comes with three runnable components namely broker core and analytics start these components in following order by executing the following scripts wso2iot 3 3 0 bin broker sh bat wso2iot 3 3 0 bin iot server sh bat wso2iot 3 3 0 bin analytics sh bat how to contribute wso2 iot server code is hosted in github https github com wso2 product iots please report issues at iot server git issues https github com wso2 product iots issues and send your pull requests to development branch https github com wso2 product iots contact us wso2 iot server developers can be contacted via the mailing lists wso2 developers list dev wso2 org wso2 architecture list architecture wso2 org | server |
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skope-rules | mode rst travis coveralls circleci python27 python35 travis image https api travis ci org skope rules skope rules svg branch master travis https travis ci org skope rules skope rules coveralls image https coveralls io repos github skope rules skope rules badge svg branch master coveralls https coveralls io github skope rules skope rules branch master circleci image https circleci com gh skope rules skope rules tree master svg style shield circle token circle token circleci https circleci com gh skope rules skope rules python27 image https img shields io badge python 2 7 blue svg python27 https badge fury io py skope rules python35 image https img shields io badge python 3 5 blue svg python35 https badge fury io py skope rules image logo png skope rules skope rules is a python machine learning module built on top of scikit learn and distributed under the 3 clause bsd license skope rules aims at learning logical interpretable rules for scoping a target class i e detecting with high precision instances of this class skope rules is a trade off between the interpretability of a decision tree and the modelization power of a random forest see the authors rst authors rst file for a list of contributors image schema png installation you can get the latest sources with pip pip install skope rules quick start skoperules can be used to describe classes with logical rules code python from sklearn datasets import load iris from skrules import skoperules dataset load iris feature names sepal length sepal width petal length petal width clf skoperules max depth duplication 2 n estimators 30 precision min 0 3 recall min 0 1 feature names feature names for idx species in enumerate dataset target names x y dataset data dataset target clf fit x y idx rules clf rules 0 3 print rules for iris species for rule in rules print rule print print 20 print skoperules can also be used as a predictor if you use the score top rules method code python from sklearn datasets import load boston from sklearn metrics import precision recall curve from matplotlib import pyplot as plt from skrules import skoperules dataset load boston clf skoperules max depth duplication none n estimators 30 precision min 0 2 recall min 0 01 feature names dataset feature names x y dataset data dataset target 25 x train y train x len y 2 y len y 2 x test y test x len y 2 y len y 2 clf fit x train y train y score clf score top rules x test get a risk score for each test example precision recall precision recall curve y test y score plt plot recall precision plt xlabel recall plt ylabel precision plt title precision recall curve plt show for more examples and use cases please check our documentation http skope rules readthedocs io en latest you can also check the demonstration notebooks notebooks links with existing literature the main advantage of decision rules is that they are offering interpretable models the problem of generating such rules has been widely considered in machine learning see e g rulefit 1 slipper 2 lri 3 mlrules 4 a decision rule is a logical expression of the form if conditions then response in a binary classification setting if an instance satisfies conditions of the rule then it is assigned to one of the two classes if this instance does not satisfy conditions it remains unassigned 1 in 2 3 4 rules induction is done by considering each single decision rule as a base classifier in an ensemble which is built by greedily minimizing some loss function 2 in 1 rules are extracted from an ensemble of trees a weighted combination of these rules is then built by solving a l1 regularized optimization problem over the weights as described in 5 in this package we use the second approach rules are extracted from tree ensemble which allow us to take advantage of existing fast algorithms such as bagged decision trees or gradient boosting to produce such tree ensemble too similar or duplicated rules are then removed based on a similarity threshold of their supports the main goal of this package is to provide rules verifying precision and recall conditions it still implement a score decision function method but which does not solve the l1 regularized optimization problem as in 1 instead weights are simply proportional to the oob associated precision of the rule this package also offers convenient methods to compute predictions with the k most precise rules cf score top rules and predict top rules functions 1 friedman and popescu predictive learning via rule ensembles technical report 2005 2 cohen and singer a simple fast and effective rule learner national conference on artificial intelligence 1999 3 weiss and indurkhya lightweight rule induction icml 2000 4 dembczy ski kot owski and s owi ski maximum likelihood rule ensembles icml 2008 5 friedman and popescu gradient directed regularization technical report 2004 dependencies skope rules requires python 2 7 or 3 3 numpy 1 10 4 scipy 0 17 0 pandas 0 18 1 scikit learn 0 17 1 for running the examples matplotlib 1 1 1 is required documentation you can access the full project documentation here http skope rules readthedocs io en latest you can also check the notebooks folder which contains some examples of utilization | ai |
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teaching-materials | gdisf teaching materials teaching materials for html css js and html5 we maintain our open source materials on this repo contributing read our docs contributing md curriculum creation read so you want to teach a workshop wiki teaching a workshop read our curriculum creation wiki curriculum creation guide in the wiki make sure that all curricula are in line with the gdi mission and an appropriate topic level for our members this may include topics that complement and expand our front end web development curriculum industry relevant topics for advanced students these students already work in the industry and range from associate junior to senior and above topics that students have requested to learn would you like to fork an existing workshop check out these resources gdi curriculum review site http girldevelopit github io gdi curriculum site girldevelopit com https www girldevelopit com materials make a pull request https github com gdisf teaching materials you may want to use the example directory to get started with the required templates your pr must must include description lecture slides hands on exercises solutions to exercises follow up email if you want your workshop to be included in our national website https www girldevelopit com materials check out this review rubric https docs google com document d 1zw7qx2eao08rgajl9683avxtsaozqlxmy7evu2my4oy edit please note that this is not a requirement for inclusion in this repository | html workshop javascript css curriculum-creation jquery curriculum-development education curriculum curriculum-design workshops workshop-materials educational-resources educational-materials | front_end |
Basic-NLP | basic nlp natural language processing | ai |
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Laravel-blockchain-api | laravel blockchain api this is a laravel package for interacting with blockchain api laravel blockchain a laravel 5 package for working with blockchain api installation php https php net 5 4 or hhvm http hhvm com 3 3 and composer https getcomposer org are required to get the latest version of blockchain api simply run the code below in your project composer require maxtee blockchain once laravel blockchain is installed you need to register the service provider open up config app php and add the following to the providers key maxtee blockchain blockchainserviceprovider class also register the facade like so php aliases blockchain maxtee blockchain facades blockchain class configuration you can publish the configuration file using this command bash php artisan vendor publish provider maxtee blockchain blockchainserviceprovider a configuration file named blockchain php with default settings will be placed in your config directory you can visit this link to get your blockchain api https api blockchain info customer signup usage open your env file and add the following in this format ensure you must have gotten your api key php blockchain api default btc fee 0 0001 transaction btc fee 0 000 using maxtee blockchain package add the following line to your controller use blockchain 1 get rates php blockchain getrates 2 convert a currency value to btc php rates blockchain convertcurrencytobtc ngn 600000 3 get statistics chart php rates blockchain getstats 4 create wallet php wallet blockchain createwallet wallet password 5 wallet balance php wallet blockchain getwalletbalance wallet guid wallet password 6 making outgoing payment php wallet blockchain makeoutgoingpayment wallet guid amount wallet password to guid 7 list address php wallet blockchain listaddress wallet guid wallet password 8 create new address php wallet blockchain createnewaddress wallet guid wallet password label credit readme document was inpsired and tuned from one of unicodedeveloper prosper otemuyiwa contributing please feel free to fork this package and contribute by submitting a pull request to enhance the functionalities how can i thank you why not star the github repo i d love the attention why not share the link for this repository on twitter or hackernews spread the word don t forget to follow me on twitter https twitter com taiwomix thanks famurewa taiwo license the mit license mit please see license file license md for more information | blockchain |
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prompt2walk | prompt a robot to walk with large language models this repository contains the code for our paper prompt a robot to walk with large language models https arxiv org abs 2309 09969 by yen jen wang https wangyenjen github io bike zhang https bikezhang106 github io jianyu chen http people iiis tsinghua edu cn jychen and koushil sreenath https hybrid robotics berkeley edu koushil overview img src figs demo png alt demo width 600 the llm directly outputs the low level action to make a robot to walk in our experiment the llm is supposed to run at 10 hz although the simulation has to be paused to wait for llm inference and the pd controller executes at 200 hz setup create conda env shell conda create n prompt2walk python 3 9 conda activate prompt2walk requirements shell pip install r requirements txt usage configuration please fill the openai api key in src llm py collect the trajectory from the llm controller shell python src run py replay the trajectory shell python src replay py training your own policy please refer to isaac gym environments for legged robots https github com leggedrobotics legged gym currently we ve tested our code on a1 and anymal c robot at 10 hz want more details please read our paper if you have further questions please feel free to contact yen jen https wangyenjen github io citation please cite our paper if you use this code or parts of it article wang2023prompt title prompt a robot to walk with large language models author wang yen jen and zhang bike and chen jianyu and sreenath koushil journal arxiv preprint arxiv 2309 09969 year 2023 | control-systems large-language-models locomotion machine-learning robotics ai artificial-intelligence ml | ai |
pycv | pycv learning opencv 3 with python second edition this is the repository and reference website for learning opencv 3 with python https www packtpub com application development learning opencv 3 computer vision python second edition a book authored by joe minichino https github com techfort and joe howse https github com joehowse code changes with the codes supplied by author 1 move the code from the folder of first version into the correct folder 2 complete the codes of second edition the codes supplied by author is not same with the book here try to complete it 3 refactor some codes to make it clear | ai |
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WaykiChain | quick resource access download waykichain coind waykichain coind binary releases https github com waykichain waykichain wiki download waykichain binary releases docker docker pull wicc waykicoind 3 2 developer documentation english version https wicc devbook readthedocs io en latest chinese version https wicc devbook readthedocs io zh cn latest | blockchain dpos smart contract cryptography p2p stablecoin c-plus-plus defi wasm dex cdp | blockchain |
secure-web-dev-backend | secure web development backend this repo contains an express app it will be the backend used for the semester prerequisites 1 fork this repository then clone it on your computer 2 install insomnia or your api testing tool of choice and import the collection provided in insomnia collection json insomnia collection json 3 if you don t have one create a db on mongo atlas 4 create an env file containing replace with your data mongo uri mongodb srv username password cluster url paris films retrywrites true w majority jwt secret your jwt secret 5 install nodejs 6 install dependencies npm install 7 if you dont have data in your db 1 download the public dataset opendata given by french gov and the city of paris named lieux de tournage paris https opendata paris fr explore dataset lieux de tournage a paris information 2 put the dataset at the root of this repository named lieux de tournage a paris json lieux de tournage a paris json 3 run the import script with npm run import 8 run the backend npm start quick information this backend connects to a mongodb database containing locations of film sets in paris france consulting locations requires an account creating updating and deleting locations requires elevated privileges there are 2 access level user and admin each user have a role property to store this data users can authenticate themselves with a json web token obtained by logging in with their username and password passwords are hashed and the hashes are never shown in api responses | front_end |
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moko-tensorflow | moko tensorflow https user images githubusercontent com 5010169 128705344 f858c4b9 db37 49f7 bb1f 9919f29cb78b png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko tensorflow https repo1 maven org maven2 dev icerock moko tensorflow kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name tensorflow mobile kotlin tensorflow this is a kotlin multiplatform library that provides access to tensorflow lite https github com tensorflow tensorflow tree master tensorflow lite functionality from common source set table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features requirements gradle version 6 8 android api 19 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko tensorflow 0 2 1 cocoapods podsproject file ios app pods pods xcodeproj here should be path to pods xcode project pod tensorflowliteobjc module tfltensorflowlite onlylink true kotlin targets filterisinstance org jetbrains kotlin gradle plugin mpp kotlinnativetarget flatmap it binaries filterisinstance org jetbrains kotlin gradle plugin mpp framework foreach framework framework linkeropts project file ios app pods tensorflowlitec frameworks path let f it framework tensorflowlitec podfile ruby pod tensorflowliteobjc 2 2 0 usage first place the model file in the multi platform resource folder commonmain resources mr files common kotlin class classifier private val interpreter interpreter fun classify inputdata any val inputshape interpreter getinputtensor 0 shape val inputsize inputshape 1 val result array 1 floatarray output classes count interpreter run listof inputdata mapof pair 0 result getting shared model file in common kotlin object resholder fun getmodelfile fileresource return mr files mymodel android kotlin class mainactivity appcompatactivity private lateinit var interpreter interpreter override fun oncreate savedinstancestate bundle interpreter interpreter resholder getmodelfile interpreteroptions 2 usennapi true this val classifier classifier interpreter classifier classify data override fun ondestroy super ondestroy interpreter close ios swift class viewcontroller uiviewcontroller private var interpreter tensorflowinterpreter override func viewdidload super viewdidload let options tensorflowinterpreteroptions tensorflowinterpreteroptions numthreads 2 let modelfileres resourcesfileresource resholder getmodelfile interpreter tensorflowinterpreter fileresource modelfileres options options let classifier classifier interpreter interpreter classifier classify data deinit interpreter close samples please see more examples in the sample directory sample set up locally the tensorflow directory tensorflow contains the tensorflow library the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps for local testing a use the publishtomavenlocal sh script so that sample apps use the locally published version contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2020 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | tensorflow kotlin moko kotlin-native kotlin-multiplatform android ios | front_end |
Vue.js-2-and-Bootstrap-4-Web-Development | vue js 2 and bootstrap 4 web development this is the code repository for web development with vue js bootstrap and firebase https www packtpub com web development web development vuejs bootstrap and firebase utm source github utm medium repository utm campaign 9781788290920 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book vue js is one of the latest new frameworks to have piqued the interest of web developers due to its reactivity reusable components and ease of use when you combine bootstrap with vue js it provides quicker ways to create responsive web applications this book will teach you to add dynamic functionality to your web applications by combining bootstrap with vue js in this book we will combine bootstrap with vue js to form a complete front end stack and build highly reactive and responsive applications we begin by creating a simple webpage combining vue js bootstrap and firebase we then go through the basic components of bootstrap and learn how to integrate these with vue js to create a visually interactive pomodoro app while developing the app you will go through the new grid system of bootstrap 4 along with the much simpler directives in vue js we also take advantage of the inbuilt utilities that come with vue js such as the webpack module to better structure our application finally we quickly deploy our application using firebase which provides backend as a service to create our fully functional responsive web application instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 codes are not present in chapter 3 the code will look like the following landingpage vue export default components logo authentication gotoapplink tagline the requirements for this book are the following computer with internet connection text editor ide related products learning web development with react and bootstrap https www packtpub com web development learning web development react and bootstrap utm source github utm medium repository utm campaign 9781786462497 web development with bootstrap 4 and angular 2 second edition https www packtpub com web development web development bootstrap 4 and angular 2 second edition utm source github utm medium repository utm campaign 9781785880810 learning web development with bootstrap and angularjs https www packtpub com web development learning web development bootstrap and angularjs utm source github utm medium repository utm campaign 9781783287550 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781788290920 https packt link free ebook 9781788290920 a p | front_end |
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Balu | balu general toolbox for image proessing pattern recogntion and computer vision balu toolbox matlab version 4 1 see wiki https github com domingomery balu wiki logo https github com domingomery balu blob master logobalu png domingo mery e mail domingo mery uc cl http dmery ing puc cl departamento de ciencia de la computacion pontificia universidad catolica de chile c 2008 2019 installation in five easy steps https github com domingomery balu wiki installation that s all balu doesn t need any compilation the installation just add the balu directories to search path of matlab note certain functions of balu call functions from the following toolboxes vlfeat image processing neural network and bioinformatics if you want to use these balu functions you must install the mentioned toolboxes see details in command list balu kid i only got so much room up in this noggin and it s fillin up fast mowgli you just don t understand balu all right how s about layin it out for me please visit balu wiki http dmery ing puc cl index php balu i d like to hear about any bugs that you find or suggestions for improvements citation apa mery d 2011 balu a matlab toolbox for computer vision pattern recognition and image processing http dmery ing puc cl index php balu bibtex misc mery2011balu author domingo mery title balu a matlab toolbox for computer vision pattern recognition and image processing http dmery ing puc cl index php balu year 2011 | ai |
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Web-Application-Development-with-MEAN | web application development with mean the code files have been placed in the folder module wise and chapter wise directory contents code files for the following modules and chapters module 1 chapter 2 chapter 3 chapter 4 chapter 5 chapter 6 chapter 7 chapter 8 chapter 9 chapter 10 chapter 11 module 2 chapter 1 chapter 2 chapter 3 chapter 4 chapter 5 chapter 6 chapter 7 chapter 8 chapter 9 chapter 10 module 3 chapter 1 chapter 2 chapter 3 chapter 4 chapter 5 chapter 6 what you need for this learning path module 1 this module is suitable for beginner and intermediate web developers with basic knowledge in html css and modern javascript development module 2 for this module you need a basic understanding of javascript html and css you will also need the following tools a text editor or ide for example sublime text cloud9 eclipse notepad vim and so on a command line terminal nodejs https nodejs org mongodb https www mongodb org accounts on social network for example facebook twitter or google and optionally accounts on paypal access to a cloud server either heroku digital ocean or similar module 3 you will require any modern web browser such as chrome s latest version or ie 10 the node js platform installed on your machine and version 3 2 or higher of mongodb optionally you can install any web server such as nginx apache iis or lighttpd to proxy requests to your node js application download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781787121720 https packt link free ebook 9781787121720 a p | front_end |
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ML-University | p align center br img src https github com d0r1h ml university blob master ml logo png width 300 br p p align center a href https hits seeyoufarm com img src https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2fd0r1h 2fml university count bg 2379c83d title bg 23555555 icon icon color 23e7e7e7 title hits edge flat false a a href https twitter com intent tweet text checkout this awesome machine learning university repo on github text url https 3a 2f 2fgithub com 2fd0r1h 2fml university img alt tweet src https img shields io twitter url style social url https 3a 2f 2fgithub com 2fd0r1h 2fml university a p h3 align center p a free machine learning university p h3 br machine learning open source university is an idea of free learning of a ml enthusiast for all other ml enthusiast this list is continuously updated and if you are a ml practitioner and have some good suggestions to improve this or have somegood resources to share you create pull request and contribute table of contents 1 getting started getting started 2 mathematics mathematics 3 machine learning machine learning 4 deep learning deep learning 5 natural language processing natural language processing 6 reinforcement learning reinforcement learning 7 books books 8 ml in production ml in production 9 quantum ml quantum ml 10 datasets datasets 11 other useful websites other useful websites 12 other useful gitrrpo other useful gitrepo 13 blogs and webinar blogs and webinar 14 must read research paper must read research paper 15 company tech blogs company tech blogs getting started title and source link elements of ai part 1 website https course elementsofai com elements of ai part 2 website https buildingai elementsofai com cs50 s introduction to ai harvard cs50 website https cs50 harvard edu ai 2020 intro to computational thinking and data science mit website https ocw mit edu courses electrical engineering and computer science 6 0002 introduction to computational thinking and data science fall 2016 practical data ethics fast ai https ethics fast ai machine learning mastery getting started machinelearningmastery https machinelearningmastery com start here design and analysis of algorithms mit ocw mit edu https ocw mit edu courses electrical engineering and computer science 6 046j design and analysis of algorithms spring 2015 ai principles and techniques stanford youtube https www youtube com playlist list ploromvodv4ro1nb9td4iuz3qghgegtqnx the private ai series openmined https courses openmined org courses mathematics title and source link statistics in machine learning krish naik youtube https www youtube com playlist list plzotaelrmxvmhvyr3ri9iq t5qpbtxzjo computational linear algebra for coders fast ai https github com fastai numerical linear algebra blob master readme md linear algebra mit website https openlearninglibrary mit edu courses course v1 ocw 18 06sc 2t2019 course statistics by zstatistics website https www zstatistics com videos essence of linear algebra by 3blue1brown youtube https www youtube com playlist list plzhqobowtqdpd3mizzm2xvfitgf8he ab seeing theory visual probability brown website https seeing theory brown edu basic probability index html matrix methods in data analysis and machine learning mit website https ocw mit edu courses mathematics 18 065 matrix methods in data analysis signal processing and machine learning spring 2018 math for machine learning youtube https www youtube com playlist app desktop list pld80i8an1oegz2tyimemzwc3xqku0jkug statistics for applications mit youtube https www youtube com playlist list plul4u3cngp60uvbmaonerc6knt mgpks0 machine learning title and source link introduction to machine learning with scikit learn dataschool https courses dataschool io introduction to machine learning with scikit learn introduction to machine learning sebastianraschka https sebastianraschka com blog 2021 ml course html open machine learning course mlcourse ai https mlcourse ai machine learning cs229 stanford website http cs229 stanford edu syllabus spring2020 html youtube https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu introduction to machine learning mit website https tinyurl com ybl6udcr machine learning systems design 2021 cs329s stanford website https stanford cs329s github io syllabus html applied machine learning 2020 cs5787 cornell tech youtube https www youtube com playlist list pl2uml kcic0uly7icqdsigdmovaupqc83 machine learning for healthcare mit website https tinyurl com yxgeesdf machine learning for trading georgia tech website https lucylabs gatech edu ml4t introduction to machine learning for coders fast ai https course18 fast ai ml html machine learning crash course google ai https developers google com machine learning crash course machine learning with python freecodecamp https www freecodecamp org learn machine learning with python deep reinforcement learning cs285 uc berkeley youtube https www youtube com playlist list pl iwqose6tfuriihcrlt wj9byivpbfgc probabilistic machine learning university of t bingen youtube https www youtube com playlist list pl05ump7r6ij1thaofy96m5ux3j21a6ynd machine learning with graphs cs224w stanford youtube https www youtube com playlist list ploromvodv4rplkxipqhjhpgdqy7imnkdn machine learning in production cmu website https ckaestne github io seai machine learning deep learning fundamentals deeplizard https deeplizard com learn video gzmobegl0yg interpretability and explainability in machine learning website https interpretable ml class github io practical machine learning 2021 stanford website https c d2l ai stanford cs329p index html machine learning vu university website https mlvu github io machine learning for cyber security purdue university youtube https www youtube com playlist list pl74sw1ohgx7ghqdhckxzeqmqbvutmrvle audio signal processing for machine learning youtube https www youtube com playlist list pl watfeyamnqiee7ch3q1bh4qjfaaenv0 machine learning causal inference stanford youtube https www youtube com playlist list plxq lxoulvqaowzeqhrqhnezs30li49g machine learning cs156 caltech youtube https www youtube com playlist list pld63a284b7615313a multimodal machine learning mmml cmu website https cmu multicomp lab github io mmml course fall2020 youtube https www youtube com playlist list pl fhd vrvisnup9yqs tdlw7dqz lda0g advanced topics in machine learning caltech website https 1five9 github io deep learning title and source link introduction to deep learning 6 s191 mit youtube https www youtube com playlist list pltbw6njqru rwp5 7c0oivt26zgjg9ni introduction to deep learning sebastianraschka https sebastianraschka com blog 2021 dl course html deep learning nyu website https atcold github io pytorch deep learning 2021 https atcold github io nyu dlsp21 deep learning cs182 uc berkeley youtube https www youtube com playlist list pl iwqose6tfvmkkqhucjpaortijyt8a5a deep learning lecture series deepmind x ucl youtube https www youtube com playlist list plqymg7htrazcdxz44o4p3n5anz3llrvzf deep learning cs230 stanford website https cs230 stanford edu lecture cnn for visual recognition cs231n stanford website 2020 https cs231n github io youtube 2017 https tinyurl com y2gghbvs full stack deep learning website https course fullstackdeeplearning com 2021 https fullstackdeeplearning com spring2021 practical deep learning for coders v3 fast ai https course19 fast ai index html deep learning crash course 2021 d2l ai youtube https www youtube com playlist list plzso 6 bsqhqsdabntcfwmqujw djfnbd deep learning for computer vision michigan website https web eecs umich edu justincj teaching eecs498 fa2020 neural networks from scratch in python by sentdex youtube https www youtube com playlist app desktop list plqvvvaa0qudcjd5baw2dxe6of2tius3v3 keras python deep learning neural network api deeplizard https deeplizard com learn video rznkvrtfkby reproducible deep learning sscardapane it https www sscardapane it teaching reproducibledl pytorch fundamentals microsoft https docs microsoft com en us learn paths pytorch fundamentals geometric deep learing gdl100 geometricdeeplearning https geometricdeeplearning com lectures deep learning neuromatch academy neuromatch https deeplearning neuromatch io tutorials intro html deep learning for molecules and materials website https whitead github io dmol book intro html deep learning course for vision arthurdouillard com https arthurdouillard com deepcourse deep multi task and meta learning cs330 stanford website https cs330 stanford edu youtube https www youtube com playlist list ploromvodv4rmc6zfymnd7ug3lvvwaity5 deep learning interviews book website https github com boltzmannentropy interviews ai deep learning for computer vision 2021 youtube https www youtube com playlist list pl z2 u9mijdngfm7 f2fz9zxjvrp jhjv deep learning 2022 cmu youtube https www youtube com playlist list plp 0k3kfddpxrmjgjm0p1wt6h gtqe8j9 uva deep learning website https uvadlc github io natural language processing title and source link natural language processing aws youtube https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw nlp krish naik youtube https www youtube com playlist list plzotaelrmxvmdj5sqbck2lim0hhqvwnzm nlp with deep learning cs224n 2019 stanford youtube https www youtube com playlist list ploromvodv4rohcuxmzknm7j3fvwbby42z 2021 https www youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj a code first introduction to natural language processing fast ai https www fast ai 2019 07 08 fastai nlp cmu neural nets for nlp 2021 carnegie mellon university youtube https www youtube com playlist list pl8pytp1v4i8akahej7loorlex pcxs xv speech and language processing stanford website https web stanford edu jurafsky slp3 natural language understanding cs224u stanford youtube https www youtube com playlist list ploromvodv4robpmcir6rnnulfan56js20 2022 https web stanford edu class cs224u nlp with dan jurafsky and chris manning 2012 stanford youtube https www youtube com playlist list ploromvodv4rofzndyrlw3 ni7tmltmijz intro to nlp with spacy youtube https www youtube com playlist list plbmcuobd5an559hbdr albnwvsgq 7utf advanced nlp with spacy website https course spacy io en applied language technology website https applied language technology readthedocs io en latest advanced natural language processing umass website https people cs umass edu miyyer cs685 schedule html youtube 2020 https www youtube com playlist list plwnsvgp6czadmqx6qevbar3 vdbiowhjl huggingface course huggingface co https huggingface co course chapter1 fw tf nlp course michigan github https github com deskool nlp class multilingual nlp 2020 cmu youtube https www youtube com playlist list pl8pytp1v4i8chhppu6n1q9 04m96d9gt5 advanced nlp 2021 cmu youtube https www youtube com playlist list pl8pytp1v4i8aysxn gkvgwxvluct9chj6 transformers united stanford website https web stanford edu class cs25 youtube https www youtube com playlist list ploromvodv4rnijrchczutfw5itr z27cm cs324 large language models website https stanford cs324 github io winter2022 reinforcement learning title and source link reinforcement learning cs234 stanford youtube 2019 https www youtube com playlist list ploromvodv4rosopzutgyctapigly2nd8u introduction to reinforcement learning deepmind youtube 2015 https www youtube com playlist list plqymg7htrazdm oyhwgpebj2mfcfzfobq reinforcement learning course deepmind ucl youtube 2018 https www youtube com playlist list plqymg7htrazbkenj je eyjhz7xgboayb advanced deep learning reinforcement learning youtube https www youtube com playlist list plqymg7htrazdnjre23vqcgivpfz k2rzs deepmind x ucl reinforcement learning 2021 youtube https www youtube com playlist list plqymg7htrazdvh599eitlewsuosjbaodm books title and source link scientific python lectures scipylectures https scipy lectures org downloads scipylectures simple pdf mathematics for machine learning mml book https mml book github io book mml book pdf an introduction to statistical learning statlearning https www statlearning com think stats think stats https greenteapress com wp think stats 2e python data science handbook python for ds https jakevdp github io pythondatasciencehandbook natural language processing with python nltk nltk https www nltk org book deep learning by ian goodfellow deeplearningbook https www deeplearningbook org dive into deep learning d2l ai https d2l ai index html approaching almost any machine learning problem aaanlp https github com abhishekkrthakur approachingalmost blob master aaamlp pdf neural networks and deep learning neuralnetworksanddeeplearning http neuralnetworksanddeeplearning com index html automl methods systems challenges first book on automl automl https www automl org book feature engineering and selection bookdown org https bookdown org max fes introduction to machine learning interviews book huyenchip com https huyenchip com ml interviews book hands on machine learning with r website https bradleyboehmke github io homl zero to mastery tensorflow for deep learning book dev mrdbourke com https dev mrdbourke com tensorflow deep learning introduction to probability for data science probability4datascience https probability4datascience com graph representation learning book cs mcgill ca https www cs mcgill ca wlh grl book interpretable machine learning christophm https christophm github io interpretable ml book computer vision algorithms and applications 2nd ed szeliski org https szeliski org book ml in production title and source link introduction to docker docker https carpentries incubator github io docker introduction mlops basics github https github com graviraja mlops basics effective mlops model development wandb https www wandb courses courses effective mlops model development quantum ml title and source link quantum machine learning pennylane ai https pennylane ai qml datasets title and source link yelp open dataset yelp https www yelp com dataset machine translation website https www manythings org anki indicnlp corpora indian languages ai4bharat https indicnlp ai4bharat org explorer amazon product co purchasing network metadata snap stanford edu https snap stanford edu data amazon meta html stanford question answering dataset squad website https rajpurkar github io squad explorer other useful websites 1 papers with code https paperswithcode com sota 2 two minute papers youtube https www youtube com c k c3 a1rolyzsolnai videos 3 the missing semester of your cs education https missing csail mit edu 2020 4 workera measure data ai skills https workera ai 5 machine learning mastery https machinelearningmastery com start here 6 from data to viz guide for your graph https www data to viz com 7 datatalks club https datatalks club 8 machine learning for art https ml4a net fundamentals 10 applyingml https applyingml com 11 deep learning drizzle https deep learning drizzle github io index html opt4ml 12 the machine deep learning compendium https book mlcompendium com 13 connectedpapers research papers https www connectedpapers com 14 papers and latest research deepai https deepai org 15 tracking progress in nlp https nlpprogress com 16 nlp blogs by sebastian ruder https ruder io 17 labmlai for papers https papers labml ai other useful gitrepo 1 applied ml papers and blogs by organizations https github com eugeneyan applied ml 2 list machine learning python libraries https github com ml tooling best of ml python 3 ml from scratch implementations of models algorithms https github com eriklindernoren ml from scratch 4 what the f ck python https github com satwikkansal wtfpython 5 scikit learn user guide step step approach https scikit learn org stable user guide html 6 nlp tutorial code with dl https github com graykode nlp tutorial 7 awesome mlops https github com visenger awesome mlops 8 text classification algorithms a survey https github com kk7nc text classification 9 ml use cases by company https github com khangich machine learning interview blob master appliedml md blogs and webinar 1 recommendation algorithms and system design https www theinsaneapp com 2021 03 system design and recommendation algorithms html m 1 2 machine learning system design https becominghuman ai machine learning system design f2f4018f2f8 gi 942874b21d0e 3 lil blog https lilianweng github io lil log must read research paper nlp text 1 text classification algorithms a survey https arxiv org abs 1904 08067 2 deep learning based text classification a comprehensive review https arxiv org abs 2004 03705 3 compression of deep learning models for text a survey https arxiv org abs 2008 05221 4 a survey on text classification from shallow to deep learning https arxiv org pdf 2008 00364 pdf 4 a survey of transformers https arxiv org abs 2106 04554 5 ammus a survey of transformer based pretrained models in natural language processing https arxiv org abs 2108 05542 6 graph neural networks for natural language processing a survey https arxiv org abs 2106 06090 8 a survey of data augmentation approaches for nlp https arxiv org abs 2105 03075 9 a survey on recent approaches for natural language processing in low resource scenarios https aclanthology org 2021 naacl main 201 pdf 10 evaluation of text generation a survey https arxiv org pdf 2006 14799 pdf 11 a survey of transfer learning in nlp https arxiv org pdf 2007 04239 pdf 12 a systematic survey of prompting methods in nlp https arxiv org pdf 2107 13586 pdf ocr optical character recognition 1 survey of post ocr processing approaches https dl acm org doi pdf 10 1145 3453476 company tech blogs 1 assemblyai https www assemblyai com blog 2 grammarly https www grammarly com blog engineering 3 huggingface https huggingface co blog 4 uber https eng uber com category articles ai 5 netflix https netflixtechblog com 6 spotify research https research atspotify com blog engineering https engineering atspotify com | machine-learning open-source university artificial-intelligence learning awsome awsome-list free deep-learning natural-language-processing mathematics course computer-science data-science reinforcement-learning neural-network | ai |
azure-iot-samples-java | all the samples have been moved to our main repository azure iot sdk java https github com azure azure iot sdk java page type sample languages java products azure azure iot hub description a set of easy to understand continuously tested samples for connecting to azure iot hub via azure azure iot sdk java azure iot samples for java azure iot samples java provides a set of easy to understand continuously tested samples for connecting to azure iot hub via azure azure iot sdk java need support if you run into any issues with these samples please file an issue on our main repository azure iot sdk java https github com azure azure iot sdk java for faster turnaround prerequisites java se 8 or later on your development machine you can download java for multiple platforms from oracle http www oracle com technetwork java javase downloads index html you can verify the current version of java on your development machine using java version maven 3 for building the sample you can download maven for multiple platforms from apache maven https maven apache org download cgi you can verify the current version of maven on your development machine using mvn version resources azure iot sdk java https github com azure azure iot sdk java contains the source code for azure iot java sdk azure iot hub documentation https docs microsoft com azure iot hub | server |
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design-system | jdrf design system build web experiences that feel beautifully at home in the jdrf ecosystem without needing to write any css or javascript using design system to use design system copy download the file found in the dist folder https github com jdrf design system tree master dist css to your website this file contains all current components and provides support for any needed icons typography or styling we recommend including it in the head html tag for use throughout your website application link rel stylesheet href path to css design system css if font files are needed for the website application copy download the font files found in the dist folder https github com jdrf design system tree master dist fonts gotham it s recommended to place the font folder in a similar path to the css files css design system css fonts gotham if icons are a requirement design system already includes all necessary styles but requires the font file from google s cdn include this in the head html tag right before the design system css file link rel stylesheet href https fonts googleapis com icon family material icons link rel stylesheet href path to css design system css design system pieces below are the different available design system elements if possible an element is built upon existing bootstrap v4 components components badges bootstrap doc reference here http v4 alpha getbootstrap com components label template path in gh pages src materials components badges html style path src scss badges scss src scss variables scss example here http jdrf github io design system dist components html badges buttons bootstrap doc reference here http v4 alpha getbootstrap com components buttons template path in gh pages src materials components buttons html style path src scss buttons scss src scss mixins buttons scss src scss variables scss example here http jdrf github io design system dist components html buttons cards bootstrap doc reference here http v4 alpha getbootstrap com components card template path in gh pages src materials components cards html style path src scss cards scss src scss variables scss example here http jdrf github io design system dist components html cards side navigation bootstrap doc reference n a template path in gh pages src materials components navigation html style path src scss nav scss example here http jdrf github io design system dist components html side navigation content colors bootstrap doc reference n a template path in gh pages src materials content colors html src data toolkit yml style path src scss variables scss example here http jdrf github io design system dist content html colors icons bootstrap doc reference n a template path in gh pages src materials content icons html style path n a example here http jdrf github io design system dist content html icons typography bootstrap doc reference here http v4 alpha getbootstrap com content typography template path in gh pages src materials content typography html style path src scss type scss src scss variables scss example here http jdrf github io design system dist content html typography layout elevation bootstrap doc reference n a template path in gh pages src materials layout elevation html style path src scss elevations scss src scss variables scss example here http jdrf github io design system dist layout html elevation grid bootstrap doc reference here http v4 alpha getbootstrap com layout grid template path in gh pages src materials layout grid html style path src scss grid scss src scss mixins grid scss example here http jdrf github io design system dist layout html grid contributing installation this will require that the master branch to be setup in a sub folder at design system be sure that you have switched to the gh pages branch and cloned another copy of the repo master branch into your working directory the structure should be similar to below fork the repo in github git clone https github com git username design system git git remote add upstream https github com jdrf design system git git checkout b gh pages upstream gh pages git push origin gh pages git clone https github com git username design system git now your file structure matches the below you can run the next below command to npm install and build the correct files working directory gh pages branch design system master branch dist src gulpfile js package json npm run pull to find available commands from this recommended setup please visit the gh pages https github com jdrf design system tree gh pages branch pull requests please review the contributing doc found here https github com jdrf design system blob master contributing md tests tests can be run from the master branch there are two kinds of testing currently in the design system visual regression and unit tests visual regression relies on phantomcss and casperjs as tools and the gh pages static site as a visual source as a result the testing actually gets executed from that branch when the environment is setup properly see installation above unit tests rely on tape and node scripting for generating mocks and tests visual regression tests npm run visual tests unit tests npm run unit tests running all tests npm test visual regression tests are run from a parent directory that relies on the above specified directory structure see the gh pages readme https github com jdrf design system tree gh pages for information on testing semantic versioning and releases we re using semantic versioning to increment the release phases of the design system this helps us to track major changes as well as minor changes and patches we can also view a history of the project s releases on github https github com jdrf design system releases due to semantic versioning a semantic version number usually looks something like this v0 0 1 the digit placement helps identify whether the release is a major release a minor release or a patch see the example below for reference major minor patch 1 0 0 major release 1 1 0 minor changes to major release 1 0 0 1 0 1 patch bug fixes to major release 1 0 0 2 0 0 a new major release to create a new release we have to tag our master branch with the new release number this can be done directly on github for further instructions on how to tag a release visit github s documentation on creating releases https help github com articles creating releases for further instructions on how to link to a release visit github s documentation on linking to releases https help github com articles linking to releases current release status we are currently at a pre release version of v0 0 1 https github com jdrf design system releases while the design system is functional it is still a work in progress build tool wise and design wise | os |
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iot-harbor | https github com quickmsg smqtt mqtt git https github com 1ssqq1lxr iot harbor reactor netty mqtt server springboot autoconfig reactor3 qos 0 1 2 ssl spring channel topic mqtt ws tcp mqtt ws 8443 java rsocketserversession serversession transportserver create 192 168 100 237 1884 auth s p true heart 100000 protocol protocoltype mqtt ssl false auth username password true log true messagehandler new memorymessagehandler exception throwable system out println throwable start block serversession closeconnect device 1 subscribe list transportconnection connections serversession getconnections block java rsocketclientsession clientsession transportclient create 127 0 0 1 1884 heart 10000 protocol protocoltype mqtt mqtt tcp ws ws 8443 ws tcp ssl false tls log true onclose clientid comsumer 3 id password 12 username 123 willmessage 123 willtopic lose topic willqos mqttqos at least once qos exception throwable system out println throwable messageacceptor topic msg system out println topic new string msg connect block clientsession sub test subscribe clientsession pub test producer 3 getbytes subscribe qos0 clientsession pub test producer 1 getbytes 1 subscribe qos1 clientsession pub test producer 1 getbytes true 1 subscribe qos1 spring yaml iot mqtt server enable true host 192 168 100 237 port 8081 log false protocol mqtt heart 100000 ssl false authencationsession exceptoracceptor rsocketmessagehandler qq 789331252 image image icon jpg | mqtt mqtt-broker mqtt-server mqtt-protocol mqtt-client spring-boot java8 reactor3 reactor-netty | server |
vis-three | p align center a href https vis three github io target blank rel noopener noreferrer img width 180 src rm logo png alt vis three logo a p align center a web 3d development framework for assembled based on three js p p br p align center img alt npm src https img shields io npm l vis three color blue img alt version src https img shields io badge version 0 6 x g svg br a target black href https space bilibili com 3048588 img alt bilibili src https img shields io badge dynamic json url https 3a 2f 2fapi swo moe 2fstats 2fbilibili 2f3048588 query count color 282c34 label bilibili labelcolor fe7398 logo data 3aimage 2fpng 3bbase64 2civborw0kggoaaaansuheugaaagaaaabgcayaaadimhc4aaad7eleqvr4no2dw9wrmbcfk6eskfajskiesqgehcabczwahepaahl2ecik5ddc 2fpxlbdlfwnlqy0xmj5bmqnq5ciigciigciigciigceibahqaemyfrgcund6wakahsekxalgx bcqd8 2fs9tmgvqedr1lligdgzvdhxso k9tytbqrwrj8ahjntl0flh5qraqk 2fmkxpeaywx2oxpbnbkihvi34b7t2mc4pavw6twr 2frwkrkpizbn8cgecuesj4lwm bwbjahek h8ewjrkhoacdzw8e1jlfkuuh1ngmr3xmhffhr1l 72bss8d7w8u jdanzerqmcv ctui7dnqfqibb4j7vtrq7xkcuaasbtmxcl4t 5avk6 2xhurwdhruar6hijcoeu2uhi8zyae2ytwfedwz9pvvq8yamiq5ddab9lfsmvav8omo2zagrc5wniarriaukr9jyed9py08aa6uuzihgrdkgkd8py0yc1wjebaqypdyoag0qazkqazkqazkqazk4vanqenjsszs3i 2fwcsbxu5jqbukrtdf4rar90v8ksv3 i3ffccspk8i 2fw lgdkdi 2fv2rep2caiwm1asdqlldad dlfxlmeaacselzdasfe5vuqnot38ckueebgassug0flvzbmeanbxfnqass0fgbyin2fiu3 2fbbmheybmdxlffa8izehb ems4wachkykrva9zfsqtl57jxzrg4a5wc 2fa8n4adizazwm2xjw75qh2kotfa0p4kygpw28ojccvgn3ndnyo2eweyrggh0qamyicmcmcmcmcmcmcmcmcmcmcfp3qwhdoq4aauektk8fabrihjnzdybvtcgc7lvmkm63gjvdinpfrqgcq5etxfmmzi90fxzpvfqt7x4reu 2fzaeccuxfvgz2zo bun6ukoaeeasptimsx5e8forycn7cvgb4vq7u 2fh50pq4jnp7qiw8ufnjwck txy wj6plevpghshv5ugwa1iqiwwyfayljin9roxygazaqikpwnmf26busc oix5tdqo5ndt f 2fss 2f9cyzwb no49zny2evkyv0lywggaxuvp6esneycqoic0w20k7cngke7jjunsglactcxf27ylmqc98t5mquh49swd i0hpxsllknt0n wnkrtki9jzl 2fl9i1sormmdeq4tqq7ofmximzgd45w8nul1im7efenzljpgpsw0pfz0cdt4u3230td 2ftvx2r6d2frhhewlkq5pelomsrphcpnazgv1xjtel7jbjiaw3sb2ndvpc 2fossyvjrqz4cj6n7ko3ryql7m l6nvtfdvraeqraeqraeqraeiz5 2fsaxmdfxaoqsaaaaasuvork5cyii 3d cacheseconds 3600 a a target black href https gitee com shiotsukikaedesari vis three img src https gitee com shiotsukikaedesari vis three badge star svg theme dark alt gitee starts a a target black href https github com shiotsukikaedesari vis three img alt github starts src https img shields io github stars shiotsukikaedesari vis three style social a a target black href https github com shiotsukikaedesari vis three img alt github fork src https img shields io github forks shiotsukikaedesari vis three style social a p br vis three three js 3d a href https github com vis three vis three graphs contributors img src https stg contrib rocks image repo vis three vis three a issue a href https space bilibili com 672852044 img src https i0 hdslb com bfs face member noface jpg 60w 60h 1c 1s webp a https vis three github io https vis three github io pnpm https www pnpm cn https www pnpm cn packages docs packages docs pnpm run build website packages examples packages examples pnpm run build website packages website packages website pnpm run build docs docs website packages create vis three packages create vis three npm three js 3d 3d 1 2 3 issue issue issue 4 issue issue 1 issue 2 github https github com vis three vis three issues 2 issue issue 3 issue 4 issue 5 issue pr issue issue img src rm github label png height 550 q issue issue https space bilibili com 3048588 https space bilibili com 3048588 issue id vis three 1 pr issue issue issue qq 586362945 qq 1025827206 gitee github github https github com vis three vis three https github com vis three vis three | front_end |
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MD | md mobile devices applications development lecture notes https docs google com presentation d 1k6mrma2an9hwelktihgky68hmimqlahjhsybpp6lgda edit usp sharing homework 1 master github how to work with github study read and understand developer android com study developer apple com ios and swift study reand and understand http cordova apache org https build phonegap com plans javascript frameworks http webix com http www idangero us framework7 vhvtx ntmko https developers facebook com docs javascript http www couchbase com nosql databases couchbase mobile https cloudant com product cloudant features sync http www iriscouch com https www smileupps com http pouchdb com bonus https golang org https xamarin com https github com google j2objc http j2objc org http www m gwt com homework 2 write todo with webix and framework7 use pouchdb to store data and sync with couchdb add authentication mechanism persona homework 3 transform spa mobile web applications in cordova android hybrid applications use couchbase lite to store data and sync with couchdb homework 4 make hybrid application work offline share with friends your todo lists | front_end |
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polyorb-hi-c | polyorb hi c runtime about polyorb hi c is a high integrity middleware it provides core middleware constructs and must be used in conjunction with code generation from aadl models by ocarina https github com openaadl ocarina it is written in c99 and is compatible with the following apis rt posix linux and os x rtems freertos windows xenomai and xtratum installation the recommended installation process is through the proper configuration and installation of ocarina https github com openaadl ocarina see ocarina documentation for details q a build status ocarina ubuntu default gnat https github com openaadl polyorb hi c workflows ocarina 20 20ubuntu 20 default 20gnat badge svg | aadl c | os |
NLP-guidance | natural language processing guidance this repo contains thoughts and guidance about the use of natural language processing based on the experience of using these techniques in a few projects here at the data science hub in the ministry of justice if you have any thoughts or feedback on any of this especially if you disagree please email me mailto samuel tazzyman justice gov uk contents introduction intro md things we have done with nlp search through a set of documents search md find topics in a set of documents topics md techniques we have used feature selection featureselection md latent semantic analysis lsa lsa md latent dirichlet allocation lda lda md word2vec doc2vec fasttext neural network models nnmodels md glossary glossary md code there is code so you can try out these techniques in practice located in the code folder in the repo https github com moj analytical services nlp guidance more details about it here code code md | nlp | ai |
dji-mobilesdk-vision | dji mobilesdk vision do computer vision with a dji drone and a mobile device this project opens the door of using dji s consumer drones to do near real time computer vision it takes advantage of dji s advanced long distance video transmission link and the mobile sdk and demonstrates how to access the frames of the live video feed do any computer vision and machine learning tricks you like on the mobile device and take actions based on your needs what you need 1 a dji drone that supports mobile sdk phantom inspire mavic and even the tiny spark i tested it on mavic and spark 2 at this moment only ios demo is provided so you ll need a mac to build the code and an iphone or ipad to run the app how to build 1 follow the dji mobile sdk s documentation https developer dji com mobile sdk documentation application development workflow workflow integrate html xcode project integration to do necessary setup basically you will need to install xcode cocoapods https guides cocoapods org using getting started html getting started and register an app key from dji developer website http developer dji com register when registering the app key you need to have a unique bundle identifier like com yourorganization yourappname 2 clone this repo to users username dji mobilesdk vision 3 download opencv2 framework i compiled opencv 3 2 0 with aruco tag support not included in official build and put it here https www dropbox com s y5q16fqt75n2o9x opencv2 framework zip dl 0 unzip and put the opencv2 framework to path dji mobilesdk vision drone cv 4 open a terminal cd into users username dji mobilesdk vision drone cv and run pod install this step may take some time 5 open users username dji mobilesdk vision drone cv drone cv xcworkspace 6 put the app key you applied from dji developer website in step 1 in the djisdkappkey entry in info plist file put the bundle identifier associated with the app key the general settings of the drone cv 7 build the code side load to your ios device connect your phone to the rc of the drone | ai |
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EG-FWD_Automotive_door_control | eg fwd automotive door control automotive door control system design for assignment no 3 for eg fwd embedded system design course report files automotive door control system static design pdf automotive door control system dynamic design pdf which contains cpu load for ecu 1 ecu 2 and can bus load on pages 4 5 also for both ecu 1 2 dynamic design from page 6 to page 16 dynamic design complete system dynamic design pdf which contains ecu 1 ecu 2 complete system dynamic design state machine and sequence diagram static design video recording https drive google com file d 1qwbcn2hncy u52rencnq7tfbuwmiih7q view usp share link dynamic design video recording https drive google com file d 1tmxn082bx8c6v1ibyfov82u1zgtymsv5 view usp share link design files using draw io application static design 1 automotive door monitor static design drawio preview link https viewer diagrams net tags 7b 7d highlight 0000ff edit blank layers 1 nav 1 title 1 automotive door monitor static design drawio uhttps 3a 2f 2fdrive google com 2fuc 3fid 3d1 3vn92qyqfwy8adnvin15p1q7e7mz6xp 26export 3ddownload screen shots located in static design folder dynamic design state machine diagram 2 dynamic deesign state machine diagram drawio preview link https viewer diagrams net tags 7b 7d highlight 0000ff edit blank layers 1 nav 1 title 2 dynamic deesign state machine diagram drawio uhttps 3a 2f 2fdrive google com 2fuc 3fid 3d1qyfu n8gy4vyfxolri bj3srjajaq qk 26export 3ddownload ecu 1 screen shots located in dynamic design ecu 1 state machine diagram folder ecu 2 screen shots located in dynamic design ecu 2 state machine diagram folder dynamic design sequence diagram 3 dynamic deesign sequence diagram drawio preview link https viewer diagrams net tags 7b 7d highlight 0000ff edit blank layers 1 nav 1 title 3 dynamic deesign sequence diagram drawio uhttps 3a 2f 2fdrive google com 2fuc 3fid 3d1cgolj0sdpmcux0 nqdqtfar9edjdyx0a 26export 3ddownload ecu 1 screen shots located in dynamic design ecu 1 sequence diagrm folder ecu 2 screen shots located in dynamic design ecu 2 sequence diagrm folder | os |
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devicescript | devicescript typescript for tiny iot devices devicescript brings a typescript developer experience to low resource microcontroller based devices devicescript is compiled to a custom vm bytecode which can run in very constrained environments read the documentation https microsoft github io devicescript experimental project from microsoft research join the a href https github com microsoft devicescript discussions discussions a to provide feedback https user images githubusercontent com 4175913 228997575 122d6ad0 f37e 4d4e ab79 3d8f680ed99d mp4 contributing contributions are welcome see contributing page https microsoft github io devicescript contributing a href https github com microsoft devicescript graphs contributors img src https contrib rocks image repo microsoft devicescript a trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies | embedded iot jacdac esp32 rp2040 typescript devicescript | server |
thcv | thcv threaded computer vision machine vision opencv https www youtube com watch v sk4by78b7v0 | ai |
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Webdev-Study-Notes | archived repo this version of my exocortex has been archived you can see the up to date version here on my personal site https www maxwellantonucci com exocortex html the exocortex an exocortex is a theoretical augmentation to a person s brain it s basically extra functions and memory you could add to a brain to make people smarter they don t really exist yet so this notebook is my exocortex in the meantime i keep it full of notes for reference and sharing most are related to web development but could honestly be about anything nonfiction fiction general life skills and topics and whatever else i want remember without relying on my unreliable brain | front_end |
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legion | legion a simple blockchain implementation build status https travis ci org aviaviavi legion svg branch master https travis ci org aviaviavi legion an as simple as possible blockchain server inspired by naivechain https github com lhartikk naivechain but written in haskell spinning up several legion nodes creates a peer to peer network that syncronizes the block chain across the network prereqs to compile from source you ll need stack https docs haskellstack org en stable readme alternatively you can get a precompiled pre release binary https github com aviaviavi legion releases note if you download the binary from github you ll need to mark it executable by running chmod x legion exe usage stack exec legion exe http port p2p port optional seedhost seedp2pport examples stack exec legion exe 8001 9001 by default legion will log what it s doing to standard out in another terminal window stack exec legion exe 8002 9002 localhost 9001 alternatively you grab the binaries from the github releases and run that directly rather than via stack exec the 3rd argument tells the node where a seed node can be found to bootstrap the connection to the peer to peer network the current state of the valid blockchain will be fetched from all servers and it will automatically keep itself updated and post its own updated to others now that 2 nodes are now synced and you can view the current chain from either node at http localhost httpport chain eg http localhost 8001 chain add a new block to the blockchain via a post request to block curl h content type application json x post d blockbody this is the data for the next block http localhost 8001 block | blockchain peer-network haskell | blockchain |
FWLR-Tool | fwlr ios framework and library reversing tool | os |
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r3 | h1 align center img src doc logo large bg svg alt r3 real time operating system h1 p align center strong experimental static component oriented rtos for deeply embedded systems strong p p align center img src https img shields io github actions workflow status r3 os r3 ci yml branch f0 9f a6 86 style for the badge img src https img shields io badge license mit 2fapache 2 0 blue style for the badge a href https crates io crates r3 img src https img shields io crates v r3 style for the badge a a href https r3 os github io r3 doc r3 index html img src https r3 os github io r3 doc badge svg a p p align center a href https replit com yvt r3 os hosted port v 1 main rs b try it on repl it b a p r3 os or simply r3 is an experimental static rtos that utilizes rust s compile time function evaluation mechanism for static configuration creation of kernel objects and memory allocation and const traits to decouple kernel interfaces from implementation all kernel objects are defined statically for faster boot times compile time checking predictable execution reduced ram consumption no runtime allocation failures and extra security a kernel and its configurator don t require an external build tool or a specialized procedural macro maintaining transparency and inter crate composability the kernel api is not tied to any specific kernel implementations kernels are provided as separate crates one of which an application chooses and instantiates using the trait system leverages rust s type safety for access control of kernel objects safe code can t access an object that it doesn t own r3 api tasks are the standard way to spawn application threads they are kernel objects encapsulating the associated threads execution states and can be activated by application code or automatically at boot time tasks have dynamic priorities and can block to relinquish the processor for lower priority tasks r3 provides a unified interface to control interrupt lines and register interrupt handlers some kernels e g the arm m profile port of the original kernel support unmanaged interrupt lines which aren t masked when the kernel is handling a system call and thus provide superior real time performance r3 supports common synchronization primitives such as mutexes semaphores and event groups the mutexes can use the priority ceiling protocol to avoid unbounded priority inversion and mutual deadlock tasks can park themselves the kernel timing mechanism drives software timers and a system global clock with microsecond precision the system clock can be rewound or fast forwarded for drift compensation bindings are a statically defined storage with runtime initialization and configuration time borrow checking they can be bound to tasks and other objects to provide safe mutable access procedural kernel configuration facilitates componentization the utility library includes safe container types such as mutex and recursivemutex which are built upon low level synchronization primitives the priority ceiling protocol https en wikipedia org wiki priority ceiling protocol the kernel the r3 original kernel is provided as a separate package r3 kernel traditional uniprocessor tickless real time kernel with preemptive scheduling implements a software based scheduler supporting a customizable number of task priorities up to 2 levels on a 32 bit target though the implementation is heavily optimized for a smaller number of priorities and an unlimited number of tasks provides a scalable kernel timing mechanism with a logarithmic time complexity this implementation is robust against a large interrupt processing delay supports arm m profile all versions shipped so far armv7 a no fpu risc v as well as the simulator port that runs on a host system r3 kernel https crates io crates r3 kernel example rust feature const refs to cell feature const trait impl feature naked functions feature const mut refs feature asm const no std no main instantiate the armv7 m port use r3 port arm m as port type system r3 kernel system systemtraits port use port unsafe struct systemtraits port use rt unsafe systemtraits port use systick tickful unsafe impl porttimer for systemtraits impl port threadingoptions for systemtraits impl port systickoptions for systemtraits stmf401 default clock configuration systick ahb 8 ahb hsi internal 16 mhz rc oscillator const frequency u64 2 000 000 use r3 bind bind kernel statictask prelude struct objects task statictask system instantiate the kernel allocate object ids const cottage objects r3 kernel build systemtraits configure app objects root configuration const fn configure app b mut r3 kernel cfg systemtraits objects system configure systick b runtime initialized static storage let count bind 1u32 finish b objects create a task giving the ownership of count task statictask define start with bind count borrow mut task body priority 2 active true finish b fn task body count mut u32 explore the examples directory for example projects prerequisites you need a nightly rust compiler this project is heavily reliant on unstable features so it might or might not work with a newer compiler version see the file rust toolchain toml to find out which compiler version this project is currently tested with you also need to install rust s cross compilation support for your target architecture if it s not installed you will see a compile error like this error e0463 can t find crate for core note the thumbv7m none eabi target may not be installed in this case you need to run rustup target add thumbv7m none eabi | embedded-systems rtos rust cortex-m cortex-a risc-v kernel memory-safety experimental embedded-rust | os |
project_azhar | project azhar https github com muhammadazhar15 project azhar collection of projects that have been made project 1 soccer robot localization based on sensor fusion from odometry and omnivision project1 robot localization div align center img src project1 robot localization image robot design robot jpg height 160px img src project1 robot localization image robot design robot 20design png height 160px img src project1 robot localization image robot design framework 20robot png height 160px div this research aims to create a new robot localization system for iris its https iris its ac id robots in the soccer field this localization system combines odometry and particle filter through sensor fusion method odometry uses 2 encoder sensors and a gyro sensor to sense robot displacement particle filter use omnivision as visual sensor to detect white line in the soccer field through image processing for more complete explanation about this research you can check this project1 robot localization paper this is my early research particle filter with python https user images githubusercontent com 120243882 213215666 4653b4f7 5cf8 48bd 91ea d05469076f68 mp4 if you want try this program you can check this project1 robot localization prototype particle filter this is particle filter that applied to the soccer robot https user images githubusercontent com 120243882 213215766 411f6838 0309 486a b52c df728a80d029 mp4 this is final localization system with senor fusion https user images githubusercontent com 120243882 213237570 8e828789 f15e 48b3 8e15 77b1aa8b0c28 mp4 the red line is the localization data generated by sensor fusion this video compare sensor fusion data with original robot location in the soccer field project 2 voice recognition system for commands on wheeled soccer robots project2 voice command div align center img src project2 voice command image voice 20activity 20detector jpg height 250px nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp img src project2 voice command image cnn 20system jpg height 250px div this research conducted to give iris its https iris its ac id soccer robots some orders through human voice there are 2 main processes in this voice recognition system record and recognition this research use cnn convolutional neural network to recognize commands from human voice there are 7 commands that can be recognized corner dropball goalkick kalibrasi kickoff stop tendang in this research mfcc is used as feature extraction method to process data from human voice data that used in cnn training process comes from 5 different people and 9 different people for test for more complete explanation about this research you can check this project2 voice command paper those are some visualization data from voice signal data and mfcc feature extraction results img src project2 voice command image data train azhar png width 120 height 120 voice commands demo for soccer robot https user images githubusercontent com 120243882 213928835 b99e419a 455b 4ef3 9824 324b2552bdfb mp4 project 3 heart disease detection system with ann artificial neural network project3 heart disease detector div align center img src project3 heart disease detector image home jpg height 160px nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp img src project3 heart disease detector image train 20and 20test jpg height 160px div research project while teaching at ma darussalam jombang https emispendis kemenag go id dashboard content madrasah action lbg nss 131235170053 introducing ai artificial intelligence to madrasah students especially about neural networks this research was conducted with students to prepare for participate in lkir lipi https kompetisi brin go id tentang lkir 2021 heart disease detection system is designed to detect disease through health status and heart sounds health status data comes from kaggle https www kaggle com datasets johnsmith88 heart disease dataset and heart sound data comes from here http www peterjbentley com heartchallenge the heart disease detection system is applied in the website for more complete explanation about this research you can check this project3 heart disease detector paper heart disease detection system demo https user images githubusercontent com 120243882 213934320 be85940c d860 4ea4 b8dd 51fb7ff6c627 mp4 project 4 line follower robot microcontroller project4 robot line follower div align center img src project4 robot line follower image line 20follower jpeg height 225px nbsp nbsp nbsp nbsp nbsp img src project4 robot line follower image robot 20lf jpeg height 225px div this project is used to introduce students in ma darussalam jombang https emispendis kemenag go id dashboard content madrasah action lbg nss 131235170053 about embedded systems and robotics this robot uses arduino nano as microcontroller and use c c programming language in arduino ide there are 3 main board in this robot sensor core and driver this line followe use 14 line sensors consist a pair of led and photodiode with switching method only need 7 adc to read sensors data in core board there are buttons lcd crystal arduino nano and voltage regulator to supply the required voltage for entire robotic system dc motor driver is used to control motors rotation to control 2 motors need 8 mosfet and 4 bjt transistors pcb design for line sensors core and dc motor driver div align center img src project4 robot line follower image sensor jpg height 200px nbsp img src project4 robot line follower image core jpg height 200px nbsp img src project4 robot line follower image driver jpg height 200px div for this pcb design you can check schematic and board with eagle here project4 robot line follower design pcb for simple mechanical design of this robot you can check here project4 robot line follower design mechanic line follower robot microcontroller demo https user images githubusercontent com 120243882 213953569 85fc9a23 e856 4095 a18f c3753762ad43 mp4 this robot can be programmed to go through certain tracks such as intersections incline black or white line this robot also participate in mrc 2020 https madrasah kemenag go id mrc2020 project 5 transporter robot iot project5 robot transporter iot div align center img src project5 robot transporter iot image robot jpeg height 225px nbsp nbsp nbsp nbsp img src project5 robot transporter iot image robot 20bottom jpeg height 225px div this project is used to teach students before participate in mrc 2021 https madrasah kemenag go id mrc2021 competition this transporter robot uses esp32 as microcontroller and can be programmed with arduino ide components of transporter robot iot table tr td esp32 td td tcs3200 td td gm66 td td oled 128x64 td td gripper servo td tr tr td line sensor td td voltage regulator td td driver motor td td gearbox motor td td lipo battery 12v td tr table there are 3 sensors used by this robot 2 line sensor barcode sensor gm66 and color sensor tcs3200 robot mission is scaning object barcode and then send object to drop location with same barcode this robot also send reports about activities that have been carried out such as take the object drop the object and object barcode the report send through mqtt protocol to this mqtt broker websockets http www hivemq com demos websocket client you can check some simple program used to access each sensor here project5 robot transporter iot program trial program and program for the entire robot system here project5 robot transporter iot program robot transporter robot iot demo https user images githubusercontent com 120243882 213965095 ef7a9108 83d1 48a8 8197 9af46c50bce0 mp4 project 6 smart home iot project6 smart home iot div align center img src project6 smart home iot image smarthome system jpg height 70 width 70 div this is iot simple project to control some devices with human voice through google assistant iot systems consist of table tr th a href http esp32 net esp32 a th th a href https blynk io blynk a th th a href https ifttt com ifttt a th th a href https assistant google com google assistant a th tr table this project uses relay as actuator which controlled by esp32 with blynk interface ifttt as third party works to make connection between blynk and google assistant arduino ide program for this project in here project6 smart home iot program if you need more clear explanation and illustration about this system check here project6 smart home iot system design smart home iot demo https user images githubusercontent com 120243882 213970831 5661dd2b 66af 45b9 8b20 814e55da1675 mp4 project 7 teaching robotics project7 teaching robotics these are some electronics projects that i made while teaching at ma darussalam jombang div align center img src project7 teaching robotics image flip flop sch jpg height 250px nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp img src project7 teaching robotics image flip flop brd jpg height 250px div nbsp div align center img src project7 teaching robotics image radio sch jpg height 250px nbsp nbsp img src project7 teaching robotics image radio brd jpg height 250px div nbsp flip flop line sensor line follower analog radio receiver arduino learning board and simple motor driver the purpose of these projects are to introduce students about electronics and simple robotics these are handbooks that i use to teach check here project7 teaching robotics handbook these are some collections of pcb designs that i have created to teach check here project7 teaching robotics design pcb project 8 stm32 projects collection project6 smart home iot these are some stm32 programming with stm32cubemx that i had made while joining iris its https iris its ac id div align center img src project8 program stm32 image stm32cube jpg height 40 width 40 div stm32 is a family of 32 bit microcontrollers based on the arm cortex m core and developed by st microelectronics stm32cubemx uses hal library to generate project in c programming language this project consists of stm32 basic programming to complex systems example gpio adc timer pwm serial odometry and robot control check my stm32 programming project here project8 program stm32 program | os |
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AWS_Cloud_Compliance_Automation_with_Python | aws cloud compliance automation with python as a cloud engineering team we take care of the aws environment and make sure it is in compliance with the organizational policies we use aws cloud watch in combination with aws lambda to govern the resources according to the policies for example we trigger a lambda function when an amazon elastic block store ebs volume is created we use amazon cloudwatch events that allow us to monitor and respond to ebs volumes that are of type gp2 and convert them to type gp3 | cloud |
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System-Design | request everyone to please contribute by raising pull requests or issues as it would help all of us to practice as many problem as possible i will review all pull requests or issues with 24 hours | ood objectorientedprogramming system-design | os |
zhihu | https img shields io badge liscense by 20nelsonzhao brightgreen svg https img shields io badge programming 20language python orange svg repo https zhuanlan zhihu com zhaoyeyu tensorflow 1 anna lstm rnn lstm tensorflow lstm https zhuanlan zhihu com p 27087310 2 skip gram skip gram word2vec tensorflow skip gram https zhuanlan zhihu com p 27296712 3 generate lyrics rnn 4 basic seq2seq rnn encoder decoder seq2seq encoder decoder seq2seq https zhuanlan zhihu com p 27608348 5 denoise auto encoder mnist https zhuanlan zhihu com p 27902193 https raw githubusercontent com nelsonzhao zhihu master denoise auto encoder dae example png 6 cifar cnn kaggle cifar knn cifar https zhuanlan zhihu com p 28035475 https raw githubusercontent com nelsonzhao zhihu master cifar cnn example pic png 7 mnist gan mnist leaky relu gan mnist https zhuanlan zhihu com p 28057434 https raw githubusercontent com nelsonzhao zhihu master mnist gan gan examples png 8 dcgan mnist dcgan batch normalization gan https zhuanlan zhihu com p 28329335 https raw githubusercontent com nelsonzhao zhihu master dcgan mnist dcgan pic png cifar dcgan https raw githubusercontent com nelsonzhao zhihu master dcgan cifar dcgan pic png 9 batch normalization discussion mnist bn tensorflow batch normalization batch normalization https zhuanlan zhihu com p 34879333 https raw githubusercontent com nelsonzhao zhihu master batch normalization discussion bn example png 10 machine translation seq2seq tensorflow 1 6 seq2seq tensorflow seq2seq https zhuanlan zhihu com p 37148308 https raw githubusercontent com nelsonzhao zhihu master machine translation seq2seq pic png 11 mt attention birnn keras seq2seq attention birnn attention attention bleu 0 9 keras attention birnn https zhuanlan zhihu com p 37290775 https raw githubusercontent com nelsonzhao zhihu master mt attention birnn pic1 png https raw githubusercontent com nelsonzhao zhihu master mt attention birnn pic2 png 12 sentiment analysis tensorflow 1 6 dnn lstm cnn sentiment analysis dnn lstm text cnn https zhuanlan zhihu com p 37978321 https raw githubusercontent com nelsonzhao zhihu master sentiment analysis images cnn png 13 image style transfer tensorflow 1 6 image style transfer tensorflow https zhuanlan zhihu com p 38315161 https raw githubusercontent com nelsonzhao zhihu master image style transfer images figures png https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel starry night gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel scream gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel guernica gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel pattern gif 14 ctr models tensorflow 2 0 deepfm deep cross xdeepfm autoint ctr deepfm deep cross xdeepfm autoint https zhuanlan zhihu com p 109933924 https raw githubusercontent com nelsonzhao zhihu master ctr models pictures ctr models png star fork | deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translation | ai |
sdk-mcumgr | mcumgr this is mcumgr version 0 2 0 mcumgr is a management library for 32 bit mcus the goal of mcumgr is to define a common management infrastructure with pluggable transport and encoding components in addition mcumgr provides definitions and handlers for some core commands image management file system management and os management mcumgr is operating system and hardware independent it relies on hardware porting layers from the operating system it runs on currently mcumgr runs on both the apache mynewt and zephyr operating systems getting started for tips on using mcumgr with your particular os see the appropriate file from the list below mynewt readme mynewt md zephyr readme zephyr md dependencies to use mcumgr s image management support your device must be running version 1 1 0 or later of the mcuboot boot loader https github com runtimeco mcuboot the other mcumgr features do not require mcuboot command line tool the mcumgr command line tool is available at https github com apache mynewt mcumgr cli the command line tool requires go 1 7 or later https golang org dl once go is installed and set up on your system you can install the mcumgr cli tool by issuing the following go get command go get github com apache mynewt mcumgr cli mcumgr the mcumgr tool allows you to manage devices running an mcumgr server architecture the mcumgr stack has the following layout command handlers mgmt transfer encoding s transport s items enclosed in angled brackets represent generic components that can be plugged into mcumgr the items in this stack diagram are defined below command handler processes incoming mcumgr requests and generates corresponding responses a command handler is associated with a single command type defined by a group id command id pair mgmt the core of mcumgr facilitates the passing of requests and responses between the generic command handlers and the concrete transports and transfer encodings transfer encoding defines how mcumgr requests and responses are encoded on the wire transport sends and receives mcumgr packets over a particular medium each transport is configured with a single transfer encoding as an example the sample application smp svr uses the following components command handlers image management img mgmt file system management fs mgmt log management log mgmt os management os mgmt transfer transports protocols smp bluetooth smp shell yielding the following stack diagram img mgmt fs mgmt log mgmt os mgmt mgmt smp smp bluetooth shell command definition an mcumgr request or response consists of the following two components mcumgr header cbor key value map how these two components are encoded and parsed depends on the transfer encoding used the mcumgr header structure is defined in mgmt include mgmt mgmt h as struct mgmt hdr the contents of the cbor key value map are specified per command type supported transfer encodings mcumgr comes with one built in transfer encoding simple management protocol smp smp requests and responses have a very basic structure for details see the comments at the top of smp include smp smp h supported transports the mcumgr project defines two transports smp console transport smp console md smp bluetooth transport smp bluetooth md implementations being hardware and os specific are not included browsing information and documentation for mcumgr is stored within the source for more information in the source here are some pointers cborattr cborattr used for parsing incoming mcumgr requests destructures mcumgr packets and populates corresponding field variables cmd cmd built in command handlers for the core mcumgr commands ext ext third party libraries that mcumgr depends on mgmt mgmt code implementing the mgmt layer of mcumgr samples samples sample applications utilizing mcumgr smp smp the built in transfer encoding simple management protocol joining developers welcome our slack channel https mynewt slack com messages c7y3k0c2j | os |
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spark-ml-streaming | join the chat at https gitter im freeman lab spark ml streaming https badges gitter im join 20chat svg https gitter im freeman lab spark ml streaming utm source badge utm medium badge utm campaign pr badge utm content badge visualize streaming machine learning in spark two dimensional demo https github com freeman lab spark streaming demos blob master animations databricks blog post 4 five clusters gif one dimensional demo https github com freeman lab spark streaming demos blob master animations databricks blog post 6 half life 5p0 gif about this python app generates data analyzes it in spark streaming and visualizes the results with lightning the analyses use streaming machine learning algorithms included with spark as of version 1 2 the demos are designed for local use but the same algorithms can run at scale on a cluster with millions of records how to use to run these demos you need a working installation of spark http spark apache org downloads html a running lightning http lightning viz org server an installation of python with standard scientific computing libraries numpy scipy scikitlearn with those three things in place install using pip install spark ml streaming then set spark home to your spark installation and start an executable streaming kmeans l lighting host where lightning host is the address of your lightning server after it starts your browser will open and you should see data appear shortly try running with different settings for example to run a 1 d version with 4 clusters and a half life of 10 points streaming kmeans p temporary path l lighting host nc 4 nd 1 hl 10 tu points where temporary path is where data will be written read if not specified the current tmp directory will be used see python tempfile gettempdir https docs python org 2 library tempfile html 2d data will make a scatter plot and 1d data will make a line plot you can set this with nd to see all options type streaming kmeans h build the demo relies on a scala package included pre built inside python mlstreaming lib to rebuild it use sbt cd scala sbt package | ai |
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worker-tgi | div align center h1 tgi endpoint serverless worker h1 ci test worker https github com runpod workers worker template actions workflows ci test worker yml badge svg https github com runpod workers worker template actions workflows ci test worker yml nbsp docker image https github com runpod workers worker template actions workflows cd docker dev yml badge svg https github com runpod workers worker template actions workflows cd docker dev yml this serverless worker utilizes vllm very large language model behind the scenes and is integrated into runpod s serverless environment it supports dynamic auto scaling using the built in runpod autoscaling feature div docker arguments 1 hugging face hub token your private hugging face token this token is essential for downloading models that require agreement to an end user license agreement eula such as the llama2 family of models 2 hf model id the hugging face model to use please ensure that the selected model is compatible with vllm refer to the list of supported models for compatibility 3 download model indicates whether to include the model in the docker image if set to false the image will need to pre download the model to network storage using the command text generation server download weights hf model id make sure to set the appropriate environment variables below when downloading the model to network storage and ensure that the network storage volume is named runpod volume environment variables env hf datasets cache runpod volume huggingface cache datasets env huggingface hub cache runpod volume huggingface cache hub env transformers cache runpod volume huggingface cache hub 4 hf model quantize specifies the type of quantization algorithm to use when loading the model e g gptq 5 sm num gpus the number of gpus to allocate for sharding the model 6 hf model revision the revision branch for the hugging face model llama2 7b chat 4bit docker build build arg hf model id thebloke llama 2 7b chat gptq build arg hf model revision main build arg sm num gpus 1 build arg hf model quantize gptq build arg hf model trust remote code true build arg hugging face hub token your hugging face token here build arg download model 1 llama2 13b chat 4bit docker build build arg hf model id thebloke llama 2 13b chat gptq build arg hf model revision main build arg sm num gpus 1 build arg hf model quantize gptq build arg hf model trust remote code true build arg hugging face hub token your hugging face token here build arg download model 1 llama2 70b chat 4bit docker build build arg hf model id thebloke llama 2 70b chat gptq build arg hf model revision main build arg sm num gpus 1 build arg hf model quantize gptq build arg hf model trust remote code true build arg hugging face hub token your hugging face token here build arg download model 1 please make sure to replace your hugging face token here with your actual hugging face token to enable model downloads that require it ensure that you have docker installed and properly set up before running the docker build commands once built you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand for further inquiries or assistance feel free to contact our support team model inputs markdown argument type default description do sample bool false use sampling for text generation max new tokens int 20 max number of new tokens to generate for each prompt repetition penalty optional float none penalty for repeating tokens in the generated text return full text bool false return full generated text or just the top n sequences seed optional int none seed for controlling randomness in text generation stop sequences optional list str none list of strings that stop text generation when encountered temperature optional float 1 0 control randomness of sampling lower values make it more deterministic higher values more random top k optional int 1 number of top tokens to consider set to 1 to consider all tokens top p optional float 1 0 cumulative probability of top tokens to consider 0 p 1 set to 1 to consider all tokens truncate optional int none max length of generated text number of tokens watermark bool false add a watermark to the generated text test inputs the following inputs can be used for testing the model json input prompt who is the president of the united states sampling params max new tokens 100 | ai |
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CVzero | cvzero making computer vision simple for everyone not much going on with this at the moment please note it still early days and we don t have running code or example code watch this space cvzero is a python 3 library that aims to simplify computer vision for the raspberry pi robotics as well as the library a companion app for the raspberry pi is being developed for selecting an object and displaying its colour signature it will be possible to save the colour signatures collected as a file for the library to use please be patient with us as we are all volunteers and have a life outside of opencv the cvzero library requires that opencv and its dependencies are installed we have a discord channel for chat and collaboration https discord gg vbdyjdn join with this url the library may be able to be used with other platforms but has not been tested or guaranteed to work recommended hardware raspberry pi raspberry pi 3 a b and b raspberry pi 4 b camera offical raspberry pi camera v1 or v2 linux support webcams design document https docs google com document d 1 cun453kmf dkiiaxqaxqbjwy bybyh1wrkkukcpaqq edit usp sharing how to help join us on our discord channel download and test the libray and example code if you wish to submit code please make a pull request building this code builds using setuptools we strongly suggest you use a virtual environment and then use python setup py develop this will symlink into your python installation such that any changes you make to the code here will be immediately reflected in the libraries imported by scripts without having to install again | ai |
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wdw | web dev weeks br wdw logo assets img logo 1x png br this is the site source for the web dev weeks and web dev weekend events we re using jekyll on github pages installation make sure you have a recent version of ruby installed and run console gem install bundler bundle install if that failed and you re on os x you might have to run this gem install nokogiri with xml2 include applications xcode app contents developer platforms macosx platform developer sdks macosx10 11 sdk usr include libxml2 use system libraries and then re run bundle install once you ve done that you can preview the site locally by running console jekyll serve if the above doesn t work try running console bundle exec jekyll serve this will compile the static files including all sass assets then in your browser navigate to http localhost 4000 to view the generated site i want to this is a typical jekyll project the jekyll documentation jekyll online is really well put together you should give it a skim in the mean time here s a list of points where you might want to look change the style of the site since this is a jekyll site the repetitive content is pulled out into templates these files are located in includes and layouts altering these files will alter all files based off of them you can tell if a file is based off another file if it includes the line layout something or include something somewhere inside it to change the stylesheets you should look in assets css here you ll see that we re using sass which is a css preprocessor this just means we re using a language that compiles to css instead of writing css directly look it up online for more information the main scss file import s the files which are located in the folder sass to change the styles you ll want to change the files located in there add content to the site any file or folder that doesn t start with an underscore will end up being a web page visible to users to add content simply create a file that doesn t have a leading underscore jekyll allows you to write content using markdown a language that compiles to html you should probably use markdown not html to create content for an example of how this is done see the frontend folder which contains numerous examples of using markdown files to generate content feel free to use these files as starting points for generating your own content license mit license see license c 2015 scottylabs jekyll http jekyllrb com | front_end |
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awesome-ukrainian-nlp | awesome ukrainian nlp curated list of ukrainian natural language processing nlp resources corpora pretrained models libriaries etc 1 datasets corpora monolingual brown uk https github com brown uk corpus carefully curated corpus of modern ukrainian language with dismabiguated tokens 1 million words ubertext 2 0 https lang org ua en ubertext over 5 gb of news wikipedia social fiction and legal texts wikipedia https dumps wikimedia org ukwiki latest oscar https oscar corpus com shuffled sentences extracted from common crawl https commoncrawl org and classified with a language detection model ukrainian portion of it is 28gb deduplicated cc 100 http data statmt org cc 100 documents extracted from common crawl https commoncrawl org automatically classified and filtered ukrainian part is 200m sentences or 10gb of deduplicated text mc4 https github com allenai allennlp discussions 5056 filtered commoncrawl again 196gb of ukrainian text ukrainian twitter corpus https github com saganoren ukr twi corpus ukrainian twitter corpus for toxic text detection ukrainian forums https github com khrystyna skopyk ukr spell check blob master data scraped txt 250k sentences scraped from forums ukrainain news headlines https huggingface co datasets yehor ukrainian news headlines 5 2m news headlines parallel opus https opus nlpl eu tatoeba mt challenge data sets https github com helsinki nlp tatoeba challenge polish ukrainian parallel corpus https clarin pl eu dspace handle 11321 535 back translated monolingual wiki data https github com helsinki nlp tatoeba challenge blob master data backtranslations md wiki edits https huggingface co datasets osyvokon wiki edits uk 5m sentence edits extracted from the ukrainian wikipedia revision history see helsinki nlp ukrainianlt https github com helsinki nlp ukrainianlt for more data and machine translation resources links labeled ua gec https github com grammarly ua gec grammatical error correction gec and fluency corpus ner uk https github com lang uk ner uk brown uk labeled for named entities yakaboo book reviews https 1drv ms f s agoifosrix8lcynbl26rru8wggo e geqlkp book reviews ratings and descriptions universal dependencies https github com universaldependencies ud ukrainian iu tree master dependency trees corpus ua news https github com fido ai ua datasets tree main ua datasets src text classification 150k news article in 5 categories ua squad https github com fido ai ua datasets tree main ua datasets src question answering ukrainian version of stanford question answering dataset ukrainian winograd schema challenge wsc dataset https github com pkuchmiichuk ua coref ukrainian wsc dataset manually translated ukrainian ontonotes dataset https github com pkuchmiichuk ua coref ukrainian ontonotes dataset scripts to build large silver dataset for coreference resolution dictionaries https github com brown uk dict uk pos tag dictionary can generate a list of all word forms valid for spelling tonal dictionary https github com lang uk tone dict uk multilingualsentiment includes ukrainian https sites google com site datascienceslab projects multilingualsentiment a list of positive negative words obscene ukr https github com saganoren obscene ukr profanity dictionary word stress dictionary https github com lang uk ukrainian word stress dictionary word stress for 2 7m word forms see ukrainian word stress https github com lang uk ukrainian word stress heteronyms https github com lang uk ukrainian heteronyms dictionary words that share the same spelling but have different meaning pronunciation abbreviations https github com lang uk ukrainian abbreviations dictionary map abbreviation to expansion 2 tools tree stem https github com amakukha stemmers ukrainian stemmer pymorphy2 https github com kmike pymorphy2 pymorphy2 dicts uk https pypi org project pymorphy2 dicts uk pos tagger and lemmatizer languagetool https languagetool org uk grammar style and spell checker stanza https stanfordnlp github io stanza python package for tokenization multi word tokenization lemmatization pos dependency parsing ner nlp uk https github com brown uk nlp uk tools for cleaning and normalizing texts tokenization lemmatization pos disambiguation 3 pretrained models language models masked xlm roberta base uk https huggingface co ukr models xlm roberta base uk truncated version of xlm roberta with only ukrainian and english embeddings left youscan ukr roberta base https huggingface co youscan ukr roberta base autoregressive pythia uk https huggingface co theodotus pythia uk mt5 finetuned on wiki and oasst1 for chats in ukrainian ualpaca https github com robinhad kruk llama fine tuned for instruction following on the machine translated alpaca dataset xglm https github com pytorch fairseq blob main examples xglm readme md multilingual autoregressive lm the 4 5b checkpoint includes ukrainian tereveni ai gpt 2 https huggingface co tereveni ai gpt2 124m uk fiction mixed electra https huggingface co lang uk machine translation helsinki nlp opus mt models https github com helsinki nlp ukrainianlt ukrainian to from 25 langaguages opus mt models at huggingface https huggingface co models language uk pipeline tag translation sort modified opus mt models evaluated on flores101 https github com helsinki nlp ukrainianlt blob main opus mt ukr flores devtest md m2m 100 https github com pytorch fairseq tree master examples m2m 100 ukrainian to from 100 languages see helsinki nlp ukrainianlt https github com helsinki nlp ukrainianlt for more sequence to sequence models mbart50 https github com pytorch fairseq tree master examples multilingual mbart50 models mt5 https github com google research multilingual t5 named entity recognition ner mitie ner model https lang org ua en models anchor1 ukr models uk ner https huggingface co ukr models uk ner lang uk flair uk ner https huggingface co lang uk flair uk ner dchaplinsky uk ner web trf large https huggingface co dchaplinsky uk ner web trf large part of speech tagging pos lang uk flair uk pos https huggingface co lang uk flair uk pos word embeddings fasttext official fasttext trained on commoncrawl and wiki https fasttext cc docs en crawl vectors html 157 languages including ukrainian older official fasttext trained on wiki https github com facebookresearch fasttext blob master docs pretrained vectors md 294 languages including ukrainian fasttext multilingual https github com babylonhealth fasttext multilingual 78 languages aligned to the same vector space fasttext uk 2023 https huggingface co dchaplinsky fasttext uk and cbow https huggingface co dchaplinsky fasttext uk cbow trained on ubertext 2 0 word2vec https lang org ua en models anchor4 glove https lang org ua en models anchor4 lexvec https lang org ua en models anchor4 bpemb subword embeddings includes ukrainian https nlp h its org bpemb easy to use with flair https github com flairnlp flair blob master resources docs embeddings byte pair embeddings md flair https github com flairnlp flair blob master resources docs embeddings flair embeddings md ukrainian https huggingface co lang uk flair uk forward added in 2022 other uk punctcase https huggingface co ukr models uk punctcase punctuation and case restoration model based on xlm roberta uk punctuation uk bert https huggingface co dchaplinsky punctuation uk bert another punctation and case restoration model based on bert base multilingual cased ukrainian word stress https github com lang uk ukrainian word stress adds word stress 4 paid lorelei ukrainian representative language pack https catalog ldc upenn edu ldc2020t24 ukrainian monolingual text ukrainian english parallel text partially annotated for named entities 5 other resources and links helsinki nlp ukrainianlt https github com helsinki nlp ukrainianlt another collection of links to ukrainian language tools egorsmkv speech recognition uk https github com egorsmkv speech recognition uk speech recognition and text to speech models and datasets unlp 2023 shared task shared task competition in grammatical error correction for ukrainian training data and evaluation scripts https github com asivokon unlp 2023 shared task public leaderboard https codalab lisn upsaclay fr competitions 10740 6 workshops and conferences ukrainian natural language processing workshop https unlp org ua | nlp ukrainian ukrainian-nlp datasets awesome-list natural-language-processing | ai |
osal_free | osal free osal free operating system abstraction for embedded systems supports pthreads freertos chibios coocox bertos and more rtos | os |
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CHRONI | chroni chroni is a mobile application that presents archived data downloaded from the geochron database in a customizable format for use by geologists in the field the project is led and maintained by cirdles https cirdles org an undergraduate research lab at the college of charleston in charleston south carolina this material is based upon work supported by the national science foundation under grant numbers 0930223 http www nsf gov awardsearch showaward awd id 0930223 and 1443037 http www nsf gov awardsearch showaward awd id 1443037 any opinions findings and conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the national science foundation for more information about this project please contact jim bowring mailto bowringj cofc edu br downloading the latest release for android and ios for ios you can download chroni from the app store https appsto re us 0sjikb i for android you can download chroni from the play store https play google com store apps details id org cirdles chroni br getting started for contributors and users please have installed git tools xcode mac and a text editor make sure there is an updated node js lts version installed http nodejs org en fork a copy of this project as your origin clone your repository onto your local and use the following commands to establish the upstream bash git remote add upstream https github com cirdles chroni git installing ionic some commands might need sudo bash npm install g cordova ionic if this result in errors use this instead bash npm uninstall g cordova npm uninstall g ionic npm install g cordova ionic ionic should now be installed with version 2 or higher use ionic v to confirm continue with bash npm install ionic cordova resources to emulate on an ios platform change directories to the chroni directory on the local and run bash ionic cordova platform add ios ionic cordova emulate ios lc to emulate on android platform have android studio installed and create an avd within android studio then add the android platform and check if its been installed bash ionic cordova platform add android ionic cordova platform change directories to the chroni directory on the local and run bash ionic cordova build android ionic cordova run android br more info on this can be found on the ionic getting started http ionicframework com getting started page and the ionic cli https github com driftyco ionic cli repo | front_end |
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yodaos | p align center img alt yodaos src logo png width 400 p p align center img src https img shields io badge base linux green svg img src https img shields io badge build openwrt blue svg p yodaos is yet another linux distribution for voice enabled iot and embrace web standards thus it uses javascript as the main application scripting language get started to start with yodaos a linux or macos is required to build the image for ubuntu sh apt get install build essential subversion libncurses5 dev zlib1g dev gawk gcc multilib flex git core gettext libssl dev unzip texinfo device tree compiler dosfstools libusb 1 0 0 dev for macos you need to install some gnu tools which the openwrt is required sh brew install gnu tar gnu getopt findutils m4 linux source tree requires a case sensitive file system make sure your workspace is apfs case sensitive before you start the openwrt check gnu getopt version openwrt include prereq build mk via the gnugetopt command name so the following link is also required sh ln sf brew list gnu getopt grep bin usr local bin gnugetopt download source shell git clone git github com yodaos project yodaos git build sh configure p rpi c rpi3b plus make c openwrt build under the openwrt directory the configure command is to select which board that you were to build board product raspberry 3b plus raspberry kamino18 kamino18 go compile run https yodaos project github io yoda book en us yodaos source system compile run html for more details community youtube https www youtube com channel ucrvbwiabcsfvctc 4ekw4lw contributing yodaos is a community driven project that we accepts any improved proposals pull requests and issues for javascript development go yodart for details for proposal yodaos project evolution is the place where someone can submit pull request to propose something documentation yodaos book https github com yodaos project yoda book license apache 2 0 yodaos https github com yodaos project yoda js https github com yodaos project yoda js flora https github com yodaos project flora yodaos project evolution https github com yodaos project evolution semver 2 0 https semver org shadownode https github com yodaos project shadownode node js https github com nodejs node raspberry products rpi kamino18 products k18 | web javascript nodejs yodaos linux openwrt | server |
Handwriting_Recogition_using_Adversarial_Learning | handwriting recognition in low resource scripts using adversarial learning cvpr 2019 ayan kumar bhunia abhirup das ankan kumar bhunia perla sai raj kishore partha pratim roy handwriting recognition in low resource scripts using adversarial learning ieee conf on computer vision and pattern recognition cvpr 2019 abstract handwritten word recognition and spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes the design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples word retrieval is therefore very difficult in low resource scripts much of the existing literature comprises preprocessing strategies which are seldom sufficient to cover all possible variations we propose the adversarial feature deformation module afdm that learns ways to elastically warp extracted features in a scalable manner the afdm is inserted between intermediate layers and trained alternatively with the original framework boosting its capability to better learn highly informative features rather than trivial ones we test our meta framework which is built on top of popular word spotting and word recognition frameworks and enhanced by the afdm not only on extensive latin word datasets but also sparser indic scripts we record results for varying training data sizes and observe that our enhanced network generalizes much better in the low data regime the overall word error rates and map scores are observed to improve as well architecture architecture model jpg citation if you find this article useful in your research please consider citing inproceedings bhunia2019handwriting title handwriting recognition in low resource scripts using adversarial learning author bhunia ayan kumar and das abhirup and bhunia ankan kumar and kishore perla sai raj and roy partha pratim booktitle proceedings of the ieee conference on computer vision and pattern recognition pages 4767 4776 year 2019 | tensorflow crnn-ocr crnn-ctc handwriting-recognition low-resource-script data-augmentation adversarial-learning thin-plate-spline featuremap-deformation generative-adversarial-networks word-spotting | ai |
EnterpriseWebBook | enterprise web development from desktop to mobile this ia a home of the new book work in progress enterprise web development from desktop to mobile there are four co authors of this book yakov fain victor rasputnis viktor gamov and anatole tartakovsky the book is released under a creative commons attribution noncommercial sharealike 3 0 unported license http creativecommons org licenses by nc sa 3 0 meaning you can both get a copy of the book for free or help to further improve it this book will be printed and available for purchase via o reilly media http oreilly com readers will have an option of purchasing this book in a number of digital formats table of contents introduction enterprisewebbook blob master 0 introduction introduction mdown part 1 desktop ch1 html5 and its new apis enterprisewebbook blob master 1 desktop 01 html ch1 html mdown ch2 advanced intro to javascript enterprisewebbook blob master 1 desktop 02 javascript ch2 advancedjs mdown ch3 mocking up the save a child web site enterprisewebbook blob master 1 desktop 03 mockup ch3 mokup mdown ch4 using ajax and json ch4 ch5 test driven development with javascript enterprisewebbook blob master 1 desktop 05 testing javascript chapter 5 test driven development with javascript mdown ch6 save a child with jquery framework ch6 ch7 save a child with ext js framework ch7 ch8 replacing http with websockets enterprisewebbook blob master 1 desktop 08 websockets chapter 8 replacing http with websockets mdown ch9 securing web applications ch9 ch10 large scale javascript projects ch10 part 2 mobile ch11 responsive design one site fits all enterprisewebbook blob master 2 mobile 11 responsive mdown ch12 save a child with jquery mobile ch12 ch13 save a child with sencha touch 13 ch14 hybrid applications html native api 14 a name ch4 using ajax and json a after explaining the json data format we ll deploy save a child under the remote tomcat server on one of our servers we ll also provide the instructions on installing tomcat on the local machine for those readers who want to do it then goes the explanation of the ajax way of retrieving data from the remote server without the need to refresh the entire page in the final version of the web site the data feed will be organized by a java program deployed under a java ee server we ll use oracle s glassfish 4 0 for being the leader in implementing all the latest java ee specifications a name ch6 save a child with jquery framework a in this chapter we ll start introducing jquery framework to save a child code developed in chapters 2 5 while this chapter won t have a formal tutorial on the jquery framework we ll briefly explain its basics and each component that ll be used in save a child by the end of this chapter the reader has a working version of save a child site built using html javascript css json ajax and jquery framework a name ch7 save a child with ext js framework a in this chapter we ll use some of the code from ch 2 5 but this time we ll use the sencha s ext js framework the reader will learn the principles of building web sites with ext js we ll demo the use of our own open source generator clear data builder that can generate the ext js code based on java classes by the end of this chapter the reader will have working version of the save a child web site we ll also compare the pros and cons of its ext js and jquery s versions a name ch9 securing web applications a this chapter will add authentication and authorization features for the users save a child they will be able to login to this web site and perform different actions according to their role a name ch10 a the save a chile site is a rather small web project but in the enterprise world lots of applications have a lot larger code base in this chapter we ll give an example of how to build modularized web applications that can load the code on as needed basis we ll also give an example of how to organize the data exchange between different modules in a loosely coupled fashion a name ch12 save a child with jquery mobile a this chapter will demonstrate how to build the mobile version of save a child using jquery mobile framework a name ch13 save a child with sencha touch a this chapter will demonstrate how to build the mobile version of save a child using the sencha touch framework a name ch14 hybrid applications html native api a this chapter explains how to add the bridge html and native mobile api with the phone gap framework it ll add the gps service to the mobile version of save a child | front_end |
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sensors | sensor data interaction and simulation this project demonstrates an interactive simulation platform for embedded software systems allowing the retrieval and manipulation of sensor data from a separate application the project comprises two main components a sensors application and a memory read write edit application e g our control panel application sensors application the sensors application emulates the behavior of various sensors including temperature and humidity sensors it provides a foundation for modeling sensor data collection and processing the application is built in c and offers an extensible class hierarchy to represent different types of sensors features include abstract sensor class a base class with common sensor attributes and methods temperaturesensor and humiditysensor derived classes for specific sensor types each with its own data collection method memory address utilities functions to retrieve memory addresses of member functions and the application s base address embedded software control panel the embedded software control panel showcases how to interact with a running device in this case an exe file that distributes sensors data it uses memory read write edit operations with the help of windows api to read sensor data directly from memory key aspects include process identification obtaining the process id of the sensors application by name memoryeditor class a template class handling memory read write and manipulation operations temperature and humidity values calculating memory offsets and reading temperature and humidity values from the sensors application console output displaying the retrieved sensor values in the console usage run the sensors application e g sensors exe execute the embedded software control panel to observe the retrieval and display of sensor data purpose this project serves as an educational resource for understanding interaction between embedded software applications as well as simulating sensor behavior within a controlled environment | os |
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real-world-machine-learning | real world machine learning this repository includes a set of ipython notebooks with functions and example code from the real world machine learning book by henrik brink joseph richards and mark fetherolf published by manning books https www manning com books real world machine learning the notebooks can be viewed directly in the github repository https github com brinkar real world machine learning to run the code yourself please clone this repo install ipython on python 2 7 and run ipython notebook in the repo directory we highly recommend installing the anaconda python distribution which includes ipython scikit learn pandas and mostly anything else used throughout the book https www continuum io downloads | ai |
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amundsenfrontendlibrary | deprecated please visit https github com amundsen io amundsen tree main frontend the amundsen project moved to a monorepo https github com amundsen io rfcs pull 31 this repository will be kept up temporarily to allow users to transition gracefully amundsen frontend service pypi version https badge fury io py amundsen frontend svg https badge fury io py amundsen frontend coverage status https img shields io codecov c github lyft amundsenfrontendlibrary master svg https codecov io github lyft amundsenfrontendlibrary branch master license https img shields io license apache 202 blue svg license pypi python version https img shields io pypi pyversions amundsen frontend svg https pypi org project amundsen frontend prs welcome https img shields io badge prs welcome brightgreen svg https img shields io badge prs welcome brightgreen svg slack status https img shields io badge slack join chat white svg logo slack style social https join slack com t amundsenworkspace shared invite enqtntk2odq1ndu1ndi0ltc3mzqyzmm0zgfjnzg5mzy1mzjlztg4yjq4ytu0zmmxywu2mmvlmzhhy2mzmtc1mdg0mzrjnta4mzrkmge0nzk amundsen is a metadata driven application for improving the productivity of data analysts data scientists and engineers when interacting with data it does that today by indexing data resources tables dashboards streams etc and powering a page rank style search based on usage patterns e g highly queried tables show up earlier than less queried tables think of it as google search for data the project is named after norwegian explorer roald amundsen https en wikipedia org wiki roald amundsen the first person to discover south pole the frontend service leverages a separate search service https github com lyft amundsensearchlibrary for allowing users to search for data resources and a separate metadata service https github com lyft amundsenmetadatalibrary for viewing and editing metadata for a given resource it is a flask application with a react frontend for information about amundsen and our other services visit the main repository https github com lyft amundsen amundsen readme md please also see our instructions for a quick start https github com lyft amundsen blob master docs installation md bootstrap a default version of amundsen using docker setup of amundsen with dummy data and an overview of the architecture https github com lyft amundsen blob master docs architecture md architecture requirements python 3 6 node v10 or v12 npm 6 x x homepage https www amundsen io documentation https www amundsen io amundsen user interface please note that the mock images only served as demonstration purpose landing page the landing page for amundsen including 1 search bars 2 popular used tables docs img landing page png search preview see inline search results as you type docs img search preview png table detail page visualization of a hive redshift table docs img table detail page png column detail visualization of columns of a hive redshift table which includes an optional stats display docs img column details png data preview page visualization of table data preview which could integrate with apache superset https github com apache incubator superset docs img data preview png installation please visit installation guideline docs installation md on how to install amundsen configuration please visit configuration doc docs configuration md on how to configure amundsen various enviroment settings local vs production developer guidelines please visit developer guidelines docs developer guide md if you want to build amundsen in your local environment license apache 2 0 license license | amundsen | front_end |
Flatiron-Procedural-Ruby | flatiron procedural ruby flatiron full stack web development curriculum amp labs procedural ruby minswan matz and the ruby language the goal of ruby is to make programmers happy i started out to make a programming language that would make me happy and as a side effect it s made many many programmers happy i hope to see ruby help every programmer in the world to be productive and to enjoy programming and to be happy that is the primary purpose of ruby language yukihiro matsumoto ruby is a dynamic object oriented general purpose programming language it was designed and developed in the mid 1990s by yukihiro matz matsumoto in japan matsumoto often referred to as matz first developed the idea for ruby in the early 1990s from matz s post to the ruby talk mailing list in 1999 i was talking with my colleague about the possibility of an object oriented scripting language i knew perl perl4 not perl5 but i didn t like it really because it had the smell of a toy language it still has the object oriented language seemed very promising i knew python then but i didn t like it because i didn t think it was a true object oriented language oo features appeared to be add on to the language as a language maniac and oo fan for 15 years i really wanted a genuine object oriented easy to use scripting language i looked for but couldn t find one so i decided to make it why do we love it matz designed ruby in order to make programmers happy he developed a programming language that would be simple to use elegant to write but capable of building vastly complex things a few of his own thoughts on ruby sum it up best make ruby natural not simple in a way that mirrors life and ruby is simple in appearance but is very complex inside just like our human body the ruby community the ruby community is characterized by respect for one another and a love of programming rubyists have a saying matz is nice so we are nice or minswan matz made a nice language to please programmers matz is nice to programmers so we are nice to each other the learn community takes this principle to heart we respect one another and the hard work that we are doing to learn how to code we support one another to overcome obstacles to our learning when another learner asks a question we answer if we can respectfully and with a positive attitude and we don t judge or criticize each other or the work that we re doing learning to code which you re doing here and will continue to do over the entire course of your programming life is hard we don t want to make it even harder on each other by failing to adhere to the minswan principle remember we are all on a journey to learn to love to code together | variables interpolation tdd rspec loops command-line-app regex hashes pry conditional-statements enumeration yields | front_end |
paozhu | english readme cn md div align center img src https www paozhu org images logo svg div paozhu is a comprehensive and fast c web framework that integrates c orm the development speed is as fast as script languages the framework integrates a webserver which can natively parse http 1 1 http 2 json websocket protocols and get and post requests the framework also distinguishes between different methods of post requests the framework comes with a built in obj micro object that can store data such as char int string float etc the framework automatically parses url and post parameters into the micro object and uses url path mapping to function mounting points it also uses coroutines thread pools and database connection pools qq group 668296235 discussing the joy of c web development with community developers 1 features 1 support json decode encode not use thirdparty support json standard 2 support multiple domain name websites 3 support multiple domain name ssl server 4 support http 1 1 and http 2 protocols 5 support websocket server 6 the framework comes with websocket push which supports regular push to websocket client 7 support httpclient get or post 8 the framework comes with an orm it uses the link pool method and currently supports mysql 9 framework has two thread pool framework thread pool user program runing thread pool 10 the framework uses asio coroutines 11 the framework features that i o uses the coroutine pool to run and thread pool to run 12 the framework supports compress gzip br out files 13 url post upload files use client get client post client files get 14 integrated sendmail 15 generating qr code requires gd and qrencode libraries 16 plugin module hot module replacement 17 the framework supports cache module orm supports result save to cache 18 the controller directory cpp files annotation auto pick to url mapping 19 struct or class annotation to json object json encode json decode function 20 support full demo admin backend visit url admin main 2 runtime environment c standard request c 20 asio mysql libmysqlclient dev zlib brotli br options boost gd qrencode 3 environment configuration 3 1 macos system requirements bigsur install necessary dependencies bash brew install asio brew install mysql brew install mysql client brew install zlib brew install brotli options brew install boost options brew install gd options brew install qrencode 3 2 ubuntu fedora system requirements ubuntu 20 04 test on fedora38 need install asan sudo yum install libasan notice requires gcc11 support for more installation details please see environment configuration paozhu linux environment configuration https github com hggq paozhu wiki linux e7 8e af e5 a2 83 e9 85 8d e7 bd ae 4 install download asio new version to project root part shell vendor cmakelists txt asio asio asio hpp directory like this shell mkdir build cd build cmake make use in production environment compile as daemon shell cmake dcmake build type release make j8 5 hosts configuration view conf server conf file and get mainhost value replace users hzq paozhu path to your project path open host file shell sudo vim etc hosts add to the last line of the hosts file if it is www 869869 com text 127 0 0 1 www 869869 com 6 https test run shell sudo bin paozhu open the browser and enter http localhost or http www 869869 com in the address bar div align center img src https www paozhu org images home png div div align center img src https www paozhu org images admin png div for more details please see the source code under the controller directory which includes crud examples 7 stress testing use h2load and ab testing apachebench test https www paozhu org images ab stress test png apachebench test h2load test https www paozhu org images h2load stress test png h2load test 8 sample hello world on controller directory testhello cpp file c include httppeer h include testhello h namespace http urlpath null hello std string testhello std shared ptr httppeer peer httppeer client peer getpeer client hello world paozhu c web framework return namespace http open the browser and enter http localhost hello urlpath null hello is annotation 9 related tutorial 1 paozhu principle https github com hggq paozhu wiki paozhu cpp web framework e6 a1 86 e6 9e b6 e5 8e 9f e7 90 86 2 paozhu hello world https github com hggq paozhu wiki paozhu e6 a1 86 e6 9e b6hello world 3 paozhu orm https github com hggq paozhu wiki paozhu e6 a1 86 e6 9e b6orm e5 85 a5 e9 97 a8 4 paozhu view https github com hggq paozhu wiki paozhu e6 a1 86 e6 9e b6view e8 a7 86 e5 9b be e5 85 a5 e9 97 a8 5 paozhu crud https github com hggq paozhu wiki paozhu e6 a1 86 e6 9e b6 crud e6 95 99 e7 a8 8b 10 roadmap x 1 improved http features full testing http 2 resist hackers scanning websites x 2 use c parse jsx file server side rendering x 3 httpclient improved support protocol forwarding x 4 websocket client x 5 support windows x 6 postgres sqlite orm x 7 support php fpm backend x 8 parsing mysql protocol for orm 11 contribute welcome to raise issues for mutual communication and of course we also welcome your active pr 12 license paozhu is provided under the mit license license | http-server orm http-client websocket | front_end |
obsidian-bmo-chatbot | bmo chatbot for obsidian generate and brainstorm ideas while creating your notes using large language models llms such as openai s gpt 3 5 turbo and gpt 4 for obsidian screenshot 1 readme images screenshot 1 png p align center img src readme images screenshot 2 png alt description of image p features chat from anywhere in obsidian chat with your bot from anywhere within obsidian chat with current note use your chatbot to reference and engage within your current note chatbot responds in markdown receive formatted responses in markdown for consistency customizable bot name personalize the chatbot s name system role prompt configure the chatbot to prompt for user roles before responding to messages set max tokens and temperature customize the length and randomness of the chatbot s responses with max tokens and temperature settings system theme color accents seamlessly matches the chatbot s interface with your system s color scheme interact with self hosted large language models llms use the rest api url provided to interact with self hosted large language models llms using localai https github com go skynet localai requirements to use this plugin you ll need an openai account with api access if you don t have an account yet you can sign up for one on the openai website https platform openai com overview additionally if you want to interact with self hosted large language models llms using localai https github com go skynet localai you will need to have the self hosted api set up and running you can follow the instructions provided by the self hosted api provider to get it up and running once you have the rest api url for your self hosted api you can use it with this plugin to interact with your models explore some models at gpt4all https gpt4all io index html under the model explorer section how to activate the plugin two methods obsidian community plugins recommended 1 search for bmo chatbot in the obsidian community plugins 2 enable bmo chatbot in the settings to activate the plugin from this repo 1 navigate to the plugin s folder in your terminal 2 run npm install to install any necessary dependencies for the plugin 3 once the dependencies have been installed run npm run build to build the plugin 4 once the plugin has been built it should be ready to activate getting started to start using the plugin enable it in your settings menu and enter your openapi key after completing these steps you can access the bot panel by clicking on the bot icon in the left sidebar if you want to clear the chat history simply click on the bot icon again in the left ribbon bar supported models openai gpt 3 5 turbo gpt 3 5 turbo 16k gpt 4 anthropic claude instant 1 2 claude 2 0 any self hosted models using localai https github com go skynet localai other notes bmo is a tag name for this project inspired by the character bmo from the animated tv show adventure time contributing if you have any bugs improvements or questions please create an issue feel free to create a pr as well d | ai |
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U-SIMonitor | u simonitor the u simonitor is an application for android that performs at commands to the baseband modem of mobile phones it can obtain the security credentials and sensitive information of the cellular technology such as permanent and temporary identities encryption keys location of users etc alternatively it can be used to evaluate the security of mobile operators by analyzing how frequently the keys are refreshed or how often the termorary identities are updated u simonitor does not disrupt the normal operation of the phone while it is running | server |
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sql-challenge | sql challenge used sql database pgadmin 4 and python to perform data modeling engineering amp analysis erd image file added quickdbd employees png table schemata added table schemata sgl all questions answered in query sql | server |
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promptrix-py | promptrix py promptrix is a prompt layout engine for large language models here is a first trivial example from promptrix import promptrixtypes volatilememory functionregistry gpt3tokenizer from promptrix prompt import prompt from promptrix systemmessage import systemmessage from promptrix usermessage import usermessage from promptrix assistantmessage import assistantmessage from promptrix conversationhistory import conversationhistory functions functionregistry tokenizer gpt3tokenizer memory volatilememory input history max tokens 2000 prompt text you are helpful creative clever and very friendly prompt prompt usermessage prompt text conversationhistory history 5 allow history to use up 1 2 the remaining token budget left after the prompt and input usermessage input async def render messages completion as msgs await prompt renderasmessages memory functions tokenizer max tokens msgs if not as msgs toolong msgs as msgs output return msgs basic chat loop while true memory set input query msgs asyncio run render messages completion response your favorite llm api model msgs print response history memory get history history append role user prefix content query history append role assistant prefix content response memory set history history | ai |
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chatbot-webui | chatbot webui supported models and their config files now support llama https huggingface co decapoda research llama 7b hf with lora https huggingface co tloen alpaca lora 7b cfgs llama 7b hf alpaca json chatglm https huggingface co thudm chatglm 6b cfgs chatglm 6b json belle llama 7b https huggingface co bellegroup belle llama 7b cfgs belle llama 7b 2m json blip2chatglm https huggingface co xipotzzz blip2zh chatglm 6b cfgs blip2zh chatglm 6b json chatgpt todo select prompt schema usage launch webui bash python launch py cfgs chatglm 6b json you should first download the huggingface model and then save the model in the location set in the config multimodal chats blip2chatglm supports multimodal chats bash python launch py cfgs chatglm 6b json json model path blip2chatglm some examples of multi modal chat doc img demo1 png doc img demo2 png doc img demo3 png | ai |
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