NLP-Guide

Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis.
https://github.com/mikeroyal/NLP-Guide

Last synced: 7 days ago
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C/C++ Learning Resources
- C - purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
- Embedded C - committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
- C & C++ Developer Tools from JetBrains
- Open source C++ libraries on cppreference.com
- C++ Graphics libraries
- C++ Libraries in MATLAB
- Google C++ Style Guide
- Introduction C++ Education course on Google Developers
- C++ style guide for Fuchsia
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ Style Guide for ROS
- Learn C++
- Learn C : An Interactive C Tutorial
- C++ Online Training Courses on LinkedIn Learning
- C++ Tutorials on W3Schools
- Learn C Programming Online Courses on edX
- Learn C++ with Online Courses on edX
- Learn C++ on Codecademy
- Coding for Everyone: C and C++ course on Coursera
- C++ For C Programmers on Coursera
- C++ Online Courses on Udemy
- Top C Courses on Udemy
- Basics of Embedded C Programming for Beginners on Udemy
- C++ For Programmers Course on Udacity
- C++ Fundamentals Course on Pluralsight
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- C++ Tools and Libraries Articles
- C++ Style Guide for ROS
C/C++ Tools and Frameworks
- AWS SDK for C++
- Visual Studio - rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
- Visual Studio Code
- ReSharper C++
- AppCode - fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
- CLion - platform IDE for C and C++ developers developed by JetBrains.
- Code::Blocks
- Conan
- High Performance Computing (HPC) SDK
- Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
- Automake
- Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
- GDB
- GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
- GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
- OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
- Libtool
- Maven
- TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
- Clang - C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
- OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- ANTLR (ANother Tool for Language Recognition)
- Oat++ - efficient web application. It's zero-dependency and easy-portable.
- Cython
- Infer - C, and C. Infer is written in [OCaml](https://ocaml.org/).
- Azure SDK for C++
- Azure SDK for C
- C++ Client Libraries for Google Cloud Services
- Vcpkg
- CppSharp
- JavaCPP
- Spdlog - only/compiled, C++ logging library.
CUDA Learning Resources
- CUDA - accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA on WSL
- NVIDIA Deep Learning cuDNN Documentation
- CUDA GPU support for TensorFlow
CUDA Tools Libraries, and Frameworks
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
- CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
- CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
- CatBoost
- cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
- ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
- AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
- Tensorman
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- cuML - learn.
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
- Minkowski Engine - differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
- CUTLASS - performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
- CUB
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
- Arraymancer - dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
- Kintinuous - time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
- GraphVite - speed and large-scale embedding learning in various applications.
Java Learning Resources
Java Tools and Frameworks
- Java SE
- JDK Development Tools
- IntelliJ IDEA
- RxJava - based programs by using observable sequences. It extends the [observer pattern](http://en.wikipedia.org/wiki/Observer_pattern) to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
- Guava
- Retrofit - safe HTTP client for Android and Java develped by Square.
- Apache Flink - and batch-processing capabilities with elegant and fluent APIs in Java and Scala.
- Fastjson
- libGDX - platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
- Jenkins - source automation server. Built with Java, it provides over 1700 [plugins](https://plugins.jenkins.io/) to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.
- DBeaver - platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).
- Redisson - Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.
- GraalVM - based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.
- Gradle - language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.
- Apache Groovy - typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
- JaCoCo
- Junit
- Mockito
- SpotBugs
- YourKit
- Java Design Patterns
- okhttp
- LeakCanary
- Elasticsearch
- Apache Groovy - typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
- NetBeans
Julia Learning Resources
Julia Tools, Libraries and Frameworks
- JuliaPro
- Juno
- Profile (Stdlib)
- JuliaGPU - level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
- CUDA.jl - friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
- Julia for VSCode
- JuMP.jl - specific modeling language for [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) embedded in Julia.
- Knet
- DataFrames.jl
- Flux.jl - Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
- Debugger.jl
- Revise.jl - compile.
- IJulia.jl
- AWS.jl
- Nanosoldier.jl
- Optim.jl
- RCall.jl
- PyCall.jl
- MXNet.jl - of-art deep learning to Julia.
- Distributions.jl
- IRTools.jl
Learning Resources for ML
MATLAB Learning Resources
MATLAB Tools, Libraries, Frameworks
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Simulink Online™
- MATLAB Drive™
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Statistics and Machine Learning Toolbox™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- UAV Toolbox
- Partial Differential Equation Toolbox™
- ROS Toolbox

Programming Languages

Python 13 Julia 10 C++ 10 Java 4 MATLAB 3 Kotlin 3 Scala 2 C# 2 HTML 2 Nim 1

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NLP-Guide

C/C++ Learning Resources

C/C++ Tools and Frameworks

CUDA Learning Resources

CUDA Tools Libraries, and Frameworks

Java Learning Resources

Java Tools and Frameworks

Julia Learning Resources

Julia Tools, Libraries and Frameworks

Learning Resources for ML

MATLAB Learning Resources

MATLAB Tools, Libraries, Frameworks