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PyTorch-Guide

PyTorch Guide
https://github.com/mikeroyal/PyTorch-Guide

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Computer Vision Learning Resources
Learning Resources for ML
ML Frameworks, Libraries, and Tools
- Amazon SageMaker
- Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
- nGraph - of-use to AI developers.
- Tensorman
- cuML - learn.
Distributed Computing Tools, Libraries, and Frameworks
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Apache Cassandra™ - tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
- Apache Flume
- Apache Mesos
- Apache HBase™ - source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
- Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
- Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- Apache Beam - specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs).
- Neo4j - strength graph database that combines native graph storage, advanced security, scalable speed-optimized architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.
- ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
- Logstash
- Kibana
- Trino - us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
- Extract, transform, and load (ETL)
- Redis(REmote DIctionary Server) - memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
- Parallel Computing - level]https://en.wikipedia.org/wiki/Bit-level_parallelism), [instruction-level](https://en.wikipedia.org/wiki/Instruction-level_parallelism), [data](https://en.wikipedia.org/wiki/Data_parallelism), and [task parallelism](https://en.wikipedia.org/wiki/Task_parallelism).
- Accelerated Computing - Training | NVIDIA Developer
- Fundamentals of Accelerated Computing with CUDA Python Course | NVIDIA
- Top Parallel Computing Courses Online | Coursera
- Top Parallel Computing Courses Online | Udemy
- Scientific Computing Masterclass: Parallel and Distributed
- Learn Parallel Computing in Python | Udemy
- GPU computing in Vulkan | Udemy
- Parallel Computing Courses | Stanford Online
- Parallel Computing | MIT OpenCourseWare
- Multithreaded Parallelism: Languages and Compilers | MIT OpenCourseWare
- Parallel Computing with CUDA | Pluralsight
- HPC Architecture and System Design | Intel
- OpenMP - platform shared-memory parallel programming in C/C++ and Fortran. The OpenMP API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer.
- Message Passing Interface (MPI) - passing standard designed to function on parallel computing architectures.
- Microsoft MPI (MS-MPI)
- Slurm - source workload manager designed specifically to satisfy the demanding needs of high performance computing.
- AWS ParallelCluster - supported open source cluster management tool that makes it easy for you to deploy and manage High Performance Computing (HPC) clusters on AWS. ParallelCluster uses a simple text file to model and provision all the resources needed for your HPC applications in an automated and secure manner.
- XGBoost
- Kibana
- Portable Batch System (PBS) Pro
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- High Performance Computing Courses | Udacity
- Fundamentals of Accelerated Computing with CUDA Python Course | NVIDIA
Computer Vision Tools, Libraries, and Frameworks
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Data Acquisition Toolbox™
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Mapping Toolbox™
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- ROS Toolbox
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- 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.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- Partial Differential Equation Toolbox™
- 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™
- UAV Toolbox
- Statistics and Machine Learning Toolbox™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
Reinforcement Learning Learning Resources
Deep Learning Tools, Libraries, and Frameworks
- NVIDIA DLSS (Deep Learning Super Sampling)
- Intel Xe Super Sampling (XeSS) - cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by [DP4a instruction](https://www.intel.com/content/dam/www/public/us/en/documents/reference-guides/11th-gen-quick-reference-guide.pdf).
- AMD FidelityFX Super Resolution (FSR) - quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
Distributed Computing Learning Resources
- Machine teaching with the Microsoft Autonomous Systems platform
- Distributed System
- Client-server - user. The end-user can also make a change from the client-side and commit it back to the server to make it permanent.
- Three-tier
- n-tier
- Peer-to-peer
- Top Distributed Systems Courses Online | Coursera
- Distributed Systems Online | Stanford Online
- Top Distributed Computing Courses Online | Udemy
- Distributed Systems & Cloud Computing with Java | Udemy
- Introduction to Distributed Systems | University of Washington
- Distributed Systems - University of Wisconsin-Madison
- A Thorough Introduction to Distributed Systems | FreeCodeCamp
- Introduction to Distributed Systems | UPenn
- Distribution System Certificate Program Online | ASU
- Autonomous Systems - Microsoft AI
- Distributed System
- Introduction to Microsoft Project Bonsai
Reinforcement Learning Tools, Libraries, and Frameworks
- OpenAI
- ReinforcementLearning.jl
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Cluster Manager for Apache Kafka(CMAK)
- Microsoft Project Bonsai - code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- ROS/ROS2 bridge for CARLA(package) - way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
- Apache MXNet
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Navigation Toolbox™ - based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
NLP Learning Resources
NLP Tools, Libraries, and Frameworks
- Natural Language Toolkit (NLTK) - to-use interfaces to over [50 corpora and lexical resources](https://nltk.org/nltk_data/) such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
- spaCy - task learning with pretrained transformers like BERT.
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- NLPnet - of-speech tagging, semantic role labeling and dependency parsing.
- CoreNLP
- Flair - of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
- Catalyst - trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
- Apache PredictionIO
- BigDL
- 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.
- Caffe
- 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.
- 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/).
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- PlaidML
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Anaconda
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Scikit-Learn
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
CUDA Learning Resources
- CUDA on WSL
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA GPU support for TensorFlow
- NVIDIA Deep Learning cuDNN Documentation
- NVIDIA GPU Cloud Documentation
- NVIDIA NGC - optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
- NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
- 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 Tools Libraries, and Frameworks
- 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).
- 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.
- 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.
- GraphVite - speed and large-scale embedding learning in various applications.
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/).
- MATLAB Online™
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Simulink Online™
- MATLAB Drive™
- SoC Blockset™
- Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
- ThingSpeak™ - of-concept IoT systems that require analytics.
- hctsa - series analysis using Matlab.
- Plotly
- YALMIP
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
- 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.
- hctsa - series analysis using Matlab.
Python Learning Resources
- Python - level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
- Python Developer’s Guide
- Azure Functions Python developer guide - us/azure/azure-functions/functions-reference).
- CheckiO
- Python Institute
- PCEP – Certified Entry-Level Python Programmer certification
- PCAP – Certified Associate in Python Programming certification
- MTA: Introduction to Programming Using Python Certification
- Getting Started with Python in Visual Studio Code
- Google's Python Style Guide
- Google's Python Education Class
- Real Python
- Intro to Python for Data Science
- Intro to Python by W3schools
- Codecademy's Python 3 course
- Learn Python with Online Courses and Classes from edX
- Python Courses Online from Coursera
- PCPP – Certified Professional in Python Programming 2
Python Frameworks and Tools
- Python Package Index (PyPI)
- PyCharm
- Django - level Python Web framework that encourages rapid development and clean, pragmatic design.
- Flask
- Web2py - source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
- Tornado - blocking network I/O, which can scale to tens of thousands of open connections.
- HTTPie
- Scrapy - level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
- Sentry
- CherryPy - oriented HTTP web framework.
- Sanic
- Pyramid - world web application development and deployment more fun and more productive.
- TurboGears
- Falcon - performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
- Dash
- NumPy
- Pillow
- IPython
- GraphLab Create - scale, high-performance machine learning models.
- Pandas
- Matplotlib - quality figures in a variety of hardcopy formats and interactive environments across platforms.
- Python Tools for Visual Studio(PTVS)
C/C++ Learning Resources
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- 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
- C++ Tools and Libraries Articles
- Google C++ Style Guide
- Introduction C++ Education course on Google Developers
- C++ style guide for Fuchsia
- C and C++ Coding Style Guide by OpenTitan
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ Style Guide for ROS
- Learn C++
- Learn C : An Interactive C Tutorial
- C++ Institute
- 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
- Top C Courses 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
- Introduction to C++ on MIT Free Online Course Materials
- Introduction to C++ for Programmers | Harvard
- Online C Courses | Harvard University
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.
- Libcu++
- 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/).

Programming Languages

Python 5 C++ 5 C# 2 Scala 2 Shell 1 Java 1 MATLAB 1 Rust 1

Ecosyste.ms: Awesome

PyTorch-Guide

Computer Vision Learning Resources

Learning Resources for ML

ML Frameworks, Libraries, and Tools

Distributed Computing Tools, Libraries, and Frameworks

Computer Vision Tools, Libraries, and Frameworks

Reinforcement Learning Learning Resources

Deep Learning Tools, Libraries, and Frameworks

Distributed Computing Learning Resources

Reinforcement Learning Tools, Libraries, and Frameworks

NLP Learning Resources

NLP Tools, Libraries, and Frameworks

CUDA Learning Resources

CUDA Tools Libraries, and Frameworks

MATLAB Learning Resources

MATLAB Tools, Libraries, Frameworks

Python Learning Resources

Python Frameworks and Tools

C/C++ Learning Resources

C/C++ Tools and Frameworks