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Jupyter-Guide

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

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Learning Resources for ML
- Machine Learning
- Machine Learning by Stanford University from Coursera
- AWS Training and Certification for Machine Learning (ML) Courses
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Machine Learning Crash Course for Google Cloud
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.
- 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.
- nGraph - of-use to AI developers.
- 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.
- 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).
Apache Spark Tools, Libraries, and Frameworks
- Apache MXNet
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Spark SQL
- Spark Streaming - tolerant stream processing engine built on the Spark SQL engine. It can express your streaming computation the same way you would express a batch computation on static data from various sources including [Apache Kafka](https://kafka.apache.org/), [Apache Flume](https://flume.apache.org/), and [Amazon Kinesis](https://aws.amazon.com/kinesis/).
- MLib - level optimization primitives and higher-level pipeline APIs.
- Graphx - parallel computation. At a high-level, GraphX extends the [Spark RDD](https://spark.apache.org/docs/latest/rdd-programming-guide.html) by introducing the Resilient Distributed Property Graph: a directed multigraph with properties attached to each vertex and edge.
- PySpark
- MLflow
- Tracking component
- Projects component
- Models component
- Model Registry
- 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 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).
- Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- 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.
- Kibana
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- 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.
Deep Learning Learning Resources
- Deep Learning - supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).
- Deep Learning Online Courses | NVIDIA
- Top Deep Learning Courses Online | Coursera
- Top Deep Learning Courses Online | Udemy
- Learn Deep Learning with Online Courses and Lessons | edX
- Deep Learning Online Course Nanodegree | Udacity
- Machine Learning Course by Andrew Ng | Coursera
- Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- How to Think About Machine Learning Algorithms | Pluralsight
- Deep Learning Courses | Stanford Online
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Machine Learning for Everyone Courses | DataCamp
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Top Artificial Intelligence Courses Online | Coursera
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Professional Certificate in Computer Science for Artificial Intelligence | edX
- Artificial Intelligence Nanodegree program
- Artificial Intelligence (AI) Online Courses | Udacity
- Intro to Artificial Intelligence Course | Udacity
- Edge AI for IoT Developers Course | Udacity
- Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
- Expert Systems and Applied Artificial Intelligence
- Autonomous Systems - Microsoft AI
- Introduction to Microsoft Project Bonsai
- Machine teaching with the Microsoft Autonomous Systems platform
- Autonomous Maritime Systems Training | AMC Search
- Top Autonomous Cars Courses Online | Udemy
- Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
- Learn Autonomous Robotics with Online Courses and Lessons | edX
- Autonomous Systems Online Courses & Programs | Udacity
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Understanding Machine Learning with Python | Pluralsight
- Edge AI for IoT Developers Course | Udacity
- Autonomous Systems Online Courses & Programs | Udacity
- Artificial Intelligence (AI) Online Courses | Udacity
- Machine Learning for Everyone Courses | DataCamp
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).
- XGBoost
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- 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.
- 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.
- 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.
- Computer Vision Toolbox™
- Caffe
- Cluster Manager for Apache Kafka(CMAK)
- 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.
- 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.
- 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.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- 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/).
- ROS Toolbox
- Scikit-Learn
- PlaidML
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- 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.
- Anaconda
- BigDL
- UAV Toolbox
- Apache PredictionIO
- Apache MXNet
- 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.
- 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.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- 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.
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- 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.
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- 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.
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Mapping Toolbox™
- 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.
- 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.
- 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.
- 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.
PyTorch Learning Resources
- PyTorch - source deep learning framework that accelerates the path from research to production, used for applications such as computer vision and natural language processing. PyTorch is developed by [Facebook's AI Research](https://ai.facebook.com/research/) lab.
- Getting Started with PyTorch
- PyTorch Documentation
- PyTorch Discussion Forum
- Top Pytorch Courses Online | Coursera
- Top Pytorch Courses Online | Udemy
- Learn PyTorch with Online Courses and Classes | edX
- PyTorch Fundamentals - Learn | Microsoft Docs
- PyTorch Development in Visual Studio Code
- PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure
- PyTorch - Azure Databricks | Microsoft Docs
- Deep Learning with PyTorch | Amazon Web Services (AWS)
- Getting started with PyTorch on Google Cloud
- Intro to Deep Learning with PyTorch | Udacity
- PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure
- Getting started with PyTorch on Google Cloud
TensorFlow Learning Resources
- Getting Started with TensorFlow
- TensorFlow Developer Certificate | TensorFlow
- TensorFlow Community
- TensorFlow Models & Datasets
- Machine learning education | TensorFlow
- Top Tensorflow Courses Online | Coursera
- Top Tensorflow Courses Online | Udemy
- Deep Learning with TensorFlow | Udemy
- Deep Learning with Tensorflow | edX
- Train and deploy a TensorFlow model - Azure Machine Learning
- Apply machine learning models in Azure Functions with Python and TensorFlow | Microsoft Azure
- Deep Learning with TensorFlow | Amazon Web Services (AWS)
- TensorFlow - Amazon EMR | AWS Documentation
- TensorFlow Enterprise | Google Cloud
- 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.
- Intro to TensorFlow for Deep Learning | Udacity
TensorFlow Tools, Libraries, and Frameworks
- TensorFlow.js
- Google Colaboratory
- TensorBoard
- XLA (Accelerated Linear Algebra) - specific compiler for linear algebra that optimizes TensorFlow computations. The results are improvements in speed, memory usage, and portability on server and mobile platforms.
- ML Perf
- TensorFlow Playground
- TPU Research Cloud (TRC)
- MLIR
- Lattice - sense shape constraints.
- TensorFlow Hub
- TensorFlow Model Optimization Toolkit
- TensorFlow Recommenders
- TensorFlow Text - and NLP-related classes and ops ready to use with TensorFlow 2.
- TensorFlow Graphics
- TensorFlow Federated
- TensorFlow Probability
- TensorFlow Privacy
- TensorFlow Agents
- TensorFlow Quantum - classical ML models.
- TRFL
- RaggedTensors - uniform shape, including text (words, sentences, characters), and batches of variable length.
- Unicode Ops
- Magenta
- Sonnet
- Neural Structured Learning
- Model Remediation
- Fairness Indicators - identified fairness metrics for binary and multiclass classifiers.
- Decision Forests - of-the-art algorithms for training, serving and interpreting models that use decision forests for classification, regression and ranking.
- What-If Tool - free probing of machine learning models, useful for model understanding, debugging, and fairness. Available in TensorBoard and jupyter or colab notebooks.
- Tensor2Tensor
- TensorFlow Ranking - to-Rank (LTR) techniques on the TensorFlow platform.
- TensorFlow Addons - established API patterns, but implement new functionality not available in core TensorFlow, maintained by [SIG Addons](https://groups.google.com/a/tensorflow.org/g/addons). TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers.
- TensorFlow I/O
- Dopamine
- Mesh TensorFlow
- Nucleus
- Sonnet
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- TensorFlow Cloud
PyTorch Tools, Libraries, and Frameworks
- PyTorch Mobile - to-end ML workflow from Training to Deployment for iOS and Android mobile devices.
- TorchScript
- TorchServe
- Kornia
- PyTorch-NLP - NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders.
- Ignite - level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
- Deep Graph Library (DGL)
- TensorLy
- GPyTorch
- Poutyne - like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.
- Forte - task interaction.
- Captum
- Hydra
- Accelerate - GPU, TPU, mixed-precision.
- ParlAI
- PyTorchVideo - focused models, datasets, training pipelines and more.
- Opacus
- PyTorch Lightning - like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.
- PyTorch Geometric
- Raster Vision
- Optuna
- Pyro
- Albumentations
- MMF
- ClinicaDL
- MONAI - optimized foundational capabilities for developing healthcare imaging training workflows.
- PyTorch3D
- Ensemble Pytorch
- Horovod
- PennyLane - classical computations.
- Fastai
- 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.
- ONNX Runtime - platform, high performance ML inferencing and training accelerator. It supports models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc.
- Hummingbird
- Transformer - of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
- Ray
Apache Spark Learning Resources
- Apache Spark Quick Start
- What is Apache Spark? | IBM
- Introduction to Apache Spark and Analytics | AWS
- Apache Spark 3.0: For Analytics & Machine Learning | NVIDIA
- .NET for Apache Spark™ | Big data analytics
- Top Apache Spark Courses Online | Coursera
- Top Apache Spark Courses Online | Udemy
- Apache Spark In-Depth (Spark with Scala) | Udemy
- Learn Apache Spark with Online Courses | edX
- Apache Spark Essential Training Online Class | LinkedIn Learning
- Cloudera Developer Training for Apache Spark™ and Hadoop | Cloudera
- Databricks Certified Associate Developer for Apache Spark 3.0 certification | Databricks
- Apache Spark Training Courses | NobleProg
- Databricks Certified Associate Developer for Apache Spark 3.0 certification | Databricks
- Cloudera Developer Training for Apache Spark™ and Hadoop | Cloudera
MATLAB Learning Resources
- MATLAB
- MATLAB Documentation
- MATLAB and Simulink Training from MATLAB Academy
- MathWorks Certification Program
- MATLAB Online Courses from Udemy
- MATLAB Online Courses from Coursera
- MATLAB Online Courses from edX
- Building a MATLAB GUI
- MATLAB Style Guidelines 2.0
- Setting Up Git Source Control with MATLAB & Simulink
- Pull, Push and Fetch Files with Git with MATLAB & Simulink
- Create New Repository with MATLAB & Simulink
- PRMLT
- Getting Started with MATLAB
- Apache Spark Basics | MATLAB & Simulink
- MATLAB Hadoop and Spark | MATLAB & Simulink
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™
- Statistics and Machine Learning Toolbox™
- Partial Differential Equation Toolbox™
- 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.
- 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.
- 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/).
Scala Learning Resources
- Scala - oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.
- Scala Style Guide
- Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
- Using Scala to Program AWS Glue ETL Scripts
- Using Flink Scala shell with Amazon EMR clusters
- AWS EMR and Spark 2 using Scala from Udemy
- Using the Google Cloud Storage connector with Apache Spark
- Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
- Scala Courses and Certifications from edX
- Scala Courses from Coursera
- Top Scala Courses from Udemy
- Using the Google Cloud Storage connector with Apache Spark
- Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
- Scala Courses and Certifications from edX
- Intro to Spark DataFrames using Scala with Azure Databricks
Scala Tools and Libraries
- Dotty
- Scala.js
- Polynote
- Scala Native - of-time compiler and lightweight managed runtime designed specifically for Scala.
- Gitbucket
- Finagle - agnostic RPC system
- Gatling - Sent-Events and JMS.
- Scalatra - performance, async web framework, inspired by [Sinatra](https://www.sinatrarb.com/).
- 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.
- Scalatra - performance, async web framework, inspired by [Sinatra](https://www.sinatrarb.com/).
- Scala.js
- Polynote
R Learning Resources
- R
- An Introduction to R
- Google's R Style Guide
- R developer's guide to Azure
- Running R on AWS
- RStudio Server Pro for AWS
- Learn R by Codecademy
- Learn R Programming with Online Courses and Lessons by edX
- R Language Courses by Coursera
- Learn R For Data Science by Udacity
R Tools, Libraries, and Frameworks
- Code Server
- VSCode-R - project.org/), including features such as extended syntax highlighting, R language service based on code analysis, interacting with R terminals, viewing data, plots, workspace variables, help pages, managing packages, and working with [R Markdown](https://rmarkdown.rstudio.com/) documents.
- R Debugger
- RStudio - highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
- Shiny
- Rmarkdown
- Plotly
- Metaflow - life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
- Prophet - linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
- LightGBM
- MLR
- CatBoost
- Plumber
- Drake - focused pipeline toolkit for reproducibility and high-performance computing.
- DiagrammeR
- Knitr - purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
- Broom
- Dash
- Shiny
- R Debugger
- Visual Studio Code
Julia Learning Resources
- Julia - level, [high-performance](https://julialang.org/benchmarks/) dynamic language for technical computing. Julia programs compile to efficient native code for [multiple platforms](https://julialang.org/downloads/#support_tiers) via LLVM.
- JuliaHub
- Julia Observer
- Julia Manual
- JuliaLang Essentials
- Julia Style Guide
- Julia By Example
- JuliaLang Gitter
- Julia Academy
- Julia Meetup groups
- Julia on Microsoft Azure
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.

Programming Languages

Python 11 C++ 8 Scala 2 C# 1 Shell 1 Jupyter Notebook 1 HTML 1 Rust 1

Ecosyste.ms: Awesome

Jupyter-Guide

Learning Resources for ML

ML Frameworks, Libraries, and Tools

Apache Spark Tools, Libraries, and Frameworks

Deep Learning Learning Resources

Deep Learning Tools, Libraries, and Frameworks

PyTorch Learning Resources

TensorFlow Learning Resources

TensorFlow Tools, Libraries, and Frameworks

PyTorch Tools, Libraries, and Frameworks

Apache Spark Learning Resources

MATLAB Learning Resources

MATLAB Tools, Libraries, Frameworks

Python Learning Resources

Python Frameworks and Tools

C/C++ Learning Resources

C/C++ Tools and Frameworks

Scala Learning Resources

Scala Tools and Libraries

R Learning Resources

R Tools, Libraries, and Frameworks

Julia Learning Resources

Julia Tools, Libraries and Frameworks