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

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

awesome awesome-list awesome-readme awesome-resources data-science deep-learning julialang jupyter jupyter-notebook jupyter-notebooks jupyterhub jupyterlab machine-learning python python3

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Jupyter Guide

Host: GitHub
URL: https://github.com/mikeroyal/Jupyter-Guide
Owner: mikeroyal
Created: 2022-01-12T23:23:10.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-01-12T23:39:09.000Z (over 2 years ago)
Last Synced: 2024-05-23T09:04:52.658Z (4 months ago)
Topics: awesome, awesome-list, awesome-readme, awesome-resources, data-science, deep-learning, julialang, jupyter, jupyter-notebook, jupyter-notebooks, jupyterhub, jupyterlab, machine-learning, python, python3
Language: Jupyter Notebook
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Open Issues: 0
Metadata Files:
- Readme: README.md

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README

Jupyter Guide

#### A guide covering Jupyter including the applications, libraries and tools that will make you better and more efficient with Jupyter development.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**

# Table of Contents

1. [Getting Started with Jupyter](https://github.com/mikeroyal/Jupyter-Guide#getting-started-with-jupyter)

2. [Machine Learning](https://github.com/mikeroyal/Jupyter-Guide#machine-learning)

3. [Deep Learning](https://github.com/mikeroyal/Jupyter-Guide#deep-learning)

4. [TensorFlow](https://github.com/mikeroyal/Jupyter-Guide#tensorflow)

5. [PyTorch](https://github.com/mikeroyal/Jupyter-Guide#pytorch)

6. [Apache Spark](https://github.com/mikeroyal/Jupyter-Guide#apache-spark)

7. [MATLAB Development](https://github.com/mikeroyal/Jupyter-Guide#matlab-development)

8. [Python Development](https://github.com/mikeroyal/Jupyter-Guide#python-development)

9. [C/C++ Development](https://github.com/mikeroyal/Jupyter-Guide#cc-development)

10. [Scala Development](https://github.com/mikeroyal/Jupyter-Guide#scala-development)

11. [R Development](https://github.com/mikeroyal/Jupyter-Guide#r-development)

12. [Julia Development](https://github.com/mikeroyal/Jupyter-Guide#julia-development)

# Getting Started with Jupyter
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

[Jupyter](https://jupyter.org) is an open-source software, open standards, and services for interactive computing across dozens of programming languages. Checkout, [Jupyter Documentation](https://jupyter.org/documentation) for more information & examples.

[JupyterLab](https://jupyter.org/install) is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning.

JupyterLab example

[Jupyter Notebook](https://jupyter.org/install) is the original web application for creating and sharing computational documents.

Jupyter Notebook Python example

[JupyterHub](https://jupyter.org/hub) is a multi-user version of the notebook designed for companies, classrooms and research labs.

JupyterHub Overview

[Voilà](https://jupyter.org/install) is a tool that helps you communicate insights by transforming notebooks into secure, stand-alone web applications that you can customize and share.

Voilà example

[Jupyter Themes](https://github.com/dunovank/jupyter-themes) is a set of custom Jupyter Notebook Themes.

[Plotly.py](https://plotly.com/python/) is an interactive graphing library for Python (includes Plotly Express).

[Pandas Profiling](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/) is a tool that create HTML profiling reports from pandas DataFrame objects.

[ComputerVision Recipes](https://github.com/microsoft/computervision-recipes) is a set of Best Practices, code samples, and documentation for Computer Vision by Microsoft.

[Recommenders](https://microsoft-recommenders.readthedocs.io/en/latest/) is a set of Best Practices on Recommendation Systems by Microsoft.

[Amazon SageMaker Examples](https://sagemaker-examples.readthedocs.io/) is an example notebook Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using brain Amazon SageMaker.

[Handson ML](https://github.com/ageron/handson-ml) is a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## Learning Resources for ML

[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)

[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)

[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)

[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)

[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)

[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)

[JupyterLab](https://jupyterlab.readthedocs.io/)

[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)

[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

## ML Frameworks, Libraries, and Tools

[TensorFlow](https://www.tensorflow.org) is an end-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](https://keras.io) is a high-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](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-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.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-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.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Apache OpenNLP](https://opennlp.apache.org/) is an open-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 Airflow](https://airflow.apache.org) is an open-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.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-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/).

[Jupyter Notebook](https://jupyter.org/) is an open-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.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-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.

[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-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.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-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.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Numba](https://github.com/numba/numba) is an open source, NumPy-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.

[Chainer](https://chainer.org/) is a Python-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.

[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

# Deep Learning
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## Deep Learning Learning Resources

[Deep Learning](https://www.ibm.com/cloud/learn/deep-learning) is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from large amounts of data. The Learning can be [supervised](https://en.wikipedia.org/wiki/Supervised_learning), [semi-supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).

[Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-us/training/online/)

[Top Deep Learning Courses Online | Coursera](https://www.coursera.org/courses?query=deep%20learning)

[Top Deep Learning Courses Online | Udemy](https://www.udemy.com/topic/deep-learning/)

[Learn Deep Learning with Online Courses and Lessons | edX](https://www.edx.org/learn/deep-learning)

[Deep Learning Online Course Nanodegree | Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101)

[Machine Learning Course by Andrew Ng | Coursera](https://www.coursera.org/learn/machine-learning?)

[Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)

[Data Science: Deep Learning and Neural Networks in Python | Udemy](https://www.udemy.com/course/data-science-deep-learning-in-python/)

[Understanding Machine Learning with Python | Pluralsight ](https://www.pluralsight.com/courses/python-understanding-machine-learning)

[How to Think About Machine Learning Algorithms | Pluralsight](https://www.pluralsight.com/courses/machine-learning-algorithms)

[Deep Learning Courses | Stanford Online](https://online.stanford.edu/courses/cs230-deep-learning)

[Deep Learning - UW Professional & Continuing Education](https://www.pce.uw.edu/courses/deep-learning)

[Deep Learning Online Courses | Harvard University](https://online-learning.harvard.edu/course/deep-learning-0)

[Machine Learning for Everyone Courses | DataCamp](https://www.datacamp.com/courses/introduction-to-machine-learning-with-r)

[Artificial Intelligence Expert Course: Platinum Edition | Udemy](https://www.udemy.com/course/artificial-intelligence-exposed-future-10-extreme-edition/)

[Top Artificial Intelligence Courses Online | Coursera](https://www.coursera.org/courses?query=artificial%20intelligence)

[Learn Artificial Intelligence with Online Courses and Lessons | edX](https://www.edx.org/learn/artificial-intelligence)

[Professional Certificate in Computer Science for Artificial Intelligence | edX](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)

[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)

[Artificial Intelligence (AI) Online Courses | Udacity](https://www.udacity.com/school-of-ai)

[Intro to Artificial Intelligence Course | Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)

[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)

[Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-3-reasoning-goal-trees-and-rule-based-expert-systems/)

[Expert Systems and Applied Artificial Intelligence](https://www.umsl.edu/~joshik/msis480/chapt11.htm)

[Autonomous Systems - Microsoft AI](https://www.microsoft.com/en-us/ai/autonomous-systems)

[Introduction to Microsoft Project Bonsai](https://docs.microsoft.com/en-us/learn/autonomous-systems/intro-to-project-bonsai/)

[Machine teaching with the Microsoft Autonomous Systems platform](https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/autonomous-systems)

[Autonomous Maritime Systems Training | AMC Search](https://www.amcsearch.com.au/ams-training)

[Top Autonomous Cars Courses Online | Udemy](https://www.udemy.com/topic/autonomous-cars/)

[Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy](https://www.udemy.com/course/applied-systems-control-for-engineers-modelling-pid-mpc/)

[Learn Autonomous Robotics with Online Courses and Lessons | edX](https://www.edx.org/learn/autonomous-robotics)

[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)

[Autonomous Systems Online Courses & Programs | Udacity](https://www.udacity.com/school-of-autonomous-systems)

[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)

[Autonomous Systems MOOC and Free Online Courses | MOOC List](https://www.mooc-list.com/tags/autonomous-systems)

[Robotics and Autonomous Systems Graduate Program | Standford Online](https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program)

[Mobile Autonomous Systems Laboratory | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/lecture-notes/)

## Deep Learning Tools, Libraries, and Frameworks

[NVIDIA DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss) is a temporal image upscaling AI rendering technology that increases graphics performance using dedicated Tensor Core AI processors on GeForce RTX™ GPUs. DLSS uses the power of a deep learning neural network to boost frame rates and generate beautiful, sharp images for your games.

[AMD FidelityFX Super Resolution (FSR)](https://www.amd.com/en/technologies/radeon-software-fidelityfx) is an open source, high-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.

[Intel Xe Super Sampling (XeSS)](https://www.youtube.com/watch?v=Y9hfpf-SqEg) is a temporal image upscaling AI rendering technology that increases graphics performance similar to NVIDIA's [DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss). Intel's Arc GPU architecture (early 2022) will have GPUs that feature dedicated Xe-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).

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-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™](https://www.mathworks.com/products/reinforcement-learning.html) is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.

[Deep Learning HDL Toolbox™](https://www.mathworks.com/products/deep-learning-hdl.html) is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-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™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-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.

[LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Microsoft Project Bonsai](https://azure.microsoft.com/en-us/services/project-bonsai/) is a low-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](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-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.

[CARLA](https://github.com/carla-simulator/carla) is an open-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)](https://github.com/carla-simulator/ros-bridge) is a bridge that enables two-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.

[ROS Toolbox](https://www.mathworks.com/products/ros.html) is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.

[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[Image Processing Toolbox™](https://www.mathworks.com/products/image.html) is a tool that provides a comprehensive set of reference-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™](https://www.mathworks.com/products/computer-vision.html) is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.

[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[Model Predictive Control Toolbox™](https://www.mathworks.com/products/model-predictive-control.html) is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.

[Predictive Maintenance Toolbox™](https://www.mathworks.com/products/predictive-maintenance.html) is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.

[Vision HDL Toolbox™](https://www.mathworks.com/products/vision-hdl.html) is a tool that provides pixel-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™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-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.

[UAV Toolbox](https://www.mathworks.com/products/uav.html) is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.

[Navigation Toolbox™](https://www.mathworks.com/products/navigation.html) is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-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.

[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.

[Mapping Toolbox™](https://www.mathworks.com/products/mapping.html) is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.

# TensorFlow
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## TensorFlow Learning Resources

[Getting Started with TensorFlow](https://www.tensorflow.org/learn)

[TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)

[TensorFlow Developer Certificate | TensorFlow](https://www.tensorflow.org/certificate)

[TensorFlow Community](https://www.tensorflow.org/community/)

[TensorFlow Models & Datasets](https://www.tensorflow.org/resources/models-datasets)

[TensorFlow Cloud](https://www.tensorflow.org/cloud)

[Machine learning education | TensorFlow](https://www.tensorflow.org/resources/learn-ml)

[Top Tensorflow Courses Online | Coursera](https://www.coursera.org/courses?query=tensorflow)

[Top Tensorflow Courses Online | Udemy](https://www.udemy.com/courses/search/?src=ukw&q=tensorflow)

[Deep Learning with TensorFlow | Udemy](https://www.udemy.com/course/deep-learning-with-tensorflow-certification-training/)

[Deep Learning with Tensorflow | edX](https://www.edx.org/course/deep-learning-with-tensorflow)

[Intro to TensorFlow for Deep Learning | Udacity ](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)

[Intro to TensorFlow: Machine Learning Crash Course | Google Developers](https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/toolkit)

[Train and deploy a TensorFlow model - Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-tensorflow)

[Apply machine learning models in Azure Functions with Python and TensorFlow | Microsoft Azure](https://docs.microsoft.com/en-us/azure/azure-functions/functions-machine-learning-tensorflow?tabs=bash)

[Deep Learning with TensorFlow | Amazon Web Services (AWS)](https://aws.amazon.com/tensorflow/)

[TensorFlow - Amazon EMR | AWS Documentation](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-tensorflow.html)

[TensorFlow Enterprise | Google Cloud](https://cloud.google.com/tensorflow-enterprise/)

## TensorFlow Tools, Libraries, and Frameworks

[TensorFlow Lite](https://www.tensorflow.org/lite/) is an open source deep learning framework for deploying machine learning models on mobile and IoT devices.

[TensorFlow.js](https://www.tensorflow.org/js) is a JavaScript Library that lets you develop or execute ML models in JavaScript, and use ML directly in the browser client side, server side via Node.js, mobile native via React Native, desktop native via Electron, and even on IoT devices via Node.js on Raspberry Pi.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Google Colaboratory](https://colab.sandbox.google.com/notebooks/welcome.ipynb) is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud, allowing you to execute TensorFlow code in your browser with a single click.

[What-If Tool](https://pair-code.github.io/what-if-tool/) is a tool for code-free probing of machine learning models, useful for model understanding, debugging, and fairness. Available in TensorBoard and jupyter or colab notebooks.

[TensorBoard](https://www.tensorflow.org/tensorboard) is a suite of visualization tools to understand, debug, and optimize TensorFlow programs.

[XLA (Accelerated Linear Algebra)](https://www.tensorflow.org/xla) is a domain-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](https://mlperf.org/) is a broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.

[TensorFlow Playground](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.04620&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false) is an development environment to tinker around with a neural network in your browser.

[TPU Research Cloud (TRC)](https://sites.research.google/trc/) is a program enables researchers to apply for access to a cluster of more than 1,000 Cloud TPUs at no charge to help them accelerate the next wave of research breakthroughs.

[MLIR](https://www.tensorflow.org/mlir) is a new intermediate representation and compiler framework.

[Lattice](https://www.tensorflow.org/lattice) is a library for flexible, controlled and interpretable ML solutions with common-sense shape constraints.

[TensorFlow Hub](https://www.tensorflow.org/hub) is a library for reusable machine learning. Download and reuse the latest trained models with a minimal amount of code.

[TensorFlow Cloud](https://www.tensorflow.org/cloud) is a library to connect your local environment to Google Cloud.

[TensorFlow Model Optimization Toolkit](https://www.tensorflow.org/model_optimization) is a suite of tools for optimizing ML models for deployment and execution.

[TensorFlow Recommenders](https://www.tensorflow.org/recommenders) is a library for building recommender system models.

[TensorFlow Text](https://www.tensorflow.org/text) is a collection of text- and NLP-related classes and ops ready to use with TensorFlow 2.

[TensorFlow Graphics](https://www.tensorflow.org/graphics) is a library of computer graphics functionalities ranging from cameras, lights, and materials to renderers.

[TensorFlow Federated](https://www.tensorflow.org/federated) is an open source framework for machine learning and other computations on decentralized data.

[TensorFlow Probability](https://www.tensorflow.org/probability) is a library for probabilistic reasoning and statistical analysis.

[Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

[TensorFlow Privacy](https://www.tensorflow.org/responsible_ai/privacy) is a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.

[TensorFlow Ranking](https://github.com/tensorflow/ranking) is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.

[TensorFlow Agents](https://www.tensorflow.org/agents) is a library for reinforcement learning in TensorFlow.

[TensorFlow Addons](https://github.com/tensorflow/addons) is a repository of contributions that conform to well-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](https://github.com/tensorflow/io) is a Dataset, streaming, and file system extensions, maintained by SIG IO.

[TensorFlow Quantum](https://www.tensorflow.org/quantum) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models.

[Dopamine](https://github.com/google/dopamine) is a research framework for fast prototyping of reinforcement learning algorithms.

[TRFL](https://deepmind.com/blog/trfl/) is a library for reinforcement learning building blocks created by DeepMind.

[Mesh TensorFlow](https://github.com/tensorflow/mesh) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.

[RaggedTensors](https://www.tensorflow.org/guide/ragged_tensor) is an API that makes it easy to store and manipulate data with non-uniform shape, including text (words, sentences, characters), and batches of variable length.

[Unicode Ops](https://www.tensorflow.org/tutorials/load_data/unicode) is an API that Supports working with Unicode text directly in TensorFlow.

[Magenta](https://magenta.tensorflow.org/) is a research project exploring the role of machine learning in the process of creating art and music.

[Nucleus](https://github.com/google/nucleus) is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF.

[Sonnet](https://github.com/deepmind/sonnet) is a library from DeepMind for constructing neural networks.

[Neural Structured Learning](https://www.tensorflow.org/neural_structured_learning) is a learning framework to train neural networks by leveraging structured signals in addition to feature inputs.

[Model Remediation](https://www.tensorflow.org/responsible_ai/model_remediation) is a library to help create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.

[Fairness Indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/guide) is a library that enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers.

[Decision Forests](https://www.tensorflow.org/decision_forests) is a State-of-the-art algorithms for training, serving and interpreting models that use decision forests for classification, regression and ranking.

# PyTorch
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## PyTorch Learning Resources

[PyTorch](https://pytorch.org) is an open-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](https://pytorch.org/get-started/locally/)

[PyTorch Documentation](https://pytorch.org/docs/stable/index.html)

[PyTorch Discussion Forum](https://discuss.pytorch.org/)

[Top Pytorch Courses Online | Coursera](https://www.coursera.org/courses?query=pytorch&page=1)

[Top Pytorch Courses Online | Udemy](https://www.udemy.com/topic/PyTorch/)

[Learn PyTorch with Online Courses and Classes | edX](https://www.edx.org/learn/pytorch)

[PyTorch Fundamentals - Learn | Microsoft Docs](https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/)

[Intro to Deep Learning with PyTorch | Udacity ](https://www.udacity.com/course/deep-learning-pytorch--ud188)

[PyTorch Development in Visual Studio Code](https://code.visualstudio.com/docs/datascience/pytorch-support)

[PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure](https://azure.microsoft.com/en-us/develop/pytorch/)

[PyTorch - Azure Databricks | Microsoft Docs](https://docs.microsoft.com/en-us/azure/databricks/applications/machine-learning/train-model/pytorch)

[Deep Learning with PyTorch | Amazon Web Services (AWS)](https://aws.amazon.com/pytorch/)

[Getting started with PyTorch on Google Cloud](https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch)

## PyTorch Tools, Libraries, and Frameworks

[PyTorch Mobile](https://pytorch.org/mobile/home/) is an end-to-end ML workflow from Training to Deployment for iOS and Android mobile devices.

[TorchScript](https://pytorch.org/docs/stable/jit.html) is a way to create serializable and optimizable models from PyTorch code. This allows any TorchScript program to be saved from a Python process and loaded in a process where there is no Python dependency.

[TorchServe](https://pytorch.org/serve/) is a flexible and easy to use tool for serving PyTorch models.

[ONNX Runtime](https://github.com/microsoft/onnxruntime) is a cross-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.

[Kornia](https://kornia.github.io/) is a differentiable computer vision library that consists of a set of routines and differentiable modules to solve generic CV (Computer Vision) problems.

[PyTorch-NLP](https://pytorchnlp.readthedocs.io/en/latest/) is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders.

[Ignite](https://pytorch.org/ignite) is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

[Hummingbird](https://github.com/microsoft/hummingbird) is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

[Deep Graph Library (DGL)](https://www.dgl.ai/) is a Python package built for easy implementation of graph neural network model family, on top of PyTorch and other frameworks.

[TensorLy](http://tensorly.org/stable/home.html) is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.

[GPyTorch](https://cornellius-gp.github.io/) is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.

[Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.

[Forte](https://github.com/asyml/forte/tree/master/docs) is a toolkit for building NLP pipelines featuring composable components, convenient data interfaces, and cross-task interaction.

[TorchMetrics](https://github.com/PyTorchLightning/metrics) is a Machine learning metrics for distributed, scalable PyTorch applications.

[Captum](https://captum.ai/) is an open source, extensible library for model interpretability built on PyTorch.

[Transformer](https://github.com/huggingface/transformers) is a State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

[Hydra](https://hydra.cc) is a framework for elegantly configuring complex applications.

[Accelerate](https://huggingface.co/docs/accelerate) is a simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

[Ray](https://github.com/ray-project/ray) is a fast and simple framework for building and running distributed applications.

[ParlAI](http://parl.ai/) is a unified platform for sharing, training, and evaluating dialog models across many tasks.

[PyTorchVideo](https://pytorchvideo.org/) is a deep learning library for video understanding research. Hosts various video-focused models, datasets, training pipelines and more.

[Opacus](https://opacus.ai/) is a library that enables training PyTorch models with Differential Privacy.

[PyTorch Lightning](https://github.com/williamFalcon/pytorch-lightning) is a Keras-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.

[PyTorch Geometric Temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) is a temporal (dynamic) extension library for PyTorch Geometric.

[PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.

[Raster Vision](https://docs.rastervision.io/) is an open source framework for deep learning on satellite and aerial imagery.

[CrypTen](https://github.com/facebookresearch/CrypTen) is a framework for Privacy Preserving ML. Its goal is to make secure computing techniques accessible to ML practitioners.

[Optuna](https://optuna.org/) is an open source hyperparameter optimization framework to automate hyperparameter search.

[Pyro](http://pyro.ai/) is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.

[Albumentations](https://github.com/albu/albumentations) is a fast and extensible image augmentation library for different CV tasks like classification, segmentation, object detection and pose estimation.

[Skorch](https://github.com/skorch-dev/skorch) is a high-level library for PyTorch that provides full scikit-learn compatibility.

[MMF](https://mmf.sh/) is a modular framework for vision & language multimodal research from Facebook AI Research (FAIR).

[AdaptDL](https://github.com/petuum/adaptdl) is a resource-adaptive deep learning training and scheduling framework.

[Polyaxon](https://github.com/polyaxon/polyaxon) is a platform for building, training, and monitoring large-scale deep learning applications.

[TextBrewer](http://textbrewer.hfl-rc.com/) is a PyTorch-based knowledge distillation toolkit for natural language processing

[AdverTorch](https://github.com/BorealisAI/advertorch) is a toolbox for adversarial robustness research. It contains modules for generating adversarial examples and defending against attacks.

[NeMo](https://github.com/NVIDIA/NeMo) is a a toolkit for conversational AI.

[ClinicaDL](https://clinicadl.readthedocs.io/) is a framework for reproducible classification of Alzheimer's Disease

[Stable Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.

[TorchIO](https://github.com/fepegar/torchio) is a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch.

[PySyft](https://github.com/OpenMined/PySyft) is a Python library for encrypted, privacy preserving deep learning.

[Flair](https://github.com/flairNLP/flair) is a very simple framework for state-of-the-art natural language processing (NLP).

[Glow](https://github.com/pytorch/glow) is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.

[FairScale](https://github.com/facebookresearch/fairscale) is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes.

[MONAI](https://monai.io/) is a deep learning framework that provides domain-optimized foundational capabilities for developing healthcare imaging training workflows.

[PFRL](https://github.com/pfnet/pfrl) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.

[Einops](https://github.com/arogozhnikov/einops) is a flexible and powerful tensor operations for readable and reliable code.

[PyTorch3D](https://pytorch3d.org/) is a deep learning library that provides efficient, reusable components for 3D Computer Vision research with PyTorch.

[Ensemble Pytorch](https://ensemble-pytorch.readthedocs.io/) is a unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

[Lightly](https://github.com/lightly-ai/lightly) is a computer vision framework for self-supervised learning.

[Higher](https://github.com/facebookresearch/higher) is a library which facilitates the implementation of arbitrarily complex gradient-based meta-learning algorithms and nested optimisation loops with near-vanilla PyTorch.

[Horovod](http://horovod.ai/) is a distributed training library for deep learning frameworks. Horovod aims to make distributed DL fast and easy to use.

[PennyLane](https://pennylane.ai/) is a library for quantum ML, automatic differentiation, and optimization of hybrid quantum-classical computations.

[Detectron2](https://github.com/facebookresearch/detectron2) is FAIR's next-generation platform for object detection and segmentation.

[Fastai](https://docs.fast.ai/) is a library that simplifies training fast and accurate neural nets using modern best practices.

# Apache Spark
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## Apache Spark Learning Resources

[Apache Spark™](https://spark.apache.org/) is a unified analytics engine for large-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.

[Apache Spark Quick Start](https://spark.apache.org/docs/latest/quick-start.html)

[What is Apache Spark? | IBM](https://www.ibm.com/cloud/learn/apache-spark)

[Introduction to Apache Spark and Analytics | AWS](https://aws.amazon.com/big-data/what-is-spark/)

[Apache Spark 3.0: For Analytics & Machine Learning | NVIDIA](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/apache-spark-3/)

[.NET for Apache Spark™ | Big data analytics](https://dotnet.microsoft.com/apps/data/spark)

[Apache Spark Basics | MATLAB & Simulink](https://www.mathworks.com/help//compiler/spark/apache-spark-basics.html)

[MATLAB Hadoop and Spark | MATLAB & Simulink](https://www.mathworks.com/products/compiler/hadoop-and-spark.html)

[Top Apache Spark Courses Online | Coursera](https://www.coursera.org/courses?query=apache%20spark)

[Top Apache Spark Courses Online | Udemy](https://www.udemy.com/topic/apache-spark/)

[Apache Spark In-Depth (Spark with Scala) | Udemy](https://www.udemy.com/course/apache-spark-in-depth-spark-with-scala/)

[Learn Apache Spark with Online Courses | edX](https://www.edx.org/learn/apache-spark)

[Apache Spark Essential Training Online Class | LinkedIn Learning](https://www.linkedin.com/learning/apache-spark-essential-training)

[Cloudera Developer Training for Apache Spark™ and Hadoop | Cloudera](https://www.cloudera.com/about/training/courses/developer-training-for-spark-and-hadoop.html)

[Databricks Certified Associate Developer for Apache Spark 3.0 certification | Databricks](https://academy.databricks.com/exam/databricks-certified-associate-developer)

[Apache Spark Training Courses | NobleProg](https://www.nobleprog.com/apache-spark-training)

## Apache Spark Tools, Libraries, and Frameworks

[Spark SQL](https://spark.apache.org/sql/) is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations.

[Spark Streaming](https://spark.apache.org/streaming/) is a scalable and fault-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](https://spark.apache.org/mllib/) is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

[Graphx](https://spark.apache.org/graphx/) is the new Spark API for graphs and graph-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](https://spark.apache.org/docs/latest/api/python/index.html) is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment.

[Koalas](https://github.com/databricks/koalas) is a project that makes data scientists more productive when interacting with big data, by implementing the [pandas DataFrame API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) on top of [Apache Spark](https://spark.apache.org/).

[MLflow](https://mlflow.org/)is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. It offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (notebooks, standalone applications or the cloud). MLflow has four main components:

- The [Tracking component](https://mlflow.org/docs/latest/tracking.html) that allows you to record machine model training sessions (called runs) and run queries using Java, Python, R, and REST APIs.
- The [Projects component](https://mlflow.org/docs/latest/projects.html) packages code that is used in data science projects to ensure it can easily be reused and experiments can be reproduced.
- The [Models component](https://mlflow.org/docs/latest/models.html) that provides a standard unit for packaging and reusing machine learning models.
- The [Model Registry](https://mlflow.org/docs/latest/model-registry.html) component that lets you centrally manage models and their lifecycle.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[Apache Cassandra™](https://cassandra.apache.org/) is an open source NoSQL distributed database trusted by thousands of companies for scalability and high availability without compromising performance. Cassandra provides linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.

[Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data.

[Apache Mesos](http://mesos.apache.org/) is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, Jenkins, Spark, Aurora, and other frameworks on a dynamically shared pool of nodes.

[Apache HBase™](https://hbase.apache.org/) is an open-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)](https://www.ibm.com/analytics/hadoop/hdfs) is a distributed file system that handles large data sets running on commodity hardware. It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of Apache Hadoop, the others being [MapReduce](https://www.ibm.com/analytics/hadoop/mapreduce) and [YARN](https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html).

[Apache Beam](https://beam.apache.org/) is an open source, unified model and set of language-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](https://arrow.apache.org/) is a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.

[Neo4j](https://neo4j.com/) is the only enterprise-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](https://www.elastic.co/) is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.

[Logstash](https://www.elastic.co/products/logstash) is a tool for managing events and logs. When used generically, the term encompasses a larger system of log collection, processing, storage and searching activities.

[Kibana](https://www.elastic.co/products/kibana) is an open source data visualization plugin for Elasticsearch. It provides visualization capabilities on top of the content indexed on an Elasticsearch cluster. Users can create bar, line and scatter plots, or pie charts and maps on top of large volumes of data.

[Trino](https://trino.io/) is a Distributed SQL query engine for big data. It is able to tremendously speed up [ETL processes](https://docs.microsoft.com/en-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)](https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl) is a data pipeline used to collect data from various sources, transform the data according to business rules, and load it into a destination data store.

[Redis(REmote DIctionary Server)](https://redis.io/) is an open source (BSD licensed), in-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.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

# MATLAB Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## MATLAB Learning Resources

[MATLAB](https://www.mathworks.com/products/matlab.html) is a programming language that does numerical computing such as expressing matrix and array mathematics directly.

[MATLAB Documentation](https://www.mathworks.com/help/matlab/)

[Getting Started with MATLAB ](https://www.mathworks.com/help/matlab/getting-started-with-matlab.html)

[MATLAB and Simulink Training from MATLAB Academy](https://matlabacademy.mathworks.com)

[MathWorks Certification Program](https://www.mathworks.com/services/training/certification.html)

[Apache Spark Basics | MATLAB & Simulink](https://www.mathworks.com/help//compiler/spark/apache-spark-basics.html)

[MATLAB Hadoop and Spark | MATLAB & Simulink](https://www.mathworks.com/products/compiler/hadoop-and-spark.html)

[MATLAB Online Courses from Udemy](https://www.udemy.com/topic/matlab/)

[MATLAB Online Courses from Coursera](https://www.coursera.org/courses?query=matlab)

[MATLAB Online Courses from edX](https://www.edx.org/learn/matlab)

[Building a MATLAB GUI](https://www.mathworks.com/discovery/matlab-gui.html)

[MATLAB Style Guidelines 2.0](https://www.mathworks.com/matlabcentral/fileexchange/46056-matlab-style-guidelines-2-0)

[Setting Up Git Source Control with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/set-up-git-source-control.html)

[Pull, Push and Fetch Files with Git with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/push-and-fetch-with-git.html)

[Create New Repository with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/add-folder-to-source-control.html)

[PRMLT](http://prml.github.io/) is Matlab code for machine learning algorithms in the PRML book.

## MATLAB Tools, Libraries, Frameworks

**[MATLAB and Simulink Services & Applications List](https://www.mathworks.com/products.html)**

[MATLAB in the Cloud](https://www.mathworks.com/solutions/cloud.html) is a service that allows you to run in cloud environments from [MathWorks Cloud](https://www.mathworks.com/solutions/cloud.html#browser) to [Public Clouds](https://www.mathworks.com/solutions/cloud.html#public-cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).

[MATLAB Online™](https://matlab.mathworks.com) is a service that allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.

[Simulink](https://www.mathworks.com/products/simulink.html) is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.

[Simulink Online™](https://www.mathworks.com/products/simulink-online.html) is a service that provides access to Simulink through your web browser.

[MATLAB Drive™](https://www.mathworks.com/products/matlab-drive.html) is a service that gives you the ability to store, access, and work with your files from anywhere.

[MATLAB Parallel Server™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. You can prototype your programs and simulations on the desktop and then run them on clusters and clouds without recoding. MATLAB Parallel Server supports batch jobs, interactive parallel computations, and distributed computations with large matrices.

[MATLAB Schemer](https://github.com/scottclowe/matlab-schemer) is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.

[LRSLibrary](https://github.com/andrewssobral/lrslibrary) is a Low-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.

[Statistics and Machine Learning Toolbox™](https://www.mathworks.com/products/statistics.html) is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.

[Partial Differential Equation Toolbox™](https://www.mathworks.com/products/pde.html) is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.

[SoC Blockset™](https://www.mathworks.com/products/soc.html) is a tool that provides Simulink® blocks and visualization tools for modeling, simulating, and analyzing hardware and software architectures for ASICs, FPGAs, and systems on a chip (SoC). You can build your system architecture using memory models, bus models, and I/O models, and simulate the architecture together with the algorithms.

[Wireless HDL Toolbox™](https://www.mathworks.com/products/wireless-hdl.html) is a tool that provides pre-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™](https://www.mathworks.com/products/thingspeak.html) is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.

[SEA-MAT](https://sea-mat.github.io/sea-mat/) is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.

[Gramm](https://github.com/piermorel/gramm) is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.

[hctsa](https://hctsa-users.gitbook.io/hctsa-manual) is a software package for running highly comparative time-series analysis using Matlab.

[Plotly](https://plot.ly/matlab/) is a Graphing Library for MATLAB.

[YALMIP](https://yalmip.github.io/) is a MATLAB toolbox for optimization modeling.

[GNU Octave](https://www.gnu.org/software/octave/) is a high-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.

# Python Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## Python Learning Resources

[Python](https://www.python.org) is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.

[Python Developer’s Guide](https://devguide.python.org) is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.

[Azure Functions Python developer guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference-python) is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the [Azure Functions developers guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference).

[CheckiO](https://checkio.org/) is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.

[Python Institute](https://pythoninstitute.org)

[PCEP – Certified Entry-Level Python Programmer certification](https://pythoninstitute.org/pcep-certification-entry-level/)

[PCAP – Certified Associate in Python Programming certification](https://pythoninstitute.org/pcap-certification-associate/)

[PCPP – Certified Professional in Python Programming 1 certification](https://pythoninstitute.org/pcpp-certification-professional/)

[PCPP – Certified Professional in Python Programming 2](https://pythoninstitute.org/pcpp-certification-professional/)

[MTA: Introduction to Programming Using Python Certification](https://docs.microsoft.com/en-us/learn/certifications/mta-introduction-to-programming-using-python)

[Getting Started with Python in Visual Studio Code](https://code.visualstudio.com/docs/python/python-tutorial)

[Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html)

[Google's Python Education Class](https://developers.google.com/edu/python/)

[Real Python](https://realpython.com)

[The Python Open Source Computer Science Degree by Forrest Knight](https://github.com/ForrestKnight/open-source-cs-python)

[Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)

[Intro to Python by W3schools](https://www.w3schools.com/python/python_intro.asp)

[Codecademy's Python 3 course](https://www.codecademy.com/learn/learn-python-3)

[Learn Python with Online Courses and Classes from edX](https://www.edx.org/learn/python)

[Python Courses Online from Coursera](https://www.coursera.org/courses?query=python)

## Python Frameworks and Tools

[Python Package Index (PyPI)](https://pypi.org/) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.

[PyCharm](https://www.jetbrains.com/pycharm/) is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.

[Python Tools for Visual Studio(PTVS)](https://microsoft.github.io/PTVS/) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.

[Pylance](https://github.com/microsoft/pylance-release) is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.

[Pyright](https://github.com/Microsoft/pyright) is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.

[Django](https://www.djangoproject.com/) is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

[Flask](https://flask.palletsprojects.com/) is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.

[Web2py](http://web2py.com/) is an open-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.

[AWS Chalice](https://github.com/aws/chalice) is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.

[Tornado](https://www.tornadoweb.org/) is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.

[HTTPie](https://github.com/httpie/httpie) is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.

[Scrapy](https://scrapy.org/) is a fast high-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](https://sentry.io/) is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.

[Pipenv](https://github.com/pypa/pipenv) is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.

[Python Fire](https://github.com/google/python-fire) is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

[Bottle](https://github.com/bottlepy/bottle) is a fast, simple and lightweight [WSGI](https://www.wsgi.org/) micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the [Python Standard Library](https://docs.python.org/library/).

[CherryPy](https://cherrypy.org) is a minimalist Python object-oriented HTTP web framework.

[Sanic](https://github.com/huge-success/sanic) is a Python 3.6+ web server and web framework that's written to go fast.

[Pyramid](https://trypyramid.com) is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.

[TurboGears](https://turbogears.org) is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.

[Falcon](https://falconframework.org/) is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.

[Neural Network Intelligence(NNI)](https://github.com/microsoft/nni) is an open source AutoML toolkit for automate machine learning lifecycle, including [Feature Engineering](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md), [Neural Architecture Search](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md), [Model Compression](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md) and [Hyperparameter Tuning](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md).

[Dash](https://plotly.com/dash) is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.

[Luigi](https://github.com/spotify/luigi) is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.

[Locust](https://github.com/locustio/locust) is an easy to use, scriptable and scalable performance testing tool.

[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython.

[NumPy](https://www.numpy.org/) is the fundamental package needed for scientific computing with Python.

[Pillow](https://python-pillow.org/) is a friendly PIL(Python Imaging Library) fork.

[IPython](https://ipython.org/) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.

[GraphLab Create](https://turi.com/) is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.

[Pandas](https://pandas.pydata.org/) is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.

[PuLP](https://coin-or.github.io/pulp/) is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.

[Matplotlib](https://matplotlib.org/) is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

# C/C++ Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

## C/C++ Learning Resources

[C++](https://www.cplusplus.com/doc/tutorial/) is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.

[C](https://www.iso.org/standard/74528.html) is a general-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](https://en.wikipedia.org/wiki/Embedded_C) is a set of language extensions for the C programming language by the [C Standards Committee](https://isocpp.org/std/the-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](https://www.jetbrains.com/cpp/)

[Open source C++ libraries on cppreference.com](https://en.cppreference.com/w/cpp/links/libs)

[C++ Graphics libraries](https://cpp.libhunt.com/libs/graphics)

[C++ Libraries in MATLAB](https://www.mathworks.com/help/matlab/call-cpp-library-functions.html)

[C++ Tools and Libraries Articles](https://www.cplusplus.com/articles/tools/)

[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)

[Introduction C++ Education course on Google Developers](https://developers.google.com/edu/c++/)

[C++ style guide for Fuchsia](https://fuchsia.dev/fuchsia-src/development/languages/c-cpp/cpp-style)

[C and C++ Coding Style Guide by OpenTitan](https://docs.opentitan.org/doc/rm/c_cpp_coding_style/)

[Chromium C++ Style Guide](https://chromium.googlesource.com/chromium/src/+/master/styleguide/c++/c++.md)

[C++ Core Guidelines](https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md)

[C++ Style Guide for ROS](http://wiki.ros.org/CppStyleGuide)

[Learn C++](https://www.learncpp.com/)

[Learn C : An Interactive C Tutorial](https://www.learn-c.org/)

[C++ Institute](https://cppinstitute.org/free-c-and-c-courses)

[C++ Online Training Courses on LinkedIn Learning](https://www.linkedin.com/learning/topics/c-plus-plus)

[C++ Tutorials on W3Schools](https://www.w3schools.com/cpp/default.asp)

[Learn C Programming Online Courses on edX](https://www.edx.org/learn/c-programming)

[Learn C++ with Online Courses on edX](https://www.edx.org/learn/c-plus-plus)

[Learn C++ on Codecademy](https://www.codecademy.com/learn/learn-c-plus-plus)

[Coding for Everyone: C and C++ course on Coursera](https://www.coursera.org/specializations/coding-for-everyone)

[C++ For C Programmers on Coursera](https://www.coursera.org/learn/c-plus-plus-a)

[Top C Courses on Coursera](https://www.coursera.org/courses?query=c%20programming)

[C++ Online Courses on Udemy](https://www.udemy.com/topic/c-plus-plus/)

[Top C Courses on Udemy](https://www.udemy.com/topic/c-programming/)

[Basics of Embedded C Programming for Beginners on Udemy](https://www.udemy.com/course/embedded-c-programming-for-embedded-systems/)

[C++ For Programmers Course on Udacity](https://www.udacity.com/course/c-for-programmers--ud210)

[C++ Fundamentals Course on Pluralsight](https://www.pluralsight.com/courses/learn-program-cplusplus)

[Introduction to C++ on MIT Free Online Course Materials](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-introduction-to-c-january-iap-2011/)

[Introduction to C++ for Programmers | Harvard ](https://online-learning.harvard.edu/course/introduction-c-programmers)

[Online C Courses | Harvard University](https://online-learning.harvard.edu/subject/c)

## C/C++ Tools and Frameworks

[AWS SDK for C++](https://aws.amazon.com/sdk-for-cpp/)

[Azure SDK for C++](https://github.com/Azure/azure-sdk-for-cpp)

[Azure SDK for C](https://github.com/Azure/azure-sdk-for-c)

[C++ Client Libraries for Google Cloud Services](https://github.com/googleapis/google-cloud-cpp)

[Visual Studio](https://visualstudio.microsoft.com/) is an integrated development environment (IDE) from Microsoft; which is a feature-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](https://code.visualstudio.com/) is a code editor redefined and optimized for building and debugging modern web and cloud applications.

[Vcpkg](https://github.com/microsoft/vcpkg) is a C++ Library Manager for Windows, Linux, and MacOS.

[ReSharper C++](https://www.jetbrains.com/resharper-cpp/features/) is a Visual Studio Extension for C++ developers developed by JetBrains.

[AppCode](https://www.jetbrains.com/objc/) is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-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](https://www.jetbrains.com/clion/features/) is a cross-platform IDE for C and C++ developers developed by JetBrains.

[Code::Blocks](https://www.codeblocks.org/) is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.

[CppSharp](https://github.com/mono/CppSharp) is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.

[Conan](https://conan.io/) is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems.

[High Performance Computing (HPC) SDK](https://developer.nvidia.com/hpc) is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.

[Thrust](https://github.com/NVIDIA/thrust) is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.

[Boost](https://www.boost.org/) is an educational opportunity focused on cutting-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](https://www.gnu.org/software/automake/) is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.

[Cmake](https://cmake.org/) is an open-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](http://www.gnu.org/software/gdb/) is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed.

[GCC](https://gcc.gnu.org/) is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.

[GSL](https://www.gnu.org/software/gsl/) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.

[OpenGL Extension Wrangler Library (GLEW)](https://www.opengl.org/sdk/libs/GLEW/) is a cross-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](https://www.gnu.org/software/libtool/) is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.

[Maven](https://maven.apache.org/) is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.

[TAU (Tuning And Analysis Utilities)](http://www.cs.uoregon.edu/research/tau/home.php) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.

[Clang](https://clang.llvm.org/) is a production quality C, Objective-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](https://opencv.org/) is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Libcu++](https://nvidia.github.io/libcudacxx) is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.

[ANTLR (ANother Tool for Language Recognition)](https://www.antlr.org/) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.

[Oat++](https://oatpp.io/) is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.

[JavaCPP](https://github.com/bytedeco/javacpp) is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language.

[Cython](https://cython.org/) is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.

[Spdlog](https://github.com/gabime/spdlog) is a very fast, header-only/compiled, C++ logging library.

[Infer](https://fbinfer.com/) is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in [OCaml](https://ocaml.org/).

# Scala Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

**[Scala for TensorFlow](https://github.com/eaplatanios/tensorflow_scala)**

## Scala Learning Resources

[Scala](https://scala-lang.org/) is a combination of object-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](https://docs.scala-lang.org/style/)

[Databricks Scala Style Guide](https://github.com/databricks/scala-style-guide)

[Data Science using Scala and Spark on Azure](https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/scala-walkthrough)

[Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-create-standalone-application)

[Intro to Spark DataFrames using Scala with Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/spark/latest/dataframes-datasets/introduction-to-dataframes-scala)

[Using Scala to Program AWS Glue ETL Scripts](https://docs.aws.amazon.com/glue/latest/dg/glue-etl-scala-using.html)

[Using Flink Scala shell with Amazon EMR clusters](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/flink-scala.html)

[AWS EMR and Spark 2 using Scala from Udemy](https://www.udemy.com/course/aws-emr-and-spark-2-using-scala/)

[Using the Google Cloud Storage connector with Apache Spark](https://cloud.google.com/dataproc/docs/tutorials/gcs-connector-spark-tutorial)

[Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud](https://cloud.google.com/dataproc/docs/tutorials/spark-scala)

[Scala Courses and Certifications from edX](https://www.edx.org/learn/scala)

[Scala Courses from Coursera](https://www.coursera.org/courses?query=scala)

[Top Scala Courses from Udemy](https://www.udemy.com/topic/scala/)

## Scala Tools and Libraries

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[Play Framework](https://github.com/playframework/playframework) is a web framework combines productivity and performance making it easy to build scalable web applications with Java and Scala.

[Dotty](https://github.com/lampepfl/dotty) is a research compiler that will become Scala 3.

[AWScala](https://github.com/seratch/AWScala) is a tool that enables Scala developers to easily work with Amazon Web Services in the Scala way.

[Scala.js](https://www.scala-js.org/) is a compiler that converts Scala to JavaScript.

[Polynote](https://polynote.org/) is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.

[Scala Native](http://scala-native.org/) is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala.

[Gitbucket](https://gitbucket.github.io/) is a Git platform powered by Scala with easy installation, high extensibility & GitHub API compatibility.

[Finagle](https://twitter.github.io/finagle) is a fault tolerant, protocol-agnostic RPC system

[Gatling](https://gatling.io/) is a load test tool. It officially supports HTTP, WebSocket, Server-Sent-Events and JMS.

[Scalatra](https://scalatra.org/) is a tiny Scala high-performance, async web framework, inspired by [Sinatra](https://www.sinatrarb.com/).

# R Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

**[R for TensorFlow](https://github.com/rstudio/tensorflow)**

## R Learning Resources

[R](https://www.r-project.org/) is an open source software environment for statistical computing and graphics. It compiles and runs on a wide variety of platforms such as Windows and MacOS.

[An Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)

[Google's R Style Guide](https://google.github.io/styleguide/Rguide.html)

[R developer's guide to Azure](https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/r-developers-guide)

[Running R at Scale on Google Compute Engine](https://cloud.google.com/solutions/running-r-at-scale)

[Running R on AWS](https://aws.amazon.com/blogs/big-data/running-r-on-aws/)

[RStudio Server Pro for AWS](https://aws.amazon.com/marketplace/pp/RStudio-RStudio-Server-Pro-for-AWS/B06W2G9PRY)

[Learn R by Codecademy](https://www.codecademy.com/learn/learn-r)

[Learn R Programming with Online Courses and Lessons by edX](https://www.edx.org/learn/r-programming)

[R Language Courses by Coursera](https://www.coursera.org/courses?query=r%20language)

[Learn R For Data Science by Udacity](https://www.udacity.com/course/programming-for-data-science-nanodegree-with-R--nd118)

## R Tools, Libraries, and Frameworks

[Visual Studio Code](https://code.visualstudio.com/) is a code editor redefined and optimized for building and debugging modern web and cloud applications.

[Code Server](https://coder.com/) is a tool that allows you to run [VS Code](https://code.visualstudio.com/) on any machine anywhere and access it in the browser.

[VSCode-R](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) is a VS Code extension provides support for the [R programming language](https://www.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](https://marketplace.visualstudio.com/items?itemName=RDebugger.r-debugger) is an extension that adds debugging capabilities for the R programming language to Visual Studio Code and depends on the R package [vscDebugger (documentation)](https://github.com/ManuelHentschel/vscDebugger).

[Language Server Protocol (LSP)](https://microsoft.github.io/language-server-protocol/) is a tool that defines the protocol used between an editor or IDE and a language server that provides language features like auto complete, go to definition, find all references.

[RStudio](https://rstudio.com/) is an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.

[Shiny](https://shiny.rstudio.com/) is a newer package from RStudio that makes it incredibly easy to build interactive web applications with R.

[Rmarkdown](https://rmarkdown.rstudio.com/) is a package helps you create dynamic analysis documents that combine code, rendered output (such as figures), and prose.

[R Host](https://github.com/microsoft/R-Host) is a host process for R that provides access and extensibility to it remotely over WebSocket and JSON.

[Rplugin](https://github.com/JetBrains/Rplugin) is R Language supported plugin for the IntelliJ IDE.

[Plotly](https://plotly-r.com/) is an R package for creating interactive web graphics via the open source JavaScript graphing library [plotly.js](https://github.com/plotly/plotly.js).

[Metaflow](https://metaflow.org/) is a Python/R library that helps scientists and engineers build and manage real-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](https://facebook.github.io/prophet) is a procedure for forecasting time series data based on an additive model where non-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](https://lightgbm.readthedocs.io/) is a gradient boosting framework that uses tree based learning algorithms, used for ranking, classification and many other machine learning tasks.

[Dash](https://plotly.com/dash) is a Python framework for building analytical web applications in Python, R, Julia, and Jupyter.

[MLR](https://mlr.mlr-org.com/) is Machine Learning in R.

[ML workspace](https://github.com/ml-tooling/ml-workspace) is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.

[CatBoost](https://catboost.ai/) is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

[Plumber](https://www.rplumber.io/) is a tool that allows you to create a web API by merely decorating your existing R source code with special comments.

[Drake](https://docs.ropensci.org/drake) is an R-focused pipeline toolkit for reproducibility and high-performance computing.

[DiagrammeR](https://visualizers.co/diagrammer/) is a package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.

[Knitr](https://yihui.org/knitr/) is a general-purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.

[Broom](https://broom.tidymodels.org/) is a tool that converts statistical analysis objects from R into tidy format.

# Julia Development
[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

**[Julia for TensorFlow](https://github.com/malmaud/TensorFlow.jl)**

## Julia Learning Resources

[Julia](https://julialang.org) is a high-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](https://juliahub.com/) contains over 4,000 Julia packages for use by the community.

[Julia Observer](https://www.juliaobserver.com)

[Julia Manual](https://docs.julialang.org/en/v1/manual/getting-started/)

[JuliaLang Essentials](https://docs.julialang.org/en/v1/base/base/)

[Julia Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/)

[Julia By Example](https://juliabyexample.helpmanual.io/)

[JuliaLang Gitter](https://gitter.im/JuliaLang/julia)

[DataFrames Tutorial using Jupyter Notebooks](https://github.com/bkamins/Julia-DataFrames-Tutorial/)

[Julia Academy](https://juliaacademy.com/courses?preview=logged_out)

[Julia Meetup groups](https://www.meetup.com/topics/julia/)

[Julia on Microsoft Azure](https://juliacomputing.com/media/2017/02/08/azure.html)

## Julia Tools, Libraries and Frameworks

[JuliaPro](https://juliacomputing.com/products/juliapro.html) is a free and fast way to setup Julia for individual researchers, engineers, scientists, quants, traders, economists, students and others. Julia developers can build better software quicker and easier while benefiting from Julia's unparalleled high performance. It includes 2600+ open source packages or from a curated list of 250+ JuliaPro packages. Curated packages are tested, documented and supported by Julia Computing.

[Juno](https://junolab.org) is a powerful, free IDE based on [Atom](https://atom.io/) for the Julia language.

[Debugger.jl](https://github.com/JuliaDebug/Debugger.jl) is the Julia debuggin tool.

[Profile (Stdlib)](https://docs.julialang.org/en/v1/manual/profile/) is a module provides tools to help developers improve the performance of their code. When used, it takes measurements on running code, and produces output that helps you understand how much time is spent on individual line's.

[Revise.jl](https://github.com/timholy/Revise.jl) allows you to modify code and use the changes without restarting Julia. With Revise, you can be in the middle of a session and then update packages, switch git branches, and/or edit the source code in the editor of your choice; any changes will typically be incorporated into the very next command you issue from the REPL. This can save you the overhead of restarting Julia, loading packages, and waiting for code to JIT-compile.

[JuliaGPU](https://juliagpu.org/) is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.

[IJulia.jl](https://github.com/JuliaLang/IJulia.jl) is the Julia kernel for Jupyter.

[AWS.jl](https://github.com/JuliaCloud/AWS.jl) is a Julia interface for [Amazon Web Services](https://aws.amazon.com/).

[CUDA.jl](https://juliagpu.gitlab.io/CUDA.jl) is a package for the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.

[XLA.jl](https://github.com/JuliaTPU/XLA.jl) is a package for compiling Julia to XLA for [Tensor Processing Unit(TPU)](https://cloud.google.com/tpu/).

[Nanosoldier.jl](https://github.com/JuliaCI/Nanosoldier.jl) is a package for running JuliaCI services on MIT's Nanosoldier cluster.

[Julia for VSCode](https://www.julia-vscode.org) is a powerful extension for the Julia language.

[JuMP.jl](https://jump.dev/) is a domain-specific modeling language for [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) embedded in Julia.

[Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl) is a univariate and multivariate optimization in Julia.

[RCall.jl](https://github.com/JuliaInterop/RCall.jl) is a package that allows you to call R functions from Julia.

[JavaCall.jl](http://juliainterop.github.io/JavaCall.jl) is a package that allows you to call Java functions from Julia.

[PyCall.jl](https://github.com/JuliaPy/PyCall.jl) is a package that allows you to call Python functions from Julia.

[MXNet.jl](https://github.com/dmlc/MXNet.jl) is the Apache MXNet Julia package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia.

[Knet](https://denizyuret.github.io/Knet.jl/latest) is the [Koç University deep](http://www.ku.edu.tr/en) learning framework implemented in Julia by [Deniz Yuret](https://www.denizyuret.com/) and collaborators. It supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.

[Distributions.jl](https://github.com/JuliaStats/Distributions.jl) is a Julia package for probability distributions and associated functions.

[DataFrames.jl](http://juliadata.github.io/DataFrames.jl/stable/) is a tool for working with tabular data in Julia.

[Flux.jl](https://fluxml.ai/) is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.

[IRTools.jl](https://github.com/FluxML/IRTools.jl) is a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs.

[Cassette.jl](https://github.com/jrevels/Cassette.jl) is a Julia package that provides a mechanism for dynamically injecting code transformation passes into Julia’s just-in-time (JIT) compilation cycle, enabling post hoc analysis and modification of "Cassette-unaware" Julia programs without requiring manual source annotation or refactoring of the target code.

## Contribute

- [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/Jupyter-Guide/pulls).

## License

[Back to the Top](https://github.com/mikeroyal/Jupyter-Guide#table-of-contents)

Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/).