{"id":1577,"url":"https://github.com/mikeroyal/Deep-Learning-Guide","name":"Deep-Learning-Guide","description":"Deep learning Guide.","projects_count":686,"last_synced_at":"2026-06-12T09:00:21.071Z","repository":{"id":46971682,"uuid":"408240969","full_name":"mikeroyal/Deep-Learning-Guide","owner":"mikeroyal","description":"Deep learning Guide.","archived":false,"fork":false,"pushed_at":"2022-02-06T20:38:02.000Z","size":146,"stargazers_count":37,"open_issues_count":0,"forks_count":5,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-05-26T17:02:51.195Z","etag":null,"topics":["ai","artificial-intelligence","awesome","awesome-list","awesome-readme","awesome-resources","computer-science","deep-learning","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mikeroyal.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-09-19T21:29:34.000Z","updated_at":"2026-05-02T20:58:59.000Z","dependencies_parsed_at":"2022-09-07T14:02:25.421Z","dependency_job_id":null,"html_url":"https://github.com/mikeroyal/Deep-Learning-Guide","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mikeroyal/Deep-Learning-Guide","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FDeep-Learning-Guide","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FDeep-Learning-Guide/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FDeep-Learning-Guide/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FDeep-Learning-Guide/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mikeroyal","download_url":"https://codeload.github.com/mikeroyal/Deep-Learning-Guide/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FDeep-Learning-Guide/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34236552,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-12T02:00:06.859Z","response_time":109,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-04T17:53:59.714Z","updated_at":"2026-06-12T09:00:21.072Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["C/C++ Tools and Frameworks","Python Frameworks, Libraries, and Tools","License","Bioinformatics Tools, Libraries, and Frameworks","ML Frameworks, Libraries, and Tools","NLP Tools, Libraries, and Frameworks","C/C++ Learning Resources","R Tools, Libraries, and Frameworks","R Learning Resources","Robotics Tools and Frameworks","LiDAR Learning Resources","CUDA Learning Resources","CUDA Tools Libraries, and Frameworks","MATLAB Learning Resources","MATLAB Tools, Libraries, Frameworks","Computer Vision Tools, Libraries, and Frameworks","Python Learning Resources","Photogrammetry Tools, Libraries, and Frameworks","Autodesk Tools and Frameworks","NLP Learning Resources","Learning Resources for ML","Computer Vision Learning Resources","Bioinformatics Learning Resources","Julia Learning Resources","Julia Tools, Libraries and Frameworks","Photogrammetry Learning Resources","Autodesk Learning Resources","LiDAR Tools \u0026 Frameworks","Robotic Arms \u0026 Dev Kits","Robotics Learning Resources"],"sub_categories":["viii. Linear Regression","i. Vector operations","iii. Matrix-vector product","iv. Linear transformations","v. Fundamental vector spaces","i. Solving systems of equations","ii. Systems of equations as matrix equations","i. Using row operations","iii. Transpose of a Matrix","i. Basis","vi. Determinants","vii. Eigenvalues and eigenvectors","ii. Matrix operations","ii. Matrix representations of linear transformations","iii. Dimension and Basis for Vector Spaces","iv. Row space, columns space, and rank of a matrix","ii. Using elementary matrices"],"readme":"\u003ch1 align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/133943699-6dcfcb40-ddf7-4501-86e0-41e8aee91fe2.png\"\u003e\n  \u003cbr /\u003e\n  Deep Learning Guide\n\u003c/h1\u003e\n\n#### A guide covering Deep Learning including the applications, libraries, frameworks, and tools that will make you a better and more efficient with Deep Learning development.\n\n**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).**\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/133943702-f78c7d7c-c27a-48d3-b30a-1785b1d7eaac.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n\u003cimg src=\"https://user-images.githubusercontent.com/45159366/133943716-5ed05027-6b17-4342-9520-4ea12f9d8f02.png\"\u003e\n\n**Deep Learning diagram.  Source: [ResearchGate](https://www.researchgate.net/figure/Deep-learning-diagram_fig5_323784695)**\n\n# Table of Contents\n\n1. [Deep Learning Learning Resources](https://github.com/mikeroyal/Deep-Learning-Guide#Deep-Learning-learning-resources)\n\n2. [Deep Learning Tools, Libraries, and Frameworks](https://github.com/mikeroyal/Deep-Learning-Guide#Deep-Learning-tools-libraries-and-frameworks)\n\n3. [Computer Vision Development](https://github.com/mikeroyal/Deep-Learning-Guide#computer-vision-development)\n\n4. [Natural Language Processing (NLP) Development](https://github.com/mikeroyal/Deep-Learning-Guide#nlp-development)\n\n5. [Bioinformatics](https://github.com/mikeroyal/Deep-Learning-Guide#bioinformatics)\n\n6. [Algorithms](https://github.com/mikeroyal/Deep-Learning-Guide#algorithms)\n\n7. [Machine Learning](https://github.com/mikeroyal/Deep-Learning-Guide#machine-learning)\n\n8. [CUDA Development](https://github.com/mikeroyal/Deep-Learning-Guide#cuda-development)\n\n9. [MATLAB Development](https://github.com/mikeroyal/Deep-Learning-Guide#matlab-development)\n\n10. [Photogrammetry Development](https://github.com/mikeroyal/Deep-Learning-Guide#photogrammetry-development)\n\n11. [Autodesk Development](https://github.com/mikeroyal/Deep-Learning-Guide#autodesk-development)\n\n12. [LiDAR Development](https://github.com/mikeroyal/Deep-Learning-Guide#lidar-development)\n\n13. [Robotics Development](https://github.com/mikeroyal/Deep-Learning-Guide#robotics-development)\n\n14. [Linear Algebra](https://github.com/mikeroyal/Deep-Learning-Guide#linear-algebra)\n\n15. [C/C++ Development](https://github.com/mikeroyal/Deep-Learning-Guide#cc-development)\n\n16. [Python Development](https://github.com/mikeroyal/Deep-Learning-Guide#python-development)\n\n17. [R Development](https://github.com/mikeroyal/Deep-Learning-Guide#r-development)\n\n18. [Julia Development](https://github.com/mikeroyal/Deep-Learning-Guide#julia-development)\n\n# Deep Learning Learning Resources\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n[Deep Learning](https://www.ibm.com/cloud/learn/deep-learning) is a subset of machine learning, which is 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).\n\n[Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-us/training/online/)\n\n[Top Deep Learning Courses Online | Coursera](https://www.coursera.org/courses?query=deep%20learning)\n\n[Top Deep Learning Courses Online | Udemy](https://www.udemy.com/topic/deep-learning/)\n\n[Learn Deep Learning with Online Courses and Lessons | edX](https://www.edx.org/learn/deep-learning)\n\n[Deep Learning Online Course Nanodegree | Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101)\n\n[Machine Learning Course by Andrew Ng | Coursera](https://www.coursera.org/learn/machine-learning?)\n\n[Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)\n\n[Data Science: Deep Learning and Neural Networks in Python | Udemy](https://www.udemy.com/course/data-science-deep-learning-in-python/)\n\n[Understanding Machine Learning with Python | Pluralsight ](https://www.pluralsight.com/courses/python-understanding-machine-learning)\n\n[How to Think About Machine Learning Algorithms | Pluralsight](https://www.pluralsight.com/courses/machine-learning-algorithms)\n\n[Deep Learning Courses | Stanford Online](https://online.stanford.edu/courses/cs230-deep-learning)\n\n[Deep Learning - UW Professional \u0026 Continuing Education](https://www.pce.uw.edu/courses/deep-learning)\n\n[Deep Learning Online Courses | Harvard University](https://online-learning.harvard.edu/course/deep-learning-0)\n\n[Machine Learning for Everyone Courses | DataCamp](https://www.datacamp.com/courses/introduction-to-machine-learning-with-r)\n\n[Artificial Intelligence Expert Course: Platinum Edition | Udemy](https://www.udemy.com/course/artificial-intelligence-exposed-future-10-extreme-edition/)\n\n[Top Artificial Intelligence Courses Online | Coursera](https://www.coursera.org/courses?query=artificial%20intelligence)\n\n[Learn Artificial Intelligence with Online Courses and Lessons | edX](https://www.edx.org/learn/artificial-intelligence)\n\n[Professional Certificate in Computer Science for Artificial Intelligence | edX](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)\n\n[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)\n\n[Artificial Intelligence (AI) Online Courses | Udacity](https://www.udacity.com/school-of-ai)\n\n[Intro to Artificial Intelligence Course | Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)\n\n[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)\n\n[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/)\n\n[Expert Systems and Applied Artificial Intelligence](https://www.umsl.edu/~joshik/msis480/chapt11.htm)\n\n[Autonomous Systems - Microsoft AI](https://www.microsoft.com/en-us/ai/autonomous-systems)\n\n[Introduction to Microsoft Project Bonsai](https://docs.microsoft.com/en-us/learn/autonomous-systems/intro-to-project-bonsai/)\n\n[Machine teaching with the Microsoft Autonomous Systems platform](https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/autonomous-systems)\n\n[Autonomous Maritime Systems Training | AMC Search](https://www.amcsearch.com.au/ams-training)\n\n[Top Autonomous Cars Courses Online | Udemy](https://www.udemy.com/topic/autonomous-cars/)\n\n[Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy](https://www.udemy.com/course/applied-systems-control-for-engineers-modelling-pid-mpc/)\n\n[Learn Autonomous Robotics with Online Courses and Lessons | edX](https://www.edx.org/learn/autonomous-robotics)\n\n[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)\n\n[Autonomous Systems Online Courses \u0026 Programs | Udacity](https://www.udacity.com/school-of-autonomous-systems)\n\n[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)\n\n[Autonomous Systems MOOC and Free Online Courses | MOOC List](https://www.mooc-list.com/tags/autonomous-systems)\n\n[Robotics and Autonomous Systems Graduate Program | Standford Online](https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program)\n\n[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/)\n\n# Deep Learning Tools, Libraries, and Frameworks\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n[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/).\n\n[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.\n\n[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.\n\n[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).\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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 \u0026 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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n# Computer Vision Development\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/129494417-b0ee8192-ac41-4a6d-8e1d-4761ffc8bab1.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## Computer Vision Learning Resources\n\n[Computer Vision](https://azure.microsoft.com/en-us/overview/what-is-computer-vision/) is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.\n\n[OpenCV Courses](https://opencv.org/courses/)\n\n[Exploring Computer Vision in Microsoft Azure](https://docs.microsoft.com/en-us/learn/paths/explore-computer-vision-microsoft-azure/)\n\n[Top Computer Vision Courses Online | Coursera](https://www.coursera.org/courses?languages=en\u0026query=computer%20vision)\n\n[Top Computer Vision Courses Online | Udemy](https://www.udemy.com/topic/computer-vision/)\n\n[Learn Computer Vision with Online Courses and Lessons | edX](https://www.edx.org/learn/computer-vision)\n\n[Computer Vision and Image Processing Fundamentals | edX](https://www.edx.org/course/computer-vision-and-image-processing-fundamentals)\n\n[Introduction to Computer Vision Courses | Udacity](https://www.udacity.com/course/introduction-to-computer-vision--ud810)\n\n[Computer Vision Nanodegree program | Udacity](https://www.udacity.com/course/computer-vision-nanodegree--nd891)\n\n[Machine Vision Course |MIT Open Courseware ](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2004/)\n\n[Computer Vision Training Courses | NobleProg](https://www.nobleprog.com/computer-vision-training)\n\n[Visual Computing Graduate Program | Stanford Online](https://online.stanford.edu/programs/visual-computing-graduate-program)\n\n## Computer Vision Tools, Libraries, and Frameworks\n\n[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.\n\n[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.\n\n[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.\n\n[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/).\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[Data Acquisition Toolbox™](https://www.mathworks.com/products/data-acquisition.html) is a tool that provides apps and functions for configuring data acquisition hardware, reading data into MATLAB® and Simulink®, and writing data to DAQ analog and digital output channels. The toolbox supports a variety of DAQ hardware, including USB, PCI, PCI Express®, PXI®, and PXI Express® devices, from National Instruments® and other vendors.\n\n[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 \u0026 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.\n\n# NLP Development\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/131386286-e23991d5-a1aa-4ee9-9582-874dc0854c1a.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## NLP Learning Resources\n\n[Natural Language Processing (NLP)](https://www.ibm.com/cloud/learn/natural-language-processing) is a branch of artificial intelligence (AI) focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, and deep learning models.\n\n[Natural Language Processing With Python's NLTK Package](https://realpython.com/nltk-nlp-python/)\n\n[Cognitive Services—APIs for AI Developers | Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/)\n\n[Artificial Intelligence Services - Amazon Web Services (AWS)](https://aws.amazon.com/machine-learning/ai-services/)\n\n[Google Cloud Natural Language API](https://cloud.google.com/natural-language/docs/reference/rest)\n\n[Top Natural Language Processing Courses Online | Udemy](https://www.udemy.com/topic/natural-language-processing/)\n\n[Introduction to Natural Language Processing (NLP) | Udemy](https://www.udemy.com/course/natural-language-processing/)\n\n[Top Natural Language Processing Courses | Coursera](https://www.coursera.org/courses?=\u0026query=natural%20language%20processing)\n\n[Natural Language Processing | Coursera](https://www.coursera.org/learn/language-processing)\n\n[Natural Language Processing in TensorFlow | Coursera](https://www.coursera.org/learn/natural-language-processing-tensorflow)\n\n[Learn Natural Language Processing with Online Courses and Lessons | edX](https://www.edx.org/learn/natural-language-processing)\n\n[Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight](https://www.pluralsight.com/courses/build-natural-language-processing-solution-microsoft-azure)\n\n[Natural Language Processing (NLP) Training Courses | NobleProg](https://www.nobleprog.com/nlp-training)\n\n[Natural Language Processing with Deep Learning Course | Standford Online](https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning)\n\n[Advanced Natural Language Processing - MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)\n\n[Certified Natural Language Processing Expert Certification | IABAC](https://iabac.org/artificial-intelligence-certification/certified-natural-language-processing-expert/)\n\n[Natural Language Processing Course - Intel](https://software.intel.com/content/www/us/en/develop/training/course-natural-language-processing.html)\n\n\n## NLP Tools, Libraries, and Frameworks\n\n[Natural Language Toolkit (NLTK)](https://www.nltk.org/) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over [50 corpora and lexical resources](https://nltk.org/nltk_data/) such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.\n\n[spaCy](https://spacy.io) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It also features neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT.\n\n[CoreNLP](https://stanfordnlp.github.io/CoreNLP/) is a set of natural language analysis tools written in Java. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.\n\n[NLPnet](https://github.com/erickrf/nlpnet) is a Python library for Natural Language Processing tasks based on neural networks. It performs part-of-speech tagging, semantic role labeling and dependency parsing.\n\n[Flair](https://github.com/flairNLP/flair) is a simple framework for state-of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.\n\n[Catalyst](https://github.com/curiosity-ai/catalyst) is a C# Natural Language Processing library built for speed. Inspired by [spaCy's design](https://spacy.io/), it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.\n\n[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).\n\n[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.\n\n[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/).\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.\n\n[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.\n\n[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.\n\n[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.\n\n# Bioinformatics\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/126912438-49cc660e-90c5-4cbc-828b-10e807b99767.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## Bioinformatics Learning Resources\n\n[Bioinformatics](https://www.genome.gov/genetics-glossary/Bioinformatics) is a field of computational science that has to do with the analysis of sequences of biological molecules. This usually refers to genes, DNA, RNA, or protein, and is particularly useful in comparing genes and other sequences in proteins and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to figure out what their function is.\n\n[European Bioinformatics Institute](https://www.ebi.ac.uk/)\n\n[National Center for Biotechnology Information](https://www.ncbi.nlm.nih.gov)\n\n[Online Courses in Bioinformatics |ISCB - International Society for Computational Biology](https://www.iscb.org/cms_addon/online_courses/index.php)\n\n[Bioinformatics | Coursera](https://www.coursera.org/specializations/bioinformatics)\n\n[Top Bioinformatics Courses | Udemy](https://www.udemy.com/topic/Bioinformatics/)\n\n[Biometrics Courses | Udemy](https://www.udemy.com/course/biometrics/)\n\n[Learn Bioinformatics with Online Courses and Lessons | edX](https://www.edx.org/learn/bioinformatics)\n\n[Bioinformatics Graduate Certificate | Harvard Extension School](https://extension.harvard.edu/academics/programs/bioinformatics-graduate-certificate/)\n\n[Bioinformatics and Biostatistics | UC San Diego Extension](https://extension.ucsd.edu/courses-and-programs/bioinformatics-and-biostatistics)\n\n[Bioinformatics and Proteomics - Free Online Course Materials | MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-bioinformatics-and-proteomics-january-iap-2005/)\n\n[Introduction to Biometrics course - Biometrics Institute](https://www.biometricsinstitute.org/event/introduction-to-biometrics-short-course/)\n\n## Bioinformatics Tools, Libraries, and Frameworks\n\n[Bioconductor](https://bioconductor.org/) is an open source project that provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the [R statistical programming language](https://www.r-project.org/about.html), and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an [AMI (Amazon Machine Image)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html) and [Docker images](https://docs.docker.com/engine/reference/commandline/images/).\n\n[Bioconda](https://bioconda.github.io) is a channel for the conda package manager specializing in bioinformatics software. It has a repository of packages containing over 7000 bioinformatics packages ready to use with conda install.\n\n[UniProt](https://www.uniprot.org/) is a freely accessible database that provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information.\n\n[Bowtie 2](https://bio.tools/bowtie2#!) is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.\n\n[Biopython](https://biopython.org/) is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.\n\n[BioRuby](https://bioruby.open-bio.org/) is a toolkit that has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.\n\n[BioJava](https://biojava.org/) is a toolkit that provides an API to maintain local installations of the PDB, load and manipulate structures, perform standard analysis such as sequence and structure alignments and visualize them in 3D.\n\n[BioPHP](https://biophp.org/) is an open source project that provides a collection of open source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools.\n\n[Avogadro](https://avogadro.cc/) is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.\n\n[Ascalaph Designer](https://www.biomolecular-modeling.com/Ascalaph/Ascalaph_Designer.html) is a program for molecular dynamic simulations. Under a single graphical environment are represented as their own implementation of molecular dynamics as well as the methods of classical and quantum mechanics of popular programs.\n\n[Anduril](https://www.anduril.org/site/) is a workflow platform for analyzing large data sets. Anduril provides facilities for analyzing high-thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.\n\n[Galaxy](https://melbournebioinformatics.github.io/MelBioInf_docs/tutorials/galaxy_101/galaxy_101/) is an open source, web-based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.\n\n[PathVisio](https://pathvisio.github.io/) is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.\n\n[Orange](https://orangedatamining.com/) is a powerful data mining and machine learning toolkit that performs data analysis and visualization.\n\n[Basic Local Alignment Search Tool](https://blast.ncbi.nlm.nih.gov/Blast.cgi) is a tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.\n\n[OSIRIS](https://www.ncbi.nlm.nih.gov/osiris/) is public-domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.\n\n[NCBI BioSystems](https://www.ncbi.nlm.nih.gov/biosystems/) is a  Database that provides integrated access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez.\n\n\n# Algorithms\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123861872-858dce80-d8dc-11eb-9a2c-51205d1541e9.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**\n\n[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123858065-ec5cb900-d8d7-11eb-81c5-c6a8feefa84f.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**\n\n[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123858036-e5ce4180-d8d7-11eb-8c52-43d7c7e6e3c4.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**\n\n[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123858026-e36be780-d8d7-11eb-9034-8859d6f09490.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**\n\n[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123858062-ebc42280-d8d7-11eb-9252-97e058bda8bd.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**\n\n[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/123858053-e8c93200-d8d7-11eb-844c-60463ecf662c.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**\n\n[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/124398881-fe21d000-dccc-11eb-8f5f-0a0730d85d55.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**\n\n[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/124398883-ffeb9380-dccc-11eb-9adb-66729a353132.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*\n\n[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/124398885-00842a00-dccd-11eb-89c1-bd4c1adbf305.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**\n\n\n# Machine Learning\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/96352527-ad077880-1078-11eb-98b7-da1c0586cf0e.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## Learning Resources for ML\n\n[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.\n\n[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)\n\n[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)\n\n[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)\n\n[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)\n\n[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)\n\n[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)\n\n[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)\n\n[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)\n\n[JupyterLab](https://jupyterlab.readthedocs.io/)\n\n[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)\n\n[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)\n\n[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)\n\n[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning\u0026)\n\n[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)\n\n## ML Frameworks, Libraries, and Tools\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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).\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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/).\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n# CUDA Development\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/94306481-e17b8f00-ff27-11ea-832f-c85374acb3b1.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/117718735-55a23480-b191-11eb-874d-e690d09cd490.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**CUDA Toolkit. Source: [NVIDIA Developer CUDA](https://developer.nvidia.com/cuda-zone)**\n\n## CUDA Learning Resources\n\n[CUDA](https://developer.nvidia.com/cuda-zone) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.\n\n[CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/index.html)\n\n[CUDA Quick Start Guide](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html)\n\n[CUDA on WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html)\n\n[CUDA GPU support for TensorFlow](https://www.tensorflow.org/install/gpu)\n\n[NVIDIA Deep Learning cuDNN Documentation](https://docs.nvidia.com/deeplearning/cudnn/api/index.html)\n\n[NVIDIA GPU Cloud Documentation](https://docs.nvidia.com/ngc/ngc-introduction/index.html)\n\n[NVIDIA NGC](https://ngc.nvidia.com/) is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.\n\n[NVIDIA NGC Containers](https://www.nvidia.com/en-us/gpu-cloud/containers/) is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.\n\n## CUDA Tools Libraries, and Frameworks\n\n[CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) is a collection of tools \u0026 libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.\n\n[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/).\n\n[CUDA-X HPC](https://www.nvidia.com/en-us/technologies/cuda-x/) is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC).\n\n[NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) is a collection of tools \u0026 libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime [library](https://github.com/NVIDIA/libnvidia-container) and utilities to automatically configure containers to leverage NVIDIA GPUs.\n\n[Minkowski Engine](https://nvidia.github.io/MinkowskiEngine) is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.\n\n[CUTLASS](https://github.com/NVIDIA/cutlass) is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.\n\n[CUB](https://github.com/NVIDIA/cub) is a cooperative primitives for CUDA C++ kernel authors.\n\n[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.\n\n[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.\n\n[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.\n\n[CuPy](https://cupy.dev/) is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.\n\n[CatBoost](https://catboost.ai/) is a fast, scalable, high performance [Gradient Boosting](https://en.wikipedia.org/wiki/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.\n\n[cuDF](https://rapids.ai/) is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers \u0026 data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.\n\n[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.\n\n[ArrayFire](https://arrayfire.com/) is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.\n\n[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.\n\n[AresDB](https://eng.uber.com/aresdb/) is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.\n\n[Arraymancer](https://mratsim.github.io/Arraymancer/) is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.\n\n[Kintinuous](https://github.com/mp3guy/Kintinuous) is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.\n\n[GraphVite](https://graphvite.io/) is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.\n\n# MATLAB Development\n[Back to the Top](https://github.com/mikeroyal/Deep-Learning-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/94306473-de809e80-ff27-11ea-924b-0a6947ae38bc.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## MATLAB Learning Resources\n\n[MATLAB](https://www.mathworks.com/products/matlab.html) is a programming language that does numerical computing such as expressing matrix and array mathematics directly.\n\n[MATLAB Documentation](https://www.mathworks.com/help/matlab/)\n\n[Getting Started with MATLAB ](https://www.mathworks.com/help/matlab/getting-started-with-matlab.html)\n\n[MATLAB and Simulink Training from MATLAB Academy](https://matlabacademy.mathworks.com)\n\n[MathWorks Certification Program](https://www.mathworks.com/services/training/certification.html)\n\n[MATLAB Online Courses from Udemy](https://www.udemy.com/topic/matlab/)\n\n[MATLAB Online Courses from Coursera](https://www.coursera.org/courses?query=matlab)\n\n[MATLAB Online Courses from edX](https://www.edx.org/learn/matlab)\n\n[Building a MATLAB GUI](https://www.mathworks.com/discovery/matlab-gui.html)\n\n[MATLAB Style Guidelines 2.0](https://www.mathworks.com/matlabcentral/fileexchange/46056-matlab-style-guidelines-2-0)\n\n[Setting Up Git Source Control with MATLAB \u0026 Simulink](https://www.mathworks.com/help/matlab/matlab_prog/set-up-git-source-control.html)\n\n[Pull, Push and Fetch Files with Git with MATLAB \u0026 Simulink](https://www.mathworks.com/help/matlab/matlab_prog/push-and-fetch-with-git.html)\n\n[Create New Repository with MATLAB \u0026 Simulink](https://www.mathworks.com/help/matlab/matlab_prog/add-folder-to-source-control.html)\n\n[PRMLT](http://prml.github.io/) is Matlab code for machine learning algorithms in the PRML book.\n\n## MATLAB Tools, Libraries, Frameworks\n\n**[MATLAB and Simulink Services \u0026 Applications List](https://www.mathworks.com/products.html)**\n\n[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/).\n\n[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.\n\n[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.\n\n[Simulink Online™](https://www.mathworks.com/products/simulink-online.html) is a service that provides access to Simulink through your web browser.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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.\n\n[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 co","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/mikeroyal%2Fdeep-learning-guide/projects"}