{"id":1591,"url":"https://github.com/mikeroyal/Autonomous-Systems-Guide","name":"Autonomous-Systems-Guide","description":"Autonomous Systems Guide","projects_count":613,"last_synced_at":"2026-08-06T14:00:23.814Z","repository":{"id":47134458,"uuid":"405735923","full_name":"mikeroyal/Autonomous-Systems-Guide","owner":"mikeroyal","description":"Autonomous Systems Guide","archived":false,"fork":false,"pushed_at":"2022-01-09T22:11:58.000Z","size":188,"stargazers_count":50,"open_issues_count":0,"forks_count":13,"subscribers_count":2,"default_branch":"main","last_synced_at":"2026-07-18T07:04:03.087Z","etag":null,"topics":["automated-machine-learning","automated-systems","automation","autonomous-robots","autonomous-vehicles","awesome","awesome-list","drones","machine-learning","resources","robotics","rover"],"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":"2021-09-12T19:34:47.000Z","updated_at":"2026-07-09T19:50:46.000Z","dependencies_parsed_at":"2022-08-20T13:20:14.908Z","dependency_job_id":null,"html_url":"https://github.com/mikeroyal/Autonomous-Systems-Guide","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mikeroyal/Autonomous-Systems-Guide","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FAutonomous-Systems-Guide","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FAutonomous-Systems-Guide/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FAutonomous-Systems-Guide/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FAutonomous-Systems-Guide/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mikeroyal","download_url":"https://codeload.github.com/mikeroyal/Autonomous-Systems-Guide/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikeroyal%2FAutonomous-Systems-Guide/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36338379,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"ssl_error","status_checked_at":"2026-08-06T04:43:02.660Z","response_time":54,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":"2024-01-04T17:54:07.659Z","updated_at":"2026-08-06T14:00:23.815Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["C/C++ Tools and Frameworks","Python Frameworks, Libraries, and Tools","License","ML Frameworks, Libraries, and Tools","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","Learning Resources for ML","Computer Vision 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/45159366/133000539-72c26930-e793-447a-ba7a-de05d55ab8c8.png\"\u003e\n  \u003cbr /\u003e\n  Autonomous Systems Guide\n\u003c/h1\u003e\n\n#### A guide covering Autonomous Systems including the applications, libraries and tools that will make you a better and more efficient Autonomous Systems 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/45159366/133000541-3da7a795-00e5-4287-b644-917f5644c610.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n\n# Table of Contents\n\n1. [Autonomous Systems Learning Resources](https://github.com/mikeroyal/Autonomous-Systems-Guide#Autonomous-Systems-learning-resources)\n\n2. [Autonomous Systems Tools, Libraries, and Frameworks](https://github.com/mikeroyal/Autonomous-Systems-Guide#Autonomous-Systems-tools-libraries-and-frameworks)\n\n3. [Computer Vision Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#computer-vision-development)\n\n4. [Algorithms](https://github.com/mikeroyal/Autonomous-Systems-Guide#algorithms)\n\n5. [Machine Learning](https://github.com/mikeroyal/Autonomous-Systems-Guide#machine-learning)\n\n6. [CUDA Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#cuda-development)\n\n7. [MATLAB Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#matlab-development)\n\n8. [Photogrammetry Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#photogrammetry-development)\n\n9. [Autodesk Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#autodesk-development)\n\n10. [LiDAR Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#lidar-development)\n\n11. [Robotics Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#robotics-development)\n\n12. [Linear Algebra](https://github.com/mikeroyal/Autonomous-Systems-Guide#linear-algebra)\n\n13. [C/C++ Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#cc-development)\n\n14. [Python Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#python-development)\n\n15. [R Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#r-development)\n\n16. [Julia Development](https://github.com/mikeroyal/Autonomous-Systems-Guide#julia-development)\n\n# Autonomous Systems Learning Resources\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-Guide#table-of-contents)\n\n[Autonomous Systems (AS)](https://www.microsoft.com/en-us/ai/autonomous-systems-project-bonsai) is a branch of artificial intelligence that provides a solution for system automation(Robotics, Cars, and Smart devices) that works alongside people for decision support or a system that works independently on its own. It can be deployed on-premises, in the cloud, IoT Edge, or in embedded devices.\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# Autonomous Systems Tools, Libraries, and Frameworks\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-Guide#table-of-contents)\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[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[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[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[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[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[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# Computer Vision Development\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-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# Algorithms\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-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/Autonomous-Systems-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\u003cimg src=\"https://user-images.githubusercontent.com/45159366/105645196-dccfd480-5e4e-11eb-95d1-c5eb560b72fd.jpeg\"\u003e\n\n**Machine Learning/Deep Learning Frameworks.**\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/Autonomous-Systems-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/Autonomous-Systems-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 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[SoC Blockset™](https://www.mathworks.com/products/soc.html) is a tool that provides Simulink® blocks and visualization tools for modeling, simulating, and analyzing hardware and software architectures for ASICs, FPGAs, and systems on a chip (SoC). You can build your system architecture using memory models, bus models, and I/O models, and simulate the architecture together with the algorithms.\n\n[Wireless HDL Toolbox™](https://www.mathworks.com/products/wireless-hdl.html) is a tool that provides pre-verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.\n\n[ThingSpeak™](https://www.mathworks.com/products/thingspeak.html) is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.\n\n[SEA-MAT](https://sea-mat.github.io/sea-mat/) is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.\n\n[Gramm](https://github.com/piermorel/gramm) is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.\n\n[hctsa](https://hctsa-users.gitbook.io/hctsa-manual) is a software package for running highly comparative time-series analysis using Matlab.\n\n[Plotly](https://plot.ly/matlab/) is a Graphing Library for MATLAB.\n\n[YALMIP](https://yalmip.github.io/) is a MATLAB toolbox for optimization modeling.\n\n[GNU Octave](https://www.gnu.org/software/octave/) is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.\n\n# Photogrammetry Development\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/129494677-0341843b-c78c-4027-8a2c-43e98a995f6f.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## Photogrammetry Learning Resources\n\n[Photogrammetry](https://www.autodesk.com/solutions/photogrammetry-software) is the art and science of extracting 3D information from photographs. The process involves taking overlapping photographs of an object, structure, or space, and converting them into 2D or 3D digital models. Photogrammetry is often used by surveyors, architects, engineers, and contractors to create topographic maps, meshes, point clouds, or drawings based on the real-world.\n\n[Aerial photogrammetry](https://www.autodesk.com/solutions/photogrammetry-software) is process of utilizing aircrafts to produce aerial photography that can be turned into a 3D model or mapped digitally. Now, it is possible to do the same work with a drone.\n\n[Terrestrial(Close-range) photogrammetry](https://www.autodesk.com/solutions/photogrammetry-software) is when images are captured using a handheld camera or with a camera mounted to a tripod. The output of this method is not to create topographic maps, but rather to make 3D models of a smaller object.\n\n[Top Photogrammetry Courses Online | Udemy](https://www.udemy.com/topic/photogrammetry/)\n\n[Photogrammetry With Drones: In Mapping Technology | Udemy](https://www.udemy.com/course/essentials-of-photogrammetry/)\n\n[Introduction to Photogrammetry Course | Coursera](https://www.coursera.org/lecture/aerial-photography-with-uav/introduction-to-photogrammetry-KyP30)\n\n[Photogrammetry Online Classes and Training | Linkedin Learning](https://www.linkedin.com/learning/search?keywords=Photogrammetry\u0026upsellOrderOrigin=default_guest_learning\u0026trk=learning-course_learning-search-bar_search-submit)\n\n[Pix4D training and certification for mapping professionals](https://training.pix4d.com/)\n\n[Drone mapping and photogrammetry workshops with Pix4D](https://training.pix4d.com/pages/workshops)\n\n[Digital Photogrammetric Systems Course | Purdue Online Learning](https://engineering.purdue.edu/online/courses/digital-photogrammetric-systems)\n\n[Photogrammetry Training | Deep3D Photogrammetry](https://deep3d.co.uk/photogrammetry-training/)\n\n[ASPRS Certification Program](https://www.asprs.org/certification)\n\n## Photogrammetry Tools, Libraries, and Frameworks\n\n[Autodesk® ReCap™](https://www.autodesk.com/products/recap/free-trial) is a software tool that converts reality captured from laser scans or photos into a 3D model or 2D drawing that's ready to be used in your design built for UAV and drone processes.\n\n[Autodesk® ReCap™ Photo](http://blogs.autodesk.com/recap/introducing-recap-photo/) is a cloud-connected solution tailored for drone/UAV photo capturing workflows. Using ReCap Photo, you can create textured meshes, point clouds with geolocation, and high-resolution orthographic views with elevation maps.\n\n[Pix4D](https://www.pix4d.com/) is a unique suite of photogrammetry software for drone mapping. Capture images with our app, process on desktop or cloud and create maps and 3D models.\n\n[PIX4Dmapper](https://www.pix4d.com/product/pix4dmapper-photogrammetry-software) is the leading photogrammetry software for professional drone mapping.\n\n[RealityCapture](https://www.capturingreality.com/) is a state-of-the-art photogrammetry software solution that creates virtual reality scenes, textured 3D meshes, orthographic projections, geo-referenced maps and much more from images and/or laser scans completely automatically.\n\n[Adobe Scantastic](https://labs.adobe.com/projects/scantastic/) is a tool that makes the creation of 3D assets accessible to everyone. It can be used with just a mobile device (combined with Adobe's server-based photogrammetry pipeline), users can easily scan objects in their physical environment and turn them into 3D models which can then be imported into tools like [Adobe Dimension](https://www.adobe.com/products/dimension.html)  and [Adobe Aero](https://www.adobe.com/products/aero.html).\n\n[Adobe Aero](https://www.adobe.com/products/aero.html) is a tool that helps you build, view, and share immersive AR experiences. Simply build a scene by bringing in 2D images from Adobe Photoshop and Illustrator, or 3D models from Adobe Dimension, Substance, third-party apps like Cinema 4D, or asset libraries like Adobe Stock and TurboSquid. Aero optimizes a wide array of assets, including OBJ, GLB, and glTF files, for AR, so you can visualize them in real time.\n\n[Agisoft Metashape](https://www.agisoft.com/) is a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales.\n\n[MicroStation](https://www.bentley.com/en/products/brands/microstation) is a CAD software platform for 2D and 3D dimensional design and drafting, developed and sold by Bentley Systems. It generates 2D/3D vector graphics objects and elements and includes building information modeling (BIM) features.\n\n[Leica Photogrammetry Suite (LPS)](https://support.hexagonsafetyinfrastructure.com/infocenter/index?page=product\u0026facRef=LPS\u0026facDisp=Leica%20Photogrammetry%20Suite%20(LPS)\u0026landing=1) is a powerful photogrammetry system that delivers full analytical triangulation, the generation of digital terrain models, orthophoto production, mosaicking, and 3D feature extraction in a user-friendly environment that guarantees results even for photogrammetry novices.\n\n[Terramodel](https://heavyindustry.trimble.com/products/terramodel) is a powerful software package for the surveyor, civil engineer or contractor who requires a CAD and design package with integrated support for raw survey data.\n\n[MicMac](https://github.com/micmacIGN/micmac) is a free and  open-source photogrammetry software tools for 3D reconstruction.\n\n[3DF Zephyr] (https://www.3dflow.net/3df-zephyr-photogrammetry-software/) is a photogrammetry software solution by 3Dflow. It allows you automatically reconstruct 3D models from photos and deal with any 3D reconstruction and scanning challenge. No matter what camera sensor, drone or laser scanner device you are going to use.\n\n[COLMAP](https://colmap.github.io/) is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections.\n\n[Multi-View Environment (MVE)](https://www.gcc.tu-darmstadt.de/home/proj/mve/) is an effort to ease the work with multi-view datasets and to support the development of algorithms based on multiple views. It features Structure from Motion, Multi-View Stereo and Surface Reconstruction. MVE is developed at the TU Darmstadt.\n\n[AliceVision](https://github.com/alicevision/AliceVision) is a Photogrammetric Computer Vision Framework which provides 3D Reconstruction and Camera Tracking algorithms. AliceVision comes up with strong software basis and state-of-the-art computer vision algorithms that can be tested, analyzed and reused.\n\n[Meshroom](https://github.com/alicevision/meshroom) is a free, open-source 3D Reconstruction Software based on the AliceVision framework.\n\n[PhotoModeler](https://www.photomodeler.com/) is a software extracts Measurements and Models from photographs taken with an ordinary camera. A cost-effective way for accurate 2D or 3D measurement, photo-digitizing, surveying, 3D scanning, and reality capture.\n\n[ODM](https://www.opendronemap.org/odm/) is an open source command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.\n\n[WebODM](https://www.opendronemap.org/webodm/) is a user-friendly, commercial grade software for drone image processing. Generate georeferenced maps, point clouds, elevation models and textured 3D models from aerial images. It supports multiple engines for processing, currently [ODM](https://github.com/OpenDroneMap/ODM) and [MicMac](https://github.com/dronemapper-io/NodeMICMAC/).\n\n[NodeODM](https://www.opendronemap.org/nodeodm/) is a [standard API specification](https://github.com/OpenDroneMap/NodeODM/blob/master/docs/index.adoc) for processing aerial images with engines such as [ODM](https://github.com/OpenDroneMap/ODM). The API is used by clients such as [WebODM](https://github.com/OpenDroneMap/WebODM), [CloudODM](https://github.com/OpenDroneMap/CloudODM) and [PyODM](https://github.com/OpenDroneMap/PyODM).\n\n[ClusterODM]https://www.opendronemap.org/clusterodm/) is a reverse proxy, load balancer and task tracker with optional cloud autoscaling capabilities for NodeODM API compatible nodes. In a nutshell, it's a program to link together multiple NodeODM API compatible nodes under a single network address.\n\n[FIELDimageR](https://www.opendronemap.org/fieldimager/) is an R package to analyze orthomosaic images from agricultural field trials.\n\n[Regard3D](https://www.regard3d.org/) is a free and open source structure-from-motion program. It converts photos of an object, taken from different angles, into a 3D model of this object.\n\n# Autodesk Development\n[Back to the Top](https://github.com/mikeroyal/Autonomous-Systems-Guide#table-of-contents)\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687186-060c4b00-d1ca-11eb-9e90-f51a3ebf4e43.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n## Autodesk Learning Resources\n\n[Autodesk](https://www.autodesk.com/) is a global leader in design and make technology, with expertise across architecture, engineering, construction, design, manufacturing, and entertainment.\n\n[CNC programming (Computer Numerical Control Programming)](https://www.autodesk.com/solutions/cnc-programming) is utilized by manufacturers to create program instructions for computers to control a machine tool. CNC is highly involved in the manufacturing process and improves automation as well as flexibility.\n\n[AutoDesk Learning \u0026 Training](https://www.autodesk.com/training)\n\n[Autodesk Certification](https://www.autodesk.com/certification/overview)\n\n[Autodesk University](https://www.autodesk.com/autodesk-university/)\n\n[Autodesk Design Academy](https://academy.autodesk.com/)\n\n[Autodesk Customer Success Hub](https://customersuccess.autodesk.com)\n\n[Software and Services for Education | Autodesk Education](https://www.autodesk.com/education/home)\n\n[AutoDesk Forums](https://forums.autodesk.com)\n\n[AutoDesk Developer Network](https://www.autodesk.com/developer-network/overview)\n\n[Learning Civil 3D on Autodesk Knowledge Network](https://knowledge.autodesk.com/support/civil-3d/learn)\n\n[Top Autodesk Courses on Udemy](https://www.udemy.com/topic/autodesk/)\n\n[Top Autodesk Courses on Coursera](https://www.coursera.org/autodesk)\n\n[Top Autodesk Fusion 360 Courses on Coursera](https://www.coursera.org/courses?query=autodesk%20fusion%20360\u0026page=1)\n\n\n## Autodesk Tools and Frameworks\n\n[AutoCAD®](https://www.autodesk.com/products/autocad/overview) is computer-aided design (CAD) software that architects, engineers, and construction professionals rely on to create precise 2D and 3D drawings. It also automates tasks such as comparing drawings, counting, adding blocks, creating schedules.\n\n[AutoCAD LT®](https://www.autodesk.com/products/autocad-lt/overview) is a powerful 2D computer-aided design (CAD) software that architects, engineers, construction professionals, and designers rely on to design, draft, and document with precise 2D geometry.\n\n[AutoCAD® Mobile App](https://www.autodesk.com/products/autocad-mobile/overview) is a mobile(smartphone or tablet) version of AutoCAD that has the core design and drafting tools. Work on your CAD drawings anytime, anywhere—even offline.\n\n[AutoCAD® Web App](https://www.autodesk.com/products/autocad-web-app/overview) is a web version of AutoCAD that can edit, create, share, and view CAD drawings in a web browser on any computer.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687196-0ad0ff00-d1ca-11eb-87d8-6a1e806dcd4c.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**AutoCAD® with Architecture toolset. Source: [Autodesk](https://www.autodesk.com/products/autocad/overview)**\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687204-13293a00-d1ca-11eb-806b-57a3526b0b75.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**AutoCAD® with  Mechanical toolset. Source: [Autodesk](https://www.autodesk.com/products/autocad/overview)**\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687201-10c6e000-d1ca-11eb-99b4-90dec581f163.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**AutoCAD® with Electrical toolset. Source: [Autodesk](https://www.autodesk.com/products/autocad/overview)**\n\n[Tinkercad®](https://www.tinkercad.com) is a free, easy-to-use app for 3D design, electronics, and coding. It's used by teachers, kids, hobbyists, and designers to imagine, design, and make anything.\n\n[Revit®](https://www.autodesk.com/products/revit/overview) is a  BIM (Building Information Modeling) software to drive efficiency and accuracy across the project lifecycle, from conceptual design, visualization, and analysis to fabrication and construction.\n\n[AEC(Architecture, Engineering \u0026 Construction) Collection®](https://www.autodesk.com/collections/architecture-engineering-construction/overview) is a set of BIM and CAD tools for designers, engineers, and contractors that is supported by a cloud-based common data environment that facilitates project delivery from early-stage design through to construction.\n\n[Fusion 360®](https://www.autodesk.com/products/fusion-360/overview) is an integrated CAD, CAM, CAE, and PCB software application. It unifies design, engineering, electronics, and manufacturing into a single software platform.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687501-98f9b500-d1cb-11eb-807b-20d24da14fc1.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® Fusion 360. Source: [Autodesk](https://www.autodesk.com/products/fusion-360/features#)**\n\n[Fusion 360 with FeatureCAM®](https://www.autodesk.com/products/featurecam/overview) is a tool that gives you access to FeatureCAM Ultimate, PartMaker, Fusion 360, Fusion Team, and HSMWorks. With FeatureCAM CNC programming software uses manufacturing knowledge to intelligently make decisions, produce results, and remove repetitive processes.\n\n[Fusion 360 with Netfabb®](https://www.autodesk.com/products/netfabb/overview) is a software that offers a complete toolset for design and implementation for additive manufacturing. It streamlines workflows and automates processes around 3D print preparation. The software also includes access to Fusion 360, Fusion 360 Team, and additional capabilities through Fusion 360 Additive Extensions.\n\n[Fusion 360 Manage](https://www.autodesk.com/products/fusion-360-manage/overview) is a product lifecycle management platform that connects your people, processes, and data across departments and geographies. It gives you the flexibility to start today and expand tomorrow with PLM that adapts to your business.\n\n[Fusion Team](https://fusionteam.autodesk.com/) is a cloud-based coll tool that helps eliminate the inefficiencies that disparate tools create when working with your internal and external teams.\n\n[Fusion 360 with PowerInspect®](https://www.autodesk.com/products/powerinspect/overview) is a 3D measurement software offers a powerful way to inspect, validate, and manage quality for all measurement equipment. Now includes access to Fusion 360, Fusion 360 Team, and Fusion 360 – Machining Extension. PowerInspect includes comprehensive inspection tools that measure parts while they’re still in On Machine Verification (OMV). Machine tool probing helps you make informed scrap or rework decisions quickly.\n\n[Fusion 360 with PowerMill® CAM](https://www.autodesk.com/products/powermill/overview) is a software provides expert CNC programming strategies for complex 3 and 5-axis manufacturing. This includes access to Fusion 360 and advanced manufacturing capabilities through Fusion 360 extensions.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687213-1f14fc00-d1ca-11eb-931e-79c7c5c58ce9.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**CNC machining with Fusion 360 with PowerMill® CAM. Source: [Autodesk](https://www.autodesk.com/products/powermill/overview)**\n\n[Fusion 360 with PowerShape®](https://www.autodesk.com/products/powershape/overview) is a  manufacturing CAD software combines surface, solid, and mesh modeling to help prepare molds, dies, and other complex parts for manufacture. This includes access to Fusion 360 and Fusion 360 Team.\n\n[Autodesk PartMaker®](https://www.autodesk.com/solutions/manufacturing-software/swiss-machining) is a software that can produce CNC programs that drive main and sub-spindle machining operations. These can be used for turning, indexed and interpolated C-axis milling, Y-axis, and B-axis milling.\n\n[Robot Structural Analysis Professional](https://www.autodesk.com/products/robot-structural-analysis/overview) is a structural load analysis software that verifies code compliance and uses BIM-integrated workflows to exchange data with Revit. It can help you to create more resilient, constructible designs that are accurate, coordinated, and connected to BIM.\n\n[Revit LT™](https://www.autodesk.com/products/revit-lt/overview) is a software that provides the most cost-effective BIM (Building Information Modeling) solution, you can produce high-quality 3D architectural designs and documentation.\n\n[Maya®](https://www.autodesk.com/products/maya/overview) is a 3D computer animation, modeling, simulation, and rendering software that can create realistic effects from explosions to cloth simulation.\n\n[Maya LT™](https://www.autodesk.com/products/maya-lt/overview) is a game design software for indie game makers that can create and animate realistic-looking characters, props, and environments using the sophisticated 3D modeling and animation tools.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687226-2a682780-d1ca-11eb-9408-4b32ddc7d493.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® Maya. Source:[Autodesk](https://www.autodesk.com/products/maya/overview)**\n\n[3DS Max®](https://www.autodesk.com/products/3ds-max/overview) is a 3D modeling and rendering software for design visualization, games, and animation.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687233-3358f900-d1ca-11eb-8e9c-0cb68b16db0e.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® 3DS Max. Source:[Autodesk](https://www.autodesk.com/products/3ds-max/overview)**\n\n[Arnold](https://www.autodesk.com/products/arnold/overview) is an advanced Monte Carlo ray tracing(Global illumination) renderer that helps you work more efficiently.\n\n[ReCap™](https://www.autodesk.com/products/recap/overview) is a Pro 3D scanning software to transform the physical world into a digital asset. With reality capture data you can better understand and verify existing and as-built conditions to gain insights and make better decisions.\n\n[Flame®](https://www.autodesk.com/products/flame/overview) is a 3D VFX and finishing software provides powerful tools for 3D compositing, visual effects, and editorial finishing. An integrated, creative environment means faster compositing, advanced graphics, color correction, and more.\n\n[Mudbox®](https://www.autodesk.com/products/mudbox/overview) is a 3D digital painting and sculpting software that let's you sculpt and paint highly detailed 3D geometry and textures.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687228-2e944500-d1ca-11eb-894e-b55e15c35620.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® Mudbox. Source:[Autodesk](https://www.autodesk.com/products/mudbox/overview)**\n\n[Character Generator®](https://www.autodesk.com/products/character-generator/overview) is a powerful 3D design and animation tools, Character Generator offers artists a web-based laboratory to create fully rigged 3D characters for animation packages and game engines.\n\n[Smoke®](https://www.autodesk.com/products/smoke/overview) is a  video effects software helps production studios increase productivity by combining editing workflows with node-based compositing tools in a timeline-centered editing environment.\n\n[ShotGrid](https://www.autodesk.com/products/shotgrid/overview) formerly Shotgun Software, is software for creative project management software and review tools for film, TV, and games that streamlines workflows for creative studios.\n\n[Advance Steel®](https://www.autodesk.com/products/advance-steel/overview) is a 3D modeling software for steel detailing, design, fabrication, and construction. it connects engineers and detailers through a seamless design and detailing workflow between Advance Steel and Revit, you can reduce the time required to move from design to fabrication while simultaneously reducing errors along the way.\n\n[Media \u0026 Entertainment Collection®](https://www.autodesk.com/collections/media-entertainment/overview) is a collection that includes all of the tools you need to build a powerful and scalable 3D animation pipeline for complex simulations, effects, and rendering.\n\n[Civil 3D®](https://www.autodesk.com/products/civil-3d/overview) is a  civil engineering design software supports BIM (Building Information Modeling) with integrated features to improve drafting, design, and construction documentation.\n\n[Inventor®](https://www.autodesk.com/products/inventor/overview) is a  CAD software that provides professional-grade 3D mechanical design, documentation, and product simulation tools. Work efficiently with a powerful blend of parametric, direct, freeform, and rules-based design capabilities.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687217-24724680-d1ca-11eb-9768-0fbcba5a0d41.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® Inventor. Source:[Autodesk](https://www.autodesk.com/products/inventor/overview)**\n\n[Inventor® CAM](https://www.autodesk.com/products/inventor-cam/overview) is an integrated CAM software for Inventor that simplifies CNC programming processes. Such as the machining workflow with CAD-embedded 2.5-axis to 5-axis milling, turning, and mill-turn capabilities.\n\n[Inventor Nastran®](https://www.autodesk.com/products/inventor-nastran/overview) is a CAD-embedded finite element analysis software that delivers finite element analysis (FEA) tools for engineers and analysts. Simulation covers multiple analysis types, such as linear and nonlinear stress, dynamics, and heat transfer.\n\n[Inventor® Nesting](https://www.autodesk.com/products/inventor-nesting/overview) is a CAD-embedded, true-shape nesting tools for Inventor that helps you optimize yield from flat raw material. Easily compare nesting studies to optimize efficiency and reduce costs, and export 3D models or DXF™ files of the completed nest for cutting path generation.\n\n[Inventor Tolerance Analysis®](https://www.autodesk.com/products/inventor-tolerance-analysis/overview) is an CAD-embedded tolerance stackup analysis software that is designed to help Inventor users make more informed decisions while specifying manufacturing tolerances.\n\n[Product Design \u0026 Manufacturing Collection](https://www.autodesk.com/collections/product-design-manufacturing/overview) is an integrated set of professional-grade applications that connect everyone, from concept to production, with shared tools to streamline your product development process.\n\n[Navisworks®](https://www.autodesk.com/products/navisworks/overview) is a project review software to improve BIM (Building Information Modeling) coordination. That can combine design and construction data into a single model.\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://user-images.githubusercontent.com/45159366/122687230-2f2cdb80-d1ca-11eb-9bcf-5dc2fb858d7d.png\"\u003e\n  \u003cbr /\u003e\n\u003c/p\u003e\n\n**Autodesk® Navisworks. Source:[Autodesk](https://www.autodesk.com/products/navisworks/overview)**\n\n[BIM Collaborate](https://www.autodesk.com/products/bim-collaborate/overview) is cloud-based design collaboration and coordination software that connects AEC teams, helping you execute on design intent and deliver high-quality constructible models on a single platform.\n\n[BIM Collaborate Pro](https://www.autodesk.com/products/bim-collaborate/overview) is cloud-based design collaboration and coordination software that connects AEC teams, helping you execute on design intent and deliver high-quality constructible models on a single platform.\n\n[InfraWorks®](https://www.autodesk.com/products/infraworks/overview) is a conceptual design software lets architecture, engineering, and construction professionals model, analyze, and visualize infrastructure design concepts within the context of the built and natural environment—improving decision making and accelerating project approvals.\n\n[SketchBook®](https://www.autodesk.com/products/sketchbook/overview) is a drawing and painting software lets designers, architects, and concept artists sketch ideas quickly and create stunning illustrations.\n\n[Alias®](https://www.autodesk.com/products/alias-products/overview) is a product design software for sketching, concept modeling, surfacing, visualization. It can create and reuse templates across design and surfacing teams sharing surfacing language.\n\n[Assemble BIM Data](https://construction.autodesk.com/products/assemble/) is a tool that helps keep projects on track. Along with condition and connect BIM data to design reviews, estimating, change management, scheduling, work-in-place tracking, and more.\n\n[Autodesk® Forge](https://forge.autodesk.com) is a cloud-based developer platform from Autodesk. That let's you access design and engineering data in the cloud with the Forge platform. Whether you want to automate processes, connect teams and workflows, or visualize your data using Forge APIs.\n\n[Autodesk® Rendering](https://www.autodesk.com/products/rendering/overview) is a fast, high-resolution cloud rendering software that let's you produce stunning, high-quality renderings from designs and models with cloud rendering. This service uses cloud credits, which is a universal measure across Autodesk consumption-based cloud services to perform certain tasks in the cloud.\n\n[Autodesk�","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/mikeroyal%2Fautonomous-systems-guide/projects"}