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MATLAB-Guide

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

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Learning Resources for ML
- Machine Learning
- Machine Learning by Stanford University from Coursera
- AWS Training and Certification for Machine Learning (ML) Courses
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
ML Frameworks, Libraries, and Tools
- Amazon SageMaker
- Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
- nGraph - of-use to AI developers.
Robotics Tools and Frameworks
- AutoGluon - to-machine-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.
- Robot Framework - readable keywords. Its capabilities can be extended by libraries implemented with Python or Java.
- The Robotics Library (RL) - contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.RL runs on many different systems, including Linux, macOS, and Windows. It uses CMake as a build system and can be compiled with Clang, GCC, and Visual Studio.
- Robot Structural Analysis Professional - 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.
- PowerMill - to-use tools for offline programming of robots. Get tools to help you optimize robotic paths and simulate virtual mock-ups of manufacturing cells and systems.
- ROS - level device control, implementation of commonly used functionality, message-passing between processes, and package management.
- ROS2 - of-the-art algorithms, and with powerful developer tools, ROS has what you need for your next robotics project. And it’s all open source.
- MoveIt - to-use robotics platform for developing advanced applications, evaluating new designs and building integrated products for industrial, commercial, R&D, and other domains.
- Gazebo - quality graphics, and programmatic and graphical interfaces.
- Intel Robot DevKit
- Neurorobotics Platform (NRP) - accessible simulation system that allows the simulation of robots controlled by spiking neural networks.
- ViSP - source visual servoing platform library, is able to compute control laws that can be applied to robotic systems.
- Arduino - source platform used for building electronics projects. Arduino consists of both a physical programmable circuit board (often referred to as a microcontroller) and a piece of software, or IDE (Integrated Development Environment) that runs on your computer, used to write and upload computer code to the physical board.
- ArduPilot
- Light Detection and Ranging(LiDAR) - driving vehicles.
- ROS-Industrial
- Azure Kinect ROS Driver - us/services/kinect-dk/) to the [Robot Operating System (ROS)](http://www.ros.org/). Developers working with ROS can use this node to connect an Azure Kinect Developer Kit to an existing ROS installation.
- Azure Cognitive Services LUIS ROS Node
- ROS - level device control, implementation of commonly used functionality, message-passing between processes, and package management.
- ArduPilot
- ROS-Industrial
- Microsoft Robotics Developer Studio - based programming environment for building robotics applications.
- The Robotics Library (RL) - contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.RL runs on many different systems, including Linux, macOS, and Windows. It uses CMake as a build system and can be compiled with Clang, GCC, and Visual Studio.
- Intel Robot DevKit
- ROS Behavior Trees - source library to create robot's behaviors in form of Behavior Trees running in ROS (Robot Operating System).
- g2core - source motion control software for CNC and Robotics, designed to run on Arduino Due class microcontrollers.
- ur5controller - source OpenRAVE controller for UR5 robot integrated with ROS.
- RBDL - source (zlib) C++ libray for both forward and inverse dynamics and kinematics. Also supports contacts and loops.
- Unity Robotics Hub - source Unity packages, tutorials, and other resources demonstrating how to use Unity for robotics simulations. Includes new support for ROS integration.
- AirSim - source, cross platform, and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations.
- The JPL Open Source Rover
- Visual Studio Code Extension for ROS
- Azure Kinect ROS Driver - us/services/kinect-dk/) to the [Robot Operating System (ROS)](http://www.ros.org/). Developers working with ROS can use this node to connect an Azure Kinect Developer Kit to an existing ROS installation.
- Azure IoT Hub for ROS
- ROS 2 with ONNX Runtime
- Azure Cognitive Services LUIS ROS Node
- ROS bridge
- Robotics System Toolbox
- CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.
- AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
Reinforcement Learning Learning Resources
- Artificial Intelligence (AI) Online Courses | Udacity
- Edge AI for IoT Developers Course | Udacity
- Reinforcement Learning - supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).
- Top Deep Learning Courses Online | Coursera
- Machine Learning Course by Andrew Ng | Coursera
- Machine Learning for Everyone Courses | DataCamp
- Top Artificial Intelligence Courses Online | Coursera
- Professional Certificate in Computer Science for Artificial Intelligence | edX
- Top Reinforcement Learning Courses | Coursera
- Top Reinforcement Learning Courses | Udemy
- Top Reinforcement Learning Courses | Udacity
- Reinforcement Learning Courses | Stanford Online
- Autonomous Systems Online Courses & Programs | Udacity
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Machine teaching with the Microsoft Autonomous Systems platform
- Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
- Machine teaching with the Microsoft Autonomous Systems platform
- Deep Learning Online Courses | NVIDIA
- Introduction to Microsoft Project Bonsai
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- How to Think About Machine Learning Algorithms | Pluralsight
- Machine Learning for Everyone Courses | DataCamp
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Autonomous Maritime Systems Training | AMC Search
- Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Learn Deep Learning with Online Courses and Lessons | edX
- Deep Learning Courses | Stanford Online
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Learn Autonomous Robotics with Online Courses and Lessons | edX
- Artificial Intelligence Nanodegree program
- Intro to Artificial Intelligence Course | Udacity
- Expert Systems and Applied Artificial Intelligence
- Autonomous Systems - Microsoft AI
- Deep Learning Online Course Nanodegree | Udacity
- Top Deep Learning Courses Online | Udemy
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
- Understanding Machine Learning with Python | Pluralsight
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Top Autonomous Cars Courses Online | Udemy
Deep Learning Tools, Libraries, and Frameworks
- NVIDIA DLSS (Deep Learning Super Sampling)
- AMD FidelityFX Super Resolution (FSR) - quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
- Intel Xe Super Sampling (XeSS) - cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by [DP4a instruction](https://www.intel.com/content/dam/www/public/us/en/documents/reference-guides/11th-gen-quick-reference-guide.pdf).
- NVIDIA DLSS (Deep Learning Super Sampling)
Reinforcement Learning Tools, Libraries, and Frameworks
- XGBoost
- OpenAI
- ReinforcementLearning.jl
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Apache MXNet
- Cluster Manager for Apache Kafka(CMAK)
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Microsoft Project Bonsai - code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
- Navigation Toolbox™ - based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
NLP Tools, Libraries, and Frameworks
- PyTorch
- Natural Language Toolkit (NLTK) - to-use interfaces to over [50 corpora and lexical resources](https://nltk.org/nltk_data/) such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
- spaCy - task learning with pretrained transformers like BERT.
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PlaidML
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- CoreNLP
- NLPnet - of-speech tagging, semantic role labeling and dependency parsing.
- Flair - of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
- Catalyst - trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
- Anaconda
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Apache PredictionIO
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- BigDL
- Scikit-Learn
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
Computer Vision Learning Resources
- Computer Vision
- OpenCV Courses
- Exploring Computer Vision in Microsoft Azure
- Top Computer Vision Courses Online | Coursera
- Top Computer Vision Courses Online | Udemy
- Learn Computer Vision with Online Courses and Lessons | edX
- Computer Vision and Image Processing Fundamentals | edX
- Introduction to Computer Vision Courses | Udacity
- Computer Vision Nanodegree program | Udacity
- Computer Vision Training Courses | NobleProg
- Visual Computing Graduate Program | Stanford Online
- Exploring Computer Vision in Microsoft Azure
- Computer Vision
- Machine Vision Course |MIT Open Courseware
Computer Vision Tools, Libraries, and Frameworks
- Data Acquisition Toolbox™
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Mapping Toolbox™
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Statistics and Machine Learning Toolbox™
- ROS Toolbox
- UAV Toolbox
- Partial Differential Equation Toolbox™
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
NLP Learning Resources
- Natural Language Processing (NLP) - based modeling of human language with statistical, machine learning, and deep learning models.
- Natural Language Processing With Python's NLTK Package
- Cognitive Services—APIs for AI Developers | Microsoft Azure
- Artificial Intelligence Services - Amazon Web Services (AWS)
- Google Cloud Natural Language API
- Top Natural Language Processing Courses Online | Udemy
- Introduction to Natural Language Processing (NLP) | Udemy
- Top Natural Language Processing Courses | Coursera
- Natural Language Processing | Coursera
- Natural Language Processing in TensorFlow | Coursera
- Learn Natural Language Processing with Online Courses and Lessons | edX
- Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight
- Natural Language Processing (NLP) Training Courses | NobleProg
- Natural Language Processing with Deep Learning Course | Standford Online
- Advanced Natural Language Processing - MIT OpenCourseWare
- Certified Natural Language Processing Expert Certification | IABAC
- Natural Language Processing Course - Intel
Robotics Learning Resources
- Carnegie Mellon Robotics Academy
- RIA Robotic Integrator Certification Program
- Learn Robotics with Online Courses and Classes from edX
- Free Online AI & Robotics Courses
- REC Foundation Robotics Industry Certification
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Microsoft AI School
- Language Understanding (LUIS) for Azure Cognitive Services
- Windows ML ROS Node
- Azure VM templates to bootstrap ROS and ROS 2 environments
- Google Robotics Research
- Top Robotics Courses Online from Udemy
- Carnegie Mellon Robotics Academy
- RIA Robotic Integrator Certification Program
- Language Understanding (LUIS) for Azure Cognitive Services
- Windows ML ROS Node
- Azure VM templates to bootstrap ROS and ROS 2 environments
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Windows ML ROS Node
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Microsoft AI School
- Free Online AI & Robotics Courses
- ROS on Windows 10
- Learn Robotics with Online Courses and Classes from edX
- RIA Robotic Integrator Certification Program
Bioinformatics Learning Resources
- Bioinformatics
- European Bioinformatics Institute
- National Center for Biotechnology Information
- Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
- Bioinformatics | Coursera
- Top Bioinformatics Courses | Udemy
- Biometrics Courses | Udemy
- Learn Bioinformatics with Online Courses and Lessons | edX
- Bioinformatics Graduate Certificate | Harvard Extension School
- Bioinformatics and Biostatistics | UC San Diego Extension
- Bioinformatics and Proteomics - Free Online Course Materials | MIT
- Introduction to Biometrics course - Biometrics Institute
Bioinformatics Tools, Libraries, and Frameworks
- Bioconductor - throughput genomic data. Bioconductor uses the [R statistical programming language](https://www.r-project.org/about.html), and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an [AMI (Amazon Machine Image)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html) and [Docker images](https://docs.docker.com/engine/reference/commandline/images/).
- Bioconda
- UniProt - quality and freely accessible set of protein sequences annotated with functional information.
- Bowtie 2 - efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
- Biopython
- BioRuby
- BioJava
- BioPHP
- Avogadro - platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
- Ascalaph Designer
- Anduril - thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
- Galaxy - based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
- PathVisio - source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
- Orange
- Basic Local Alignment Search Tool
- OSIRIS - domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
- NCBI BioSystems
- Anduril - thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
LiDAR Learning Resources
- Introduction to Lidar Course - NOAA
- Lidar 101:An Introduction to Lidar Technology, Data, and Applications(PDF) - NOAA
- Understanding LiDAR Technologies - GIS Lounge
- LiDAR University Free Lidar Training Courses on MODUS AI
- LiDAR | Learning Plan on ERSI
- Light Detection and Ranging Sensors Course on Coursera
- Quick Introduction to Lidar and Basic Lidar Tools(PDF)
- LIDAR - GIS Wiki
- OpenStreetMap Wiki
- OpenStreetMap Frameworks
- Back to the Top
Photogrammetry Tools, Libraries, and Frameworks
- COLMAP - 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.
- Multi-View Environment (MVE) - 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.
- Autodesk® ReCap™
- Autodesk® ReCap™ Photo - 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.
- Pix4D
- PIX4Dmapper
- MicroStation
- RealityCapture - 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.
- Adobe Scantastic - 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).
- Adobe Aero - 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.
- Agisoft Metashape - 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.
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- Terramodel
- PhotoModeler - effective way for accurate 2D or 3D measurement, photo-digitizing, surveying, 3D scanning, and reality capture.
- ODM
- WebODM - 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/).
- NodeODM
- FIELDimageR
- Regard3D - from-motion program. It converts photos of an object, taken from different angles, into a 3D model of this object.
- MicroStation
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- MicMac - source photogrammetry software tools for 3D reconstruction.
- Meshroom - source 3D Reconstruction Software based on the AliceVision framework.
- AliceVision - of-the-art computer vision algorithms that can be tested, analyzed and reused.
LiDAR Tools & Frameworks
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- National Map Data Download and Visualization Services
- Mola
- MOLA
- LASer(LAS) - dimensional point cloud data data between data users. Although developed primarily for exchange of lidar point cloud data, this format supports the exchange of any 3-dimensional x,y,z tuplet. This binary file format is an alternative to proprietary systems or a generic ASCII file interchange system used by many companies. The problem with proprietary systems is obvious in that data cannot be easily taken from one system to another. There are two major problems with the ASCII file interchange. The first problem is performance because the reading and interpretation of ASCII elevation data can be very slow and the file size can be extremely large even for small amounts of data. The second problem is that all information specific to the lidar data is lost. The LAS file format is a binary file format that maintains information specific to the lidar nature of the data while not being overly complex.
- 3D point cloud - dimensional coordinates system.. Point clouds can be produced directly by 3D scanner which records a large number of points returned from the external surfaces of objects or earth surface. These data are exchanged between LiDAR users mainly through LAS format files (.las).
- ArcGIS Desktop - effective desktop geographic information system (GIS) software. It is the essential software package for GIS professionals. ArcGIS Desktop users can create, analyze, manage, and share geographic information so decision-makers can make intelligent, informed decisions.
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
- National Geospatial Program
- USGS Lidar Base Specification(LBS) online edition
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
Photogrammetry Learning Resources
- Photogrammetry Training | Deep3D Photogrammetry
- Top Photogrammetry Courses Online | Udemy
- Photogrammetry With Drones: In Mapping Technology | Udemy
- Introduction to Photogrammetry Course | Coursera
- Photogrammetry Online Classes and Training | Linkedin Learning
- Pix4D training and certification for mapping professionals
- Drone mapping and photogrammetry workshops with Pix4D
- Digital Photogrammetric Systems Course | Purdue Online Learning
- ASPRS Certification Program
- Photogrammetry Online Classes and Training | Linkedin Learning
- Terrestrial(Close-range) photogrammetry
CUDA Learning Resources
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA on WSL
- CUDA GPU support for TensorFlow
- NVIDIA Deep Learning cuDNN Documentation
- NVIDIA GPU Cloud Documentation
- NVIDIA NGC - optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
- NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
- CUDA - accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
CUDA Tools Libraries, and Frameworks
- - CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
  - CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
  - Minkowski Engine - differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
  - CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
  - CatBoost
  - cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
  - ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
  - AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
  - GraphVite - speed and large-scale embedding learning in various applications.
  - Linear algebra
  - Linear Algebra - Online Courses | Harvard University
  - Linear Algebra | MIT Open Learning Library
  - Linear Algebra - Khan Academy
  - Top Linear Algebra Courses on Coursera
  - Mathematics for Machine Learning: Linear Algebra on Coursera
  - Learn Linear Algebra with Online Courses and Classes on edX
  - The Math of Data Science: Linear Algebra Course on edX
  - Linear Algebra in Twenty Five Lectures | UC Davis
  - Linear Algebra | UC San Diego Extension
  - Linear Algebra for Machine Learning | UC San Diego Extension
  - Introduction to Linear Algebra, Interactive Online Video | Wolfram
  - Linear Algebra Resources | Dartmouth
  - Linear algebra
  - Linear Algebra | MIT Open Learning Library
  - Linear Algebra - Khan Academy
  - Mathematics for Machine Learning: Linear Algebra on Coursera
  - Top Linear Algebra Courses on Udemy
  - Learn Linear Algebra with Online Courses and Classes on edX
  - Linear Algebra in Twenty Five Lectures | UC Davis
  - Linear Algebra for Machine Learning | UC San Diego Extension
  - Linear Algebra Resources | Dartmouth
  - Tensorman
  - cuML - learn.
  - NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
  - CUTLASS - 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.
  - CUB
  - Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
  - Arraymancer - 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.
  - Kintinuous - 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.
  - Linear Algebra | UC San Diego Extension
  - Linear Algebra for Machine Learning | UC San Diego Extension
  - Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
  - NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
  - Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- i. Vector operations
  - slideserve
  - pinterest
- viii. Linear Regression
  - Fuzzy logic - tree processing and better integration with rules-based programming.
  - Linear regression
  - Medium
  - ResearchGate
  - Support Vector Machine (SVM) - group classification problems.
  - OpenClipArt
  - Convolutional Neural Networks (R-CNN)
  - CS231n
  - Recurrent neural networks (RNNs)
  - Slideteam
  - Random forest - 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.
  - wikimedia
  - Decision trees - structured models for classification and regression.
  - CMU
  - Naive Bayes - theorem.html) with strong independence assumptions between the features.
  - mathisfun
  - IBM
  - Linear regression
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - wikimedia
  - Medium
  - Medium
  - Medium
  - Medium
  - wikimedia
  - Medium
  - Medium
  - Medium
  - Medium
  - wikimedia
  - Medium
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - Medium
  - DeepAI
  - wikimedia
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
  - Support Vector Machine (SVM) - group classification problems.
  - Medium
  - Medium
  - Medium
  - Medium
  - Medium
- ii. Matrix operations
  - DPS Concepts
  - DPS Concepts
- iii. Matrix-vector product
- iv. Linear transformations
- v. Fundamental vector spaces
  - wikimedia
  - wolfram
- i. Solving systems of equations
  - slideshare
  - slideshare
- ii. Systems of equations as matrix equations
- i. Using row operations
  - YouTube
  - YouTube
- ii. Using elementary matrices
  - SDSU Physics
- iii. Transpose of a Matrix
  - slideserve
  - slideserve
- i. Basis
- ii. Matrix representations of linear transformations
  - slideserve
  - slideserve
- iii. Dimension and Basis for Vector Spaces
  - sliderserve
  - sliderserve
- iv. Row space, columns space, and rank of a matrix
- vi. Determinants
- vii. Eigenvalues and eigenvectors
  - YouTube
  - YouTube
- v. Invertible matrix theorem
  - SDSU Physics
Uncategorized
- Uncategorized
  - SoC Blockset™
  - Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
  - ThingSpeak™ - of-concept IoT systems that require analytics.
  - MATLAB Online Courses | Udemy
  - MATLAB Online Courses | Coursera
  - MATLAB Online Courses | edX
  - MATLAB Essentials | edX
  - MATLAB Online Training Courses | LinkedIn Learning
  - Introduction to MATLAB - MIT OpenCourseWare
  - Building a MATLAB GUI
  - MATLAB Style Guidelines 2.0
  - Advanced Programming Techniques in MATLAB by Loren Shure (PDF)
  - Setting Up Git Source Control with MATLAB & Simulink
  - Pull, Push and Fetch Files with Git with MATLAB & Simulink
  - Create New Repository with MATLAB & Simulink
  - MATLAB GPU Computing Support for NVIDIA CUDA-Enabled GPUs
  - MATLAB for GPU Computing
  - MATLAB Programming at Wikibooks
  - PRMLT
  - Awesome Matlab Robotics
  - Awesome MATLAB & Simulink Hackathons

Programming Languages

C++ 18 Python 8 Shell 2 C# 2 Scala 2 Nim 1 Cuda 1 TypeScript 1 Prolog 1 Dockerfile 1

Ecosyste.ms: Awesome

MATLAB-Guide

Learning Resources for ML

ML Frameworks, Libraries, and Tools

Robotics Tools and Frameworks

Reinforcement Learning Learning Resources

Deep Learning Tools, Libraries, and Frameworks

Reinforcement Learning Tools, Libraries, and Frameworks

NLP Tools, Libraries, and Frameworks

Computer Vision Learning Resources

Computer Vision Tools, Libraries, and Frameworks

NLP Learning Resources

Robotics Learning Resources

Bioinformatics Learning Resources

Bioinformatics Tools, Libraries, and Frameworks

LiDAR Learning Resources

Photogrammetry Tools, Libraries, and Frameworks

LiDAR Tools & Frameworks

Photogrammetry Learning Resources

CUDA Learning Resources

CUDA Tools Libraries, and Frameworks

i. Vector operations

viii. Linear Regression

ii. Matrix 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

ii. Using elementary matrices

iii. Transpose of a Matrix

i. Basis

ii. Matrix representations of linear transformations

iii. Dimension and Basis for Vector Spaces

iv. Row space, columns space, and rank of a matrix

vi. Determinants

vii. Eigenvalues and eigenvectors

v. Invertible matrix theorem

Uncategorized

Uncategorized