Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
LiDAR-Guide
LiDAR Guide
https://github.com/mikeroyal/LiDAR-Guide
Last synced: 21 minutes ago
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ML Frameworks, Libraries, and Tools
- Apache MXNet
- 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.
- 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.
- PyTorch
- Amazon SageMaker
- 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.
- 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.
- 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 Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Anaconda
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- 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.
- 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 PredictionIO
- BigDL
- 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.
- XGBoost
- Point Cloud Processing with NVIDIA DriveWorks SDK
- 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.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- PlaidML
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- nGraph - of-use to AI developers.
- 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.
- Cluster Manager for Apache Kafka(CMAK)
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MATLAB Tools
- MATLAB Online™
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Plotly
- UAV Toolbox
- ROS Toolbox
- hctsa - series analysis using Matlab.
- YALMIP
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
- MATLAB Schemer
- 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.
- SEA-MAT
- Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
- 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.
- Plotly
- Simulink Online™
- MATLAB Drive™
- 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™
- Mapping Toolbox™
- Partial Differential Equation 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.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- 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.
- 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.
- SoC Blockset™
- Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
- ThingSpeak™ - of-concept IoT systems that require analytics.
- hctsa - series analysis using Matlab.
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Learning Resources for ML
- Machine Learning
- Machine Learning by Stanford University from Coursera
- AWS Training and Certification for Machine Learning (ML) Courses
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Machine Learning Crash Course for Google Cloud
- Learning Machine learning and artificial intelligence from Google Cloud Training
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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 GPU support for TensorFlow
- 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.
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CUDA Tools
- 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.
- Accelerating Lidar for Robotics with NVIDIA CUDA-based PCL
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
- 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.
- 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.
- 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.
- 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.
- Tensorman
- cuML - learn.
- 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/).
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Robotics Learning Resources
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Free Online AI & Robotics Courses
- REC Foundation Robotics Industry Certification
- Carnegie Mellon Robotics Academy
- RIA Robotic Integrator Certification Program
- 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
- Free Online AI & Robotics Courses
- Learn Robotics with Online Courses and Classes from edX
- Carnegie Mellon Robotics Academy
- RIA Robotic Integrator Certification Program
- Windows ML ROS Node
- Microsoft AI School
- Windows ML ROS Node
- ROS on Windows 10
- Learn Robotics with Online Courses and Classes from edX
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- RIA Robotic Integrator Certification Program
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Tools for Robotics
- 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.
- 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
- 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
- AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
- 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
- Lidar Processing - MATLAB & Simulink
- Lidar Toolbox Documentation - MATLAB & Simulink
- Automated Driving Toolbox - MATLAB
- Getting Started with Lidar Acquisition in MATLAB Video
- Point Cloud Processing - MATLAB & Simulink
- 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.
- Point Cloud Processing - MATLAB & Simulink
- Microsoft Robotics Developer Studio - based programming environment for building robotics applications.
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- ROS - level device control, implementation of commonly used functionality, message-passing between processes, and package management.
- ArduPilot
- ROS-Industrial
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- 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
- 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
- AliceVision - of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage.
- 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.
- ROS bridge
- 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
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Lidar Toolbox - MATLAB
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- 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.
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Robotics System Toolbox
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
- Point Cloud Processing - MATLAB & Simulink
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MATLAB Learning Resources
- MATLAB
- MATLAB Documentation
- MATLAB and Simulink Training from MATLAB Academy
- MathWorks Certification Program
- MATLAB Online Courses from Udemy
- MATLAB Online Courses from Coursera
- MATLAB Online Courses from edX
- Building a MATLAB GUI
- MATLAB Style Guidelines 2.0
- Setting Up Git Source Control with MATLAB & Simulink
- Pull, Push and Fetch Files with Git with MATLAB & Simulink
- Create New Repository with MATLAB & Simulink
- PRMLT
- Getting Started with MATLAB
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cuda
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deep-learning
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ros
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gpu
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nvidia
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computer-vision
4
cpp
3
machine-learning
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matlab
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2
cpp11
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