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Autonomous-Systems-Guide

Autonomous Systems Guide
https://github.com/mikeroyal/Autonomous-Systems-Guide

Last synced: 5 days ago
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Autodesk Learning Resources

Autodesk Tools and Frameworks

C/C++ Learning Resources

viii. Linear Regression

C/C++ Tools and Frameworks

viii. Linear Regression

  • AWS SDK for C++
  • Visual Studio - rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
  • Visual Studio Code
  • ReSharper C++
  • AppCode - fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
  • CLion - platform IDE for C and C++ developers developed by JetBrains.
  • Code::Blocks
  • Conan
  • High Performance Computing (HPC) SDK
  • Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
  • Automake
  • Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
  • GDB
  • GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
  • GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
  • OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
  • Libtool
  • Maven
  • TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
  • Clang - C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
  • OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
  • ANTLR (ANother Tool for Language Recognition)
  • Oat++ - efficient web application. It's zero-dependency and easy-portable.
  • Cython
  • Infer - C, and C. Infer is written in OCaml.
  • Azure SDK for C++
  • Azure SDK for C
  • C++ Client Libraries for Google Cloud Services
  • Vcpkg
  • CppSharp
  • JavaCPP
  • Spdlog - only/compiled, C++ logging library.

Computer Vision Learning Resources

Computer Vision Tools, Libraries, and Frameworks

  • 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.
  • Computer Vision Toolbox™
  • Statistics and Machine Learning Toolbox™
  • Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
  • Mapping Toolbox™
  • UAV Toolbox
  • 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.
  • Partial Differential Equation Toolbox™
  • ROS Toolbox
  • Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
  • 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.
  • Data Acquisition Toolbox™
  • Microsoft AirSim - source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop 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.
  • Fuzzy logic - tree processing and better integration with rules-based programming.
  • ResearchGate
  • Support Vector Machine (SVM) - group classification problems.
  • OpenClipArt
  • Convolutional Neural Networks (R-CNN)
  • CS231n
  • Slideteam
  • DeepAI
  • wikimedia
  • Decision trees - structured models for classification and regression.
  • CMU
  • Naive Bayes - theorem.html) with strong independence assumptions between the features.