Unreal-Engine-Guide

Unreal Engine 5 Guide. Learn to develop games for Windows, Linux, macOS, iOS, Android, Xbox Series X|S, PlayStation 5, Nintendo Switch.
https://github.com/mikeroyal/Unreal-Engine-Guide

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Computer Vision Tools, Libraries, and Frameworks
- VS Code Extensions for Developer Productivity
  - 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.
  - 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.
  - OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
  - 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.
CUDA Learning Resources
- viii. Linear Regression
  - 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 Toolkit Documentation
  - CUDA Quick Start Guide
  - CUDA on WSL
  - NVIDIA Deep Learning cuDNN Documentation
  - CUDA GPU support for TensorFlow
CUDA Tools Libraries, and Frameworks
- viii. Linear Regression
  - 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.
  - 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).
  - 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.
  - 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.
Deep Learning Learning Resources
- viii. Linear Regression
Deep Learning Tools, Libraries, and Frameworks
- viii. Linear Regression
  - Open Neural Network Exchange(ONNX) - in operators and standard data types.
  - Anaconda
  - 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 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.
  - LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
  - 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.
  - Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
  - 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.
  - 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.
  - ROS/ROS2 bridge for CARLA(package) - 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.
DirectX Learning Resources
- VS Code Extensions for Developer Productivity
  - Microsoft DirectX® - level API that handles tasks related to multimedia for game programming and video on Microsoft platforms(Windows & Xbox).
  - Getting Started with the DirectX 12 Agility SDK
  - DirectX— Feature Level 12_2
  - AMD DirectX® 12 (DX12) Technology | AMD
  - Top Microsoft DirectX Courses Online | Udemy
  - DirectX - Learn Microsoft DirectX from Scratch Course | Udemy
  - DirectX 11 Programming Course | Udemy
  - DirectX 12 Technology | NVIDIA
DirectX Tools, Libraries, and Frameworks
- VS Code Extensions for Developer Productivity
  - NVIDIA® Nsight™ Visual Studio Edition
  - Radeon™ GPU Profiler
  - Radeon™ GPU Analyzer
  - Radeon™ Memory Visualizer (RMV)
  - Visual Studio Code
  - PIX on Windows
  - DirectStorage API - us/2020/07/14/a-closer-look-at-xbox-velocity-architecture/) to Windows. The DirectX API is architected in a way that takes all this into account and maximizes performance throughout the entire pipeline from NVMe drive all the way to the GPU. It does this in several ways: by reducing per-request NVMe overhead, enabling batched many-at-a-time parallel IO requests which can be efficiently fed to the GPU, and giving games finer grain control over when they get notified of IO request completion instead of having to react to every tiny IO completion. The DirectStorage API will be available on [Windows 11](https://www.microsoft.com/en-us/windows/windows-11) PCs with NVMe SSDs, but will also be support in [Windows 10](https://www.microsoft.com/software-download/windows10) version 1909 and newer.
  - FAudio - us/windows/win32/xaudio2/xaudio2-introduction), [X3DAudio](https://docs.microsoft.com/en-us/windows/win32/xaudio2/x3daudio-overview), [XAPO](https://docs.microsoft.com/en-us/windows/win32/xaudio2/xapo-overview), and [XACT3](https://en.wikipedia.org/wiki/Cross-platform_Audio_Creation_Tool).
  - Simple DirectMedia Layer - platform development library designed to provide low level access to audio, keyboard, mouse, joystick, and graphics hardware via OpenGL and Direct3D. It is used by video playback software, emulators, and popular games including Valve's award winning catalog.
  - DXVK - based translation layer for Direct3D 9/10/11 which allows running 3D applications on Linux using Wine.
  - VKD3D-Proton
  - NVRHI (NVIDIA Rendering Hardware Interface)
  - RTXMU - RTX Memory Utility SDK
  - NVRHI (NVIDIA Rendering Hardware Interface)
  - RTXMU - RTX Memory Utility SDK
Game Development Tools
- VS Code Extensions for Developer Productivity
  - Metal - level GPU programming framework used for rendering 2D and 3D graphics on Apple platforms such as iOS, iPadOS, macOS, watchOS and tvOS.
  - 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).
  - Frostbite - platform use on Microsoft Windows and game consoles such as PlayStation 3 Xbox 360, PlayStation 4, Xbox One, Nintendo Switch, PlayStation 5, and Xbox Series X/S.
  - Panda3D - source and free for any purpose, including commercial ventures.
  - Source 2 - Life: Alyx.
  - Havok
  - AutoDesk 3ds Max
  - Houdini
  - Amazon Lumberyard
  - Open Graphics Library(OpenGL) - accelerated rendering of 2D/3D vector graphics currently developed by the [Khronos Group](https://www.khronos.org/).
  - OpenCL
  - Mesa 3D Graphics Library - source implementation of the OpenGL specification. A system for rendering interactive 3D graphics. Mesa ties into several other open-source projects: the [Direct Rendering Infrastructure](https://dri.freedesktop.org/), [X.org](https://x.org/), and [Wayland](https://wayland.freedesktop.org/) to provide OpenGL support on Linux, FreeBSD, and other operating systems.
  - OpenGL ES
  - VDPAU
  - VA API - source library and API specification, which provides access to graphics hardware acceleration capabilities for video processing.
  - XvMC
  - AMD Radeon ProRender - based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
  - Superpowers - time collaborative projects . You can use it solo like a regular offline game maker, or setup a password and let friends join in on your project through their Web browser.
  - URHO3D - platform 2D and 3D game engine implemented in C++ and released under the MIT license. Greatly inspired by OGRE and Horde3D.
  - Vivox
  - HGIG
  - NVIDIA Reflex - in-time for rendering, eliminating the GPU render queue and reducing CPU back pressure in GPU-bound scenarios. In addition to latency reduction functions, the SDK also features measurement markers to calculate both Game and Render Latency that are great for debugging and visualizing in-game performance counter.
  - DirectX 12 Ultimate
  - LibGDX - platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
  - cocos2d-x - platform framework for building 2d games, interactive books, demos and other graphical applications. It is based on cocos2d-iphone, but instead of using Objective-C, it uses C++. It works on iOS, Android, macOS, Windows and Linux.
  - MonoGame - platform games. The spiritual successor to XNA with thousands of titles shipped across desktop, mobile, and console platforms. MonoGame is a fully managed .NET open source game framework without any black boxes.
  - AppGameKit
  - Godot - packed, cross-platform game engine to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools, so that users can focus on making games without having to reinvent the wheel. Games can be exported in one click to a number of platforms, including the major desktop platforms (Linux, Mac OSX, Windows) as well as mobile (Android, iOS) and web-based (HTML5) platforms.
  - Vulkan - platform graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms. Vulkan is currently in development by the Khronos consortium.
  - Blender
  - High Level Shading Language(HLSL) - like programmable shaders for the Direct3D pipeline. HLSL was first created with DirectX 9 to set up the programmable 3D pipeline.
  - MoltenVK
  - OpenGL Shading Language(GLSL) - style language, so it covers most of the features a user would expect with such a language. Such as control structures (for-loops, if-else statements, etc) exist in GLSL, including the switch statement.
  - Unigine - platform game engine designed for development teams (C++/C# programmers, 3D artists) working on interactive 3D apps.
  - Superpowers - time collaborative projects . You can use it solo like a regular offline game maker, or setup a password and let friends join in on your project through their Web browser.
Getting Started with Xcode
- VS Code Extensions for Developer Productivity
  - Compiling Game Projects in Xcode
  - Apple Developer Documentation for Xcode
  - Apple
  - SwiftUI
  - UIKit - Touch and other types of input to your app, and the main run loop needed to manage interactions among the user, the system, and your app.
  - AppKit
  - ARKit - reality apps for iOS developed by Apple. The latest version ARKit 3.5 takes advantage of the new LiDAR Scanner and depth sensing system on iPad Pro(2020) to support a new generation of AR apps that use Scene Geometry for enhanced scene understanding and object occlusion.
  - RealityKit - performance 3D simulation and rendering with information provided by the ARKit framework to seamlessly integrate virtual objects into the real world.
  - Mac Catalyst
  - Instruments - analysis and testing tool that’s part of the Xcode tool set. It’s designed to help you profile your iOS, watchOS, tvOS, and macOS apps, processes, and devices in order to better understand and optimize their behavior and performance.
  - TestFlight
  - Xcode - based CPUs and Apple Silicon. It includes a unified macOS SDK that features all the frameworks, compilers, debuggers, and other tools you need to build apps that run natively on Apple Silicon and the Intel x86_64 CPU.
  - Mac Catalyst
Learning Resources for ML
- viii. Linear Regression
LiDAR Learning Resources
- VS Code Extensions for Developer Productivity
LiDAR Tools & Frameworks
- i. Basis
  - wikimedia
  - wikimedia
- iii. Dimension and Basis for Vector Spaces
  - sliderserve
- iii. Matrix-vector product
  - wikimedia
  - mathisfun
- iii. Transpose of a Matrix
  - slideserve
- ii. Matrix operations
  - DPS Concepts
  - SDSU Physics
- ii. Matrix representations of linear transformations
  - slideserve
- ii. Systems of equations as matrix equations
  - mathisfun
  - pinterest
- ii. Using elementary matrices
  - SDSU Physics
- i. Solving systems of equations
  - slideshare
  - slideshare
- i. Using row operations
  - YouTube
- i. Vector operations
  - slideserve
  - pinterest
- iv. Linear transformations
  - slideserve
  - Quora
- iv. Row space, columns space, and rank of a matrix
  - slideshare
  - slideshare
- v. Fundamental vector spaces
  - wikimedia
  - wolfram
- vi. Determinants
  - stackexchange
  - onlinemathlearning
- vii. Eigenvalues and eigenvectors
  - YouTube
- viii. Linear Regression
  - Linear regression
  - Medium
  - Fuzzy logic - tree processing and better integration with rules-based programming.
  - ResearchGate
  - OpenClipArt
  - Convolutional Neural Networks (R-CNN)
  - CS231n
  - Slideteam
  - DeepAI
  - wikimedia
  - Decision trees - structured models for classification and regression.

Programming Languages

C++ 28 Python 15 C 9 C# 7 MATLAB 3 Rust 3 Scala 3 Lua 3 Objective-C 2 Go 2

Unreal-Engine-Guide

Computer Vision Tools, Libraries, and Frameworks

VS Code Extensions for Developer Productivity

CUDA Learning Resources

viii. Linear Regression

CUDA Tools Libraries, and Frameworks

viii. Linear Regression

Deep Learning Learning Resources

viii. Linear Regression

Deep Learning Tools, Libraries, and Frameworks

viii. Linear Regression

DirectX Learning Resources

VS Code Extensions for Developer Productivity

DirectX Tools, Libraries, and Frameworks

VS Code Extensions for Developer Productivity

Game Development Tools

VS Code Extensions for Developer Productivity

Getting Started with Xcode

VS Code Extensions for Developer Productivity

Learning Resources for ML

viii. Linear Regression

LiDAR Learning Resources

VS Code Extensions for Developer Productivity

LiDAR Tools & Frameworks

i. Basis

iii. Dimension and Basis for Vector Spaces

iii. Matrix-vector product

iii. Transpose of a Matrix

ii. Matrix operations

ii. Matrix representations of linear transformations

ii. Systems of equations as matrix equations

ii. Using elementary matrices

i. Solving systems of equations

i. Using row operations

i. Vector operations

iv. Linear transformations

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

v. Fundamental vector spaces

vi. Determinants

vii. Eigenvalues and eigenvectors

viii. Linear Regression