amx-guide
Advanced Matrix Extensions (AMX) Guide
https://github.com/mikeroyal/amx-guide
Last synced: 5 days ago
JSON representation
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Deep Learning Learning Resources
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viii. Linear Regression
- Autonomous Systems - Microsoft AI
- Top Deep Learning Courses Online | Udemy
- Learn Deep Learning with Online Courses and Lessons | edX
- Deep Learning Online Course Nanodegree | Udacity
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- Understanding Machine Learning with Python | Pluralsight
- How to Think About Machine Learning Algorithms | Pluralsight
- Deep Learning Courses | Stanford Online
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Artificial Intelligence Nanodegree program
- Artificial Intelligence (AI) Online Courses | Udacity
- Intro to Artificial Intelligence Course | Udacity
- Edge AI for IoT Developers Course | Udacity
- Expert Systems and Applied Artificial Intelligence
- Introduction to Microsoft Project Bonsai
- Autonomous Maritime Systems Training | AMC Search
- Top Autonomous Cars Courses Online | Udemy
- Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
- Learn Autonomous Robotics with Online Courses and Lessons | edX
- Autonomous Systems Online Courses & Programs | Udacity
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
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C/C++ Learning Resources
- Learn C++
- Learn C : An Interactive C Tutorial
- C++ Online Training Courses on LinkedIn Learning
- C++ Tutorials on W3Schools
- Learn C Programming Online Courses on edX
- Learn C++ with Online Courses on edX
- Learn C++ on Codecademy
- Coding for Everyone: C and C++ course on Coursera
- C++ For C Programmers on Coursera
- C++ Online Courses on Udemy
- Top C Courses on Udemy
- Basics of Embedded C Programming for Beginners on Udemy
- C++ For Programmers Course on Udacity
- C++ Fundamentals Course on Pluralsight
- C & C++ Developer Tools from JetBrains
- Open source C++ libraries on cppreference.com
- C++ Graphics libraries
- C++ Libraries in MATLAB
- Google C++ Style Guide
- C - purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
- Embedded C - committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
- C++ style guide for Fuchsia
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- C++ Tools and Libraries Articles
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C/C++ Tools and Frameworks
- AWS SDK for C++
- Azure SDK for C++
- Azure SDK for C
- C++ Client Libraries for Google Cloud Services
- 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.
- Vcpkg
- 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
- CppSharp
- Conan
- High Performance Computing (HPC) SDK
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
- 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.
- 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.
- JavaCPP
- Cython
- Spdlog - only/compiled, C++ logging library.
- Infer - C, and C. Infer is written in [OCaml](https://ocaml.org/).
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Linear Algebra Learning Resources
- 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 | UC San Diego Extension
- Linear Algebra for Machine Learning | UC San Diego Extension
- Linear Algebra Resources | Dartmouth
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Defintions
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i. Vector operations
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ii. Matrix operations
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iii. Matrix-vector product
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iv. Linear transformations
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v. Fundamental vector spaces
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Computational Linear Algebra
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ii. Systems of equations as matrix equations
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Computing the Inverse of a Matrix
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ii. Systems of equations as matrix equations
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ii. Using elementary matrices
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iii. Transpose of a Matrix
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i. Using row operations
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Other Linear Topics
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i. Basis
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ii. Matrix representations of linear transformations
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iii. Dimension and Basis for Vector Spaces
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iv. Row space, columns space, and rank of a matrix
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vi. Determinants
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vii. Eigenvalues and eigenvectors
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viii. Linear Regression
- Linear regression
- Medium
- PV(ParaVirtualization) - assisted virtualization.
- Virtualized Infrastructure Manager (VIM)
- Management and Orchestration(MANO) - hosted initiative to develop an Open Source NFV Management and Orchestration (MANO) software stack aligned with ETSI NFV. Two of the key components of the ETSI NFV architectural framework are the NFV Orchestrator and VNF Manager, known as NFV MANO.
- OpenRAN - vendor deployments.
- Open vSwitch(OVS)
- Edge
- Multi-access edge computing (MEC) - parties across multi-vendor Multi-access Edge Computing platforms.
- Cloud-Native Network Functions(CNF)
- Physical Network Function(PNF)
- KVM (for Kernel-based Virtual Machine) - V). It consists of a loadable kernel module, kvm.ko, that provides the core virtualization infrastructure and a processor specific module, kvm-intel.ko or kvm-amd.ko.
- VirtManager
- HyperKit - level components such as the [VPNKit](https://github.com/moby/vpnkit) and [DataKit](https://github.com/moby/datakit). HyperKit currently only supports macOS using the [Hypervisor.framework](https://developer.apple.com/library/mac/documentation/DriversKernelHardware/Reference/Hypervisor/index.html) making it a core component of Docker Desktop for Mac.
- Intel® Graphics Virtualization Technology (Intel® GVT) - through, starting from 4th generation Intel Core (TM) processors with Intel processor graphics(Broadwell and newer). It can be used to virtualize the GPU for multiple guest virtual machines, effectively providing near-native graphics performance in the virtual machine and still letting your host use the virtualized GPU normally.
- Apple Hypervisor - party kernel extensions. Hypervisor provides C APIs so you can interact with virtualization technologies in user space, without writing kernel extensions (KEXTs). As a result, the apps you create using this framework are suitable for distribution on the [Mac App Store](https://www.appstore.com/).
- Apple Virtualization Framework - level APIs for creating and managing virtual machines on Apple silicon and Intel-based Mac computers. This framework is used to boot and run a Linux-based operating system in a custom environment that you define. It also supports the [Virtio specification](https://www.redhat.com/en/virtio-networking-series), which defines standard interfaces for many device types, including network, socket, serial port, storage, entropy, and memory-balloon devices.
- Apple Paravirtualized Graphics Framework - accelerated graphics for macOS running in a virtual machine, hereafter known as the guest. The operating system provides a graphics driver that runs inside the guest, communicating with the framework in the host operating system to take advantage of Metal-accelerated graphics.
- Cloud Hypervisor - lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.
- Xen
- Ganeti
- Packer
- Vagrant - to-use workflow and focus on automation, Vagrant lowers development environment setup time, increases production parity, and makes the "works on my machine" excuse a relic of the past. It provides easy to configure, reproducible, and portable work environments built on top of industry-standard technology and controlled by a single consistent workflow to help maximize the productivity and flexibility of you and your team.
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Parallel Computing Learning Resources
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viii. Linear Regression
- Parallel Computing - level]https://en.wikipedia.org/wiki/Bit-level_parallelism), [instruction-level](https://en.wikipedia.org/wiki/Instruction-level_parallelism), [data](https://en.wikipedia.org/wiki/Data_parallelism), and [task parallelism](https://en.wikipedia.org/wiki/Task_parallelism).
- Accelerated Computing - Training | NVIDIA Developer
- Fundamentals of Accelerated Computing with CUDA Python Course | NVIDIA
- Top Parallel Computing Courses Online | Coursera
- Top Parallel Computing Courses Online | Udemy
- Scientific Computing Masterclass: Parallel and Distributed
- Learn Parallel Computing in Python | Udemy
- GPU computing in Vulkan | Udemy
- High Performance Computing Courses | Udacity
- Parallel Computing Courses | Stanford Online
- Parallel Computing with CUDA | Pluralsight
- HPC Architecture and System Design | Intel
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Parallel Computing Tools, Libraries, and Frameworks
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viii. Linear Regression
- MATLAB Parallel Server™
- Statistics and Machine Learning Toolbox™
- OpenMP - platform shared-memory parallel programming in C/C++ and Fortran. The OpenMP API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer.
- CUDA®
- Message Passing Interface (MPI) - passing standard designed to function on parallel computing architectures.
- Slurm - source workload manager designed specifically to satisfy the demanding needs of high performance computing.
- AWS ParallelCluster - supported open source cluster management tool that makes it easy for you to deploy and manage High Performance Computing (HPC) clusters on AWS. ParallelCluster uses a simple text file to model and provision all the resources needed for your HPC applications in an automated and secure manner.
- 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.
- cuML - learn.
- Apache Flume
- Apache HBase™ - source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
- Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
- Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- 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
- 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.
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Apache Beam - specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs).
- 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.
- Neo4j - strength graph database that combines native graph storage, advanced security, scalable speed-optimized architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.
- ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
- Logstash
- Kibana
- Trino - us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
- Redis(REmote DIctionary Server) - memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
- 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).
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
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OpenCL Learning Resources
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viii. Linear Regression
- Open Computing Language (OpenCL) - to-parallel-computing-zNrIS) of heterogeneous platforms consisting of CPUs, GPUs, and other hardware accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms.
- OpenCL | NVIDIA Developer
- Introduction to OpenCL on FPGAs Course | Coursera
- Compiling OpenCL Kernel to FPGAs Course | Coursera
- OpenCL Tutorials - StreamHPC
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OpenCL Tools, Libraries and Frameworks
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viii. Linear Regression
- GPUVerify
- OpenCL ICD Loader
- clBLAS
- clFFT
- clSPARSE
- clRNG
- CLsmith - core environment, OpenCL. Its primary feature is the generation of random OpenCL kernels, exercising many features of the language. It also brings a novel idea of applying EMI, via dead-code injection.
- Oclgrind - races and barrier divergence, collecting instruction histograms, and for interactive OpenCL kernel debugging. The simulator is built on an interpreter for LLVM IR.
- NVIDIA® Nsight™ Visual Studio Edition
- Radeon™ GPU Profiler
- Radeon™ GPU Analyzer
- 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.
- Intel® SDK For OpenCL™ Applications - intensive workloads. Customize heterogeneous compute applications and accelerate performance with kernel-based programming.
- 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/).
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
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CUDA Learning Resources
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viii. Linear Regression
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CUDA Tools Libraries, and Frameworks
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viii. Linear Regression
- 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).
- 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
- Tensorman
- 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.
- 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.
- 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
- Multilayer Perceptrons (MLPs) - layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.
- wikimedia
- Decision trees - structured models for classification and regression.
- CMU
- Naive Bayes - theorem.html) with strong independence assumptions between the features.
- mathisfun
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Learning Resources for ML
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viii. Linear Regression
- Machine Learning by Stanford University from Coursera
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Machine Learning Crash Course for Google Cloud
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- Machine Learning Courses Online from Udemy
- Learn Machine Learning with Online Courses and Classes from edX
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ML Frameworks, Libraries, and Tools
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viii. Linear Regression
- 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.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Anaconda
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- 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.
- 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.
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Deep Learning Tools, Libraries, and Frameworks
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viii. Linear Regression
- 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).
- 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.
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- 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.
- 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.
- ROS Toolbox
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- 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™
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- 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.
- 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.
- UAV Toolbox
- 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.
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
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Contribute
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viii. Linear Regression
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Types of Accelerators
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FPGA Learning Resources
- FPGA(Field Programmable Gate Arrays)
- SiFive FPGA shells
- FPGA & SoC Design Tools from Microsemi
- FPGA Courses on Udemy
- FPGAs & SoCs Training from Microsemi
- Verilog Courses on Coursera
- FPGA Courses on Coursera
- FPGA Online Training Courses on LinkedIn Learning
- UMass Lowell's Graduate Certificate in Field Programmable Gate Arrays(FPGA)
- FPGA for Beginners with Development Boards from Digilent®
- DSP fundamentals for FPGAs course from MATLAB and Simulink Training
- Programming and FPGA Basics - INTEL® FPGAS
- Intel FPGA Training Program
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FPGA Tools
- Apio - built packages, project configuration tools and easy command interface to verify, synthesize, simulate and upload your verilog designs.
- IceStorm
- Icestudio
- FuseSoC - winning package manager and a set of build tools for HDL (Hardware Description Language) code and FPGA/ASIC development.
- OpenWiFi - source IEEE802.11/Wi-Fi baseband chip/FPGA design.
- PipeCNN - based FPGA Accelerator for Large-Scale Convolutional Neural Networks (CNNs). Currently, there is a growing trend among developers in the FPGA community to utilize High Level Synthesis (HLS) tools to design and implement customized circuits on FPGAs.
- The Eclipse Embedded CDT - ins for Arm & RISC-V C/C++ developers.
- Unicorn - platform, multi-architecture CPU emulator framework(ARM, AArch64, M68K, Mips, Sparc, X86) based on [QEMU](https://www.qemu.org/).
- Keystone - platform, multi-architecture(Arm, Arm64, Hexagon, Mips, PowerPC, Sparc, SystemZ & X86) assembler framework.
- Verilog to Routing(VTR) - source framework for conducting FPGA architecture and CAD Research & Development. The VTR design flow takes as input a Verilog description of a digital circuit, and a description of the target FPGA architecture.
- PlatformIO - in. It provides support for multiplatforms and frameworks such as IoT, Arduino, CMSIS, ESP-IDF, FreeRTOS, libOpenCM3, mbed OS, Pulp OS, SPL, STM32Cube, Zephyr RTOS, ARM, AVR, Espressif (ESP8266/ESP32), FPGA, MCS-51 (8051), MSP430, Nordic (nRF51/nRF52), NXP i.MX RT, PIC32, RISC-V.
- PlatformIO for VSCode
- Chipyard - based systems-on-chip. It will allow you to leverage the Chisel HDL, Rocket Chip SoC generator, and other [Berkeley](https://berkeley.edu/) projects to produce a RISC-V SoC with everything from MMIO-mapped peripherals to custom accelerators.
- Reko
- Renode
- Diosix - metal hypervisor written in Rust for RISC-V.
- OpenTimer - Performance Timing Analysis Tool for VLSI Systems.
- Tock - M and RISC-V based embedded platforms. Tock's design centers around protection, both from potentially malicious applications and from device drivers.
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LLVM Tools, Libraries and Frameworks
- FileCheck
- tblgen
- clang-tblgen
- lldb-tblgen
- llvm-tblgen
- mlir-tblgen
- lit
- llvm-exegesis
- llvm-pdbutil
- llvm-profgen
- Code Server
- Clang-Format - C/Objective-C++/Protobuf code.
- Clang-Tidy - based C++ "linter" tool. Its purpose is to provide an extensible framework for diagnosing and fixing typical programming errors, like style violations, interface misuse, or bugs that can be deduced via static analysis. clang-tidy is modular and provides a convenient interface for writing new checks.
- Clangd
- Visual Studio Code
- LLD - in replacement for system linkers and runs much faster than them. It also provides features that are useful for toolchain developers. The linker supports ELF (Unix), PE/COFF (Windows), Mach-O (macOS) and WebAssembly in descending order.
- llvm-locstats
- bugpoint
- llvm-extract
- llvm-bcanalyzer
- llvm-addr2line - in replacement for addr2line.
- llvm-ar
- llvm-cxxfilt
- llvm-install-name-tool - names and rpaths.
- llvm-nm
- llvm-objcopy
- llvm-objdump
- llvm-ranlib
- llvm-readelf - style LLVM Object Reader.
- llvm-size
- llvm-strings
- llvm-strip
- TinyGo - line tools.
- Back to the Top
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LLVM Learning Resources
- Clang - end and tooling infrastructure for languages in the C language family (C, C++, Objective C/C++, OpenCL, CUDA, and RenderScript) for the LLVM project.
- LLVM | Apple Developer Forums
- LLVM Documentation
- Contributing to LLVM
- Getting Started with LLVM
- Getting Started with Clang
- How To Setup Clang Tooling For LLVM
- Using Clang-Tidy in Visual Studio
- Configure VS Code for Clang/LLVM on macOS
- LLVM Project GitHub
- LLVM Project GitHub
- LLVM Discussion Forum
- LLVM | Apple Developer Forums
- Using Clang-Tidy in Visual Studio
Categories
LLVM Tools, Libraries and Frameworks
34
Other Linear Topics
31
C/C++ Tools and Frameworks
31
Parallel Computing Tools, Libraries, and Frameworks
29
Deep Learning Learning Resources
26
C/C++ Learning Resources
26
CUDA Tools Libraries, and Frameworks
25
Deep Learning Tools, Libraries, and Frameworks
21
FPGA Tools
18
OpenCL Tools, Libraries and Frameworks
15
LLVM Learning Resources
14
ML Frameworks, Libraries, and Tools
13
FPGA Learning Resources
13
Parallel Computing Learning Resources
12
Types of Accelerators
10
Defintions
10
Linear Algebra Learning Resources
10
Learning Resources for ML
6
OpenCL Learning Resources
5
Computing the Inverse of a Matrix
4
CUDA Learning Resources
3
License
1
Computational Linear Algebra
1
Contribute
1
Sub Categories
viii. Linear Regression
180
vi. Determinants
2
v. Fundamental vector spaces
2
i. Basis
2
iv. Linear transformations
2
iii. Matrix-vector product
2
i. Vector operations
2
ii. Systems of equations as matrix equations
2
ii. Matrix operations
2
iii. Dimension and Basis for Vector Spaces
1
i. Using row operations
1
iii. Transpose of a Matrix
1
iv. Row space, columns space, and rank of a matrix
1
vii. Eigenvalues and eigenvectors
1
ii. Matrix representations of linear transformations
1
ii. Using elementary matrices
1
Keywords
cuda
8
cpp
8
deep-learning
6
gpu
5
fpga
5
verilog
4
nvidia
4
arm
4
python
4
c
3
cpp11
3
cxx14
3
reverse-engineering
3
x86-64
3
machine-learning
3
cpp17
3
opencl
3
x86
3
cpp14
3
hls
2
systemz
2
sparc
2
security
2
iot
2
embedded
2
azure-sdk
2
azure
2
hardware
2
cxx20
2
cxx17
2
cxx11
2
cxx
2
risc-v
2
algorithms
2
compiler
2
deep-neural-networks
2
dotnet
2
kvm
2
virtualization
2
arm64
2
cpu
2
eda
2
parallel-computing
2
nvidia-hpc-sdk
2
framework
2
m68k
2
mips
2
powerpc
2
cpp20
2
gpu-computing
2