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TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. Develop ML in the Browser Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

deep-learning typescript webgl machine-learning neural-network deep-neural-network gpu-accelerationNOTE: Building on the momentum of deeplearn.js, we have joined the TensorFlow family and we are starting a new ecosystem of libraries and tools for Machine Learning in Javascript, called TensorFlow.js. This repo moved from PAIR-code/deeplearnjs to tensorflow/tfjs-core. A part of the TensorFlow.js ecosystem, this repo hosts @tensorflow/tfjs-core, the TensorFlow.js Core API, which provides low-level, hardware-accelerated linear algebra operations and an eager API for automatic differentiation.

deep-learning typescript webgl machine-learning neural-network deep-neural-networks gpu-accelerationThere are certainly a lot of guides to assist you build great deep learning (DL) setups on Linux or Mac OS (including with Tensorflow which, unfortunately, as of this posting, cannot be easily installed on Windows), but few care about building an efficient Windows 10-native setup. Most focus on running an Ubuntu VM hosted on Windows or using Docker, unnecessary - and ultimately sub-optimal - steps. We also found enough misguiding/deprecated information out there to make it worthwhile putting together a step-by-step guide for the latest stable versions of Keras, Tensorflow, CNTK, MXNet, and PyTorch. Used either together (e.g., Keras with Tensorflow backend), or independently -- PyTorch cannot be used as a Keras backend, TensorFlow can be used on its own -- they make for some of the most powerful deep learning python libraries to work natively on Windows.

theano gpu-acceleration deep-learning tensorflow cudnn cntk gpu-mode kerasA Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

bayesian-inference bayesian-data-analysis gpu-computing gpu-acceleration statistics machine-learning clojure-library bayesian opencl cuda high-performance-computing gpu mcmc markov-chain-monte-carloConverts gl-matrix(1) mat4 objects into strings that can be applied as values for css transform properties on elements. Because if you move elements using...

matrix3d css transform gl-matrix animation gpu-accelerationIf you compile on the RPi: Please note that the compiling of the release version (-O3) requires a big amount of memory. If the compiling fails reduce the memory for the gpu at 64MB (config.txt) or enable swapping. If you compile on other systems: Add the cmake flag -DWITH_RPI=0 to disable the parts which requires RPi dependecies.

raspivid rpi detection opengl camera blob-detection gpu-accelerationAn embedded language for GPU kernel programming.

haskell-library gpu gpu-computing gpu-acceleration edsl haskellC++ DBSCAN VP tree kNN

c-plus-plus dbscan gpu clustering gpu-acceleration cuda dbscan-clustering dbscan-vpAnvilKit tames Metal. It's a collection of code that seems to come up in just about every project that everyone seems to roll themselves. Object that wraps MTLDevice and makes it into a singleton so that you don't need to pass it around.

metal metalkit gpu gpu-acceleration gpu-computingPHCpack is a software package to solve polynomial systems by homotopy continuation methods. A polynomial system is given as a sequence of polynomials in several variables. Homotopy continuation methods operate in two stages. In the first stage, a family of polynomial systems (the so-called homotopy) is constructed. This homotopy contains a polynomial system with known solutions. In the second stage, numerical continuation methods are applied to track the solution paths defined by the homotopy, starting at the known solutions and leading to the solutions of the given polynomial system.

polynomial-systems homotopy-continuation-methods homotopy algebraic-geometry parallel-computing gpu-acceleration:framed_picture: Actionscript 3, GPU accelerated 2D game engine using Stage3D

spritesheet engine rendering gpu-accelerationLearn OpenCL step by step as below. Using Docker is convenient, which you don't need config and install enviroments for all about OpenCL. Of course, install Docker Community Edition first and then search relative images in DockerHub.

opencl tutorials tutorial-code guides scratch gpu-programming gpu-accelerationNeuralNetwork.NET is a .NET Standard 2.0 library that implements sequential and computation graph neural networks with customizable layers, built from scratch with C#. It provides simple APIs designed for quick prototyping to define and train models using stochastic gradient descent, as well as methods to save/load a network model and its metadata and more. The library also exposes CUDA-accelerated layers with more advanced features that leverage the GPU and the cuDNN toolkit to greatly increase the performances when training or using a neural network.

neural-network convolutional-neural-networks backpropagation-algorithm gradient-descent machine-learning classification-algorithims cnn supervised-learning ai cuda gpu-acceleration netstandard net-framework visual-studioAfter checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

cuda high-performance-computing gpu-computing gpu-accelerationClojure library for CUDA development. See the documentation at ClojureCUDA website. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

clojure-library cuda-development cuda high-performance gpu-computing gpu-accelerationDeep learning library for F#. Provides tensor functionality, symbolic model differentiation, automatic differentiation and compilation to CUDA GPUs. It includes optimizers and model blocks used in deep learning. Deep.Net is currently being ported to .NET Standard 2.0.

deep-learning symbolic-computation symbolic-execution-engine differentiation fsharp machine-learning cuda gpu-acceleration gpu gpu-computing ndarray tensor
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