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Search results for deep learning automatic differentiation

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54 search results found

Gorgonia ⭐ 5,330

Gorgonia is a library that helps facilitate machine learning in Go.

Tangent ⭐ 2,066

Source-to-Source Debuggable Derivatives in Pure Python

Pennylane ⭐ 2,022

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Spago ⭐ 1,691

Self-contained Machine Learning and Natural Language Processing library in Go

Effectivepytorch ⭐ 1,321

PyTorch tutorials and best practices.

Arraymancer ⭐ 1,177

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

Enzyme ⭐ 1,142

High-performance automatic differentiation of LLVM and MLIR.

Deeplearning.scala ⭐ 759

A simple library for creating complex neural networks

Deepdarkfantasy ⭐ 464

A Programming Language for Deep Learning

Torchopt ⭐ 460

TorchOpt is an efficient library for differentiable optimization built upon PyTorch.

Deep Learning From Scratch 3 ⭐ 426

『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)

Rust Autograd ⭐ 413

Tensors and differentiable operations (like TensorFlow) in Rust

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

Tensorlang ⭐ 303

Tensorlang, a differentiable programming language based on TensorFlow

Kayak is a library for automatic differentiation with applications to deep neural networks.

Neuraloperators.jl ⭐ 206

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Jaxfluids ⭐ 203

Differentiable Fluid Dynamics Package

Nnlib.jl ⭐ 187

Neural Network primitives with multiple backends

Backprop ⭐ 180

Heterogeneous automatic differentiation ("backpropagation") in Haskell

Autograd.jl ⭐ 160

Julia port of the Python autograd package.

Neograd ⭐ 117

A deep learning framework created from scratch with Python and NumPy

scorch is a deep learning framework in Scala inspired by PyTorch

Infermo ⭐ 102

Tensors and dynamic Neural Networks in Mojo

Notebooks ⭐ 101

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

Deep.Net machine learning framework for F#

Autograd From Scratch ⭐ 82

Documented and Unit Tested educational Deep Learning framework with Autograd from scratch.

A fast and expressive Matlab/MatConvNet deep learning API, with native automatic differentiation

Exprgrad ⭐ 70

An experimental deep learning framework for Nim based on a differentiable array programming language

Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux

Recurrentjava ⭐ 46

Deep Recurrent Neural Nets in Java

Neural Network Playground ⭐ 43

A visual Deep Learning Framework for the Web - Built with WebGPU, Next.js and ReactFlow.

Qualia2.0 ⭐ 41

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

Keras-like APIs for JAX framework

Python package for Geometric / Clifford Algebra with TensorFlow

Deep Learning Optimization Algorithms ⭐ 37

Visualization of various deep learning optimization algorithms using PyTorch automatic differentiation and optimizers.

Numpy Nn Model ⭐ 32

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Protograd.jl ⭐ 31

Deep learning 99% fat free

A numerical optimisation and deep learning framework for D.

Diffhask ⭐ 26

DSL for forward and reverse mode automatic differentiation in Haskell. Port of DiffSharp.

Appendices ⭐ 25

Complement the article 'Differential Machine Learning' (Huge & Savine, 2020), including mathematical proofs and important implementation details for production

JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

SANNet Neural Network Framework

Machine Learning Summer Schools ⭐ 14

Curated materials for different machine learning related summer schools

A Python library for training neural ODEs.

Toeffipy ⭐ 13

ToeffiPy is a PyTorch like autograd/deep learning library based only on NumPy.

A small framework that can perform automatic differentiation to calculate first-order gradients of numpy arrays.

Gradflow ⭐ 10

A small, educational autograd system with deep neural networks support

SemiFlow is a deep learning framework with automatic differentiation and automatic shape inference, developing from Numpy. 一个基于Numpy支持自动求导的深度学习框架

Automatic differentiation in python

Physics Driven Ml ⭐ 8

Physics-driven machine learning using PyTorch and Firedrake

A small framework that can perform automatic differentiation.

Albiruniml ⭐ 7

AlbiruniML is a linear algebra and machine learning library written in pure c# language inspired from tensorflow

Reverse Mode Automatic Differentiation Cpp ⭐ 6

A simple implementation of reverse mode automatic differentiation in C++ without the use of any libraries.

An imperative deep learning framework with customized GPU and CPU backend

🌲 Ahead-of-time Static Macro-gen Automatic Differentiation. A little bit like Jax. Now in Beta

a Tiny Automatic Differentiation Library for understanding how neural networks works, implemented in pure Java

Automatic differentiation deep neural network framework.

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