#Autograd
Showing 41 of 41 repositories tagged #autograd, ranked by stars
Tensors and Dynamic neural networks in Python with strong GPU acceleration
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
Efficiently computes derivatives of NumPy code.
A C++ standalone library for machine learning
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
An Engine-Agnostic Deep Learning Framework in Java
MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
JAX - A curated list of resources https://github.com/google/jax
C++ Implementation of PyTorch Tutorials for Everyone
Deep learning in Rust, with shape checked tensors and neural networks
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
Owl - OCaml Scientific Computing @ https://ocaml.xyz
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
Deep learning with spiking neural networks (SNNs) in PyTorch.
R Interface to Torch
Tensors and differentiable operations (like TensorFlow) in Rust
A Machine Learning framework from scratch in Pure Mojo 🔥
A complete GPT language model (training and inference) in ~600 lines of pure C#, zero dependencies
A deep learning framework created from scratch with Python and NumPy
A deep learning framework built on an autograd engine with high level abstractions and low level control.
Julia port of the Python autograd package.
The V Tensor Library
A machine learning library built with user convenience at its core
S + Autograd + XLA :: S-parameter based frequency domain circuit simulations and optimizations using JAX.
Interactive visualization of a minimal GPT implementation with autograd engine.
A deep learning library built from scratch with complex neural networks examples built on top for learning purposes.
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
A learning-focused, high-performance tensor computation library built from scratch in Rust, featuring automatic differentiation and CPU/CUDA backends.
A minimal OpenCL, CUDA, Vulkan and host CPU array manipulation engine / framework.
Andrej Karpathy's Micrograd in Go
Error propagation and statistical analysis for Monte Carlo simulations in lattice QCD and statistical mechanics using autograd.
Port of Andrej Karpathy's python microGPT to Rust
Deep Learning framework in Go with Tensors, AutoGrad, and GPU acceleration
Build a GPT from scratch, one concept at a time, from pure Python to PyTorch, JAX, MLX, and production serving. Inspired by Andrej Karpathy's microGPT.
Recreating PyTorch from scratch, using Numpy. Supports FCN, CNN, RNN layers.
European Distributed Deep Learning (EDDL) library. A general-purpose library initially developed to cover deep learning needs in healthcare use cases within the DeepHealth project.
RusTorch is a production-grade deep learning framework re-imagined in Rust. It combines the usability you love from PyTorch with the performance, safety, and concurrency guarantees of Rust. Say goodbye to GIL locks, GC pauses, and runtime errors. Say hello to RusTorch.
A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
Toy autograd engine in OCaml with Apple Accelerate backend
Automatic differentiation library written in pure Vim script.