Torch Containers simplified in PyTorch

Autograd (backpropagation, reverse-mode auto differentiation) in Nim

Machine Learning models for large datasets

Autograd compatible Givens Transforms which is especially useful for optimization on a Stiefel Manifold.

a simple implementation of autograd engine

Deep-Learning framework from scratch

Examples from scratch using PyTorch

用例子学习PyTorch1.0（Learning PyTorch with Examples 中文翻译与学习）

An implementation of simple deep learning framework with autograd, from scratch (numpy)

Pytorch Autograd Tutorial: contains the official tutorial notebook (autograd_tutorial.ipynb) with a couple of basic examples added.

A set of autograd tutorial notebooks

A pedagogical implementation of Automatic Differation on multi-dimensional tensors.

Computation graphs, automatic differentiation and machine learning for Kotlin

Source code for Deep Multigrid method https://arxiv.org/pdf/1711.03825.pdf

Testing a simple autogradient calculation for a Neural Network

Multilayer neural network framework implementation, used for classification and regression task. Can use multiple activation functions with backpropagation based on autograd library. Contains polynomial activation function for regression task.

Forward and backward mode automatic differentiation implemented in Python

barebones implementation of autograd

scorch is a deep learning framework in Scala inspired by PyTorch