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.

An algorithm that implements intelligence based on a Method pool (a collection containing multiple types of functions). 一种基于方法池（包含多种类型的函数的集合）实现智能的算法

Flow Based Programming in Go

A rust implementation of Andrej Karpathy's Micrograd

Realization of computational graph as multilist in C with neural network implementation.

Lightweight performat Python 3.12+ automatic differentiation system that leverages PyTorch’s computational graph to compute arbitrary-order partial derivatives.

This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.

A Pure Python Deep Learning Framework with Automatic Differentiation and PyTorch-like API

vanilla, simple, node-oriented, compositive, optimized, frameworkn't{torchn't, TFn't, candlen't}

A minimal vectorized automatic differentiation engine for learning how backpropagation, broadcasting, and neural networks work under the hood.

Understanding of Deep Learning Training Framework

Deep learning Library and mini-Framework built from Scratch,implementing an simplified version of PyTorch using only NumPy

The implementation of automatic differentiation based on NumPy

Manual implementation of backpropagation on a custom computational graph with gradient checking. Benchmarks Vanilla SGD, Momentum, and Adam optimizers from first principles using NumPy.

A lightweight, reverse-mode Automatic Differentiation (AD) engine built from scratch using Python and NumPy. Supports dynamic computational graphs and complex linear algebra operations.

🎢 IaaS visual editor to create & deploy data processing pipelines - python, rmq, react, meteorjs