Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken

• tensorken

  2 97 8.2 Rust

  A fun, hackable, GPU-accelerated, neural network library in Rust, written by an idiot

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

• tinygrad

  17 23,864 10.0 Python

  You like pytorch? You like micrograd? You love tinygrad! ❤️

  

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• swift

  214 65,927 10.0 C++

  The Swift Programming Language

  

Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.

• Pytorch

  337 77,783 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

Suggest a related project