Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
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Updated
Dec 4, 2024 - Rust
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Source-to-Source Debuggable Derivatives in Pure Python
Deep learning in Rust, with shape checked tensors and neural networks
automatic differentiation made easier for C++
DiffSharp: Differentiable Functional Programming
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
End-to-end Generative Optimization for AI Agents
AutoBound automatically computes upper and lower bounds on functions.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
An interface to various automatic differentiation backends in Julia.
Drop-in autodiff for NumPy.
Autodifferentiation package in Rust.
A JIT compiler for hybrid quantum programs in PennyLane
[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
Tensors and dynamic Neural Networks in Mojo
Automatic differentiation of implicit functions
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.
An experimental deep learning framework for Nim based on a differentiable array programming language
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