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
Jul 31, 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.
AutoBound automatically computes upper and lower bounds on functions.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Drop-in autodiff for NumPy.
Trace, the New AutoDiff for AI Systems and LLM Agents
Autodifferentiation package in Rust.
An interface to various automatic differentiation backends in Julia.
[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
Tensors and dynamic Neural Networks in Mojo
Automatic differentiation of implicit functions
An experimental deep learning framework for Nim based on a differentiable array programming language
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.
A JIT compiler for hybrid quantum programs in PennyLane
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