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 6, 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.
Autodifferentiation package in Rust.
An interface to various automatic differentiation backends in Julia.
Automatic differentiation of implicit functions
[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
Tensors and dynamic Neural Networks in Mojo
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 probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.
Solve ODEs fast, with support for PyMC
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