Deep learning in Rust, with shape checked tensors and neural networks
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Updated
Jul 23, 2024 - Rust
Deep learning in Rust, with shape checked tensors and neural networks
automatic differentiation made easier for C++
Tensors and dynamic neural networks in pure Rust.
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Drop-in autodiff for NumPy.
FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
Differentiate python calls from Julia
Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.
[wip] Lightweight Automatic Differentiation & DeepLearning Framework implemented in pure Julia.
A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
Yaae: Yet another autodiff engine (written in Numpy).
Forward mode automatic differentiation for Fortran
A minimalist neural networks library built on a tiny autograd engine
Algorithmic differentiation with hyper-dual numbers in C++ and Python
Scala embedded universal probabilistic programming language
C++20 numerical and analytical derivative computations
Chemical Explosive Mode Analysis for computational/experimental combustion diagnostics using Julia SciML features
Assignments for Data Intensive Systems for Machine Learning Coursework
Experiments with forward gradients on optimization test functions
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