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neural-tangents

Fast and Easy Infinite Neural Networks in Python

networks

Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.

descent

First-order optimization tools

torchzero

Modular optimization library for PyTorch (work-in-progress).

ml-python

Easy to use high level python library for popular machine learning algorithms. Has in-built support for graphing and optimizers based in C++.

grande

(ICLR 2024) GRANDE: Gradient-Based Decision Tree Ensembles

spsa

A simple implementation of SPSA with automatic learning rate tuning

torchimize

numerical optimization in pytorch

mlektic

A Python ML library to use and visualize gradient descent for linear and logistic regression optimization.

nets

Recreating PyTorch from scratch, using Numpy. Supports FCN, CNN, RNN layers.

rose-opt

🌹 Rose: Range-Of-Slice Equilibration PyTorch optimizer. Stateless optimization through range-normalized gradient updates.

yodf

'Hello, World!' Forward Mode Autdiff library with Tensorflow 1 like interface.

torch-cgd

A fast implementation of adaptive competitive gradient descent in PyTorch 🤺

mlr-gd

A package for multiple linear regression by gradient descent.

simpliml

Machine Learning, Artificial Intelligence, Mathematics

fastcpd

Fast Change Point Detection in R

dlfs

implement from scratch (using numpy arrays) a package based on tensorflow architecture which allows to build and train Fully Connected Networks and Convolutional Neural Networks (CNNs).

gradient-equilibrum

An implementation of an Gradient Algorithm that fines the equilibrium instead of the minimum or maximum

tno-mpc-mpyc-secure-learning

TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Secure Learning

waveletml

WaveletML: A Scalable and Extensible Wavelet Neural Network Framework

psd-optimizer

Perturbed Saddle-escape Descent (PSD): a first-order optimizer that escapes strict saddle points in nonconvex problems.

findi-descent

FinDi: Finite Difference Gradient Descent can optimize any function, including the ones without analytic form, by employing finite difference numerical differentiation within a gradient descent algorithm.

optimisation-algorithms

A collection of the most commonly used Optimisation Algorithms for Data Science & Machine Learning

optschedule

Flexible parameter scheduler that can be implemented with proprietary and open source optimizers.