DFO-GN: Derivative-Free Optimization using Gauss-Newton

Teaching Materials for Dr. Waleed A. Yousef

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

This a JavaScript Library for the Numerical Javascript and Machine Learning

Share resources, assets and information for the math sessions 🚀

First-order optimization tools

Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.

Investment portfolio and stocks analyzing tools for Python with free historical data

a lightweight C++17 library of numerical optimization methods for nonlinear functions (Including L-BFGS-B for TensorFlow)

🎯 A comprehensive gradient-free optimization framework written in Python

Modern Fortran Numerical Differentiation Library

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.

https://machinelearning.wtf/ - An online glossary of machine learning terms.

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

🤖 An authorial collection of fundamental python recipes on Machine Learning and Artificial Intelligence.

Rcpp Integration for Numerical Computing Libraries

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries