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56 search results found

Pyswarms ⭐ 1,154

A research toolkit for particle swarm optimization in Python

Awesome Robotics ⭐ 817

A curated list of awesome links and software libraries that are useful for robots.

Ensmallen ⭐ 675

A header-only C++ library for numerical optimization --

Dist Keras ⭐ 611

Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.

🎯 A comprehensive gradient-free optimization framework written in Python

Abagail ⭐ 249

The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.

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.

Sgdlibrary ⭐ 181

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

L4casadi ⭐ 175

Use PyTorch Models with CasADi and Acados in Python, C(++) or Matlab

PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms

Matlab and Python toolbox for fast Total Variation proximity operators

A Python library for mathematical optimization

Typesafe, purely functional Computational Intelligence

Proximalalgorithms.jl ⭐ 117

Proximal algorithms for nonsmooth optimization in Julia

Genetic algorithm for unsupervised machine learning in Go.

C++ Large Scale Genetic Programming

Optimizer Visualization ⭐ 101

Visualize Tensorflow's optimizers.

Viz_torch_optim ⭐ 96

Videos of deep learning optimizers moving on 3D problem-landscapes

Distributedevolutionarycomputation ⭐ 84

A (still growing) paper list of Evolutionary Computation (EC) published in some (rather all) top-tier (and also EC-focused) journals and conferences. For EC-focused publications, only Parallel/Distributed EC are covered.

Jestimator ⭐ 77

Amos optimizer with JEstimator lib.

Csmath 2020 ⭐ 71

This mathematics course is taught for the first year Ph.D. students of computer science and related areas @ZJU

Hyper Engine ⭐ 69

Python library for Bayesian hyper-parameters optimization

Machine Learning In Python Workshop ⭐ 54

My workshop on machine learning using python language to implement different algorithms

Riemannian stochastic optimization algorithms: Version 1.0.3

Hashedexpression ⭐ 41

Type-safe modelling DSL, symbolic transformation, and code generation for solving optimization problems.

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

Spark Parameter Optimization and Tuning

Gdlibrary ⭐ 27

Matlab library for gradient descent algorithms: Version 1.0.1

Csmath 2021 ⭐ 26

This mathematics course is taught for the first year Ph.D. students of computer science and related areas @zju

Stochasticoptimization.jl ⭐ 26

Implementations of stochastic optimization algorithms and solvers

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

Learning to Learn: Gradient-free Optimization framework

Ml Optimizers Jax ⭐ 23

Toy implementations of some popular ML optimizers using Python/JAX

Sparsegdlibrary ⭐ 21

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

Chameleonquant ⭐ 11

chameleonQuant was born as an open-source Java framework to help enthusiast quants to implement system trading strategies and dynamic portfolio trading systems using advanced optimization techniques, machine learning, and deep learning techniques.

Proxasaga ⭐ 11

Proximal Asynchronous SAGA

Parallelgso ⭐ 11

A highly scalable parallel version of Galactic Swarm Optimisation Algorithm

Logistic Regression Swift ⭐ 9

A basic example of how to implement logistic regression in Swift

Zkml Optimization ⭐ 9

Optimization for on-chain private machine learning.

Neural Networks in Java 8 with CuDNN and Aparapi

Optimizers for/and sklearn compatible Machine Learning models

Machine_learning_workbench ⭐ 7

Workbench for practical machine learning in Ruby.

Optimizing Readability Using Genetic Algorithms

Official JAX/Haiku implementation of the paper "Differentially Private Adaptive Optimization with Delayed Preconditioners"

Spark Optim ⭐ 7

A library of scalable optimization algorithms based on Spark

Eeg Emotion Recognition ⭐ 7

EEG-Based Emotion Recognition

Java Genetic Programming ⭐ 6

Genetic-programming framework for various genetic programming paradigms such as linear genetic programming, tree genetic programming, gene expression programming, etc

Optimisation Algorithms ⭐ 6

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

Simple Evolutionary Feature Search ⭐ 6

evolutionary feature algorithm for finding features in the models

Optimization algorithms for Machine Learning problems like Hyperparameter tuning and Ensembling.

Sdd Gp Mpc ⭐ 6

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

Random Bases ⭐ 5

Improving Neural Network Training in Low Dimensional Random Bases

Submodularmaximization ⭐ 5

A collection of optimization algorithms for maximizing unconstrained submodular set functions.

ODA is a non-parametric machine-learning algorithm that maximizes classification accuracy. This package provides an R-based interface for the MegaODA software suite.

Gradient Descent Optimization ⭐ 5

a python script of a function summarize some popular methods about gradient descent

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