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

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their *Large-Scale* versions/variants. https://pypop.rtfd.io/

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Python library for Bayesian hyper-parameters optimization

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Spark Parameter Optimization and Tuning

Feature selection package of the mlr3 ecosystem.

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

Hyperparameters-Optimization

Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets

Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.

Ithaka board game is played on a four by four square grid with three pieces in each of four colors.

The python implementation of Partition-based Random Search for stochastic multi-objective optimization via simulation

A simple random searching technique which provides a competitive approach to Reinforcement learning for Locomotion related tasks on Mu-Jo-Co bodies like Humanoid, Half-Cheetah etc

Global optimization by uniform random global search

These are Stochastic Optimization Codes by using various Techniques to optimize the function/Feature Selection

The repository includes the Augmented Random Search algorithm implemented from scratch in Python. This AI algorithm as released on March 2018 research paper is a faster and more efficient than other reinforcement algorihtms.