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

Lightweight optimization with local, global, population-based and sequential techniques across mixed search spaces

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

[JMLR (CCF-A)] PyPop7: A Pure-PYthon LibrarY for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* algorithm variants. In the near future, we will add Learning-Based Optimizers as its extensions. [https://jmlr.org/papers/v25/23-0386.html]

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

Python library for Bayesian hyper-parameters optimization

Heuristic Optimization for Python

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)

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

Feature selection package of the mlr3 ecosystem.

Spark Parameter Optimization and Tuning

Mixed Adaptive Random Search (MARS) for Optimization

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

A simple JAX-based implementation of random search for locomotion tasks using MuJoCo XLA (MJX).

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

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

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