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

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

Ontology for the description of human clinical features

An automatic ML model optimization tool.

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

The open-source version of PhenoTips is no longer maintained. PhenoTips makes it simple to record clinical findings observed in patients with possible genetic disorders through an easy-to-use Web interface

[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets

Automatic machine learning for tabular data. ⚡🔥⚡

PhenoTagger

Bayesian Optimization Hyperband Hyperparameter Optimization

Java library to map LOINC-encoded test results to Human Phenotype Ontology

A Python library to work with, analyze, filter and inspect the Human Phenotype Ontology

Collections of GA4GH phenopackets that represent individuals with Mendelian diseases.

Flexible Bayesian Optimization in R

HPO and Architecture Benchmarking for RL: Dynamically, Reactive and Efficient

loinc2hpo Annotation Data

Internationalisation of the HPO content

Hyperparameters-Optimization

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.