Open-source implementation of Google Vizier for hyper parameters tuning

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps enviro…

Tuning hyperparams fast with Hyperband

ML hyperparameters tuning and features selection, using evolutionary algorithms.

Machine learning algorithms in Dart programming language

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

Workflow engine for exploration of simulation models using high throughput computing

Configure Python functions explicitly and safely

A library for composing end-to-end tunable machine learning pipelines.

Streamlined machine learning experiment management.

Adventures using keras on Google's Cloud ML Engine

How to initialize Anchors in Faster RCNN for custom dataset?

Purely functional genetic algorithms for multi-objective optimisation

ES6 hyperparameters search for tfjs

Awesome series for Large Language Model(LLM)s

An interactive framework to visualize and analyze your AutoML process in real-time.

Easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them.

Deep learning, architecture and hyper parameters search with genetic algorithms