hyperparameter-optimization

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

A hyperparameter optimization framework

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Fast and Accurate ML in 3 Lines of Code

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Automated Machine Learning with scikit-learn

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

A curated list of automated machine learning papers, articles, tutorials, slides and projects

AI Infra / AI Orchestration / AI Control Plane

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

A Hyperparameter Tuning Library for Keras

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Hyperparameter Experiments with TensorFlow and Keras

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

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

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

HyperView is a terminal-first TradingView strategy lab for downloading market data, backtesting Python strategies with Pine-like behavior, and optimizing SL/TP parameters.

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

LLM agents as your hyperparameter optimizer.

Examples for https://github.com/optuna/optuna

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Real-time Web Dashboard for Optuna.

Automated modeling and machine learning framework FEDOT

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 environments.

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

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.