Trending repositories for topic hyperparameter-optimization

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

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

A hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

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

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

QSARtuna: QSAR model building with the optuna framework

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

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

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 Neur...

Automated Machine Learning with scikit-learn

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 Hyperparameter Tuning Library for Keras

QSARtuna: QSAR model building with the optuna framework

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

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

A hyperparameter optimization framework

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

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

Fast and Accurate ML in 3 Lines of Code

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

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

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 Neur...

A Hyperparameter Tuning Library for Keras

Automated Machine Learning with scikit-learn

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

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

A hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

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

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

QSARtuna: QSAR model building with the optuna framework

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

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

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 Neur...

Automated Machine Learning with scikit-learn

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 Hyperparameter Tuning Library for Keras

QSARtuna: QSAR model building with the optuna framework

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

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

A hyperparameter optimization framework

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

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

Fast and Accurate ML in 3 Lines of Code

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

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

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 Neur...

A Hyperparameter Tuning Library for Keras

Automated Machine Learning with scikit-learn

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

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

Fast and Accurate ML in 3 Lines of Code

A hyperparameter optimization framework

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

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 Neur...

Parallel Hyperparameter Tuning in Python

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

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

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

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

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

Automated Machine Learning with scikit-learn

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

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

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

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 throug...

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

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

Python library for CMA Evolution Strategy.

Parallel Hyperparameter Tuning in Python

Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.

QSARtuna: QSAR model building with the optuna framework

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

A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!

Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

Code for intrusion detection system (IDS) development using CNN models and transfer learning

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 portfolio optimization tool with scikit-learn interface. Hyperparameters selection and easy plotting of efficient frontiers.

Python library for CMA Evolution Strategy.

Autonomous Performance Tuning for Kubernetes!

Fast and Accurate ML in 3 Lines of Code

Benchmark for bi-level optimization solvers

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

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

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 Neur...

A curated list of awesome Distributed Deep Learning resources.

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 Clojure machine learning library

Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems

A Library for Conformal Hyperparameter Tuning

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

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 Neur...

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

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

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

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

Automated Machine Learning with scikit-learn

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

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

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 throug...

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

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

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems

QSARtuna: QSAR model building with the optuna framework

Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252)

An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.

A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!

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

Code for intrusion detection system (IDS) development using CNN models and transfer learning

Distributed hyperparameter optimization made easy

Benchmark for bi-level optimization solvers

A portfolio optimization tool with scikit-learn interface. Hyperparameters selection and easy plotting of efficient frontiers.

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

Deterministic algorithms for objective Bayesian inference and hyperparameter optimization

Improve prompts for e.g. GPT3 and GPT-J using templates and hyperparameter optimization.

🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.

Python library for CMA Evolution Strategy.

Real-time Web Dashboard for Optuna.

Propulate is an asynchronous population-based optimization algorithm and software package for global optimization and hyperparameter search on high-performance computers.