Trending repositories for topic 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.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
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...
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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...
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
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 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.
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.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
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...
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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...
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Sequential model-based optimization with a `scipy.optimize` interface
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
A collection of scripts aimed at efficiently tuning chess engine parameters.
An automated machine learning tool aimed to facilitate AutoML research.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Code for intrusion detection system (IDS) development using CNN models and transfer learning
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
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...
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
SMAC3: A Versatile Bayesian Optimization Package for 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.
Notes for Deep Learning Specialization Courses led by Andrew Ng.
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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...
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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.
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 training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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...
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
Code for intrusion detection system (IDS) development using CNN models and transfer learning
[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.
A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
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...
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 collection of scripts aimed at efficiently tuning chess engine parameters.
Multiobjective black-box optimization using gradient-boosted trees
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...
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
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.
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 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.
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
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...
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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 curated list of automated machine learning papers, articles, tutorials, slides and projects
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" pu...
Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.
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
🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.
A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Deterministic algorithms for objective Bayesian inference and hyperparameter optimization
A portfolio optimization tool with scikit-learn interface. Hyperparameters selection and easy plotting of efficient frontiers.
Official implementation of NeurIPS'22 paper "Monte Carlo Tree Search based Variable Selection for High-Dimensional Bayesian Optimization"
