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.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Sequential model-based optimization with a `scipy.optimize` interface
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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 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
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...
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Sequential model-based optimization with a `scipy.optimize` interface
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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..)
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Sequential model-based optimization with a `scipy.optimize` interface
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
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...
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Sequential model-based optimization with a `scipy.optimize` interface
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.
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.
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.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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...
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.
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..)
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.
Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.
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...
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
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 training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents 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...
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 convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
Notes for Deep Learning Specialization Courses led by Andrew Ng.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems
Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems
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...
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.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems
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.
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.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://proceedings.neurips.cc/paper_files/paper/2023/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets...
Code for intrusion detection system (IDS) development using CNN models and transfer learning
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
Official implementation of NeurIPS'22 paper "Monte Carlo Tree Search based Variable Selection for High-Dimensional Bayesian Optimization"
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.