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.
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 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.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Notes for Deep Learning Specialization Courses led by Andrew Ng.
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 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..)
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...
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Notes for Deep Learning Specialization Courses led by Andrew Ng.
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.
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.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter 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.
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...
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
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..)
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 Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
SMAC3: A Versatile Bayesian Optimization Package for 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.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Notes for Deep Learning Specialization Courses led by Andrew Ng.
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.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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.
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..)
A curated list of automated machine learning papers, articles, tutorials, slides and projects
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...
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
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
Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
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...
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252)
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...
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.
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...
A curated list of automated machine learning papers, articles, tutorials, slides and projects
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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
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.
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252)
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
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
🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.
Improve prompts for e.g. GPT3 and GPT-J using templates and hyperparameter optimization.
Open Source version of SigOpt API, performing hyperparameter optimization and visualization