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 a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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 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.
Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.
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
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)
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Interactive coding assistant for data scientists and machine learning developers, empowered by large language 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...
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.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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 curated list of automated machine learning papers, articles, tutorials, slides and projects
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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)
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Ray is a unified framework for scaling AI and Python applications. 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 tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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 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.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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 for Deep Learning Specialization Courses led by Andrew Ng.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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 IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Notes for Deep Learning Specialization Courses led by Andrew Ng.
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 tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Ray is a unified framework for scaling AI and Python applications. 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 tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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.
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.
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...
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
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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!
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 Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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 Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
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.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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...
ML hyperparameters tuning and features selection, using evolutionary algorithms.
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...
Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction
Ray is a unified framework for scaling AI and Python applications. 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 tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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.
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...
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 training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
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
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
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.
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 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!
Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.
🐟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.
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..)
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Deterministic algorithms for objective Bayesian inference and hyperparameter optimization
Open Source version of SigOpt API, performing hyperparameter optimization and visualization
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models