Statistics for topic hyperparameter-optimization

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 hyperparameter optimization framework

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..)

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

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.

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

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 hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

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

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

Real-time Web Dashboard for Optuna.

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

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

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 hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

QSARtuna: QSAR model building with the optuna framework

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

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.

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

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 hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

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.