Hyperparameter Tuning
Showing 26 of 26 projects
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
An AutoML library for deep learning that automates model selection and hyperparameter tuning using Keras and TensorFlow.
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
A Python research toolkit for implementing and visualizing particle swarm optimization algorithms.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A web-based tool for automated hyperparameter tuning and stacked ensemble creation in Python.
A versatile Bayesian optimization package for hyperparameter optimization of machine learning algorithms.
High-performance, end-to-end reinforcement learning implementations fully written in JAX for massive parallelization on GPUs.
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
A Julia implementation of the scikit-learn API, providing a uniform interface for machine learning models from both Julia and Python ecosystems.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
A Swiss knife collection of utility functions for developing and evaluating machine learning algorithms in Julia.
A lightweight Python library for explicit, type-checked function configuration via a centralized Python file.
A tutorial and demo using Hyperopt to auto-optimize CNN architecture and hyperparameters for the CIFAR-100 dataset with Keras/TensorFlow.
A lightweight Bayesian optimization library built on JAX for efficient optimization of expensive-to-evaluate functions.
A simple yet essential Python framework for Bayesian optimization, enabling efficient hyperparameter tuning and black-box function optimization.
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