Automated Machine Learning
Showing 20 of 20 projects
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
An AutoML library for deep learning that automates model selection and hyperparameter tuning using Keras and TensorFlow.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
A Python library for distributed asynchronous hyperparameter optimization over complex search spaces.
Automatic neural architecture search and hyperparameter optimization for PyTorch, focusing on tabular data and time series forecasting.
A simple wrapper that combines Keras and Hyperopt for convenient hyperparameter optimization in deep learning models.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
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.
A scikit-learn compatible hyperparameter optimization tool using evolutionary algorithms instead of grid search.
Automated modeling and machine learning framework FEDOT
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.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
A Python library for fast, reproducible, and modular Neural Architecture Search (NAS) to generate efficient deep networks.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
A tutorial and demo using Hyperopt to auto-optimize CNN architecture and hyperparameters for the CIFAR-100 dataset with Keras/TensorFlow.
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