103 Open source projects that are alternatives of or similar to shadho

Python library for CMA Evolution Strategy.

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...

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MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

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Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Sequential Model-based Algorithm Configuration

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 Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

A hyperparameter optimization framework

[UNMAINTAINED] Automated machine learning for analytics & production

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

A general, modular, and programmable architecture search framework

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

An automatic ML model optimization tool.

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

A scikit-learn compatible implementation of hyperband

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

Purely functional genetic algorithms for multi-objective optimisation

Bayesian Optimization using GPflow

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Time Series Cross-Validation -- an extension for scikit-learn

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

DeepArchitect: Automatically Designing and Training Deep Architectures

Python library to easily log experiments and parallelize hyperparameter search for neural networks

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

Automated Machine Learning with scikit-learn

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

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Notes for Deep Learning Specialization Courses led by Andrew Ng.

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Asynchronous Distributed Hyperparameter Optimization.

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A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

A toolset for black-box hyperparameter optimisation.