auto-sklearn - Automated Machine Learning with scikit-learn

•    Python

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auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

automl scikit-learn automated-machine-learning hyperparameter-optimization hyperparameter-tuning hyperparameter-search bayesian-optimization metalearning meta-learning smac

hyperband - Tuning hyperparams fast with Hyperband

•    Python

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Code for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Use defs.meta/defs_regression.meta to try many models in one Hyperband run. This is an automatic alternative to constructing search spaces with multiple models (like defs.rf_xt, or defs.polylearn_fm_pn) by hand.

hyperparameters hyperparameter-optimization hyperparameter-tuning gradient-boosting-classifier gradient-boosting machine-learning

SMAC3 - Sequential Model-based Algorithm Configuration

•    Python

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Attention: This package is under heavy development and subject to change. A stable release of SMAC (v2) in Java can be found here. The documentation can be found here.

bayesian-optimization bayesian-optimisation hyperparameter-optimization hyperparameter-tuning hyperparameter-search configuration algorithm-configuration automl automated-machine-learning

spotz - Spark Parameter Optimization and Tuning

•    Scala

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A library for performing hyperparameter optimization on top of Spark

hyperparameter-optimization spark optimizer optimization-algorithms machine-learning machinelearning hyperparameter-tuning hyperparameters grid-search random-search vowpal-wabbit

mgo - Purely functional genetic algorithms for multi-objective optimisation

•    Scala

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MGO implements NGSAII, CP (Calibration Profile), PSE (Pattern Search Experiment). All algorithm in MGO have version to compute on noisy fitness function. MGO handle noisy fitness functions by resampling only the most promising individuals. It uses an aggregation function to aggregate the multiple sample when needed.

genetic-algorithm optimisation functional-programming parameter-tuning hyperparameter-optimization hyperparameters hyperparameter-tuning

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

•    Python

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Milano (Machine learning autotuner and network optimizer) is a tool for enabling machine learning researchers and practitioners to perform massive hyperparameters and architecture searches. Your script can use any framework of your choice, for example, TensorFlow, PyTorch, Microsoft Cognitive Toolkit etc. or no framework at all. Milano only requires minimal changes to what your script accepts via command line and what it returns to stdout.

deep-learning deep-neural-networks automl hyperparameter-tuning hyperparameter-optimization machine-learning

Hyperopt-Keras-CNN-CIFAR-100 - Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset

•    Python

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This project acts as both a tutorial and a demo to using Hyperopt with Keras, TensorFlow and TensorBoard. Not only we try to find the best hyperparameters for the given hyperspace, but also we represent the neural network architecture as hyperparameters that can be tuned. This automates the process of searching for the best neural architecture configuration and hyperparameters. Here, we are meta-optimizing a neural net and its architecture on the CIFAR-100 dataset (100 fine labels), a computer vision task. This code could be easily transferred to another vision dataset or even to another machine learning task.

hyperopt hyperparameter-optimization hyperparameter-tuning hyperparameters-optimization hyperparameter-search keras cnn cnn-keras tensorflow

sentence-classification - Sentence Classifications with Neural Networks

•    Python

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Each of the above broad sentence categories can be expanded and can be made more indepth. The way these networks and scripts are designed it should be possible expand to classify other sentence types, provided the data is provided. This was developed for applications at Metacortex and is accompanied by a guide on building practical/applied neural networks on austingwalters.com.

sentence-classification hyperparameter-tuning fasttext cnn rnn neural-network