111 Open source projects that are alternatives of or similar to keras-hypetune

Parallel Hyperparameter Tuning in Python

Easy Hyper Parameter Optimization with mlr and mlrMBO.

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A Clojure machine learning library

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

An automatic ML model optimization tool.

A scikit-learn compatible implementation of hyperband

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

Polyaxon Core Client & CLI to streamline MLOps

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

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

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

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

基于tf.keras的多标签多分类模型

DeepArchitect: Automatically Designing and Training Deep Architectures

Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data

Notes for Deep Learning Specialization Courses led by Andrew Ng.

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

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.

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for voxel-level clinically significant prostate cancer detection in multi-channel 3D bpMRI scans.

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

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

Purely functional genetic algorithms for multi-objective optimisation

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

Hyperparameter optimization package of the mlr3 ecosystem

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A general-purpose pythonic genetic algorithm.

A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

A simple, extensible library for developing AutoML systems

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.

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

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

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

Set model hyperparameters using deterministic, exact algorithms.

State-of-the art Automated Machine Learning python library for Tabular Data

A High Level Python Library to empower students, developers to build applications and systems enabled with computer vision capabilities.

[UNMAINTAINED] Automated machine learning for analytics & production

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

A toolset for black-box hyperparameter optimisation.

Building an image classifier in TF2

A fully decentralized hyperparameter optimization framework

Real-time Web Dashboard for Optuna.

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Python code for bayesian optimization using Gaussian processes

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

Distribution transparent Machine Learning experiments on Apache Spark

Python library for CMA Evolution Strategy.

Library for Semi-Automated Data Science

Determined: Deep Learning Training Platform

Simple, but essential Bayesian optimization package

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 simple collection of well working NLP models (Keras, H2O, StarSpace) tuned and benchmarked on a variety of datasets.

Streamlined machine learning experiment management.

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Scalable, structured, dynamically-scheduled hyperparameter optimization.