Open-source implementation of Google Vizier for hyper parameters tuning

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

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches"

Tuning hyperparams fast with Hyperband

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.

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

Workflow engine for exploration of simulation models using high throughput computing

A thoughtful approach to hyperparameter management.

Adventures using keras on Google's Cloud ML Engine

Streamlined machine learning experiment management.

A library for composing end-to-end tunable machine learning pipelines.

Purely functional genetic algorithms for multi-objective optimisation

Machine learning algorithms in Dart programming language

Easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them.

Deep learning, architecture and hyper parameters search with genetic algorithms

How to initialize Anchors in Faster RCNN for custom dataset?

ES6 hyperparameters search for tfjs

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

Tuning XGBoost hyper-parameters with Simulated Annealing

Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent

Spark Parameter Optimization and Tuning

How optimizer and learning rate choice affects training performance

ParamHelpers Next Generation

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI

🤖 bare-bones implementation of a neural network with numpy

Using DDPG and A2C reinforcement learning algorithms to solve a math puzzle

Introductory Kaggle competition

Argload, easy reloading of command line arguments

A library for the hyperparameter optimization of deep neural networks