Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Interactive deep learning book with code, math, and discussions. Available in multi-frameworks. Adopted at 140 universities.

AutoGluon: AutoML Toolkit for Deep Learning

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

📐 Jekyll theme for building a personal site, blog, project documentation, or portfolio.

a distributed Hyperband implementation on Steroids

Differentiable architecture search for convolutional and recurrent networks

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Gaussian processes in TensorFlow

RoBO: a Robust Bayesian Optimization framework

Augment images for neural networks/machine learning

Gaussian processes framework in python

convnet hyperparameter optimization tools

Python package for modular Bayesian optimization

Functional ANOVA

Caffe: a Fast framework for neural networks. For the most recent version, check out branch dev. For a more stable version, check out branch master.

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Laroche…

simple python interface to SMAC.