aaronkl

emukit

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

RoBO

RoBO: a Robust Bayesian Optimization framework

autogluon

AutoGluon: AutoML Toolkit for Deep Learning

d2l-en

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

darts

Differentiable architecture search for convolutional and recurrent networks

ObjectDetection

aaronkl.github.io

amazon-sagemaker-examples

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

caffe

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.

OptSizeChooser

cappuccino

convnet hyperparameter optimization tools

fanova

Functional ANOVA

GPflow

Gaussian processes in TensorFlow

GPy

Gaussian processes framework in python

HpBandSter

a distributed Hyperband implementation on Steroids

imageaugmenter

Augment images for neural networks/machine learning

minimal-mistakes

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

pybo

Python package for modular Bayesian optimization

pysmac

simple python interface to SMAC.

pytorch-sentiment-analysis

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

spearmint

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 Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

transformers

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