A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Automatic extraction of relevant features from time series:

Python code for "Probabilistic Machine learning" book by Kevin Murphy

"Probabilistic Machine Learning" - a book series by Kevin Murphy

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

DeltaPy - Tabular Data Augmentation (by @firmai)

pca: A Python Package for Principal Component Analysis.

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This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble

Bayesian time series forecasting and decision analysis

A normalizing flow using Bernstein polynomials for conditional density estimation.

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