Bayesian machine learning is a subfield of machine learning that incorporates Bayesian
principles and probabilistic models into the learning process. It provides a principled …

Deep learning is a form of machine learning for nonlinear high dimensional pattern
matching and prediction. By taking a Bayesian probabilistic perspective, we provide a …

While perception tasks such as visual object recognition and text understanding play an
important role in human intelligence, subsequent tasks that involve inference, reasoning …

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

The engineering of machine learning systems is still a nascent field; relying on a seemingly
daunting collection of quickly evolving tools and best practices. It is our hope that this …

Many modern machine learning algorithms have a large number of hyperparameters. To
effectively use these algorithms, we need to pick good hyperparameter values. In this article …

In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence,
and it has been deployed in different fields of healthcare applications such as image …

This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases
a few different applications of them for classification and regression problems. BNNs are …

A detailed and up-to-date introduction to machine learning, presented through the unifying
lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and …

The performance of machine learning algorithms highly depends on the tuning of
hyperparameters. Unfortunately, hyperparameter tuning is a complicated process that …