Probabilistic Deep Learning

With Python, Keras and TensorFlow Probability

Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.

Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Oliver Du¨rr is professor for data science at the University of Applied Sciences in Konstanz, Germany.

Beate Sick holds a chair for applied statistics at ZHAW, and works as a researcher and lecturer at the University of Zurich, and as a lecturer at ETH Zurich.

Elvis Murina is a research assistant, responsible for the extensive exercises that accompany the book "Probabilistic Deep Learning" (Manning, 2020).