Beate Sick

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.

Dürr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelor’s, master’s, and PhD theses on the topic of deep learning, and planned and conducted several postgraduate- and master’s-level deep learning courses. All three authors have worked with deep learning methods since 2013, and have extensive experience in both teaching the topic and developing probabilistic deep learning models.

books by Beate Sick

Probabilistic Deep Learning

  • October 2020
  • ISBN 9781617296079
  • 296 pages
  • printed in black & white
  • Available translations: Korean, Simplified Chinese

Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.