Probabilistic Deep Learning you own this product

With Python, Keras and TensorFlow Probability
Oliver Dürr, Beate Sick, Elvis Murina
  • October 2020
  • ISBN 9781617296079
  • 296 pages
  • printed in black & white
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A deep dive through the choppy probabilistic waters that will help reveal the treasures hidden beneath the surface.

Richard Vaughan, Purple Monkey Collective
Look inside
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data.

about the technology

The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work.

about the book

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.

what's inside

  • Explore maximum likelihood and the statistical basis of deep learning
  • Discover probabilistic models that can indicate possible outcomes
  • Learn to use normalizing flows for modeling and generating complex distributions
  • Use Bayesian neural networks to access the uncertainty in the model

about the reader

For experienced machine learning developers.

about the authors

Oliver Dürr is a professor 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. Elvis Murina is a data scientist.

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Read this book if you are curious about what really happens inside a deep learning network.

Kim Falk Jorgensen, Binary Vikings

This book opens up a completely new view on many aspects of deep learning.

Zalán Somogyváry, Vienna University of Technology

A comprehensive, thorough walkthrough in the marvelous world of probabilistic deep learning, with lots of practical examples.

Diego Casella, Centrica Business Solutions Belgium