Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability


Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability
By 作者: Oliver Duerr
Pub Date: 2020
ISBN: 9781617296079
Pages 页数: 252
Language 语言: English
pages 页数: PDF
Size: 60 Mb

The Book Description robot was collected from Amazon and arranged by Finelybook
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.
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.
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


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