Probabilistic Deep Learning. This item is unavailable.
Language: English
Published by Manning Publications, 2021
- Softcover
- Used
Seller: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc
AbeBooks seller since March 23, 2026
Condition: Used - Good
US$ 15.67
Item description from seller
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. Key Features · The maximum likelihood principle that underlies deep learning applications · Probabilistic DL models that can indicate the range of possible outcomes · Bayesian deep learning that allows for the uncertainty occurring in real-world situations · Applying probabilistic principles to variational auto-encoders Aimed at a reader experienced with developing machine learning or deep learning applications. About the technology Probabilistic deep learning models are better suited to dealing with the noise and uncertainty of real world data ?a crucial factor for self-driving cars, scientific results, financial industries, and other accuracy-critical applications. Oliver Dürr 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 this book. Dürr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelors, masters, and PhD the seson the topic of deep learning, and planned and conducted several postgraduate and masters-level deep learning courses. All three authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing probabilistic deep learning models.…
Seller Inventory # CIN1617296074G
- Title
- Probabilistic Deep Learning
- Author
- Oliver Durr
- Publisher
- Manning Publications
- Publication year
- 2021
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1617296074
- ISBN 13
- 9781617296079
Summary
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 author
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.
Table of Contents
PART 1 - BASICS OF DEEP LEARNING
1 Introduction to probabilistic deep learning
2 Neural network architectures
3 Principles of curve fitting
PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS
4 Building loss functions with the likelihood approach
5 Probabilistic deep learning models with TensorFlow Probability
6 Probabilistic deep learning models in the wild
PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS
7 Bayesian learning
8 Bayesian neural networks
"Synopsis" may belong to another edition of this title.
About the Author
Duerr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelors, masters, and PhD theses on the topic of deep learning, and planned and conducted several postgraduate and masters- level deep learning courses. All three authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing probabilistic deep learning models.
"About the title" may belong to another edition of this title.