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Enhancing Deep Learning with Bayesian Inference: Create more powerful.. PAPER...
US $73.95
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eBay item number:204411008517
Item specifics
- Condition
- Like New
- Seller Notes
- “This item looks like Brand new. satisfaction guaranteed.”
- Book Title
- Enhancing Deep Learning with Bayesian Inference: PAPERBACK
- Subject
- Neural Networks, General
- Original Language
- English
- ISBN
- 180324688X
- Publication Year
- 2023
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Subject Area
- Computers, Science
- Publication Name
- Enhancing Deep Learning with Bayesian Inference : Create More Powerful, Robust Deep Learning Systems with Bayesian Deep Learning in Python
- Publisher
- Packt Publishing, The Limited
- Item Length
- 92.5 in
- Item Width
- 75 in
- Number of Pages
- 249 Pages
About this product
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
180324688X
ISBN-13
9781803246888
eBay Product ID (ePID)
24061938890
Product Key Features
Number of Pages
249 Pages
Language
English
Publication Name
Enhancing Deep Learning with Bayesian Inference : Create More Powerful, Robust Deep Learning Systems with Bayesian Deep Learning in Python
Subject
Neural Networks, General
Publication Year
2023
Type
Textbook
Subject Area
Computers, Science
Format
Trade Paperback
Dimensions
Item Length
92.5 in
Item Width
75 in
Additional Product Features
Intended Audience
Trade
Synopsis
Develop Bayesian Deep Learning models to help make your own applications more robust. Key Features Gain insights into the limitations of typical neural networks Acquire the skill to cultivate neural networks capable of estimating uncertainty Discover how to leverage uncertainty to develop more robust machine learning systems Book Description Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don't know.The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more in how we incorporate model predictions within our applications.Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios.By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems. What you will learn Understand advantages and disadvantages of Bayesian inference and deep learning Understand the fundamentals of Bayesian Neural Networks Understand the differences between key BNN implementations/approximations Understand the advantages of probabilistic DNNs in production contexts How to implement a variety of BDL methods in Python code How to apply BDL methods to real-world problems Understand how to evaluate BDL methods and choose the best method for a given task Learn how to deal with unexpected data in real-world deep learning applications Who this book is for This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models. ]]>
LC Classification Number
Q325.73
Item description from the seller
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