CANCEL

Subscription

0

Cart

You have no products in your basket yet

Save more on your purchases!
Savings automatically calculated. No voucher code required

Account

€18.99
per month
~~
€39.99
~~
€27.98

eBook

Print

€48.99
Subscription

Free Trial

Renews at €18.99p/m
eBook

Print

€48.99
Subscription

Free Trial

Renews at €18.99p/m
Free for first 7 days. **$19.99 p/m** after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

View plans & pricing

- 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

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.

- 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

Free for first 7 days. **$19.99 p/m** after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

View plans & pricing

Publication date :
Jun 30, 2023

Length
386 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781803246888

Category :

Concepts :

Total
€
64.95
93.97
29.02 saved

€15.99
~~€23.99~~

€20.98
~~€29.99~~

€27.98
~~€39.99~~

=

Total
€
64.95
93.97
29.02 saved

Preface

1. Chapter 1: Bayesian Inference in the Age of Deep Learning

2. Chapter 2: Fundamentals of Bayesian Inference

3. Chapter 3: Fundamentals of Deep Learning

4. Chapter 4: Introducing Bayesian Deep Learning

5. Chapter 5: Principled Approaches for Bayesian Deep Learning

6. Chapter 6: Using the Standard Toolbox for Bayesian Deep Learning

7. Chapter 7: Practical Considerations for Bayesian Deep Learning

8. Chapter 8: Applying Bayesian Deep Learning

9. Chapter 9: Next Steps in Bayesian Deep Learning

10. Why subscribe?

No reviews found

What is included in a Packt subscription?

How can I cancel my subscription?

What are credits?

What happens if an Early Access Course is cancelled?

Where can I send feedback about an Early Access title?

Can I download the code files for Early Access titles?

How accurate is the publication date?

How will I know when new chapters are ready?

I am a Packt subscriber, do I get Early Access?

How is Early Access delivered?

How do I buy Early Access content?

What is Early Access?