Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Deep Learning

You're reading from   Python Deep Learning Understand how deep neural networks work and apply them to real-world tasks

Arrow left icon
Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837638505
Length 362 pages
Edition 3rd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1:Introduction to Neural Networks
2. Chapter 1: Machine Learning – an Introduction FREE CHAPTER 3. Chapter 2: Neural Networks 4. Chapter 3: Deep Learning Fundamentals 5. Part 2: Deep Neural Networks for Computer Vision
6. Chapter 4: Computer Vision with Convolutional Networks 7. Chapter 5: Advanced Computer Vision Applications 8. Part 3: Natural Language Processing and Transformers
9. Chapter 6: Natural Language Processing and Recurrent Neural Networks 10. Chapter 7: The Attention Mechanism and Transformers 11. Chapter 8: Exploring Large Language Models in Depth 12. Chapter 9: Advanced Applications of Large Language Models 13. Part 4: Developing and Deploying Deep Neural Networks
14. Chapter 10: Machine Learning Operations (MLOps) 15. Index 16. Other Books You May Enjoy

6

Natural Language Processing and Recurrent Neural Networks

This chapter will introduce two different topics that nevertheless complement each other – natural language processing (NLP) and recurrent neural networks (RNNs). NLP teaches computers to process and analyze natural language text to perform tasks such as machine translation, sentiment analysis, and text generation. Unlike images in computer vision, natural text represents a different type of data, where the order (or sequence) of the elements matters. Thankfully, RNNs are suitable for processing sequential data, such as text or time series. They help us deal with sequences of variable length by defining a recurrence relation over these sequences (hence the name). This makes NLP and RNNs natural allies. In fact, RNNs can be applied to any problem since it has been proven that they are Turing-complete – theoretically, they can simulate any program that a regular computer would not be able to compute.

However...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime