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

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Hands-On Graph Neural Networks Using Python

Maxime Labonne

ISBN: 978-1-80461-752-6

  • Understand the fundamental concepts of graph neural networks
  • Implement graph neural networks using Python and PyTorch Geometric
  • Classify nodes, graphs, and edges using millions of samples
  • Predict and generate realistic graph topologies
  • Combine heterogeneous sources to improve performance
  • Forecast future events using topological information
  • Apply graph neural networks to solve real-world problems

Enhancing Deep Learning with Bayesian Inference

Matt Benatan, Jochem Gietema, Marian Schneider

ISBN: 978-1-80324-688-8

  • Understand advantages and disadvantages of Bayesian inference and deep learning
  • Understand the fundamentals of Bayesian Neural Networks
  • Understand the differences...
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