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
Deep Learning for Beginners

You're reading from   Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python

Arrow left icon
Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Length 432 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Pablo Rivas Pablo Rivas
Author Profile Icon Pablo Rivas
Pablo Rivas
Dr. Pablo Rivas Dr. Pablo Rivas
Author Profile Icon Dr. Pablo Rivas
Dr. Pablo Rivas
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Getting Up to Speed
2. Introduction to Machine Learning FREE CHAPTER 3. Setup and Introduction to Deep Learning Frameworks 4. Preparing Data 5. Learning from Data 6. Training a Single Neuron 7. Training Multiple Layers of Neurons 8. Section 2: Unsupervised Deep Learning
9. Autoencoders 10. Deep Autoencoders 11. Variational Autoencoders 12. Restricted Boltzmann Machines 13. Section 3: Supervised Deep Learning
14. Deep and Wide Neural Networks 15. Convolutional Neural Networks 16. Recurrent Neural Networks 17. Generative Adversarial Networks 18. Final Remarks on the Future of Deep Learning 19. Other Books You May Enjoy

Summary

In this chapter, we discussed many data manipulation techniques that we will come back to use all the time. It is good for you to spend time doing this now rather than later. It will make our modeling of deep learning architectures easier.

After reading this chapter, you are now able to manipulate and produce binary data for classification or for feature representation. You also know how to deal with categorical data and labels and prepare it for classification or regression. When you have real-valued data, you now know how to identify statistical properties and how to normalize such data. If you ever have the problem of data that has non-normal or non-uniform distributions, now you know how to fix that. And if you ever encounter problems of not having enough data, you learned a few data augmentation techniques. Toward the end of this chapter, you learned some of the most popular dimensionality reduction techniques. You will learn more of these along the road, for example, when...

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 $19.99/month. Cancel anytime