Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Deep Learning Algorithms with Python

You're reading from   Hands-On Deep Learning Algorithms with Python Master deep learning algorithms with extensive math by implementing them using TensorFlow

Arrow left icon
Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789344158
Length 512 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Deep Learning FREE CHAPTER
2. Introduction to Deep Learning 3. Getting to Know TensorFlow 4. Section 2: Fundamental Deep Learning Algorithms
5. Gradient Descent and Its Variants 6. Generating Song Lyrics Using RNN 7. Improvements to the RNN 8. Demystifying Convolutional Networks 9. Learning Text Representations 10. Section 3: Advanced Deep Learning Algorithms
11. Generating Images Using GANs 12. Learning More about GANs 13. Reconstructing Inputs Using Autoencoders 14. Exploring Few-Shot Learning Algorithms 15. Assessments 16. Other Books You May Enjoy

Chapter 6 - Demystifying Convolutional Networks

  1. The different layers of CNN include convolution, pooling, and fully connected layers.
  2. We slide over the input matrix with the filter matrix by one pixel and perform the convolution operation. But we can not only slide over the input matrix by one pixel-we can also slide over the input matrix by any number of pixels. The number of pixels we slide over the input matrix by the filter matrix is called stride.

  3. With the convolution operation, we slide over the input matrix with a filter matrix. But in some cases, the filter does not perfectly fit the input matrix. That is, there exists a situation that when we move our filter matrix by two pixels, it reaches the border and the filter does not fit the input matrix, that is, some part of our filter matrix is outside the input matrix. In this case, we perform padding.
  4. The pooling layer...
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
Banner background image