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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

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789344158
Length 512 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Deep Learning
2. Introduction to Deep Learning FREE CHAPTER 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

Capsule networks

Capsule networks (CapsNets) were introduced by Geoffrey Hinton to overcome the limitations of convolutional networks.

Hinton stated the following:

"The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster."

But what is wrong with the pooling operation? Remember when we used the pooling operation to reduce the dimension and to remove unwanted information? The pooling operation makes our CNN representation invariant to small translations in the input.

This translation invariance property of a CNN is not always beneficial, and can be prone to misclassifications. For example, let's say we need to recognize whether an image has a face; the CNN will look for whether the image has eyes, a nose, a mouth, and ears. It does not care about which location they are in. If it finds all such...

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