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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction 2. Regression FREE CHAPTER 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

Learning to beat the previous MNIST state-of-the-art results with Capsule Networks

Capsule Networks (or CapsNets) is a very recent and innovative type of deep learning network. This technique was introduced at the end of October 2017 in a seminal paper titled Dynamic Routing Between Capsules by Sara Sabour, Nicholas Frost and Geoffrey Hinton (https://arxiv.org/abs/1710.09829). Hinton is one of the fathers of Deep Learning and, therefore, the whole Deep Learning community is excited to see the progress made with capsules. Indeed, CapsNets are already beating the best CNN at MNIST classification which is... well, impressive!

So what is the problem with CNNs? In CNNs each layer understands an image at a progressive level of granularity. As we discussed in multiple recipes, the first layer will most likely recognize straight lines or simple curves and edges, while subsequent layers...

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