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Hands-On Deep Learning Architectures with Python

You're reading from   Hands-On Deep Learning Architectures with Python Create deep neural networks to solve computational problems using TensorFlow and Keras

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Length 316 pages
Edition 1st Edition
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Authors (2):
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Saransh Mehta Saransh Mehta
Author Profile Icon Saransh Mehta
Saransh Mehta
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: The Elements of Deep Learning
2. Getting Started with Deep Learning FREE CHAPTER 3. Deep Feedforward Networks 4. Restricted Boltzmann Machines and Autoencoders 5. Section 2: Convolutional Neural Networks
6. CNN Architecture 7. Mobile Neural Networks and CNNs 8. Section 3: Sequence Modeling
9. Recurrent Neural Networks 10. Section 4: Generative Adversarial Networks (GANs)
11. Generative Adversarial Networks 12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13. New Trends of Deep Learning 14. Other Books You May Enjoy

Restricted Boltzmann Machines and Autoencoders

When you are shopping online or surfing movies, you may wonder how the products you may also like or movies that may also interest you works. In this chapter, we will explain the algorithm behind the scene, called the restricted boltzmann machine (RBM). We will start with reviewing RBMs and their evolution path. We will then dig deeper into the logic behind them and implement RBMs in TensorFlow. We will also apply them to build a movie recommender. Beyond a shallow architecture, we will move on with a stacked version of RBMs called deep belief networks (DBNs) and use it to classify images, of course, with our implementation in TensorFlow from scratch.

RBMs find a latent representation of the input by attempting to reconstruct the input data. In this chapter, we will also discuss the autoencoders as another...

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