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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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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

The evolution path of RNNs

RNNs actually have a long history and were first developed in the 1980s. The Hopfield network, as the first neural network with recurrent links, was invented by John Hopfield in Neurons with graded response have collective computational properties like those of two-state neurons (PNAS. 1984 May; 81(10): 3088-3092).

Inspired by the Hopfield network, the fully connected neural network—the Elman network—was introduced in Finding structure in time (Cognitive Science, 1990 March; 14(2): 179-211). The Elman network has one hidden layer and a set of context units connected to the hidden layer. At each time step, the context units keep track of the previous values of the hidden units.

In 1992, Schmidhuber discovered the vanishing gradient problem due to memorizing long-term dependencies. Five years later, the long short-term memory (LSTM...

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