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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 FREE CHAPTER
2. Getting Started with Deep Learning 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

Summary

Let's take a quick look at what we learned in this chapter. We began by briefly discussing artificial intelligence and its evolution through machine learning and then deep learning. We then saw details about some interesting applications of deep learning like machine translation, chatbots, and optical character recognition. This being the first chapter of the book, we focus on learning the fundamentals for deep learning.

We learned how ANN works with the help of some mathematics. Also, we saw different types of activation functions used in ANN and deep learning. Finally, we moved to set our coding environment with TensorFlow and Keras for building deep learning models.

In the next chapter, we will see how neural networks evolved into deep feedforward networks and deep learning. We will also code our first deep learning model with TensorFlow and Keras!

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