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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

TensorFlow and Keras

Before proceeding any further, let us quickly set up our coding environment. This book uses Python programming language all throughout the chapters. So, we expect you to have prior knowledge of Python. We will be using two of the most popular deep learning open source frameworks—TensorFlow and Keras. Let's begin with setting up Python first (in case you don't have it installed already).

We highly recommend using a Linux (Ubuntu preferably) or macOS operating system. The reason for this is most of the libraries for deep learning are built to work best with a Linux/Unix operating system. All the setup instructions will be covered for these operating systems.

While installing Python, it is recommended to install version 3.6 rather than the latest 3.7 or beyond. This is to avoid unpredicted conflicts between TensorFlow and Python due to...
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