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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

Understanding TensorFlow 1.x

It is generally the tradition that the first program one learns to write in any computer language is "hello world." We maintain the convention in this book! Let's begin with a Hello World program:

import tensorflow as tf
message = tf.constant('Welcome to the exciting world of Deep Neural Networks!')
with tf.Session() as sess:
    print(sess.run(message).decode())

Let us go in depth into this simple code. The first line imports tensorflow. The second line defines the message using tf.constant. The third line defines the Session() using with, and the fourth runs the session using run(). Note that this tells us that the result is a "byte string." In order to remove string quotes and b (for byte) we use the method decode().

TensorFlow 1.x computational graph program structure

TensorFlow 1.x is unlike other programming languages. We first need to build a blueprint of whatever neural network we want...

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Deep Learning with TensorFlow 2 and Keras - Second Edition
Published in: Dec 2019
Publisher: Packt
ISBN-13: 9781838823412
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