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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
Author Profile Icon Dr. Amita Kapoor
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 FREE CHAPTER 2. TensorFlow 1.x and 2.x 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

Some mathematical tools

Before introducing backpropagation, we need to review some mathematical tools from calculus. Don't worry too much; we'll briefly review a few areas, all of which are commonly covered in high school-level mathematics.

Derivatives and gradients everywhere

Derivatives are a powerful mathematical tool. We are going to use derivatives and gradients for optimizing our network. Let's look at the definition. The derivative of a function y = f(x) of a variable x is a measure of the rate at which the value y of the function changes with respect to the change of the variable x. If x and y are real numbers, and if the graph of f is plotted against x, the derivative is the "slope" of this graph at each point.

If the function is linear, y = f(x) = ax + b, the slope is . This is a simple result of calculus that can be derived by considering that:

In Figure 1 we show the geometrical meaning of , and the angle between the linear...

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