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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

The Math Behind Deep Learning

In this chapter we discuss the math behind deep learning. This topic is quite advanced and not necessarily required for practitioners. However, it is recommended reading if you are interested in understanding what is going on under the hood when you play with neural networks. We start with an historical introduction, and then we will review the high school concept of derivatives and gradients. We will also introduce the gradient descent and backpropagation algorithms commonly used to optimize deep learning networks.

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