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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

C/G/T processing units

In this section we discuss CPUs, GPUs, and TPUs. Before discussing TPUs, it will be useful for us to review CPUs and GPUs.

CPUs and GPUs

You are probably somewhat familiar with the concept of a CPU, a general-purpose chip sitting in each computer, tablet, and smartphone. CPUs are in charge of all of the computations: from logical controls, to arithmetic, to register operations, to operations with memory, and much more. CPUs are subject to the well-known Moore's law [1], which states that the number of transistors in a dense integrated circuit doubles about every two years.

Many people believe that we are currently in an era where this trend cannot be sustained for long, and indeed it has already declined during the past few years. Therefore, we need some additional technology if we want to support the demand for faster and faster computation to process the ever-growing amount of data that is available out there.

One improvement came from so...

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