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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction FREE CHAPTER 2. Regression 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

TensorFlow Processing Units

Google services such as Google Search (RankBrain), Street View, Google Photos, and Google Translate have one thing in common: they all use Google’s Tensor Processing Unit, or TPU, for their computations.

You might be thinking what is a TPU and what is so great about these services? All these services use state-of-the-art machine learning algorithms in the background, and these algorithms involve large computations. TPUs help to accelerate the neural network computations involved. Even AlphaGo, the deep learning program that defeated Lee Sedol in the game of Go, was powered by TPUs. So let us see what exactly a TPU is.

A TPU is a custom application-specific integrated circuit (ASIC) built by Google specifically for machine learning and is tailored for Tensorflow. It is built on a 28-nm process, it runs at 700 MHz, and consumes 40 W of energy...

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