Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
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
Arrow right icon
View More author details
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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image