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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

Product type Paperback

Published in Dec 2017

Publisher Packt

ISBN-13 9781788293594

Length 536 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (2):

Dr. Amita Kapoor

Antonio Gulli

View More author details

Table of Contents (15) Chapters

Preface

1. TensorFlow - An Introduction

2. Regression FREE CHAPTER

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

Components of TPUs

Introduction

Each TensorFlow computation is described in terms of a graph. This allows a natural degree of flexibility in the structure and the placement of operations that can be split across distributed nodes of computation. The graph can be split into multiple subgraphs that are assigned to different nodes in a cluster of servers.

I strongly suggest the reader have a look to the paper Large Scale Distributed Deep Networks Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng. NIPS, 2012, https://research.google.com/archive/large_deep_networks_nips2012.html

One key result of the paper is to prove that it is possible to run distributed stochastic gradient descent (SDG) where multiple nodes are working in parallel on data-shards and update independently and...

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