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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 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

Components of TPUs

Classifying videos with pre-trained nets in six different ways

Classifying videos is an area of active research because of a large amount of data needed for processing this type of media. Memory requirements are frequently reaching the limits of modern GPUs and a distributed form of training on multiple machines might be required. Research is currently exploring different directions with increased levels of complexity, let's review them.

The first approach consists of classifying one video frame at a time by considering each of them as a separate image processed with a 2D CNN. This approach simply reduces the video classification problem to an image classification problem. Each video frame emits a classification output, and the video is classified by taking into account the more frequently chosen category for each frame.

The second approach consists of creating one single...

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