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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

Creating a ConvNet to classify handwritten MNIST numbers

In this recipe, you will learn how to create a simple three-layer convolutional network to predict the MNIST digits. The deep network consists of two convolutional layers with ReLU and maxpool and two fully connected final layers.

Getting ready

MNIST is a set of 60,000 images representing handwritten numbers. The goal of this recipe is to recognize those numbers with high accuracy.

How to do it...

Let us start with the recipe:

Import tensorflow, matplotlib, random, and numpy. Then, import the minst data and perform...

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