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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

Reading from CSV files and preprocessing data

Most of you will already be familiar with Pandas and its usefulness in handling large dataset files. TensorFlow also offers methods to read from the files. In the first chapter, we went through the recipe for reading from files in TensorFlow; in this recipe, we will focus on how to read from CSV files and preprocess the data before training.

Getting ready

We will consider the Boston housing price dataset (http://lib.stat.cmu.edu/datasets/boston) collected by Harrison and Rubinfield in 1978. The dataset contains 506 sample cases. Each house is assigned 14 attributes:

CRIM: per capita crime rate by town
ZN: Proportion of residential land zoned for lots over 25,000 sq.ft
INDUS: Proportion...

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