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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

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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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
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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

Introduction

Regression is one of the oldest and yet quite powerful tools for mathematical modelling, classification, and prediction. Regression finds application in varied fields ranging from engineering, physical science, biology, and the financial market to social sciences. It is the basic tool in the hand of a data scientist.

Regression is normally the first algorithm that people in machine learning work with. It allows us to make predictions from data by learning the relationship between the dependent and independent variables. For example, in the case of house price estimation, we determine the relationship between the area of the house (independent variable) and its price (dependent variable); this relationship can be then used to predict the price of any house given its area. We can have multiple independent variables impacting the dependent variable. Thus, there are two...

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