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Keras 2.x Projects

You're reading from   Keras 2.x Projects 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789536645
Length 394 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Keras FREE CHAPTER 2. Modeling Real Estate Using Regression Analysis 3. Heart Disease Classification with Neural Networks 4. Concrete Quality Prediction Using Deep Neural Networks 5. Fashion Article Recognition Using Convolutional Neural Networks 6. Movie Reviews Sentiment Analysis Using Recurrent Neural Networks 7. Stock Volatility Forecasting Using Long Short-Term Memory 8. Reconstruction of Handwritten Digit Images Using Autoencoders 9. Robot Control System Using Deep Reinforcement Learning 10. Reuters Newswire Topics Classifier in Keras 11. What is Next? 12. Other Books You May Enjoy

Summary

In this chapter, you've learned about the different types of regression techniques. Regression analysis is a statistical process that's done to study the relationship between a set of independent variables (explanatory variables) and a dependent variable (response variable). Regression algorithms show you how the value of the response variable changes when the explanatory variable is varied. The concepts underlying regression were explored. Furthermore, we gained an understanding of the differences between simple and multiple regression. Later, we saw how a simple and multiple linear regression problem is represented algebraically. Thus, we have analyzed how a regression problem is solved through the least squares algorithm.

The second part of this chapter was dedicated to the practical resolution of a multiple regression problem using the keras library. Modeling...

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