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You're reading from  Java for Data Science

Product typeBook
Published inJan 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781785280115
Edition1st Edition
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Authors (2):
Richard M. Reese
Richard M. Reese
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Richard M. Reese

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java
Read more about Richard M. Reese

Jennifer L. Reese
Jennifer L. Reese
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Jennifer L. Reese

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.
Read more about Jennifer L. Reese

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Deep learning and regression analysis


Neural networks can be used to perform regression analysis. However, other techniques (see the early chapters) may offer a more effective solution. With regression analysis, we want to predict a result based on several input variables.

We can perform regression analysis using an output layer that consists of a single neuron that sums the weighted input plus bias of the previous hidden layer. Thus, the result is a single value representing the regression.

Preparing the data

We will use a car evaluation database to demonstrate how to predict the acceptability of a car based on a series of attributes. The file containing the data we will be using can be downloaded from: http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data. It consists of car data such as price, number of passengers, and safety information, and an assessment of its overall quality. It is this latter element, quality, that we will try to predict. The comma-delimited values in...

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Java for Data Science
Published in: Jan 2017Publisher: PacktISBN-13: 9781785280115

Authors (2)

author image
Richard M. Reese

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java
Read more about Richard M. Reese

author image
Jennifer L. Reese

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.
Read more about Jennifer L. Reese