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Deep Learning For Dummies
book

Deep Learning For Dummies

by John Paul Mueller, Luca Massaron
May 2019
Intermediate to advanced content levelIntermediate to advanced
368 pages
9h 55m
English
For Dummies
Content preview from Deep Learning For Dummies

Chapter 6

Laying Linear Regression Foundations

IN THIS CHAPTER

Bullet Performing various tasks with variables

Bullet Dealing with probabilities

Bullet Considering which features to use

Bullet Learning by using Stochastic Gradient Descent (SGD)

The term linear regression may seem complicated, but it’s not, as you see in this chapter. A linear regression is essentially a straight line drawn through a series of x/y coordinates that determine the location of a data point. The data points may not always lie directly on the line, but the line shows where the data points would fall in a perfect world of linear coordinates. By using the line, you can predict a value of y (the criterion variable) given a value of x (the predictor variable). When you have just one predictor variable, you have a simple linear regression. As a contrast, when you have many predictors, you have a multiple linear regression, which doesn’t rely on a line but rather on a plane extending through multiple dimensions. Deep learning uses data inputs to guess the nonlinear plane that will most correctly go through the middle of a set of data points ...

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Publisher Resources

ISBN: 9781119543046Purchase book