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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

What are neural networks?


Neural networks are one of the most interesting machine learning models. Neural networks are inspired by the structures of the brain. Neural networks are algorithms that mimic the functioning of the brain. They are unsupervised algorithms, which means that we do not always know what the outputs should be.

Neural networks have layers, which can be categorized into the following:

  • Input

  • Middle

  • Output layers

The input layer consumes the data, and the output layer represents the result. The middle layer represents the part of the algorithm that indicates how the input layer gets to the output layer.

Different types of neural networks

The simplest type of neural network is known as a Feedforward Neural Network. It feeds information in one direction only, from the front to the back. This type of network is also known as a perceptron. The following figure illustrates a perceptron:

Neural network training process

Neural networks can also feed information back down through the layers...

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