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R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
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Author (1):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Modeling using neural networks


Neural networks, or to be more specific in this case, artificial neural networks, is a family of machine learning models whose concepts are based on the working of biological neural networks, just like our nervous system. Neural networks have been there for a long time, but recently there has been an upsurge of interest in building highly intelligent systems using deep learning and artificial intelligence. Deep learning makes use of deep neural networks, which are essentially neural networks with a huge number of hidden layers between the input and output layers. A typical neural network can be visualized with the following figure:

From the figure, you can deduce that this neural network is an interconnected network of various nodes, also called neurons. Each node represents a neuron which is nothing but a mathematical function. It is impossible to go into every detail of how to represent a node mathematically but we will give the gist here. These mathematical...

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