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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Deep neural networks


Neural networks are extremely popular today, thanks to major research advancement over the last 10 years. The result of this research has culminated in deep learning algorithms and architecture. Big technology giants such as Google, Facebook, and Microsoft are heavily investing in deep learning network research. Complex neural networks powered by deep learning are considered state of the art in AI and machine learning. We see them being used in everyday life. For example, Google's image search is powered by deep learning. Google Translate is another application powered by deep learning today. The field of computer vision has made several advancements thanks to deep learning.

The following diagram is a typical neural network, commonly called a multi-layer perceptron:

This network architecture has a single hidden layer with two nodes. The output layer is activated by a softmax function. This network is built for a classification task. The hidden layer can be activated by...

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