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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

Introduction to the MXNet R package


We will use the package MXNet R to build our neural networks. It implements state-of-the-art deep learning algorithms and enables efficient GPU computing. We can work in our familiar R environment and at the same time harness the power of the GPUs (though access to GPU is available through the Python API now, we still need to wait for it to be available for R). It will be useful to give you a small overview about the basic building blocks of MXNet before we start using it for our time series predictions.

Note

Refer to the https://github.com/apache/incubator-mxnet/tree/master/R package for more details on the MXNet R package.

In MXNet, NDArray is the basic operation unit. It's a vectorized operation unit for matrix and tensor computations. All operations on this operation unit can be run on either the CPU or GPUs. The most important point is that all these operations are parallel. It's the basic data structure for manipulating and playing around with data...

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