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Neural Networks with R

You're reading from Neural Networks with R

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781788397872

Pages 270 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Balaji Venkateswaran

Giuseppe Ciaburro

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Table of Contents (14) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Neural Network and Artificial Intelligence Concepts

2. Learning Process in Neural Networks

3. Deep Learning Using Multilayer Neural Networks

4. Perceptron Neural Network Modeling – Basic Models

5. Training and Visualizing a Neural Network in R

6. Recurrent and Convolutional Neural Networks

7. Use Cases of Neural Networks – Advanced Topics

Multilayer neural networks with neuralnet

After understanding the basics of deep learning, it's time to apply the skills acquired to a practical case. We've seen in the previous section that two libraries we know are listed in packages available in R for DNNs section. I refer to the nnet and neuralnet packages that we learned to use in the previous chapters through practical examples. Since we have some practice with the neuralnet library, I think we should start our practical exploration of the amazing world of deep learning from here.

To start, we introduce the dataset we will use to build and train the network. It is named the College dataset, and it contains statistics for a large number of US colleges, collected from the 1995 issue of US News and World Report. This dataset was taken from the StatLib library, which is maintained at Carnegie Mellon University, and was used in the ASA Section on Statistical Graphics.

Things for us are further simplified because we do not have to retrieve...

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Authors (2)

Balaji Venkateswaran

Balaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management. He has worked for major companies such as ADP, Goldman Sachs, MasterCard, and Wipro. Balaji is a trainer in data science, Hadoop, and Tableau. He holds a postgraduate degree PG in business analytics from Great Lakes Institute of Management, Chennai. Balaji has expertise relating to statistics, classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures. His main interests are neural networks and deep learning. Balaji holds various certifications in IBM SPSS, IBM Watson, IBM big data architect, cloud architect, CEH, Splunk, Salesforce, Agile CSM, and AWS. If you have any questions, don't hesitate to message him on LinkedIn (balvenkateswaran); he will be more than glad to help fellow data scientists.

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Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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