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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

TensorFlow integration with R

TensorFlow is an open source numerical computing library provided by Google for machine intelligence. It hides all of the programming required to build deep learning models and gives the developers a black box interface to program. The Keras API for TensorFlow provides a high-level interface for neural networks.

Python is the de facto programming language for deep learning, but R is catching up. Deep learning libraries are now available with R and a developer can easily download TensorFlow or Keras similar to other R libraries and use them.

In TensorFlow, nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. TensorFlow was originally developed by the Google Brain Team within Google's machine intelligence research for machine learning and deep neural networks research, but it is now available in the public domain. TensorFlow exploits GPU processing when configured...

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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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