Ciaburro Giuseppe - Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles
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- Book:Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles
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Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles: summary, description and annotation
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Uncover the power of artificial neural networks by implementing them through R code.
About This Book
- Build smart systems using the power of deep learning
- Real-world case studies to illustrate the power of neural network models
Who This Book Is For
This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!
What You Will Learn
- Set up R packages for neural networks and deep learning
- Understand the core concepts of artificial neural networks
- Understand neurons, perceptrons, bias, weights, and activation functions
- Implement supervised and unsupervised machine learning in R for neural...
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BIRMINGHAM - MUMBAI
Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: September 2017
Production reference: 1220917
ISBN 978-1-78839-787-2
www.packtpub.com
Authors Giuseppe Ciaburro Balaji Venkateswaran | Copy Editors Safis Editing Alpha Singh Vikrant Phadkay |
Reviewer Juan Toms Oliva Ramos | Project Coordinator Nidhi Joshi |
Commissioning Editor Sunith Shetty | Proofreader Safis Editing |
Acquisition Editor Varsha Shetty | Indexer Mariammal Chettiyar |
Content Development Editor Cheryl Dsa | Graphics Tania Dutta |
Technical Editor Suwarna Patil | Production Coordinator Arvindkumar Gupta |
Giuseppe Ciaburro ; holds a master's degree in chemical engineering from Universit degli Studi di Napoli Federico II, and a master's degree in acoustic and noise control from Seconda Universit degli Studi di Napoli. He works at the Built Environment Control Laboratory of Universit degli Studi della Campania "Luigi Vanvitelli".
He has over 15 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 Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control.
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 (linkedin.com/in/balvenkateswaran); he will be more than glad to help fellow data scientists.
Juan Toms Oliva Ramos is an environmental engineer from the university of Guanajuato, Mexico, with a master's degree in administrative engineering and quality. He has more than 5 years of experience in management and development of patents, technological innovation projects, and development of technological solutions through the statistical control of processes. He has been a teacher of statistics, entrepreneurship, and technological development of projects since 2011. He became an entrepreneur mentor, and started a new department of technology management and entrepreneurship at instituto Tecnologico Superior de Purisima del Rincon.
Juan is a Alfaomega reviewer and has worked on the book Wearable designs for Smart watches, Smart TVs and Android mobile devices.
He has developed prototypes through programming and automation technologies for the improvement of operations, which have been registered for patents.
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