ᥠ ᘫ Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles library ᦣ Ebook Author Giuseppe Ciaburro ᦧ Uncover the power of artificial neural networks by implementing them through R code.
About This BookDevelop a strong background in neural networks with R, to implement them in your applicationsBuild smart systems using the power of deep learningReal world case studies to illustrate the power of neural network models
Who This Book Is ForThis 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 LearnSet up R packages for neural networks and deep learningUnderstand the core concepts of artificial neural networksUnderstand neurons, perceptrons, bias, weights, and activation functionsImplement supervised and unsupervised machine learning in R for neural networksPredict and classify data automatically using neural networksEvaluate and fine tune the models you build.
In DetailNeural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics The book begins with neural network design using the neural net package, then you ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it This book covers various types of neural network including recurrent neural networks and convoluted neural networks You will not only learn how to train neural networks, but will also explore generalization of these networks Later we will delve into combining different neural network models and work with the real world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Style and approachA step by step guide filled with real world practical examples.
Table of ContentsNeural Network and Artificial Intelligence ConceptsLearning Process in Neural NetworksDeep Learning Using Multilayer Neural NetworksPerceptron Neural Network Modeling Basic ModelsTraining and Visualizing a Neural Network in RRecurrent and Convolutional Neural NetworksUse Cases of Neural Networks Advanced TopicsGiuseppe 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 balvenkateswaran he will be than glad to help fellow data scientists. 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- Format Kindle
- Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Giuseppe Ciaburro
- 09 June 2017 Giuseppe Ciaburro
- 270 pages