Neural Networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Giuseppe Ciaburro, Balaji Venkateswaran.

Format:

Book

Author/Creator:

Ciaburro, Giuseppe, author.

Venkateswaran, Balaji, author.

Language:

English

Subjects (All):

Neural networks (Computer science).

R (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Venkateswaran Balaji: 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. Ciaburro Giuseppe: Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Universita 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).

Summary:

Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications 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 networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural 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 approach A step-by-step guide filled with real-world practical examples.

Contents:

Cover

Title Page

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Neural Network and Artificial Intelligence Concepts

Introduction

Inspiration for neural networks

How do neural networks work?

Layered approach

Weights and biases

Training neural networks

Supervised learning

Unsupervised learning

Epoch

Activation functions

Different activation functions

Linear function

Unit step activation function

Sigmoid

Hyperbolic tangent

Rectified Linear Unit

Which activation functions to use?

Perceptron and multilayer architectures

Forward and backpropagation

Step-by-step illustration of a neuralnet and an activation function

Feed-forward and feedback networks

Gradient descent

Taxonomy of neural networks

Simple example using R neural net library - neuralnet()

Let us go through the code line-by-line

Implementation using nnet() library

Deep learning

Pros and cons of neural networks

Pros

Cons

Best practices in neural network implementations

Quick note on GPU processing

Summary

Chapter 2: Learning Process in Neural Networks

What is machine learning?

Reinforcement learning

Training and testing the model

The data cycle

Evaluation metrics

Confusion matrix

True Positive Rate

True Negative Rate

Accuracy

Precision and recall

F-score

Receiver Operating Characteristic curve

Learning in neural networks

Back to backpropagation

Neural network learning algorithm optimization

Supervised learning in neural networks

Boston dataset

Neural network regression with the Boston dataset

Unsupervised learning in neural networks&amp.

#160

Competitive learning

Kohonen SOM

Chapter 3: Deep Learning Using Multilayer Neural Networks

Introduction of DNNs

R for DNNs

Multilayer neural networks with neuralnet

Training and modeling a DNN using H2O

Deep autoencoders using H2O

Chapter 4: Perceptron Neural Network Modeling - Basic Models

Perceptrons and their applications

Simple perceptron - a linear separable classifier

Linear separation

The perceptron function in R

Multi-Layer Perceptron

MLP R implementation using RSNNS

Chapter 5: Training and Visualizing a Neural Network in R

Data fitting with neural network

Exploratory analysis

Neural network model

Classifing breast cancer with a neural network

The network training phase

Testing the network

Early stopping in neural network training

Avoiding overfitting in the model

Generalization of neural networks

Scaling of data in neural network models

Ensemble predictions using neural networks

Chapter 6: Recurrent and Convolutional Neural Networks

Recurrent Neural Network

The rnn package in R

LSTM model

Convolutional Neural Networks

Step #1 - filtering

Step #2 - pooling

Step #3 - ReLU for normalization

Step #4 - voting and classification in the fully connected layer

Common CNN architecture - LeNet

Humidity forecast using RNN

Chapter 7: Use Cases of Neural Networks - Advanced Topics

TensorFlow integration with R

Keras integration with R

MNIST HWR using R

LSTM using the iris dataset

Working with autoencoders

PCA using H2O

Autoencoders using H2O

Breast cancer detection using darch

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed October 23, 2017).

OCLC:

1008968699