Neural Networks with R

Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro

Buy this Book

Neural Networks with R

By: Balaji Venkateswaran, Giuseppe Ciaburro

Buy this Book

Overview of this book

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.

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Neural Network and Artificial Intelligence Concepts

Introduction

Inspiration for neural networks

How do neural networks work?

Layered approach

Weights and biases

Training neural networks

Epoch

Activation functions

Different activation functions

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()

Implementation using nnet() library

Deep learning

Pros and cons of neural networks

Best practices in neural network implementations

Quick note on GPU processing

Summary

Learning Process in Neural Networks

What is machine learning?

Supervised learning

Unsupervised learning

Reinforcement learning

Training and testing the model

The data cycle

Evaluation metrics

Learning in neural networks

Back to backpropagation

Neural network learning algorithm optimization

Supervised learning in neural networks

Unsupervised learning in neural networks

Summary

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

Summary

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

Summary

Training and Visualizing a Neural Network in R

Data fitting with neural network

Classifing breast cancer with a neural 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

Summary

Recurrent and Convolutional Neural Networks

Recurrent Neural Network

The rnn package in R

LSTM model

Convolutional Neural Networks

Common CNN architecture - LeNet

Humidity forecast using RNN

Summary

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

Summary

Customer Reviews

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

We shall take a step-by-step approach to understand the forward and reverse pass with a single hidden layer. The input layer has one neuron and the output will solve a binary classification problem (predict 0 or 1). In the following figure is shown a forward and reverse pass with a single hidden layer:

Next, let us analyze in detail, step by step, all the operations to be done for network training:

Take the input as a matrix.
Initialize the weights and biases with random values. This is one time and we will keep updating these with the error propagation process.
Repeat the steps 4 to 9 for each training pattern (presented in random order), until the error is minimized.
Apply the inputs to the network.
Calculate the output for every neuron from the input layer, through the hidden layer(s), to the output layer.
Calculate the error at the outputs: actual minus predicted.

Use the output error to compute error signals for previous layers. The partial derivative of the activation function is used to compute the error signals.
Use the error signals to compute weight adjustments.
Apply the weight adjustments.

Steps 4 and 5 are forward propagation and steps 6 through 9 are backpropagation.

The learning rate is the amount that weights are updated is controlled by a configuration parameter.

The complete pass back and forth is called a training cycle or epoch. The updated weights and biases are used in the next cycle. We keep recursively training until the error is very minimal.

We shall cover more about the forward and backpropagation in detail throughout this book.

Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro

Neural Networks with R

By: Balaji Venkateswaran, Giuseppe Ciaburro

Overview of this book

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