Book Image

Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro
Book Image

Neural Networks with R

By: Balaji Venkateswaran, Giuseppe Ciaburro

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.

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Table of Contents (14 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Neural Network and Artificial Intelligence Concepts
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
2
Learning Process in Neural Networks
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
3
Deep Learning Using Multilayer Neural Networks
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
4
Perceptron Neural Network Modeling – Basic Models
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
5
Training and Visualizing a Neural Network in R
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
6
Recurrent and Convolutional Neural Networks
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
7
Use Cases of Neural Networks – Advanced Topics
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

Weights and biases

Weights in an ANN are the most important factor in converting an input to impact the output. This is similar to slope in linear regression, where a weight is multiplied to the input to add up to form the output. Weights are numerical parameters which determine how strongly each of the neurons affects the other.

For a typical neuron, if the inputs are x1, x2, and x3, then the synaptic weights to be applied to them are denoted as w1, w2, and w3.

Output is

 

where i is 1 to the number of inputs.

Simply, this is a matrix multiplication to arrive at the weighted sum.

Bias is like the intercept added in a linear equation. It is an additional parameter which is used to adjust the output along with the weighted sum of the inputs to the neuron.

The processing done by a neuron is thus denoted as :

 

A function is applied on this output and is called an activation function. The input of the next layer is the output of the neurons in the previous layer, as shown in the following image:

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