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Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro

4 (10)

Neural Networks with R

4 (10)

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

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

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

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

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

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

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

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

Best practices in neural network implementations

The following are some best practices that will help in the implementation of neural network:

Neural networks are best implemented when there is good training data
More the hidden layers in an MLP, the better the accuracy of the model for predictions
It is best to have five nodes in the hidden layer
ReLU and Sum of Square of Errors (SSE) are respectively best techniques for activation function and error deduction

Visually different images

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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