Neural Networks With R by Ciaburro, Giuseppe -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Neural Networks with R

Credits About the Authors About the Reviewer www.PacktPub.com

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Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

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

Let us go through the code line-by-line

Deep learning Pros and cons of neural networks

Pros Cons

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

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

Competitive learning Kohonen SOM

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

Exploratory analysis Neural network model

Classifing breast cancer with a neural network

Exploratory analysis Neural network model 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 Summary

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 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