Book Image

Advanced Deep Learning with R

By : Bharatendra Rai
Book Image

Advanced Deep Learning with R

By: Bharatendra Rai

Overview of this book

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Revisiting Deep Learning Basics
Section 1: Revisiting Deep Learning Basics
2
Revisiting Deep Learning Architecture and Techniques
Revisiting Deep Learning Architecture and Techniques
Deep learning with R
Process of developing a deep network model
Deep learning techniques with R and RStudio
Summary
3
Section 2: Deep Learning for Prediction and Classification
Section 2: Deep Learning for Prediction and Classification
4
Deep Neural Networks for Multi-Class Classification
Deep Neural Networks for Multi-Class Classification
Cardiotocogram dataset
Preparing the data for model building
Creating and fitting a deep neural network model
Model evaluation and predictions
Performance optimization tips and best practices
Summary
5
Deep Neural Networks for Regression
Deep Neural Networks for Regression
Understanding the Boston Housing dataset
Preparing the data
Creating and fitting a deep neural network model for regression
Model evaluation and prediction
Performance optimization tips and best practices
Summary
6
Section 3: Deep Learning for Computer Vision
Section 3: Deep Learning for Computer Vision
7
Image Classification and Recognition
Image Classification and Recognition
Handling image data
Data preparation
Creating and fitting the model
Model evaluation and prediction
Performance optimization tips and best practices
Summary
8
Image Classification Using Convolutional Neural Networks
Image Classification Using Convolutional Neural Networks
Data preparation
Layers in the convolutional neural networks
Fitting the model
Model evaluation and prediction
Performance optimization tips and best practices
Summary
9
Applying Autoencoder Neural Networks Using Keras
Applying Autoencoder Neural Networks Using Keras
Types of autoencoders
Dimension reduction autoencoders
Denoising autoencoders
Image correction
Summary
10
Image Classification for Small Data Using Transfer Learning
Image Classification for Small Data Using Transfer Learning
Using a pretrained model to identify an image
Working with the CIFAR10 dataset
Image classification with CNN
Classifying images using the pretrained RESNET50 model
Model evaluation and prediction
Performance optimization tips and best practices
Summary
11
Creating New Images Using Generative Adversarial Networks
Creating New Images Using Generative Adversarial Networks
Generative adversarial network overview
Processing MNIST image data
Developing the generator network
Developing the discriminator network
Training the network
Reviewing results
Performance optimization tips and best practices
Summary
12
Section 4: Deep Learning for Natural Language Processing
Section 4: Deep Learning for Natural Language Processing
13
Deep Networks for Text Classification
Deep Networks for Text Classification
Text datasets
Preparing the data for model building
Developing deep neural networks
Model evaluation and prediction
Performance optimization tips and best practices
Summary
14
Text Classification Using Recurrent Neural Networks
Text Classification Using Recurrent Neural Networks
Preparing data for model building
Developing a recurrent neural network model
Fitting the model
Model evaluation and prediction
Performance optimization tips and best practices
Summary
15
Text classification Using Long Short-Term Memory Network
Text classification Using Long Short-Term Memory Network
Why do we use LSTM networks?
Preparing text data for model building
Creating a long short-term memory network model
Fitting the LSTM model
Evaluating model performance
Performance optimization tips and best practices
Summary
16
Text Classification Using Convolutional Recurrent Neural Networks
Text Classification Using Convolutional Recurrent Neural Networks
Working with the reuter_50_50 dataset
Preparing the data for model building
Developing the model architecture
Compiling and fitting the model
Evaluating the model and predicting classes
Performance optimization tips and best practices
Summary
17
Section 5: The Road Ahead
Section 5: The Road Ahead
18
Tips, Tricks, and the Road Ahead
Tips, Tricks, and the Road Ahead
TensorBoard for training performance visualization
Visualizing deep network models with LIME
Visualizing model training with tfruns
Early stopping of network training
Summary
19
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Performance optimization tips and best practices

In this section, we will explore changes we can make to the model architecture and other settings to improve author classification performance. We will carry out two experiments, and, for both of these two experiments, we will increase the number of most frequent words from 500 to 1,500 and increase the length of the sequences of integers from 300 to 400. For both experiments, we will also add a dropout layer after the pooling layer.

Experimenting with reduced batch size

The code that we'll be using for this experiment is as follows:

# Model architecture
model <- keras_model_sequential() %>%
         layer_embedding(input_dim = 1500, 
                         output_dim...
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