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.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Revisiting Deep Learning Basics
3
Section 2: Deep Learning for Prediction and Classification
6
Section 3: Deep Learning for Computer Vision
12
Section 4: Deep Learning for Natural Language Processing
17
Section 5: The Road Ahead

Fitting the model

For fit the model, we will continue with the format that we have been using in the earlier chapters. The following code is used to fit the model:

# Fit model
model_one <- model %>% fit(trainx,
trainy,
epochs = 15,
batch_size = 128,
validation_split = 0.2)
plot(model_one)

Here, we are using 20 epochs, a batch size of 128, and 20% of the training data is reserved for validation. Since the neural network used here is more complex than the previous chapters, each run is likely to take relatively more time.

Accuracy and loss

After fitting the model, accuracy and loss values for the 15 epochs are plotted as follows...