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Advanced Deep Learning with R

Advanced Deep Learning with R

By : Rai
4.3 (3)
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Advanced Deep Learning with R

Advanced Deep Learning with R

4.3 (3)
By: 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)
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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

Deep Neural Networks for Multi-Class Classification

When developing prediction and classification models, depending on the type of response or target variable, we come across two potential type of problems: the target variable is of categorical type (this is a classification type of problem) or the target variable is of a numeric type (this is a regression type of problem). It has been observed that about 70% of the data belongs to problems arising from classification categories and the remaining 30% are regression problems (here is the reference: https://www.topcoder.com/role-of-statistics-in-data-science/). In this chapter, we will provide steps for applying deep learning neural networks for classification problems. The steps are illustrated using the fetal cardiotocograms, or CTGs.

In this chapter, we will cover the following topics:

  • A brief understanding of the fetal cardiotocogram...
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