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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 Regression

In the previous chapter, we worked with a dataset that had a categorical target variable, and we went over the steps for developing a classification model using Keras. In situations where the response variable is numeric, supervised learning problems are categorized as regression problems. In this chapter, we will develop a prediction model for numeric response variables. To illustrate the process of developing the prediction model, we will make use of the Boston Housing dataset, which is available within the mlbench package.

In this chapter, we will cover the following topics:

  • 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
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