In this chapter, we went through the steps for developing a prediction model when the response variable is of a numeric type. We started with a neural network model that had 201 parameters and then developed deep neural network models with over 7,000 parameters. You may have noticed that, in this chapter, we made use of comparatively deeper and more complex neural network models compared to the previous chapter, where we developed a classification model for the target variable that was of a categorical nature. In both Chapter 2, Deep Neural Networks for Multiclass Classification, and Chapter 3, Deep Neural Networks for Regression, we developed models based on data that was structured. In the next chapter, we move on to problems where the data type is unstructured. More specifically, we'll deal with the image type of data and go over the problem of image classification...
Advanced Deep Learning with R
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Advanced Deep Learning with R
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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)
Preface
Revisiting Deep Learning Architecture and Techniques
Section 2: Deep Learning for Prediction and Classification
Deep Neural Networks for Multi-Class Classification
Deep Neural Networks for Regression
Section 3: Deep Learning for Computer Vision
Image Classification and Recognition
Image Classification Using Convolutional Neural Networks
Applying Autoencoder Neural Networks Using Keras
Image Classification for Small Data Using Transfer Learning
Creating New Images Using Generative Adversarial Networks
Section 4: Deep Learning for Natural Language Processing
Deep Networks for Text Classification
Text Classification Using Recurrent Neural Networks
Text classification Using Long Short-Term Memory Network
Text Classification Using Convolutional Recurrent Neural Networks
Section 5: The Road Ahead
Tips, Tricks, and the Road Ahead
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