Deep learning is part of a broader machine learning and artificial intelligence field that uses artificial neural networks. One of the main advantages of deep learning methods is that they help to capture complex relationships and patterns contained in data. When the relationships and patterns are not very complex, traditional machine learning methods may work well. However, with the availability of technologies that help to generate and process more and more unstructured data, such as images, text, and videos, deep learning methods have become increasingly popular as they are almost a default choice to deal with such data. Computer vision and natural language processing (NLP) are two areas that are seeing interesting applications in a wide variety of fields, such as driverless cars, language translation, computer games, and even creating new artwork.
Within the deep learning toolkit, we now have an increasing array of neural network techniques that can be applied to a specific type of task. For example, when developing image classification models, a special type of deep network called a convolutional neural network (CNN) has proved to be effective in capturing unique patterns that exist in image-related data. Similarly, another popular deep learning network called recurrent neural networks (RNNs) and its variants have been found useful in dealing with data involving sequences of words or integers. Another popular and interesting deep learning network called a generative adversarial network (GAN) has the capability to generate new images, speech, music, or artwork.
In this book, we will use these and other popular deep learning networks using R software. Each chapter presents a complete example that has been specifically developed to run on a regular laptop or computer. The main idea is to avoid getting bogged down by a huge amount of data needing advanced computing resources at the first stage of applying deep learning methods. You will be able to go over all the steps using the illustrated examples in this book. The examples used also include the best practices for each topic, and you will find them useful. You will also find a hands-on and applied approach helpful in quickly seeing the big picture when trying to replicate these deep learning methods when faced with a new problem.
This chapter provides an overview of the deep learning methods with R that are covered in this book. We will go over the following topics in this chapter:
- Deep learning with R
- The process of developing a deep network model
- Popular deep learning techniques with R and RStudio