Revisiting Deep Learning Architecture and Techniques

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...

Deep learning with R

We will start by looking at the popularity of deep learning networks and also take a look at a version of some of the important R packages used in this book.

Deep learning trend

Deep learning techniques make use of neural network-based models and have seen increasing interest in the last few years.A Google trends website for the search term deep learning provides the following plot:

The preceding plot has 100 as the peak popularity of a search term, and other numbers are relative to this highest point. It can be observed that the interest in the term deep learning has gradually increased in popularity since around 2014. For the last two years, it has enjoyed peak popularity. One of the reasons for the...

Process of developing a deep network model

Developing a deep learning network model can be broken down into five key steps shown in the following flowchart:

Each step mentioned in the preceding flowchart can have varying requirements based on the type of data used, the type of deep learning network being developed, and also the main objective of developing a model. We will go over each step to develop a general idea about what is involved.

Preparing the data for a deep network model

Developing deep learning neural network models requires the variables to have a certain format. Independent variables may come with a varying scale, with some variable values in decimals and some other variables in thousands. Using such varying...

Deep learning techniques with R and RStudio

The term deep in deep learning refers to a neural network model having several layers, and the learning takes place with the help of data. And based on the type of data used, deep learning may be categorized into two major categories, as shown in the following screenshot:

As shown in the preceding diagram, the type of data used for developing a deep neural network model can be of a structured or unstructured type. In Chapter 2, Deep Neural Networks for Multi-Class Classification, we illustrate the use of a deep learning network for classification problems using structured data where the response variable is of the categorical type. In Chapter 3, Deep Neural Networks for Regression, we illustrate the use of a deep learning network for regression problems using structured data where the response is a continuous type of variable. Chapters...

Summary

Deep learning methods that make use of artificial neural networks have been increasing in popularity in recent years. A number of areas of application involving deep learning methods include driverless cars, image classification, natural language processing, and new image generation. We started this first chapter by looking at the popularity of the deep learning term as reported from a Google trend website. We described a general five-step process for applying deep learning methods and developed some broad ideas about details within each step. We then briefly looked at deep learning techniques covered in each chapter and situations in which they are applied, along with some best practices.

In the next chapter, we get started with an application example and illustrate steps for developing a deep network model for multi-class classification problems.

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