ABSTRACT
This chapter is the introductory chapter and it sets the context for reading the further chapters in the book. This chapter is useful for the readers interested in understanding deep learning starting from the basics of neural networks to its advanced counterpart. However, it assumes that the readers have a basic understanding of machine learning concepts, including types of learning; different machine learning tasks, such as classification, clustering, etc.; and the machine learning life cycle consisting of training, validation, testing, different performance measures, etc. This chapter introduces deep learning and the basics of neural networks, including their history and applications. Some concepts are illustrated by using solved examples and small understandable programs wherever possible. The main intention of the chapter is to make artificial neural networks and deep learning very easily understood to even below-average learners to advanced learners and thus to remove the fear of understanding the internal functioning of the algorithms, including mathematical and logical understanding. Nowadays, due to advancements in programming languages and readily available APIs, different inbuilt machine learning algorithms are implemented that can be imported easily in the program with few lines of code, and thus learners lack a thorough understanding of these algorithms. So the aim of this chapter is to make things simple and to create interests through understanding the deep learning field.
