ML is thus the task of identifying patterns from training examples and applying these learned patterns (or representations) to new unseen data. ML is also sometimes termed as shallow learning because of its nature of learning single layered representations (in most cases). This brings us to the questions of what layers of representation are? and what deep learning is? We will answer these questions in the subsequent chapters. Let's have a quick overview of deep learning.

Deep learning is a subfield of ML that is concerned with learning successive meaningful representations from training examples to solve a given task. Deep learning is closely associated with artificial neural networks that consist of multiple layers stacked one after the other, which capture successive representations.

Do not worry if it was difficult to digest and understand, as mentioned, we will cover more in considerable depth in subsequent chapters.

ML has become a buzzword thanks to the amount of data we are generating and collecting along with faster compute. Let's look at ML in more depth in the following sections.