What is deep learning?

In machine learning (ML), we try to automatically discover rules for mapping input data to a desired output. In this process, it's very important to create appropriate representations of data. For example, if we want to create an algorithm to classify an email as spam/ham, we need to represent the email data numerically. One simple representation could be a binary vector where each component depicts the presence or absence of a word from a predefined vocabulary of words. Also, these representations are task-dependent, that is, representations may vary according to the final task that we desire our ML algorithm to perform.

In the preceding email example, instead of identifying spam/ham if we want to detect sentiment in the email, a more useful representation of the data could be binary vectors where the predefined vocabulary consists of words with positive or negative polarity. The successful application of most of the ML algorithms, such as random forests and logistic...