What is deep learning?

DL is a subcategory of ML, based on extracting patterns from data by implementing successive layers that are responsible for extracting relevant features. These patterns are learned through ML models called neural networks (inspired by our brain neurons) and structured in layers stacked one on top of the other, but what is a layer? A layer is a set of nodes called cells that perform an operation by processing an input and generating an output. This kind of operation can be stateless but it usually has a state that is stored in an array of float numbers, called weights.

Let's look at a multilayer-depth neural network recognizing a single-digit image, as follows:

Figure 2.1 – Visual representation of the layers of a neural network for digit classification

We can think of the network as a funnel with several filters, in which each layer is equivalent to a filter that reduces impurities until the desired value is obtained.

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