Introducing deep learning

In Part 2 of this book, we will also use deep learning methodologies when solving the use cases. Deep learning models employ multiple layers of interconnected nodes called neurons, which process input data and produce outputs based on learned weights and activation functions. The connections between neurons facilitate information flow, and the architecture of the network determines how information is processed and transformed.

We will study three types of neural network architectures in detail in their corresponding chapters. For now, let’s introduce the framework and terminology that we will use in them.

The neuron serves as the fundamental building block of the system and can be defined as a node with one or more input values, weights, and output values:

Figure 7.9 – A neuron’s structure

When we stack multiple layers with this structure, it becomes a neural network. This architecture typically consists...