This chapter lays the groundwork for understanding artificial neural networks. We start by positioning deep learning relative to traditional machine learning techniques, highlighting the key distinctions.

You will examine the biological neuron as the conceptual origin for artificial models and then define the mathematical components of an artificial neuron: inputs, weights, bias, the summation function, and the activation step. We'll analyze the Perceptron, the earliest form of a neural network, understand its capabilities, and discuss its limitations, particularly with non-linearly separable data like the XOR problem.

This leads to the introduction of Multi-Layer Perceptrons (MLPs), showing how adding hidden layers increases model complexity and representational capacity. The chapter concludes with a practical exercise where you will implement a simple Perceptron model using Python.

By completing this chapter, you will grasp the historical context and fundamental building blocks upon which more complex deep learning architectures are built.