The basic building blocks from a single neuron to a multi-layer network.
The journey into deep learning begins with its most fundamental unit: the perceptron. Conceived by Frank Rosenblatt in the 1950s, the perceptron is a simplified model of a biological neuron. It takes a set of binary inputs, multiplies each input by a corresponding 'weight' (which signifies the input's importance), and sums them up. This weighted sum is then compared against a threshold. If the sum exceeds the threshold, the perceptron 'fires' and outputs 1; otherwise, it outputs 0. The learning process for a perceptron involves iteratively adjusting its weights to correctly classify a set of training examples. While a single perceptron can learn to solve linearly separable problems (like implementing the logical AND or OR functions), it famously cannot solve non-linearly separable problems like XOR. This limitation is overcome by stacking perceptrons (or more modern neurons) into layers, creating an Artificial Neural Network (ANN), also known as a Multi-Layer Perceptron (MLP). An ANN consists of at least three layers: an input layer, which receives the raw data; one or more hidden layers, where the actual processing and feature extraction occurs; and an output layer, which produces the final prediction. Each neuron in a layer is typically connected to all neurons in the next layer. This layered structure allows the network to learn a hierarchy of features. The first hidden layer might learn simple patterns (like edges in an image), and subsequent layers can combine these to recognize more complex patterns (like shapes, objects, and scenes). This ability to learn hierarchical representations from data is the core strength of deep learning.