Neural networks are composed of several fundamental components that work together to enable learning and make predictions. Let's explore these building blocks:

Neurons: Neurons are the basic units of a neural network. They receive input values, apply weights to them, and calculate an output using an activation function. Think of neurons as entities that simulate biological neurons in the human brain, firing when the input exceeds a certain threshold.

Activation Functions: Activation functions introduce non-linearity in neural networks and determine the output of a neuron. Common activation functions include the sigmoid function, which squashes the input between 0 and 1, and the ReLU (Rectified Linear Unit) function, which outputs the input as is if it is positive, otherwise, it outputs 0.

Layers: In a neural network, neurons are organized into layers. These layers can be categorized as input, hidden, and output layers. The input layer receives data and passes it on to the hidden layers, which perform computations. Finally, the output layer provides the network's prediction based on the computations performed in the hidden layers.