This chapter introduces neural networks as flexible function approximators built by composing layers of simple processing units. A network with no hidden layers performs linear regression if its output layer is linear and logistic regression if its output layer uses softmax. Hidden layers increase expressivity: a network with one hidden layer and ReLU activations can approximate any continuous function on a closed and bounded input domain, though complex functions may require many units. Deep networks, with multiple hidden layers, are more efficient and scalable than shallow ones, especially for learning hierarchical structure. Neural networks are trained using gradient-based optimisation, with gradients computed via backpropagation. Training adjusts weights to minimise a loss function, using small batches of data. Techniques like early stopping and small batches act as implicit regularisers, while weight decay provides explicit regularisation. Convolutional neural networks use convolution and pooling layers to exploit spatial structure in image data. More broadly, architectural choices often reflect domain-specific assumptions.