How do neural networks learn from data?

Neural networks learn from data through a process called training, which involves adjusting weights based on error minimisation.

Neural networks, also known as artificial neural networks (ANNs), are a type of machine learning model inspired by the human brain. They consist of interconnected layers of nodes, or "neurons", which transmit signals to each other. Each connection between neurons has a weight, which determines the strength of the signal that is passed on. The goal of training a neural network is to adjust these weights in a way that allows the network to accurately map inputs to outputs.

The training process begins with the initialisation of the weights, which is often done randomly. The network is then presented with a set of training data, which includes both the inputs and the correct outputs. The network makes a prediction based on the inputs and the current weights, and the difference between this prediction and the correct output is calculated. This difference is known as the error.

The next step is to adjust the weights in order to minimise this error. This is done using a technique called backpropagation. In backpropagation, the error is propagated backwards through the network, from the output layer to the input layer. The weights are adjusted proportionally to their contribution to the error. This process is repeated for each piece of training data, in a cycle known as an epoch.

The number of epochs and the rate at which the weights are adjusted, known as the learning rate, are key parameters in the training process. Too few epochs or a low learning rate can result in underfitting, where the network fails to learn the underlying patterns in the data. Conversely, too many epochs or a high learning rate can lead to overfitting, where the network becomes too specialised to the training data and performs poorly on new data.

In summary, neural networks learn from data by iteratively adjusting their weights based on the error they make in predicting the correct output. This process, known as training, involves a delicate balance of parameters to ensure that the network learns effectively without overfitting or underfitting the data.