How does a neural network learn?

A neural network learns primarily by adjusting weights based on input data and its outcomes. This process is integral to how neural networks operate. During training, the network receives a set of input data and generates output. It then compares this output to the actual target values, using a loss function to quantify the error.

The network uses an optimization algorithm, often stochastic gradient descent, to minimize this error. By backpropagating the error through the network, it adjusts the weights of the connections between neurons. These adjustments are aimed at improving the accuracy of future predictions for similar inputs. The continual refinement of these weights based on the feedback from each iteration allows the neural network to learn complex patterns and relationships within the data.

In contrast to the other options, simply following a linear regression model does not capture the complexities that a neural network can, as it is limited to linear relationships. Memorizing all data points would lead to overfitting, where the network cannot generalize to new data. Randomly generating predictions does not facilitate learning, as it lacks the systematic adjustment of weights based on input and outcome relationships that enables effective learning.