How Neural Networks Learn

• Neural networks learn through a process called training, where they adjust their internal parameters to minimize the difference between their predicted outputs and the desired outputs.
• This is typically done using a technique called backpropagation, which involves computing the gradient of a loss function with respect to the network’s parameters and updating the parameters accordingly.
• During training, the network is presented with a set of input-output pairs, and it iteratively adjusts its parameters to improve its predictions.
• This process continues until the network’s performance reaches a satisfactory level.