A neural network learns through a process called training, which involves feeding it with a dataset containing input data and their corresponding correct output or labels. The network then uses an algorithm called gradient descent to adjust its internal parameters, known as weights and biases, based on the error between its predicted output and the correct output.

  During training, the inputs are processed through the network, passing through multiple layers called neurons or nodes. Each neuron applies a mathematical operation to the input, and the result is passed through an activation function. This activation function introduces non-linear transformations and allows the network to capture complex patterns and relationships in the data.

  The initial weights and biases of the network are randomly assigned. The network's predicted output is compared with the actual output from the dataset, and the error is calculated using a loss function, such as mean squared error or cross-entropy.

  The gradient descent algorithm then calculates the gradients of the weights and biases with respect to the error. These gradients indicate the direction and magnitude of adjustments needed to minimize the error. The weights and biases are updated by subtracting a small fraction of the gradients multiplied by a learning rate, which determines the step size of the updates.

  This process is repeated iteratively for all the samples in the dataset, and it is known as a forward and backward pass. Each pass helps the network to fine-tune its weights and biases, reducing the error and improving its ability to make accurate predictions. This iterative process is called an epoch.

  The training process continues for multiple epochs until the network's performance on the training data reaches satisfactory levels. However, it is essential to prevent overfitting, which occurs when the network becomes too specialized for the training data and performs poorly on new, unseen data. Techniques like regularization, early stopping, and dropout are commonly used to mitigate overfitting and improve generalization.

  In summary, a neural network learns by iteratively adjusting its internal parameters based on the error between its predictions and the correct outputs in a training dataset. This process of gradient descent helps the network capture complex patterns and improve its predictive ability.