Neural networks are trained using a process called gradient-based optimization, where the model learns to minimize errors in its predictions. This involves feeding input data through the network, comparing the predicted output to the actual labels, and updating the network’s parameters to reduce the error. The difference between predictions and labels is measured using a loss function, such as Mean Squared Error or Cross-Entropy.
The training process typically uses an algorithm like Stochastic Gradient Descent (SGD) or one of its variants (e.g., Adam). These algorithms calculate gradients of the loss function with respect to the network's weights through backpropagation, a technique that applies the chain rule to distribute errors backward from the output layer to the input layer. The weights are then adjusted incrementally to improve predictions.
Neural networks are trained iteratively over multiple epochs, where the entire dataset is passed through the model several times. Techniques like learning rate scheduling, batch normalization, and early stopping help ensure efficient and effective training while avoiding problems like overfitting or underfitting.