Neural networks are trained through an iterative process of adjusting their internal parameters (weights and biases) to minimize prediction errors. The core steps involve feeding input data forward through the network, calculating the error between predictions and actual targets, then propagating this error backward to update the parameters. This cycle—forward pass, loss calculation, backward pass (backpropagation), and parameter update—repeats until the model performs adequately. For example, in image classification, a network might start with random weights, generate incorrect labels for cat/dog images, and gradually improve as it adjusts weights to reduce the difference between predictions and true labels.
Training begins with data preparation. Input data is split into batches (e.g., 32–256 samples per batch) to make computation manageable. During the forward pass, data flows through layers (like convolutional or dense layers), applying operations such as matrix multiplications and activation functions (e.g., ReLU). The loss function (e.g., cross-entropy for classification, mean squared error for regression) quantifies prediction errors. Backpropagation then calculates gradients—partial derivatives of the loss with respect to each parameter—using the chain rule. Optimizers like stochastic gradient descent (SGD) or Adam use these gradients to update weights. For instance, SGD might adjust a weight by subtracting the product of the gradient and a learning rate (e.g., 0.001), nudging the network toward better performance.
Key challenges include avoiding overfitting (memorizing training data) and ensuring efficient learning. Techniques like dropout (randomly disabling neurons during training), L2 regularization (penalizing large weights), and early stopping (halting training when validation performance plateaus) address these issues. Developers often split data into training, validation, and test sets to monitor generalization. For example, a network trained on MNIST digit data might use dropout layers with a 0.5 probability to prevent overfitting. Hyperparameters like batch size, learning rate, and optimizer choice are tuned experimentally. Frameworks like PyTorch or TensorFlow automate gradient calculations and parameter updates, letting developers focus on architecture design and evaluation.