Training Neural Networks

This chapter covers the full training loop: measuring error with loss functions, minimizing it through gradient descent and backpropagation, and accelerating convergence with adaptive optimizers like Adam. We then tackle the practical challenges every practitioner faces — vanishing gradients, overfitting, and proper data splitting — and the techniques that address them: dropout, batch normalization, regularization, and early stopping.