Training Deep Learning Models Questions

Understand the training process: feeding data through the network, computing loss, backpropagating, updating weights, and iterating until convergence. Know about batching, epochs, validation splits, and early stopping. Understand overfitting, underfitting, and the bias-variance trade-off. Know techniques to address overfitting: regularization, dropout, data augmentation, batch normalization.