Overfitting: the model performs perfectly on the data it has already seen, but fails on new data. Underfitting: the model is too simple to capture the fundamental trend or relationship within the training data. Gradient descent: It is an iterative optimization algorithm used to find the best set of parameters (weights and biases) that will make a model's predictions as accurate as possible.