Deep learning book chapter 6: In 22.214.171.124 last paragraph:
Unfortunately, mean squared error and mean absolute error often lead to poor results when used with gradient-based optimization. Some output units that saturate produce very small gradients when combined with these cost functions. This is one reason that the cross-entropy cost function is more popular than mean squared error or mean absolute error, even when it is not necessary to estimate an entire distribution p(y | x).
explain the above sentence
Doubt: But we use mean squared error (MSE) and mean absolute error (MAE) for regression.