Variance and Bias is another way of saying overfitting and underfitting respectively, as discussed in Chapter 2, Deep Learning and Convolutional Neural Networks. We can diagnose the problem of "underfitting" and "overfitting" using the train set, dev set and test set errors.
Consider the following scenario where we have data coming from two different distributions named as Distribution 1 and Distribution 2. Distribution 2 represents the target application which we care about. The question is, how do we define train, dev and test sets on such distributions.
The best way to do so is to split it according to the preceding figure. Distribution 1 is split in to train set and part of it is used as the dev set. Here we are calling it the "Train-Dev set" ( because the dev set has same distribution as train set). Distribution 1 is used mainly for training as it is a large dataset. Distribution 2 is split into test set and dev set which are independent of either sets from Distribution...