Determine Optimal CNN Complexity: Achieve ~100% Training Accuracy, Then Add Regularization?
Yes, that is in my opinion the best methodology for approaching Deep Learning. This isn't supported by just me but by many researchers in the field. (I can't cite it but I'm pretty sure even Andrew Ng mentioned it in a lesson in the Deep Learning Specialization in coursera.)

The first step is to make sure you have a model with a sufficient capacity to be able to learn all the relationships (useful and not) in the training set.

Then you start applying regularization techniques (dropout, augmentation, early stopping, etc.), to prevent overfitting. During this step your training accuracy is expected to take a drop (or at least take longer to reach 100%), but the accuracy on the validation set should increase.

This methodology, however, is only applicable in the field of deep learning, because of the strong regularization techniques available.