When trying to improve a deep learning system, you often have a lot of ideas or things you could try. And the problem is that if you choose poorly, it is entirely possible that you end up spending six months charging in some direction only to realize after six months that that didn't do any good.
So, assuming you don't have six months to waste on your problem, won't it be nice if you had quick and effective ways to figure out which of all of these ideas and maybe even other ideas, are worth pursuing and which ones you can safely discard. So what I hope to do in this course is teach you a number of strategies, that is, ways of analyzing a machine learning problem that will point you in the direction of the most promising things to try.
⇒ One of the challenges with building machine learning systems is that there's so many things you could try, so many things you could change. Including, for example, so many hyperparameters you could tune.
One of the things I've noticed is about the most effective machine learning people is they're very clear-eyed about what to tune in order to try to achieve one effect. This is a process we call orthogonalization.
