Hi, welcome to this course on how to structure your machine learning project,
that is on machine learning strategy.
I hope that through this course you will learn how to much more
quickly and efficiently get your machine learning systems working.
So, what is machine learning strategy.
Let's start with a motivating example.
Let's say you are working on your cat cost file.
And after working it for some time,
you've gotten your system to have 90% accuracy,
but this isn't good enough for your application.
You might then have a lot of ideas as to how to improve your system.
For example, you might think well let's collect more data, more training data.
Or you might say,
maybe your training set isn't diverse enough yet,
you should collect images of cats in more diverse poses,
or maybe a more diverse set of negative examples.
Well maybe you want to train the algorithm longer with gradient descent.
Or maybe you want to try a different optimization algorithm,
like the Adam optimization algorithm.
Or maybe trying a bigger network or a smaller network or maybe you want
to try to dropout or maybe L2 regularization.
Or maybe you want to change
the network architecture such as changing activation functions,
changing the number of hidden units and so on and so on.
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.
For example, I've seen some teams spend literally six months collecting
more data only to realize after
six months that it barely improved the performance of their system.
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.
What I will do in this course also is share with
you a number of lessons I've learned through
building and shipping large number of deep learning products.
And I think these materials are actually quite unique to this course.
I don't see a lot of these ideas being taught
in universities' deep learning courses for example.
It turns out also that machine learning strategy is
changing in the era of deep learning because the things you could
do are now different with deep learning algorithms
than with previous generation of machine learning algorithms.
I hope that these ideas will help you become much more
effective at getting your deep learning systems to work.