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 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 CAD classifier, and after working on 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 for 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. Or maybe you want to train the algorithm longer with gradient descents, or maybe you want to try a different optimization algorithm, like the Adam optimization algorithm. Or maybe try a bigger network, or a smaller network, or maybe you want to try dropout, or maybe L2 regularization, or maybe you want to change the network architecture, such as try new activation functions, change 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 for things you could try. And the problem is that if you choose poorly, it's entirely impossible. 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 wait on your problem, wouldn'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 I hope point you in the direction of the most promising things to try. What I'll do in this course is also share with you a number of lessons I've learned through building and shipping a 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. But I hope that these ideas will help you become much more effective at getting your deep learning systems to work.