深度学习学习笔记——C2W1-3——机器学习的基本方法

Basic Recipe for Machine Learning机器学习的基本方法

In the previous video, you saw how looking at training error and depth error can help you diagnose whether your algorithm has a bias or a variance problem, or maybe both. It turns out that this information that lets you much more systematically, using what they call a basic recipe for machine learning and lets you much more systematically go about improving your algorithms' performance. Let's take a look. When training a neural network, here's a basic recipe I will use. After having trained in an initial model, I will first ask, does your algorithm have high bias? And so, to try and evaluate if there is high bias, you should look at, really, the training set or the training data performance. Right. And so, if it does have high bias, does not even fitting in the training set that well, some things you could try would be to try pick a network, such as more hidden layers or more hidden units, or you could train it longer, you know, maybe run trains longer or try some more advanced optimization algorithms, which we'll talk about later in this course.

Or, you can also try, this is kind of a, maybe it work, maybe it won't. But we'll see later that there are a lot of different neural network architectures and maybe you can find a new network architecture that's better suited for this problem. Putting this in parentheses because one of those things that, you know, you just have to try, maybe you can make it work, maybe not. Whereas, getting a bigger network almost always helps, and training longer, well, doesn't always help, but it certainly never hurts. But,so when training a learning algorithm, I would try these things until