Video: Best Practices for Training Your AI
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Cullen Gallagher: There's sort of this push and pull between the business and the technical side. On the business side, you want to be constantly listening to your customers, asking what they want, implementing those things. But that has an effect on the technical side that you have to account for. So for example, we had a hockey customer that said, "Hey, can you pull out all the face-offs?" And we said, "Yeah, sure." So we taught a classifier how to understand just from the video, no metadata at all, when a face-off was occurring, and clipped those out.
That's one of those things when you go and you change the model, it starts to do other things into the neural networks, and in your total package that you have to worry about from a technical standpoint. For us, we ran into things like overfitting of the model, so now it was keying in and seeing in many thousands of different games, but scoreboards, now that we've implemented this new module, it started to hone in only on the kind of scoreboards that it knew, and became inflexible and rigid.
When you're creating your AI strategy and using the best practices and things like that, just understand the push and the pull between the product requirements and the business side stuff, with what it's going to do on the technical side, and how it's going to affect your product in the long run.
Josh Gray: All right, I want to add one thing to that, that overfitting problem is an incredibly common first result when you're going through some of these exercises. And a lot of that is related to the selection of your training dataset relative to your output. If what you're after is looking for scoreboards, and you train a bunch of pictures, they have variety but the scoreboards are always prominent, then when you start throwing real images where maybe the scoreboard is a little more off to the side, or different angles, you might find that you have overfit to a very clean scoreboard classifier. As you're understanding what you want to get out of it, make sure that you're throwing the right dataset into it, to make sure that you get the variety that you want it to be able to handle.
Jason Hofmann: After you've selected the data that you want to train it with, there are also best practices for how to train your algorithm, like, for example, using a random subset of the training data instead of all of the training data, or using five random overlapping subsets of the data to train it, and then test it against some of set of data that it's never seen. So there are best practices that a lot of the workbenches that are out there, point-and-click workbenches. There's quite a few, like from our MATLAB, others, a lot of them will guide you through that, and say, "I know you have a million data points. Don't train me on all of them. Let's do 15 iterations of 10,000 data points each, see what we come up with, and then let's go see how it works against 100,000 data points I've never seen before."
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