this is a really nice example of a simple, practical, "non-deep" NN. I'm wondering, are there any good learning resources you could recommend to start a project like this?

It makes me want to try a personal project that makes use of a tiny light-weight NN as well. I see giant AlphaGo neural nets mentioned so frequently that I forgot they could be lean :)

I read some tutorials on back propagation for this, but if you want to get into ML in general, I highly recommend fast.ai and Andrew Ng's free Stanford ML course.

I’m still quite new to neural networks. Can you explain the material benefit to using a neural network over a priority queue with a similar weighting system? Are you giving any other input to the neural network than simply the metrics you listed?

P.S. e.g. a polynomial over the metrics. P.P.S. I imagine you’re using the neural network to tune the weights?

I started with a list of commands prioritized by a linear function of the metrics listed. It did okay, but since I was learning the linear function with back propagation, I figured a "real" network would do slightly better, and it seems to (but possibly only slightly).

That’s interesting. What’s the more real network than the one with back propagation?

Rather, the first one was simply a single node linear perceptron (a linear function) that I trained with backprop because I could, even though there are better techniques for fitting a linear function. Now that it's a "real" network, backprop is appropriate.

Makes sense. Thanks.

> I plan to make it do online learning

From what source? Do users need to worry about people gaming this to have ads show up in results?

Sorry, I agree that was confusing. Online learning is a term in ML for when you train a model incrementally over time. McFly won't connect to the Internet.

Ah, thanks for the clarification! To a non-ML person, this brought back memories of what Canonical was trying to do with search functionality on Ubuntu, by fetching ads over the internet to interleve in local search results.