| Learning enough to make your own spin on things by mixing and matching parts is really easy once you understand the mathematical underpinnings of things. It's true, many API-level things are opaque about how they work, but their theoretical foundations are usually possible to pick up. I guess a good way to do this is via an example. I had to build a system to hit the following criteria: * The data is scarce * The data is time series * The data follows a state machine * The data is noisy, and contains a signal that's unique across all training samples Gluing together multiple domains of knowledge, from generative tactics for label propagation, to EM for state-machine convolutional decoding, to 5 or 6 others gave the company something that worked, in a short timescale, that scaled well. It's understanding where things glue together and under what circumstances to apply that glue has been what's been most helpful to me. A great place to get an intuition of what I mean is Louppe Giles's PhD thesis on random forests, specifically stuff like the section on the bias-variance tradeoff. https://arxiv.org/abs/1407.7502 |