AI Playbook
I found this really frustrating to read; on 'training your own models':> Deep learning is a class of machine learning that has gotten a lot of well-deserved attention in recent years because it's working to solve a wide variety of AI problems in vision, natural language processing, and many others.What others? Be specific.The problem is not that people don't know how to build neural networks.The tensorflow tutorial is extremely comprehensive and there are lots of resources for people who know what problem they're solving to try to build a solution.The problem is that people don't understand what they can do with this technology, or even, in a meaningful sense, what it is.Given a array of input numbers [i1, i2, i3...] it provides a black box that can map that input to a set of output numbers [o0, o2, ...] where the dimension of the input and output are different.That's. All. It. Does.It takes an input array and outputs an output array; a magic f(x) function.The thing people don't understand well is not how do I implement f(x); the problem is how do I convert my domain problem (I have 2000000 shelf products in my database and a tonnes of sales information) how to convert that into an input array of numbers? What is the output array of numbers I want out of the function?The reason this works well for images is because you can say, ok, this output number represents a tag (dog, cat, etc) for each discrete value, and the input numbers are just an array of the raw pixel data.That's why its good for images; because a traditional function implementation of f(x) that takes a I of dimension say, 65535, and produces an output of dimension 1 is a hard problem....but if your problem is, given an input of say [price, date, origin] and an output of [value to business], you can solve that problem quite trivially without needing any neural network.tldr; The problem people are having is figuring out input and output for domains other than image processing; not how to build a NN, or how to choose which NN to build.Guide misses the point entirely.(You might argue this is somewhat uncharitable to what is actually a well written guide to ML; but you can read the about section for yourself and see what you think; I feel like the guide needs to focus sharply on practical business outcomes if it wants to do what it says its doing)