AI Playbook
I found this really frustrating to read; on &#x27;training your own models&#x27;:<p>&gt; Deep learning is a class of machine learning that has gotten a lot of well-deserved attention in recent years because it&#x27;s working to solve a wide variety of AI problems in vision, natural language processing, and many others.<p>What others? Be specific.<p>The problem <i>is not</i> that people don&#x27;t know how to build neural networks.<p>The tensorflow tutorial is extremely comprehensive and there are lots of resources for people who know what problem they&#x27;re solving to try to build a solution.<p>The <i>problem</i> is that people don&#x27;t understand what they can <i>do</i> with this technology, or even, in a meaningful sense, what it is.<p>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.<p>That&#x27;s. All. It. Does.<p>It takes an input array and outputs an output array; a magic f(x) function.<p>The thing people don&#x27;t understand well is <i>not</i> 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?<p>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.<p>That&#x27;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.<p>...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.<p>tldr; The problem people are having is figuring out input and output for domains other than image processing; <i>not</i> how to build a NN, or how to choose which NN to build.<p>Guide misses the point entirely.<p>(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)