How are Neural Networks made so general?

After reading this blog about Deep Neural Networks learning about selfies I'm struck by how generic the network in question is.

In short: I'm thinking of trying to write something vaguely similar for solving cryptic crosswords, but I can't fathom how to make the general algorithms it uses generic enough that it can develop them ?

A contextual example from the blog is how the network 'understands' filters? Would I need to teach the network about tokenizing the input around keywords, or is that something the network has to 'figure out'? How do I 'seed' the operations the network can perform?

Neural nets don't "understand", they are trained. Despite the fancy term, a neural net is simple a regression model on steroid - often high-dimensional. It's a bunch of weights (vectors) connected in a graph, with an input side and some updating rule (for example, gradient descent).

You'll need a lot of labeled data for the training set. The neural net is trained by feeding these input and telling it about the supposed output, and it readjusts its weights in the regression model.

Apart from that it's a blackbox - kinda magical. But you can't use a neural net trained to recognize selfies to do cryptography (or anything else).

StackExchange Computer Science Q#48824, answer score: 3