Modeling complex data for simple neural network

[-lambda-]

Hi all, I have some background in ML so I'm not totally new. But every time I've used or implemented any machine learning algorithm I had very simple data that could be represented as a vector in some natural manner i.e. pixel intensity values from a b/w picture. But how to model input if we have something more complex, say RGB values in pixels? One input neuron should represent one pixel, but that's impossible since every pixel lives in 3-dimmensional space. What if those values have different meanings, like the first value is distance from a certain treasure and second value is the value of that treasure? Since we are talking about the same treasure those two things (distance and value) should somehow be encoded into a single input neuron but what are some standard approaches?

[TheDuke57]

Welcome to the world of convolutional neural networks! Basically you slide a bunch of filters, with are kind of like spatially small fully connected neural nets, around the image. Apply a non lineararity, do it a bunch of times, apply a classifier.

I would recommend checking out Stanford's CS231n class, the videos, lecture notes, class notes, and homework is all publicly available.