As other people have said, for MNIST, CPU is fine. One core. You can get over 90% accuracy with a network with just 1 hidden layer with only a few seconds of

As other people have said, for MNIST, CPU is fine. One core. You can get over 90% accuracy with a network with just 1 hidden layer with only a few seconds of training. Or something like that. If you've never played with MNIST before, it's kind of amazing how easy it is. For instance, the following idea "works", in that you get results that are pretty bad, but much better than chance. (Even more than 50% right, I think.) Suppose you have 10 arrays: zeros: each element of this array is a picture of a "0" ones: each element is a picture of a "1" ... nines: each element is a "9" Compute the average of each array: the "average" 0, the average 1, and so on. Then classify new digits based on which average element they are closest to, using Euclidean distance. This sounds way too dumb to do any good, but the MNIST digits are normalized so well that this actually does something. Even better: you can have a neural "network" that has zero hidden layers. This actually achieves almost respectable performance, believe it or not.