Very short article, didn't understand a thing. (I haven't started to learn about neural networks yet, but still..)

For those of you following at home, to get this working you need to add an import and the tabs to make it valid python. Technically a neural network in 12 lines of python, but still quite impressive.

Here it is in pure python

import math, random
X = [ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ]
y = zip(*[[0,1,1,0]])  # transpose
syn0 = [[2*random.random()-1 for x in range(4)] for _ in range(3)]  # 3x4
syn1 = [[2*random.random()-1 for x in range(1)] for _ in range(4)]  # 4x1
for j in xrange(60000):
    l1 = [[1/(1+math.exp(-sum(a*b for a,b in zip(r, c)))) for c in zip(*syn0)] for r in X]
    l2 = [[1/(1+math.exp(-sum(a*b for a,b in zip(r, c)))) for c in zip(*syn1)] for r in l1]
    l2_delta = [[(ry[0]-a)*(a*(1-a)) for a in r] for r, ry in zip(l2, y)]
    l1_delta = [[n*a*(1-a) for a,n  in zip(r,m)] for r, m in zip(l1, [[sum(a2*b2 for a2,b2 in zip(r2,c2)) for c2 in syn1] for r2 in l2_delta])]
    syn1 = [[n+sum(a*b for a,b in zip(r,c)) for c, n in zip(zip(*l2_delta), m)] for r, m in zip(zip(*l1), syn1)]
    syn0 = [[n+sum(a*b for a,b in zip(r,c)) for c, n in zip(zip(*l1_delta), m)] for r, m in zip(zip(*X), syn0)]