I.D. This is probably not what he means, but it comes from what I know. The dot product is x*a+y*b+z*c -> on and on...
xyz are the weights on the synapses/connections (and these are just 1 or 0.) u can imagine them as being 1's when the pixel is existing in the picture in the neural network, and 0 if its not. and a,b,c would be the image coming in. (and this is where it might differ from what Sean is doing) and its a match when you get a larger sum. if you had a separate synapse for white and black, it would be just be a pick max of which was the nearest neuron to match to. The problem is, youd have to create a dot product for every single neuron in the net, and you get a square cost, I dont know Sean is doing to get rid of the square cost, but that what he says hes got. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T894f73971549b2ee-M810891dc9284ea741a6a078b Delivery options: https://agi.topicbox.com/groups/agi/subscription
