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A little GA Tech humor/puzzle..
I got this in an e-mail today, thought I'd share.
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Lord, JC, what are you taking? Quantum physics?
Is the answer, because the net is a male model? ( lol.) Good stuff, keep it coming, our (my!) brains can always use some weighted inputs. |
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Because you know that a surplus of grad students creates a scarcity of interesting projects --- as they all vie to promote their own project as critical to the advancement of science, when in reality it's just another useless niche with great buzzwords. |
Sounds like that Neural Net can't learn anything. Even if the weights are being modified during the learning procedure, each perceptron in the layer will be outputting the same value to the next layer, thereby removing any learned behavior. Essentially, the network will act as a simple adding machine where the result is scaled by the learned weights.
...I think, I never really learned much about neural networks. And JC, stop getting other people to do your homework. :P |
Now, correct me if I'm wrong, but won't each node only recieve and transmit the value 1?
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:?: :?: :?: :?:
wow. my head hurts. |
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Sounds like a cascading pyramid of nodes resulting in a flat "true" at the edge of the net. Boring....
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Um, 9?
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Answer:
The Neural Net is uninteresting because, *no matter* how large and complicated it is, it can be replaced by a single perceptron (node) for each output. Training a single node is far faster than training 500! A single perceptron here represents a linear combination of the weighted inputs. Without thresholds, the output will always just be a linear combination of the weighted inputs. The perceptron can represent a vector in an N dimensional space, where N is the number of inputs. If you add two/three/a billion vectors together you just get another vector. So the entire Net only has the power to represent a vector for each of its outputs. This means that it can be replaced by a single node for each output with no loss of representational ability. |
Cool, looks like we were on the right track for the answer, but I like the way it's phrased better in JC's solution.
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Damn, so close...
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