Historical background:
First generation neural networks
Bomb
Toy
Perceptrons (~1960)
used a layer of hand-
coded features and tried
to recognize objects by
learning how to weight
these features.
There was a neat
learning algorithm for
adjusting the weights.
But perceptrons are
fundamentally limited
in what they can learn
to do.
output units
e.g. class labels
non-adaptive
hand-coded
features
input units
e.g. pixels
Sketch of a typical
perceptron from the 1960s