Backpropagation Algorithm
From Ufldl
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The intuition behind the backpropagation algorithm is as follows. | The intuition behind the backpropagation algorithm is as follows. | ||
| - | Given a training example <math>(x,y)</math>, we will first run a | + | Given a training example <math>(x,y)</math>, we will first run a "forward pass" to |
compute all the activations throughout the network, including the output value | compute all the activations throughout the network, including the output value | ||
of the hypothesis <math>h_{W,b}(x)</math>. Then, for each node <math>i</math> in layer <math>l</math>, we would like | of the hypothesis <math>h_{W,b}(x)</math>. Then, for each node <math>i</math> in layer <math>l</math>, we would like | ||
