Neural Networks
From Ufldl
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We have so far focused on one example neural network, but one can also build neural | We have so far focused on one example neural network, but one can also build neural | ||
| - | networks with other | + | networks with other """architectures""" (meaning patterns of connectivity between neurons), including ones with multiple hidden layers. |
| - | architectures | + | |
The most common choice is a <math>n_l</math>-layered network | The most common choice is a <math>n_l</math>-layered network | ||
where layer <math>1</math> is the input layer, layer <math>n_l</math> is the output layer, and each | where layer <math>1</math> is the input layer, layer <math>n_l</math> is the output layer, and each | ||
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output of the network, we can successively compute all the activations in layer | output of the network, we can successively compute all the activations in layer | ||
<math>L_2</math>, then layer <math>L_3</math>, and so on, up to layer <math>L_{n_l}</math>, using Equations~(\ref{eqn-forwardprop1}-\ref{eqn-forwardprop2}). This is one | <math>L_2</math>, then layer <math>L_3</math>, and so on, up to layer <math>L_{n_l}</math>, using Equations~(\ref{eqn-forwardprop1}-\ref{eqn-forwardprop2}). This is one | ||
| - | example of a | + | example of a """feedforward""" neural network, since the connectivity graph |
does not have any directed loops or cycles. | does not have any directed loops or cycles. | ||
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Neural networks can also have multiple output units. For example, here is a network | Neural networks can also have multiple output units. For example, here is a network | ||
