Neural Networks

 Revision as of 22:56, 26 February 2011 (view source)Ang (Talk | contribs)← Older edit Revision as of 23:05, 26 February 2011 (view source)Ang (Talk | contribs) Newer edit → Line 119: Line 119: 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 {\bf + networks with other """architectures""" (meaning patterns of connectivity between neurons), including ones with multiple hidden layers. - architectures} (meaning patterns of connectivity between neurons), including ones with multiple hidden layers. + The most common choice is a $n_l$-layered network The most common choice is a $n_l$-layered network where layer $1$ is the input layer, layer $n_l$ is the output layer, and each where layer $1$ is the input layer, layer $n_l$ is the output layer, and each Line 126: Line 125: 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 $L_2$, then layer $L_3$, and so on, up to layer $L_{n_l}$, using Equations~(\ref{eqn-forwardprop1}-\ref{eqn-forwardprop2}).  This is one $L_2$, then layer $L_3$, and so on, up to layer $L_{n_l}$, using Equations~(\ref{eqn-forwardprop1}-\ref{eqn-forwardprop2}).  This is one - example of a {\bf feedforward} neural network, since the connectivity graph + 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. - %We will write $s_l$ to denote the - %number of units in layer $l$ of the network (not counting the bias unit). 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