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more boring typos

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mhjensen
2018-10-19 06:07:18 +02:00
parent a067cf3e04
commit 092fdecd9b
18 changed files with 384 additions and 384 deletions
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doc/pub/NeuralNet/html/._NeuralNet-bs089.html
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@@ -381,17 +381,17 @@ and biases for each layer.
<p>
It is assumed that there are no weights and
bias at the input layer, so \( P = \{ P_{\text{hidden}},
P_{\text{output}} \} \). If there are \( N_{\text{hidden} } \) neurons in
the hidden layer, then \( P_{\text{hidden}} \) is an \( N_{\text{hidden} }
bias at the input layer, so \( P = \{ P_{\mathrm{hidden}},
P_{\mathrm{output}} \} \). If there are \( N_{\mathrm{hidden} } \) neurons in
the hidden layer, then \( P_{\mathrm{hidden}} \) is an \( N_{\mathrm{hidden} }
\times 2 \) matrix.
<p>
The first column in \( P_{\text{hidden} } \) represents
The first column in \( P_{\mathrm{hidden} } \) represents
the bias for each neuron in the hidden layer and the second column
represents the weigths for each neuron. If there are \( N_{\text{output}
} \) neurons in the output layer, then \( P_{\text{output}} \) is a
\( N_{\text{output} } \times (1 + N_{\text{hidden} }) \) matrix. Its first
represents the weigths for each neuron. If there are \( N_{\mathrm{output}
} \) neurons in the output layer, then \( P_{\mathrm{output}} \) is a
\( N_{\mathrm{output} } \times (1 + N_{\mathrm{hidden} }) \) matrix. Its first
column represents the bias of each neuron and the remaining columns
represents the weights to each neuron.