
 \begin{theglossary} 

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  \item \textbf{backpropagation} Backpropagation is a learning algorithm. After running a training set, the algorithm adepts the weights in that kind of way that nodes which are near the result get higher weights. \textit{First explained in chapter} 
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  \item \textbf{branching factor} A program for move bassed games such as GO or chess often calculates further moves. That way it examines chances for the next move. The deeper the branch goes, the longer calculation takes. \textit{First explained in chapter} 
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  \item \textbf{full connected layer} A layer is called full connected, if every node from itself is connected to every node of his surrounding layers. \textit{First explained in chapter} 
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  \item \textbf{hidden layer} The layer structures between the input and the output layer are called hidden layer. \textit{First explained in chapter} 
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  \item \textbf{three layer network} This nettype consists of an input, hidden and an output layer, which may be fully connected. Information processes only in one direction, from the input, over the hidden to the output layer. \textit{First explained in chapter} 
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 \end{theglossary}
