Q$1.$ Consider a feed$-$forward neural network with two inputs $($numbered as $1$&$2),$ one

hidden layer including two nodes $(3$& & $4)$ and two outputs $(5$& & $6).$

A weight on connection between nodes i and $j$ is denoted by $w_{ij},$ such as $w_{13}$ is the weight

on the connection between nodes $1$ and $3.$ The following table lists all the weights and bias

terms in the network:

Each of the nodes $3,4,5$ and $6$ uses the following activation function:

$(v)=\{\begin{array}{ccc}1& if& v0\mathrm{.}5\\ 0& \text{o}\text{t}\text{h}\text{e}\text{r}\text{w}\text{i}\text{s}\text{e}& \end{array}$

where $v$ denotes the weighted sum of a node. Each of the input nodes $(1$ and $2)$ can only

receive binary values $($either $0$ or $1).$ Calculate the output of the network $(y_5$ and $y_6)$ for

each of the input patterns and tabularize the findings for each input and output pattern.