FINAL Ss Ch Data Mining Last Name:Secdarla ID: 1. Design a Feed Forward f a classifier to predict whard Fully Connected 3-Layered Neural Nas 1 Cough, Test1, Test2, Thether a patient has Ehnin 1 hidden layer with 2 hids. in variable \\( \\times 1, \\times 2, \\times 3 \\) notation for parameters as covered in \\( W^{1}{ }_{i j}: \\) weight be the final output calculated \\( b_{i} \\) : bias in from unit \\( j \\) in layer I to \\( i \\) unit in \\( N N \\) with the set of weights \\( W \\) and bias \\( b \\) \\( \\mathrm{bl}_{i} \\) : bias in I layer \\( \\mathrm{h} 1, \\mathrm{~h} 2 \\) : hidden nodes in layer 2 Your \\( N N \\) has parameters denoted as \\( (W, b)=\\left(w_{i j}^{1}, b_{i}, w_{i j}, b^{2} i\ ight) \\) Indicate a weight of each edge in your \\( N N \\) in the notation \\( W_{i j}^{l_{i j}} \\) as given. Specify the dimension of the Weight matrix of each Input, Hidden, and Output layer