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Texts: (a) Consider a two-input XNOR gate and simulate a perceptron algorithm for it, where the learning rate is 0.02 and the threshold is 0.2. [2] (b) Represent using a multilayer neural network A O BOCODOE, where O represents XNOR. What will be the optimal depth and width for this network? [3] (c) You'd like to train a fully-connected neural network with 5 hidden layers, each with 10 hidden units. The input is 20-dimensional, and the output is binary. What is the total number of trainable parameters in your network?

          Texts: 

(a) Consider a two-input XNOR gate and simulate a perceptron algorithm for it, where the learning rate is 0.02 and the threshold is 0.2. [2]

(b) Represent using a multilayer neural network A O BOCODOE, where O represents XNOR. What will be the optimal depth and width for this network? [3]

(c) You'd like to train a fully-connected neural network with 5 hidden layers, each with 10 hidden units. The input is 20-dimensional, and the output is binary. What is the total number of trainable parameters in your network?

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