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~ Lab 3. Advanced Machine Learning Neural Networks This is the Third in-class exercise for CS 767. ~ Task 0. Forward pass and Network Architecture Definition (5 points) We have a network with: * 1 input layer (5 features) 2-nd layer has 10 neurons (has bias) * 3-rd layer has 15 neurons (does not have bias) * Final layer has 2 neurons (does not have bias) * No Activation functions between layers Your task is: * Draw the network on the board/paper and embed the photo of the network to the colab Compute the number of learnable parameters Provide shapes of Weight Matrices (should be W1, W2, W3), incorporate bias into W matrices Provide Formula for Forward Pass Your input. has the shape (32, 5) Provide answers below Number of learnable parameters: ? * Shapes of Weight Matrices: = W1 shape = (?, ?) W2 shape = (?, ?) W3 shape = (?, ?) * Formula for Forward Pass as a function of (W1, W2, W3, X), where.X is the input features: FCW Wor W399) = Wa W232 Picture of Neural Network Architecture with given shapes: Start coding or generate with AI