Consider the neural network shown below: Z 2 Z W The weight matrix, W, is: [1,...

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Consider the neural network shown below: Z₁ 2₂ Z₂ W₂ The weight matrix, W, is: [1, 1, -1, 0.5, 1, 2]. Assume that the hidden layer uses RelU and the output layer uses Sigmoid activation function. Assume squared error loss, i.e., Loss = (y - y)². The input x = 4, and the output y = 0. Using this information, answer the questions below: (Show all work, and all answers should be rounded to 2 decimal places OR POINTS WILL BE TAKEN OFF!) (a) [2 points] Use forward propagation to compute the predicted output. (b) [1 point] What is the loss or error value? (c) [4 points] Using backpropagation, compute the gradient of the weight vector, that is, compute the partial derivative of the error with respect to all of the weights. Consider the neural network shown below: Z₁ 2₂ Z₂ W₂ The weight matrix, W, is: [1, 1, -1, 0.5, 1, 2]. Assume that the hidden layer uses RelU and the output layer uses Sigmoid activation function. Assume squared error loss, i.e., Loss = (y - y)². The input x = 4, and the output y = 0. Using this information, answer the questions below: (Show all work, and all answers should be rounded to 2 decimal places OR POINTS WILL BE TAKEN OFF!) (a) [2 points] Use forward propagation to compute the predicted output. (b) [1 point] What is the loss or error value? (c) [4 points] Using backpropagation, compute the gradient of the weight vector, that is, compute the partial derivative of the error with respect to all of the weights.

Answer rating: 100% (QA)

Part a Forward propagation Compute the weighted sum of the inputs in the hidden layer Z1 4 1 4 1 1 0... View the full answer