We consider a neural network with 100 neurons in the input layer and 10 neurons in the hidden layer and these two layers are fully connected between, how many weigh parameters do we have between these two layers?

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Consider the following neural network which takes two binary-valued inputs and outputs. Which logical function does it approximately compute? +1 ... -12 +8 X1 he(x) +8 X2

Suppose we are fitting a neural network with three hidden layers to a training set. It is found that the cross validation error Jcv(0) is much larger than the training error Jtrain (0). Should we increase the number of hidden layers?

Give an example of how a high number of layers in a neural network might cause a problem.Discuss overfitting and how to prevent it in the following paragraphs.

Please show the steps to solve as well

Q1. Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let the weight matrices between the input and hidden, and hidden and output layers, respectively, be: W = (w1, w2, w3) = (1, 1, –1) W = (w, w½, wý)™ = (0.5, 1, 2)" Assume that the hidden layer uses ReLU, whereas the output layer uses sigmoid activation. Assume SSE error. Answer the following questions, when the input is x = 4 and the true response is y = 0: z1 wi w2 22 ws 23 Figure 25.13. Neural network for Q1. (a) Use forward propagation to compute the predicted output. (b) What is the loss or error value? (c) Compute the net gradient vector 8º for the output layer. (d) Compute the net gradient vector &ª for the hidden layer.

Question 16 Joe designed two neural networks: NN1 and NN2. Both networks have a softmax output layer with three classes and a cross-entropy loss. He feeds an input from class 1 to each network. The output of NN1 is 0.41, 0.37, and 0.22 for classes 1, 2, and 3, respectively. The output of NN2 is 0.74, 0.11, and 0.15 for classes 1, 2, and 3, respectively. Calculate the loss of each network and report it for NN1 and NN2 Based on these findings, (NN1 or NN2) is a better design.

Please provide an illustration of how a neural network that has an excessive number of layers could result in an issue.Overfitting will be discussed, along with ways to prevent it, in the following paragraphs.

Show the output for each node during the forward pass. The output of C is (round to 3 decimals):  The output of D is (round to 3 decimals):