1: A four-layer neural network is to have five inputs, four nodes for first hidden layer, three nodes for second hidden layer, and six outputs. The range of the outputs is to be continuous between 0 and 1. a. How many neurons are required for this network? b. What are the dimensions of the weight matrices? c. What kinds of transfer functions can be used in each layer? d. Are biases required in any layer? XI X₂ X3 -1 2 0 -1 1 0 1 -2 2 8=0) 9=+1 -1

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

Exercise 10: Assume that you are trying to solve a binary classification problem with a feed-forward neural network. Assume that the training set has 1250 observations and 5 input features. Finally, assume that the network has only one layer of hidden neurons, and that layer contains 3 neurons. What is the total number of connections of the network? (Remark: to answer this question, you have to take into account also the threshold connections of each neuron). Answer 1. 9 Answer 2. 5 Answer 3. 22 Answer 4. 18 Answer 5. 1255 Answer 6. 3 Answer 7. 1262 Answer 8. None of the other alternatives. Answer 9. 13 Answer 10. 1250 Answer 11. 20 Answer 12. 1258