Reference no: EM133627075
Question 1. What is the main difference between multiple linear and logistic regression?
Question 2. What is profiling?
Question 3. What are the two steps in logistic regression?
Question 4. Why do we define the logistic response function as it is given in equation 10.2?
Question 5. How do we compute the probability given the odds of an event?
Question 6. How is the logit defined?
Question 7. Are neural networks (NN) used for classification or prediction?
Question 8. What is the NN based on and what does it mimic?
Question 9. What is ALVINN?
Question 10. Where does the main strength of neural networks stem from?
Question 11. Which are the main layers of a typical NN?
Question 12. What is a weakness of the neural network?
Question 13. What are the general guidelines for choosing an architecture of a NN listed in the textbook?
Question 14. What is a key parameter that the user can control the learning rate with that is used primarily to avoid overfitting?
Question 15. Why are the NNs known to be "black boxes"?
Question 16. Name three libraries of Python that are used for deep learning.