## Examining *L*_{2} regularization

_{2}

This exercise contains a small, noisy training data set. In this kind of setting, overfitting is a real concern. Fortunately, regularization might help.

This exercise consists of three related tasks. To simplify comparisons across the three tasks, run each task in a separate tab.

**Task 1:**Run the model as given for at least 500 epochs. Note the following:- Test loss.
- The delta between Test loss and Training loss.
- The learned weights of the features and the feature crosses. (The relative thickness of each line running from FEATURES to OUTPUT represents the learned weight for that feature or feature cross. You can find the exact weight values by hovering over each line.)

**Task 2:**(*Consider doing this Task in a separate tab.*) Increase the regularization rate from`0`to`0.3`. Then, run the model for at least 500 epochs and find answers to the following questions:- How does the Test loss in Task 2 differ from the Test loss in Task 1?
- How does the delta between Test loss and Training loss in Task 2 differ from that of Task 1?
- How do the learned weights of each feature and feature cross differ from Task 2 to Task 1?
- What do your results say about model complexity?

**Task 3:**Experiment with regularization rate, trying to find the optimum value.

(Answers appear just below the exercise.)