This report contains the run for the grid search of different mini-batch sizes and learning rates. We have the following list for the two hyperparameters lr_list=[0.0001, 0.001, 0.01, 0.1] batch_size_list = [1,8,16,32,64]

This report contains the run for the elastic net regression using sklearn. The parameters that we are tuning are alpha and l1_ratio. We have the following alpha and l1_ratio search space. alpha_list=[0.001, 0.01, 0.1, 1, 10] l1_ratio_list = np.arange(0.0,1.0,0.1).tolist()

This report contains the run for the grid search of different mini-batch sizes and learning rates. We have the following list for the two hyperparameters lr_list=[0.0001, 0.001, 0.01, 0.1] batch_size_list = [1,8,16,32,64]