# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## Appendix A Pre-Test and Post-Test Questions
### Regularization Post-Test Questions  
1. Overfitting usually manifests itself as a high variance problem. (T/F) 2. The regularization parameter \(\lambda\) serves to increase model complexity. (T/F) 3. Why is L2 regularization usually performed only for the parameter w and not for the parameter \"b\"? 1. \"b\" is one of many parameters that have a small effect on model complexity. 2. The dimension of \"b\" is usually higher than that of \"w\". 3. The range of values for \"b\" is usually larger than for \"w\", which does not lend itself easily to regularization. 4. The update of \"b\" has no significant effect on model performance. 4. What is the main purpose of regularization techniques in deep learning? 1. Improving model accuracy 2. Accelerating the model training process 3. Reducing model overfitting 4. Simplifying model complexity 5. In machine learning and deep learning, L2 regularization is achieved by adding a regularization term to the loss function, which is 1. The cube of the L2 norm of the model parameters (usually referred to as weights w) 2. The square of the L2 norm of the model parameters (usually referred to as weights w) 3. Minus the cube of the L2 norm of model parameters (usually referred to as weights w) 4. Minus the cube of the L2 norm of model parameters (usually referred to as weights w) 6. How can Dropout help improve model performance in neural networks (Multiple choice)? 1. Reducing overfitting 2. Increasing the complexity of the model 3. Improving the generalization of models 4. Accelerating the training process 7. What are the correct statements about the regularization term \(\lambda/2m\times||W||_{F}^{2}\) (the Frobenius paradigm) (Multiple choice)? 1. It sums the sum of squares of all layer weight matrices W 2. It helps to reduce the number of paradigms in the weight matrix, thus avoiding overfitting 3. It has the same effect as L2 regularization in logistic regression 4. It only applies to fully connected layers, not to convolutional layers 8. Which of the following options are correct in the description of L2 regularization in neural networks (Multiple choice)? 1. L2 regularization is achieved by adding a term to the loss function that is proportional to the square of the weights 2. L2 regularization terms usually do not include bias terms 3. During gradient descent, L2 regularization causes the weights to shrink towards zero, thus preventing overfitting 4. The larger the L2 regularization factor (\(\lambda\)), the weaker the effect of regularization 5. L2 regularization is achieved by directly modifying the weights during the forward propagation of the neural network 9. What is the main difference between L1 regularization and L2 regularization in deep learning? 1. L1 regularization takes into account the square of the weights, while L2 regularization does not take into account the square of the weights. 2. There is no difference in effect between L1 regularization and L2 regularization. 3. L1 regularization focuses on preventing overfitting, while L2 regularization is usually used for feature selection. 4. L1 regularization tends to produce sparse weight matrices, while L2 regularization makes the weights smoother. 10. There is a growing tendency to use L2 regularization when training networks to better achieve the effect of avoiding overfitting, does it mean that L1 regularization has gradually lost its usefulness? Please write down what you think.