{"description":"Test your understanding of different loss functions and their roles in training deep learning models.","questions":[{"answer":"To measure the error between predicted and actual values","number":1,"options":["To generate synthetic data","To improve image resolution","To measure the error between predicted and actual values","To reduce training time"],"question":"What is the primary role of a loss function in a neural network?"},{"answer":"Log Loss","number":2,"options":["Mean Squared Error","Log Loss","Huber Loss","Mean Absolute Error"],"question":"Which loss function is typically used in binary classification tasks with probability outputs?"},{"answer":"Mean Squared Error","number":3,"options":["Mean Absolute Error","Log Loss","Mean Squared Error","Binary Cross-Entropy"],"question":"Which loss function penalizes large errors more heavily due to squaring the differences?"},{"answer":"Treats all errors equally","number":4,"options":["Highly sensitive to outliers","Differentiable everywhere","Treats all errors equally","Increases exponentially"],"question":"Which of the following is a key characteristic of Mean Absolute Error (MAE)?"},{"answer":"Huber Loss","number":5,"options":["Softmax Loss","Huber Loss","Hinge Loss","Categorical Cross-Entropy"],"question":"Which loss function combines MSE and MAE to be both sensitive to small errors and robust to outliers?"},{"answer":"It penalizes wrong confident predictions more heavily","number":6,"options":["It always outputs binary values","It penalizes wrong confident predictions more heavily","It ignores small errors","It works only for regression"],"question":"Why is Log Loss useful in classification tasks?"},{"answer":"Huber Loss","number":7,"options":["Mean Squared Error","Log Loss","Huber Loss","Categorical Cross-Entropy"],"question":"Which of these loss functions is best suited for a regression problem with expected outliers?"},{"answer":"Mean Absolute Error","number":8,"options":["Mean Squared Error","Mean Absolute Error","Huber Loss","Log Loss"],"question":"Which loss function is not differentiable at zero?"},{"answer":"Closer predicted and actual values","number":9,"options":["Worse model accuracy","More overfitting","Closer predicted and actual values","Larger gradients"],"question":"Which property is generally associated with a lower loss value?"},{"answer":"The right choice depends on the task (e.g., classification vs regression)","number":10,"options":["Any loss function can be used for any problem","The right choice depends on the task (e.g., classification vs regression)","Loss functions don\u2019t impact learning","Only linear models need loss functions"],"question":"Which of the following is true about selecting a loss function?"}],"title":"Loss Functions"}