The video tutorial discusses the differences between binary and multi-class classification tasks. It introduces the concept of cross entropy loss, which is used for multi-class problems, as opposed to binary cross entropy loss for binary classification. An exercise is presented to explore cross entropy loss when the number of classes is greater than two, such as 10, 20, or even 345.

What type of loss function is typically used for binary classification tasks?

• Hinge Loss

• Binary Cross Entropy Loss

• Categorical Cross Entropy Loss

• Mean Squared Error

When dealing with more than two classes, which loss function is more appropriate?

• Huber Loss

• Cross Entropy Loss

• Mean Absolute Error

• Binary Cross Entropy Loss

What is the main difference between binary cross entropy loss and cross entropy loss?

• Binary cross entropy loss is more complex

• Cross entropy loss is used for multi-class classification

• Cross entropy loss is used for binary classification

• Binary cross entropy is used for multi-class problems

For how many classes is cross entropy loss applicable?

• Only for a fixed number of classes

• For any number of classes greater than two

• Only for ten classes

• Only for two classes

What is a key consideration when choosing cross entropy loss for classification tasks?

• The number of classes should be less than ten

• The number of classes should be greater than two

• The number of classes should be exactly two

• The number of classes should be a prime number