The video tutorial explains the concept of one-hot encoding for class labels, where each class is represented by a vector with a single high (1) value and the rest low (0). It then discusses how predicted labels are presented as probability vectors using softmax. The tutorial further details the calculation of cross entropy loss, which measures the difference between predicted and true labels. Finally, it compares cross entropy loss with binary cross entropy loss, emphasizing the importance of choosing the right loss function as a hyperparameter for model performance.

How is a class represented in a one-hot encoded vector?

• By a vector with a 0 at the class index and 1s elsewhere

• By a vector of random numbers

• By a vector with a 1 at the class index and 0s elsewhere

• By a vector of all ones

What is the purpose of using softmax in predicted labels?

• To normalize the predicted labels into a probability distribution

• To increase the dimensionality of the labels

• To convert labels into binary format

• To decrease the complexity of the model

How is cross-entropy loss calculated for a true class?

• By adding a constant to the predicted probability

• By taking the sum of all predicted probabilities

• By taking the negative logarithm of the predicted probability for the true class

• By multiplying the predicted probability by the true probability

Why is the choice of loss function considered a hyperparameter?

• Because it can significantly impact the model's performance

• Because it does not affect the model's performance

• Because it is fixed for all models

• Because it is only used during model evaluation

Which loss function is mentioned as being simpler than binary cross-entropy loss?

• Huber Loss

• Cross Entropy Loss

• Hinge Loss

• Mean Squared Error