Fundamentals of Neural Networks - Cross-Entropy Loss Function

The video tutorial covers two primary loss functions used in machine learning: mean square error and binary cross entropy. It explains the mathematical formulations of these functions and their applications in neural networks. The tutorial also delves into statistical inference, particularly the maximum likelihood estimation, and its connection to binary cross entropy. The video provides a practical understanding of how these loss functions work, especially in binary classification tasks, and emphasizes the importance of choosing the right loss function based on the data type.

How does the concept of maximum likelihood estimator relate to binary cross entropy?

What happens to the loss when the predicted value (Y hat) is incorrect?

In what scenarios should a different loss function be considered instead of binary cross entropy?