Deep Learning - Deep Neural Network for Beginners Using Python - Discrete Versus Continuous Error Function

The video tutorial explains the concept of error functions, emphasizing the importance of having a continuous error function rather than a discrete one. It uses a line-fitting problem to illustrate how a discrete error function provides limited information for improvement. The tutorial further explains the concept using a height analogy, demonstrating how a continuous error function allows for better error reduction and optimization.

How does changing the line affect the error function in the context of misclassified points?

What is the significance of having a continuous error function instead of a discrete one?

In what way can an error function provide useful information for reducing errors?

Explain the relationship between height and error function as described in the text.

What does it mean when the error function is described as having discrete values?