Deep Learning - Deep Neural Network for Beginners Using Python - Early Stopping

The video tutorial explains the concept of early stopping in neural networks, focusing on how model complexity evolves over epochs. It discusses the relationship between training and testing errors, highlighting the importance of choosing the right stopping point to avoid overfitting or underfitting. The elbow rule is introduced as a method to determine the optimal number of epochs. The tutorial emphasizes the need for experimenting with hyperparameters to achieve a well-fitted model.

How can one determine the optimal number of epochs for training a model?

What are the implications of underfitting and overfitting in a neural network model?