In deep learning, a model can either underfit (not learn enough from the data) or overfit (memorize the data, losing the ability to generalize).
In this 90-minute session, we will dive deep into the concepts of overfitting and underfitting, with a special focus on how to address overfitting in your neural networks.
You will learn various techniques like L2 regularization, dropout, early stopping, and model checkpointing to improve your model’s ability to generalize. These methods can significantly improve model performance, especially when working with limited data or large, complex models.
We’ll provide practical, hands-on examples using Keras to apply these techniques to a real-world dataset, so you can directly see how to improve your model’s accuracy and prevent overfitting.
Duration: 90 mins
Course Code: BDT498
Learning Objectives:
After this course, you will be able to:
Training material provided: Yes (Digital format)
Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools
