Playground (Keeping it simple)
Explore methods to combat overfitting in neural networks by applying regularization techniques such as L1 regularization, early stopping, and node removal. This lesson helps you understand how to optimize model performance on validation and test datasets by experimenting with different approaches and tuning hyperparameters effectively.
Revision
Before we move on, let’s practice the code for a little while. That’s optional, but it’s a good way to revise these concepts.
Go through all the codes which we have covered in this chapter by launching the below app:
Hands on
In this chapter, we applied L1 regularization to reduce overfitting on our four-layers neural network. Now it’s up to us to try a few other regularization techniques.
- How does early stopping work on this network?
- What about removing a few nodes from each layer?
Try out those techniques, and keep an eye on the accuracy of the validation set. Maybe we’ll find a more accurate result than we did in this chapter. Do not worry if we do not! The point of this exercise is experimenting with regularization, not necessarily exceeding that 92% score. ...