How to Implement Regularization on Neural Networks

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Overfitting is one of the main problems we face when building neural networks. Before jumping into trying out fixes for over or underfitting, it is important to understand what it means, why it happens and what problems it causes for our neural networks. In this video, we will see how to implement all the regularization techniques we learned about hands-on. This includes, L1/L2 regularization and how to set up its paramaters, Dropout regularization, Data Augmentation and Early Stoppingg.

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As I see the Early Stopping method is the easiest and most reasonable way for Regularization

abdulazizmohammedtaib

Can we call Early Stopping as one of the Regularization Techniques?

mohammedzia

Hello Mam,

I want to ask that whenever I run the command "mnist = keras.datasets.cifar10". It gives me an error stating that the certificate has been expired. Can you guide me that what kind of problem it is?

ahmedmustafa

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