If the training accuracy is 99% and test accuracy is 98%, is this model considered to be overfitting?

Are there any other ways to check for overfitting?

What do you mean by:

Let me put the question in a different way, I am training a Neural Network model and I happen to get an accuracy value of 99% on training data and accuracy value of 98% on validation data, is this model considered as overfitting?

And what are the way/methods to find or check if my model is overfitting?

Given that training accuracy is pretty close to validation accuracy, the model is slightly overfitting on the training dataset (assuming that both training and validation data come from the same distribution). Your method of looking at training and validation accuracies of the model is fine.

If this level of overfitting is of concern, go ahead and try augmentation methods taught in later lectures and of course, the methods taught in `deep learning specialization`

to better generalize the model.

Thanks for the information. It really helped!