Questions

1. What are VGG and ResNet pre-trained architectures trained on?
2. Why does VGG11 have an inferior accuracy to VGG16?
3. What does the number 11 in VGG11 represent?
4. What is residual in the residual network?
5. What is the advantage of a residual network?
6. What are the various popular pre-trained models?
7. During transfer learning, why should images be normalized with the same mean and standard deviation as those that were used during the training of a pre-trained model?
8. Why do we freeze certain parameters in a model?
9. How do we know the various modules that are present in a pre-trained model?
10. How do we train a model that predicts categorical and numerical values together?
11. Why might age and gender prediction code not always work for an image of your own interest if we execute the same code as we wrote in the age and gender estimation section?
12. How can we further improve the accuracy of the facial keypoint recognition model that we wrote about in the facial key points prediction section?

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