Chapter 5 - Transfer Learning for Image Classification

1. What are VGG and ResNet pre-trained architectures trained on?
   The images in the Imagenet dataset.
2. Why does VGG11 have an inferior accuracy to VGG16?
   VGG11 has fewer layers when compared to VGG16.
3. What does the number 11 in VGG11 represent?
   11 layers.
4. What is residual in the residual network?
   The layer returns input in addition to the layer's transformation.
5. What is the advantage of a residual network?
   It helps in avoiding vanishing gradients and also helps in increasing model depth.
6. What are the various popular pre-trained models?
   VGG, ResNet, Inception, AlexNet.
7. During transfer learning, why should images be normalized with the same mean and standard deviation as those which were used during training of a pre-trained model?
   Models are trained such that they expect input images to be normalized with a specific mean and standard deviation.
8. Why do we freeze certain parameters in a model?
   We freeze so that the parameters will not...