Exploring popular ConvNet architectures

Now that we know how CNNs are built and trained, it is time to explore some of the popular architectures that are used and understand what makes them so powerful.

VGG-16

The VGG network is a derivation of AlexNet that was created by Andrew Zisserman and Karen Simonyan at the Visual Geometry Group (VGG) at the University of Oxford in 2015. This architecture is simpler than the one we saw earlier, but it gives us a much better framework to work with. VGGNet was also trained on the ImageNet dataset, except it takes images with a size of 224 × 224 × 3 that are sampled from the rescaled images in the dataset as input. You may have noticed that we have headed this section VGG-16...