The highest tier (again, which you are referring to) includes 800+ pages, detailed experiment journals on how to reproduce the state-of-the-art publications (ResNet, SqueezeNet, VGG, etc.) on ImageNet (which is 1.2 million images). I demonstrate how to implement each model from scratch and then train them, detailing which parameters to change and when. The highest tier is for people looking to train really large networks on massive datasets where you could be spending thousands of dollars in the cloud for GPU costs (you can't train these networks without a GPU, or ideally multiple GPUs). I've also included the pre-trained models as well if people want to get started with them and skip training. This tier is really for researchers/practitioners who need to save time and finances by starting with experiment journals that detail how to replicate the results.
The lower tiers are for people just (1) getting started with deep learning in context of computer vision and/or (2) looking to apply best practices. Each book also includes video tutorials/lectures once I have finished putting them together. Realistically I should rebrand the book as a course as it's much more in line with something you would get from Udacity (only with more theory and more detailed code and implementations).
If anyone has any questions about the book do feel free to ask.