Deep Learning for Computer Vision with Python — Practitioner Bundle

Deep Learning for Computer Vision with Python #2

by Adrian Rosebrock

The Practitioner Bundle is appropriate if you want to take a deeper dive in deep learning. Inside this bundle, I cover more advanced techniques and best practices/rules of thumb. When you factor in the cost/time of training these deeper networks, the techniques I cover in the Practitioner Bundle will save you so much time that the bundle will pay for itself, guaranteed.

While the Starter Bundle focuses on learning the fundamentals of deep learning, the Practitioner Bundle takes the next logical step and covers more advanced techniques, including transfer learning, fine-tuning, networks as feature extractors, working with HDF5 + large datasets, and object detection and localization.

I also review Deep Dreaming and Neural Style, Generative Adversarial Networks (GANs), and Image Super Resolution in detail.

Using the techniques discussed in this bundle, you'll be able to compete in image classification competitions such as the Kaggle Dog vs. Cats Challenge (claiming a position in the top-25 leaderboard) and Stanford's cs231n Tiny ImageNet challenge.

This bundle is perfect for you if you are ready to study deep learning in-depth, understand advanced techniques, and discover common best practices and rules of thumb.

280 pages, ebook

Published September 1, 2017

Reviews

[Nguyen Le · Rating 2 out of 5]

substantially worse. I fail to see the point of this second book. a lot of topics introduced, such as image augmentation and transfer learning are rather simple. The book fails to actually go to more advance topic. Instead, it just introduces different architecture for CNN, while introducing these differences in a very shallow way. The part on different optimization, he literally throw in there 4-5 new optimization, without really showing the difference in performance