Deep Learning for Computer Vision with Python
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. 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.Formats : PDF
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.Formats : PDF
卷:
2
年:
2017
语言:
english
页:
210
系列:
Practitioner Bundle
文件:
PDF, 9.75 MB
IPFS:
,
english, 2017
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