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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781838821654

Length 512 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Rowel Atienza

View More author details

Table of Contents (16) Chapters

Preface

1. Introducing Advanced Deep Learning with Keras

2. Deep Neural Networks FREE CHAPTER

3. Autoencoders

4. Generative Adversarial Networks (GANs)

5. Improved GANs

6. Disentangled Representation GANs

7. Cross-Domain GANs

8. Variational Autoencoders (VAEs)

9. Deep Reinforcement Learning

10. Policy Gradient Methods

11. Object Detection

12. Semantic Segmentation

13. Unsupervised Learning Using Mutual Information

14. Other Books You May Enjoy

15. Index

15. References

Krizhevsky Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
Liu Wei, et al. "SSD: Single Shot MultiBox Detector." European conference on computer vision. Springer, Cham, 2016.
Girshick Ross. "Fast R-CNN." Proceedings of the IEEE international conference on computer vision. 2015.
Lin Tsung-Yi, et al. "Focal loss for Dense Object Detection. "Proceedings of the IEEE international conference on computer vision. 2017.

Dutta, et al. VGG Image Annotator http://www.robots.ox.ac.uk/~vgg/software/via/

Bodla Navaneeth, et al. "Soft-NMS--Improving Object Detection With One Line of Code." Proceedings of the IEEE international conference on computer vision. 2017.
Everingham Mark, et al. "The Pascal Visual Object Classes (VOC) challenge." International...

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Authors (1)

Rowel Atienza

Rowel Atienza

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).

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