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Advanced Deep Learning with Keras

You're reading from   Advanced Deep Learning with Keras Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788629416
Length 368 pages
Edition 1st Edition
Languages
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (13) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras FREE CHAPTER 2. Deep Neural Networks 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 Other Books You May Enjoy Index

References

  1. Yuval Netzer and others. Reading Digits in Natural Images with Unsupervised Feature Learning. NIPS workshop on deep learning and unsupervised feature learning. Vol. 2011. No. 2. 2011(https://www-cs.stanford.edu/~twangcat/papers/nips2011_housenumbers.pdf).
  2. Zhu, Jun-Yan and others. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017 (http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf).
  3. Phillip Isola and others. Image-to-Image Translation with Conditional Adversarial Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017 (http://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf).
  4. Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784, 2014(https://arxiv.org/pdf/1411.1784.pdf).
  5. Xudong...
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