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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

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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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 (16) Chapters Close

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

5. References

  1. Ian Goodfellow et al.: Generative Adversarial Nets. Advances in neural information processing systems, 2014 (http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf).
  2. Martin Arjovsky, Soumith Chintala, and Léon Bottou: Wasserstein GAN. arXiv preprint, 2017 (https://arxiv.org/pdf/1701.07875.pdf).
  3. Xudong Mao et al.: Least Squares Generative Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE 2017 (http://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf).
  4. Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional Image Synthesis with Auxiliary Classifier GANs. ICML, 2017 (http://proceedings.mlr.press/v70/odena17a/odena17a.pdf).
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