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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. Sutton and Barto. Reinforcement Learning: An Introduction, 2017 (http://incompleteideas.net/book/bookdraft2017nov5.pdf).
  2. Volodymyr Mnih and others, Human-level control through deep reinforcement learning. Nature 518.7540, 2015: 529 (http://www.davidqiu.com:8888/research/nature14236.pdf)
  3. Hado Van Hasselt, Arthur Guez, and David Silver Deep Reinforcement Learning with Double Q-Learning. AAAI. Vol. 16, 2016 (http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12389/11847).
  4. Kai Arulkumaran and others A Brief Survey of Deep Reinforcement Learning. arXiv preprint arXiv:1708.05866, 2017 (https://arxiv.org/pdf/1708.05866.pdf).
  5. David Silver Lecture Notes on Reinforcement Learning, (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html).
  6. Tom Schaul and others. Prioritized experience replay. arXiv preprint arXiv:1511.05952, 2015 (https://arxiv.org/pdf/1511.05952.pdf).
  7. Ziyu Wang and others. Dueling Network Architectures for Deep Reinforcement Learning. arXiv preprint arXiv:1511...
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