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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

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788629416

Length 368 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Learning

Author (1):

Rowel Atienza

View More author details

Table of Contents (13) Chapters

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

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Index

References

Ian Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv preprint arXiv:1701.00160, 2016 (https://arxiv.org/pdf/1701.00160.pdf).
Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434, 2015 (https://arxiv.org/pdf/1511.06434.pdf).
Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784, 2014 (https://arxiv.org/pdf/1411.1784.pdf).
Tero Karras and others. Progressive Growing of GANs for Improved Quality, Stability, and Variation. ICLR, 2018 (https://arxiv.org/pdf/1710.10196.pdf).

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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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