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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

Symbols

100-layer DenseNet-BC for CIFAR10

building 69, 70, 71, 72, 73

Semantic Segmentation 422

A

accuracy 19

Actor-Critic method 338, 339, 340, 341

Adaptive Moments (Adam) 20

Advantage Actor-Critic (A2C) method 341, 342, 344

AE

CNN, using 268, 270, 271, 272, 273

Anaconda

URL 4

anchor box 373, 375, 377, 380

Artificial Intelligence (AI) 289

autoencoder

building, with Keras 81, 84, 85, 86, 87, 88, 90

decoder 78

encoder 78

principles 78, 79, 80

automatic colorization autoencoder 96, 101, 102, 103

auxiliary classifier GAN (ACGAN) 171

Auxiliary Classifier GAN (ACGAN) 133, 155, 156, 157, 159, 161, 163, 166, 167, 168

B

backbone network 391

backpropagation 23

Batch Normalization (BN) 112, 229

Bottleneck 43

C

callbacks 62

class imbalance 391

CNN

used, for AE 268, 270, 271, 272, 273

CNN MNIST digit classifier

summary 32

conditional GAN (CGAN) 171

Conditional...

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