Advanced Deep Learning with Keras

Index
A
accuracy 17
Actor-Critic (A2C) method
about 315
advantages 317
Adaptive Moments (Adam) 17
Artificial Intelligence (AI) 271
Asynchronous Advantage Actor-Critic
(A3C) 317
autoencoders
building, with Keras 74, 78-80, 83
decoder 72
encoder 72
principles 72-74
automatic colorization
autoencoders 89, 90, 95
Auxiliary Classifier GAN (ACGAN) 125,
147-150, 152-157, 161
B
backpropagation
reference 20
Batch Normalization (BN) 25, 105, 212
Bellman Equation 275
bootstrapping 288
C
Conditional GAN (CGAN) 114-205
Conditional loss function 184
Conditional VAE (CVAE) 254, 261, 263, 264
Conv2D -Batch Normalization (BN)-ReLU 51
convolution 26
Convolutional Neural Networks (CNN)
about 23, 25
convolution 26, 27
performance evaluation 28, 29
pooling operation 28
summary 28
Core Deep Learning Models
difference between 5
implementing 4, 5
critic 100
cross-domain transfer 203
CyCADA (Cycle-Consistent Adversarial Domain
Adaptation) 230
CycleGAN
about 203
principles 204, 205
using, in MNIST 227-234
using, in SVHN datasets 227-234
CycleGAN model
about 207-210
implementing, with Keras 211-227
D
decoder 72
Deep Learning (DL)
about 271
URL 97
Deep Q-network (DQN)
on Keras 296, 297
Deep Reinforcement Learning
(DRL) 271, 293-296
deep residual networks (ResNet) 49-58
denoising autoencoders (DAE) 84-86
densely connected convolutional networks
(DenseNet) 62-68
[ 347 ]