Advanced Deep Learning with Keras

DenseNet 39
DenseNet-BC (Bottleneck-Compression) 65
disentangled representations 162-174
Double Q-learning (DDQN) 302
dropout 14
E
Earth-Mover Distance (EMD) 127
Entropy loss function 185
evidence lower bound (ELBO) 241
experience replay 296
F
feature maps 26
FractalNet 39
Functional API
about 40-45
conclusion 68
layer 41
model 41
reference 49
two input and one output model,
creating 43-49
G
Gated Recurrent Unit (GRU) 35
Generative Adversarial Network (GAN) 71
about 99,125, 203
distance function 128-131
implementing, in Keras 105-113
principles 100-105
gradient descent (GD) 17
Gym
URL 288
H
hyperparameter 12
I
InfoGAN
about 162-174
conclusion 200
Generator Outputs 177, 179
Instance Normalization (IN) 212
J
Jensen-Shannon (JS) 126, 128
K
Keras
about 2
Deep Q-network (DQN) 296, 297
GAN implementation 105-113
installing 3, 4
policy gradient methods 318-333
reference 4
used, for building autoencoders 74-83
used, for building model 12-14
used, for implementing CycleGAN 211-227
used, for implementing WGAN 135, 141
Keras Sequential API 2
Kullback-Leibler (KL) 126, 240
L
label flipping 230
Leaky ReLU 106
Least Squares GAN
(LSGAN) 125, 208, 142-146
logistic sigmoid 15
Long Short Term Memory (LSTM) 35
M
Markov Decision Process (MDP) 273
Mean Absolute Error (MAE) 207
Mean Squared Error (MSE) 17, 73, 208, 317
Monte Carlo policy gradient (REINFORCE)
method
about 311, 312
Actor-Critic method 315
[ 348 ]