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DenseNets 160HighwaysNets 160residual networks 159xception networks 160-162CNNs, composing for complex tasksclassification 140, 141instance segmentation 145-147localization 140, 141object detection 142-145semantic segmentation 141Colaboratory Notebookreference link 454ColabsTPUs, using with 580color mappingwith SOM 387-392computational graph, TensorFlow 1.xabout 52example 53, 54execution 52need for 52program structure 51Compute Enginereference link 445ConceptNet Numberbatch 247config parameters, for convolutionlayer configurationkernel size 184padding 184stride 184constants 54content-based attention 329Contextualized Vectors (CoVe) 261continuous backpropagationhistory 543Contrastive Divergence (CD) 392ConvNet 183convolution 110convolutional autoencoderabout 360implementing 360used, for removing noise fromimages 360-364Convolutional Neural Network (CNN)about 183composing, for complex tasks 139issues 185using, for audio 178using, for sentiment analysis 175-178convolutional neural networks (ConvNets)about 109summarizing 113TensorFlow 2.x 112convolution operationsabout 183depthwise convolution 185depthwise separable convolution 185separable convolution 184Corpus of Linguistic Acceptability (COLA) 270cross entropyabout 561-563derivative 561-563Custom Estimators 94custom layers, building__init__() method, using 349build() method, using 349call() method, using 349CycleGANabout 210-212in TensorFlow 2.0 218-228reference link 228Ddata preparation, AutoMLdata cleansing 493data synthesis 493decision boundaries 101Deep Averaging Network (DAN) 263deep belief networks (DBNs) 397Deep Convolutional Neural Network (DCNN)about 110example, LeNet 114local receptive fields 110, 111mathematical example 111, 112pooling layers 113shared weights and bias 111using, for large-scale imagerecognition 132, 133deep convolution GAN (DCGAN)about 198, 199building, for generation of MNISTdigits 200-209changes 198[ 599 ]
Deep Deterministic Policy Gradient(DDPG) 434, 435DeepDream networkcreating 168-171Deep Inception-v3 Netused, for transfer learning 151-153DeepLab 146Deep Learning Containersreference link 446Deep Learning (DL)about 478history 543used, for recognizing CIFAR-10 images 122Deep Learning (DL) model, on cloudabout 439advantages 439, 440AWS 442categories 440IBM cloud 447Microsoft Azure 440Platform as a Service (PaaS) 440Software as a Service (SaaS) 440DeepMindreference link 179Deep Q-Networks (DQNs)about 420-422for CartPole 422-426used, for playing Atari game 427-430variants 430Deep Reinforcement Learning (DRL)about 411policy-based methods 412value-based methods 411working 412-414denoising autoencodersabout 356used, for clearing images 357DenseNets 160dependent variablesabout 88reference link 88deprecated endpointsreference link 80derivatives 544differentiation rules 547Dilated Causal Convolutions 179Dilated ConvNets 178-180dilated convolutionabout 184dilation rate 184transposed convolution 184distributed representations 233, 234distributed training, TensorFlow 2.xabout 76multiple GPUs 76-78MultiWorkerMirroredStrategy 78ParameterServerStrategy 78TPUStrategy 78dot product 232double DQN 430DQN variantsabout 430double DQN 430, 431dueling DQN 431-433rainbow 434Dropoutused, for improving simple Net inTensorFlow 2.0 24, 25dueling DQN 431-433dynamic embeddings 260, 262Eeager execution 60edge computingFederated Learning (FL) 474edge TPU 578Efficient Neural Architecture Search(ENAS) 495eigen decomposition 375Embedding Projectorabout 379Inspector Panel 380Projections Panel 379Embeddings from Language Models(ELMo) 261end of sentence (EOS) 318environments, OpenAI Gymalgorithms 417Atari 417Box2D 417classic control 417MuJoCo 417robotics 417[ 600 ]
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- Page 633: AutoML pipelinedata preparation 493
- Page 637 and 638: Google cloud consolereference link
- Page 639 and 640: used, for building GAN 193-198MNIST
- Page 641 and 642: regularizersreference link 38reinfo
- Page 643 and 644: TensorFlow Lite 81TensorFlow Core r
- Page 645: Xxception networks 160, 162YYOLO ne
DenseNets 160
HighwaysNets 160
residual networks 159
xception networks 160-162
CNNs, composing for complex tasks
classification 140, 141
instance segmentation 145-147
localization 140, 141
object detection 142-145
semantic segmentation 141
Colaboratory Notebook
reference link 454
Colabs
TPUs, using with 580
color mapping
with SOM 387-392
computational graph, TensorFlow 1.x
about 52
example 53, 54
execution 52
need for 52
program structure 51
Compute Engine
reference link 445
ConceptNet Numberbatch 247
config parameters, for convolution
layer configuration
kernel size 184
padding 184
stride 184
constants 54
content-based attention 329
Contextualized Vectors (CoVe) 261
continuous backpropagation
history 543
Contrastive Divergence (CD) 392
ConvNet 183
convolution 110
convolutional autoencoder
about 360
implementing 360
used, for removing noise from
images 360-364
Convolutional Neural Network (CNN)
about 183
composing, for complex tasks 139
issues 185
using, for audio 178
using, for sentiment analysis 175-178
convolutional neural networks (ConvNets)
about 109
summarizing 113
TensorFlow 2.x 112
convolution operations
about 183
depthwise convolution 185
depthwise separable convolution 185
separable convolution 184
Corpus of Linguistic Acceptability (COLA) 270
cross entropy
about 561-563
derivative 561-563
Custom Estimators 94
custom layers, building
__init__() method, using 349
build() method, using 349
call() method, using 349
CycleGAN
about 210-212
in TensorFlow 2.0 218-228
reference link 228
D
data preparation, AutoML
data cleansing 493
data synthesis 493
decision boundaries 101
Deep Averaging Network (DAN) 263
deep belief networks (DBNs) 397
Deep Convolutional Neural Network (DCNN)
about 110
example, LeNet 114
local receptive fields 110, 111
mathematical example 111, 112
pooling layers 113
shared weights and bias 111
using, for large-scale image
recognition 132, 133
deep convolution GAN (DCGAN)
about 198, 199
building, for generation of MNIST
digits 200-209
changes 198
[ 599 ]
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