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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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Deep Learning withTensorFlow 2 and
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packt.comSubscribe to our online di
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I want to thank my kids, Aurora, Le
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Sujit Pal is a Technology Research
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Table of ContentsPrefacexiChapter 1
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[ iii ]Table of ContentsConverting
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Table of ContentsSo what is the pro
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[ vii ]Table of ContentsChapter 10:
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Table of ContentsPretrained models
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PrefaceDeep Learning with TensorFlo
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• Supervised learning, in which t
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PrefaceThe complexity of deep learn
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PrefaceFigure 5: Adoption of deep l
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Chapter 1, Neural Network Foundatio
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PrefaceChapter 13, TensorFlow for M
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ConventionsThere are a number of te
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PrefaceReferences1. Deep Learning w
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Neural Network Foundations with Ten
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TensorFlow 1.x and 2.xThe intent of
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An example to start withWe'll consi
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Chapter 23. Placeholders: Placehold
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• To create random values from a
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To know the value, we need to creat
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Chapter 2Both PyTorch and TensorFlo
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Chapter 2state = [tf.zeros([100, 10
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Chapter 2For now, there's no need t
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Chapter 2Let's see an example of a
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Chapter 2If you want to save a mode
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Chapter 2supervised=True)train_data
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Chapter 2There, tf.feature_column.n
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Chapter 2print (dz_dx)print (dy_dx)
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Chapter 2In our toy example we use
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Chapter 2For multi-machine training
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Chapter 25. Use tf.layers modules t
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Chapter 2Keras or tf.keras?Another
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• tf.data can be used to load mod
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RegressionLet us imagine a simpler
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RegressionTake a look at the last t
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Regression3. Now, we calculate the
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RegressionIn the next section we wi
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Regression2. Now, we define the fea
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Regression2. Download the dataset:(
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RegressionThe following is the Tens
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RegressionIn regression the aim is
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RegressionThe Estimator outputs the
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RegressionThe following is the grap
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RegressionReferencesHere are some g
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Convolutional Neural NetworksIn thi
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Convolutional Neural NetworksIn thi
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Convolutional Neural NetworksIn oth
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Convolutional Neural NetworksThen w
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Convolutional Neural NetworksHoweve
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Convolutional Neural NetworksPlotti
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Convolutional Neural NetworksIn gen
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Convolutional Neural NetworksOur ne
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Convolutional Neural NetworksThese
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Convolutional Neural NetworksSo, we
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Convolutional Neural NetworksEach i
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Convolutional Neural NetworksVery d
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Convolutional Neural NetworksRecogn
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Convolutional Neural NetworksIf we
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Convolutional Neural NetworksRefere
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Advanced Convolutional Neural Netwo
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GenerativeAdversarial NetworksIn th
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[ 193 ]Chapter 6Eventually, we reac
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[ 195 ]Chapter 6Next, we combine th
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Chapter 6And handwritten digits gen
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Chapter 6Figure 1: Visualizing the
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Chapter 6The resultant generator mo
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Chapter 6Figure 4: A summary of res
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Chapter 6def train(self, epochs, ba
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Chapter 6The preceding images were
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Chapter 6Another interesting paper
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Chapter 6To elaborate, let us say t
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Chapter 6Figure 7: The architecture
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Chapter 6Figure 11: Illegible initi
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Chapter 6Bedrooms: Generated bedroo
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Chapter 6The images need to be norm
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Chapter 6initializer = tf.random_no
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Cool, right? Now we can define the
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Chapter 6d_loss = (dA_loss + dB_los
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Chapter 6generator_AB.save_weights(
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6. Ledig, Christian, et al. Photo-R
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Word EmbeddingsDeep learning models
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Word EmbeddingsFor example, "crucia
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Word EmbeddingsAssuming a window si
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Word EmbeddingsGloVeThe Global vect
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Word Embeddingsgensim is an open so
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Word Embeddingsgensim also provides
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Word EmbeddingsSpecifically, we wil
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Word EmbeddingsWe will also convert
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Word EmbeddingsE = np.zeros((vocab_
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Word Embeddingsx = self.embedding(x
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Word EmbeddingsThe change in valida
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Word EmbeddingsThe dataset is a 114
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Word Embeddingsprint("random walks
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Word Embeddingssize=128, # size of
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Word EmbeddingsfastText computes em
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Word EmbeddingsIn the future, once
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Word EmbeddingsA much earlier relat
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Word EmbeddingsOnce you have the fi
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Word EmbeddingsThis will create the
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Word EmbeddingsClassifying with BER
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Word Embeddings2. Each Transformer
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Word EmbeddingsOnce trained, we sav
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Word Embeddings4. Pennington, J., S
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Word Embeddings34. Google Research,
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Recurrent Neural NetworksWe will th
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Recurrent Neural NetworksFor notati
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Recurrent Neural NetworksThis probl
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Recurrent Neural NetworksThe line a
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Recurrent Neural NetworksGated recu
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Recurrent Neural NetworksThis probl
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Recurrent Neural NetworksThe topolo
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Recurrent Neural Networkstexts = do
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Recurrent Neural Networksdef call(s
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Recurrent Neural Networks# callback
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Recurrent Neural NetworksExample
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Recurrent Neural NetworksAs can be
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Recurrent Neural Networksdata_dir =
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Recurrent Neural NetworksWe can als
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Recurrent Neural NetworksIn order t
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Recurrent Neural Networkssource_voc
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Recurrent Neural NetworksFinally, w
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Recurrent Neural Networks38 - val_l
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Recurrent Neural NetworksIf you wou
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Recurrent Neural NetworksExample
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Recurrent Neural NetworksNext we ha
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Recurrent Neural Networksself.embed
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Recurrent Neural NetworksThis is a
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Recurrent Neural Networksreturn np.
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Recurrent Neural NetworksAttention
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Recurrent Neural NetworksFinally, V
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Recurrent Neural Networks# query.sh
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Recurrent Neural Networksself.atten
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Recurrent Neural Networks30 try to
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Recurrent Neural Networks3. Because
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Recurrent Neural NetworksSummaryIn
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Recurrent Neural Networks18. Shi, X
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AutoencodersAutoencoders are feed-f
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Depending upon the actual dimension
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• __init__(): Here, you define al
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Chapter 9And then we reshape the te
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Chapter 9plt.imshow(x_test[index].r
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Chapter 9Keeping the rest of the co
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noise = np.random.normal(loc=0.5, s
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Chapter 9x_train,validation_data=(x
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Chapter 9import matplotlib.pyplot a
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Chapter 9self.conv4 = Conv2D(1, 3,
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Chapter 9You can see that the image
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[ 367 ]Chapter 9Let us use the prec
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Chapter 9Our autoencoder model take
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We train the autoencoder for 20 epo
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Chapter 90.97905576229095460.989323
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Unsupervised LearningThis chapter d
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Chapter 10Next we load the MNIST da
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Chapter 10TensorFlow Embedding APIT
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3. Recompute the centroids using cu
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Chapter 10Figure 4: Plot of the fin
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Chapter 10In SOMs, neurons are usua
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[ 387 ]Chapter 10Colour mapping usi
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Chapter 10# Calculating Neighbourho
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We will also need to normalize the
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Chapter 10ρρ(vv oo |h oo ) = σσ
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# Generate the sample probabilityde
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Chapter 10And the reconstructed ima
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Chapter 10inpX = rbm.rbm_output(inp
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Chapter 10(60000, 28, 28) (60000,)(
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Chapter 10Figure 11: Summary of the
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Chapter 10This chapter, along with
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Reinforcement LearningThis chapter
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Chapter 11And unlike unsupervised l
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Chapter 11Normally, the value is de
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Chapter 11• The next question tha
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Chapter 11This neural network takes
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Chapter 11The MuJoCo environment re
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Chapter 11We will first import the
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Chapter 11The αα is the learning
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Chapter 11We set up the global valu
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Chapter 11else:return np.argmax(sel
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Chapter 11DQN to play a game of Ata
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Chapter 11self.model.add( Conv2D(64
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Chapter 11Here the action A was sel
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Chapter 11Image source: https://arx
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Chapter 11A neural network is used
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Chapter 1111. Details regarding ins
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TensorFlow and Cloud• Scalability
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TensorFlow and Cloud• Azure DevOp
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TensorFlow and Cloud• Lambda: The
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TensorFlow and Cloud• Deep Learni
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TensorFlow and CloudEC2 on AmazonTo
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TensorFlow and CloudCompute Instanc
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TensorFlow and CloudYou just share
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TensorFlow and CloudIn case you req
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TensorFlow and CloudIt starts with
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TensorFlow and CloudTFX librariesTF
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TensorFlow and CloudReferences1. To
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TensorFlow for Mobile and IoT and T
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An introduction to AutoMLThat is pr
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An introduction to AutoMLFeature co
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An introduction to AutoMLThis Effic
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An introduction to AutoMLGoogle Clo
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An introduction to AutoMLThen, we c
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An introduction to AutoMLOnce the d
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An introduction to AutoMLIf your mo
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An introduction to AutoMLClicking o
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An introduction to AutoMLFigure 16:
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An introduction to AutoMLYou can al
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An introduction to AutoMLPut simply
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An introduction to AutoMLLet's star
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An introduction to AutoMLThe token
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An introduction to AutoMLThis will
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An introduction to AutoMLAt the end
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An introduction to AutoMLUsing Clou
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An introduction to AutoMLOnce the d
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An introduction to AutoMLAt the end
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An introduction to AutoMLAs the nex
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An introduction to AutoMLOnce the m
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An introduction to AutoMLOnce the m
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An introduction to AutoMLWe can als
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An introduction to AutoMLThe most e
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An introduction to AutoMLReferences
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The Math Behind Deep LearningSome m
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The Math Behind Deep LearningSuppos
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Page 607 and 608: Tensor Processing UnitMany people b
Page 609 and 610: Tensor Processing UnitThe sequentia
Page 611 and 612: Tensor Processing UnitIf you want t
Page 613 and 614: Tensor Processing UnitOn the other
Page 615 and 616: Tensor Processing UnitHow to use TP
Page 617 and 618: Tensor Processing UnitNote that ful
Page 619 and 620: Tensor Processing UnitEpoch 10/1060
Page 621 and 622: Tensor Processing UnitFigure 11: Go
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Page 626 and 627: Other Books YouMay EnjoyIf you enjo
Page 628 and 629: Other Books You May EnjoyAI Crash C
Page 630: Other Books You May EnjoyLeave a re
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
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