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IndexAAccelerated Linear Algebra (XLA) 82achievements, RLAI controlled sailplanes 415AlphaGo Zero 414locomotion behavior 415Action-Value function 411activation functionsabout 548derivative of ReLU 550derivative of sigmoid 548, 549derivative of tanh 549additive attention 329adversarial training 192Agent 408AI controlled sailplanes 415AlexNet 159AlphaGo Zero 415Amazon EC2virtual machines, creating 448, 449Amazon Machine Image (AMI)reference link 449selecting 449Amazon Web Service (AWS)about 439-443reference link 442services, reference link 443Android Studioinstallation link 467reference link 466App Enginereference link 446applications, GANalbum covers 217bedroom 217Application-Specific Integrated Circuit(ASIC) 573Arduino Nano 33 BLE Sensereference link 464Area Under the Cover Receiver OperatingCharacteristic (AUC ROC) 506Area Under the Curve (AUC) 506Arm Cortex-M Series Processorsreference link 464Artificial General Intelligence (AGI) 407, 491artificial neural networks (ANNs) 5AtrousConvolution 180Attention mechanismabout 328-330integrating, with seq2seq network 330-336machine translation example 330-336autoencodersabout 345-347denoising autoencoders 356sparse autoencoder 354, 355stacked autoencoder 360vanilla autoencoder 347Auto-Encoding Variational Bayesreference link 402AutoGraph 61, 62AutoKeras 497automatic differentiation 568AutoMLabout 491, 492achieving 492benefits 492AutoML Natural LanguageAutoML Entity Extraction 498AutoML Sentiment Analysis 498AutoML Text Classification 498Cloud Natural Language API 498[ 597 ]
AutoML pipelinedata preparation 493feature engineering 493model generation 494-496AutoML Translation 498AutoML Video 498AutoML Visionabout 498AutoML Object Detection 498average pooling 113AWS servicesAWS IoT 444Elastic Beanstalk 444Elastic Compute Cloud (EC2) 443Lambda 444SageMaker 444Bbackpropagationabout 550-553and convnets 564, 565and RNNs 565-568backstep 554, 555forward step 553limits 561overview 46, 47Backpropagation Through Time (BTT) 566backstepabout 554, 555neuron equation, from hidden layer to hiddenlayer 557-560neuron equation, from hidden layer tooutput layer 556, 557backward pass 393Bahdanau attention 329batch computationsize, increasing 35Batch Gradient Descent (BGD) 563, 564BatchNormalization 38, 39beginning-of-string (BOS) 317Bidirectional Encoder Representations (BERT)about 266classifying 270, 271fine-tuning 269using, as feature extractor 268using, as part of network 271-275bidirectional RNNs 289, 290BiLingual Evaluation Understudy (BLEU) 324Blender learning environment 416Boston housing price datasetreference link 97CCaffereference link 1callbacks 67Canned Estimators 94capsule 186Capsule Networks (CapsNets)about 185features 186, 187versus CNN 186, 187CartPoleDQN 422-426reference link 422Central Processing Units (CPUs) 571, 572chain rule 547character embeddings 259CIFAR-10used, for prediction 130CIFAR-10 imagesrecognizing, with deep learning 122-125CIFAR-10 performanceimproving, with data augmentation 128-130improving, with deeper network 125-128CIFAR repositoryreference link 122classification head 140classification task 101CLEVR datasetreference link 164Cloud AutoMLreference link 446Cloud Functionsreference link 446Cloud IoT Corereference link 446cloud storagecreating, reference link 516CNN architecturesabout 159AlexNet 159[ 598 ]
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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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Page 607 and 608: Tensor Processing UnitMany people b
Page 609 and 610: Tensor Processing UnitThe sequentia
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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
Page 623 and 624: Tensor Processing UnitThen the usag
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 635 and 636: Deep Deterministic Policy Gradient(
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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