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toy text 417epochscount, increasing 32Epsilon-Greedy (Є−greedy) policy 413estimator modelabout 147running, on GPUs 150, 151Estimators 72-74Evolutionary algorithm (EA) 496Experience Replay method 413explorationversus exploitation 413FFacebook AI Research (FAIR) 259False Positive Rate (FPR) 506Fashion-MNISTclassifying, with tf.Keras 147-150reference link 147Faster R-CNNreference link 144fastText 247feature engineering, AutoMLabout 493feature construction 494feature extraction 494feature selection 493feature extraction 494feature selection 493federated core (FC)reference link 476Federated Learning (FL), edge computingissues 475overview 474, 475Feed Forward Network (FFN) 338, 339FigureQA datasetreference link 164first-generation TPU (TPU v1) 574-576FlatBuffersabout 463reference link 463forward pass 392GGAN architecturesabout 209CycleGAN 210-212InfoGAN 212, 213SRGAN 209, 210Gated Recurrent Unit (GRU) 279Gazebo 416GCP servicesApp Engine 446Cloud AutoML 446Cloud Functions 446Cloud IoT Core 446Compute Engine 445Deep Learning Containers 446Generative Adversarial Networks (GANs)about 191-193applications 214-217building, with MNIST in TensorFlow 193-198convergence, reference link 193learning 193reference link 191Generative Pretraining (GPT) 266Genetic Programming (GA) 496gensiminstallation link 240reference link 240used, for creating word embedding 239, 240used, for exploring embeddingspace 240-243Global vectors for word representation (GloVe)about 238, 247download link 239Google AutoMLintegrating, with Kaggle 540, 541Google Cloud AutoMLabout 498cost 539reference link 498Google Cloud AutoML Tableabout 498using 499-513Google Cloud AutoML Textusing 524-528Google Cloud AutoML Translationusing 528-532Google Cloud AutoML Videousing 533-538Google Cloud AutoML Visionusing 513-522[ 601 ]
Google cloud consolereference link 587Google Cloud Platform (GCP)about 444, 516Pricing Calculator, reference link 499reference link 444services 445Google Colab (Google Colaboratory)CPUs 39-41GPUs 39-41playing with 39-42reference link 39TPUs 39-41Google Compute Engine (GCE) 577Google Kubernetes Engine (GKE) 577Google Neural Machine Translation(Google NMT) 530Google powered Dataset Searchreference link 82Gradient Descent (GD) 26gradients 544-546Gradlereference link 469Graphic Processing Units (GPUs)about 571, 572estimator model, running 150, 151Hhandwritten chartsrecognizing, with experiments 36handwritten digitsrecognizing 14reconstructing, with vanillaautoencoders 350-353hard clustering 384hard update 414hidden layer 392HighwaysNets 160hyperparameter tuningabout 494-496grid search 496random search 496IIBM cloudabout 447reference link 447ImageNet ILSVRC-2012reference link 132imagesclearing, denoising autoencoderused 357-360noise, removing with convolutionalautoencoder 360-365independent variablesabout 88reference link 88InfoGAN 212, 213Information Retrieval (IR) 232internal hidden neuronscount, increasing 34Internet of Things (IoT) 578Item2Vec 253JJava Caffereference link 132Jupyter Notebooks, on cloudabout 451Google Colaboratory 452-454Microsoft Azure Notebook 455SageMaker 452Just In Time (JIT) compilation 62KKaggleGoogle AutoML, integrating into 540, 541reference link 528Kaggle VQA challengereference link 164Kerasabout 3, 83pooling layers, reference link 113Keras APIsabout 63Functional API 64-66model subclassing 66, 67Sequential API 63Keras applicationsabout 158reference link 152[ 602 ]
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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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The Math Behind Deep LearningNote t
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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
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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 and 634: AutoML pipelinedata preparation 493
Page 635: Deep Deterministic Policy Gradient(
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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