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optimizersreference link 17, 27testing 26-32outputpredicting 45Pparagraph embedding 262, 264Paragraph Vectors - Distributed Bagof Words (PV-DBOW) 264Paragraph Vectors - Distributed Memory(PV-DM) 264paraphrase database (PPDB) 247Part-of-Speech (POS) analysis 175peephole LSTM 288, 289perceptron 6, 7placeholderabout 55defining 58plot command 383policy-based methods 412pooling layers, DCNNabout 113average pooling 113max pooling 113pose estimationreference link 471Positive Rate (TPR) 506POS tagging 307-316prebuilt deep learning modelsrecycling, for feature extraction 136, 137pre-trained models, TensorFlow.jsBodyPix, reference link 486Coco SSD, reference link 486DeepLab v3, reference link 486KNN Classifier, reference link 487MobileNet, reference link 486PoseNet, reference link 486Speech Commands, reference link 487Toxicity 487Universal Sentence Encoder, referencelink 487pre-trained models, TensorFlow Liteimage classification 468, 470mobile GPUs 473object detection 468, 471pose estimation 468, 471question and answer 468, 472reference link 468segmentations 468, 471smart reply 468, 471style transfers 468, 471text classification 468, 472pretrained TPU modelsusing 584, 585principal component analysis (PCA)about 375, 376implementing, on MNIST dataset 376-378k-means clustering 380, 381reference link 379TensorFlow embeddings API 379principal components 375principles, reinforcement learning (RL)goal 407interaction, with environment 407trial and error 407Prioritized Experience Replay (PER) 414PyTorchreference link 1, 272Qquantizationabout 462post-training quantization 462quantization-aware training 462Rragged tensors 74rainbow 434Recurrent Neural Networks (RNN) 279Region of Interest (ROI) 144Region Proposal Network (RPN) 144regression 87, 88regularizationabout 36adoption, for avoiding overfitting 36-38BatchNormalization 38elastic regularization 38L1 regularization (LASSO) 38L2 regularization (Ridge) 38[ 605 ]
regularizersreference link 38reinforcement learning (RL)about 407, 409achievements 414action 409model of the environment 411policy 410return 410reward 410state 409value function 410ReLU (REctified Linear Unit) 11representational state transfer (REST)reference link 508residual networks 159Restricted Boltzmann Machines (RBM)about 393backward pass 393deep belief networks (DBNs) 397, 398forward pass 392used, for reconstructing images 393-397restriction 392right singular matrix 376RNN cellabout 280-282backpropagation through time(BPTT) 283, 284gradients, exploding 284, 285gradients, vanishing 284, 285variants 285RNN cell variantsgated recurrent unit (GRU) 288long short-term memory (LSTM) 285-287peephole LSTM 288, 289RNN topologiesabout 291, 292many-to-many network 307-316many-to-one network 300-307one-to-many network 292-299RNN variantsabout 289bidirectional RNNs 289, 290stateful RNNs 290Robot Operating System (ROS) 416SSageMakerabout 452reference link 452scaled dot-product attention 330Scheduled Sampling 324second-generation TPUs (TPU2) 576, 577segmentationreference link 471Selective Searchreference link 143self-organized maps (SOM)about 384-386used, for color mapping 387-391sentence embeddings 262, 263sentence vectorsgenerating 365-372sentiment analysis 42-45seq2seq model (Encoder-Decoderarchitecture)about 316, 317machine translation example 318-327seq2seq network with Attention modelversus transformer network 336, 337services, Microsoft AzureAzure DevOps 442Function 441IoT Hub 441Storage Services 441Virtual Machines 441shallow neural networks 392Short Message Service (SMS) 243simple linear regression 89-92simple Net, TensorFlow 2.0improving, with Dropout 24, 26improving, with hidden layers 21-23singular value decomposition (SVD) 375Skip-gram with Negative Sampling (SGNS)model 237smart replyreference link 471soft update 414Something2Vec page 253SparkFun edgereference link 464sparse autoencoder[ 606 ]
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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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The Math Behind Deep LearningTheref
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The Math Behind Deep LearningThe ea
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Page 607 and 608: Tensor Processing UnitMany people b
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Page 613 and 614: Tensor Processing UnitOn the other
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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 and 636: Deep Deterministic Policy Gradient(
Page 637 and 638: Google cloud consolereference link
Page 639: used, for building GAN 193-198MNIST
Page 643 and 644: TensorFlow Lite 81TensorFlow Core r
Page 645: Xxception networks 160, 162YYOLO ne
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