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Chapter 59. Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan,Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and KorayKavukcuoglu. WaveNet: A Generative Model for Raw Audio. arXiv preprint,2016.10. Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, OriolVinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart,Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, SebNoury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, AlexGraves, Helen King, Tom Walters, Dan Belov, Demis Hassabis, ParallelWaveNet: Fast High-Fidelity Speech Synthesis, 2017.11. Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick, Mask R-CNN,2018.12. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff,Hartwig Adam, Encoder-Decoder with Atrous Separable Convolution forSemantic Image Segmentation, 2018.13. Lex Flagel Yaniv Brandvain Daniel R Schrider, The Unreasonable Effectivenessof Convolutional Neural Networks in Population Genetic Inference, 2018.14. Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, Dynamic Routing BetweenCapsules, https://arxiv.org/abs/1710.09829.[ 189 ]
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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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Page 175 and 176: Advanced Convolutional Neural Netwo
Page 223: Advanced Convolutional Neural Netwo
Page 227 and 228: Generative Adversarial NetworksThe
Page 229 and 230: Generative Adversarial NetworksWe w
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Page 241 and 242: Generative Adversarial Networks# If
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Page 255 and 256: Generative Adversarial NetworksTo c
Page 257 and 258: Generative Adversarial NetworksLet
Page 259 and 260: Generative Adversarial NetworksNow
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Page 263 and 264: Generative Adversarial NetworksWe s
Page 266 and 267: Word EmbeddingsIn the last few chap
Page 268 and 269: Chapter 7Word embeddings are based
Page 270 and 271: Chapter 7Word2VecThe models known a
Page 272 and 273: Chapter 7([Earth, The], 1)([Earth,
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Chapter 7GloVe vectors trained on v
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Chapter 7if i >= k-1:breakif k < le
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Chapter 7We can also compute the di
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Chapter 7labels.append(1 if label =
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Building the embedding matrixThe ge
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Chapter 7Output of the convolutiona
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Chapter 7class_weight=CLASS_WEIGHTS
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Chapter 7The idea of neural embeddi
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continue# compute non-zero elements
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0 3422 3455 118 4527 2304 772 3659
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Chapter 71971 5443 0.000 0.3481971
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The earliest dynamic embedding was
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Chapter 7A more convenient source o
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Chapter 7Figure 4: Different stages
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As with the OpenAI paper, BERT prop
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Chapter 7Fine-tuning BERTBecause BE
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Chapter 7$ python run_classifier.py
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Chapter 7We then instantiate a toke
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Chapter 7The usage pattern of insta
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18. Bojanowski, P., et al. (2017, 1
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Recurrent Neural NetworksIn chapter
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Chapter 8Just as in a traditional n
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Chapter 8However, an understanding
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As we backpropagate across multiple
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Chapter 8Here i, f, and o are the i
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Chapter 8Notice that the only diffe
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Chapter 8RNN topologiesWe have seen
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Chapter 8That is, if the input is t
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Chapter 8sequences = sequences.map(
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[ 297 ]Chapter 8The logic follows a
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Chapter 8Alice, make there some tha
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Chapter 8Our objective is to train
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Chapter 8Next we define our model.
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accuracy: 0.9962 - val_loss: 0.7770
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0 1 there is so much good food in v
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Chapter 8for idx, line in enumerate
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poss_as_catints.append(tf.keras.uti
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Chapter 8Perhaps the best approach
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Chapter 819/19 [===================
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Chapter 8This tensor is fed into an
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Chapter 8The first sequence starts
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Chapter 8batch_size, drop_remainder
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Chapter 8The output of the encoder
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Chapter 8decoder_in = tf.expand_dim
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Chapter 8Epoch-#LossBLEU ScoreEngli
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Chapter 8The Attention context sign
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self.encoder_dim = encoder_dimself.
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Chapter 8In order to verify that th
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Chapter 8loss += loss_fn(decoder_ou
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3. The target sequences are fed int
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Chapter 8Since the entire sequence
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Chapter 84. Hadjeres, G., Pachet, F
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Chapter 834. Honnibal, M. (2016). E
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AutoencodersYou might think that im
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AutoencodersThis results in produci
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Autoencodersself.encoder = Encoder(
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Autoencodersepoch_loss = 0for step,
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AutoencodersYou can see that the co
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AutoencodersWe can see that in the
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Autoencodersx_train_noisy = x_train
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AutoencodersAn impressive reconstru
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Autoencoders4. Let us now define ou
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Autoencoders8. You can see the loss
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Autoencodersimport collectionsimpor
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AutoencodersThe embedding is genera
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Autoencodersyield Xbatch, XbatchXba
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AutoencodersFirst, we extract the e
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AutoencodersReferences1. Rumelhart,
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Unsupervised LearningPCA reduces th
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Unsupervised LearningA comparison o
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Unsupervised Learning• Inspector
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Unsupervised LearningFigure 3: Rand
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Unsupervised LearningK-means cluste
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Unsupervised LearningTo decide whic
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Unsupervised Learningself._neighbou
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Unsupervised LearningThen there are
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Unsupervised Learningplt.text(m[1],
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Unsupervised LearningWe define a cl
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Unsupervised Learning#Find the erro
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Unsupervised LearningLet us try sta
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Unsupervised LearningNow that we ha
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Unsupervised LearningWe define the
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Unsupervised Learning# Backprop and
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Unsupervised Learning16. Sculley, D
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Reinforcement LearningSo, the first
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Reinforcement Learning• Reward R(
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Reinforcement Learning• Consider,
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Reinforcement LearningA modified ve
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Reinforcement LearningToday there e
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Reinforcement Learningenv_name = 'B
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Reinforcement LearningBy default, i
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Reinforcement LearningIn the next s
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Reinforcement LearningThe DQN that
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Reinforcement LearningLet us now in
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Reinforcement LearningAnother impor
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Reinforcement Learningself.replay(s
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Reinforcement LearningIn the follow
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Reinforcement LearningRainbowRainbo
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Reinforcement LearningSummaryReinfo
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TensorFlow and CloudAI algorithms r
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Chapter 12Figure 1: The Microsoft A
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Chapter 12You can learn about all t
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Chapter 12Figure 3: The console of
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Chapter 12Having covered GCP, let's
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Chapter 12After clicking Launch Ins
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Chapter 12• Nvidia Tesla K80• N
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Chapter 12When you log in to Colabo
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Chapter 12Microsoft Azure Notebooks
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Chapter 12In a TFX pipeline, a unit
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Chapter 12TFX uses the open source
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TensorFlow for Mobile andIoT and Te
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Chapter 13Figure 1: Trade-offs for
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Chapter 13Figure 2: TensorFlow Lite
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Chapter 13Then you need to install
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Chapter 13In this section, we will
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[ 471 ]Chapter 13We will discuss Au
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Chapter 13Figure 9: An example of Q
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Chapter 13Traditional machine learn
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Chapter 13keras_model = …keras_mo
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The steps here are similar to a nor
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Chapter 13}model.add(tf.layers.dens
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}Chapter 13const container = {name:
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Chapter 13We have seen how to use T
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Chapter 13AudioTextGeneralUtilities
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Chapter 13In this section, we have
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An introduction to AutoMLThe goal o
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Automatic data preparationThe first
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Chapter 14On the CIFAR-10 dataset,
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AutoKerasAutoKeras [6] provides fun
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Chapter 14Using Cloud AutoML ‒ Ta
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Chapter 14Figure 8: AutoML Tables:
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Chapter 14The ANALYZE tab (see Figu
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Chapter 14This price includes the u
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Chapter 14Figure 15: AutoML Tables:
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curl -X POST -H "Content-Type: appl
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Chapter 14Figure 21: AutoML Table:
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We believe that AI might advance me
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Chapter 14The dataset is hosted on
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Chapter 14The first thing is to cre
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Chapter 14Figure 35: AutoML Vision
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Chapter 14There are two options: ei
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Chapter 14Figure 43: AutoML Vision
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Chapter 14Figure 47: AutoML Text Cl
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Chapter 14Figure 51: AutoML Text Cl
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Chapter 14Figure 55: AutoML Text Tr
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Chapter 14Figure 59: AutoML Text Tr
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Chapter 14Using Cloud AutoML ‒ Vi
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We can now start to build a model.
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Chapter 14Figure 71: AutoML Video I
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Chapter 14Figure 74: AutoML Video I
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Chapter 14The final step consists s
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The Math BehindDeep LearningIn this
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Chapter 15If the function is not li
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Chapter 15Chain ruleThe chain rule
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Chapter 15The derivative can be com
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Remember that a neural network can
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Chapter 15Let's see in detail how t
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Chapter 15For a function in multipl
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Chapter 15The gradient of the error
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Chapter 15= ww jjjj δδ′ jj (zz
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Chapter 153. Backpropagate the erro
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Chapter 15Combining the results, we
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Chapter 15Thinking about backpropag
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Chapter 15Figure 17: RNN equations
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Chapter 15The error is computed via
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Tensor Processing UnitThis chapter
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Chapter 16It was clear that neither
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Chapter 16Figure 2: TPU v1 design s
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Chapter 16TPU2 has MMU for matrix m
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Chapter 16Figure 7: Linear scalabil
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Chapter 16Loading data with tf.data
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[ 583 ]Chapter 16The execution is s
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Chapter 16The best way to play with
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Chapter 16Using TensorFlow 2.1 and
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References1. Moore's law https://en
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Other Books You May Enjoy●●●
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Other Books You May EnjoyDancing wi
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IndexAAccelerated Linear Algebra (X
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DenseNets 160HighwaysNets 160residu
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toy text 417epochscount, increasing
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k-means clusteringabout 380in Tenso
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optimizersreference link 17, 27test
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about 354, 355reference link 355SRG
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textual documents 174, 175tfjs-mode
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