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Chapter 834. Honnibal, M. (2016). Embed, encode, attend, predict: The new deep learningformula for state of the art NLP models. URL: https://explosion.ai/blog/deep-learning-formula-nlp.35. Papineni, K., Roukos, S., Ward, T., and Zhu, W. (2002). BLEU: A Method forAutomatic Evaluation of Machine Translation. Proceedings of the 40th AnnualMeeting for the Association of Computational Linguistics (ACL).36. Project Gutenberg (2019), URL: https://www.gutenberg.org/.[ 343 ]
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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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Page 337 and 338: Recurrent Neural NetworksAs can be
Page 339 and 340: Recurrent Neural Networksdata_dir =
Page 341 and 342: Recurrent Neural NetworksWe can als
Page 343 and 344: Recurrent Neural NetworksIn order t
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Page 347 and 348: Recurrent Neural NetworksFinally, w
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Page 355 and 356: Recurrent Neural NetworksNext we ha
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Page 359 and 360: Recurrent Neural NetworksThis is a
Page 361 and 362: Recurrent Neural Networksreturn np.
Page 363 and 364: Recurrent Neural NetworksAttention
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Page 367 and 368: Recurrent Neural Networks# query.sh
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Page 373 and 374: Recurrent Neural Networks3. Because
Page 375 and 376: Recurrent Neural NetworksSummaryIn
Page 377: Recurrent Neural Networks18. Shi, X
Page 381 and 382: AutoencodersYou might think that im
Page 383 and 384: AutoencodersThis results in produci
Page 385 and 386: Autoencodersself.encoder = Encoder(
Page 387 and 388: Autoencodersepoch_loss = 0for step,
Page 389 and 390: AutoencodersYou can see that the co
Page 391 and 392: AutoencodersWe can see that in the
Page 393 and 394: Autoencodersx_train_noisy = x_train
Page 395 and 396: AutoencodersAn impressive reconstru
Page 397 and 398: Autoencoders4. Let us now define ou
Page 399 and 400: Autoencoders8. You can see the loss
Page 401 and 402: Autoencodersimport collectionsimpor
Page 403 and 404: AutoencodersThe embedding is genera
Page 405 and 406: Autoencodersyield Xbatch, XbatchXba
Page 407 and 408: AutoencodersFirst, we extract the e
Page 409 and 410: AutoencodersReferences1. Rumelhart,
Page 411 and 412: Unsupervised LearningPCA reduces th
Page 413 and 414: Unsupervised LearningA comparison o
Page 415 and 416: Unsupervised Learning• Inspector
Page 417 and 418: Unsupervised LearningFigure 3: Rand
Page 419 and 420: Unsupervised LearningK-means cluste
Page 421 and 422: Unsupervised LearningTo decide whic
Page 423 and 424: Unsupervised Learningself._neighbou
Page 425 and 426: Unsupervised LearningThen there are
Page 427 and 428: 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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