09.05.2023
•
Views
Chapter 13In this section, we have discussed how to use TensorFlow.js with both vanillaJavaScript and with Node.js with sample applications for both the browser and forbackend computation.SummaryIn this chapter we have discussed how to use TensorFlow Lite for mobile devicesand IoT and deployed real applications on Android devices. Then, we also talkedabout Federated Learning for distributed learning across thousands (millions) ofmobile devices, taking into account privacy concerns. The last section of the chapterwas devoted to TensorFlow.js for using TensorFlow with vanilla JavaScript or withNode.js.The next chapter is about AutoML, a set of techniques used to enable domainexperts who are unfamiliar with machine learning technologies to use MLtechniques easily.References1. Quantization-aware training https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize2. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference, Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu,Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko(Submitted on 15 Dec 2017); https://arxiv.org/abs/1712.058773. MobileNetV2: Inverted Residuals and Linear Bottlenecks, Mark Sandler, AndrewHoward, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen (Submittedon 13 Jan 2018 (v1), last revised 21 Mar 2019 (v4)) https://arxiv.org/abs/1806.083424. MnasNet: Platform-Aware Neural Architecture Search for Mobile, Mingxing Tan,Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard,Quoc V. Le https://arxiv.org/abs/1807.116265. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,Atrous Convolution, and Fully Connected CRFs, Liang-Chieh Chen, GeorgePapandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille, May2017, https://arxiv.org/pdf/1606.00915.pdf6. BERT: Pre-training of Deep Bidirectional Transformers for LanguageUnderstanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, KristinaToutanova (Submitted on 11 Oct 2018 (v1), last revised 24 May 2019 v2))https://arxiv.org/abs/1810.04805[ 489 ]
TensorFlow for Mobile and IoT and TensorFlow.js7. MOBILEBERT: TASK-AGNOSTIC COMPRESSION OF BERT BYPROGRESSIVE KNOWLEDGE TRANSFER, Anonymous authors,Paper under double-blind review, https://openreview.net/pdf?id=SJxjVaNKwB, 25 Sep 2019 (modified: 25 Sep 2019)ICLR 2020Conference Blind Submission Readers: Everyone8. Communication-Efficient Learning of Deep Networks from Decentralized Data,H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, BlaiseAgüera y Arcas (Submitted on 17 Feb 2016 (v1), last revised 28 Feb 2017 (thisversion, v3)) https://arxiv.org/abs/1602.056299. Federated Learning: Strategies for Improving Communication Efficiency, JakubKonečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, AnandaTheertha Suresh, Dave Bacon (Submitted on 18 Oct 2016 (v1), last revised 30Oct 2017 (this version, v2)) https://arxiv.org/abs/1610.0549210. TOWARDS FEDERATED LEARNING AT SCALE: SYSTEM DESIGN, KeithBonawitz et al. 22 March 2019 https://arxiv.org/pdf/1902.01046.pdf[ 490 ]
-
Page 2 and 3:
Deep Learning withTensorFlow 2 and
-
Page 4 and 5:
packt.comSubscribe to our online di
-
Page 6 and 7:
I want to thank my kids, Aurora, Le
-
Page 8 and 9:
Sujit Pal is a Technology Research
-
Page 10 and 11:
Table of ContentsPrefacexiChapter 1
-
Page 12 and 13:
[ iii ]Table of ContentsConverting
-
Page 14 and 15:
Table of ContentsSo what is the pro
-
Page 16 and 17:
[ vii ]Table of ContentsChapter 10:
-
Page 18 and 19:
Table of ContentsPretrained models
-
Page 20 and 21:
PrefaceDeep Learning with TensorFlo
-
Page 22 and 23:
• Supervised learning, in which t
-
Page 24 and 25:
PrefaceThe complexity of deep learn
-
Page 26 and 27:
PrefaceFigure 5: Adoption of deep l
-
Page 28 and 29:
Chapter 1, Neural Network Foundatio
-
Page 30 and 31:
PrefaceChapter 13, TensorFlow for M
-
Page 32 and 33:
ConventionsThere are a number of te
-
Page 34:
PrefaceReferences1. Deep Learning w
-
Page 37 and 38:
Neural Network Foundations with Ten
-
Page 39 and 40:
Neural Network Foundations with Ten
-
Page 41 and 42:
Neural Network Foundations with Ten
-
Page 43 and 44:
Neural Network Foundations with Ten
-
Page 45 and 46:
Neural Network Foundations with Ten
-
Page 47 and 48:
Neural Network Foundations with Ten
-
Page 49 and 50:
Neural Network Foundations with Ten
-
Page 51 and 52:
Neural Network Foundations with Ten
-
Page 53 and 54:
Neural Network Foundations with Ten
-
Page 55 and 56:
Neural Network Foundations with Ten
-
Page 57 and 58:
Neural Network Foundations with Ten
-
Page 59 and 60:
Neural Network Foundations with Ten
-
Page 61 and 62:
Neural Network Foundations with Ten
-
Page 63 and 64:
Neural Network Foundations with Ten
-
Page 65 and 66:
Neural Network Foundations with Ten
-
Page 67 and 68:
Neural Network Foundations with Ten
-
Page 69 and 70:
Neural Network Foundations with Ten
-
Page 71 and 72:
Neural Network Foundations with Ten
-
Page 73 and 74:
Neural Network Foundations with Ten
-
Page 75 and 76:
Neural Network Foundations with Ten
-
Page 77 and 78:
Neural Network Foundations with Ten
-
Page 79 and 80:
Neural Network Foundations with Ten
-
Page 81 and 82:
Neural Network Foundations with Ten
-
Page 83 and 84:
Neural Network Foundations with Ten
-
Page 86 and 87:
TensorFlow 1.x and 2.xThe intent of
-
Page 88 and 89:
An example to start withWe'll consi
-
Page 90 and 91:
Chapter 23. Placeholders: Placehold
-
Page 92 and 93:
• To create random values from a
-
Page 94 and 95:
To know the value, we need to creat
-
Page 96 and 97:
Chapter 2Both PyTorch and TensorFlo
-
Page 98 and 99:
Chapter 2state = [tf.zeros([100, 10
-
Page 100 and 101:
Chapter 2For now, there's no need t
-
Page 102 and 103:
Chapter 2Let's see an example of a
-
Page 104 and 105:
Chapter 2If you want to save a mode
-
Page 106 and 107:
Chapter 2supervised=True)train_data
-
Page 108 and 109:
Chapter 2There, tf.feature_column.n
-
Page 110 and 111:
Chapter 2print (dz_dx)print (dy_dx)
-
Page 112 and 113:
Chapter 2In our toy example we use
-
Page 114 and 115:
Chapter 2For multi-machine training
-
Page 116 and 117:
Chapter 25. Use tf.layers modules t
-
Page 118 and 119:
Chapter 2Keras or tf.keras?Another
-
Page 120:
• tf.data can be used to load mod
-
Page 123 and 124:
RegressionLet us imagine a simpler
-
Page 125 and 126:
RegressionTake a look at the last t
-
Page 127 and 128:
Regression3. Now, we calculate the
-
Page 129 and 130:
RegressionIn the next section we wi
-
Page 131 and 132:
Regression2. Now, we define the fea
-
Page 133 and 134:
Regression2. Download the dataset:(
-
Page 135 and 136:
RegressionThe following is the Tens
-
Page 137 and 138:
RegressionIn regression the aim is
-
Page 139 and 140:
RegressionThe Estimator outputs the
-
Page 141 and 142:
RegressionThe following is the grap
-
Page 143 and 144:
RegressionReferencesHere are some g
-
Page 145 and 146:
Convolutional Neural NetworksIn thi
-
Page 147 and 148:
Convolutional Neural NetworksIn thi
-
Page 149 and 150:
Convolutional Neural NetworksIn oth
-
Page 151 and 152:
Convolutional Neural NetworksThen w
-
Page 153 and 154:
Convolutional Neural NetworksHoweve
-
Page 155 and 156:
Convolutional Neural NetworksPlotti
-
Page 157 and 158:
Convolutional Neural NetworksIn gen
-
Page 159 and 160:
Convolutional Neural NetworksOur ne
-
Page 161 and 162:
Convolutional Neural NetworksThese
-
Page 163 and 164:
Convolutional Neural NetworksSo, we
-
Page 165 and 166:
Convolutional Neural NetworksEach i
-
Page 167 and 168:
Convolutional Neural NetworksVery d
-
Page 169 and 170:
Convolutional Neural NetworksRecogn
-
Page 171 and 172:
Convolutional Neural NetworksIf we
-
Page 173 and 174:
Convolutional Neural NetworksRefere
-
Page 175 and 176:
Advanced Convolutional Neural Netwo
-
Page 177 and 178:
Advanced Convolutional Neural Netwo
-
Page 179 and 180:
Advanced Convolutional Neural Netwo
-
Page 181 and 182:
Advanced Convolutional Neural Netwo
-
Page 183 and 184:
Advanced Convolutional Neural Netwo
-
Page 185 and 186:
Advanced Convolutional Neural Netwo
-
Page 187 and 188:
Advanced Convolutional Neural Netwo
-
Page 189 and 190:
Advanced Convolutional Neural Netwo
-
Page 191 and 192:
Advanced Convolutional Neural Netwo
-
Page 193 and 194:
Advanced Convolutional Neural Netwo
-
Page 195 and 196:
Advanced Convolutional Neural Netwo
-
Page 197 and 198:
Advanced Convolutional Neural Netwo
-
Page 199 and 200:
Advanced Convolutional Neural Netwo
-
Page 201 and 202:
Advanced Convolutional Neural Netwo
-
Page 203 and 204:
Advanced Convolutional Neural Netwo
-
Page 205 and 206:
Advanced Convolutional Neural Netwo
-
Page 207 and 208:
Advanced Convolutional Neural Netwo
-
Page 209 and 210:
Advanced Convolutional Neural Netwo
-
Page 211 and 212:
Advanced Convolutional Neural Netwo
-
Page 213 and 214:
Advanced Convolutional Neural Netwo
-
Page 215 and 216:
Advanced Convolutional Neural Netwo
-
Page 217 and 218:
Advanced Convolutional Neural Netwo
-
Page 219 and 220:
Advanced Convolutional Neural Netwo
-
Page 221 and 222:
Advanced Convolutional Neural Netwo
-
Page 223 and 224:
Advanced Convolutional Neural Netwo
-
Page 226 and 227:
GenerativeAdversarial NetworksIn th
-
Page 228 and 229:
[ 193 ]Chapter 6Eventually, we reac
-
Page 230 and 231:
[ 195 ]Chapter 6Next, we combine th
-
Page 232 and 233:
Chapter 6And handwritten digits gen
-
Page 234 and 235:
Chapter 6Figure 1: Visualizing the
-
Page 236 and 237:
Chapter 6The resultant generator mo
-
Page 238 and 239:
Chapter 6Figure 4: A summary of res
-
Page 240 and 241:
Chapter 6def train(self, epochs, ba
-
Page 242 and 243:
Chapter 6The preceding images were
-
Page 244 and 245:
Chapter 6Another interesting paper
-
Page 246 and 247:
Chapter 6To elaborate, let us say t
-
Page 248 and 249:
Chapter 6Figure 7: The architecture
-
Page 250 and 251:
Chapter 6Figure 11: Illegible initi
-
Page 252 and 253:
Chapter 6Bedrooms: Generated bedroo
-
Page 254 and 255:
Chapter 6The images need to be norm
-
Page 256 and 257:
Chapter 6initializer = tf.random_no
-
Page 258 and 259:
Cool, right? Now we can define the
-
Page 260 and 261:
Chapter 6d_loss = (dA_loss + dB_los
-
Page 262 and 263:
Chapter 6generator_AB.save_weights(
-
Page 264:
6. Ledig, Christian, et al. Photo-R
-
Page 267 and 268:
Word EmbeddingsDeep learning models
-
Page 269 and 270:
Word EmbeddingsFor example, "crucia
-
Page 271 and 272:
Word EmbeddingsAssuming a window si
-
Page 273 and 274:
Word EmbeddingsGloVeThe Global vect
-
Page 275 and 276:
Word Embeddingsgensim is an open so
-
Page 277 and 278:
Word Embeddingsgensim also provides
-
Page 279 and 280:
Word EmbeddingsSpecifically, we wil
-
Page 281 and 282:
Word EmbeddingsWe will also convert
-
Page 283 and 284:
Word EmbeddingsE = np.zeros((vocab_
-
Page 285 and 286:
Word Embeddingsx = self.embedding(x
-
Page 287 and 288:
Word EmbeddingsThe change in valida
-
Page 289 and 290:
Word EmbeddingsThe dataset is a 114
-
Page 291 and 292:
Word Embeddingsprint("random walks
-
Page 293 and 294:
Word Embeddingssize=128, # size of
-
Page 295 and 296:
Word EmbeddingsfastText computes em
-
Page 297 and 298:
Word EmbeddingsIn the future, once
-
Page 299 and 300:
Word EmbeddingsA much earlier relat
-
Page 301 and 302:
Word EmbeddingsOnce you have the fi
-
Page 303 and 304:
Word EmbeddingsThis will create the
-
Page 305 and 306:
Word EmbeddingsClassifying with BER
-
Page 307 and 308:
Word Embeddings2. Each Transformer
-
Page 309 and 310:
Word EmbeddingsOnce trained, we sav
-
Page 311 and 312:
Word Embeddings4. Pennington, J., S
-
Page 313 and 314:
Word Embeddings34. Google Research,
-
Page 315 and 316:
Recurrent Neural NetworksWe will th
-
Page 317 and 318:
Recurrent Neural NetworksFor notati
-
Page 319 and 320:
Recurrent Neural NetworksThis probl
-
Page 321 and 322:
Recurrent Neural NetworksThe line a
-
Page 323 and 324:
Recurrent Neural NetworksGated recu
-
Page 325 and 326:
Recurrent Neural NetworksThis probl
-
Page 327 and 328:
Recurrent Neural NetworksThe topolo
-
Page 329 and 330:
Recurrent Neural Networkstexts = do
-
Page 331 and 332:
Recurrent Neural Networksdef call(s
-
Page 333 and 334:
Recurrent Neural Networks# callback
-
Page 335 and 336:
Recurrent Neural NetworksExample
-
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
-
Page 345 and 346:
Recurrent Neural Networkssource_voc
-
Page 347 and 348:
Recurrent Neural NetworksFinally, w
-
Page 349 and 350:
Recurrent Neural Networks38 - val_l
-
Page 351 and 352:
Recurrent Neural NetworksIf you wou
-
Page 353 and 354:
Recurrent Neural NetworksExample
-
Page 355 and 356:
Recurrent Neural NetworksNext we ha
-
Page 357 and 358:
Recurrent Neural Networksself.embed
-
Page 359 and 360:
Recurrent Neural NetworksThis is a
-
Page 361 and 362:
Recurrent Neural Networksreturn np.
-
Page 363 and 364:
Recurrent Neural NetworksAttention
-
Page 365 and 366:
Recurrent Neural NetworksFinally, V
-
Page 367 and 368:
Recurrent Neural Networks# query.sh
-
Page 369 and 370:
Recurrent Neural Networksself.atten
-
Page 371 and 372:
Recurrent Neural Networks30 try to
-
Page 373 and 374:
Recurrent Neural Networks3. Because
-
Page 375 and 376:
Recurrent Neural NetworksSummaryIn
-
Page 377 and 378:
Recurrent Neural Networks18. Shi, X
-
Page 380 and 381:
AutoencodersAutoencoders are feed-f
-
Page 382 and 383:
Depending upon the actual dimension
-
Page 384 and 385:
• __init__(): Here, you define al
-
Page 386 and 387:
Chapter 9And then we reshape the te
-
Page 388 and 389:
Chapter 9plt.imshow(x_test[index].r
-
Page 390 and 391:
Chapter 9Keeping the rest of the co
-
Page 392 and 393:
noise = np.random.normal(loc=0.5, s
-
Page 394 and 395:
Chapter 9x_train,validation_data=(x
-
Page 396 and 397:
Chapter 9import matplotlib.pyplot a
-
Page 398 and 399:
Chapter 9self.conv4 = Conv2D(1, 3,
-
Page 400 and 401:
Chapter 9You can see that the image
-
Page 402 and 403:
[ 367 ]Chapter 9Let us use the prec
-
Page 404 and 405:
Chapter 9Our autoencoder model take
-
Page 406 and 407:
We train the autoencoder for 20 epo
-
Page 408 and 409:
Chapter 90.97905576229095460.989323
-
Page 410 and 411:
Unsupervised LearningThis chapter d
-
Page 412 and 413:
Chapter 10Next we load the MNIST da
-
Page 414 and 415:
Chapter 10TensorFlow Embedding APIT
-
Page 416 and 417:
3. Recompute the centroids using cu
-
Page 418 and 419:
Chapter 10Figure 4: Plot of the fin
-
Page 420 and 421:
Chapter 10In SOMs, neurons are usua
-
Page 422 and 423:
[ 387 ]Chapter 10Colour mapping usi
-
Page 424 and 425:
Chapter 10# Calculating Neighbourho
-
Page 426 and 427:
We will also need to normalize the
-
Page 428 and 429:
Chapter 10ρρ(vv oo |h oo ) = σσ
-
Page 430 and 431:
# Generate the sample probabilityde
-
Page 432 and 433:
Chapter 10And the reconstructed ima
-
Page 434 and 435:
Chapter 10inpX = rbm.rbm_output(inp
-
Page 436 and 437:
Chapter 10(60000, 28, 28) (60000,)(
-
Page 438 and 439:
Chapter 10Figure 11: Summary of the
-
Page 440 and 441:
Chapter 10This chapter, along with
-
Page 442 and 443:
Reinforcement LearningThis chapter
-
Page 444 and 445:
Chapter 11And unlike unsupervised l
-
Page 446 and 447:
Chapter 11Normally, the value is de
-
Page 448 and 449:
Chapter 11• The next question tha
-
Page 450 and 451:
Chapter 11This neural network takes
-
Page 452 and 453:
Chapter 11The MuJoCo environment re
-
Page 454 and 455:
Chapter 11We will first import the
-
Page 456 and 457:
Chapter 11The αα is the learning
-
Page 458 and 459:
Chapter 11We set up the global valu
-
Page 460 and 461:
Chapter 11else:return np.argmax(sel
-
Page 462 and 463:
Chapter 11DQN to play a game of Ata
-
Page 464 and 465:
Chapter 11self.model.add( Conv2D(64
-
Page 466 and 467:
Chapter 11Here the action A was sel
-
Page 468 and 469:
Chapter 11Image source: https://arx
-
Page 470 and 471:
Chapter 11A neural network is used
-
Page 472:
Chapter 1111. Details regarding ins
-
Page 475 and 476:
TensorFlow and Cloud• Scalability
-
Page 477 and 478:
TensorFlow and Cloud• Azure DevOp
-
Page 479 and 480:
TensorFlow and Cloud• Lambda: The
-
Page 481 and 482:
TensorFlow and Cloud• Deep Learni
-
Page 483 and 484:
TensorFlow and CloudEC2 on AmazonTo
-
Page 485 and 486:
TensorFlow and CloudCompute Instanc
-
Page 487 and 488:
TensorFlow and CloudYou just share
-
Page 489 and 490:
TensorFlow and CloudIn case you req
-
Page 491 and 492:
TensorFlow and CloudIt starts with
-
Page 493 and 494:
TensorFlow and CloudTFX librariesTF
-
Page 495 and 496:
TensorFlow and CloudReferences1. To
-
Page 497 and 498:
TensorFlow for Mobile and IoT and T
-
Page 499 and 500:
TensorFlow for Mobile and IoT and T
-
Page 501 and 502:
TensorFlow for Mobile and IoT and T
-
Page 503 and 504:
TensorFlow for Mobile and IoT and T
-
Page 505 and 506:
TensorFlow for Mobile and IoT and T
-
Page 507 and 508:
TensorFlow for Mobile and IoT and T
-
Page 509 and 510:
TensorFlow for Mobile and IoT and T
-
Page 511 and 512:
TensorFlow for Mobile and IoT and T
-
Page 513 and 514:
TensorFlow for Mobile and IoT and T
-
Page 515 and 516:
TensorFlow for Mobile and IoT and T
-
Page 517 and 518:
TensorFlow for Mobile and IoT and T
-
Page 519 and 520:
TensorFlow for Mobile and IoT and T
-
Page 521 and 522:
TensorFlow for Mobile and IoT and T
-
Page 523:
TensorFlow for Mobile and IoT and T
-
Page 527 and 528:
An introduction to AutoMLThat is pr
-
Page 529 and 530:
An introduction to AutoMLFeature co
-
Page 531 and 532:
An introduction to AutoMLThis Effic
-
Page 533 and 534:
An introduction to AutoMLGoogle Clo
-
Page 535 and 536:
An introduction to AutoMLThen, we c
-
Page 537 and 538:
An introduction to AutoMLOnce the d
-
Page 539 and 540:
An introduction to AutoMLIf your mo
-
Page 541 and 542:
An introduction to AutoMLClicking o
-
Page 543 and 544:
An introduction to AutoMLFigure 16:
-
Page 545 and 546:
An introduction to AutoMLYou can al
-
Page 547 and 548:
An introduction to AutoMLPut simply
-
Page 549 and 550:
An introduction to AutoMLLet's star
-
Page 551 and 552:
An introduction to AutoMLThe token
-
Page 553 and 554:
An introduction to AutoMLThis will
-
Page 555 and 556:
An introduction to AutoMLFigure 37:
-
Page 557 and 558:
An introduction to AutoMLAt the end
-
Page 559 and 560:
An introduction to AutoMLUsing Clou
-
Page 561 and 562:
An introduction to AutoMLOnce the d
-
Page 563 and 564:
An introduction to AutoMLAt the end
-
Page 565 and 566:
An introduction to AutoMLAs the nex
-
Page 567 and 568:
An introduction to AutoMLOnce the m
-
Page 569 and 570:
An introduction to AutoMLFigure 65:
-
Page 571 and 572:
An introduction to AutoMLOnce the m
-
Page 573 and 574:
An introduction to AutoMLWe can als
-
Page 575 and 576:
An introduction to AutoMLThe most e
-
Page 577 and 578:
An introduction to AutoMLReferences
-
Page 579 and 580:
The Math Behind Deep LearningSome m
-
Page 581 and 582:
The Math Behind Deep LearningSuppos
-
Page 583 and 584:
The Math Behind Deep LearningNote t
-
Page 585 and 586:
The Math Behind Deep LearningTheref
-
Page 587 and 588:
The Math Behind Deep LearningThe ea
-
Page 589 and 590:
The Math Behind Deep LearningThe re
-
Page 591 and 592:
The Math Behind Deep LearningCase 2
-
Page 593 and 594:
The Math Behind Deep LearningIn thi
-
Page 595 and 596:
The Math Behind Deep LearningHere,
-
Page 597 and 598:
The Math Behind Deep Learning(Note
-
Page 599 and 600:
The Math Behind Deep LearningIn man
-
Page 601 and 602:
The Math Behind Deep LearningIf we
-
Page 603 and 604:
The Math Behind Deep LearningChapte
-
Page 605 and 606:
The Math Behind Deep LearningThis c
-
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
-
Page 619 and 620:
Tensor Processing UnitEpoch 10/1060
-
Page 621 and 622:
Tensor Processing UnitFigure 11: Go
-
Page 623 and 624:
Tensor Processing UnitThen the usag
-
Page 626 and 627:
Other Books YouMay EnjoyIf you enjo
-
Page 628 and 629:
Other Books You May EnjoyAI Crash C
-
Page 630:
Other Books You May EnjoyLeave a re
-
Page 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 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
Change language