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Chapter 10This chapter, along with chapters 6 and 9, covered models that were trainedusing unsupervised learning. In the next chapter, we move on to another learningparadigm: reinforcement learning.References1. https://arxiv.org/abs/1404.11002. http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf3. http://mplab.ucsd.edu/tutorials/pca.pdf4. http://projector.tensorflow.org/5. http://web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.htm6. https://www.deeplearningbook.org7. Kanungo, Tapas, et al. An Efficient k-Means Clustering Algorithm: Analysisand Implementation. IEEE transactions on pattern analysis and machineintelligence 24.7 (2002): 881-892.8. Ortega, Joaquín Pérez, et al. Research issues on K-means Algorithm: AnExperimental Trial Using Matlab. CEUR Workshop Proceedings: SemanticWeb and New Technologies.9. A Tutorial on Clustering Algorithms, http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html.10. Chen, Ke. On Coresets for k-Median and k-Means Clustering in Metric andEuclidean Spaces and Their Applications. SIAM Journal on Computing 39.3(2009): 923-947.11. https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_ data_set.12. Least Squares Quantization in PCM, Stuart P. Lloyd (1882), http://www-evasion.imag.fr/people/Franck.Hetroy/Teaching/ProjetsImage/2007/Bib/lloyd-1982.pdf13. Dunn, J. C. (1973-01-01). A Fuzzy Relative of the ISODATA Process and Its Usein Detecting Compact Well-Separated Clusters. Journal of Cybernetics. 3(3):32–57.14. Bezdek, James C. (1981). Pattern Recognition with Fuzzy Objective FunctionAlgorithms.15. Peters, Georg, Fernando Crespo, Pawan Lingras, and Richard Weber. Softclustering–Fuzzy and rough approaches and their extensions and derivatives.International Journal of Approximate Reasoning 54, no. 2 (2013): 307-322.[ 405 ]
Unsupervised Learning16. Sculley, David. Web-scale k-means clustering. In Proceedings of the 19thinternational conference on World wide web, pp. 1177-1178. ACM, 2010.17. Smolensky, Paul. Information Processing in Dynamical Systems: Foundationsof Harmony Theory. No. CU-CS-321-86. COLORADO UNIV AT BOULDERDEPT OF COMPUTER SCIENCE, 1986.18. Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. RestrictedBoltzmann Machines for Collaborative Filtering. Proceedings of the 24thinternational conference on Machine learning. ACM, 2007.19. Hinton, Geoffrey. A Practical Guide to Training Restricted BoltzmannMachines. Momentum 9.1 (2010): 926.20. http://deeplearning.net/tutorial/rbm.html[ 406 ]
- 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
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- 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 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
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- Page 470 and 471: Chapter 11A neural network is used
- Page 472: Chapter 1111. Details regarding ins
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Unsupervised Learning
16. Sculley, David. Web-scale k-means clustering. In Proceedings of the 19th
international conference on World wide web, pp. 1177-1178. ACM, 2010.
17. Smolensky, Paul. Information Processing in Dynamical Systems: Foundations
of Harmony Theory. No. CU-CS-321-86. COLORADO UNIV AT BOULDER
DEPT OF COMPUTER SCIENCE, 1986.
18. Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. Restricted
Boltzmann Machines for Collaborative Filtering. Proceedings of the 24th
international conference on Machine learning. ACM, 2007.
19. Hinton, Geoffrey. A Practical Guide to Training Restricted Boltzmann
Machines. Momentum 9.1 (2010): 926.
20. http://deeplearning.net/tutorial/rbm.html
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