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0
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94.7 KB
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01-Part 1 Introduction.mp4
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MP4
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21.3 MB
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1
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35.1 KB
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02-Chapter 1 Machine learning and graphs - An introduction.mp4
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MP4
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69.7 MB
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2
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938.6 KB
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3
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259.2 KB
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03-Chapter 1 Business understanding.mp4
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MP4
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39.1 MB
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4
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66.1 KB
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04-Chapter 1 Machine learning challenges.mp4
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MP4
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49.8 MB
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05-Chapter 1 Performance.mp4
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MP4
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53.1 MB
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5
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896.8 KB
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06-Chapter 1 Graphs.mp4
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33.3 MB
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6
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427.3 KB
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07-Chapter 1 Graphs as models of networks.mp4
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MP4
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71.3 MB
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08-Chapter 1 The role of graphs in machine learning.mp4
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MP4
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73.8 MB
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8
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714.2 KB
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09-Chapter 2 Graph data engineering.mp4
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MP4
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82 MB
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9
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529.1 KB
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10
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77.3 KB
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10-Chapter 2 Velocity.mp4
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MP4
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50.8 MB
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11
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367 KB
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11-Chapter 2 Graphs in the big data platform.mp4
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MP4
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49.4 MB
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12-Chapter 2 Graphs are valuable for big data.mp4
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MP4
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43.2 MB
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12
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657.5 KB
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13
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338.2 KB
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13-Chapter 2 Graphs are valuable for master data management.mp4
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MP4
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75.7 MB
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14-Chapter 2 Graph databases.mp4
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MP4
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52.1 MB
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14
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177.7 KB
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15
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408.9 KB
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15-Chapter 2 Sharding.mp4
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MP4
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70.5 MB
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16-Chapter 2 Native vs. non-native graph databases.mp4
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MP4
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79.9 MB
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16
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723.1 KB
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17
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490.3 KB
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17-Chapter 2 Label property graphs.mp4
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MP4
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37.7 MB
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18-Chapter 3 Graphs in machine learning applications.mp4
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MP4
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65.9 MB
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19-Chapter 3 Managing data sources.mp4
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MP4
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77.4 MB
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19
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309.6 KB
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20-Chapter 3 Detect a fraud.mp4
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MP4
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52.3 MB
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20
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978 KB
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21
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13 KB
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21-Chapter 3 Recommend items.mp4
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MP4
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63.6 MB
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22-Chapter 3 Algorithms.mp4
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MP4
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48.2 MB
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22
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360.7 KB
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23
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669.9 KB
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23-Chapter 3 Find keywords in a document.mp4
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MP4
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53.6 MB
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24
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411.1 KB
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24-Chapter 3 Storing and accessing machine learning models.mp4
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MP4
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31.4 MB
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25
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533 KB
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25-Chapter 3 Monitoring a subject.mp4
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MP4
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55.5 MB
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26-Chapter 3 Visualization.mp4
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MP4
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37.9 MB
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26
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674.9 KB
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27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4
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MP4
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52.8 MB
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27
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131.1 KB
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28
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356.1 KB
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28-Part 2 Recommendations.mp4
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MP4
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148.9 MB
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29-Chapter 4 Content-based recommendations.mp4
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MP4
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67.5 MB
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29
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864.7 KB
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30
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408.8 KB
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30-Chapter 4 Representing item features.mp4
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MP4
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63.4 MB
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31
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452.1 KB
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31-Chapter 4 Representing item features.mp4
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MP4
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60.2 MB
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32
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628.5 KB
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32-Chapter 4 User modeling.mp4
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MP4
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33.6 MB
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33
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215.1 KB
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33-Chapter 4 Providing recommendations.mp4
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MP4
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56.8 MB
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34
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744.9 KB
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34-Chapter 4 Providing recommendations.mp4
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MP4
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66.3 MB
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35-Chapter 4 Providing recommendations.mp4
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MP4
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72.6 MB
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35
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787.2 KB
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36
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928.4 KB
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36-Chapter 5 Collaborative filtering.mp4
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MP4
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99 MB
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37
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357.2 KB
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37-Chapter 5 Collaborative filtering recommendations.mp4
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MP4
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92.7 MB
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38-Chapter 5 Computing the nearest neighbor network.mp4
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MP4
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69 MB
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38
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214.8 KB
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39-Chapter 5 Computing the nearest neighbor network.mp4
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MP4
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47.9 MB
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39
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470.6 KB
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40-Chapter 5 Providing recommendations.mp4
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MP4
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53.8 MB
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40
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522.4 KB
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41
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249.9 KB
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41-Chapter 5 Dealing with the cold-start problem.mp4
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MP4
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40.2 MB
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42-Chapter 6 Session-based recommendations.mp4
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MP4
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61.8 MB
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42
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410.1 KB
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43
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475.4 KB
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43-Chapter 6 The events chain and the session graph.mp4
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MP4
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68.3 MB
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44-Chapter 6 Providing recommendations.mp4
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MP4
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81.3 MB
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44
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876.3 KB
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45
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132.8 KB
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45-Chapter 6 Session-based k-NN.mp4
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MP4
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63.6 MB
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46-Chapter 7 Context-aware and hybrid recommendations.mp4
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MP4
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67.6 MB
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46
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225.4 KB
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47
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685.9 KB
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47-Chapter 7 Representing contextual information.mp4
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MP4
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42.9 MB
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48-Chapter 7 Providing recommendations.mp4
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MP4
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85.9 MB
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48
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896.1 KB
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49
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907.8 KB
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49-Chapter 7 Providing recommendations.mp4
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MP4
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85.1 MB
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50-Chapter 7 Advantages of the graph approach.mp4
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MP4
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51.8 MB
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50
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193.1 KB
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51
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193.7 KB
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51-Chapter 7 Providing recommendations.mp4
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MP4
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38.6 MB
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52
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412.6 KB
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52-Part 3 Fighting fraud.mp4
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MP4
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34.4 MB
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53
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444.3 KB
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53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4
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MP4
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48.5 MB
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54
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582 KB
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54-Chapter 8 Fraud prevention and detection.mp4
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MP4
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45.2 MB
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55-Chapter 8 The role of graphs in fighting fraud.mp4
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MP4
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47.1 MB
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55
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121.3 KB
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56
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165.3 KB
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56-Chapter 8 Warm-up - Basic approaches.mp4
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MP4
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55.5 MB
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57-Chapter 8 Identifying a fraud ring.mp4
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MP4
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46.9 MB
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57
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633.8 KB
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58
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525.1 KB
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58-Chapter 9 Proximity-based algorithms.mp4
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MP4
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69 MB
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59
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832.9 KB
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59-Chapter 9 Distance-based approach.mp4
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MP4
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49.9 MB
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60-Chapter 9 Creating the k-nearest neighbors graph.mp4
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MP4
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52.1 MB
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60
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129.6 KB
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61-Chapter 9 Identifying fraudulent transactions.mp4
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MP4
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82.6 MB
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61
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913.6 KB
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62-Chapter 9 Identifying fraudulent transactions.mp4
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MP4
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32.5 MB
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62
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93.3 KB
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63
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572.9 KB
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63-Chapter 10 Social network analysis against fraud.mp4
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MP4
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79.6 MB
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64-Chapter 10 Social network analysis concepts.mp4
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MP4
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46.4 MB
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64
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131.4 KB
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65
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777.2 KB
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65-Chapter 10 Score-based methods.mp4
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MP4
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32.2 MB
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66
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169.7 KB
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66-Chapter 10 Neighborhood metrics.mp4
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MP4
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45.9 MB
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67-Chapter 10 Centrality metrics.mp4
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MP4
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61.3 MB
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67
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835.6 KB
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68
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122.6 KB
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68-Chapter 10 Collective inference algorithms.mp4
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MP4
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50.6 MB
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69-Chapter 10 Cluster-based methods.mp4
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MP4
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65.7 MB
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69
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496 KB
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70
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834.6 KB
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70-Part 4 Taming text with graphs.mp4
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MP4
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24.5 MB
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71
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921.5 KB
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71-Chapter 11 Graph-based natural language processing.mp4
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MP4
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57.7 MB
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72-Chapter 11 A basic approach - Store and access sequence of words.mp4
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MP4
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53.5 MB
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72
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450.3 KB
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73
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652.2 KB
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73-Chapter 11 NLP and graphs.mp4
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MP4
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80.5 MB
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74
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100.1 KB
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74-Chapter 11 NLP and graphs.mp4
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MP4
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70 MB
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75
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317.3 KB
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75-Chapter 12 Knowledge graphs.mp4
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MP4
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60.1 MB
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76
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113 KB
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76-Chapter 12 Knowledge graph building - Entities.mp4
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MP4
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94.1 MB
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77-Chapter 12 Knowledge graph building - Relationships.mp4
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MP4
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68.6 MB
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77
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630.5 KB
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78-Chapter 12 Semantic networks.mp4
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MP4
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38.4 MB
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78
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438.7 KB
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79-Chapter 12 Unsupervised keyword extraction.mp4
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MP4
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52.9 MB
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79
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700.4 KB
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80-Chapter 12 Unsupervised keyword extraction.mp4
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MP4
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35.9 MB
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80
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503.4 KB
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81-Chapter 12 Keyword co-occurrence graph.mp4
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MP4
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50.6 MB
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81
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777.3 KB
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82
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637.4 KB
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82-Appendix A. Machine learning algorithms taxonomy.mp4
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MP4
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65.2 MB
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83-Appendix C Graphs for processing patterns and workflows.mp4
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MP4
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43.8 MB
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84-Appendix C Graphs for defining complex processing workflows.mp4
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MP4
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50.4 MB
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85-Appendix D. Representing graphs.mp4
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MP4
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40.5 MB
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TutsNode.com.txt
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TXT
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102.4 B
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[TGx]Downloaded from torrentgalaxy.to .txt
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TXT
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614.4 B
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83
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561.2 KB
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