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Название Graph-Powered Machine Learning, Video Edition
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Размер 4.87Гб

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[TGx]Downloaded from torrentgalaxy.to .txt 585б
0 94.66Кб
01-Part 1 Introduction.mp4 21.31Мб
02-Chapter 1 Machine learning and graphs - An introduction.mp4 69.70Мб
03-Chapter 1 Business understanding.mp4 39.10Мб
04-Chapter 1 Machine learning challenges.mp4 49.84Мб
05-Chapter 1 Performance.mp4 53.14Мб
06-Chapter 1 Graphs.mp4 33.32Мб
07-Chapter 1 Graphs as models of networks.mp4 71.29Мб
08-Chapter 1 The role of graphs in machine learning.mp4 73.83Мб
09-Chapter 2 Graph data engineering.mp4 82.01Мб
1 35.06Кб
10 77.31Кб
10-Chapter 2 Velocity.mp4 50.81Мб
11 366.98Кб
11-Chapter 2 Graphs in the big data platform.mp4 49.38Мб
12 657.48Кб
12-Chapter 2 Graphs are valuable for big data.mp4 43.18Мб
13 338.18Кб
13-Chapter 2 Graphs are valuable for master data management.mp4 75.67Мб
14 177.73Кб
14-Chapter 2 Graph databases.mp4 52.12Мб
15 408.88Кб
15-Chapter 2 Sharding.mp4 70.52Мб
16 723.06Кб
16-Chapter 2 Native vs. non-native graph databases.mp4 79.92Мб
17 490.25Кб
17-Chapter 2 Label property graphs.mp4 37.69Мб
18 1001.98Кб
18-Chapter 3 Graphs in machine learning applications.mp4 65.87Мб
19 309.61Кб
19-Chapter 3 Managing data sources.mp4 77.36Мб
2 938.57Кб
20 978.03Кб
20-Chapter 3 Detect a fraud.mp4 52.33Мб
21 13.05Кб
21-Chapter 3 Recommend items.mp4 63.56Мб
22 360.68Кб
22-Chapter 3 Algorithms.mp4 48.19Мб
23 669.92Кб
23-Chapter 3 Find keywords in a document.mp4 53.60Мб
24 411.05Кб
24-Chapter 3 Storing and accessing machine learning models.mp4 31.38Мб
25 532.97Кб
25-Chapter 3 Monitoring a subject.mp4 55.54Мб
26 674.85Кб
26-Chapter 3 Visualization.mp4 37.90Мб
27 131.10Кб
27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4 52.78Мб
28 356.11Кб
28-Part 2 Recommendations.mp4 148.91Мб
29 864.74Кб
29-Chapter 4 Content-based recommendations.mp4 67.48Мб
3 259.17Кб
30 408.84Кб
30-Chapter 4 Representing item features.mp4 63.39Мб
31 452.06Кб
31-Chapter 4 Representing item features.mp4 60.23Мб
32 628.45Кб
32-Chapter 4 User modeling.mp4 33.57Мб
33 215.13Кб
33-Chapter 4 Providing recommendations.mp4 56.79Мб
34 744.89Кб
34-Chapter 4 Providing recommendations.mp4 66.34Мб
35 787.23Кб
35-Chapter 4 Providing recommendations.mp4 72.60Мб
36 928.38Кб
36-Chapter 5 Collaborative filtering.mp4 98.97Мб
37 357.15Кб
37-Chapter 5 Collaborative filtering recommendations.mp4 92.75Мб
38 214.78Кб
38-Chapter 5 Computing the nearest neighbor network.mp4 69.04Мб
39 470.62Кб
39-Chapter 5 Computing the nearest neighbor network.mp4 47.87Мб
4 66.10Кб
40 522.43Кб
40-Chapter 5 Providing recommendations.mp4 53.76Мб
41 249.87Кб
41-Chapter 5 Dealing with the cold-start problem.mp4 40.18Мб
42 410.07Кб
42-Chapter 6 Session-based recommendations.mp4 61.79Мб
43 475.41Кб
43-Chapter 6 The events chain and the session graph.mp4 68.35Мб
44 876.35Кб
44-Chapter 6 Providing recommendations.mp4 81.30Мб
45 132.76Кб
45-Chapter 6 Session-based k-NN.mp4 63.60Мб
46 225.45Кб
46-Chapter 7 Context-aware and hybrid recommendations.mp4 67.60Мб
47 685.92Кб
47-Chapter 7 Representing contextual information.mp4 42.88Мб
48 896.13Кб
48-Chapter 7 Providing recommendations.mp4 85.94Мб
49 907.85Кб
49-Chapter 7 Providing recommendations.mp4 85.12Мб
5 896.77Кб
50 193.11Кб
50-Chapter 7 Advantages of the graph approach.mp4 51.81Мб
51 193.74Кб
51-Chapter 7 Providing recommendations.mp4 38.56Мб
52 412.63Кб
52-Part 3 Fighting fraud.mp4 34.38Мб
53 444.28Кб
53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4 48.49Мб
54 582.00Кб
54-Chapter 8 Fraud prevention and detection.mp4 45.24Мб
55 121.27Кб
55-Chapter 8 The role of graphs in fighting fraud.mp4 47.11Мб
56 165.26Кб
56-Chapter 8 Warm-up - Basic approaches.mp4 55.49Мб
57 633.81Кб
57-Chapter 8 Identifying a fraud ring.mp4 46.91Мб
58 525.13Кб
58-Chapter 9 Proximity-based algorithms.mp4 68.99Мб
59 832.93Кб
59-Chapter 9 Distance-based approach.mp4 49.88Мб
6 427.27Кб
60 129.59Кб
60-Chapter 9 Creating the k-nearest neighbors graph.mp4 52.11Мб
61 913.57Кб
61-Chapter 9 Identifying fraudulent transactions.mp4 82.58Мб
62 93.35Кб
62-Chapter 9 Identifying fraudulent transactions.mp4 32.51Мб
63 572.87Кб
63-Chapter 10 Social network analysis against fraud.mp4 79.64Мб
64 131.36Кб
64-Chapter 10 Social network analysis concepts.mp4 46.44Мб
65 777.21Кб
65-Chapter 10 Score-based methods.mp4 32.24Мб
66 169.74Кб
66-Chapter 10 Neighborhood metrics.mp4 45.87Мб
67 835.59Кб
67-Chapter 10 Centrality metrics.mp4 61.27Мб
68 122.61Кб
68-Chapter 10 Collective inference algorithms.mp4 50.60Мб
69 495.97Кб
69-Chapter 10 Cluster-based methods.mp4 65.65Мб
7 1009.59Кб
70 834.56Кб
70-Part 4 Taming text with graphs.mp4 24.45Мб
71 921.52Кб
71-Chapter 11 Graph-based natural language processing.mp4 57.65Мб
72 450.33Кб
72-Chapter 11 A basic approach - Store and access sequence of words.mp4 53.54Мб
73 652.23Кб
73-Chapter 11 NLP and graphs.mp4 80.48Мб
74 100.06Кб
74-Chapter 11 NLP and graphs.mp4 70.02Мб
75 317.31Кб
75-Chapter 12 Knowledge graphs.mp4 60.09Мб
76 113.03Кб
76-Chapter 12 Knowledge graph building - Entities.mp4 94.08Мб
77 630.46Кб
77-Chapter 12 Knowledge graph building - Relationships.mp4 68.65Мб
78 438.75Кб
78-Chapter 12 Semantic networks.mp4 38.36Мб
79 700.45Кб
79-Chapter 12 Unsupervised keyword extraction.mp4 52.87Мб
8 714.25Кб
80 503.42Кб
80-Chapter 12 Unsupervised keyword extraction.mp4 35.89Мб
81 777.29Кб
81-Chapter 12 Keyword co-occurrence graph.mp4 50.57Мб
82 637.37Кб
82-Appendix A. Machine learning algorithms taxonomy.mp4 65.16Мб
83 561.17Кб
83-Appendix C Graphs for processing patterns and workflows.mp4 43.83Мб
84-Appendix C Graphs for defining complex processing workflows.mp4 50.43Мб
85-Appendix D. Representing graphs.mp4 40.52Мб
9 529.07Кб
TutsNode.com.txt 63б
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