| 文件 | 大小 |
|---|
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| 01-Part 1 Introduction.mp4 | 21.31 MB |
| 02-Chapter 1 Machine learning and graphs - An introduction.mp4 | 69.70 MB |
| 03-Chapter 1 Business understanding.mp4 | 39.10 MB |
| 04-Chapter 1 Machine learning challenges.mp4 | 49.84 MB |
| 05-Chapter 1 Performance.mp4 | 53.14 MB |
| 06-Chapter 1 Graphs.mp4 | 33.32 MB |
| 07-Chapter 1 Graphs as models of networks.mp4 | 71.29 MB |
| 08-Chapter 1 The role of graphs in machine learning.mp4 | 73.83 MB |
| 09-Chapter 2 Graph data engineering.mp4 | 82.01 MB |
| 10-Chapter 2 Velocity.mp4 | 50.81 MB |
| 11-Chapter 2 Graphs in the big data platform.mp4 | 49.38 MB |
| 12-Chapter 2 Graphs are valuable for big data.mp4 | 43.18 MB |
| 13-Chapter 2 Graphs are valuable for master data management.mp4 | 75.67 MB |
| 14-Chapter 2 Graph databases.mp4 | 52.12 MB |
| 15-Chapter 2 Sharding.mp4 | 70.52 MB |
| 16-Chapter 2 Native vs. non-native graph databases.mp4 | 79.92 MB |
| 17-Chapter 2 Label property graphs.mp4 | 37.69 MB |
| 18-Chapter 3 Graphs in machine learning applications.mp4 | 65.87 MB |
| 19-Chapter 3 Managing data sources.mp4 | 77.36 MB |
| 20-Chapter 3 Detect a fraud.mp4 | 52.33 MB |
| 21-Chapter 3 Recommend items.mp4 | 63.56 MB |
| 22-Chapter 3 Algorithms.mp4 | 48.19 MB |
| 23-Chapter 3 Find keywords in a document.mp4 | 53.60 MB |
| 24-Chapter 3 Storing and accessing machine learning models.mp4 | 31.38 MB |
| 25-Chapter 3 Monitoring a subject.mp4 | 55.54 MB |
| 26-Chapter 3 Visualization.mp4 | 37.90 MB |
| 27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4 | 52.78 MB |
| 28-Part 2 Recommendations.mp4 | 148.91 MB |
| 29-Chapter 4 Content-based recommendations.mp4 | 67.48 MB |
| 30-Chapter 4 Representing item features.mp4 | 63.39 MB |
| 31-Chapter 4 Representing item features.mp4 | 60.23 MB |
| 32-Chapter 4 User modeling.mp4 | 33.57 MB |
| 33-Chapter 4 Providing recommendations.mp4 | 56.79 MB |
| 34-Chapter 4 Providing recommendations.mp4 | 66.34 MB |
| 35-Chapter 4 Providing recommendations.mp4 | 72.60 MB |
| 36-Chapter 5 Collaborative filtering.mp4 | 98.97 MB |
| 37-Chapter 5 Collaborative filtering recommendations.mp4 | 92.75 MB |
| 38-Chapter 5 Computing the nearest neighbor network.mp4 | 69.04 MB |
| 39-Chapter 5 Computing the nearest neighbor network.mp4 | 47.87 MB |
| 40-Chapter 5 Providing recommendations.mp4 | 53.76 MB |
| 41-Chapter 5 Dealing with the cold-start problem.mp4 | 40.18 MB |
| 42-Chapter 6 Session-based recommendations.mp4 | 61.79 MB |
| 43-Chapter 6 The events chain and the session graph.mp4 | 68.35 MB |
| 44-Chapter 6 Providing recommendations.mp4 | 81.30 MB |
| 45-Chapter 6 Session-based k-NN.mp4 | 63.60 MB |
| 46-Chapter 7 Context-aware and hybrid recommendations.mp4 | 67.60 MB |
| 47-Chapter 7 Representing contextual information.mp4 | 42.88 MB |
| 48-Chapter 7 Providing recommendations.mp4 | 85.94 MB |
| 49-Chapter 7 Providing recommendations.mp4 | 85.12 MB |
| 50-Chapter 7 Advantages of the graph approach.mp4 | 51.81 MB |
| 51-Chapter 7 Providing recommendations.mp4 | 38.56 MB |
| 52-Part 3 Fighting fraud.mp4 | 34.38 MB |
| 53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4 | 48.49 MB |
| 54-Chapter 8 Fraud prevention and detection.mp4 | 45.24 MB |
| 55-Chapter 8 The role of graphs in fighting fraud.mp4 | 47.11 MB |
| 56-Chapter 8 Warm-up - Basic approaches.mp4 | 55.49 MB |
| 57-Chapter 8 Identifying a fraud ring.mp4 | 46.91 MB |
| 58-Chapter 9 Proximity-based algorithms.mp4 | 68.99 MB |
| 59-Chapter 9 Distance-based approach.mp4 | 49.88 MB |
| 60-Chapter 9 Creating the k-nearest neighbors graph.mp4 | 52.11 MB |
| 61-Chapter 9 Identifying fraudulent transactions.mp4 | 82.58 MB |
| 62-Chapter 9 Identifying fraudulent transactions.mp4 | 32.51 MB |
| 63-Chapter 10 Social network analysis against fraud.mp4 | 79.64 MB |
| 64-Chapter 10 Social network analysis concepts.mp4 | 46.44 MB |
| 65-Chapter 10 Score-based methods.mp4 | 32.24 MB |
| 66-Chapter 10 Neighborhood metrics.mp4 | 45.87 MB |
| 67-Chapter 10 Centrality metrics.mp4 | 61.27 MB |
| 68-Chapter 10 Collective inference algorithms.mp4 | 50.60 MB |
| 69-Chapter 10 Cluster-based methods.mp4 | 65.65 MB |
| 70-Part 4 Taming text with graphs.mp4 | 24.45 MB |
| 71-Chapter 11 Graph-based natural language processing.mp4 | 57.65 MB |
| 72-Chapter 11 A basic approach - Store and access sequence of words.mp4 | 53.54 MB |
| 73-Chapter 11 NLP and graphs.mp4 | 80.48 MB |
| 74-Chapter 11 NLP and graphs.mp4 | 70.02 MB |
| 75-Chapter 12 Knowledge graphs.mp4 | 60.09 MB |
| 76-Chapter 12 Knowledge graph building - Entities.mp4 | 94.08 MB |
| 77-Chapter 12 Knowledge graph building - Relationships.mp4 | 68.65 MB |
| 78-Chapter 12 Semantic networks.mp4 | 38.36 MB |
| 79-Chapter 12 Unsupervised keyword extraction.mp4 | 52.87 MB |
| 80-Chapter 12 Unsupervised keyword extraction.mp4 | 35.89 MB |
| 81-Chapter 12 Keyword co-occurrence graph.mp4 | 50.57 MB |
| 82-Appendix A. Machine learning algorithms taxonomy.mp4 | 65.16 MB |
| 83-Appendix C Graphs for processing patterns and workflows.mp4 | 43.83 MB |
| 84-Appendix C Graphs for defining complex processing workflows.mp4 | 50.43 MB |
| 85-Appendix D. Representing graphs.mp4 | 40.52 MB |
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