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[] MANNING - Graph-Powered Machine Learning [Video Edition]

  • 收录时间:2022-06-18 02:26:44
  • 文件大小:5GB
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文件列表

  1. 28-Part 2 Recommendations.mp4 149MB
  2. 36-Chapter 5 Collaborative filtering.mp4 99MB
  3. 76-Chapter 12 Knowledge graph building - Entities.mp4 94MB
  4. 37-Chapter 5 Collaborative filtering recommendations.mp4 93MB
  5. 48-Chapter 7 Providing recommendations.mp4 86MB
  6. 49-Chapter 7 Providing recommendations.mp4 85MB
  7. 61-Chapter 9 Identifying fraudulent transactions.mp4 83MB
  8. 09-Chapter 2 Graph data engineering.mp4 82MB
  9. 44-Chapter 6 Providing recommendations.mp4 81MB
  10. 73-Chapter 11 NLP and graphs.mp4 80MB
  11. 16-Chapter 2 Native vs. non-native graph databases.mp4 80MB
  12. 63-Chapter 10 Social network analysis against fraud.mp4 80MB
  13. 19-Chapter 3 Managing data sources.mp4 77MB
  14. 13-Chapter 2 Graphs are valuable for master data management.mp4 76MB
  15. 08-Chapter 1 The role of graphs in machine learning.mp4 74MB
  16. 35-Chapter 4 Providing recommendations.mp4 73MB
  17. 07-Chapter 1 Graphs as models of networks.mp4 71MB
  18. 15-Chapter 2 Sharding.mp4 71MB
  19. 74-Chapter 11 NLP and graphs.mp4 70MB
  20. 02-Chapter 1 Machine learning and graphs - An introduction.mp4 70MB
  21. 38-Chapter 5 Computing the nearest neighbor network.mp4 69MB
  22. 58-Chapter 9 Proximity-based algorithms.mp4 69MB
  23. 77-Chapter 12 Knowledge graph building - Relationships.mp4 69MB
  24. 43-Chapter 6 The events chain and the session graph.mp4 68MB
  25. 46-Chapter 7 Context-aware and hybrid recommendations.mp4 68MB
  26. 29-Chapter 4 Content-based recommendations.mp4 67MB
  27. 34-Chapter 4 Providing recommendations.mp4 66MB
  28. 18-Chapter 3 Graphs in machine learning applications.mp4 66MB
  29. 69-Chapter 10 Cluster-based methods.mp4 66MB
  30. 82-Appendix A. Machine learning algorithms taxonomy.mp4 65MB
  31. 45-Chapter 6 Session-based k-NN.mp4 64MB
  32. 21-Chapter 3 Recommend items.mp4 64MB
  33. 30-Chapter 4 Representing item features.mp4 63MB
  34. 42-Chapter 6 Session-based recommendations.mp4 62MB
  35. 67-Chapter 10 Centrality metrics.mp4 61MB
  36. 31-Chapter 4 Representing item features.mp4 60MB
  37. 75-Chapter 12 Knowledge graphs.mp4 60MB
  38. 71-Chapter 11 Graph-based natural language processing.mp4 58MB
  39. 33-Chapter 4 Providing recommendations.mp4 57MB
  40. 25-Chapter 3 Monitoring a subject.mp4 56MB
  41. 56-Chapter 8 Warm-up - Basic approaches.mp4 55MB
  42. 40-Chapter 5 Providing recommendations.mp4 54MB
  43. 23-Chapter 3 Find keywords in a document.mp4 54MB
  44. 72-Chapter 11 A basic approach - Store and access sequence of words.mp4 54MB
  45. 05-Chapter 1 Performance.mp4 53MB
  46. 79-Chapter 12 Unsupervised keyword extraction.mp4 53MB
  47. 27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4 53MB
  48. 20-Chapter 3 Detect a fraud.mp4 52MB
  49. 14-Chapter 2 Graph databases.mp4 52MB
  50. 60-Chapter 9 Creating the k-nearest neighbors graph.mp4 52MB
  51. 50-Chapter 7 Advantages of the graph approach.mp4 52MB
  52. 10-Chapter 2 Velocity.mp4 51MB
  53. 68-Chapter 10 Collective inference algorithms.mp4 51MB
  54. 81-Chapter 12 Keyword co-occurrence graph.mp4 51MB
  55. 84-Appendix C Graphs for defining complex processing workflows.mp4 50MB
  56. 59-Chapter 9 Distance-based approach.mp4 50MB
  57. 04-Chapter 1 Machine learning challenges.mp4 50MB
  58. 11-Chapter 2 Graphs in the big data platform.mp4 49MB
  59. 53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4 48MB
  60. 22-Chapter 3 Algorithms.mp4 48MB
  61. 39-Chapter 5 Computing the nearest neighbor network.mp4 48MB
  62. 55-Chapter 8 The role of graphs in fighting fraud.mp4 47MB
  63. 57-Chapter 8 Identifying a fraud ring.mp4 47MB
  64. 64-Chapter 10 Social network analysis concepts.mp4 46MB
  65. 66-Chapter 10 Neighborhood metrics.mp4 46MB
  66. 54-Chapter 8 Fraud prevention and detection.mp4 45MB
  67. 83-Appendix C Graphs for processing patterns and workflows.mp4 44MB
  68. 12-Chapter 2 Graphs are valuable for big data.mp4 43MB
  69. 47-Chapter 7 Representing contextual information.mp4 43MB
  70. 85-Appendix D. Representing graphs.mp4 41MB
  71. 41-Chapter 5 Dealing with the cold-start problem.mp4 40MB
  72. 03-Chapter 1 Business understanding.mp4 39MB
  73. 51-Chapter 7 Providing recommendations.mp4 39MB
  74. 78-Chapter 12 Semantic networks.mp4 38MB
  75. 26-Chapter 3 Visualization.mp4 38MB
  76. 17-Chapter 2 Label property graphs.mp4 38MB
  77. 80-Chapter 12 Unsupervised keyword extraction.mp4 36MB
  78. 52-Part 3 Fighting fraud.mp4 34MB
  79. 32-Chapter 4 User modeling.mp4 34MB
  80. 06-Chapter 1 Graphs.mp4 33MB
  81. 62-Chapter 9 Identifying fraudulent transactions.mp4 33MB
  82. 65-Chapter 10 Score-based methods.mp4 32MB
  83. 24-Chapter 3 Storing and accessing machine learning models.mp4 31MB
  84. 70-Part 4 Taming text with graphs.mp4 24MB
  85. 01-Part 1 Introduction.mp4 21MB
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