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Graph Neural Networks in Action Video Edition

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Type: Tutorials
Language: English
Category: Other
Size: 1.8 GB
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Files:
  1. Get Bonus Downloads Here.url 180 bytes
  2. 001. Part 1. First steps.mp4 3.0 MB
  3. 002. Chapter 1. Discovering graph neural networks.mp4 30.8 MB
  4. 003. Chapter 1. Graph-based learning.mp4 57.8 MB
  5. 004. Chapter 1. GNN applications Case studies.mp4 21.4 MB
  6. 005. Chapter 1. When to use a GNN.mp4 25.9 MB
  7. 006. Chapter 1. Understanding how GNNs operate.mp4 23.7 MB
  8. 007. Chapter 1. Summary.mp4 6.6 MB
  9. 008. Chapter 2. Graph embeddings.mp4 72.2 MB
  10. 009. Chapter 2. Creating embeddings with a GNN.mp4 34.0 MB
  11. 010. Chapter 2. Using node embeddings.mp4 50.3 MB
  12. 011. Chapter 2. Under the Hood.mp4 62.4 MB
  13. 012. Chapter 2. Summary.mp4 6.4 MB
  14. 013. Part 2. Graph neural networks.mp4 3.0 MB
  15. 014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4 84.0 MB
  16. 015. Chapter 3. Aggregation methods.mp4 51.3 MB
  17. 016. Chapter 3. Further optimizations and refinements.mp4 39.9 MB
  18. 017. Chapter 3. Under the hood.mp4 64.8 MB
  19. 018. Chapter 3. Amazon Products dataset.mp4 17.1 MB
  20. 019. Chapter 3. Summary.mp4 7.9 MB
  21. 020. Chapter 4. Graph attention networks.mp4 14.7 MB
  22. 021. Chapter 4. Exploring the review spam dataset.mp4 48.4 MB
  23. 022. Chapter 4. Training baseline models.mp4 25.2 MB
  24. 023. Chapter 4. Training GAT models.mp4 37.7 MB
  25. 024. Chapter 4. Under the hood.mp4 32.0 MB
  26. 025. Chapter 4. Summary.mp4 5.9 MB
  27. 026. Chapter 5. Graph autoencoders.mp4 32.5 MB
  28. 027. Chapter 5. Graph autoencoders for link prediction.mp4 39.2 MB
  29. 028. Chapter 5. Variational graph autoencoders.mp4 34.3 MB
  30. 029. Chapter 5. Generating graphs using GNNs.mp4 48.8 MB
  31. 030. Chapter 5. Under the hood.mp4 32.3 MB
  32. 031. Chapter 5. Summary.mp4 6.3 MB
  33. 032. Part 3. Advanced topics.mp4 4.4 MB
  34. 033. Chapter 6. Dynamic graphs Spatiotemporal GNNs.mp4 26.1 MB
  35. 034. Chapter 6. Problem definition Pose estimation.mp4 38.6 MB
  36. 035. Chapter 6. Dynamic graph neural networks.mp4 28.6 MB
  37. 036. Chapter 6. Neural relational inference.mp4 82.7 MB
  38. 037. Chapter 6. Under the hood.mp4 32.6 MB
  39. 038. Chapter 6. Summary.mp4 4.7 MB
  40. 039. Chapter 7. Learning and inference at scale.mp4 25.2 MB
  41. 040. Chapter 7. Framing problems of scale.mp4 41.9 MB
  42. 041. Chapter 7. Techniques for tackling problems of scale.mp4 16.6 MB
  43. 042. Chapter 7. Choice of hardware configuration.mp4 31.0 MB
  44. 043. Chapter 7. Choice of data representation.mp4 17.1 MB
  45. 044. Chapter 7. Choice of GNN algorithm.mp4 24.3 MB
  46. 045. Chapter 7. Batching using a sampling method.mp4 26.2 MB
  47. 046. Chapter 7. Parallel and distributed processing.mp4 28.7 MB
  48. 047. Chapter 7. Training with remote storage.mp4 25.7 MB
  49. 048. Chapter 7. Graph coarsening.mp4 26.9 MB
  50. 049. Chapter 7. Summary.mp4 5.6 MB
  51. 050. Chapter 8. Considerations for GNN projects.mp4 23.8 MB
  52. 051. Chapter 8. Designing graph models.mp4 57.1 MB
  53. 052. Chapter 8. Data pipeline example.mp4 76.4 MB
  54. 053. Chapter 8. Where to find graph data.mp4 12.9 MB
  55. 054. Chapter 8. Summary.mp4 12.3 MB
  56. 055. appendix A. Discovering graphs.mp4 53.3 MB
  57. 056. appendix A. Graph representations.mp4 68.5 MB
  58. 057. appendix A. Graph systems.mp4 14.6 MB
  59. 058. appendix A. Graph algorithms.mp4 11.7 MB
  60. 059. appendix A. How to read GNN literature.mp4 9.7 MB
  61. Bonus Resources.txt 70 bytes

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