Graph Neural Networks in Action, Video Edition

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• 001. Part 1. First steps.mp4 (3.0 MB)
• 002. Chapter 1. Discovering graph neural networks.mp4 (30.8 MB)
• 003. Chapter 1. Graph-based learning.mp4 (57.8 MB)
• 004. Chapter 1. GNN applications Case studies.mp4 (21.4 MB)
• 005. Chapter 1. When to use a GNN.mp4 (25.9 MB)
• 006. Chapter 1. Understanding how GNNs operate.mp4 (23.7 MB)
• 007. Chapter 1. Summary.mp4 (6.6 MB)
• 008. Chapter 2. Graph embeddings.mp4 (72.2 MB)
• 009. Chapter 2. Creating embeddings with a GNN.mp4 (34.0 MB)
• 010. Chapter 2. Using node embeddings.mp4 (50.3 MB)
• 011. Chapter 2. Under the Hood.mp4 (62.4 MB)
• 012. Chapter 2. Summary.mp4 (6.4 MB)
• 013. Part 2. Graph neural networks.mp4 (3.0 MB)
• 014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4 (84.0 MB)
• 015. Chapter 3. Aggregation methods.mp4 (51.3 MB)
• 016. Chapter 3. Further optimizations and refinements.mp4 (39.9 MB)
• 017. Chapter 3. Under the hood.mp4 (64.8 MB)
• 018. Chapter 3. Amazon Products dataset.mp4 (17.1 MB)
• 019. Chapter 3. Summary.mp4 (7.9 MB)
• 020. Chapter 4. Graph attention networks.mp4 (14.7 MB)
• 021. Chapter 4. Exploring the review spam dataset.mp4 (48.4 MB)
• 022. Chapter 4. Training baseline models.mp4 (25.2 MB)
• 023. Chapter 4. Training GAT models.mp4 (37.7 MB)
• 024. Chapter 4. Under the hood.mp4 (32.0 MB)
• 025. Chapter 4. Summary.mp4 (5.9 MB)
• 026. Chapter 5. Graph autoencoders.mp4 (32.5 MB)
• 027. Chapter 5. Graph autoencoders for link prediction.mp4 (39.2 MB)
• 028. Chapter 5. Variational graph autoencoders.mp4 (34.3 MB)
• 029. Chapter 5. Generating graphs using GNNs.mp4 (48.8 MB)
• 030. Chapter 5. Under the hood.mp4 (32.3 MB)
• 031. Chapter 5. Summary.mp4 (6.3 MB)
• 032. Part 3. Advanced topics.mp4 (4.4 MB)
• 033. Chapter 6. Dynamic graphs Spatiotemporal GNNs.mp4 (26.1 MB)
• 034. Chapter 6. Problem definition Pose estimation.mp4 (38.6 MB)
• 035. Chapter 6. Dynamic graph neural networks.mp4 (28.6 MB)
• 036. Chapter 6. Neural relational inference.mp4 (82.7 MB)
• 037. Chapter 6. Under the hood.mp4 (32.6 MB)
• 038. Chapter 6. Summary.mp4 (4.7 MB)
• 039. Chapter 7. Learning and inference at scale.mp4 (25.2 MB)
• 040. Chapter 7. Framing problems of scale.mp4 (41.9 MB)
• 041. Chapter 7. Techniques for tackling problems of scale.mp4 (16.6 MB)
• 042. Chapter 7. Choice of hardware configuration.mp4 (31.0 MB)
• 043. Chapter 7. Choice of data representation.mp4 (17.1 MB)
• 044. Chapter 7. Choice of GNN algorithm.mp4 (24.3 MB)
• 045. Chapter 7. Batching using a sampling method.mp4 (26.2 MB)
• 046. Chapter 7. Parallel and distributed processing.mp4 (28.7 MB)
• 047. Chapter 7. Training with remote storage.mp4 (25.7 MB)
• 048. Chapter 7. Graph coarsening.mp4 (26.9 MB)
• 049. Chapter 7. Summary.mp4 (5.6 MB)
• 050. Chapter 8. Considerations for GNN projects.mp4 (23.8 MB)
• 051. Chapter 8. Designing graph models.mp4 (57.1 MB)
• 052. Chapter 8. Data pipeline example.mp4 (76.4 MB)
• 053. Chapter 8. Where to find graph data.mp4 (12.9 MB)
• 054. Chapter 8. Summary.mp4 (12.3 MB)
• 055. appendix A. Discovering graphs.mp4 (53.3 MB)
• 056. appendix A. Graph representations.mp4 (68.5 MB)
• 057. appendix A. Graph systems.mp4 (14.6 MB)
• 058. appendix A. Graph algorithms.mp4 (11.7 MB)
• 059. appendix A. How to read GNN literature.mp4 (9.7 MB)
• Bonus Resources.txt (0.1 KB)