Graph Neural Networks in Action Video Edition

Graph Neural Networks in Action Video Edition
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     001. Part 1. First steps.mp4 -


     002. Chapter 1. Discovering graph neural networks.mp4 -


     003. Chapter 1. Graph-based learning.mp4 -


     004. Chapter 1. GNN applications Case studies.mp4 -


     005. Chapter 1. When to use a GNN.mp4 -


     006. Chapter 1. Understanding how GNNs operate.mp4 -


     007. Chapter 1. Summary.mp4 -


     008. Chapter 2. Graph embeddings.mp4 -


     009. Chapter 2. Creating embeddings with a GNN.mp4 -


     010. Chapter 2. Using node embeddings.mp4 -


     011. Chapter 2. Under the Hood.mp4 -


     012. Chapter 2. Summary.mp4 -


     013. Part 2. Graph neural networks.mp4 -


     014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4 -


     015. Chapter 3. Aggregation methods.mp4 -


     016. Chapter 3. Further optimizations and refinements.mp4 -


     017. Chapter 3. Under the hood.mp4 -


     018. Chapter 3. Amazon Products dataset.mp4 -


     019. Chapter 3. Summary.mp4 -


     020. Chapter 4. Graph attention networks.mp4 -


     021. Chapter 4. Exploring the review spam dataset.mp4 -


     022. Chapter 4. Training baseline models.mp4 -


     023. Chapter 4. Training GAT models.mp4 -


     024. Chapter 4. Under the hood.mp4 -


     025. Chapter 4. Summary.mp4 -


     026. Chapter 5. Graph autoencoders.mp4 -


     027. Chapter 5. Graph autoencoders for link prediction.mp4 -


     028. Chapter 5. Variational graph autoencoders.mp4 -


     029. Chapter 5. Generating graphs using GNNs.mp4 -


     030. Chapter 5. Under the hood.mp4 -


     031. Chapter 5. Summary.mp4 -


     032. Part 3. Advanced topics.mp4 -


     033. Chapter 6. Dynamic graphs Spatiotemporal GNNs.mp4 -


     034. Chapter 6. Problem definition Pose estimation.mp4 -


     035. Chapter 6. Dynamic graph neural networks.mp4 -


     036. Chapter 6. Neural relational inference.mp4 -


     037. Chapter 6. Under the hood.mp4 -


     038. Chapter 6. Summary.mp4 -


     039. Chapter 7. Learning and inference at scale.mp4 -


     040. Chapter 7. Framing problems of scale.mp4 -


     041. Chapter 7. Techniques for tackling problems of scale.mp4 -


     042. Chapter 7. Choice of hardware configuration.mp4 -


     043. Chapter 7. Choice of data representation.mp4 -


     044. Chapter 7. Choice of GNN algorithm.mp4 -


     045. Chapter 7. Batching using a sampling method.mp4 -


     046. Chapter 7. Parallel and distributed processing.mp4 -


     047. Chapter 7. Training with remote storage.mp4 -


     048. Chapter 7. Graph coarsening.mp4 -


     049. Chapter 7. Summary.mp4 -


     050. Chapter 8. Considerations for GNN projects.mp4 -


     051. Chapter 8. Designing graph models.mp4 -


     052. Chapter 8. Data pipeline example.mp4 -


     053. Chapter 8. Where to find graph data.mp4 -


     054. Chapter 8. Summary.mp4 -


     055. appendix A. Discovering graphs.mp4 -


     056. appendix A. Graph representations.mp4 -


     057. appendix A. Graph systems.mp4 -


     058. appendix A. Graph algorithms.mp4 -


     059. appendix A. How to read GNN literature.mp4 -


     Bonus Resources.txt -

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