Download Graph Neural Networks in Action, Video Edition Torrent

Torrent Contents Size: 1.8 GB

Graph Neural Networks in Action, Video Edition

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001. Part 1. First steps.mp4	MP4	3 MB
002. Chapter 1. Discovering graph neural networks.mp4	MP4	30.8 MB
003. Chapter 1. Graph-based learning.mp4	MP4	57.8 MB
004. Chapter 1. GNN applications Case studies.mp4	MP4	21.4 MB
005. Chapter 1. When to use a GNN.mp4	MP4	25.9 MB
006. Chapter 1. Understanding how GNNs operate.mp4	MP4	23.7 MB
007. Chapter 1. Summary.mp4	MP4	6.6 MB
008. Chapter 2. Graph embeddings.mp4	MP4	72.2 MB
009. Chapter 2. Creating embeddings with a GNN.mp4	MP4	34 MB
010. Chapter 2. Using node embeddings.mp4	MP4	50.3 MB
011. Chapter 2. Under the Hood.mp4	MP4	62.4 MB
012. Chapter 2. Summary.mp4	MP4	6.4 MB
013. Part 2. Graph neural networks.mp4	MP4	3 MB
014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4	MP4	84 MB
015. Chapter 3. Aggregation methods.mp4	MP4	51.3 MB
016. Chapter 3. Further optimizations and refinements.mp4	MP4	39.9 MB
017. Chapter 3. Under the hood.mp4	MP4	64.8 MB
018. Chapter 3. Amazon Products dataset.mp4	MP4	17.1 MB
019. Chapter 3. Summary.mp4	MP4	7.9 MB
020. Chapter 4. Graph attention networks.mp4	MP4	14.7 MB
021. Chapter 4. Exploring the review spam dataset.mp4	MP4	48.4 MB
022. Chapter 4. Training baseline models.mp4	MP4	25.2 MB
023. Chapter 4. Training GAT models.mp4	MP4	37.7 MB
024. Chapter 4. Under the hood.mp4	MP4	32 MB
025. Chapter 4. Summary.mp4	MP4	5.9 MB
026. Chapter 5. Graph autoencoders.mp4	MP4	32.5 MB
027. Chapter 5. Graph autoencoders for link prediction.mp4	MP4	39.2 MB
028. Chapter 5. Variational graph autoencoders.mp4	MP4	34.3 MB
029. Chapter 5. Generating graphs using GNNs.mp4	MP4	48.8 MB
030. Chapter 5. Under the hood.mp4	MP4	32.3 MB
031. Chapter 5. Summary.mp4	MP4	6.3 MB
032. Part 3. Advanced topics.mp4	MP4	4.4 MB
033. Chapter 6. Dynamic graphs Spatiotemporal GNNs.mp4	MP4	26.1 MB
034. Chapter 6. Problem definition Pose estimation.mp4	MP4	38.6 MB
035. Chapter 6. Dynamic graph neural networks.mp4	MP4	28.6 MB
036. Chapter 6. Neural relational inference.mp4	MP4	82.7 MB
037. Chapter 6. Under the hood.mp4	MP4	32.6 MB
038. Chapter 6. Summary.mp4	MP4	4.7 MB
039. Chapter 7. Learning and inference at scale.mp4	MP4	25.2 MB
040. Chapter 7. Framing problems of scale.mp4	MP4	41.9 MB
041. Chapter 7. Techniques for tackling problems of scale.mp4	MP4	16.6 MB
042. Chapter 7. Choice of hardware configuration.mp4	MP4	31 MB
043. Chapter 7. Choice of data representation.mp4	MP4	17.1 MB
044. Chapter 7. Choice of GNN algorithm.mp4	MP4	24.3 MB
045. Chapter 7. Batching using a sampling method.mp4	MP4	26.2 MB
046. Chapter 7. Parallel and distributed processing.mp4	MP4	28.7 MB
047. Chapter 7. Training with remote storage.mp4	MP4	25.7 MB
048. Chapter 7. Graph coarsening.mp4	MP4	26.9 MB
049. Chapter 7. Summary.mp4	MP4	5.6 MB
050. Chapter 8. Considerations for GNN projects.mp4	MP4	23.8 MB
051. Chapter 8. Designing graph models.mp4	MP4	57.1 MB
052. Chapter 8. Data pipeline example.mp4	MP4	76.4 MB
053. Chapter 8. Where to find graph data.mp4	MP4	12.9 MB
054. Chapter 8. Summary.mp4	MP4	12.3 MB
055. appendix A. Discovering graphs.mp4	MP4	53.3 MB
056. appendix A. Graph representations.mp4	MP4	68.5 MB
057. appendix A. Graph systems.mp4	MP4	14.6 MB
058. appendix A. Graph algorithms.mp4	MP4	11.7 MB
059. appendix A. How to read GNN literature.mp4	MP4	9.7 MB
Bonus Resources.txt	TXT	102.4 B
Get Bonus Downloads Here.url	URL	204.8 B

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Graph Neural Networks in Action, Video Edition

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