WebToolTip com Graph Neural Networks in Action Video Edition | 44193ED76A9C65A213E0B4615E756ACFE93961DB

WebToolTip com Graph Neural Networks in Action Video Edition

Name	[ WebToolTip.com ] Graph Neural Networks in Action, Video Edition	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.9 GB
Total Files	61
Last Seen	2025-06-28 00:32
Hash	44193ED76A9C65A213E0B4615E756ACFE93961DB

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001. Part 1. First steps.mp4	3.1 MB
002. Chapter 1. Discovering graph neural networks.mp4	32.3 MB
003. Chapter 1. Graph-based learning.mp4	60.6 MB
004. Chapter 1. GNN applications Case studies.mp4	22.5 MB
005. Chapter 1. When to use a GNN.mp4	27.2 MB
006. Chapter 1. Understanding how GNNs operate.mp4	24.9 MB
007. Chapter 1. Summary.mp4	6.9 MB
008. Chapter 2. Graph embeddings.mp4	75.7 MB
009. Chapter 2. Creating embeddings with a GNN.mp4	35.7 MB
010. Chapter 2. Using node embeddings.mp4	52.7 MB
011. Chapter 2. Under the Hood.mp4	65.4 MB
012. Chapter 2. Summary.mp4	6.7 MB
013. Part 2. Graph neural networks.mp4	3.2 MB
014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4	88.1 MB
015. Chapter 3. Aggregation methods.mp4	53.8 MB
016. Chapter 3. Further optimizations and refinements.mp4	41.9 MB
017. Chapter 3. Under the hood.mp4	68.0 MB
018. Chapter 3. Amazon Products dataset.mp4	17.9 MB
019. Chapter 3. Summary.mp4	8.3 MB
020. Chapter 4. Graph attention networks.mp4	15.4 MB
021. Chapter 4. Exploring the review spam dataset.mp4	50.8 MB
022. Chapter 4. Training baseline models.mp4	26.4 MB
023. Chapter 4. Training GAT models.mp4	39.5 MB
024. Chapter 4. Under the hood.mp4	33.5 MB
025. Chapter 4. Summary.mp4	6.2 MB
026. Chapter 5. Graph autoencoders.mp4	34.0 MB
027. Chapter 5. Graph autoencoders for link prediction.mp4	41.1 MB
028. Chapter 5. Variational graph autoencoders.mp4	36.0 MB
029. Chapter 5. Generating graphs using GNNs.mp4	51.2 MB
030. Chapter 5. Under the hood.mp4	33.9 MB
031. Chapter 5. Summary.mp4	6.6 MB
032. Part 3. Advanced topics.mp4	4.6 MB
033. Chapter 6. Dynamic graphs Spatiotemporal GNNs.mp4	27.4 MB
034. Chapter 6. Problem definition Pose estimation.mp4	40.5 MB
035. Chapter 6. Dynamic graph neural networks.mp4	30.0 MB
036. Chapter 6. Neural relational inference.mp4	86.8 MB
037. Chapter 6. Under the hood.mp4	34.1 MB
038. Chapter 6. Summary.mp4	4.9 MB
039. Chapter 7. Learning and inference at scale.mp4	26.4 MB
040. Chapter 7. Framing problems of scale.mp4	43.9 MB
041. Chapter 7. Techniques for tackling problems of scale.mp4	17.4 MB
042. Chapter 7. Choice of hardware configuration.mp4	32.5 MB
043. Chapter 7. Choice of data representation.mp4	18.0 MB
044. Chapter 7. Choice of GNN algorithm.mp4	25.5 MB
045. Chapter 7. Batching using a sampling method.mp4	27.4 MB
046. Chapter 7. Parallel and distributed processing.mp4	30.1 MB
047. Chapter 7. Training with remote storage.mp4	27.0 MB
048. Chapter 7. Graph coarsening.mp4	28.2 MB
049. Chapter 7. Summary.mp4	5.8 MB
050. Chapter 8. Considerations for GNN projects.mp4	25.0 MB
051. Chapter 8. Designing graph models.mp4	59.9 MB
052. Chapter 8. Data pipeline example.mp4	80.1 MB
053. Chapter 8. Where to find graph data.mp4	13.5 MB
054. Chapter 8. Summary.mp4	12.9 MB
055. appendix A. Discovering graphs.mp4	55.9 MB
056. appendix A. Graph representations.mp4	71.8 MB
057. appendix A. Graph systems.mp4	15.3 MB
058. appendix A. Graph algorithms.mp4	12.2 MB
059. appendix A. How to read GNN literature.mp4	10.1 MB
Bonus Resources.txt	0.1 KB
Total files 61