| 001. Part 1. First steps.mp4 | 3 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 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 MB | ||
| 014. Chapter 3. Graph convolutional networks and GraphSAGE.mp4 | 84 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 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 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 | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 61 total files | |||
Graph Neural Networks in Action, Video Edition
https://WebToolTip.com
Released 2/2025
By Namid Stillman, Keita Broadwater
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11h 2m | Size: 1.8 GB
A hands-on guide to powerful graph-based deep learning models.
Graph Neural Networks in Action teaches you to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale.
In Graph Neural Networks in Action, you will learn how to
Train and deploy a graph neural network
Generate node embeddings
Use GNNs at scale for very large datasets
Build a graph data pipeline
Create a graph data schema
Understand the taxonomy of GNNs
Manipulate graph data with NetworkX
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.9 GB | freecoursewb | 4 years | 2 | 2 |
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