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⚡️ Graph Machine Learning
Бесплатный продвинутый курс: Машинное обучение на графах.
Курс регулярно дополняется практическими задачками и слайдами. Автор Ксавье Брессон - профессор национального университета Сингапура.
▪Введение
▪Погружение в графы
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
▪Кластеризация графов
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb https://pic.twitter.com/vSXCx364pe
▪Лекции 4 Graph SVM
- Lab1: Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2: Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3: Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4: Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Инструкции по запуску: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf
💡 Github
@ai_machinelearning_big_data
BY Machinelearning

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