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      Eyedicamp 개발 이야기

      Big Data, Machine Learning, AI 등의 다양한 이야기를 하는 곳.

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Tag Archive

  • AI Expert Roadmap 2
  • Database 1
  • English Writing Study 2
  • Fundamentals 1
  • IT 1
  • Machine Learning 24
  • Machine Learning with Graphs 24

AI Expert Roadmap

  • 관계형 vs 비관계형 데이터베이스
  • 블로그를 시작하며

Database

  • 관계형 vs 비관계형 데이터베이스

English Writing Study

  • Should Space Exploration Budget be Increased?
  • Should Generative AI be Regulated?

Fundamentals

  • 관계형 vs 비관계형 데이터베이스

IT

  • 사과와 로봇

Machine Learning

  • [8.2] Training Graph Neural Networks
  • [8.1] Graph Augmentation for GNNs
  • [7.3] Stacking layers of a GNN
  • [7.2] A Single Layer of a GNN
  • [7.1] A general Perspective on GNNs
  • [6.3] Deep Learning for Graphs
  • [6.2] Basics of Deep Learning
  • [6.1] Introduction to Graph Neural Networks
  • [5.3] Collective Classification
  • [5.2] Relational and Iterative Classification
  • [5.1] Message passing and Node Classification
  • [4.4] Matrix Factorization and Node Embeddings
  • [4.3] Random Walk with Restarts
  • [4.2] PageRank: How to solve?
  • [4.1] PageRank
  • [3.3] Embedding Entire Graphs
  • [3.2] Random Walk Approaches for Node
  • [3.1] Node Embeddings
  • [2.3] Traditional Feature-based Methods : Graph
  • [2.2] Traditional Feature-based Methods : Link
  • [2.1] Traditional Feature-based Methods : Node
  • [1.3] Choice of Graph Representation
  • [1.2] Applications of Graph ML
  • [1.1] Why Graphs?

Machine Learning with Graphs

  • [8.2] Training Graph Neural Networks
  • [8.1] Graph Augmentation for GNNs
  • [7.3] Stacking layers of a GNN
  • [7.2] A Single Layer of a GNN
  • [7.1] A general Perspective on GNNs
  • [6.3] Deep Learning for Graphs
  • [6.2] Basics of Deep Learning
  • [6.1] Introduction to Graph Neural Networks
  • [5.3] Collective Classification
  • [5.2] Relational and Iterative Classification
  • [5.1] Message passing and Node Classification
  • [4.4] Matrix Factorization and Node Embeddings
  • [4.3] Random Walk with Restarts
  • [4.2] PageRank: How to solve?
  • [4.1] PageRank
  • [3.3] Embedding Entire Graphs
  • [3.2] Random Walk Approaches for Node
  • [3.1] Node Embeddings
  • [2.3] Traditional Feature-based Methods : Graph
  • [2.2] Traditional Feature-based Methods : Link
  • [2.1] Traditional Feature-based Methods : Node
  • [1.3] Choice of Graph Representation
  • [1.2] Applications of Graph ML
  • [1.1] Why Graphs?