핸즈온 그래프 인공신경망 with Python

MaximeLabonne

MaximeLabonneiscurrentlyaseniorappliedresearcheratAirbus.HereceivedaM.Sc.degreeincomputersciencefromINSACVL,andaPh.D.inmachinelearningandcybersecurityfromthePolytechnicInstituteofParis.Duringhiscareer,heworkedoncomputernetworksandtheproblemofrepresentationlearning,whichledhimtoexploregraphneuralnetworks.Heappliedthisknowledgetovariousindustrialprojects,includingintrusiondetection,satellitecommunications,quantumnetworks,andAI-poweredaircrafts.HeisnowanactivegraphneuralnetworkevangelistthroughTwitterandhispersonalblog.

제1장그래프학습시작하기
제2장그래프인공신경망을위한그래프이론
제3장딥워크(DeepWalk)로노드표현(NodeRepresentations)생성
제4장노드투벡(Node2Vec)의편향된랜덤워크(RandomWalk)를사용한임베딩개선
제5장기본인공신경망(VanillaNeuralNetworks)을사용한노드특성값(NodeFeatures)포함시키기
제6장그래프컨볼루션신경망
제7장그래프어텐션신경망
제8장GraphSAGE를통한그래프인공신경망확장
제9장그래프분류를위한표현력정의
제10장그래프신경망을이용한링크예측
제11장그래프신경망을이용한그래프생성
제12장이종그래프인공신경망학습
제13장시간적그래프인공신경망
제14장그래프인공신경망설명하기
제15장A3T-GCN을사용한교통예측
제16장이종그래프인공신경망을활용한이상감지
제17장LightGCN을활용한추천시스템구축
제18장실세계응용을위한그래프인공신경망의잠재력활용하기