深蓝 人工智能 深蓝图卷积神经网络

深蓝 人工智能 深蓝图卷积神经网络课程介绍(A001047)：

深蓝 人工智能 深蓝图卷积神经网络

文件目录：

深蓝 人工智能 深蓝图卷积神经网络

│ ├─

│ ├─图卷积神经网络开课仪式.pptx 362.7x p 3 G Y X _ 22KB

│ ├─图神经网络（GNN）100篇论文集

│ │ ├─Applications

│ │ │ ├─comy 6 V \ B r h Q 2binatorial optimization

│ │ │ │ ├─Comq { f _ & Q o Y wbinatorial Optimization with Graph Convolutional Networks and Guided Tg a D h @ J 7 \ree Search(1).pdf 718.71KB

│ │ │ │ └─Learning Combinatorial Optimization AlgorithO P d Z h . Zms over Graphs.pdf 3.09MB

│ │ │ ├I z n l = % &─g: Z i %raph generation

│ │ │ │ ├─Graph ConvolutionalT I Y Pol! ` L r 7 : \ –icy Network for Goal-Directed Molecular Graph Generation/ 9 ? 0 ; B m.pdf 701.09KB

│ │ │ │ ├─MolGAN- An implicit generative model for small molecular graphs(1).pdf 1.27MB

│ │ │ │ └─NetGAN- Generating Gra@ Z [ r A Y U h Cphs via Random Walks(1).pdf 1.# B h / 78MB

│ │ │ ├─image

│ │ │ │ ├─Image classification

│ │ │ │ │ └─w L = wFew-Shot Lea6 \ f brning with Graph Neural Networks.pdf 1.86MB

│ │ │ │ ├─Interaction Dete3 } 7 ~ 1 \ s Q \ction

│ │ │ │ │ └─Structural-RNN- Deep Learning on S# K Ypatio-Temporal Graphs.pdf 1.27MB

│ │ │ │ ├─Object Detection

│ │ │ │ │ ├─Learning Region features for Object DS J u uej N ^ g & y \ Ctection.pdf 1.86MB

│ │ │ │ │ └─t 5 P @ sRelation Networks for Object De q M t l 5 ; b 8etection.pdf 1.06MB

│ │ │G ] w │ ├─Region Classification

│ │ │ │ │ └─Iterative Visual Reasoning Beyond Convolutions..pdh V g Z Y Qf 4.08MB

│ │ │ │ ├─Semantic Segmentation

│ │ │ │ │ ├─3D Graph NeM J Z [ # H f ^urw P ; $ v Ial Networks for RGBD Semantic Segmentation.pdf 2.4MB

│ │ │ │ │ ├─Dynamic Graph CNN for Learning on Point Clouds.pdf 5.2m u w W 95MB

│ │ │ │ │ ├─Large-scale Point Cloud Se, 0 \mantic Segmentation with Superp` 9 V b Aoint Graphs.pdf 5MB

│ │ │ │ │ ├─Modeling polyp( S 1 m n { Qharmacy side efI Q r n \fects with graph convolutional networks.pdf 4.28MB

│ │ │ │ │ └─m [ W @ ^ v * y oPointNet- Deep Learning on Pu # |oint Sets for 3D Classification and Se: h A _ O a w ^gmentation.pdf 8.84MB

│ │ │ │ ├─Social ReC U @ N \lationship Understanding

│ │ │ │ └─Visual Question Answering

│ │ │ │ ├─Gx % } d 4 F V u yra[ a . ] j ;ph-Strt O & nuctured Representations for Visual Question Answering.pdf 3.92MB

│ │ │ │ └─Out of tO Z % x F Y = `he Box- Reasoning with Graph ConvolutiI X 8 4 * ) H 0 Fon Nets for Factual Visual Queste $ @ 1 \ a ? wion Answering(1).pdf 2.62MB

│ │ │ ├─knowledge graph

│ │ │ │ ├─Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf 610.44KB

│ │ │ │ ├─Deep Reasoning with Knowledge Graph for Social Relationship Understanding.% ^ updf 2.93MB

│ │ │ │ ├─Dynamic/ G 6 T $ y = 1 \ Gru 9 [ ~ [ . C & Raph Generation Network- Generating ReZ K G % f U Qlational Knowledge from DiF D ` tagrams.pdf 1.37MB

│ │ │ │ ├─Knowledge Transfer for Out-of-Knowledge-Base Entities – A Graph Neural Network Approach.pdf 531.96KB

│ │ │ │ ├─Modeling Semantics w{ & 9 J n Q 1ith Gated Graph Neural Networks for Knowledge Base Que$ U % ; D x ] | 8stion Au o n * ? bnswe@ q j xring.pdf 618.29KB

│ │ │ │ ├─Multi-Label Zero-Shot+ 8 Z O ] | n y i Learning with Structureo D 4 Ad Knowledge Graphs.pdf 1.54MB

│ │ │ │ ├─Repb c M H { F Kresentatt + ~ion learni~ } o .ng for visual-r( N Z W . Y 9 zelati– G | 9onal knowledge graphs.pdf 7.08MB

│ │ │ │ ├─TheN 9 2 : More You Know- Using Kno* * y A Xwledge Graphs for Image Classification.pdf 2.48MB

│ │ │ │ └─Zero-shot Recognition viaZ 1 N ( $ Z P ] [ Semantic Embeddings and KnowlS ; = – x + } f –edge Graphs.pdf 1.8MB

│ │ │ ├─science

│ │ │ │ ├─A Compositional Object-Based App( N p : croach to Learning Physical Dynamics.pdf 4.44MB

│ │ │ │ ├─A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf 517.84KB

│ │ │ │ ├─A siD O xmple neural network module for relational reasd d 8 ) / ? [oning.pdf 1.55MB

│ │ │ │ ├─Action Schema Networks- Generalised Policies with Deep Learning.pdf 1.84MB

│ │ │ │ ├─Adversarial Attack on Graph Structured Data.pdf 770.47KB

│ │ │ │ ├─Attend, Infer, Repeat- Fast Scene Understanding with Get y + }nerative Models.pdf 1.48MB

│ │ │ │ ├─A v m = V 4 u v PAttention, Learn to S\ f g O 8 ^olve Routing Problems!.pdf 1.67MB

│ │ │ │ ├─Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.i m lpdf 797.68KB

│ │ │Q ~ U \ v 7 [ _ H │ ├─Co) r z r K z $ Ymbining Neural Networks wiR o U y Mth Personalized PageRank for Classification on Gra0 8 O , N d qph, Z : /s.pdf 666.15KB

│ │ │ │ ├─Constrained Generation of Semantically Valid Graphs via Regularizin[ Y ; X ] . J `g Variational Autoencoders.pdf 744.82KB

│ │ │ │ ├─Constr, 0 j –ucting Nar@ F z m qrative Eventa K @ ] Evolutionary Graph for Script Event Prediction.pdf 829KB

│ │ │ │ ├D _ S A J _ ( h g─Conversation Modeling on Reddit using a Graph-StructuredP ~ C c @ 8 = V LSTM.pdf 867.19KU g 7 ] K 6 HB

│ │ │ │ ├─Convolutional^ p \ networks on graZ c o . : N 0 uphs for learning molecular fingerprints.pdf 964.62KB

│ │ │ │ ├─Cross-Sentence N-ary Relation Extraction with Grap1 X 8 j Z lh LSTMs.pdf 729 J j _3.8KB

│ │ │ │ ├─Deep Graph Infomax.pdf 8.33MB

│ │ │ │ ├─Deez \ UpInf- Modeling influence locality in large social networks.pdf 1.24MB

│ │` ] } Z ; = X | f │ │ ├─Discovering objects and their relations from entangled scene representations.pdf 5.17MB

│ │ │ │ ├─Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf 746.4/ q ^ 3 o – j $ V1KB

│ │ │+ t i = t 8 ` 2 & │ ├S y I \ c ! `─Effective Approaches to Attention-bM ) Based Neural Machine Translation.pdf 42^ c b h z8.27KB

│ │ │ │ ├─Geometric Matrix Compl& 9 9 @ p )etion wiG _ X e qth Rec1 M i 1 ; ,urrent Multi-Graph Neural Networks.pdf 7.16MB

│ │ │ │ ├─Graph Convolutional Matrix Completion.pdf 907.15KB

│ │ │ │ ├─Graph_ 6 | n Convolutional Neural Networks for Web-ScaY * N 4 Ile Recommender Systems.pdf 10.01MB

│ │ │ │ ├─Grap, I g r s Z t . &h networks as l4 j V =earnable physics engines for inference and control.pdf 2.9MB

│ │ │ │ ├─GraphRNN- Generating Realis1 e R 2tic Graphs with Deep Auto-regres5 \ ? ? b – \ Qsive Models.pdf 2.61MB

│ │ │ │ ├─Hybrid Approach oft K i R h Rel# E – d J *ation Neh ^ v S w U Ntwork and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf 2.69MB

│ │ │M X = 6 │ ├─HyR p a | Q R F ^perbolic Attention Networks.pdf 3.26MB

│ │E U @ D ; │ │ ├─Improved Semantic Representations From Tree-Structured Long Short$ R , _ & +-Term Memory Networks.pdf 489.27KB

│ │ │ │ ├─Infe5 Q f ;rence in Probabilistic Graphical Models by Graph] M x F W ~ NeR = u Oural Networks.pdf 3.24MB

│ │ │ │ ├Y K z B a─Interaction Networks for Learu h hning a5 9 x # s 7bout Objects, Relations and Physics.pdf 2h F u b | + 6 U.08MB

│ │ │| S _ + │ ├─Learning a SAT Solver from Single-Bit Supervision.pdf 2.07MB

│ │ │ │ ├─Leas h k erning Condition, . X $ ( /ed Graph Structures for{ r / . 7 Interpretable Visual Question Answering.pdf 8.66B ! * 8 [ s @MB

│ │ │ │ ├─Learning Deep Generative Models of Graphs.pdf 2.45MB

│ │ │ │ ├─Learning Graphical State Transitions.pdf 1.66MB

│ │ │ │ ├─Learning Human-Object Interactions by Gr* g \ M p 3aph Parsing Neural Networks.pdf 4.08MB L q 8 ( ; @ ! gB

│ │ │ │ ├─Learning model-based planning from scratch.pdD D 4 + 4f 1.46MB

│ │ │ │ ├─Learning Multiagenk e a \ 1 Tt Communication wiq v Z C rth Backpropagation.pdf 4.18MB

│– R D : { x b │ │ │ ├─Learning to Represent Programs with Graphs.pdf 607.09KB

│ │ │ │ ├─MetacontroV R %l for Adaptive Imagination-Based Optimization.pdf 1.79MB

│T = 0 _ & q ; – │ │ │ ├─Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf 2.28M\ U } y Q [ `B

│ │ │ │ ├─NerveNet Learning Struc3 t , / s Ftured Policy with Graph Neural Networks.pdf 3.31MB

│ │ │ │ ├─Neural Combinatorial Optimization with Reinforcement Learning.0 i 2 T Z f ~ M Lpdf 582.63KB

│ │ │ │ ├─Neural Module Networks.pdf 1.21MB

│ │ │ │ ├─Neural Relational Inference for Interacting Systems.pdf 3MB

│ │ │ │ ├─Protein Interface Prediction using Graph Convolutio[ Q @ N +nal Networks.pdf 1016.82KB

│ │ │ │ ├─Relational Deep Reinforcement Learning.pdf 6.99MB

│ │ │ │ ├─Relational inductive bias for physical constructio0 ; ( b O w g ]n in humans and maf h I # ] y p 4 Tchines.pdf 1.17MB

│ │ │ │ ├─Relational neural expectB H K )ation maximization- Unsupervised discovery of objects and their interactions.pdf 1.32MB

│ │ │ │ ├─Self-Attention with Rel_ y A } ! g Gative Position Representations.pdf 404.98KB

│ │ │ │ ├─Sem= / a , 2 l N *i. s ?-supervised User Geolocation via Graph C+ ? R donvolutional Networks.pdf 1.31MB

│ │ │# Y \ H Q │ ├─Situation Recognition with Graph Neural Networr 3 ` E 4 ; ? 7ks.pdf 5.45MB

│ │$ [ 3 │ │ ├A H 2 \ m ^ ]─Spatial Temporal Graph Co} Y } N b v t 5nvolutional Networks for Skeleton-Based Action Recognition.pdf 1% i , x } Q.67MB

│ │ │ │ ├─Spatio-Temporal Graph Convolutione $ b d 6 _ 4 ,al Networks- A Deep Lear) | % S u U + # gning Framework for Traffic Forecasting.pdf 1.05MB

│ │ │ │ ├─Structured Dialogue Policy with Graph Neural Networks.pdf 965.29KB

│ │ │ │ ├─Symbolic Graph Reasoning Meets Convolutions.pg . 8 ! $df 3.41MB

│ │ │ │ ├─Traffic Graph Convolutional Recurrent Neural Network- A Deep Learnina P @g Framework for Network-Scale TraD Z W O t 3 iffic Learning and Forecasting.pdf 1.64MB

│ │ │ │ ├─Translating Embeddings for Modeling Multi-rela^ ? q k s T d *tional Data.pdf 588.79KB

│ │ │ │ ├─I \ { Q rUnderstay U q b ( ~ l wnding Kin Relationo ) Z \ ? ? + Aships in a Photo.pdf 1.62MB

│ │ │ │ ├─VAIN- Attentional Multi6 ) r . 0 F B %-agent Predictive Modeling.pdf 608.35KB

│ │ │ │ └─Visual Interaction Networks- Learning a Physics Simulator from Videz i \.o.pdf 5.58MB

│ │ │ └─text

│ │ │ ├─A Graph-to-Sequence Model for AMR-to-Text Generation.pd{ B t K f Of 470.98KB

│ │ │ ├─Encoding Sentences with Graph Convolutional Networks forC N ? h ! p W ] SemU ! ) c Iantic Role Labeling.pdf 789.3KB

│ │ │ ├─End-to-End Relation Extract+ ? @ \ Vion using LSTMs on Sequences and Tre. ) * 4 + q e le Structures.pdf 546.\ m R ;26KB

│ │ │ ├─Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf 784.41KV L } b Y j , Y YBy v ;

│ │ │ ├─Exploring Graph-structured Passage Repres) J P sentation for M| H [ o Vulti-hop ReQ c B N [ ~ Iading Comprehension with Grl @ Caph Neural Networks..pdf 628.84KB

│ │ │ ├─Grap3 R , M ^ E [ Q wh Convolut! – jion ovg T Y C F 7er Pr* c p J f 3 funed Dependency Trees Improves Relation Extraction.pdf 962.87KB

│ │ │ ├─Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf 523.78KB

│ │ │ ├─Graph Convolutional NeZ % * G I r 1tworks for Text Classification.pdk A Z 3 8 W j *f 2MB

│ │ │ ├─Graph Convolutional Netf % | \works with Argument-AwarZ x \e PoolinA 5 M k , W Ng for Event Detep B ` \ F H zction.pdf 506.13KB

│ │ │ ├─Jointly Multiple Events Extraction via Attention-based Graph.pdf 609.6KB

│ │ │ ├─N-ary relation extraction using graph state LSTM.pdf 633.56KB

│ │ │ ├─Recz = , C $ $urrent Relational Networks$ ) D 0 $ H ~ b 4.pdf 482.13KB

│ │ │ ├─Sequence Labeling

│ │ │ └─Text classification

│ │ ├─Models

│ │ │ ├─graphtype

│ │ │ │ ├─Adaptive Graph Convolutional Neural Networks.pdf 980.99KB

│ │ │ │ ├─directed graph

│ │ │ │ │ └─Rethinking Knowledg# { ( g I ^ x ve Graph Propagation for Zero-Shot Learning.pdf 4.38MB

│ │ │ │ ├─edgP J p r Ne-informatim @ Mve grapc g 7 6h

│ │ │ │ │ ├z E , ~─Graph-to-Sequence Learning using Gated Graph Neural Networks.pdf 4.23MB

│ │ │ │ │ └─Modeling relational data with graph coS 3 a &nvolutional networks.pdf 505.33KB

│ │ │ │ ├─Graph Capsule Convolutional Neural Networks.pdf 2.11MB

│ │ │ │ ├─Graph Neural Networks for Object Localization.pdf 397E c : 1 9 c _ l.84KB

│ │ │ │ ├─Graph Neural Networks for Ranking WebN – + Y Pages.pdf 1.18MB

│ │ │ │ ├─Graph Partition Neural Networks` | + for Semi-i 5 3 mSupervised Classification.pdf 894KB

│ │ │ │ ├─heterogeneous graf G jphs

│V X 8 @ H i { │ │ │ ├─How Powerful are Graph Neural Networks-U i n W | C.pdf 871.25KB

│ │ │ │ ├─Mean-X Y ` $ p v h 5 Lfiel( ` 6 –d theory of graph neural networks in graph partition1 9 { f #ing.pdf 550.54KB

│ │ │g b ~ R t 5 V │ └─Spectral Networks and Locally Connb * w Kected Networks on Graphs.pdf 2.04MB

│ │ │ ├─others

│ │ │ │ ├─A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf 427.38KB

│ │ │ │ ├─A new model fo( \ ? y ;r learning in graph doma5 J wins.pdf 356.89KB

│ │ │ │ ├─CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf 16.52MB

│ │ │ │ ├─Contextual Graph Markov M* B D 4 dodel- A Deep and Generative Approach to Graph Processing.pdf 750.55KB

│ │ │ │ ├─Deep Sets.pdf 5.3MB

│ │ │ │ ├─Deri% T e nving Neural Architectures fra S bom S/ @ ] M K hequence and Graph Kernels.pdf 880.73KB

│ │ │ │ ├─Diffusion-Convolutional Neural Networks.pdf 545.23KB

│ │ │ │ └─Geometric deep learning on graphs and manifolds using mixture model cnns.pdf 7.41MB

│ │ │ ├─propagationtype

│ │d V X h J A V a │ │ ├─attentI | : ! 4 ] W \ion

│ │ │ │ │ ├─Attention Is All You Need.pdf 2.22MB

│ │ │ │ │ ├─Graph( + u G + e Attention Networks.pdf 1.66MB

│ │ │ │ │ └─Graph Classi5 ` b & 1 Ufication using Structul U Y q =ral Atter I l Z 8ntion.pdf 2.64MB

│ │ │ │ ├─convolution

│ │ │ │ │ ├─Bayesian S7 R / 8 Xemi-supervised Learni1 ^ 6 ) J U e c +ng with Graph Gaussian Processes/ @ Z.pdf 872\ : 8 v M.04KB

│ │ │ │ │ ├─Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.pdf 627.75KB

│ │ │ │ │! ; G D ` i / % ├─Deep ConvoluV ! s 2 V M [ + –tional Networks on Go d ( o lraph-Strucn ! _ 2 N o 2 utured Data.pdf 4.75MB

│ │ │ │ │ ├─LearL 4 Rning Convolutional Neural Networks for Graphs.pdf 823.63KB

│ │ │ │ │ ├─Spectral Networks and Deep Locally ConnecteI k \ D – .d.pdf 2.04MB

│ │ │ │ │ └─Structure-Aware Convolut# H 8 $ M 7 =ional Nec x 1 f Mural NetH ) Y \ r – :works.pdf 1.53MB

│ │ │ │ ├─gate

│ │ │ │ │ ├─Gated Graph Sequence Neural Networks.pt | Y B :df 931.94KB

│ │ │ │ │ └─V O 4Sentence-State LSTM for Text Representation.pdf 620.68KB

│ │ │ │ └─skip

│ │ │ │ ├─Representation Learning on Graphs with Jumping Knowledge Networks.pdf 3.33MB

│ │ │ │ └─Semi-Supervis+ Z N % 7 Q I L qed ClaV I q n W 1ssification with Graph Convolutional Networks.pdf 1.01MB/ ; z | ( K

│ │ │ └─training methods

│ │ │ ├─boosp U x p 5 S Wting

│ │ │ │ └─Deeper Insights into Graph Convolutional NetwoL a ; 9 K V orks for Semi-Supervised Learning.pdf 2.14MB

│ │ │ ├─i M 7 * % q P #Covariant Compositional N3 Z O z * N Setworks For Learning Graphs.pdf 669.84KB

│ │ │ ├─Graphical-Based Learning E– A 1 }nvironments for Pattern Recognition.pdf 521.43KB

│ │ │ ├─Hierarchical Graph Representation Learning with Differentiable Pooling.pdf 2.49M} ` @B

│ │ │ ├─Knowledge-Guided Recurrent Neural Network Learning for Task-Orm P ] 6iented Action Prediction; 4 G.pdf 1.15MB

│ │ │ ├─Learning Steady-States of Iterative Algorithms over Graphs.pdf 3.27MB

│ │ │ ├─neighborhood samp1 7 * x z 7 Vling

│ │ │ │ ├─Adaptive Sampling Towards Fast Gra1 N p 5 n Fph Representation Learning.pdf 760.23KB

│ │ │ │ ├─FastGCN- FasU 6 ( M Nt Learning with Graph ConvC f +olut: / g + ; l ) / ;ional NetT y m A ` g d Qworks via ImpoH O 3 J | .rtance Sampling.pdf 544.03KB

│ │ │ │ └j . M L V Y u ^ J─Inductive Representatik H * P t 7 k #on Lr J 5 Cearning on Large Graph8 5 S Y = ^ / is.pdf 1.22MB

│ │ │ ├─6 – w # V r 0 .Neural networks for relational learning- an experimental com| J ? z [ vparison.pdf 1.36MB

│ │ │ └─receptive field control

│ │ │ └─SC w m { n ,tochastic Training of Graph ConvG s – ! – &olutional Networks with Variance Reduction.pdf 1.44MB

│l y ~ │ └─Surve} P ! s Dy

│ │ ├─一般推荐

│ │ │ ├─A Comprehensive Survey on Graph Neural Networks.pdf 1.98MB

│ │ │ ├─CompuY q j & 7tational Capabilities of Graph Neural Networks(1).pdf 1.48MB

│ │ │ ├─Deep Learning on Graphs-8 D n ~ I A Survey.pdf 1.96MB

│ │ │ ├─Geometric Deep Learningj z 2 I Y : F d r– Going beyond Euclidean data.pdf 5.44MB

│ │ │ └─Neural Message Passing for Quantum Chemistry.\ \ d i F S J :pdf 694.46KB

│ │ └─极力推C b C * L ] Z } i荐

│q k V E k I A a W │ ├─Graph Neural Networks：A Revie= L | Z 0 \w of Methods an. 0 # G t }d Applications.pdf 2.85MB

│ │ ├─Non-local Neural Networks.pdf 1.41Mu G j j mB

│ │ ├─Relational Inductive Biases, Deep Learning, and Graph Networks.pdf 9.14MB

│ │ └─The Graph Neural Network Model.pdfe w 1 a & 1.62M3 N C C ( P V 8 ^B

│ ├─第1章 从欧几里得空间到非欧几里得空间

│ │ ├─Chapter1卷积神经网络-从欧式空间到非欧式空间.mp4 2= Q \ 4 !39.53MB

│ │ └─GCN第一节课9 + + 1 3 } R.pdf 2.1MB

│ ├─第2章 谱域图卷积介绍

│ │ ├─第2章谱域图卷积介绍.mp4 325.09MB

│ │ └─第二节课-谱域图卷积.pdf 3.59MB

│ ├─第3章 空域图卷积介绍

│ │ ├` H ! z ^ $ e d─3.1-3.2 空域卷积.mp4 357.35MB

│ │ ├─3.1-3.2-3.3-3.4–L3空域图卷积介绍（一）.pdf 2.43MB

│ │ ├─3.3-3.4 空域卷积.mp4 133.48MB

│ │ ├─3.5-3.6-v5.0过平滑现象.pdf 2.66MB

│ │ ├─3.5图卷积网络回顾 空域图卷积2.mp4 164.45MB

│ │ └─3.6过平滑现象.mp4 310.82MB

│ ├─e v v第4章p d h 图卷积的实践应用

│ │ ├─图卷积神经网络的应用.mp4 461.09MB

│ │ └─第五节课.pdf 3.34MB

│ ├─第5章 实践：基于PyG的图卷积的节点分类1 ( 8 ) i ? & T K(1)

│ │ ├─19：第五章作业讲评K 3 d d B ,.mp4 62.08MB

│ │ ├─保存模型与相关代码.zip 4B ] + p % * N &.4MB

│ │ ├─实践作业.pdf 455.01KB

│ │ ├─第1节 环境搭建

│ │ │ └─【视频】环境搭建.mp4 336.6MB

│ │ ├─第2节 基于PyG框架的节点分类实践

│ │ │ ├─P r ~ ` 8 . Y 8 @16：【视频】节点分类实践（上）.mp4 229.86MB

│ │ │ └─16：【视频】节点分类实践（下）.mp4 200.78MB

│ │ ├─第3节 构造自己的数据集&查阅其他GCN方法

│ │ │ └─17：【视频】构造自己的数据集&查阅其他GCN方法.\ 5 B _ !mp4 309.64MB

│ │ └─第4. – {节 实践作业

│ │ ├─第六次课.pdf 304.15KB

│ │ └─节点分类code.rar 2.09! 0 * I d n n ;KB

│ └─第6章 实践：基于Pytorch的图卷积的交通预测

│ ├─图卷积第6章优秀作业（PCCH）.zip 32.7y A I8MB

│ ├─第1节 课件&代码

│ │ ├─code.rar 31.12MBf & ] w ! 9 1 z v

│ │ └─第七次课.pdf 420.48KB

│ ├─第2节 时序数据处理及建模

│ │ └─20：【视频】时7 T l T v 9 Z序数据处理及建模.mp4 349.96MB

│ ├─第3节 基于Pytorch的交通流量预测

│ │ └─21：【视频】基于? ; z U \ | P vPytorch的交通流量预测.mp4 427.72MB

│ └─第4节 作业

│ └─作业.pdf 441.68KB