All Projects → dglai → Kdd 2019 Hands On

dglai / Kdd 2019 Hands On

DGL tutorial in KDD 2019

Projects that are alternatives of or similar to Kdd 2019 Hands On

S3contents
A S3 backed ContentsManager implementation for Jupyter
Stars: ✭ 175 (-1.69%)
Mutual labels:  jupyter-notebook
Tensorflow Ml Nlp
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Ocaml Jupyter
An OCaml kernel for Jupyter (IPython) notebook
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Compact bilinear pooling
MatConvNet and Caffe repo with compact bilinear and bilinear pooling functionality added
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Egg
EGG: Emergence of lanGuage in Games
Stars: ✭ 175 (-1.69%)
Mutual labels:  jupyter-notebook
Advance Bayesian Modelling With Pymc3
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Neural image captioning
Neural image captioning (NIC) implementation with Keras 2.
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Julia Tutorial
高速でJuliaを学ぶ入門チュートリアル
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Ppnp
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Ethereum demo
This is the code for "Ethereum Explained" by Siraj Raval on Youtube
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Nel
Entity linking framework
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Jetbot
An educational AI robot based on NVIDIA Jetson Nano.
Stars: ✭ 2,391 (+1243.26%)
Mutual labels:  jupyter-notebook
Super resolution with cnns and gans
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
Stars: ✭ 176 (-1.12%)
Mutual labels:  jupyter-notebook
Prefetch generator
Simple package that makes your generator work in background thread
Stars: ✭ 174 (-2.25%)
Mutual labels:  jupyter-notebook
Unet In Tensorflow
U-Net implementation in Tensorflow
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Debiaswe
Remove problematic gender bias from word embeddings.
Stars: ✭ 175 (-1.69%)
Mutual labels:  jupyter-notebook
Tamburetei
Fazendo de tamburete as cadeiras de [email protected]
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Tensorflow2 Docs Zh
TF2.0 / TensorFlow 2.0 / TensorFlow2.0 官方文档中文版
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
Notebook
📒 notebook
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook
2017 Ccf Bdci Aijudge
2017-CCF-BDCI-让AI当法官(初赛):7th/415 (Top 1.68%)
Stars: ✭ 177 (-0.56%)
Mutual labels:  jupyter-notebook

Learning Graph Neural Networks with Deep Graph Library -- KDD'19 hands-on tutorial

Presenters: Minjie Wang, Lingfan Yu, Da Zheng, Nicholas Choma

Opening speaker: Alex Smola

Time: Wed, August 07, 2019, 1:30 pm - 4:30 pm

Abstract

Learning from graph data has played a substantial role in many real world scenarios including social network analysis, knowledge graph construction, protein function prediction and so on. Recent burst of researches on Graph Neural Networks (GNNs) brings representation learning to non-euclidean space and achieves state-of-art results in community detection, drug discovery, recommendation, etc. More recent perspective begins to view GNN as a more general form of the neural network models, such as attention architecture, that have dominated areas of computer vision and natural language processing. As graph is essentially relation, modeling explicit or inferring latent graph structure is crucial to the ability of relational reasoning for model AI.

This tutorial focuses on this recent trend in geometric deep learning including how and why graph neural networks are widely applied, its foundation and recent development. We then introduce a new framework called Deep Graph Library (DGL) that is designed to ease deep learning on graphs. The hands-on part starts with basic concepts in DGL for easier understanding, and later walks the audience through several end-to-end examples including community detection, hierarchical clustering and building recommender system using GNNs.

Prerequisite

Basic understanding of Machine Learning and Deep Learning. Have experience with either Pytorch or Apache MXNet.

Agenda

Time Session Slides Notebooks Presenter
1:30-2:00 Opening talk - - Alex Smola
2:00-2:45 DGL 101
(Hands-on) Semi-supervised Community Detection using Graph Convolutional Network
link link Lingfan Yu
2:45-3:30 Scalable Clustering with Graph Neural Networks using DGL
(Hands-on) GNNs for clustering TrackML dataset
link link Nicholas Choma / Minjie Wang
3:30-3:45 Coffee Break
3:45-4:30 Building Recommender Systems using Graph Neural Networks
(Hands-on) GraphSage for MovieLens
link link Da Zheng

Community

Join our Slack using this invitation link (expired in a week). Jumpy to the kdd19-tutorial channel for community meetup!

Play locally

Build a docker image with all the environment installed.

docker build --force-rm -t dgl-kdd19 -f Dockerfile .

Start a container using the image,

docker run -it --rm -p 8888:8888 dgl-kdd19 bash

Within the docker image,

cd ~/KDD-2019-Hands-on
conda activate kdd19
jupyter notebook --ip 0.0.0.0 --allow-root

Finally, open the url with browswer.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].