Скачать с ютуб Train a Graph Neural Network for Note Classification Using DGL в хорошем качестве

Train a Graph Neural Network for Note Classification Using DGL

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks. Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (currently supporting PyTorch, MXNet and TensorFlow). In this tutorial, we will learn about how to use DGL to classify node types using the famous Zachary's karate club data. Topics include: How to create customized DGL Dataset? How to create deep convolutional graph network using DGL? How to train the model and evaluate its performance? How to visualize a network using the networkx module? Code used in this video can be downloaded from GitHub: https://github.com/DreamJarsAI/Apply-... Hashtags: #networkanalysis #artificialintelligence #machinelearning #deeplearning #python #pythonprogramming #pythontutorial #aitutorial #coding #neuralnetworks #neuralnetwork #pytorch #graph #tutorial #tutorials