An Introduction to Graph Neural Networks скачать в хорошем качестве

An Introduction to Graph Neural Networks

In this video, we explore graph neural networks, which learn by passing messages between nodes to capture complex relationships in structured data. These models excel at tasks from molecular property prediction to social network analysis by leveraging the inherent connectivity of graphs. Related Videos ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Bayesian Optimization:    • Bayesian Optimization   Hyperparameters Tuning: Grid Search vs Random Search:    • Hyperparameters Tuning: Grid Search vs Ran...   The Kernel Trick:    • The Kernel Trick   Cross-Entropy - Explained:    • Cross-Entropy - Explained   Dropout - Explained:    • Dropout in Neural Networks - Explained   Overfitting vs Underfitting:    • Overfitting vs Underfitting - Explained   Why Models Overfit and Underfit - The Bias Variance Trade-off:    • Bias-Variance Trade-off - Explained   Least Squares vs Maximum Likelihood:    • Least Squares vs Maximum Likelihood   XGBoost Explained in Under 3 Minutes:    • XGBoost Explained in Under 3 Minutes   Contents ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Intro 00:42 - GNN Examples 01:03 - GNN Tasks 01:33 - GNN Challenges 02:08 - Simplest GNN 02:34 - Message Passing 03:20 - Graph Convolutional Network (GCN) 04:01 - Graph Attention Network (GAT) 05:05 - Summary 05:30 - Outro Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic   / datamlistic   📸 Instagram: @datamlistic   / datamlistic   📱 TikTok: @datamlistic   / datamlistic   👔 Linkedin:   / datamlistic   Channel Support ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon:   / datamlistic   ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #machinelearning #datascience #gnn #graphneuralnetworks #gat #gcn