Graph Neural Network Applications - Search Videos

A Gentle Introduction to Graph Neural Networks

A Gentle Introduction to Graph Neural Networks

24 views1 day ago

YouTuberitvikmath

Graph Neural Networks (GNNs) Explained in 17 Minutes | GCN, SHAP & GNNExplainer

Graph Neural Networks (GNNs) Explained in 17 Minutes | GCN, SHAP & GNNExplainer

1 views20 hours ago

YouTubeCloudvala

DLSG#10 Module 2.3 - Graph Neural Networks

DLSG#10 Module 2.3 - Graph Neural Networks

YouTubeinzva team

What are computational graphs in TensorFlow? | TECH Global University

What are computational graphs in TensorFlow? | TECH Global University

YouTubeTECH Global University

What are computational graphs in TensorFlow? | TECH Global University

What are computational graphs in TensorFlow? | TECH Global University

YouTubeTECH Global University

Graph Neural Networks: The Deep Learning Revolution for Complex Data

Graph Neural Networks: The Deep Learning Revolution for Complex Data

YouTubeNikhil Tech

CARE-AI: Graph Neural Network Based Multi Head Disease Risk Prediction System with a Medical Chatbot

CARE-AI: Graph Neural Network Based Multi Head Disease Risk Prediction System with a Medical Chatbot

YouTubeDepartment of Electronic and Telecommunication Engineering, Univer…

A Graph Neural Network WithReinforcement Learning | ClickMyProject

YouTubeClickMyProject

🔹 Artificial neural networks mimic the brain’s functioning in a simplified way through interconnected neurons that process signals and adjust their weights to learn from data. Their layers, activation functions, and learning mechanisms help solve complex problems and recognise patterns. Their adaptability, generalisation, and parallel processing capabilities support applications in prediction, robotic control, and trajectory planning. Evolutionary optimisation can also automate the exploration

178 views1 week ago

TikToktechglobaluniversity

🔹 Neuroevolutionary hybrid models combine neural networks with evolutionary algorithms to optimise architectures, connections, and learning parameters. Their applications span medicine, economics, and computer vision, from clinical decision support to fraud detection and object recognition. Convolutional networks and architectures such as U-Net enable detailed analysis of biomedical images. However, addressing bias, overfitting, privacy, and interpretability is essential to building reliable an

184 views1 week ago

TikToktechglobaluniversity

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