Sep 28, 2024 · 6 min read · This article explains how to adapt Graph Neural Networks (GNNs) for image classification. It covers the process from converting images into graphs to updating the model’s parameters. I explore how message passing and global pooling can enhance spatia...
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Sep 14, 2024 · 10 min read · In traditional machine learning models, including neural networks, data is often assumed to follow the IID (independent and identically distributed) assumption. This means each data point is independent of others and follows the same underlying proba...
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Sep 14, 2024 · 10 min read · Graph theory and neural networks are two seemingly distinct fields in computer science and mathematics, but they have an intrinsic connection that has become increasingly significant as advancements in both areas have evolved. Graph theory, which dea...
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Mar 9, 2024 · 3 min read · In part one of our blog series, we explored the world of graphs and graph neural networks (GNNs). We learned that GNNs are a powerful tool for machine learning tasks involving graphs, like social networks and molecule structures. But how exactly do G...
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Feb 28, 2024 · 11 min read · The Ant Colony Optimization Algorithm is a computational technique that is heavily inspired by the behavior of ants in finding the shortest path to the food sources from the colony. Introduction The Ant Colony Optimization Algorithm is a probabilisti...
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Jun 28, 2023 · 7 min read · 📚 Table of Contents 📚 Introduction to Graphs and GNNs 📊 🌟 What are graphs? 📈 🌟 Why are GNNs important? 🧠 🌟 How do GNNs work? 🤖 GNN Architecture and Components 🏗️ 🌟 Nodes, edges, and features 🧑🤝🧑🔗📝 🌟 Message Passing and Agg...
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Sep 7, 2021 · 14 min read · Introduction Graph neural networks provide a more flexible paradigm for machine learning models by explicitly accounting for entities and their relationships through neural networks. For example, It is easier to model a real problem with graphs in so...
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