Graph Neural Network

🎓 Course Title: Graph Neural Network (Complete Introductory Course to GNN)

🚀 Headline: From Graph Representation Learning to Graph Neural Network with Younes Sadat-Nejad

Unlock the Secrets of Complex Data with Graph Neural Networks! 🌐🧠

Course Description:

Graph Neural Networks (GNNs) have revolutionized the way we process and analyze data structured in graphs. Their ability to handle complex relationships and diverse datasets has made them indispensable in fields such as recommendation systems, bioinformatics, social network analysis, and more. With their roots tracing back to 1997, GNNs truly came into their own around 2017, transforming the landscape of deep learning on graphs.

Why This Course?

• Comprehensive Introduction: Dive into the world of Graph Neural Networks with a course designed to cover all the fundamentals from scratch.
• Structured Learning Journey: Ease into the subject with a well-organized curriculum that guides you through both theoretical concepts and practical applications.
• Expert Insights: Learn from Younes Sadat-Nejad, an instructor whose journey reflects the challenges and triumphs of mastering GNNs.

What You'll Learn:

• 📚 Theoretical Foundations: Grasp the core principles behind graph representation learning and understand the mathematical underpinnings of GNNs.
• 🧬 Practical Implementation: Work with real-world examples and learn how to implement GNN models using PyTorch Geometric, a powerful library for geometric deep learning.
• 📈 Real-World Applications: Discover how GNNs are applied in various domains such as classification, clustering, link prediction, and more.

Course Structure:

1. Introduction to Graph Neural Networks:

   • What are Graph Neural Networks?
   • The evolution of GNNs from 1997 to present.

2. Graph Representation Learning:

   • Types of graph data and their structures.
   • Key concepts in graph representation learning.

3. Theoretical Framework:

   • Understanding the math behind GNNs.
   • Different architectures of GNNs (GCN, RGCN, GAT, etc.).

4. Practical Implementation:

   • Setting up your environment with PyTorch Geometric.
   • Step-by-step tutorials to build and train GNN models.

5. Applications and Case Studies:

   • Explore various applications of GNNs in different domains.
   • Case studies on how GNNs have solved real-world problems.

Who Is This Course For?

• Researchers and practitioners interested in the cutting-edge field of graph deep learning.
• Data scientists eager to expand their skill set with advanced machine learning techniques.
• Students and professionals looking for a structured path to learn GNNs from scratch.

By the end of this course, you'll have a solid understanding of Graph Neural Networks, both in theory and practice, ready to tackle complex data structures with confidence. 🌟

Enroll now and embark on your journey to mastering Graph Neural Networks with Younes Sadat-Nejad! 🚀✨