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How to get started with Graph Machine Learning

A talk by Aleksa Gordic
Software - Deep Learning engineer, Microsoft | The AI Epiphany

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What can you learn about Graph Machine Learning in 2 months?

Aleksa Gordic, Machine Learning engineer @ Microsoft and Founder @ The AI Epiphany, shares his journey in the world of Graph Machine Learning. Aleksa started exploring the basics in the world of Graph Machine Learning, and ended up implementing and open sourcing his own Graph Attention Network on PyTorch.

In this talk, Aleksa will share the fundamentals of Graph Machine Learning, provide real-world examples, resources, and everything his younger self would be grateful for. Aleksa will also be available to answer questions.

What is Graph Machine Learning? Simply put, Graph Machine Learning is a branch of machine learning that deals with graph data.

Graphs consist of nodes, that may have feature vectors associated with them, and edges, which again may or may not have feature vectors attached. The applications are endless. Massive-scale recommender systems, particle physics, computational pharmacology / chemistry / biology, traffic prediction, fake news detection, and the list goes on and on.

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Aleksa Gordic

Aleksa is a software/deep learning engineer at Microsoft, as well as the founder of AI Epiphany. A huge fan of self-education with a strong focus on mathematics and everything deep learning related. He channeled that passion into The AI Epiphany where he educates others on AI through his open-source GitHub projects, blogs, and YouTube videos.

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