Book · Machine Learning with PyTorch and Scikit-Learn

Graph Neural Networks with PyTorch Geometric

An advanced deep-learning milestone for graph-structured data, message passing, and molecular property prediction with PyTorch Geometric.

Graph neural networksMessage passingPyTorch GeometricQM9 moleculesSet2Set pooling

GitHub

What this project is about

This milestone goes beyond images and sequences into graph-structured data. Molecules are represented as nodes and edges, then processed by message passing.

Implementation focus

The important idea was non-Euclidean learning: not all data is a grid or a sequence. Graph neural networks learn from relationships directly.

Key results and artifacts

QM9 dataset size: 130,831 molecules
Example graph: node features, edge index, edge attributes, targets, positions
Validation loss improved to around 0.056 by epoch 3

What I learned

This expanded the learning path beyond images and text into non-Euclidean data, where relationships are the structure.

Milestone deep dive

What this project proves in my learning path.

An advanced deep-learning milestone for graph-structured data, message passing, and molecular property prediction with PyTorch Geometric. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layernodes, edges, edge attributes, and graph batches

Model layermessage passing / graph neural network

Evaluation layertests, metrics, plots, or qualitative output

Artifact layergraph-level prediction and learned representations

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Graph neural networksMessage passingPyTorch GeometricQM9 moleculesSet2Set pooling

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This expanded the learning path beyond images and text into non-Euclidean data, where relationships are the structure.

Important outputs and evidence

QM9 dataset size: 130,831 molecules
Example graph: node features, edge index, edge attributes, targets, positions
Validation loss improved to around 0.056 by epoch 3

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.