newsletterSeptember 2, 2026·Towards Data Science

Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply

A visual guide to how graph neural networks work under the hood The post Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply appeared first on Towards Data Science.

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•A visual guide to how graph neural networks work under the hood The post Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply appeared first on Towards Data Science.
•This story was reported by Towards Data Science, covering developments in the newsletter space.
•AI advancements continue to reshape industries — read the full article on Towards Data Science for complete coverage.

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Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working

I built a matcher meant to finish the cleanup that normalization left behind. Testing it against real data showed that no version of it could be made safe. What follows is the architecture that was left once the matcher was set aside. The post Avoiding Entity Key Drift in a Data Lake: Step 2, When F

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A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

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A Practical Introduction to PySpark Window Functions

Why the standard groupBy function isn’t enough The post A Practical Introduction to PySpark Window Functions appeared first on Towards Data Science.

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Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch

Five failure modes that survive constrained decoding, and why your schema validator will never catch them. The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.

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Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply

Key Takeaways

Related Articles

Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working

A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

A Practical Introduction to PySpark Window Functions

Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch

Discussion

Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply

Key Takeaways

Related Articles

Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working

A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

A Practical Introduction to PySpark Window Functions

Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch

Discussion

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Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working
I built a matcher meant to finish the cleanup that normalization left behind. Testing it against real data showed that no version of it could be made safe. What follows is the architecture that was left once the matcher was set aside. The post Avoiding Entity Key Drift in a Data Lake: Step 2, When F

Towards Data Science
A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence
Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare “no answer” with no justification is almost as bad. Each of the four bricks has one piece of evidence to show The post A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence appeared first on

Towards Data Science
A Practical Introduction to PySpark Window Functions
Why the standard groupBy function isn’t enough The post A Practical Introduction to PySpark Window Functions appeared first on Towards Data Science.

Towards Data Science
Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
Five failure modes that survive constrained decoding, and why your schema validator will never catch them. The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.