newsletterOctober 23, 2026·Towards Data Science

Skewness and Kurtosis: From Moments to Machine Learning

The mathematics, intuition, interpretation, and practical use of higher-order moments. The post Skewness and Kurtosis: From Moments to Machine Learning appeared first on Towards Data Science.

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Don’t Let Messy Data Bend Your Curves

Robust Estimation Series: Building Accurate Curves with Polynomials and Splines With Spurious Data The post Don’t Let Messy Data Bend Your Curves appeared first on Towards Data Science.

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Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension

What does effective dimension reveal about redundancy, generalization, and trainability that parameter count, loss, and accuracy do not? The post Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension appeared first on Towards Data Science.

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Your Six AI Models Are Really Two: The Math of Correlated Mistakes

Ask several models the same question, take a vote, and you get fewer independent opinions than you paid for. One formula says how many, and I checked it on six open models and 1,500 exam questions. The post Your Six AI Models Are Really Two: The Math of Correlated Mistakes appeared first on Towards

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Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions

I let an AI agent search again whenever it chose. It found some papers plain search had missed, lost some plain search had found, and gained too little to trust. The post Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions appeared first on Towards Data Science.

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Skewness and Kurtosis: From Moments to Machine Learning

Key Takeaways

Related Articles

Don’t Let Messy Data Bend Your Curves

Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension

Your Six AI Models Are Really Two: The Math of Correlated Mistakes

Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions

Discussion

Skewness and Kurtosis: From Moments to Machine Learning

Key Takeaways

Related Articles

Don’t Let Messy Data Bend Your Curves

Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension

Your Six AI Models Are Really Two: The Math of Correlated Mistakes

Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions

Discussion

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Don’t Let Messy Data Bend Your Curves
Robust Estimation Series: Building Accurate Curves with Polynomials and Splines With Spurious Data The post Don’t Let Messy Data Bend Your Curves appeared first on Towards Data Science.

Towards Data Science
Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension
What does effective dimension reveal about redundancy, generalization, and trainability that parameter count, loss, and accuracy do not? The post Why Parameter Count Is Not Model Complexity: An Introduction to Effective Dimension appeared first on Towards Data Science.

Towards Data Science
Your Six AI Models Are Really Two: The Math of Correlated Mistakes
Ask several models the same question, take a vote, and you get fewer independent opinions than you paid for. One formula says how many, and I checked it on six open models and 1,500 exam questions. The post Your Six AI Models Are Really Two: The Math of Correlated Mistakes appeared first on Towards

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Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions
I let an AI agent search again whenever it chose. It found some papers plain search had missed, lost some plain search had found, and gained too little to trust. The post Does Agentic RAG Actually Retrieve Better Evidence? I Tested It on 298 Questions appeared first on Towards Data Science.