While Model Trains

Read data blog posts.
Carefully handpicked.
Presented 3 at a time.

Variance after scaling and summing: One of the most useful facts from statistics

Chris Said

"What do R2, laboratory error analysis, ensemble learning, meta-analysis, and financial portfolio risk all have in common? The answer is that they all depend on a fundamental principle of statistics that is not as widely known as it should be. Once this principle is understood, a lot of stuff starts to make more sense."

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How much data should you allocate to training and validation?

Francesco Pochetti

To avoid responding with "that's what Andrew NG said" when asked about the reason behind choosing an 80% training and 20% validation split, consider this explanation.

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The AI Hierarchy of Needs

Monica Rogati

"Think of AI as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water and shelter (data literacy, collection and infrastructure)."

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