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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Real-time machine learning: challenges and solutions

Chip Huyen

"Real-time machine learning is largely an infrastructure problem. Solving it will require the data science/ML team and the platform team to work together."

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A better lesson

Rodney Brooks

"So my take on Rich Sutton’s piece is that the lesson we should learn from the last seventy years of AI research is not at all that we should just use more computation and that always wins. "

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