While Model Trains

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

A few reasons to be skeptical of machine learning

Julia Evans

"why, even though machine learning is really awesome and cool and you can do super powerful and interesting things with it – why you should still be skeptical"

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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 Bitter Lesson

Richard Sutton

"The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin."

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