"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."
Read it!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.
Read it!"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)."
Read it!