Explain bias–variance, overfitting, and vanishing gradients

Q: Explain bias–variance, overfitting, and vanishing gradients

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Answer the following ML fundamentals questions:

Bias–variance tradeoff: What are bias and variance? How do they relate to underfitting/overfitting? What practical actions increase/decrease each?
Overfitting: What does it mean for a model to overfit? How would you detect it, and what are common mitigation strategies?
Vanishing gradients: What is the vanishing gradient problem? When does it occur (e.g., deep nets / RNNs), what are its symptoms, and what techniques mitigate it?

Explain bias–variance, overfitting, and vanishing gradients

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