Analysis of Chip Huyen's chapter on AI system evaluation, covering evaluation-driven development, criteria, and practical implementation.
Explains polynomial regression as a solution to under-fitting in machine learning when data has a nonlinear correlation.
A guide to model evaluation, selection, and algorithm comparison in machine learning to ensure models generalize well to new data.
A guide to evaluating machine learning models, selecting the best models, and choosing appropriate algorithms to ensure good generalization performance.
Explores the concept of object signatures for defining parameters, focusing on applications in libraries like scikit-learn for model selection and avoiding type checking.
