MLE from scratch #1 - Linear regression beyond the textbook
In the current AI landscape, classical machine learning models are often considered obsolete. Large-scale deep learning and foundation models dominate most discussions, driven by abundant compute, massive datasets, and rapidly evolving tooling. In many teams, reaching for a large model has become the default response, sometimes before the problem itself is fully understood. What is discussed far less is the cost of this default. Infrastructure expenses grow quickly, data requirements escalate, and system complexity increases in ways that are difficult to reason about. In practice, many production failures attributed to model limitations are symptoms of deeper issues, weak signals, noisy or poorly curated data, and objectives that were never clearly defined. ...