ML foundations (math-first)¶
This chapter is math-first: it defines precisely what "learning" is, derives the loss functions and their gradients from first principles, connects loss and regularization back to probability theory, derives the metrics you evaluate with (and computes them on numeric examples you can check by hand), and states the statistical tests that separate a real signal from luck. It is the bedrock for MLOps (see MLOps principles) and for every algorithm chapter in Part X — classical ML (17), deep learning (18), NLP/LLMs (19), computer vision (20), generative AI (21), reinforcement learning (22), and time series forecasting (23). …
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