A practical guide to loss functions, gradient descent, learning rates, feature scaling, regularization, logistic regression, softmax, and production-scale ML training.
Machine learning models do not simply “learn” from data.
They solve an optimization problem.
Given a dataset, a model, and an objective function, training is the process of finding parameter values that make the model perform well according to that objective.
That seemingly simple idea connects a large part of classical machine learning:
