Logistic Regression and Text Classification: From Features to Probability and Loss | Stanford CS124 Week 3
Week 3 teaches logistic regression: turn a document into a feature vector, pass the weighted sum through a sigmoid to get a probability, measure the error with cross-entropy, and fix the weights with gradient descent. This post works the slides' movie-review example through a prediction, a loss and one weight update, and explains why evaluation looks at precision and recall.