generalization

Many state-of-the-art training runs keep a shadow copy of the weights that's a moving average of the trajectory, and serve that instead of the final step. It's a few lines and a real accuracy boost. Here it is.

The most common ML fundamentals question, and a quiet seniority test: anyone recites the definition, but can you decompose the error and turn it into a concrete debugging plan?

Data augmentation cheaply expands training data and regularizes models, but valid transforms differ by modality. The signal is the label-preserving constraint and why text is the hard one. Here is the answer.

Two cheap regularizers that fix overconfident classifiers. The signal is knowing that one softens the target and the other softens the input, and exactly why softer signals calibrate the model. Here is the answer.

Classic bias-variance says bigger models eventually overfit, yet deep nets keep improving past the point where they memorize the data. The signal is explaining the second descent and why over-parameterized models generalize. Here is the answer.

Batch size is a training knob with non-obvious effects on speed, memory, and generalization. The signal is the large-batch tradeoffs and the learning-rate-scaling and gradient-accumulation tricks that make scaling actually work.