generalization

Many state-of-the-art training runs hold a shadow copy of the weights that averages the trajectory, then serve that rather than the final step. It's a few lines and a genuine accuracy gain. Here it is.

The most frequent ML fundamentals question, and a subtle seniority check: anyone can repeat the definition, but can you break down the error and convert it into a concrete debugging plan?

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

Two inexpensive regularizers that cure overconfident classifiers. What proves you understand them is knowing that one softens the target while the other softens the input, and precisely why gentler signals produce calibrated models. Here is the answer.

Classic bias-variance predicts that larger models eventually overfit, but deep nets keep improving even after they memorize the data. What shows depth is explaining the second descent and why over-parameterized models generalize. Here is the answer.

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