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Articles tagged with #python
Every course teaches you how to train a model. Almost none teach you how to keep it alive. This is the part of ML that pays the bills — and the part that breaks. Eighteen months ago I built a demand-f
An honest comparison of regression, trees, ensembles, SVMs, and neural networks — scored on the criteria that actually matter in production, with a decision rule you can use today. A few months ago, a
A practical guide to neural network pruning — the concepts (magnitude pruning, structured vs unstructured, iterative pruning, distillation), the PyTorch implementation, and what actually happens when
A practical deep dive into model quantization — the precision-ladder trick that takes a 14GB model down to 3.5GB, what you lose, what you keep, and the code to measure both. Six months ago I was tryin
The honest, practical guide to hyperparameter tuning — what the knobs actually do, when to turn them by hand, when to automate, and the learning-rate mistake that wastes more GPU hours than anything e
A practical, honest path from zero to your first working model — the Keras Sequential API, a real data pipeline, and the production lessons that tutorials skip. I spent a week once helping a team of i
