Model-evaluation - TechnoScripts

model-evaluation

Guides on evaluating machine learning models with metrics, validation, and test sets.

AI Training Data: Where Datasets Come From and Why They Matter

AI training data is the single biggest reason a model works or quietly falls apart, and this tutorial explains where datasets come from, what a label really is, how bias sneaks in, and why the train/validation/test split is the habit that keeps you honest. If you remember one thing from the whole AI Foundations chapter, make it this: the data you feed a model matters more than the model itself. “Real stupidity beats artificial intelligence every time.” Terry Pratchett, Hogfather ... Read More

Dropout and Batch Normalization: DL Regularization Explained

A big neural network is a champion memorizer, and deep learning regularization is how you keep it honest. Give it enough neurons and it will learn your training data by heart, noise and all, then fall flat on data it has never seen. This post shows you how to stop that, using dropout, batch normalization, and weight decay to close the gap between training and test accuracy. Every number below comes from real code run on Python 3.14.6. “Dropout provides ... Read More

ML: Best Practices, the Ethics and Gotchas Nobody Warns You About

These ML best practices cover the ethics and the catches that nobody warns you about: how to spot bias in your data, how to measure fairness across different groups of people, how to explain what your machine learning model is actually doing, and the responsible AI checklist that separates a production-grade model from a dangerous prototype. Fairness in machine learning is not a purely technical problem. That is the core lesson of the Gender Shades study. after Joy Buolamwini & ... Read More