Embeddings: How Machines Represent Meaning
What embeddings are, how vector spaces capture similarity, and how teams use them for search, recommendations, clustering, and deduplication.
8 min read
The archive
Machine Learning
Research results and the fundamentals behind the models.
Overfitting: Why Models Memorize and How to Stop It
Why a model can look excellent in training and fail on real data, how to spot overfitting early, and the practical techniques that keep models honest.
8 min read
Data Quality for Machine Learning: The Unglamorous Work That Decides Results
The data problems that quietly sink machine learning projects, from label noise and leakage to drift, and a practical checklist for catching them early.
8 min read
The Transformer Architecture, Explained Without the Math Wall
A plain-language tour of the transformer: how attention works, why position has to be added in, and what each layer contributes to the final result.
8 min read
How to Evaluate a Machine Learning Model
A practical guide to precision, recall, F1, ROC-AUC, and calibration, and how to choose evaluation metrics that reflect what mistakes actually cost.
8 min read
Supervised, Unsupervised, and Self-Supervised Learning: A Practical Comparison
How the three main ways of learning from data differ, what each one needs from you, and how to pick the right approach for a real project.
9 min read