Tag: #statistics
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 6 posts
Building an Eval Set for Your Own Service — From Traffic Collection to Statistical Significance
Public benchmarks cannot measure your problem for you, because the input distribution, the definition of a correct answer, and the cost structure of failure are all different. This post walks through, with real code, how
2026-08-02 · 23 min read #llm-evaluation#eval-set#rubric#statistics#regression-testingThe Dunning-Kruger Effect — That Famous Graph Is Not in the Original Paper
The Peak of Mount Stupid graph that circulates online appears nowhere in the 1999 paper by Dunning and Kruger. The original was four studies that gave Cornell undergraduates tasks in humor, logic and grammar and compared
2026-07-26 · 9 min read #psychology#paper-review#metacognition#self-assessment#statisticsHow to Stop Evaluating LLMs on Vibes — Sample Size, Judge Bias, and CI Regression Tests
A team that edits a prompt and ships on "it seems better now" has no way to see the regressions quietly piling up. This post splits evaluation into four layers — assertions, golden datasets, LLM-as-judge, and human revie
2026-07-26 · 12 min read #llm#evaluation#llm-as-judge#statistics#regression-testingEngineering Math Series 24: Numerical Methods, Optimization, Graphs, Probability and Statistics Roadmap
Wraps up the first cycle of the engineering math series and organizes why numerical methods, optimization, graphs, and probability and statistics matter, plus the recommended study order.
2026-03-19 · 4 min read #engineering-math#numerical-methods#optimization#probability#statisticsMathematical Foundations for AI/ML: Complete Guide - Linear Algebra, Calculus, Probability
A complete guide to mastering the core mathematics required to understand AI and machine learning. Covers linear algebra (vectors, matrices, eigenvalues), calculus (partial derivatives, chain rule), probability and stati
2026-03-17 · 21 min read #mathematics#linear-algebra#calculus#probability#statisticsComplete Math Guide for AI — From Linear Algebra to Information Theory
A guide to the math needed for AI/deep learning, explained with code and intuition. Linear algebra (matrices, eigenvalues), calculus (partial derivatives, backpropagation), probability/statistics (Bayes, distributions),
2026-03-02 · 13 min read #ai#mathematics#linear-algebra#calculus#probability