#AI & Machine Learning

4 article(s)

Engineering & AI Jun 2026 · 4 min read

Preface to the “Foundations and Frontiers of Large Language Models” Series

A structured, engineer‑focused introduction to the “Foundations and Frontiers of Large Language Models” series — connecting the core principles, architectural breakthroughs, engineering paradigms, and emerging system‑level patterns behind modern LLMs. This preface outlines why the series exists, who it’s for, and how it maps the evolution from statistical NLP to Transformers, from prompting to RAG, from reasoning to agentic systems, and from classical recommenders to LLM‑native paradigms.

#AI & Machine Learning #AI Engineering #Software Engineering #Knowledge & Thinking

Engineering & AI Jun 2026 · 5 min read

Rapid ML Prototyping: Re-evaluating Weka for Classic Classification Tasks

A pragmatic guide to using Weka's GUI for quick baseline classification, parsing CSV constraints, and extracting the power of traditional ML models like SMO (SVM).

#AI & Machine Learning

Engineering & AI Jun 2026 · 4 min read

Dynamic Equivalence in Translation: From Sociosemiotics to Vector Spaces

A retrospective analysis of Eugene Nida's functional equivalence theory and sociosemiotics, and how these classical translation philosophies mirror modern cross-lingual LLM alignment.

#AI & Machine Learning #Humanities

Engineering & AI May 2026 · 16 min read

Demystifying Conditional Random Fields (CRF) for NER: From Mathematical Elegance to Practical Implementation

A deep dive into the underlying mathematics of Conditional Random Fields (CRF), why they still matter in the age of LLMs, and a practical step-by-step guide to implementing sequence labeling using CRF++. Note: this is a combination of 2 old blog posts of me: https://blog.csdn.net/Felomeng/article/details/4288492 https://felomeng.blog.csdn.net/article/details/4367250

#AI & Machine Learning #Tech History