nlp

The formal machinery under masked text generation: categorical transition matrices, the kernel choices that were tried and discarded, the variational bound, and the two parameterizations that made the objective trainable at scale.

Words have internal structure, and modelling it is computational linguistics' cleanest success. This lesson covers morphemes and the ways languages build words, why the finite-state machines at the bottom of the hierarchy are enough, and how finite-state transducers analyze and generate word forms, up to the modern neural shared tasks.

Computational linguistics treats human language as something to be modelled precisely, and language fights back at every level. This lesson maps the levels of linguistic structure, shows why ambiguity, recursion, and Zipf's law make naive approaches fail, and frames the formal and statistical traditions the rest of the path builds on.

A rigorous guide to measuring RAG quality using RAGAS metrics — faithfulness, answer relevancy, context precision, and context recall — plus how to build a golden dataset and recognize where automated metrics fall short.

Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.

A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.

From the limits of RNNs to the self-attention mechanism that replaced them. Learn how queries, keys, and values implement scaled dot-product attention, why multi-head attention captures richer structure, how positional encodings inject order, and how all of this assembles into a transformer block.

Explore Retrieval-Augmented Generation (RAG), a powerful technique that enhances Large Language Models (LLMs) by grounding their responses in external, up-to-date, and domain-specific information, mitigating hallucinations and improving factual accuracy. This lesson covers its core components, workflow, and practical considerations.