DLAI Study Notes
A corpus of personal study notes synthesized from deeplearning.ai short courses. DLAI courses are video + handouts; these are the readable, searchable, math-rendered companion form that didn't otherwise exist.
Compliance with DLAI Community Code of Conduct : non-commercial (CC-BY-NC-4.0), DLAI cited and instructor named, no quizzes / graded assignments / lab solutions / verbatim transcripts. Takedown requests: brandon.m.behring@gmail.com (48-hour response).
Books in this corpus
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Knowledge Graphs for RAG
Study notes on DLAI's Knowledge Graphs for RAG (Andreas Kollegger, in partnership with Neo4j). Graph data modelling (nodes / relationships / properties / labels), Cypher querying, preparing text for RAG with vector indexes, constructing and expanding a knowledge graph from SEC filings, and chatting with the graph via LLM-generated Cypher — with applied practice questions, a glossary, and spaced-recall flashcards.
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Fine-tuning & RL for LLMs: Intro to Post-training
Study notes on DLAI's Fine-tuning & RL for LLMs: Intro to Post-training (Sharon Zhou, in partnership with AMD). Where post-training sits in the LLM pipeline, the SFT-vs-RL tradeoff, data and grading requirements, reasoning and safety alignment, evaluation, and production pipelines — with applied practice questions, a glossary, and spaced-recall flashcards.
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Evaluating AI Agents
Study notes on DLAI's Evaluating AI Agents (John Gilhuly & Aman Khan, in partnership with Arize AI). Why LLM systems break traditional testing, decomposing agents into router/skills/memory, observability and tracing, component and trajectory evaluation, and LLM-as-judge with production monitoring — with applied practice questions, a glossary, and spaced-recall flashcards.
About this corpus
Author: Brandon Behring — applied mathematician who builds and learns. Source code: github.com/brandon-behring/dlai-study-notes.
Each book is paraphrased synthesis written by hand, not transcripts; includes original margin notes, interview prompts, worked problems, and Anki flashcards. Math via KaTeX, search via Pagefind, offline PDF via Paged.js. No tracking, no ads.
Full license, attribution & AI-assisted-authoring disclosure →