brandon@seoul:~/studyen / ko

Study

A public study shelf for material I have actually worked through.

Each section turns notes I worked through — Georgia Tech DSA and AWS certification prep — into inspectable examples: visual demos, comparison maps, and quick recall prompts to keep the material in long-term memory.

dsa-i/ · 2026-06-18[active notes]

Data Structures & Algorithms I

ArrayLists, LinkedLists, stacks, queues, recursion, binary search, iterators, comparators, and Big-O from the DSA I study folder.learnedBig-O scaling · Array-backed lists · Recursive traces · Pointer updatesmodulesModule 0Module 1Module 2Module 3# Modules 0–3 · 5 interactive demos
dsa-ii/ · 2026-06-19[active notes]

Data Structures & Algorithms II

Binary search trees and traversals, BST removal, SkipLists, binary heaps and priority queues, and HashMaps with collisions and load factor, from the DSA II study folder.learnedBST traversals · BST removal · Heap sift · Hash collisionsmodulesModule 4Module 5Module 6Module 7# Modules 4-7 · 4 interactive demos
dsa-iii/ · 2026-07-06[fresh notes]

Data Structures & Algorithms III

AVL trees and rotations, (2,4) trees with splits and fusions, iterative sorts, and the divide-and-conquer sorts (merge, quicksort, LSD radix, and quickselect), from the DSA III study folder.learnedAVL rotations · (2,4) tree splits · Sort trade-offs · QuickselectmodulesModule 8Module 9Module 10Module 11# Modules 8-11 · 4 interactive demos
dsa-iv/ · 2026-07-31[fresh notes]

Data Structures & Algorithms IV

Pattern matching with Boyer-Moore, KMP, and Rabin-Karp, graph traversals and Dijkstra's shortest paths, minimum spanning trees with Prim's and Kruskal's, and dynamic programming from the LCS table to Bellman-Ford, from the DSA IV study folder.learnedPattern preprocessing · Graph traversals · MST cut property · DP tablesmodulesModule 12Module 13Module 14Module 15# Modules 12-15 · 4 interactive demos
aws-ai-practitioner/ · 2026-09-14[exam passed]

AWS Certified AI Practitioner

A visual AIF-C01 cram sheet: the AI → ML → GenAI hierarchy, Amazon Bedrock and SageMaker AI sub-services, Amazon Q by user, prompt vs RAG vs fine-tuning, evaluation metrics, responsible AI, and keyword-to-answer exam traps.learnedService routing · RAG vs fine-tuning · Metric choice · Responsible AIsectionsMental mapAWS service familiesGenAI decisionsML coreResponsible AI + governanceExam traps# 6 sections · 36 exam traps

approach.txt · 3 lines

  • Visual demos you can step through
  • A module roadmap for each course
  • Quick self-check prompts to fight forgetting
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