Binary and Number Systems in Computing
How computers represent all data in base-2 (binary), why transistors make this fundamental, and how number systems connect to AI model …
How computers represent all data in base-2 (binary), why transistors make this fundamental, and how number systems connect to AI model …
Arrays, hash maps, trees, graphs, queues, and vector stores - how the choice of data structure shapes the performance of AI pipelines.
IEEE 754, FP32, FP16, BF16, and INT8 - how number precision determines model size, inference speed, and accuracy tradeoffs in AI deployment.
CPU vs GPU, VRAM limits, memory bandwidth, and how hardware choices determine what AI models you can run and at what cost.
Classes, objects, inheritance, encapsulation, and polymorphism - how OOP concepts apply directly to AI frameworks like CrewAI and Pydantic.
How sorting and search algorithms underpin AI pipeline design: complexity trade-offs, partial sorting for top-k selection, tiered analysis …
An introduction to Big-O notation and how it describes the asymptotic behavior of algorithms.