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 …
Cs-Fundamentals
Data Structures for AI Applications
Arrays, hash maps, trees, graphs, queues, and vector stores - how the choice of data structure shapes the performance of AI pipelines.
Floating-Point Arithmetic and Model Precision
IEEE 754, FP32, FP16, BF16, and INT8 - how number precision determines model size, inference speed, and accuracy tradeoffs in AI deployment.
Hardware Constraints for AI Systems
CPU vs GPU, VRAM limits, memory bandwidth, and how hardware choices determine what AI models you can run and at what cost.
Object-Oriented Programming (OOP)
Classes, objects, inheritance, encapsulation, and polymorphism - how OOP concepts apply directly to AI frameworks like CrewAI and Pydantic.
Sorting and Search Algorithms for AI Pipelines
How sorting and search algorithms underpin AI pipeline design: complexity trade-offs, partial sorting for top-k selection, tiered analysis …
Time Complexity and Big-O Notation
An introduction to Big-O notation and how it describes the asymptotic behavior of algorithms.