cnns, transformers, and useful llm internals

Architecture literacy helps builders predict cost and behavior. This chapter keeps CNNs, token budgets, attention, decoding, KV cache, and quantization grounded in the tradeoffs a product builder actually reviews.

The exercises use small Python dictionaries and lists so every check can run in the browser. Real-world tools may be larger, but the review shape stays the same: input, decision, evidence, blocker, and next step.

By the end of the chapter, learners should be able to turn this topic into a concrete handoff instead of a vague model claim.