Jay Alammar

Jay Alammar is widely credited with helping a generation of practitioners understand attention, transformers, and representation learning through illustration rather than dense algebra alone. His articles walk readers through tensors, masking, positional encodings, and tokenizer behavior using progressive diagrams that mirror how people actually debug or teach these ideas on a whiteboard.

Beyond mechanical explanations of architectures, he emphasizes how small implementation choices—byte-pair encoding, layer norms, sampling strategies—shape model behavior in ways that aren’t obvious from headlines. That makes his work valuable both for newcomers building a mental model and for experienced engineers who need quick, trustworthy refreshers before designing training or inference stacks.

Alammar is worth following if you prefer learning by seeing data flow through a system. His combination of blog posts and video walkthroughs is especially helpful when a paper’s notation feels abstract; the visuals often make the “why this matrix multiply” click faster than repeated rereads of the original PDF.