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Embeddings, Attention, FNN and Everything You Wanted to Know but Were Afraid to Ask Introduction With this article, I’m attempting to start a series dedicated to LLMs (Large Language Models), neural networks, and everything related to AI. The goals of writing these articles are, of course, self-serving, as I myself have relatively recently started diving into these topics and encountered a situation where there’s a wealth of information, articles, and documents written in fine print with complex diagrams and formulas. When reading them, by the time you finish a paragraph, you forget what the previous one was about. Therefore, here I will try to describe the essence of the subject matter at a conceptual level - and thus I promise to avoid mathematical formulas and intricate graphs as much as possible, which inevitably make readers want to close their browser tab and visit the nearest liquor store. So - no formulas (except the simplest ones), no complexity, no pretense to appear smarter than I am. Your grandmother should be able to understand these articles, and if that doesn’t happen - it means I’ve failed at my task. ...

Introduction Recently, I’ve noticed a slight tremor among colleagues preparing for Go interviews - everyone understands that questions about Go’s most hyped feature are inevitable. Sure, you’ve all read those articles - with pictures and letters M, G, P in circles, you know about GOMAXPROCS (though probably not what you should), but when the interviewer dives deeper into details, like asking - why do we need these goroutines and why weren’t OS threads enough? - that’s when the incoherent mumbling begins. ...