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How Transformers Work: A Plain Explanation Let’s revisit transformers and try to figure out how they actually work — as usual, minimal math and no AI-generated filler. The author makes no claims to absolute truth and may occasionally get things slightly wrong. 1. Let’s Start Simple Let’s refresh our memory on how recurrent networks worked — the ones that “loop back to themselves”. Hard not to think of Hegel’s Absolute Spirit, which travels a path of self-alienation only to return to itself — pure Hegelian recurrence. ...

“Study, Study, and Study Again!” (c) V.I. Lenin Why bother? So here’s the task: build an AI assistant that uses an SLM (Small Language Model) to extract personalized information about the user. Why SLM? Well, privacy — data doesn’t leave the device, plus cost savings. We don’t need the details here — the point is to figure out whether this is even feasible with acceptable quality, what thorns await us, and what to do about it. Let’s go! ...

Agents, Agents Everywhere… Our icons are the prettiest (c) Preface: why bother? As you know, in IT there’s no such thing as “doing nothing.” We call it “researching.” That’s the moment when, barely awake before standup, you frantically try to come up with a justification for yesterday’s idleness in a matter of seconds. But even your research might need some artifacts you can present while insisting you’ve been working on it day and night. Not to mention perfectly pragmatic tasks — exploring new topics and terms to impress people and boost your authority (and hang on to that contract in these grim times). ...

Embeddings, Attention, FNN and Everything You Wanted to Know But Were Afraid to Ask Introduction With this, I’m trying to start a series of articles dedicated to LLMs (large language models), neural networks, and everything related to the AI abbreviation. The goals of writing these articles are, of course, self-serving, because I myself started diving into these topics relatively recently and faced the fact that there seems to be a mass of information, articles, and documents written in small print with pretentious diagrams and formulas, reading which, by the end of a paragraph, you forget what the previous one was about. Therefore, here I will try to describe the essence of the subject area at a conceptual level - and so I promise to avoid mathematical formulas and tricky graphs as much as possible, seeing which, the reader inevitably catches themselves wanting to close the browser tab and visit the nearest liquor store. So - no formulas (except the simplest ones), no pretentiousness, no pretensions of looking smarter than I am. Your grandmother should understand these articles, and if that didn’t work out - then I failed the task. ...

Neural Networks for Beginners - a simple and accessible introduction to the amazing world of artificial intelligence. We’ll start by explaining the basic principles of how neural networks work, using clear analogies and visual examples. Then we’ll move on to practical aspects - how to create, train, and use neural networks to solve real problems. By the end of this article, you’ll have a clear understanding of what neural networks are and how they work. ...