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Channel

Машиннное обучение | Наука о данных Библиотека

@machinelearning_books

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

16,878subscribers

+30 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001716596674
TypeChannel
Username@machinelearning_books
CreatedBetween 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live25 September 2026
Measurements held29
Confirmed unchanged1 time, most recently 25 September 2026
On Telegramt.me/machinelearning_books

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Growth

16,82716,89016,858.57 August 2026 — 16,848 subscribers7 August 2026 — 16,850 subscribers9 August 2026 — 16,840 subscribers10 August 2026 — 16,839 subscribers11 August 2026 — 16,836 subscribers12 August 2026 — 16,831 subscribers14 August 2026 — 16,827 subscribers17 August 2026 — 16,828 subscribers18 August 2026 — 16,830 subscribers20 August 2026 — 16,831 subscribers21 August 2026 — 16,837 subscribers22 August 2026 — 16,853 subscribers24 August 2026 — 16,864 subscribers25 August 2026 — 16,861 subscribers26 August 2026 — 16,856 subscribers28 August 2026 — 16,858 subscribers29 August 2026 — 16,859 subscribers31 August 2026 — 16,857 subscribers1 September 2026 — 16,868 subscribers2 September 2026 — 16,886 subscribers3 September 2026 — 16,887 subscribers5 September 2026 — 16,890 subscribers8 September 2026 — 16,880 subscribers11 September 2026 — 16,879 subscribers13 September 2026 — 16,876 subscribers14 September 2026 — 16,888 subscribers16 September 2026 — 16,881 subscribers18 September 2026 — 16,882 subscribers25 September 2026 — 16,878 subscribers16,8787 August 202625 September 2026
29 measurements spanning 49 days, net +30. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 16,818–16,899 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 29
Measured (UTC)SubscribersChange
25 Sept 2026, 05:3816,878-4
18 Sept 2026, 21:1616,882+1
16 Sept 2026, 14:2116,881-7
14 Sept 2026, 16:1816,888+12
13 Sept 2026, 05:5816,876-3
11 Sept 2026, 10:3616,879-1
8 Sept 2026, 20:0116,880-10
5 Sept 2026, 10:3716,890+3
3 Sept 2026, 13:5816,887+1
2 Sept 2026, 04:3416,886+18
1 Sept 2026, 07:4416,868+11
31 Aug 2026, 10:4316,857-2
29 Aug 2026, 14:5516,859+1
28 Aug 2026, 16:3316,858+2
26 Aug 2026, 18:3716,856-5
25 Aug 2026, 18:5816,861-3
24 Aug 2026, 16:2316,864+11
22 Aug 2026, 23:4416,853+16
21 Aug 2026, 13:2816,837+6
20 Aug 2026, 09:5216,831first reading

Engagement

28 posts held, back to 9 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 55 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
2.90%
avg views ÷ 16,878 subscribers
Avg views / post
489
1 post measured
Reaction rate
1.84%
reactions ÷ views · ER floor
Posts in window
1
of 28 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 30 August 2026
Posts held28 (9 June 2026 – 30 August 2026)
Views total489
Reactions total9
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Sept 2026, 09:35 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Video runtime
4m 35s
Average length
2m 18s

Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Reaction mix

285 reactions across 27 posts, in 8 distinct kinds. The most used accounts for 41.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤11841.4%
👍8630.2%
🔥5920.7%
😁82.81%
🥰72.46%
👎51.75%
🗿10.351%
🤡10.351%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 27 of the 28 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 285 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 28 most recent posts we hold, published 9 June 2026 to 30 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Advertising

Ad load
3.57%
1 of 28 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
1,400
over 1 measured post
Median views · rest
1,740
over 27 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2W5zFGVyJ8h115 July 202615 July 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 28 most recent posts we hold, published 9 June 2026 to 30 August 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

30 Aug 2026, 11:29 UTC489 views9 reactionsread 3 September 2026
Forwarded from @ai_machinelearning_big_dataVideo

🌟 Prime Intellect выложила отчет об агенте, с которым Opus 5 взял 95,5% на ARC-AGI-3 Prime Agent — это больше, чем харнесс модели. Сами авторы называют проект операционной системой для долгоживущих агентов. Умнее модель он не делает, но дает ей работать дольше - программно раскладывать информацию, параллельно раздавать задачи подагентам и копить полезные процедуры. Идея в том, чтобы у языковой модели была устойчив…

👍4❤3🔥2

26 Aug 2026, 07:02 UTC≈1,100 views3 reactionsread 3 September 2026
Photo

🔥 Prime Agent выпустили технический отчёт "Prime Agent: A Self-Improving RLM Harness" В работе подробнее разобрана архитектура Prime Agent и его роль как harness для долгих agentic-оценок. Один из самых интересных экспериментов - Factorio: - 7 дней непрерывной траектории на Sonnet 5 - 23,4 млн output tokens - завершено 24 из 196 технологий - ещё 71% прогресса до следующей технологии - 633 запущенных subagents - м…

🔥3

25 Aug 2026, 15:01 UTC≈1,190 views11 reactionsread 3 September 2026
Photo

🤯 Локальные AI-модели становятся пугающе мощными Qwen3.8-27B вошла в топ-10 рейтинга Arena WebDev, оказавшись всего в нескольких позициях от моделей, которые больше неё на порядки. Модель всего на 27 млрд параметров, которую можно запускать локально. Ещё недавно такие возможности требовали огромных облачных моделей и дорогой инфраструктуры. Теперь open-source модели начинают конкурировать с лидерами рынка прямо н…

🔥6❤4🥰1

25 Aug 2026, 10:05 UTC≈1,040 views8 reactionsread 3 September 2026
Photo

🤖 AI-агенты уже пишут код. Но умеют ли они чинить настоящие проблемы? Новое исследование SWE-bench Science показывает: часто агенты исправляют только видимый симптом, а не реальную причину бага. Учёные проверили агентов на задачах из открытых научных репозиториев: * публичные тесты можно использовать для итераций; * скрытые тесты показывают, действительно ли проблема решена. Результат удивляет: Claude Code с Opu…

👍5❤2🔥1

23 Aug 2026, 14:48 UTC≈1,350 views12 reactionsread 3 September 2026
Photo

У Microsoft вышла очень показательная работа про надёжность AI-агентов. Лучший агент в эксперименте смог успешно выполнить 91% бизнес-задач хотя бы один раз. Но если требовать, чтобы он выполнял ту же задачу правильно каждый раз, показатель падал до 25%. И есть ещё более неприятная часть. В 4 из 5 неудачных запусков агент: — вежливо завершал работу — вызывал инструмент записи в базу — сообщал, что всё выполнено …

❤6👍4🔥2

23 Aug 2026, 10:11 UTC971 views2 reactionsread 3 September 2026
Photo

🔥 Хочешь быстрее расти в IT? Хватит учиться в одиночку Окружение решает больше, чем кажется. Собрал папки и каналы, где можно быстрее влиться в нужное направление, следить за трендами и не вариться в своём пузыре. AI: t.me/ai_machinelearning_big_data Python: t.me/pythonl Linux: t.me/linuxacademiya Хакинг: t.me/linuxkalii DevOps: t.me/DevOPSitsec Docker: https://t.me/+90Z5TAyfuNU5YmRi Golang: t.me/Golang_google Rus…

❤2

19 Aug 2026, 12:28 UTC≈4,370 views12 reactionsread 3 September 2026
Photo

Бесплатная книга по performance engineering В Algorithmica хорошо разобрали, почему классическая оценка сложности всё хуже отражает реальную производительность на современном железе. Раньше модель была довольно логичной: процессор выполняет инструкции почти последовательно, у каждой есть своя стоимость, а значит можно примерно оценить время работы алгоритма количеством операций. Потом всё упростили до асимптотики.…

❤7👍4🥰1

18 Aug 2026, 09:46 UTC≈1,410 views3 reactionsread 3 September 2026
Photo

🔥 Год production-трафика показал: быстрый LLM-serving начинается не со scheduler, а с понимания поведения пользователей Исследователи из Harvard и University of Chicago проанализировали год production-трафика Chutes - миллиарды запросов к тысячам моделей. Главный вывод: реальные LLM-нагрузки сильно меняются со временем, поэтому короткие synthetic benchmarks дают слишком упрощённую картину. Пользователь часто возвр…

❤3

15 Aug 2026, 11:12 UTC≈1,560 views14 reactionsread 3 September 2026
Photo

⚡️ Harness Engineering - полный курс на русском Как заставить AI-агента писать код надёжно. Не «какую модель выбрать», а как спроектировать вокруг неё рабочую систему: инструкции, инструменты, среду, состояние и верификацию. Курс - от нуля до продвинутых тем: теоретическая база из 11 блоков, 14 модулей, 8 практических проектов, 14 лабораторных работ, диагностический протокол «как починить агента», библиотека готовы…

❤6👍5🔥3

13 Aug 2026, 13:56 UTC≈1,420 views12 reactionsread 3 September 2026
Forwarded from @data_analysis_mlPhoto

🔥 Нашёл отличный бесплатный ресурс по Harness Engineering - тому, что делает coding agents реально надёжными Курс посвящён не очередному «как написать хороший промпт», а инженерии окружения вокруг Codex, Claude Code и других AI-агентов. Разбирают: * почему сильные агенты всё равно проваливают задачи; * как сохранять контекст между длинными сессиями; * зачем нужны AGENTS.md, feature_list.json и progress-файлы; * ка…

🔥8👍3🥰1

11 Aug 2026, 13:25 UTC≈1,740 views17 reactionsread 3 September 2026
Photo

🧠 Любишь ложиться поздно? Исследования действительно находили связь между «совами» и более высокими результатами когнитивных тестов, но всё не так просто. В работе 2009 года исследователи проанализировали данные более 10 тысяч человек и обнаружили: участники с более высокими результатами тестов интеллекта в детстве чаще становились взрослыми, которые позже ложатся и позже встают. Авторы объясняли это через гипотезу…

❤7👍5😁3👎1🔥1

28 Jul 2026, 10:27 UTC≈2,410 views22 reactionsread 3 September 2026
Photo

Qwen научила модель создавать себе учебную программу Команда Qwen представила Skill Self-Play - подход, в котором LLM сама генерирует задания, решает их и постепенно расширяет библиотеку проверенных навыков. Проблема обычного self-play: модель либо остаётся в узких средах с простой проверкой, либо создаёт много разнообразных, но ненадёжных задач. В Skill-SP работают три компонента: proposer придумывает сложные за…

👍9❤8🔥5

Showing the 12 most recent of 28 posts we hold for @machinelearning_books. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 3 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Data Science
@datascienceiot · 42,634
Telegram ranks this channel #2 of 76 here — alongside 75 others — read 29 August 2026
Python вопросы с собеседований
@python_job_interview · 24,881
Telegram ranks this channel #13 of 90 here — alongside 89 others — read 15 September 2026
Python/ django
@pythonl · 58,866
Telegram ranks this channel #17 of 84 here — alongside 83 others — read 22 August 2026
Data Science. SQL hub
@sqlhub · 35,960
Telegram ranks this channel #18 of 87 here — alongside 86 others — read 2 September 2026
Machinelearning
@ai_machinelearning_big_data · 279,417
Telegram ranks this channel #23 of 95 here — alongside 94 others — read 10 August 2026
Machine learning Interview
@machinelearning_interview · 30,308
Telegram ranks this channel #26 of 98 here — alongside 97 others — read 7 September 2026
Data Science Jobs
@datascienceml_jobs · 21,227
Telegram ranks this channel #31 of 97 here — alongside 96 others — read 24 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 50,656
Telegram ranks this channel #38 of 93 here — alongside 92 others — read 25 August 2026
Physics.Math.Code
@physics_lib · 146,712
Telegram ranks this channel #47 of 72 here — alongside 71 others — read 13 August 2026
Учебные фильмы 🎞
@maths_lib · 25,423
Telegram ranks this channel #57 of 67 here — alongside 66 others — read 14 September 2026
Python learning
@python3learning · 22,563
Telegram ranks this channel #58 of 71 here — alongside 70 others — read 20 September 2026
Sber AI
@SberAIScience · 27,144
Telegram ranks this channel #61 of 95 here — alongside 94 others — read 10 September 2026
DevOps
@DevOPSitsec · 23,684
Telegram ranks this channel #68 of 90 here — alongside 89 others — read 18 September 2026
Искусственный интеллект. Высокие технологии
@vistehno · 71,491
Telegram ranks this channel #68 of 94 here — alongside 93 others — read 20 August 2026
Artificial Intelligence && Deep Learning
@DeepLearning_ai · 57,432
Telegram ranks this channel #79 of 87 here — alongside 86 others — read 23 August 2026

This channel appears in 15 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 25 September 2026 — this entry's latest reading, not the date you are reading this.

“Машиннное обучение | Наука о данных Библиотека” (@machinelearning_books), 16,878 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/machinelearning_books.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.