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Channel

Машинное обучение. Книги по программированию

@maschinelearning

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

10,658subscribers

-8 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1001404791780
TypeChannel
Username@maschinelearning
DescriptionИз своего опыта мы будем делиться нужной информацией по : Maschine Learning(ML) Big Data, Deep Learning(DL). Преемущественно книги Реклама - @viktorreh @anothertechrock РКН: https://clck.ru/3R3tnH
CreatedBetween 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged2 times, most recently 12 August 2026
On Telegramt.me/maschinelearning

Growth

10,65810,67010,6646 August 2026 — 10,666 subscribers6 August 2026 — 10,666 subscribers7 August 2026 — 10,668 subscribers8 August 2026 — 10,670 subscribers9 August 2026 — 10,668 subscribers11 August 2026 — 10,663 subscribers11 August 2026 — 10,658 subscribers6 August 202611 August 2026
7 measurements spanning 5 days, net -8. 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 10,656–10,672 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
11 Aug 2026, 21:0210,658-5
11 Aug 2026, 00:0710,663-5
9 Aug 2026, 21:2110,668-2
8 Aug 2026, 22:2110,670+2
7 Aug 2026, 19:2710,668+2
6 Aug 2026, 19:0010,666no change
6 Aug 2026, 18:5210,666first reading

Engagement

21 posts held, back to 3 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 15 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
15.1%
avg views ÷ 10,658 subscribers
Avg views / post
1,610
15 posts measured
Reaction rate
0.265%
reactions ÷ views · ER floor
Posts in window
15
of 21 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. It is computed over the 13 of 15 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 10 August 2026
Posts held21 (3 July 202610 August 2026)
Views total24,136
Reactions total58
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 13:53 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

Photos
200
Links
213

Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

73 reactions across 15 posts, in 10 distinct kinds. The most used accounts for 31.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
2331.5%
👍1926.0%
🔥1723.3%
👎45.48%
🤔34.11%
❤‍🔥22.74%
🙏22.74%
👏11.37%
💩11.37%
🥴11.37%

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 17 of the 21 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 73reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 21 most recent posts we hold, published 3 July 2026 to 10 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
4.76%
1 of 21 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
1,640
over 1 measured post
Median views · rest
1,730
over 20 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
2W5zFJrQWam115 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 21 most recent posts we hold, published 3 July 2026 to 10 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

10 Aug 2026, 13:50 UTC866 viewsread 12 August 2026
Photo

Build a Large Language Model Автор: Raschka Sebastian Год издания: 2025 #ml #en Скачать книгу

6 Aug 2026, 07:56 UTC≈1,350 viewsread 12 August 2026
Photo

PythonBooks - самый большой 38.000+ и старый c 2017 года канал для скачивания Python книг в PDF формате. Что в канале: 6️⃣ Книги по питону, которые помогут вам пройти собеседование на позицию Python Developer. 2️⃣ Все книги в PDF формате 3️⃣ Все книги можно скачать в 2 клика 4️⃣ Всё, никакой другой воды. Подписывайтесь и качайте книги: @pythonbooks

29 Jul 2026, 12:02 UTC≈1,810 views5 reactionsread 12 August 2026
Photo

Machine Learning - The Mastery Bible Автор: Bill Hanson Год издания: 2020 #ml #en Скачать книгу

3🔥1🤔1

29 Jul 2026, 07:33 UTC≈1,630 views5 reactionsread 12 August 2026
Photo

⚡️Детекторы объектов быстро меняются: подходы, которые недавно считались стандартом, уже уступают трансформерам реального времени. Если вы работаете с компьютерным зрением и опираетесь только на YOLO, легко пропустить важный сдвиг в архитектурах. 🗓19 августа в 20:00 МСК приглашаем вас на открытый урок курса «Компьютерное зрение. Экспертный уровень». На занятии разберём путь от R-CNN и семейства YOLO до RT-DETR и RF

👍3🔥1🙏1

27 Jul 2026, 18:04 UTC≈1,430 views2 reactionsread 12 August 2026
Photo

Introduction to Algorithms & Data Structures 1 Автор: Bolakale Aremu Год издания: 2023 #ml #en Скачать книгу

🤔2

27 Jul 2026, 13:02 UTC≈1,380 views9 reactionsread 12 August 2026
Photo

Открыли регистрацию на E-CUP 2026 Students 🎓 В этом сезоне — только для студентов. Будет интересно тем, кто изучает ML / DS / big data / аналитику данных. Сможете ускорить модель по поиску дубликатов на 20%? Получится создать классификатор для модерации товаров? Сумеете предсказать поведение покупателя? Как минимум — попробуете и получите фидбэк от тех, кто делает это в Ozon Tech каждый день. Как максимум — раздел

🔥4👍3❤‍🔥2

27 Jul 2026, 10:47 UTC≈1,370 views11 reactionsread 12 August 2026
Photo

Машинное обучение: основы Автор: Сергей Николенко Год издания: 2025 #ml #ru Скачать книгу

6👍3🔥2

25 Jul 2026, 09:00 UTC≈1,600 views5 reactionsread 12 August 2026

Фундаментальное понимание машинного обучения дает возможность быстро адаптироваться ко всем новым моделям и технологиям. Курс Евгения Разинкова «ИИ: от основ до трансформеров» создан для тех, у кого уже есть техническая база и кто хочет стать сильным и востребованным на рынке ML-специалистом, а не просто собирать пайплайны из чужих кусков. Пишете алгоритмы сами, а не импортируете. Сначала реализуете ключевые методы

🔥21👍1👎1

20 Jul 2026, 07:10 UTC≈2,020 views1 reactionsread 12 August 2026
Photo

Машинное обучение и безопасность Автор: Кларенс Чио Год издания: 2020 #ml #ru Скачать книгу

🥴1

17 Jul 2026, 09:57 UTC≈1,980 views4 reactionsread 12 August 2026
Photo

Оффер редко приходит сам. Обычно между «без работы» и «оффер получен» стоят десятки откликов, доработок резюме и часов поиска. Talanto.work помогает пройти этот путь быстрее: 🟠 50 000+ IT-вакансий с разных сайтов 🟠 Telegram-бот с уведомлениями по вашим фильтрам 🟠 Проверка резюме и рекомендации по улучшению 🟠 Проверка соответствия резюме конкретной вакансии 🟠 Генератор персональных сопроводительных писем А в кан

👎3💩1

16 Jul 2026, 18:03 UTC≈1,910 views0 reactionsread 12 August 2026
Photo

Как учится машина Автор: Ян Лекун Год издания: 2021 #ml #ru Скачать книгу

16 Jul 2026, 13:50 UTC≈1,730 views5 reactionsread 12 August 2026
Photo

📹Вебинар: Выбор между Serverless и Kubernetes для AI-ворклоадов: как определить оптимальную платформу под задачу На открытом уроке рассмотрим: - В чем различаются Serverless-подходы и Kubernetes при работе с AI-ворклоадами; - Какие преимущества и ограничения есть у каждого подхода с точки зрения масштабируемости, стоимости и сложности эксплуатации; - Какие трейдоффы нужно учитывать при выборе платформы: холодный ста

2👍2🙏1

Showing the 12 most recent of 21 posts we hold for @maschinelearning. 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.

Citation-graph rank

Citation-graph rank — 1,145,048 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Republishes

Channels on the register whose posts this channel has forwarded.

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 1 registered channel — 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.

Named by

Channels on the register whose posts name this channel's handle.

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.

Physics.Math.Code
@physics_lib · 145,853
Telegram ranks this channel #32 of 72 here — alongside 71 others — read 13 August 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list 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 11 August 2026 — this entry's latest reading, not the date you are reading this.

“Машинное обучение. Книги по программированию” (@maschinelearning), 10,658 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/maschinelearning.

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.