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

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 |
|---|---|
| Type | Channel |
| Username | @maschinelearning |
| Description | Из 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 |
| Created | Between 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 2 times, most recently 12 August 2026 |
| On Telegram | t.me/maschinelearning |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 21:02 | 10,658 | -5 |
| 11 Aug 2026, 00:07 | 10,663 | -5 |
| 9 Aug 2026, 21:21 | 10,668 | -2 |
| 8 Aug 2026, 22:21 | 10,670 | +2 |
| 7 Aug 2026, 19:27 | 10,668 | +2 |
| 6 Aug 2026, 19:00 | 10,666 | no change |
| 6 Aug 2026, 18:52 | 10,666 | first reading |
Engagement
21 posts held, back to 3 July 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 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.
| Window | Rolling 30 days · latest post in window 10 August 2026 |
|---|---|
| Posts held | 21 (3 July 2026 – 10 August 2026) |
| Views total | 24,136 |
| Reactions total | 58 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 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.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 23 | 31.5% | |
| 👍 | 19 | 26.0% | |
| 🔥 | 17 | 23.3% | |
| 👎 | 4 | 5.48% | |
| 🤔 | 3 | 4.11% | |
| ❤🔥 | 2 | 2.74% | |
| 🙏 | 2 | 2.74% | |
| 👏 | 1 | 1.37% | |
| 💩 | 1 | 1.37% | |
| 🥴 | 1 | 1.37% |
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.
| erid | Posts | First seen | Last seen |
|---|---|---|---|
| 2W5zFJrQWam | 1 | 15 July 2026 | 15 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
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
Machine Learning - The Mastery Bible Автор: Bill Hanson Год издания: 2020 #ml #en Скачать книгу
❤3🔥1🤔1
⚡️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…
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Introduction to Algorithms & Data Structures 1 Автор: Bolakale Aremu Год издания: 2023 #ml #en Скачать книгу
🤔2
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…
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Машинное обучение: основы Автор: Сергей Николенко Год издания: 2025 #ml #ru Скачать книгу
❤6👍3🔥2
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…
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Машинное обучение и безопасность Автор: Кларенс Чио Год издания: 2020 #ml #ru Скачать книгу
🥴1
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…
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Как учится машина Автор: Ян Лекун Год издания: 2021 #ml #ru Скачать книгу
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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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
Handles this channel named that no longer answer
- Dead references
- 1
- handles named in this channel’s posts, vacant today
- Evidenced gone
- 0
- we ourselves saw one of these resolve, at some point
- Never seen alive
- 1
- vacant every time we have ever looked
@maschinelearning named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
named in 3 posts, 8 August 2026 – 10 August 2026
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_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.