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Channel

📚Python Books

@pythonlbooks

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

33,388subscribers

-289 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001441817281
TypeChannel
Username@pythonlbooks
Description📚Python библиотека admin - @workakkk @ai_machinelearning_big_data - машинное обучение @programming_books_it - бесплатные it книги @pythonl - 🐍 @ArtificialIntelligencedl - AI @datascienceiot - ml РКН: clck.ru/3FmsTi
CreatedBetween 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live25 September 2026
Measurements held36
Confirmed unchanged1 time, most recently 25 September 2026
On Telegramt.me/pythonlbooks

Topic

Education — 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 80% 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

33,38833,67733,532.56 August 2026 — 33,677 subscribers6 August 2026 — 33,677 subscribers6 August 2026 — 33,675 subscribers7 August 2026 — 33,661 subscribers8 August 2026 — 33,657 subscribers9 August 2026 — 33,658 subscribers10 August 2026 — 33,653 subscribers11 August 2026 — 33,640 subscribers12 August 2026 — 33,644 subscribers13 August 2026 — 33,625 subscribers14 August 2026 — 33,627 subscribers16 August 2026 — 33,618 subscribers17 August 2026 — 33,604 subscribers18 August 2026 — 33,594 subscribers19 August 2026 — 33,580 subscribers20 August 2026 — 33,590 subscribers22 August 2026 — 33,588 subscribers23 August 2026 — 33,597 subscribers24 August 2026 — 33,583 subscribers26 August 2026 — 33,567 subscribers27 August 2026 — 33,564 subscribers28 August 2026 — 33,550 subscribers29 August 2026 — 33,545 subscribers30 August 2026 — 33,543 subscribers31 August 2026 — 33,526 subscribers1 September 2026 — 33,527 subscribers2 September 2026 — 33,548 subscribers3 September 2026 — 33,549 subscribers5 September 2026 — 33,542 subscribers8 September 2026 — 33,502 subscribers11 September 2026 — 33,486 subscribers13 September 2026 — 33,456 subscribers14 September 2026 — 33,457 subscribers16 September 2026 — 33,435 subscribers19 September 2026 — 33,418 subscribers25 September 2026 — 33,388 subscribers6 August 202625 September 2026
36 measurements spanning 51 days, net -289. 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 33,345–33,720 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 36
Measured (UTC)SubscribersChange
25 Sept 2026, 19:5933,388-30
19 Sept 2026, 07:4033,418-17
16 Sept 2026, 18:5833,435-22
14 Sept 2026, 20:2133,457+1
13 Sept 2026, 05:4133,456-30
11 Sept 2026, 06:5833,486-16
8 Sept 2026, 14:4033,502-40
5 Sept 2026, 08:5933,542-7
3 Sept 2026, 15:3933,549+1
2 Sept 2026, 07:0633,548+21
1 Sept 2026, 05:0633,527+1
31 Aug 2026, 04:0533,526-17
30 Aug 2026, 01:2933,543-2
29 Aug 2026, 00:2333,545-5
28 Aug 2026, 03:1233,550-14
27 Aug 2026, 06:0633,564-3
26 Aug 2026, 02:4433,567-16
24 Aug 2026, 23:0833,583-14
23 Aug 2026, 12:0433,597+9
22 Aug 2026, 00:2433,588first reading

Engagement

31 posts held, back to 29 May 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 88 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
8.94%
avg views ÷ 33,388 subscribers
Avg views / post
2,990
6 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
6
of 31 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 22 September 2026
Posts held31 (29 May 2026 – 22 September 2026)
Views total17,910
Reactions total—
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken27 Sept 2026, 07:04 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
≈1,140
Videos
≈5
Links
≈1,130

Lifetime counters from Telegram’s own channel header, read 27 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
37s
Average length
19s

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.

Advertising

Ad load
3.23%
1 of 31 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
3,510
over 1 measured post
Median views · rest
3,920
over 30 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
2Vtzqv11YLS14 August 20264 August 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 31 most recent posts we hold, published 29 May 2026 to 22 September 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

22 Sept 2026, 09:35 UTC≈1,520 viewsread 27 September 2026
Photo

Бесплатная книга по Physics-based Deep Learning 461-страничный учебник о применении глубокого обучения в физических симуляциях. Внутри: - Physics-Informed Neural Networks - физические ограничения в loss-функциях - дифференцируемые симуляции - обучение с подкреплением - моделирование неопределённости - практические примеры в Jupyter Notebook Полезно для изучения научного ML, PINN и нейросетевого решения физических…

17 Sept 2026, 09:53 UTC≈2,570 viewsread 27 September 2026
Photo

📘 Elementary Probability for Applications - бесплатная книга по теории вероятностей 163 страницы с базой по вероятностям и прикладными примерами. Подойдёт тем, кто хочет нормально разобраться в случайных событиях, распределениях, ожиданиях и других основах, которые постоянно встречаются в Data Science и Machine Learning. Хороший компактный материал без перегруза. https://clcoding.com/2026/07/elementary-probabilit…

11 Sept 2026, 13:19 UTC≈3,210 viewsread 27 September 2026

🔥 Хак в Django: Meta.indexes можно использовать для собственных миграций Оказывается, models.Index в Django можно превратить почти в универсальную migration-операцию. Идея: class CustomMigrationOperation(models.Index): def create_sql(self, model, schema_editor, **kwargs): ... def remove_sql(self, model, schema_editor, **kwargs): ... А затем добавить её прямо в модель: class Meta: i…

5 Sept 2026, 12:42 UTC≈3,650 viewsread 27 September 2026
Photo

📚 Бесплатный 17-страничный PDF по Hidden Markov Models Короткий и плотный разбор HMM от Stanford. Внутри: - скрытые состояния и наблюдения - transition и emission probabilities - вычисление вероятности последовательности - Forward algorithm - Viterbi algorithm - обучение параметров модели - базовые примеры применения Хороший материал, чтобы быстро понять HMM без огромного учебника. PDF: https://web.stanford.edu/…

31 Aug 2026, 14:20 UTC≈3,840 viewsread 27 September 2026
Forwarded from @machinelearning_interviewPhoto

MIT бесплатно выложил 920-страничный учебник Mathematics for Computer Science. Это большой курс по математике, которая реально нужна в Computer Science. Внутри: - логика и доказательства - множества и отношения - индукция - графы - теория чисел - рекуррентные соотношения - комбинаторика - вероятность - дискретные структуры и алгоритмическое мышление Хорошая база для алгоритмов, теории вычислений и более серьёзног…

31 Aug 2026, 12:40 UTC≈3,120 viewsread 27 September 2026
Photo

🔥 Python + AI без игрушечных демок. Курс для тех, кто хочет собирать рабочие системы. Stepik: «Python современный AI для разработчика и автоматизации задач» 63 урока, 382 шага, практика с кодом и автопроверкой. Внутри: RAG, tool calling, агенты, evals, MCP, Ollama, vLLM, pgvector + HNSW, безопасный text-to-SQL, prompt injection, кэш, очереди и sandbox для агентного кода. Плюс реальные автоматизации: почта, отчёты…

27 Aug 2026, 11:05 UTC≈3,220 viewsread 27 September 2026
Forwarded from @pythonlVideo

🔥 Face Anything - восстановление 4D-лица из любой последовательности изображений На ECCV 2026 представили Face Anything - unified feed-forward модель для высокоточного 4D face reconstruction и плотного трекинга лица. Ключевая идея - canonical facial point prediction: каждому пикселю назначается нормализованная координата лица в общем каноническом пространстве. Это позволяет объединить dense tracking и динамическую…

27 Aug 2026, 09:00 UTC≈2,690 viewsread 27 September 2026
Video

Интегрируйте OCR в ваш Python-продукт за 15 минут 🚀 Создаёте SaaS, автоматизируете B2B или собираете ИИ-агентов на Python? Забудьте про облачные костыли и отправку конфиденциальных данных на чужие сервера! Provision OCR — российский движок для локального распознавания документов в защищенном контуре. ⚡️ Python-friendly: интеграция через REST API и gRPC для FastAPI, Django или Flask. ⚡️ Docker-ready: быстрый запуск…

25 Aug 2026, 20:53 UTC≈2,590 viewsread 27 September 2026
Photo

📚 Бесплатная работа по Information Geometry и Fisher–Rao Distance Исследование посвящено численному приближению расстояния Fisher–Rao между многомерными нормальными распределениями. Внутри разбираются: - Fisher–Rao Distance - Information Geometry - Fisher Information - KL и Jeffreys Divergence - статистические многообразия и геодезические - SPD-матрицы - Mahalanobis Distance - численные методы приближения Полезно…

24 Aug 2026, 15:12 UTC≈2,690 viewsread 27 September 2026
Photo

📚 Бесплатная книга по causal inference + ML Applied Causal Inference Powered by Machine Learning and AI - большая открытая книга про причинно-следственный анализ с современным уклоном в машинное обучение и AI. Внутри: * causal graphs и DAG * потенциальные исходы * A/B и observational data * propensity score * matching и weighting * heterogeneous treatment effects * Double/Debiased Machine Learning * causal forests…

22 Aug 2026, 15:20 UTC≈2,840 viewsread 27 September 2026

10 бесплатных книг по программированию, которые можно читать прямо с GitHub 1. The Rust Programming Language https://github.com/rust-lang/book 2. Rust by Example https://github.com/rust-lang/rust-by-example 3. The Little Book of Rust Books https://github.com/lborb/book 4. Python Data Science Handbook https://github.com/jakevdp/PythonDataScienceHandbook 5. Dive Into Deep Learning https://github.com…

22 Aug 2026, 13:20 UTC≈2,460 viewsread 27 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…

Showing the 12 most recent of 31 posts we hold for @pythonlbooks. 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

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.

Python/ django
@pythonl · 58,866
Telegram ranks this channel #5 of 84 here — alongside 83 others — read 22 August 2026
Machine Learning with Python
@CodeProgrammer · 68,301
Telegram ranks this channel #33 of 84 here — alongside 83 others — read 20 August 2026
Computer Science and Programming
@computer_science_and_programming · 139,806
Telegram ranks this channel #38 of 87 here — alongside 86 others — read 13 August 2026
Python Programming Books
@dsabooks · 59,137
Telegram ranks this channel #54 of 89 here — alongside 88 others — read 22 August 2026
Artificial Intelligence && Deep Learning
@DeepLearning_ai · 57,432
Telegram ranks this channel #65 of 87 here — alongside 86 others — read 23 August 2026
Python Projects & Resources
@pythondevelopersindia · 63,430
Telegram ranks this channel #74 of 86 here — alongside 85 others — read 21 August 2026
Artificial Intelligence
@Artificial_intelligence_in · 65,766
Telegram ranks this channel #81 of 90 here — alongside 89 others — read 21 August 2026
Искусственный интеллект. Высокие технологии
@vistehno · 71,491
Telegram ranks this channel #93 of 94 here — alongside 93 others — read 20 August 2026

This channel appears in 8 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.

“📚Python Books” (@pythonlbooks), 33,388 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/pythonlbooks.

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.