Telegram RegisterThe public register of Telegram

Channel

Data Scientist | ML & AI

@datascience_tg

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

2,965subscribers

-4 since we began measuring on 9 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1002309388740
TypeChannel
Username@datascience_tg
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 August 2026
Last confirmed live13 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/datascience_tg

Growth

2,9652,9692,9679 August 2026 — 2,969 subscribers9 August 2026 — 2,969 subscribers10 August 2026 — 2,968 subscribers13 August 2026 — 2,965 subscribers9 August 202613 August 2026
4 measurements spanning 4 days, net -4. 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 2,964–2,970 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 11:152,965-3
10 Aug 2026, 05:112,968-1
9 Aug 2026, 19:002,969no change
9 Aug 2026, 18:522,969first reading

Engagement

20 posts held, back to 21 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 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
16.0%
avg views ÷ 2,965 subscribers
Avg views / post
475
20 posts measured
Reaction rate
0.842%
reactions ÷ views · ER floor
Posts in window
20
of 20 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 9 August 2026
Posts held20 (21 July 20269 August 2026)
Views total9,502
Reactions total80
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken9 Aug 2026, 19:00 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
2m 27s
Average length
37s

Measured directly from 4 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

80 reactions across 20 posts, in 5 distinct kinds. The most used accounts for 41.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
3341.3%
👍2430.0%
🔥2126.3%
11.25%
😐11.25%

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 20 of the 20 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 80reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 21 July 2026 to 9 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.

Recent posts

9 Aug 2026, 11:00 UTC146 views3 reactionsread 9 August 2026
Photo

🔖 Большая подборка ресурсов по MLOps Нашли полезную подборку для тех, кто хочет выйти за рамки обучения моделей и разобраться с их эксплуатацией. В репозитории собраны лучшие материалы по деплою, мониторингу, автоматизации ML-пайплайнов, CI/CD и другим практикам, без которых сложно представить production-системы. ⛓ Ссылка на GitHub tags: #полезное ➡ Data Scientist | Чат

1👍1🔥1

8 Aug 2026, 11:00 UTC225 views3 reactionsread 9 August 2026
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🔖 Google DeepMind выпустила книгу How to Scale Your Model В ней разбирают, как масштабировать и запускать модели при ограниченных вычислительных ресурсах. Полезно для тех, кто работает с оптимизацией и развёртыванием ML-моделей. ⛓ Ссылка на книгу tags: #полезное ➡ Data Scientist | Чат

1👍1🔥1

7 Aug 2026, 16:17 UTC271 views3 reactionsread 9 August 2026
Video

🔖 Добавляем DeepSeek и Kimi в Codex Нашли полезный инструмент для тех, кто пользуется Codex. Что умеет: 🫡 добавляет Kimi K3 в список моделей; 🫡 подключает DeepSeek V4 Flash и DeepSeek V4 Pro; 🫡 не ломает встроенные GPT-модели; 🫡 автоматически создаёт резервную копию конфигурации; 🫡 поддерживает безопасный откат изменений. Проект полностью бесплатный и с открытым исходным кодом. ⛓ Забираем тут tags: #полезное ➡

1👍1🔥1

6 Aug 2026, 11:00 UTC343 views4 reactionsread 9 August 2026
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🔖 Инструкция для AI-агентов Обратите внимание на AGENTS.md — справочник, посвящённый одноимённому файлу с инструкциями для AI-агентов. На сайте объясняют, что стоит включать в AGENTS.md, как его правильно оформлять и показывают примеры из более чем 60 тысяч проектов. tags: #полезное ➡ Data Scientist | Чат

👍21🔥1

5 Aug 2026, 17:00 UTC378 views4 reactionsread 9 August 2026
Photo

🔖 5 бесплатных курсов по AI-агентам Внутри: 1. Hugging Face — AI Agents Course 2. DeepLearningAI — AI Agents in LangGraph 3. DeepLearningAI — Multi AI Agent Systems with CrewAI 4. Microsoft Learn — AI Agents for Beginners 5. DeepLearningAI — Building Code Agents with Hugging Face smolagents Если хотите разобраться в Agentic AI, сохраняйте подборку. tags: #полезное ➡ Data Scientist | Чат

2👍1🔥1

4 Aug 2026, 11:00 UTC432 views3 reactionsread 9 August 2026
Photo

📰 Градиентный спуск: фундамент машинного обучения Нашли подробный гайд, который объясняет один из самых важных алгоритмов в ML — от теории до реализации на Python. В статье простым языком объясняют принцип работы градиентного спуска, разбирают популярные оптимизаторы (SGD, Momentum, RMSprop, Adam) и показывают, как реализовать алгоритм с нуля. ⛓️ Читать статью tags: #статья ➡ Data Scientist | Чат

1👍1🔥1

3 Aug 2026, 07:54 UTC462 views4 reactionsread 9 August 2026
Video

🔖 Как создать свою LLM с нуля? Нашли практический ресурс для тех, кто хочет разобраться в устройстве AI-моделей. Пошагово показывают создание собственной модели: подготовка данных, токенизация, pre-training и fine-tuning. ⛓ Забираем тут tags: #полезное ➡ Data Scientist | Чат

2👍1🔥1

2 Aug 2026, 11:00 UTC476 views3 reactionsread 9 August 2026
Video

🔖 AI-инструмент для работы с данными от Microsoft Microsoft выложила в опенсорс приложение, которое использует большие языковые модели для преобразования данных и построения визуализаций. Инструмент помогает быстрее исследовать датасеты, готовить данные к анализу и создавать графики с помощью ИИ. ⛓ Ссылка на GitHub tags: #полезное ➡ Data Scientist | Чат

1👍1🔥1

1 Aug 2026, 11:00 UTC458 views5 reactionsread 9 August 2026
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🔖 Бесплатная книга по Data Science Если хотите глубже разобраться в анализе данных на Python, этот материал стоит сохранить. В книге подробно разбираются NumPy, Pandas, визуализация данных, машинное обучение и другие ключевые инструменты, которые ежедневно используются. ⛓ Ссылка на книгу tags: #полезное ➡ Data Scientist | Чат

3👍1🔥1

31 Jul 2026, 11:00 UTC474 views3 reactionsread 9 August 2026
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📰 Практика по Deep Learning вместо теории Если давно хотели написать свою первую нейросеть, сохраните эту статью. Автор показывает полный цикл разработки текстового классификатора: 🫡 подготовка датасета; 🫡 токенизация и эмбеддинги; 🫡 построение модели в Keras; 🫡 обучение и оценка качества; 🫡 сохранение модели и развёртывание. ⛓️ Читать статью tags: #статья ➡ Data Scientist | Чат

1👍1🔥1

30 Jul 2026, 16:00 UTC476 views5 reactionsread 9 August 2026
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🔖 300+ реальных кейсов ML-систем от топовых компаний Нашли репозиторий, где собран настоящий опыт ML-инженерии — не теория из учебников, а реальные истории внедрения моделей в продакшене. Внутри кейсы от Uber, Netflix, Google и других компаний: как строили архитектуру, какие проблемы возникали, где системы ломались и какие решения помогали их восстановить. ⛓ Ссылка на GitHub tags: #полезное ➡ Data Scientist | Ча

👍31🔥1

29 Jul 2026, 11:00 UTC561 views3 reactionsread 9 August 2026
Video

🔖 Учим Data Science через интерактивные примеры Один из самых полезных репозиториев для тех, кто хочет лучше понять машинное обучение. Он превращает сложные концепции в наглядные эксперименты: можно изучать модели, менять параметры и сразу видеть результат. ⛓ Ссылка на GitHub tags: #полезное ➡ Data Scientist | Чат

1👍1🔥1

Showing the 12 most recent of 20 posts we hold for @datascience_tg. 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 — 1,162,418 of 1,345,403entries 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.

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.

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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“Data Scientist | ML & AI” (@datascience_tg), 2,965 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/datascience_tg.

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.