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Channel

Machine learning Interview

@machinelearning_interview

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Telegram's recommendations · Cite this entry

30,334subscribers

+200 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-1001713271750
TypeChannel
Username@machinelearning_interview
DescriptionИИ, Rust, вайбкодинг, Data Science, Deep Learning и делюсь тем, что интересно и полезно! Вопросы - @workakkk РКН: clck.ru/3FmwRz
CreatedBetween 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live4 October 2026
Measurements held34
Confirmed unchanged1 time, most recently 4 October 2026
On Telegramt.me/machinelearning_interview

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 9 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

30,09630,33430,2156 August 2026 — 30,134 subscribers6 August 2026 — 30,131 subscribers7 August 2026 — 30,119 subscribers8 August 2026 — 30,120 subscribers9 August 2026 — 30,121 subscribers10 August 2026 — 30,129 subscribers11 August 2026 — 30,121 subscribers12 August 2026 — 30,111 subscribers13 August 2026 — 30,102 subscribers15 August 2026 — 30,100 subscribers16 August 2026 — 30,096 subscribers17 August 2026 — 30,097 subscribers18 August 2026 — 30,142 subscribers20 August 2026 — 30,146 subscribers21 August 2026 — 30,145 subscribers22 August 2026 — 30,152 subscribers24 August 2026 — 30,185 subscribers25 August 2026 — 30,200 subscribers26 August 2026 — 30,195 subscribers27 August 2026 — 30,204 subscribers28 August 2026 — 30,197 subscribers29 August 2026 — 30,194 subscribers30 August 2026 — 30,195 subscribers31 August 2026 — 30,202 subscribers1 September 2026 — 30,222 subscribers2 September 2026 — 30,246 subscribers3 September 2026 — 30,261 subscribers5 September 2026 — 30,264 subscribers8 September 2026 — 30,274 subscribers11 September 2026 — 30,267 subscribers13 September 2026 — 30,273 subscribers15 September 2026 — 30,292 subscribers17 September 2026 — 30,308 subscribers4 October 2026 — 30,334 subscribers6 August 20264 October 2026
34 measurements spanning 59 days, net +200. 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 30,060–30,370 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)SubscribersChange
4 Oct 2026, 12:5930,334+26
17 Sept 2026, 08:5530,308+16
15 Sept 2026, 05:1730,292+19
13 Sept 2026, 13:1830,273+6
11 Sept 2026, 14:5730,267-7
8 Sept 2026, 21:2130,274+10
5 Sept 2026, 15:4230,264+3
3 Sept 2026, 16:3530,261+15
2 Sept 2026, 08:5830,246+24
1 Sept 2026, 06:3730,222+20
31 Aug 2026, 03:4330,202+7
30 Aug 2026, 06:0630,195+1
29 Aug 2026, 03:2830,194-3
28 Aug 2026, 06:5830,197-7
27 Aug 2026, 10:3430,204+9
26 Aug 2026, 08:4230,195-5
25 Aug 2026, 06:2730,200+15
24 Aug 2026, 03:5630,185+33
22 Aug 2026, 08:1530,152+7
21 Aug 2026, 02:2430,145first reading

Engagement

124 posts held, back to 26 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 96 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
9.89%
avg views ÷ 30,334 subscribers
Avg views / post
3,000
50 posts measured
Reaction rate
0.809%
reactions ÷ views · ER floor
Posts in window
50
of 124 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 49 of 50 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 6 October 2026
Posts held124 (26 July 2026 – 6 October 2026)
Views total150,010
Reactions total1,195
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Oct 2026, 20:16 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
≈1,890
Videos
≈168
Links
≈1,310

Lifetime counters from Telegram’s own channel header, read 6 October 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
17m 52s
Average length
1m 17s

Measured directly from 14 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

3,308 reactions across 118 posts, in 48 distinct kinds. The most used accounts for 25.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍85825.9%
❤73322.2%
🔥68920.8%
😁47814.4%
🤣1414.26%
💯722.18%
🥰601.81%
👏361.09%
💊361.09%
🥱351.06%
🥴260.786%
🤔220.665%
😱100.302%
🌚90.272%
🙈90.272%
👀80.242%
⚡70.212%
😐70.212%
🤪60.181%
🤩50.151%
28 further kinds611.84%

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 122 of the 124 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 3,377 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 124 most recent posts we hold, published 26 July 2026 to 6 October 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
3.23%
4 of 124 posts carry an ad marker
Regulatory tokens
4
posts carrying an erid · 4 distinct tokens
Median views · ads
2,280
over 4 measured posts
Median views · rest
3,170
over 119 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
2VtzqxF9x3v122 September 202622 September 2026
2W5zFGUKxVF118 September 202618 September 2026
2W5zFKAbF53122 September 202622 September 2026
CQH36pWzJqVJCbWwcsXL4MHgQs4xaEvn9ZDjZ1jckjmgft12 September 20262 September 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 124 most recent posts we hold, published 26 July 2026 to 6 October 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

6 Oct 2026, 13:08 UTC≈1,360 views20 reactionsread 6 October 2026
Photo

🧮 Математика для ИИ на русском: от школьной базы до трансформеров и диффузии Бесплатный курс-шпаргалка на GitHub, который связывает формулы с их применением в ML: интуиция → формула → иллюстрация → код на NumPy → задачи. Внутри: • Линейная алгебра, производные, вероятности и статистика. • Backpropagation, оптимизаторы, функции потерь и attention. • Генеративные модели, обучение с подкреплением, RAG и рекомендации. …

❤8👍8🔥3😁1

5 Oct 2026, 11:31 UTC≈2,150 views15 reactionsread 6 October 2026
Photo

Hugging Face исследовали multi-harness RL: обучение с подкреплением сразу через Claude Code, Codex, OpenCode и Mini-SWE-Agent. Для этого объединили три проекта: • OpenEnv записывает обращения к модели, токены и вероятности генерации. • Harbor запускает одинаковые задачи в контейнерах через разные обвязки. • TRL использует собранные данные для обучения. Каждая обвязка сохраняет собственные инструменты, промпты и ци…

🔥7❤5⚡2👍1

4 Oct 2026, 14:01 UTC≈2,500 views24 reactionsread 6 October 2026
Photo

Apache Kafka курс 2026: бесплатный курс по Kafka с нуля до профи на русском Kafka обучение без воды: каждый модуль состоит из понятной теории, схем, команд, которые можно запустить у себя, типичных ошибок и вопросов для самопроверки. Курс подходит, чтобы выучить Kafka с нуля, подготовиться к собеседованию на backend, data engineer или DevOps-позицию и спроектировать надёжную систему на Kafka в продакшене. https://g…

❤11👍9🥰4

4 Oct 2026, 10:03 UTC≈2,280 views21 reactionsread 6 October 2026
Photo

🧠 ИИ лучше улучшает агентов, когда развивает несколько специализаций Исследователи предложили новый подход к оптимизации harness: кода вокруг LLM, который управляет инструментами, поиском информации и проверкой ответов. Обычно ИИ переписывает эту обвязку и оценивает версии на одном наборе задач. Но полезная специализация может потеряться, если проигрывает по общему баллу. Авторы разделили поиск на две ветки: • Ка…

👍9🔥6❤4💘2

2 Oct 2026, 11:58 UTC≈2,510 views23 reactionsread 6 October 2026
Photo

🔥 ChatGPT PRO на русском: открытый курс от первого промпта до агентов и Codex На GitHub появился бесплатный практический курс по ChatGPT, целиком на русском. Он умещается в один репо, поэтому ничего не нужно ставить и регистрироваться не надо. Программа идёт от простого к сложному в четыре уровня: база, работа, расширение и PRO. Всего в ней 13 модулей. Первые разбирают выбор между быстрыми и рассуждающими моделями,…

❤7👍7🔥4🥱3🥰2

2 Oct 2026, 11:32 UTC≈2,260 views19 reactionsread 6 October 2026
Photo

MIT с помощью AI разработал более термостабильную формулу для mRNA-вакцин. Новая формула сохраняла стабильность: - до 1 года при комнатной температуре - до 2 месяцев при ~37°C AI подбирал оптимальную комбинацию компонентов вместо долгого перебора тысяч вариантов. По словам исследователей, это сократило поиск с месяцев до нескольких недель. В экспериментах на мышах вакцина после такого хранения вызывала сопоставим…

🔥10❤5🌚2🥰2

2 Oct 2026, 09:32 UTC≈2,400 views5 reactionsread 6 October 2026
Photo

One Day Offer: ищем инженера для решения задач по безопасности ИИ-агентов Хочешь работать над крупными проектами и умеешь создавать надёжные системы управления рисками? Приходи 10 октября на экспресс-отбор на позицию Senior DS/ML Engineer для AI Safety в Сбер — все этапы отбора за 1 день. Ты будешь работать на стыке машинного обучения, инженерии и безопасности: ▪️ проектировать архитектуру безопасного ИИ ▪️ выводи…

❤5

1 Oct 2026, 13:03 UTC≈2,670 views22 reactionsread 6 October 2026
Photo

11 бесплатных книг по AI и Machine Learning, которые можно читать онлайн или скачать 📚 Подборка сильная: от классического ML и deep learning до RL, multi-agent систем и probabilistic ML. 1. Foundations of Machine Learning https://cs.nyu.edu/~mohri/mlbook/ 2. Understanding Deep Learning https://udlbook.github.io/udlbook/ 3. Introduction to Machine Learning Systems Vol. 1: https://mlsysbook.ai/vol1/ Vol. …

👍8🔥8❤3🥰3

1 Oct 2026, 09:50 UTC≈8,030 views26 reactionsread 6 October 2026
Photo

🔥 Бесплатный курс по Claude Code на русском: от установки до команды агентов На GitHub выложили Claude Code PRO Course, бесплатный практический курс по Claude Code на русском языке. Он ведёт от первого запуска до настройки многоагентной работы в команде. Внутри 12 модулей в четырёх уровнях сложности: основы, рабочий процесс, расширения и масштабирование. Сначала установка, промптинг, память проекта через CLAUDE.md,…

❤8👍8🔥4😁4💊1🥰1

30 Sept 2026, 09:31 UTC≈3,670 views48 reactionsread 6 October 2026
Photo

🔥 DeepSeek и Huawei строят китайскую альтернативу CUDA DeepSeek выпустила open-source инструменты для ускорителей Huawei Ascend, включая поддержку TileLang для Ascend 950. Они упрощают написание вычислительных ядер, перекладывая часть низкоуровневой работы на компилятор. Смысл шире одной библиотеки: Китай пытается снизить зависимость не только от чипов NVIDIA, но и от всей экосистемы CUDA. Именно за этим сейчас ст…

👍27🔥13🎉4❤2😁2

29 Sept 2026, 12:42 UTC≈2,990 views16 reactionsread 6 October 2026
Video

«В 2026 году мировой спрос составляет около 31,7 млрд токенов каждые 10 секунд. К 2030 году он вырастет до 1,27 трлн токенов, то есть примерно в 40 раз». — CEO Qualcomm Кристиано Амон Взрывной рост числа токенов связан с тем, что ИИ переходит от взаимодействия в темпе человека к активности в темпе AI-агентов. За каждым полезным действием скрывается вычислительная цена: нужно передать контекст, обновить память, иног…

❤7🔥4💊2🥰2👍1

Showing the 12 most recent of 124 posts we hold for @machinelearning_interview. 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 48 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.

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.

Channels Telegram recommends alongside this one

Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

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#66
ML — это ОК
@mlvok · 2,103
#67
Neural Networks | Нейронные сети
@neural · 11,582
#68
XOR
@xor_journal · 213,509
#69
Data Science 🛸 & ML Jobs за рубежом
@opento_data · 7,466
#70
Data Science | Вопросы собесов
@easy_ds · 4,902
#71
Derp Learning
@derplearning · 13,896
#72
AbstractDL
@abstractDL · 18,672
#73
Искусственный интеллект. Высокие технологии
@vistehno · 71,011
#74
PyMagic
@pymagic · 5,580
#75
Инжиниринг Данных
@rockyourdata · 23,707
#76
Neural Shit
@NeuralShit · 54,321
#77
AI VK Hub
@aihubvk · 2,482
#78
AI для Всех
@nn_for_science · 15,482
#79
karpov.courses
@KarpovCourses · 27,406
#80
Библиотека задач по Data Science | тесты, код, задания
@ds_problems_lib · 3,975
#81
ML Advertising
@dsinsights · 1,252
#82
ODS #jobs
@odsjobs · 14,195
#83
New Yorko Times
@new_yorko_times · 10,606
#84
Data Science Jobs
@datasciencejobs · 22,197
#85
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 14,863
#86
Старший Авгур
@senior_augur · 8,634
#87
Нескучный Data Science Jobs
@not_boring_ds_jobs · 9,188
#88
Поступашки - ШАД, Стажировки и Магистратура
@postypashki_old · 46,014
#89
Заметки Computer Vision инженера
@CVML_team · 6,067
#90
Тагир Анализирует
@tagir_analyzes · 11,702
#91
Take Friends to Luna Park
@hrlunapark · 20,411
#92
Job for Analysts & Data Scientists
@foranalysts · 36,999
#93
(sci)Berloga Всех Наук и Технологий
@sberlogabig · 8,013
#94
ФКН: Вакансии
@vacancy_cs · 17,444
#95
Нейронки и вино
@neural_wine · 1,928
#96
Зарплатник Аналитика
@zarplatnik_analytics · 11,646
#97
̶с̶а̶м̶̶о̶изолента мёбиуса
@izolenta_mebiusa · 2,772
#98

Read from Telegram’s recommendation API, most recently 7 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

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 Jobs
@datascienceml_jobs · 21,310
Telegram ranks this channel #3 of 97 here — alongside 96 others — read 24 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 50,680
Telegram ranks this channel #3 of 93 here — alongside 92 others — read 25 August 2026
Data Science | Machinelearning [ru]
@devsp · 19,787
Telegram ranks this channel #4 of 91 here — alongside 90 others — read 30 September 2026
Python вопросы с собеседований
@python_job_interview · 24,860
Telegram ranks this channel #4 of 90 here — alongside 89 others — read 15 September 2026
Data Science
@datascienceiot · 42,567
Telegram ranks this channel #6 of 76 here — alongside 75 others — read 29 August 2026
Machinelearning
@ai_machinelearning_big_data · 278,381
Telegram ranks this channel #6 of 95 here — alongside 94 others — read 10 August 2026
Data Science. SQL hub
@sqlhub · 35,948
Telegram ranks this channel #7 of 87 here — alongside 86 others — read 2 September 2026
Искусственный интеллект. Высокие технологии
@vistehno · 71,011
Telegram ranks this channel #9 of 94 here — alongside 93 others — read 20 August 2026
DevOps
@DevOPSitsec · 23,671
Telegram ranks this channel #13 of 90 here — alongside 89 others — read 18 September 2026
Python/ django
@pythonl · 58,613
Telegram ranks this channel #15 of 84 here — alongside 83 others — read 22 August 2026
Data Secrets
@data_secrets · 94,538
Telegram ranks this channel #17 of 98 here — alongside 97 others — read 17 August 2026
Yandex for ML
@yandexforml · 18,948
Telegram ranks this channel #23 of 96 here — alongside 95 others — read 4 October 2026
Dev & ML Connectable Jobs
@dev_connectablejobs · 28,088
Telegram ranks this channel #27 of 94 here — alongside 93 others — read 10 September 2026
XOR
@xor_journal · 213,509
Telegram ranks this channel #27 of 93 here — alongside 92 others — read 12 August 2026
karpov.courses
@KarpovCourses · 27,406
Telegram ranks this channel #31 of 96 here — alongside 95 others — read 10 September 2026
Kali Novskaya
@rybolos_channel · 19,054
Telegram ranks this channel #36 of 93 here — alongside 92 others — read 4 October 2026
gonzo-обзоры ML статей
@gonzo_ML · 24,543
Telegram ranks this channel #37 of 93 here — alongside 92 others — read 16 September 2026
Stepik – онлайн-курсы
@stepik_courses · 24,429
Telegram ranks this channel #42 of 93 here — alongside 92 others — read 16 September 2026
Время Валеры
@cryptovalerii · 31,566
Telegram ranks this channel #42 of 94 here — alongside 93 others — read 6 September 2026
LLM под капотом
@llm_under_hood · 29,482
Telegram ranks this channel #44 of 96 here — alongside 95 others — read 9 September 2026
Job for Analysts & Data Scientists
@foranalysts · 36,999
Telegram ranks this channel #44 of 96 here — alongside 95 others — read 1 September 2026
эйай ньюз
@ai_newz · 97,222
Telegram ranks this channel #47 of 94 here — alongside 93 others — read 16 August 2026
Senior Python Developer
@seniorpy · 39,657
Telegram ranks this channel #48 of 92 here — alongside 91 others — read 30 August 2026
Physics.Math.Code
@physics_lib · 146,792
Telegram ranks this channel #48 of 72 here — alongside 71 others — read 13 August 2026

This channel appears in 35 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. 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 4 October 2026 — this entry's latest reading, not the date you are reading this.

“Machine learning Interview” (@machinelearning_interview), 30,334 subscribers as measured 4 October 2026. Telegram Register, tgregister.com/channel/machinelearning_interview.

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.