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Channel

Machine learning Interview

@machinelearning_interview

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

30,560subscribers

+426 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 live9 November 2026
Measurements held62
Confirmed unchanged1 time, most recently 9 November 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 the other topics.

Growth

30,09630,58430,34030,5606 August 20269 November 2026
62 measurements spanning 96 days, net +426. 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 30,023–30,657 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 62
Measured (UTC)SubscribersChange
9 Nov 2026, 20:0930,560-4
8 Nov 2026, 09:3530,564-5
7 Nov 2026, 07:0530,569-12
6 Nov 2026, 07:3930,581+6
5 Nov 2026, 10:3130,575+1
4 Nov 2026, 07:2630,574+4
3 Nov 2026, 07:5530,570-11
2 Nov 2026, 06:2030,581+4
1 Nov 2026, 03:5030,577-4
31 Oct 2026, 00:2930,581+5
30 Oct 2026, 01:0830,576-8
28 Oct 2026, 21:3030,584+6
27 Oct 2026, 22:5130,578+14
26 Oct 2026, 22:3730,564-4
25 Oct 2026, 22:3630,568+10
25 Oct 2026, 01:2730,558-1
23 Oct 2026, 23:4830,559-3
22 Oct 2026, 23:1530,562+14
21 Oct 2026, 20:5130,548+128
20 Oct 2026, 15:1130,420first reading

Engagement

199 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 166 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
10.6%
avg views ÷ 30,560 subscribers
Avg views / post
3,240
69 posts measured
Reaction rate
0.701%
reactions ÷ views · ER floor
Posts in window
69
of 199 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 10 November 2026
Posts held199 (26 July 2026 – 10 November 2026)
Views total223,720
Reactions total1,569
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken10 Nov 2026, 23:10 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
≈1,960
Videos
≈177
Links
≈1,380

Lifetime counters from Telegram’s own channel header, read 10 November 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
29m 12s
Average length
1m 16s

Measured directly from 23 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

5,133 reactions across 193 posts, in 50 distinct kinds. The most used accounts for 24.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍1,27724.9%
❤1,20423.5%
🔥1,16922.8%
😁65412.7%
🤣2234.34%
🥰1182.30%
💯741.44%
👏551.07%
💊500.974%
🥱430.838%
🥴360.701%
🤔260.507%
🗿190.37%
⚡130.253%
👀120.234%
😭120.234%
😱120.234%
❤‍🔥110.214%
🌚110.214%
😢110.214%
30 further kinds1032.01%

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 197 of the 199 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 5,202 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 199 most recent posts we hold, published 26 July 2026 to 10 November 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
2.51%
5 of 199 posts carry an ad marker
Regulatory tokens
5
posts carrying an erid · 5 distinct tokens
Median views · ads
2,970
over 5 measured posts
Median views · rest
3,210
over 193 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. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2Vtzqw8Csuo18 October 20268 October 2026
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 199 most recent posts we hold, published 26 July 2026 to 10 November 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

Telegram’s own ‘edited’ marker: 5 of 69 posts held from the last 30 days (7.25%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 09:34 UTC≈2,130 views16 reactionsread 10 November 2026
Photo

🍏 Apple научила Claude управлять роботом без специального обучения Исследователи Apple опубликовали работу *On Agentic Manipulation Policies*, в которой показали, что универсальные ИИ-модели способны управлять роботизированными манипуляторами без предварительного обучения на записях действий роботов. Обычно робототехнические модели обучают на тысячах демонстраций: как захватывать предметы, перемещать их и размещать…

❤10👍4🔥2

9 Nov 2026, 18:33 UTC≈2,570 views19 reactionsread 10 November 2026
Photo

183 мс против 3,5 секунды у Claude Opus 5.5. Drex Max ускоряет принятие решений ИИ-агентами почти в 20 раз. Nace AI представила Drex Max - мультимодальную decision-модель с контекстом до 256K токенов. Её особенность: модель вообще не генерирует текст. Вместо этого она получает контекст, варианты ответа, текст, скриншоты или графики и за один проход возвращает вероятности для каждого варианта. Сравнение задержки: -…

❤9🔥5👍2😁2💊1

9 Nov 2026, 14:10 UTC≈2,540 views7 reactionsread 10 November 2026
Photo

Как рассчитать размер выборки для A/B-теста и объяснить бизнесу p-value? Классический вопрос на собеседовании аналитика. Теперь добавьте к нему ещё один: как вы используете в этой работе LLM и как проверяете её выводы? Разобраться в этом можно на AI Analytics Week — бесплатном онлайн-интенсиве Школы анализа данных Яндекса. С 23 по 27 ноября на кейсах Яндекса пройдёте путь от первых этапов до агентной системы. Что р…

❤3👍2🥰2

9 Nov 2026, 13:20 UTC≈2,610 views21 reactionsread 10 November 2026
Photo

DeepMind обещает перелом в научных открытиях. Первые результаты покажут уже в ближайшие недели. Вице-президент Google DeepMind по науке заявил, что человечество переживает настоящий «фазовый переход» в темпах научных открытий. По его словам, первые доказательства этого станут видны уже в ближайших релизах Google. «Мы наблюдаем настоящий фазовый переход в скорости научных открытий». Если кто и способен приблизить …

🔥10👍6❤4😁1

8 Nov 2026, 10:19 UTC≈3,070 views37 reactionsread 10 November 2026

Карпати собрал YouTube-клон с 47 000 видео за 3 минуты с помощью Claude Code Андрей Карпати превратил подборку YouTube-каналов Тристана Хьюма в полноценную видеотеку TristanTube. Внутри около 47 000 технических и научных видео, собранных из рейтинга каналов Хьюма. Что получилось: - Интерфейс в стиле YouTube с поиском и просмотром видео. - Отдельная страница About. - Тёмная тема. - TV Mode для фонового просмотра и…

🔥16🗿9🍾4😁4🤣3👍1

7 Nov 2026, 09:45 UTC≈3,100 views36 reactionsread 10 November 2026
Photo

PostgreSQL: от SQL до внутреннего устройства Практический курс на русском для разработчиков, аналитиков и инженеров, которым важно понимать не только синтаксис запросов, но и поведение базы под нагрузкой. Вы построите учебный сервис заказов, исследуете планы запросов, воспроизведёте блокировки и разберёте MVCC, WAL и vacuum. https://github.com/justxor/postgresql-deep-dive-ru

👍25😭6❤4🔥1

6 Nov 2026, 15:40 UTC≈4,790 views20 reactionsread 10 November 2026
Photo

35-летнюю математическую задачу решили с помощью GPU и ИИ-агентов В сентябре 2026 года инженер Cognition Эрик Лу разложил число RSA-260 длиной 260 десятичных цифр (862 бита) на два простых множителя по 130 цифр. Задача оставалась нерешённой с 1991 года. Для факторизации использовали General Number Field Sieve (GNFS), один из самых эффективных известных алгоритмов разложения больших чисел. Его реализацию CADO-NFS с…

❤12👀4👍2🔥2

6 Nov 2026, 11:00 UTC≈3,000 views17 reactionsread 10 November 2026

10 Open Source проектов для создания собственного ИИ-агента по покупкам Можно собрать персонального ИИ-помощника, который ищет товары в разных магазинах, сравнивает характеристики, учитывает ваши предпочтения и отслеживает изменения цен. Для этого необязательно писать всё с нуля. Вот 10 проектов, из которых можно собрать такую систему. 1. LangChain - оркестрация LLM, инструментов и логики агента. https://github.c…

❤11🔥4👍1🥰1

6 Nov 2026, 09:04 UTC≈2,610 views9 reactionsread 10 November 2026
Photo

Кто в IT платит выше рынка? И какой оффер считается хорошим? Предлагаем опытным IT-специалистам поделиться мнением о зарплатах и бенефитах в крупных технологических компаниях. Кто, на ваш взгляд, платит выше рынка? У кого привлекательнее премии? Насколько важны ДМС, спорт, питание и другие бонусы при выборе работы? 👉 Поделиться мнением: https://clck.ru/3WNkGe

🔥5🌭3❤1

6 Nov 2026, 07:11 UTC≈2,810 views10 reactionsread 10 November 2026
Photo

Ant Group представила Ling-3.2-flash: мультимодальная модель на 560 млрд параметров, обошедшая Claude Opus 5 в ряде тестов Ant Ling выпустила Ling-3.2-flash, модель с нативной мультимодальностью и архитектурой MoE: 560 млрд параметров, из которых активируются 25 млрд. Модель работает с изображениями, документами, 3D-сценами и сложными агентными задачами. Результаты против Claude Opus 5 (max): - OmniDocBench 1.5: …

❤4🔥2🗿2😱2

5 Nov 2026, 14:29 UTC≈3,110 views25 reactionsread 10 November 2026
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OpenAI может пересмотреть планы строительства дата-центров после выборов в США По данным The Wall Street Journal, руководство OpenAI обсуждает возможность пересмотра прогнозов по вычислительным мощностям на 2027 год. Причина неожиданная: сопротивление строительству новых дата-центров на местном уровне. На прошедших выборах американские избиратели поддержали кандидатов, критически настроенных к расширению инфрастру…

❤11👍6🥰5🤔2😨1

4 Nov 2026, 13:03 UTC≈3,700 views45 reactionsread 10 November 2026
Photo

Вышел htop для видеокарт, который показывает, куда на самом деле уходит VRAM Скотт Хансельман из Microsoft опубликовал gputaskman, терминальный монитор GPU с открытым исходным кодом. Обычный диспетчер задач показывает, что занято 15 ГБ видеопамяти. Но сколько из них съела нейросеть, сколько занимает KV-кеш, а сколько забрал браузер? gputaskman разбирает потребление памяти по процессам и пытается определить, какие …

🔥20👍10❤9🤣3🥰3

Showing the 12 most recent of 199 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.

Posts recorded as deleted after publishing

We recorded 3 posts on @machinelearning_interview as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
21 August 2026
Most recent seen missing
21 October 2026
Median time before seen missing
39 hours
Seen missing in the last 30 days
2

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 50 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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#56
ODS Events
@datafest · 8,857
#57
DziS Science | Data Science
@dzis_science · 2,186
#58
Стать специалистом по машинному обучению
@toBeAnMLspecialist · 10,879
#59
Агенты ИИ | AGI_and_RL
@AGI_and_RL · 6,280
#60
MLinside - школа ML
@ml_inside · 4,088
#61
КПД
@quant_prune_distill · 3,510
#62
epsilon correct
@epsiloncorrect · 8,199
#63
Пресидский залив
@neural_prosecco · 8,653
#64
Python Jobs
@python_djangojobs · 15,125
#65
Пристанище Дата Сайентиста
@renat_alimbekov · 6,287
#66
ML — это ОК
@mlvok · 2,094
#67
Neural Networks | Нейронные сети
@neural · 11,724
#68
XOR
@xor_journal · 195,549
#69
Data Science 🛸 & ML Jobs за рубежом
@opento_data · 7,784
#70
Data Science | Вопросы собесов
@easy_ds · 4,864
#71
Derp Learning
@derplearning · 13,992
#72
AbstractDL
@abstractDL · 18,849
#73
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
#74
PyMagic
@pymagic · 5,521
#75
Инжиниринг Данных
@rockyourdata · 23,685
#76
Neural Shit
@NeuralShit · 54,643
#77
AI VK Hub
@aihubvk · 2,485
#78
AI для Всех
@nn_for_science · 15,453
#79
karpov.courses
@KarpovCourses · 27,313
#80
Библиотека задач по Data Science | тесты, код, задания
@ds_problems_lib · 3,966
#81
ML Advertising
@dsinsights · 1,462
#82
ODS #jobs
@odsjobs · 14,582
#83
New Yorko Times
@new_yorko_times · 10,720
#84
Data Science Jobs
@datasciencejobs · 22,587
#85
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 15,139
#86
Старший Авгур
@senior_augur · 8,372
#87
Нескучный Data Science Jobs
@not_boring_ds_jobs · 9,430
#88
Поступашки - ШАД, Стажировки и Магистратура
@postypashki_old · 47,200
#89
Заметки Computer Vision инженера
@CVML_team · 6,118
#90
Тагир Анализирует
@tagir_analyzes · 11,650
#91
Take Friends to Luna Park
@hrlunapark · 21,006
#92
Job for Analysts & Data Scientists
@foranalysts · 37,857
#93
(sci)Berloga Всех Наук и Технологий
@sberlogabig · 8,021
#94
ФКН: Вакансии
@vacancy_cs · 18,429
#95
Нейронки и вино
@neural_wine · 1,934
#96
Зарплатник Аналитика
@zarplatnik_analytics · 11,694
#97
̶с̶а̶м̶̶о̶изолента мёбиуса
@izolenta_mebiusa · 2,776
#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 · 21,642
Telegram ranks this channel #3 of 97 here — alongside 96 others — read 24 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
Telegram ranks this channel #3 of 93 here — alongside 92 others — read 25 August 2026
Data Science | Machinelearning [ru]
@devsp · 19,659
Telegram ranks this channel #4 of 91 here — alongside 90 others — read 30 September 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #4 of 90 here — alongside 89 others — read 15 September 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #6 of 76 here — alongside 75 others — read 29 August 2026
Machinelearning
@ai_machinelearning_big_data · 275,659
Telegram ranks this channel #6 of 95 here — alongside 94 others — read 10 August 2026
Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #7 of 87 here — alongside 86 others — read 2 September 2026
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
Telegram ranks this channel #9 of 94 here — alongside 93 others — read 20 August 2026
DevOps
@DevOPSitsec · 23,892
Telegram ranks this channel #13 of 90 here — alongside 89 others — read 18 September 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #15 of 84 here — alongside 83 others — read 22 August 2026
Data Secrets
@data_secrets · 96,035
Telegram ranks this channel #17 of 98 here — alongside 97 others — read 17 August 2026
Yandex for ML
@yandexforml · 19,136
Telegram ranks this channel #23 of 96 here — alongside 95 others — read 4 October 2026
Dev & ML Connectable Jobs
@dev_connectablejobs · 29,084
Telegram ranks this channel #27 of 94 here — alongside 93 others — read 10 September 2026
XOR
@xor_journal · 195,549
Telegram ranks this channel #27 of 93 here — alongside 92 others — read 12 August 2026
karpov.courses
@KarpovCourses · 27,313
Telegram ranks this channel #31 of 96 here — alongside 95 others — read 10 September 2026
Kali Novskaya
@rybolos_channel · 19,136
Telegram ranks this channel #36 of 93 here — alongside 92 others — read 4 October 2026
gonzo-обзоры ML статей
@gonzo_ML · 24,695
Telegram ranks this channel #37 of 93 here — alongside 92 others — read 16 September 2026
Stepik – онлайн-курсы
@stepik_courses · 28,865
Telegram ranks this channel #42 of 93 here — alongside 92 others — read 16 September 2026
Время Валеры
@cryptovalerii · 32,082
Telegram ranks this channel #42 of 94 here — alongside 93 others — read 6 September 2026
LLM под капотом
@llm_under_hood · 30,219
Telegram ranks this channel #44 of 96 here — alongside 95 others — read 9 September 2026
Job for Analysts & Data Scientists
@foranalysts · 37,857
Telegram ranks this channel #44 of 96 here — alongside 95 others — read 1 September 2026
эйай ньюз
@ai_newz · 98,262
Telegram ranks this channel #47 of 94 here — alongside 93 others — read 16 August 2026
Senior Python Developer
@seniorpy · 39,134
Telegram ranks this channel #48 of 92 here — alongside 91 others — read 30 August 2026
Physics.Math.Code
@physics_lib · 146,676
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 9 November 2026 — this entry's latest reading, not the date you are reading this.

“Machine learning Interview” (@machinelearning_interview), 30,560 subscribers as measured 9 November 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.