Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 11 August 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.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (6 of the pairs behind the counts below)
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 3 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 17 comparable posts for this entry, running 5 May 2023 to 6 November 2023. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Across the whole group of 2, the earliest publisher we hold is @MathStaDatasci_IDSchools. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_source, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
2 measurements taken within a single day. 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 656–658 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
8 Aug 2026, 06:01
657
no change
7 Aug 2026, 07:46
657
first reading
Engagement
17 posts held, back to 5 May 2023 — the 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 17 posts for this entry, the most recent from 6 November 2023. An engagement rate over an empty window would be a number about nothing.
Reaction mix
24 reactions across 9 posts, in 4 distinct kinds. The most used accounts for 54.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
13
54.2%
❤
9
37.5%
👎
1
4.17%
😁
1
4.17%
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 12 of the 17 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 24reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 5 May 2023 to 6 November 2023, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
❇️ معرفی دوره های پاییزی در یک نگاه- اولین دوره
🔸عنوان دوره: «پایتون از مقدماتی تا پیشرفته، مهندسی داده با پایتون»
🔸زمان برگزاری جلسه اول: سه شنبه، 16 آبان ساعت 18
🔻سرفصل جلسات:
🔹هفته اول: مبانی پایتون
▫️مباحث این دو جلسه: مقدمه-نصب-آشنایی با محیط-انواع داده
🔹هفته دوم: ادامه انواع داده و توابع
▫️مباحث این دو جلسه: معرفی دیکشنری، توابع، شیء گرایی در پایتون، ارث بری در پایتون
🔹هفته سوم: پکیج ها
▫️مباحث این دو جلس…
❇️ یادآوری- شروع دوره
🔸جلسه اول «دوره مقدماتی آشنایی با متلب»
🔸 عنوان جلسه اول: ویژگیهای اصلی متلب
🔸زمان برگزاری جلسه اول: چهارشنبه، 18 مرداد ساعت 17
🔸با ارائه دکتر امین اصغرزاده، عضو هیات علمی دانشگاه علوم پزشکی ایران
🔸چهارشنبه ها، ساعت 17 تا 20
🔸کسب اطلاعات بیشتر:
@M_Solh
🆔 | @IDSchools|
🆔 | @IDS_NeuroTalk|
🆔 | @AiMlearningPl_IDSchools|
❇️ معرفی دوره های تابستانی در یک نگاه- پنجمین دوره
🔸عنوان دوره: دوره مقدماتی آشنایی با متلب
🔸زمان برگزاری جلسه اول: چهارشنبه، 18 مرداد ساعت 17
🔻سرفصل جلسات:
🔹هفته اول:
▫️جلسه اول: ویژگیهای اصلی متلب
▫️جلسه دوم: آرایه ها، مفاهیم ساختار داده ها و ماتریس ها
🔹هفته دوم:
▫️جلسه سوم: مبانی اجرای عملیات پردازشی بر روی داده ها
▫️جلسه چهارم: نحوه دسترسی، فراخوانی، مقداردهی و ذخیره سازی داده ها
🔹هفته سوم:
▫️جلسه پنجم: اصو…
سلام دوستان🌸🤓🌹
نمونه تدریس استاد "دوره مقدماتی آشنایی با متلب" ، دکتر امین اصغرزاده، عضو هیات علمی دانشگاه علوم پزشکی ایران✨🧠
⭕️اگه سوالی درباره دوره داشتین و یا قصد ثبت نام دارین، کافیه به ادمین کانال به آی دیِ (@M_Solh) پیام بدین🌱🌺
🆔 | @IDSchools|
🆔 | @AiMlearningPl_IDSchools|
دوستان سلام🙋🏻♂️🙋🏼♀️🌻✨
بریم با هم سری به مطالبی که قراره توی دو هفته دوم (4 جلسه آخر) یادبگیریم بندازیم🌹
توی جلسه هفتم با عملیات منطقی و رابطه ای مثل کار با عملگرهای منطقی مثل AND,OR,NOT و بقیه عملگرها آشنا میشیم🤓🧠👨🏼💻👩🏻💻🌸 بعدش استفاده از حلقه ها و توابع شرطی، که از ارکان اصلی هر زبان برنامه نویسی محسوب میشه رو مفصل باهم یادمیگیریم🤩🌸
بعدش میرسیم به مفهوم تابع در متلب و نحوه فراخوانی تابع رو یاد میگیریم✨ و در جلسه د…
دوستان سلام🙋🏻♂🙋🏼♀🌻✨
بریم با هم سری به مطالبی که قراره توی سه هفته اول (6 جلسه اول) یادبگیریم بندازیم🌹
قراره از ویژگیهای متلب که یه زبان برنامه نویسی قوی و خفن😎هست و پکیج ها و تولباکس هاش و قابلیتهاشون توی برنامه نویسی، پیش پردازش و تحلیل انواع داده ها وحتی محاسبات ترسیمی شروع کنیم👩🏻💻👨🏼💻🤩👌بعدش به مفاهیمی مثل آرایه ها،مفاهیم ساختار داده ها و ماتریس ها میرسیم و مفصل یادمیگیریمشون🤓🌸🧠در ادامه با انواع دیتا و پردازش ها…
شاید برای شما هم این سوال پیش اومده باشه که : اگه بخوام کار با یه پلتفرم قوی رو یاد بگیرم که هم بتونم باهاش روی دیتاها کار کنم و هم بتونم دیتاها رو تصویرسازی کنم. و از همه مهمتر🤩 به روز و معتبر و رزومه ساز باشه چه پلتفرمی رو باید مسلط بشم؟🤔🤓👨💻👩💻🧠🌻✨
جواب این سوال شما، در این دوره پرطرفدار مدارس میان رشته ای هستش😍🤩✌️🌸
بله 😊✨ شما با مسلط شدن روی متلب میتونین هم برای تحلیل داده هاتون درحوزه هایی مثل نوروساینس،علوم شناخ…
سلام دوستان✨🌺👋
نمونه تدریس استاد دوره"ریاضیات،آمار و احتمال برای دیتاساینس" ، دکتر امین محبی، خدمت شما🌸🌹🤓🧑💻👩💻📊📈✨
⭕️اگه سوالی درباره دوره داشتین و یا قصد ثبت نام دارین، کافیه به ادمین کانال به آی دیِ (@M_Solh) پیام بدین🌱🌺
🆔 | @IDSchools|
🆔 | @AiMlearningPl_IDSchools|
🔰🔰🔰
🔸فایل تصویری
🔸توضیحات مدیر آکادمی زبانهای خارجی مدارس میان رشته ای پیرامون نحوه فعالیت این آکادمی
🔸لیلا شادمانی، دانشجوی دکتری در فرانسه، مدیر آکادمی زبانهای خارجی مدارس میان رشته ای
🔸برای اطلاع از فعالیت های این آکادمی و تقویت مهارت های زبانی خود در انگلیسی، فرانسه و آلمانی به این کانال با لینک زیر جوین شوید:
🆔 |@IDS_language|
❇️ یادآوری- شروع دوره
🔸جلسه اول دوره «ریاضیات، آمار و احتمال برای دیتاساینس»
🔸 عنوان جلسه اول: احتمال (تعاریف اولیه،امید ریاضی و فراوانی)
🔸زمان برگزاری جلسه اول: دوشنبه، 25 اردیبهشت ساعت 19
🔸با ارائه دکتر امین محبی، دانشگاه هایدلبرگ آلمان
🔸دوشنبه ها، ساعت 19 تا 20:30، به مدت 10 جلسه
🔸کسب اطلاعات بیشتر:
@M_Solh
🆔 |@IDSchools|
🆔 |@AiMlearningPl_IDSchools|
🆔 |@MathStaDatasci_IDSchools|
❇️ معرفی دوره های هشت گانه بهاری در یک نگاه- هفتمین دوره
🔸عنوان دوره: ریاضیات، آمار و احتمال برای دیتاساینس
🔸شروع دوره: دوشنبه، 25 اردیبهشت ساعت 19
🔸استاد این دوره: دکتر امین محبی، دانشگاه هایدلبرگ آلمان
🔸روز و ساعت برگزاری: دوشنبه ها، به مدت 10 هفته از ساعت 19 تا 20:39
🔸محتوای این دوره:
🔸جلسه اول:احتمال(بخش اول)
🔹مباحث:احتمالات(تعاریف اولیه،فراوانی)
🔸جلسه دوم:احتمال(بخش دوم)
🔹مباحث:ترکیبات(مفاهیم اولیه،جایگشت)…
Showing the 12 most recent of 17 posts we hold for @AiMlearningPl_IDSchools. 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
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
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
3
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
3
vacant every time we have ever looked
@AiMlearningPl_IDSchools named 3 handles 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.
@ids_neurotalk named in 3 posts, 8 August 2026 – 8 August 2026
@ids_language named in 1 post, 8 August 2026 – 8 August 2026
@neurosci_idschools named in 1 post, 8 August 2026 – 8 August 2026
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 8 August 2026 — this
entry's latest reading, not the date you are reading this.
“مدارس میان رشته ای- هوش مصنوعی، یادگیری ماشین و زبانهای برنامه نویسی” (@AiMlearningPl_IDSchools), 657 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/AiMlearningPl_IDSchools.
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.