5 measurements spanning 6 days, net +43. 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 1,743–1,798 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 03:32
1,792
+13
9 Aug 2026, 13:01
1,779
+22
6 Aug 2026, 22:26
1,757
+8
6 Aug 2026, 07:03
1,749
no change
6 Aug 2026, 01:52
1,749
first reading
Engagement
20 posts held, back to 28 January 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 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
27.8%
avg views ÷ 1,792 subscribers
Avg views / post
498
3 posts measured
Reaction rate
1.81%
reactions ÷ views · ER floor
Posts in window
3
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
Window
Rolling 30 days · latest post in window 3 August 2026
Posts held
20 (28 January 2026 – 3 August 2026)
Views total
1,495
Reactions total
27
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 17:37 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
11s
Average length
11s
Measured directly from 1 video 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
128 reactions across 17 posts, in 5 distinct kinds. The most used accounts for 76.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
98
76.6%
👍
22
17.2%
🔥
5
3.91%
🏆
2
1.56%
🤷♀
1
0.781%
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 17 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 128reactions 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 28 January 2026 to 3 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.
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
📈🎯 قسمت 10: دقت (Accuracy) چیست و چگونه عملکرد مدل را میسنجیم؟
تا اینجا یاد گرفتیم که مدل با دادهها آموزش میبیند و تلاش میکند پیشبینیهای بهتری انجام دهد.
اما یک سؤال مهم:
❓ از کجا بفهمیم یک مدل خوب کار میکند؟
اینجا مفهوم معیار ارزیابی (Evaluation Metric) وارد میشود.
💡 یکی از سادهترین معیارها، دقت (Accuracy) است.
📌 واژه Accuracy یعنی چه؟
یعنی چند درصد از پیشبین…
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🤖📚 قسمت 9: مدل چگونه یاد میگیرد؟
تا اینجا فهمیدیم که مدل با دادههای آموزشی کار میکند. اما سؤال اصلی این است:
❓ آیا مدل واقعاً «فکر» میکند؟
جواب کوتاه: خیر! 😄
مدل نه فکر میکند، نه احساس دارد و نه مفهوم دادهها را درک میکند.
فقط با استفاده از الگوهای موجود در دادهها یاد میگیرد.
📌 یک مثال ساده:
فرض کنید میخواهیم مدلی بسازیم که قیمت خانه را پیشبینی کند.
ابتدا مدل ه…
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🧪📊 قسمت 8: دادههای آموزشی و آزمایشی چه تفاوتی دارند؟
تا اینجا فهمیدیم که مدل یادگیری ماشین با استفاده از دادهها یاد میگیرد.
اما یک سؤال مهم وجود دارد:
❓ از کجا بفهمیم مدل واقعاً یاد گرفته است؟
🤔 اگر تمام دادهها را برای آموزش به مدل بدهیم، دیگر نمیتوانیم بهدرستی بررسی کنیم که آیا مدل توانایی پیشبینی دادههای جدید را دارد یا نه.
به همین دلیل، دیتاست را معمولاً به دو …
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🏷🤖 قسمت 7: ویژگی (Feature) و برچسب (Label) چیست؟
اگر میخواهی یادگیری ماشین را واقعاً یاد بگیری، باید با دو مفهوم بسیار مهم آشنا شوی: Feature و Label.
💡 ویژگی (Feature)
ویژگیها همان اطلاعاتی هستند که به مدل میدهیم تا از آنها یاد بگیرد.
مثلاً اگر بخواهیم قیمت یک خانه را پیشبینی کنیم، ویژگیها میتوانند اینها باشند:
🏠 متراژ خانه
🛏 تعداد اتاقها
📍 موقعیت مکانی
🏗 سال ساخت…
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
❤️📊 قسمت 6: دیتاست (Dataset) چیست و چرا قلب یادگیری ماشین است؟
تا اینجا چند بار اسم «داده» و «دیتاست» را شنیدیم. اما دیتاست دقیقاً چیست؟ 🤔
💡 دیتاست یعنی مجموعهای از دادههای مرتب و سازماندهیشده که مدل یادگیری ماشین از آنها برای آموزش استفاده میکند.
📌 یک مثال ساده:
فرض کنید میخواهیم مدلی بسازیم که قیمت خانه را پیشبینی کند.
دیتاست ما میتواند چیزی شبیه این باشد:
🏠 خا…
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🏠📈 قسمت 5: اولین پروژه واقعی؛ پیشبینی قیمت خانه با یادگیری ماشین
حالا وقت آن رسیده که ببینیم یک مدل یادگیری ماشین در دنیای واقعی چگونه استفاده میشود. 🚀
فرض کنید میخواهیم قیمت یک خانه را پیشبینی کنیم.
برای این کار، اطلاعات خانههای مختلف را به مدل میدهیم، مانند:
🏠 متراژ خانه
🛏 تعداد اتاقها
📍 موقعیت مکانی
🏗 سال ساخت
و در کنار این اطلاعات، قیمت واقعی هر خانه را هم به م…
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🐍🚀 قسمت 4: اولین مدل یادگیری ماشین با پایتون
تا اینجا با مفهوم یادگیری ماشین و انواع آن آشنا شدیم.
حالا وقت آن رسیده که اولین مدل ساده خودمان را بسازیم! 😍
💡 برای ساخت مدلهای یادگیری ماشین در پایتون، کتابخانههای زیادی وجود دارد.
یکی از معروفترین و سادهترین آنها scikit-learn است.
📦 این کتابخانه ابزارهای آمادهای برای ساخت مدلهای مختلف در اختیار ما قرار میدهد.
📌 مراحل …
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
🤖📚 قسمت 3: انواع یادگیری ماشین؛ نظارتشده و بدون نظارت
تا اینجا فهمیدیم دادهها چقدر برای یادگیری ماشین مهم هستند 🚀
اما یک سؤال مهم داریم:
❓ آیا همه مدلهای هوش مصنوعی به یک شکل یاد میگیرند؟
جواب: نه! 😮
یادگیری ماشین روشهای مختلفی دارد.
💡 1️⃣ یادگیری نظارتشده (supervised learning)
در این روش، ما به مدل هم داده میدهیم و هم جواب درست را نشان میدهیم.
مثلاً:
📷 عکسهای ز…
کانالمون رو به همکلاسیهای خودتون و اگر معلم هستید به دانشآموزها، توی گروههای درسی معرفی کنید.
https://t.me/studentai
📖 هوش مصنوعی برای دانشآموزان
➖➖➖➖➖
🆔 : @StudentAI
#فصل_چهارم
#آموزش_قدمبهقدم_هوش_مصنوعی
📊🤖 قسمت 2: دادهها در یادگیری ماشین چه نقشی دارن؟
در قسمت قبل فهمیدیم که در یادگیری ماشین، به جای اینکه همه قوانین را خودمان بنویسیم، به کامپیوتر داده میدهیم تا الگوها را یاد بگیرد 🚀
اما یک سؤال مهم:
❓ آیا هر دادهای باعث میشود AI خوب یاد بگیرد؟
جواب: نه! 😮
💡 دادهها مثل سوخت هوش مصنوعی هستند ⛽️
اگر دادهها خوب باشند، مدل هم بهتر یاد میگیرد.
اگر دادهها اشتباه یا ناقص با…
👍11
Showing the 12 most recent of 20 posts we hold for @studentai. 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.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 20 most recent posts we hold, published 28 January 2026 to 3 August 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Citation-graph rank
Citation-graph rank — 222,467 of 1,160,990entries 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.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 5 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.
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.
Handles this channel named that no longer answer
Dead references
2
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
2
vacant every time we have ever looked
@studentai named 2 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.
@studentaistudentai named in 1 post, 8 August 2026 – 8 August 2026
@studentaistudentaistudentai 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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“آموزش هوش مصنوعی” (@studentai), 1,792 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/studentai.
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.