13 measurements spanning 32 days, net +152. 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 1,726–1,924 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
6 Sept 2026, 14:22
1,901
+9
2 Sept 2026, 13:23
1,892
+7
30 Aug 2026, 13:37
1,885
+7
27 Aug 2026, 23:44
1,878
+11
25 Aug 2026, 05:48
1,867
+16
22 Aug 2026, 03:47
1,851
+17
18 Aug 2026, 21:37
1,834
+11
16 Aug 2026, 01:54
1,823
+31
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 pages of 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 20 posts for this entry, the most recent from 3 August 2026. An engagement rate over an empty window would be a number about nothing.
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 128 reactions 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.
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 6 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
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.
مجله هوش مصنوعی @HomeAI · 32,742 Telegram ranks this channel #54 of 91 here — alongside 90 others — read 5 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. 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 6 September 2026 — this
entry's latest reading, not the date you are reading this.
“آموزش هوش مصنوعی” (@studentai), 1,901 subscribers as measured 6 September 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.