Channel
@matlabanyone
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry
7,310subscribers
-59 since we began measuring on 5 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001382337201 |
|---|---|
| Type | Channel |
| Username | @matlabanyone |
| Created | Between 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 10 September 2026 |
| Measurements held | 13 |
| Confirmed unchanged | 1 time, most recently 10 September 2026 |
| On Telegram | t.me/matlabanyone |
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 20 August 2026 and assigned it the closest of 31 fixed categories, at 99% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 10 Sept 2026, 12:17 | 7,310 | -3 |
| 5 Sept 2026, 15:57 | 7,313 | -9 |
| 1 Sept 2026, 21:13 | 7,322 | -5 |
| 30 Aug 2026, 07:47 | 7,327 | -9 |
| 26 Aug 2026, 21:36 | 7,336 | -1 |
| 23 Aug 2026, 12:13 | 7,337 | -6 |
| 20 Aug 2026, 04:11 | 7,343 | -7 |
| 16 Aug 2026, 20:05 | 7,350 | -2 |
| 13 Aug 2026, 13:17 | 7,352 | -5 |
| 10 Aug 2026, 05:50 | 7,357 | -10 |
| 6 Aug 2026, 21:55 | 7,367 | -2 |
| 6 Aug 2026, 03:07 | 7,369 | no change |
| 5 Aug 2026, 22:11 | 7,369 | first reading |
20 posts held, back to 19 January 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 14 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 13 July 2026. An engagement rate over an empty window would be a number about nothing.
208 reactions across 20 posts, in 6 distinct kinds. The most used accounts for 55.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 115 | 55.3% | |
| ❤ | 62 | 29.8% | |
| 🙏 | 23 | 11.1% | |
| 🏆 | 6 | 2.88% | |
| 👌 | 1 | 0.481% | |
| 🔥 | 1 | 0.481% |
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 20 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 208 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 19 January 2025 to 13 July 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.
❇️ بهبود شبکه عصبی عمیق UNet با الگوریتم گرگ خاکستری ❇️ Optimizing U-Net using GWO 🔶 توضیح کد اصلی برای بخش بندی 🔶 مفهوم و نوشتن تابع هزینه 🔶 نکات ترکیب unet و گرگ خاکستری 🔶 ارزیابی ✳️ لینک سرفصل و معرفی دوره : https://matlablearning.com/courses/unet-gery-wolf-optimizer/ #GWO #segmentation #Unet ✳️ آموزش متلب 🆔: t.me/matlabanyone www.aparat.com/matlablearning
👍6❤1
❇️ آموزش تحلیل سیگنال های ECG با هوش مصنوعی در متلب ❇️ ECG Signals analysis using artificial intelligence in MATLAB 🔶 مفهوم و تئوری سیگنالهای قلبی ECG 🔶 تبدیل سیگنال به تصویر با ویولت 🔶 طبقه بندی با یادگیری عمیق CNN 🔶 تحلیل و تست نهایی سیگنال ✳️ لینک مشاهده سرفصل و معرفی آموزش: https://matlablearning.com/courses/ecg-signal-deep-learning/ ⏱ مدت زمان کل آموزش: حدود ۳ و نیم ساعت #ECG #Classification #CWT …
👍7❤2
❇️ کاهش ابعاد Dimension Reduction با الگوریتم PCA و اتوانکدر ❇️ Dimension Reduction using PCA and Autoencoder 🔶 بررسی مفهوم 🔶 آموزش کدنویسی در متلب ✳️ لینک مشاهده معرفی آموزش: https://aparat.com/v/jB328 ⏱ مدت زمان کل آموزش: 1 ساعت #datamining #autoencoder #unsupervised #PCA #DimensionReduction ✳️ آموزش متلب 🆔: t.me/matlabanyone www.aparat.com/matlablearning
👍6❤3
✅ آموزش های عمومی متلب مخصوص دوستانی که علاقه مند به متلب هستند بخش اول https://t.me/matlabanyone/145 https://t.me/matlabanyone/154 https://t.me/matlabanyone/601 https://t.me/matlabanyone/393 https://t.me/matlabanyone/665 https://t.me/matlabanyone/316 https://t.me/matlabanyone/473 https://t.me/matlabanyone/649 https://t.me/matlabanyone/484 https://t.me/matlabanyone/668
🙏10❤4
❇️ الگوریتم فراابتکاری ستاره دریایی در متلب 2025 ❇️ Starfish Optimization Algorithm SFOA in Matlab 🟩 الگوریتم فراابتکاری جدید ستاره دریایی 🔶 بررسی مفهوم و تئوری SFOA 🔶 آموزش کدنویسی SFOA در متلب 🔶 مفهوم exploration و exploitation ✳️ لینک سرفصل معرفی آموزش: https://matlablearning.com/courses/starfish-optimization-algorithm-description/ #metaheuristic #optimization #SFOA #Starfish ✳️ آموزش متلب 🆔: t.me/matlaban…
🏆5
❇️ الگوریتم فراابتکاری جست و جوی خزنده RSA در متلب ❇️ Reptile search Algorithm in Matlab 🟩 الگوریتم فراابتکاری جدید 🔶 بررسی مفهوم و تئوری RSA 🔶 آموزش کدنویسی RSA در متلب 🔶 مفهوم exploration و exploitation ✳️ لینک سرفصل معرفی آموزش: RSA_link #metaheuristic #crocodile #optimization #RSA #Reptile ✳️ آموزش متلب 🆔: t.me/matlabanyone www.aparat.com/matlablearning
❤10
🟩آموزش تخمین تابع یا رگرسیون با الگوریتم PSO ✅لینک معرفی و صرفصل دوره : https://matlablearning.com/courses/curve-fitting-pso/ 🆔 www.matlablearning.com 🆔 t.me/matlabanyone
👍6
❇️ بهینه سازی کنترلر فازی با گرگ خاکستری در سیمولینک متلب ✳️ Optimizing Fuzzy controller using Gray Wolf Optimizer ✳️ لینک مشاهده معرفی آموزش: https://matlablearning.com/courses/fuzzy-controller-grey-wolf-simulink/ ⏱ مدت زمان کل آموزش: حدود 2 ساعت 🚹 مدرس ID: @hassan_saadatmand #control #fuzzy #controller #Simulink #GWO #FIS ✳️ آموزش متلب 🆔: t.me/matlabanyone www.matlablearning.com
❤5👍5
❇️ بهینه سازی شبکه عصبی MLP با الگوریتم گرگ خاکستری GWO ❇️ Optimizing MLP using Gray Wolf Optimizer ✳️ سرفصل و معرفی آموزش: https://matlablearning.com/courses/optimizing-mlp-using-gwo/ ⏱ مدت زمان کل آموزش: 1 ساعت #MLP #optimization #neuralnetworks #GWO ✳️ آموزش متلب 🆔: t.me/matlabanyone www.aparat.com/matlablearning
🙏5❤2
(خلاصه ۳ جملهای از کتاب اثر مرکب): تغییرات کوچک روزانه نتایج بزرگ میسازند. ثبات و تکرار نیروهای قدرتمند موفقیت هستند. هر انتخاب امروز آیندهات را شکل میدهد. چالش امروز: یک عادت مثبت کوچک را شروع کن! 🕵♂@Aghaye_Eghtesad
👍42
❇️ الگوریتم فراابتکاری ستاره دریایی در متلب 2025 ❇️ Starfish Optimization Algorithm SFOA in Matlab 🟩 الگوریتم فراابتکاری جدید ستاره دریایی 🔶 بررسی مفهوم و تئوری SFOA 🔶 آموزش کدنویسی SFOA در متلب 🔶 مفهوم exploration و exploitation ✳️ لینک سرفصل معرفی آموزش: https://matlablearning.com/courses/starfish-optimization-algorithm-description/ #metaheuristic #optimization #SFOA #Starfish ✳️ آموزش متلب 🆔: t.me/matlaban…
👍12❤3
🎯 پیشبینی سری زمانی با پشتیبانی داده چند متغیره با LSTM 🔹 LSTM: Long Short Term Memory 🔶 آموزش پیش بینی با LSTM برای داده Sequence و سری زمانی 🔶 آموزش پیش بینی با افزایش تعداد داده ها و ایجاد sub-sample 🔶 آموزش تحلیل یا پیش بینی سری زمانی Time Series Forecasting برای حالت چند متغیره مناسب برای پیش بینی آب و هوا، بورس، قیمت طلا، زلزله، فراگیری یک نوع بیماری و ... ❇️ مدت زمان: 5 ساعت ❇️ لینک مشاهده سرفصل و دمو …
👍5❤1
Showing the 12 most recent of 20 posts we hold for @matlabanyone. 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.
Named by 1 registered channel — 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.
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.
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 10 September 2026 — this entry's latest reading, not the date you are reading this.
“MATLAB Learning” (@matlabanyone), 7,310 subscribers as measured 10 September 2026. Telegram Register, tgregister.com/channel/matlabanyone.
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.