Channel
@Machine_Learnings
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
3,343subscribers
-1 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001125125573 |
|---|---|
| Type | Channel |
| Username | @Machine_Learnings |
| Created | 8 July 2017 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/Machine_Learnings |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 02:55 | 3,343 | -2 |
| 9 Aug 2026, 17:41 | 3,345 | +2 |
| 7 Aug 2026, 02:37 | 3,343 | -1 |
| 6 Aug 2026, 23:33 | 3,344 | first reading |
20 posts held, back to 9 October 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 3 pagesof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 17 July 2026 |
|---|---|
| Posts held | 20 (9 October 2024 – 17 July 2026) |
| Views total | 810 |
| Reactions total | 10 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 23:27 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.
413 reactions across 20 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 413 | 100.0% |
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 413reactions 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 9 October 2024 to 17 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.
لینوس توروالدز دیروز در لیست ایمیل کرنل گفت: لینوکس پروژه ضد-AI نیست و AI ابزاری مفید است. او در جواب مخالفان استفاده از LLM نوشت: «شاید یک سال پیش جای شک داشت، ولی امروز دیگر شکی نیست که مفید است. هر کسی که به این شک دارد، واضح است که واقعا از آن استفاده نکرده است.» او اضافه کرد اگر کسی با این موضوع مشکل دارد، میتواند پروژه را فورک کند یا برود، و راهحل این نیست که سر را در برف کرد و گفت «لا لا لا نمیشنوم». https…
👍10
خروجی یک ایجنت که بطور روزانه، اخبار و مقالات حوزهی هوش مصنوعی را در ۲۴ ساعت اخیر بررسی کرده و طبق یک معیار امتیازدهی، موارد با بیشترین امتیاز را نمایش میدهد: https://hossein-amirkhani.github.io/ai-daily-reads
👍26
در راستای پست قبل، این راهنما هم بسیار مفید است: https://every.to/guides/compound-engineering علاوه بر معرفی پلاگین کاربردی Compound Engineering، تلاشی ساختارمند برای تدوین اصول برنامهنویسی در عصر جدید هم انجام دادهاند که ارزش مطالعه دارد.
👍14
متاسفانه با شرایط فعلی در ایران عزیز، انتشار مطلب در این کانال میتواند نامناسب تلقی شود، ولی به هرحال ما باید سعی کنیم با وجود شرایط سخت موجود، راهمان را ادامه دهیم. به همین دلیل تصمیم گرفتم بعد از مدتها پستی منتشر کنم. مهمترین مهارتی که در زمان فعلی توصیه میشود، یادگیری نحوهی کار حرفهای با ابزارهایی مانند Claude Code است. در آیندهی نزدیک، مهارت بکارگیری مناسب این ابزارها (فراتر از Vibe Coding معمولی)، جزء مه…
👍38
درس یادگیری عمیق استنفورد مربوط به ترم جاری که ویدئوها در طول ترم بارگذاری میشوند: https://youtube.com/playlist?list=PLoROMvodv4rNRRGdS0rBbXOUGA0wjdh1X&si=rxVW_A-bb9FB7IN7
👍25
نسخهی سوم کتاب یادگیری عمیق با پایتون آقای فرانسوا شوله که به زودی منتشر میشود، در وبسایت کتاب به آدرس زیر بطور رایگان در دسترس قرار گرفته است. این کتاب یکی از بهترین منابع این حوزه است و در این نسخه به مباحث جدیدتر مانند ترنسفورمرها و مدلهای بزرگ زبانی هم پرداخته شده است. https://deeplearningwithpython.io/chapters/
👍39
https://youtu.be/7_K3soZkmWc?si=N_OYf021w0ra-piv
👍31
پادکست اخبار هوش مصنوعی (۲۵ ژوئن ۲۰۲۵)، تولید شده با NotebookLM مربوط به لینک زیر: https://www.deeplearning.ai/the-batch/issue-307
👍5
پادکست اخبار هوش مصنوعی (۱۸ ژوئن ۲۰۲۵)، تولید شده با NotebookLM مربوط به لینک زیر: https://www.deeplearning.ai/the-batch/issue-306
👍7
پادکست اخبار هوش مصنوعی (١١ ژوئن ۲۰۲۵)، تولید شده با NotebookLM مربوط به لینک زیر: https://www.deeplearning.ai/the-batch/issue-305
👍10
مطالعهای جدید از MIT نشان داده که افرادی که برای نوشتن انشا از ChatGPT استفاده کردهاند، فعالیت مغزی بسیار کمتری نسبت به افرادی داشتهاند که خودشان متن را نوشته یا از گوگل برای جستجو استفاده کردهاند. یافتهی دیگر این بود که متنهای تولیدشده توسط این کاربران تمایل به استفاده از واژگان و ایدههای مشترک داشتند؛ استفاده از هوش مصنوعی نوعی اثر همگنسازی ایجاد کرده بود. http://newyorkermag.visitlink.me/hWuRQz
👍17
پادکست اخبار هوش مصنوعی (۴ ژوئن ۲۰۲۵)، تولید شده با NotebookLM مربوط به لینک زیر: https://www.deeplearning.ai/the-batch/issue-304
👍12
Showing the 12 most recent of 20 posts we hold for @Machine_Learnings. 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.
Citation-graph rank — 1,181,240 of 1,340,412entries 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.
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.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“یادگیری ماشین” (@Machine_Learnings), 3,343 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/Machine_Learnings.
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.