Channel
@ml_learning
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
886subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001220997296 |
|---|---|
| Type | Channel |
| Username | @ml_learning |
| Description | Insights in recent Machine Learning topics, approaches, models and papers. Interested in collaboration, DM @infatum |
| Created | 15 December 2019 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 7 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/ml_learning |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 6 Aug 2026, 14:21 | 886 | no change |
| 6 Aug 2026, 12:49 | 886 | first reading |
20 posts held, back to 2 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 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 20 posts for this entry, the most recent from 18 June 2025. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 6 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
114 reactions across 15 posts, in 10 distinct kinds. The most used accounts for 46.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 53 | 46.5% | |
| ❤ | 15 | 13.2% | |
| 👍 | 15 | 13.2% | |
| 😭 | 8 | 7.02% | |
| 🤡 | 8 | 7.02% | |
| 💯 | 6 | 5.26% | |
| 🔥 | 6 | 5.26% | |
| 🐳 | 1 | 0.877% | |
| 👎 | 1 | 0.877% | |
| 🙈 | 1 | 0.877% |
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 15 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 114reactions 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 2 October 2024 to 18 June 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
⚡️Java-мітап від Levi9: Java x AI — майбутнє твого коду Як Java-інженеру вписатися в нову реальність, де AI змінює правила розробки? Ми покажемо на живих прикладах, як інтегрувати AI у продакшн-код, розповімо про AI-агентів, інструменти та типові помилки. Спікери: Себастьян Дашнер — Java Champion, автор книги «Architecting Modern Java EE Applications», tech-евангеліст. 👉 AI Tools and Agents That Make You a More E…
Machine, are you learning? pinned a photo
🔥 Стань експертом зі штучного інтелекту з Google! Google запускає оновлений “Інтенсивний курс з машинного навчання” українською. За 15 годин ви зможете безкоштовно опанувати ШІ і навіть навчитися створювати власні “розумні” програми. Що всередині курсу: • 12 модулів із практичними темами • 9 відео з поясненнями • Понад 100 вправ та тестів • Бейджі за успішне проходження • Реальні приклади та інтерактивні візуалізац…
❤3👍1
https://youtu.be/ZqWyqwd9_Pk?si=krhc6o5wVlw0FyKt Якщо ви мріяли осягнути топологію, але задихались в сухих формалізмах і термінах, раджу до перегляду це відео. Просте і елегантне пояснення складних теорій
👍8
https://matharena.ai/
🔥3😁1
ML ops in one pic
😁12
Given rising interest to Reinforcement Learning recently recollected this course which I’ve personally took during spring 2023: https://huggingface.co/learn/deep-rl-course/en/unit0/introduction #reinforcementlearning #RL #DeepRL #DRL
🔥3😁1
https://youtu.be/7xTGNNLPyMI?si=nv7EJ0_EmcLFN3Hf Andrey Karpathy’s tutorial on LLMs #llm #tutorial #chagpt #ArtificialIntelligence
👍2👎1
https://thehealthcareinsights.com/swedish-scientists-unveil-the-worlds-first-living-computer-built-from-human-brain-tissue/ Swedish scientists have created the world’s first ‘living computer’ and it is made out of human brain tissue. It is composed of 16 organoids also called clumps of brain cells. Organoids are tiny, self-organized three-dimensional tissue cultures made from stem cells. This is an alternative (out…
Andrej Karpathy: “I don't have too too much to add on top of this earlier post on V3 and I think it applies to R1 too (which is the more recent, thinking equivalent). I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable in…
❤4
https://www.youtube.com/watch?v=_CXwZ5xyFno DeepSeek-R1 Crash course #deepseekr1 #llm #DeepSeek #course
https://arxiv.org/pdf/2501.12948 DeepSeek-R1-Zero is a pure RL model without any supervised data and fine-tuning which achieved paramount reasoning capabilities and was actually trained on a DeepSeek-V3-Base model using GRPO(Group Relative Policy Optimisation) approach. Which is truly an amazing result, that shows how undervalued RL potential is. As I foreseen — the next big leap in AI will be achieved by RL massive…
❤5
Showing the 12 most recent of 20 posts we hold for @ml_learning. 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 — 1,055,598 of 1,176,251entries 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.
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.
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 August 2026 — this entry's latest reading, not the date you are reading this.
“Machine, are you learning?” (@ml_learning), 886 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/ml_learning.
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.