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Channel

Machine learning books and papers

@Machine_learn

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

24,464subscribers

-11 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001304758333
TypeChannel
Username@Machine_learn
CreatedBetween 1 March 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live22 August 2026
Measurements held17
Confirmed unchanged1 time, most recently 22 August 2026
On Telegramt.me/Machine_learn

Growth

24,45524,48024,467.56 August 2026 — 24,475 subscribers6 August 2026 — 24,473 subscribers7 August 2026 — 24,470 subscribers8 August 2026 — 24,480 subscribers9 August 2026 — 24,478 subscribers10 August 2026 — 24,477 subscribers11 August 2026 — 24,471 subscribers12 August 2026 — 24,466 subscribers13 August 2026 — 24,459 subscribers14 August 2026 — 24,455 subscribers16 August 2026 — 24,460 subscribers17 August 2026 — 24,466 subscribers18 August 2026 — 24,457 subscribers19 August 2026 — 24,460 subscribers20 August 2026 — 24,464 subscribers21 August 2026 — 24,462 subscribers22 August 2026 — 24,464 subscribers24,4646 August 202622 August 2026
17 measurements spanning 17 days, net -11. 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 24,451–24,484 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
22 Aug 2026, 21:3624,464+2
21 Aug 2026, 09:2824,462-2
20 Aug 2026, 08:4224,464+4
19 Aug 2026, 09:5624,460+3
18 Aug 2026, 11:4324,457-9
17 Aug 2026, 12:4824,466+6
16 Aug 2026, 06:1724,460+5
14 Aug 2026, 19:0524,455-4
13 Aug 2026, 11:2624,459-7
12 Aug 2026, 13:4824,466-5
11 Aug 2026, 14:1224,471-6
10 Aug 2026, 16:3124,477-1
9 Aug 2026, 17:4124,478-2
8 Aug 2026, 14:5324,480+10
7 Aug 2026, 13:2424,470-3
6 Aug 2026, 13:2424,473-2
6 Aug 2026, 05:1524,475first reading

Engagement

26 posts held, back to 29 June 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 35 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
7.17%
avg views ÷ 24,464 subscribers
Avg views / post
1,750
12 posts measured
Reaction rate
0.197%
reactions ÷ views · ER floor
Posts in window
14
of 26 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. It is computed over the 11 of 12 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 19 August 2026
Posts held26 (29 June 202619 August 2026)
Views total21,047
Reactions total38
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 19:52 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
17s
Average length
9s

Measured directly from 2 videos 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

71 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 88.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
6388.7%
👍79.86%
🔥11.41%

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 19 of the 26 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 71reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 26 most recent posts we hold, published 29 June 2026 to 19 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.

Recent posts

19 Aug 2026, 01:08 UTC777 views2 reactionsread 22 August 2026
Photo

"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊 It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮 In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of

2

13 Aug 2026, 13:25 UTC≈1,450 views2 reactionsread 22 August 2026

🔥 8 skills = 8 free certifications >>> AI (Microsoft) - https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/ Deep learning (NVIDIA) - https://learn.nvidia.com/en-us/training/self-paced-courses Data science (IBM) - https://skillsbuild.org/students/course-catalog/data-science Data Analyst (Microsoft) - https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/ Pytho

2

10 Aug 2026, 07:13 UTC≈1,720 viewsread 22 August 2026

اخرین زمان سابمیت این مقاله امشب...! @Raminmousa1

9 Aug 2026, 08:22 UTCviews —

Machine learning books and papers pinned «با عرض سلام در مقاله زیر جهت سابمیت نیاز به نفر دوم داریم Title: FFChurn: Fusion Former for Customer Churn Classification Based on Transformer, FEDformer, and Informer Abstract: Customer churn prediction is a key issue in customer relationship management…»

9 Aug 2026, 08:22 UTC≈1,890 views2 reactionsread 22 August 2026

با عرض سلام در مقاله زیر جهت سابمیت نیاز به نفر دوم داریم Title: FFChurn: Fusion Former for Customer Churn Classification Based on Transformer, FEDformer, and Informer Abstract: Customer churn prediction is a key issue in customer relationship management that directly impacts organizational profitability and has created challenges for researchers and organizations. Machine learning (ML), Ensemble Learning (EL), and

2

6 Aug 2026, 06:07 UTC≈1,960 views5 reactionsread 22 August 2026
File

هر هفته با یک موضوع تحقیقی موضوع :تولید داده های سری زمانی با استفاده از شبکه های عصبی تخاصمی در شبکه های هوشمند #Thesis #proposed_research @Raminmousa1 @Machine_learn

4👍1

4 Aug 2026, 06:45 UTC≈2,150 views5 reactionsread 22 August 2026
Video

🔖 Learning Data Science through interactive examples One of the most useful repositories for those who want to better understand machine learning. It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results. ⛓ Link to GitHub https://github.com/GeostatsGuy/DataScienceInteractivePython @Machine_learn

4👍1

4 Aug 2026, 06:10 UTC≈1,840 views1 reactionsread 22 August 2026
Video

Attention Heatmap vs Token Pruning 🔍✂️ 🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms #AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM @Machine_learn

1

2 Aug 2026, 08:24 UTCviews —

Machine learning books and papers pinned «با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه. 1: Survey on knowledge graph and large language models _ auth2: 300$ _auth3:200$ 2: Survey…»

2 Aug 2026, 08:24 UTC≈2,080 views3 reactionsread 22 August 2026

با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه. 1: Survey on knowledge graph and large language models _ auth2: 300$ _auth3:200$ 2: Survey on challenges of large language models _ auth2: 300$ _auth3:200$ 3: New learning model for skin cancer detection _ auth2: 300$ _auth3:200$ جهت مشارکت میتونین

3

2 Aug 2026, 05:22 UTC≈1,660 views5 reactionsread 22 August 2026
Photo

🔖 One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents. It's an excellent option to get a holistic picture and understand which topics deserve deeper study. ⛓️ Link to the book https://arxiv.org/abs/2606.24937 @Machine_learn

4👍1

2 Aug 2026, 05:21 UTC≈1,500 views3 reactionsread 22 August 2026
Photo

Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers 🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars. 📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context. 📖 It contains 20 chapters: * Vectors,

2👍1

Showing the 12 most recent of 26 posts we hold for @Machine_learn. 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

Citation-graph rank — 256,492 of 1,584,142entries 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 3 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.

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.

Machine Learning with Python
@CodeProgrammer · 68,188
Telegram ranks this channel #10 of 84 here — alongside 83 others — read 20 August 2026
AI and Machine Learning
@machine_learning_courses · 95,211
Telegram ranks this channel #14 of 74 here — alongside 73 others — read 16 August 2026
Artificial Intelligence
@Artificial_intelligence_in · 65,539
Telegram ranks this channel #23 of 90 here — alongside 89 others — read 21 August 2026
Data Science & Machine Learning
@datasciencefun · 77,305
Telegram ranks this channel #36 of 85 here — alongside 84 others — read 19 August 2026
Machine Learning & Artificial Intelligence | Data Science Free Courses
@datasciencefree · 67,978
Telegram ranks this channel #38 of 88 here — alongside 87 others — read 20 August 2026
هوش مصنوعی در پژوهش
@AI_in_Research · 249,739
Telegram ranks this channel #39 of 92 here — alongside 91 others — read 19 August 2026
Python Projects & Resources
@pythondevelopersindia · 63,316
Telegram ranks this channel #43 of 86 here — alongside 85 others — read 21 August 2026
Coding Projects
@Programming_experts · 67,332
Telegram ranks this channel #53 of 84 here — alongside 83 others — read 20 August 2026
آموزش مقاله و پایان‌ نامه‌ نویسی
@dr_amaniiii · 62,427
Telegram ranks this channel #58 of 79 here — alongside 78 others — read 21 August 2026
MS Excel for Data Analysis
@excel_analyst · 72,374
Telegram ranks this channel #62 of 84 here — alongside 83 others — read 20 August 2026
SQL Programming Resources
@sqlanalyst · 76,646
Telegram ranks this channel #72 of 85 here — alongside 84 others — read 19 August 2026
Data Analytics
@sqlspecialist · 110,775
Telegram ranks this channel #74 of 83 here — alongside 82 others — read 14 August 2026

This channel appears in 12 seed channels' Telegram-generated recommendation lists 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 22 August 2026 — this entry's latest reading, not the date you are reading this.

“Machine learning books and papers” (@Machine_learn), 24,464 subscribers as measured 22 August 2026. Telegram Register, tgregister.com/channel/Machine_learn.

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.