5 Aug 2026, 11:14 UTC≈48 viewsread 6 August 2026
I Compressed Bad Apple into a 3MB Neural Network [P] https://redd.it/1vfrco1 @datascientology
Channel
Data Scientology
@datascientology
On this record: Growth · Engagement · Posts · Citations · Cite this entry
1,150subscribers
-3 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
Register entry
| Telegram ID | -1001076468917 |
|---|---|
| Type | Channel |
| Username | @datascientology |
| Created | 21 September 2016 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 26 August 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 26 August 2026 |
| On Telegram | t.me/datascientology |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 26 Aug 2026, 01:39 | 1,150 | -1 |
| 22 Aug 2026, 19:34 | 1,151 | -1 |
| 12 Aug 2026, 03:42 | 1,152 | -2 |
| 9 Aug 2026, 08:51 | 1,154 | +1 |
| 6 Aug 2026, 09:06 | 1,153 | no change |
| 6 Aug 2026, 00:39 | 1,153 | first reading |
Engagement
17 posts held, back to 24 June 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 1 pageof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 13.7%
- avg views ÷ 1,150 subscribers
- Avg views / post
- 158
- 4 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 4
- of 17 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.
| Window | Rolling 30 days · latest post in window 5 August 2026 |
|---|---|
| Posts held | 17 (24 June 2026 – 5 August 2026) |
| Views total | 631 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Aug 2026, 09:06 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.
Recent posts
3 Aug 2026, 11:15 UTC≈102 viewsread 6 August 2026
Is it too late regain some coherence in the ML research space in our life time? D Was just looking at the list of preprints on Arxiv cs.LG https://arxiv.org/list/cs.LG/recent?skip=0&show=500 Everyday 100 - 400 new machine learning papers gets uploaded on this server. Looking at this unending list of preprints is as if you stepped into a crowded room, like the stock trading floor on wall st. in the 1980s. Everyone…
29 Jul 2026, 11:14 UTC≈214 viewsread 6 August 2026
Video
Beginner here: My pothole detection model mistakes the roadside for potholes. https://redd.it/1v90113 @datascientology
27 Jul 2026, 11:14 UTC≈267 viewsread 6 August 2026
Photo
30+ officially free AI/ML books, all in one curated repo https://redd.it/1v7cvqr @datascientology
20 Jul 2026, 11:14 UTC≈415 viewsread 6 August 2026
Are there some textbooks that take a primarily engineering approach to machine learning (as opposed to a "scientific" approach)? D As someone who studied stats undergrad and industrial engineering operations research grad, and who thinks about the practical business of ML components in software.... I get lost and a bit hopeless when I think about how to make useful software out of ML models in a reasonable amount o…
17 Jul 2026, 11:14 UTC≈250 viewsread 6 August 2026
Photo
SenseNova-Vision is open-sourced: handle every CV task as unified multimodal generation https://redd.it/1uyorje @datascientology
15 Jul 2026, 11:14 UTC≈244 viewsread 6 August 2026
Photo
I reviewed that boy. https://redd.it/1uwsmbo @datascientology
13 Jul 2026, 11:14 UTC≈186 viewsread 6 August 2026
Prompt-engineering paper accepted to ICML R "Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity" This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this. Even if it works, I’m not sure this kin…
10 Jul 2026, 11:14 UTC≈150 viewsread 6 August 2026
Hyperparameter tuning approach question R I am doing some work with cell type classification, where I have 4.3 million cells and 512 features (condensed embeddings from the encoder of a transformer). The broader goal is to implement a contextual bandit for augmenting the training set of the dataset, as it is currently imbalanced, and rare cell type classification is poor when I tried a baseline logistic regression …
8 Jul 2026, 11:14 UTC≈108 viewsread 6 August 2026
TorchJD: Training with multiple losses in PyTorch P Hi everyone! I wanted to share some recent progress on TorchJD that might be useful to the machine learning community. When training models with multiple losses (multiple tasks, constraints, auxiliary losses, regularization terms, etc.), you typically have two options: Scalarization: Various ways to combine those losses into a single loss (e.g. average them or c…
6 Jul 2026, 11:14 UTC≈122 viewsread 6 August 2026
I trained a local AI model that generated 22,000+ novel drug-like molecules — verified against 4.6M known compounds. Dataset available. Built an 80M parameter causal transformer on consumer hardware (RTX 5070), trained on MOSES + ZINC-250k. Generated and filtered for QED ≥ 0.5, SA ≤ 4.0, MW ≤ 500. Top compound hits QED 0.947. 100% novel against MOSES, ZINC, and ChEMBL. HuggingFace: https://huggingface.co/datasets/M…
3 Jul 2026, 11:14 UTC≈129 viewsread 6 August 2026
Books/Resources to improve mathematical foundations for ML research D I am a mid to late stage PhD student in ML. I've known this before, but only recently I started feeling this urgently: my mathematical foundations are shaky, because I kept "learning-things-as-I-go" when working on various problems. I likely have only a year or two left until I graduate, and before I do so, I want to really dedicate some time and …
Showing the 12 most recent of 17 posts we hold for @datascientology. 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 — 1,555,570 of 1,603,893entries 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.
Mentions
Named by 7 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.
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 26 August 2026 — this entry's latest reading, not the date you are reading this.
“Data Scientology” (@datascientology), 1,150 subscribers as measured 26 August 2026. Telegram Register, tgregister.com/channel/datascientology.
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.