Channel
@prakta_AI
On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Cite this entry
261subscribers
+5 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003713066344 |
|---|---|
| Type | Channel |
| Username | @prakta_AI |
| Created | Between 1 February 2026 and 13 April 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 8 August 2026 |
| On Telegram | t.me/prakta_AI |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 18:22 | 261 | +5 |
| 8 Aug 2026, 09:17 | 256 | no change |
| 7 Aug 2026, 23:16 | 256 | first reading |
20 posts held, back to 13 April 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 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 29 July 2026 |
|---|---|
| Posts held | 20 (13 April 2026 – 29 July 2026) |
| Views total | 1,530 |
| Reactions total | 57 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 09:17 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.
192 reactions across 20 posts, in 8 distinct kinds. The most used accounts for 50.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 97 | 50.5% | |
| 🔥 | 44 | 22.9% | |
| ⚡ | 26 | 13.5% | |
| 👍 | 14 | 7.29% | |
| 💘 | 8 | 4.17% | |
| 🍓 | 1 | 0.521% | |
| 👻 | 1 | 0.521% | |
| 🤓 | 1 | 0.521% |
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 192reactions 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 13 April 2026 to 29 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.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @prakta_AI. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 20 most recent posts we hold for this entry, published 13 April 2026 to 29 July 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Давайте поговорим о роли нейросетей в математике уже сейчас на примере нескольких кейсов, что я смог найти (да, некоторые очень нашумевшие и вам известные, но, надеюсь, не все): 1. Гипотеза Якобиана (1939г.) На протяжении 87 лет считалась верной, но ИИ нашёл контрпример, который её опровергает для случая трёх и более переменных (частный случай до сих пор открытая проблема). Самая важная гипотеза, что была опровергну…
❤6🔥6⚡4
Приёмная кампания подходит к концу Сегодня последний день приёма документов для зачисления в ВУЗы. В каких-то регионах приём документов уже завершён, поэтому для тех, у кого осталось < 2 часов: 1. Проверьте свои заявления на госуслугах 2. Проверьте, чтобы вы были в списках подавших заявление на сайтах своего ВУЗа и на госуслугах 3. Проверьте, что вы подгрузили все свои достижения, аттестат, баллы за ЕГЭ, олимпиады,…
❤6⚡4🔥3👍1
🏠5.Данные | Дом | Площадь (м²) | Этаж | Продался? | |-----|--------------|------|-----------| | 1 | 50 | 3 | Да (1) | | 2 | 80 | 7 | Да (1) | | 3 | 30 | 1 | Нет (0) | | 4 | 90 | 10 | Да (1) | | 5 | 45 | 2 | Нет (0) | | 6 | 70 | 8 | Нет (0) | Задача: предсказать, продастся ли дом с площадью 60 м² и этажом 5 Строим…
❤2⚡1🔥1
Порог 60 - Левая (≤60): {50} → [1] → Gini=0 - Правая (>60): {70,90,90} → [0,1,1] → Gini = 1 - (2/3)² - (1/3)² = 4/9 ≈ 0.444 - Gini_w = (1/4)*0 + (3/4)*0.444 = 0.333 - Прирост = 0.375 – 0.333 = 0.042 [18.07.2026 00:42] IT: Порог 80 - Левая (≤80): {50,70} → [1,0] → Gini = 1 - (1/2)² - (1/2)² = 0.5 - Правая (>80): {90,90} → [1,1] → Gini=0 - Gini_w = (2/4)*0.5 + (2/4)*0 = 0.25 - Прирост = 0.375 – 0.25 = 0.125 ✅ Выбираем…
❤2⚡1🔥1
⚙️ 6. Гиперпараметры и их настройка 1. `n_estimators` (количество деревьев, B). Чем больше, тем лучше качество и стабильнее результат — до определённого предела. Ошибка OOB или тестовая ошибка выходит на плато. Обычно ставят 100–500. Для больших данных можно 1000, но выигрыш мизерный, а время растёт линейно. *Подбираем:* строим график ошибки от числа деревьев, ищем колено. 2. `max_features` (число признаков дл…
❤2⚡1🔥1
🧑💻7.Пример кода import numpy as np from sklearn.ensemble import RandomForestClassifier Сама модель – случайный лес для классификации. from sklearn.datasets import make_classification Генератор синтетических данных. Принимает параметры (число объектов, признаков, информативных, избыточных) и выдаёт `X, y from sklearn.model_selection import train_test_split - `train_test_split` – разбиение на обучающую и тестовую в…
❤4⚡1🔥1
🌲Случайный лес (🔤🔤🔤🔤🔤🔤 🔤🔤🔤🔤🔤🔤) 📦 1. Что это вообще такое и зачем придумали Одиночное решающее дерево — отличный интерпретируемый алгоритм, но у него большая проблема: высокая дисперсия (variance). Чуть-чуть измени обучающую выборку — и дерево может перестроиться совершенно иначе, а предсказания поплывут. На практике это означает переобучение. Чтобы победить дисперсию, не потеряв в точности, придумали ансамблевый …
❤2⚡1👍1
🧠 4. Почему это работает: Bias-Variance разбор - Смещение (bias): отдельное дерево — модель с низким смещением (гибкая, может идеально подогнаться под любую сложную границу). Когда мы усредняем много таких деревьев, смещение остаётся почти таким же низким. Небольшой рост смещения возможен из-за того, что каждое дерево видит меньше признаков и меньше данных, но на практике это незначительно. - Дисперсия (variance): к…
❤3⚡1🔥1
🔤🔤🔤🔤🔤🔤🔤🔤🔤🔤 (ПЕРЕОБУЧЕНИЕ) ⚙️1. Интуиция и определение Представь студента, который вместо понимания предмета выучил наизусть ответы на все билеты из прошлогодней сессии. Если вопрос на экзамене совпадёт дословно — он получит «отлично». Но стоит преподавателю чуть переформулировать или задать смежную тему — студент провалится. Примерно так ведёт себя переобученная модель. 🔖Формальное определение: Переобучение (overf…
❤4⚡2🔥2
В алгоритмах вроде случайного леса мы голосуем многочисленными деревьями, и именно усреднение снижает дисперсию (variance), что и борется с переобучением. 🔥4. Как обнаружить переобучение на практике 🦖4.1. Обучающая и валидационная кривые Строим две кривые: ошибка на train и ошибка на валидации (val) в зависимости от сложности модели (глубина дерева, степень полинома, число итераций и т.д.). Ошибка │ │ …
❤5⚡2🔥2
4️⃣6.4. Ранняя остановка (Early Stopping) При итеративном обучении отслеживаем ошибку на валидационной выборке. Как только она перестаёт улучшаться и начинает расти, останавливаем обучение и возвращаем лучшие веса. Математически ранняя остановка эквивалентна L2-регуляризации: она ограничивает эффективную ёмкость модели, не давая весам уйти далеко от начальных малых значений. 5️⃣6.5. Кросс-валидация для подбора гипер…
❤4⚡2🔥2🍓1
🌳 Решающие деревья 1. Решающее дерево — это модель, которая предсказывает значение целевой переменной, задавая последовательность вопросов с ответом «да/нет». Каждый вопрос проверяет один признак объекта. Например: «Площадь ≤ 75 м²?» → если да, идём налево, иначе направо. Структура: - Корень — первый (самый информативный) вопрос. - Внутренние узлы — последующие вопросы. - Листья — финальное предсказание (класс, веро…
❤4⚡1🔥1
Showing the 12 most recent of 20 posts we hold for @prakta_AI. 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 — 787,048 of 1,345,403entries 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.
Named by 2 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.
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 8 August 2026 — this entry's latest reading, not the date you are reading this.
“ПрактИИкум” (@prakta_AI), 261 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/prakta_AI.
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.