Математика Дата саентиста
@workakkk - админ @data_analysis_ml - ds https://gosuslugi.ru/snet/67b55bb01a1c5a6fb6ecc946
Show more📈 Analytical overview of Telegram channel Математика Дата саентиста
Channel Математика Дата саентиста (@data_math) in the Russian language segment is an active participant. Currently, the community unites 14 048 subscribers, ranking 9 115 in the Technologies & Applications category and 47 031 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 048 subscribers.
According to the latest data from 27 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -37 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 14.97%. Within the first 24 hours after publication, content typically collects 6.22% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 102 views. Within the first day, a publication typically gains 874 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 28.
- Thematic interests: Content is focused on key topics such as llm, программирование, параметр, визуализация, stepik.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“@workakkk - админ
@data_analysis_ml - ds
https://gosuslugi.ru/snet/67b55bb01a1c5a6fb6ecc946”
Thanks to the high frequency of updates (latest data received on 28 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
from sympy import symbols
from galgebra.ga import Ga
o3d = Ga('e', g=[1,1,1], coords=symbols('x,y,z',real=True))
(grad,rgrad) = o3d.grads()
https://galgebra.readthedocs.io/en/latest/
@data_mathNVIDIA для настройки математических моделей, содержащий 1,8 млн пар "задача-решение".
> Используются обучающие датасеты GSM8K и MATH.
> Для создания ланных используется Mixtral 8x7B.
> Модель использует текстовые рассуждения + интерпретатор кода при генерации.
> Выпущены LLama, CodeLlama, Mistral, Mixtral fine-tunes.
> Лицензия Apache 2.0!
Блестящая работа команды Nvidia AI - 2024 год станет годом синтетических данных и еще более мощных моделей! 🔥
▪Dataset: https://huggingface.co/datasets/nvidia/OpenMathInstruct-1
▪Paper: https://huggingface.co/papers/2402.10176
ai_machinelearning_big_data
Available now! Telegram Research 2025 — the year's key insights 
