DLeX: AI Python
هوشمصنوعی و برنامهنویسی توییتر : https://twitter.com/NaviDDariya لینک پیام ناشناس، جهت انتقادات، حرف و حدیث و پیشنهادات : https://t.me/mahtabak_nashenas_bot?start=1dOIcE18ITmb5Htw1M7zgq
Show more📈 Analytical overview of Telegram channel DLeX: AI Python
Channel DLeX: AI Python (@ai_python) in the Farsi language segment is an active participant. Currently, the community unites 21 284 subscribers, ranking 6 234 in the Technologies & Applications category and 16 500 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 21 284 subscribers.
According to the latest data from 13 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -58 over the last 30 days and by -9 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.24%. Within the first 24 hours after publication, content typically collects 2.67% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 754 views. Within the first day, a publication typically gains 569 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 18.
- Thematic interests: Content is focused on key topics such as مصنوعی, توییتر, ماهواره, داده, فناوری.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“هوشمصنوعی و برنامهنویسی
توییتر :
https://twitter.com/NaviDDariya
لینک پیام ناشناس، جهت انتقادات، حرف و حدیث و پیشنهادات : https://t.me/mahtabak_nashenas_bot?start=1dOIcE18ITmb5Htw1M7zgq”
Thanks to the high frequency of updates (latest data received on 14 November, 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 sklearn.linear_model import LinearRegression import numpy as np lr = LinearRegression() x=np.reshape(np.array([1.0,2.0,3.0]),(3,1)) y=np.reshape(np.array([-45,14,5]),(3,1)) lr.fit(x,y) print(lr.predict(np.reshape([2,5,6],(3,1)))) print(lr.coef_)❇️ @AI_Python 🗣 @AI_Python_arXiv ✴️ @AI_Python_EN
