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Coding Learning

Coding Learning

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πŸ“ˆ Analytical overview of Telegram channel Coding Learning

Channel Coding Learning (@codingdrive) in the English language segment is an active participant. Currently, the community unites 12 325 subscribers, ranking 16 477 in the Education category and 34 554 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 12 325 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 364 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 20.65%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
  • Thematic interests: Content is focused on key topics such as implementation, llm, dive, autogen, structure.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œBackup”

Thanks to the high frequency of updates (latest data received on 12 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 Education category.

12 325
Subscribers
+624 hours
+897 days
+36430 days
Posts Archive
10 - LLMs Implementation - Part 01

09 - LLMs Intuition

08 - A3C Implementation - Part 04

08 - A3C Implementation - Part 03

08 - A3C Implementation - Part 02

08 - A3C Implementation - Part 01

07 - A3C Intuition

06 - Deep Convolutional QLearning Implementation - Part 02

06 - Deep Convolutional QLearning Implementation - Part 01

05 - Deep Convolutional QLearning Intuition

04 - Deep QLearning Implementation - Part 03

04 - Deep QLearning Implementation - Part 02

04 - Deep QLearning Implementation - Part 01

03 - Deep QLearning Intuition

02 - QLearning Intuition

01 - Welcome to the course

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