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AI & Deep Learning

AI & Deep Learning

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All about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML Follow for quality content amid all the noise in #AI.

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πŸ“ˆ Analytical overview of Telegram channel AI & Deep Learning

Channel AI & Deep Learning (@deeplearning005) in the English language segment is an active participant. Currently, the community unites 11 015 subscribers, ranking 10 976 in the Technologies & Applications category and 35 734 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 11 015 subscribers.

According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 239 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 12.83%. Within the first 24 hours after publication, content typically collects 2.45% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 413 views. Within the first day, a publication typically gains 270 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
  • Thematic interests: Content is focused on key topics such as developer, openai.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œAll about Deep Learning, LLMs #deeplearning #deep_learning #AI #ML Follow for quality content amid all the noise in #AI.”

Thanks to the high frequency of updates (latest data received on 26 August, 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.

11 015
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Attracting Subscribers
August '26
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June '26
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February '26
+196
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January '26
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December '25
+198
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+184
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April '25
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March '25
+297
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February '25
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January '25
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December '24
+818
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December '23
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January '23
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December '22
+30
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February '22
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January '22
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December '21
+25
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November '21
+18
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October '21
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April '21
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March '21
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February '21
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Date
Subscriber Growth
Mentions
Channels
26 August+1
25 August+1
24 August+4
23 August+4
22 August+6
21 August+4
20 August+2
19 August+50
18 August+5
17 August+6
16 August+4
15 August+10
14 August+7
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12 August+8
11 August+9
10 August+6
09 August+9
08 August+2
07 August+38
06 August+7
05 August+7
04 August+16
03 August+6
02 August+10
01 August+2
Channel Posts
Great News! MCP will now be stateless, this was a much awaited change. Read full blog: https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/

2
https://gemini.google/overview/agent/spark/
847
3
Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. It matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index, which is a composite score of nine benchmarks. https://x.ai/news/grok-4-6
1 203
4
https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
1 454
5
https://github.com/yc-software/qm
1 399
6
https://qwen.ai/blog?id=qwen3.8
1 459
7
https://thinkingmachines.ai/news/introducing-inkling/
1 603
8
TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples. https://github.com/google-research/tabfm
1 754
9
https://www.anthropic.com/news/claude-opus-5
1 659
10
https://www.zdnet.com/article/claude-ai-shared-chats-indexed-by-google/
1 690
11
https://github.com/hkuds/lightrag
1 774
12
TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples. https://github.com/google-research/tabfm
1
13
https://platform.kimi.ai/docs/guide/kimi-k3-quickstart
1 923
14
https://x.com/Alibaba_Qwen/status/2078754377473601787
1 738
15
https://openai.com/index/previewing-gpt-5-6-sol/
1 792
16
https://github.com/0xNyk/council-of-high-intelligence
1 986
17
https://opencv.org/opencv-5/
2 010
18
https://www.anthropic.com/news/claude-sonnet-5
2 204
19
https://github.com/cloudflare/security-audit-skill
2 237
20
https://x.com/OpenAI/status/2070555272230384038?s=20
https://x.com/OpenAI/status/2070555272230384038?s=20
2 092