Available now! Telegram Research 2025 — the year's key insights 
Computer Science and Programming
Open in Telegram
Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers * Related Courses and Ebooks With advertising offers contact:
Show more📈 Analytical overview of Telegram channel Computer Science and Programming
Channel Computer Science and Programming (@machinelearning_programming) in the English language segment is an active participant. Currently, the community unites 14 851 subscribers, ranking 8 724 in the Technologies & Applications category and 29 599 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 851 subscribers.
According to the latest data from 03 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -150 over the last 30 days and by -3 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 14.63%. 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 learning, github, engineer, quantization, detection.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
* Related Courses and Ebooks
With advertising offers contact:”
Thanks to the high frequency of updates (latest data received on 04 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.
14 851
Subscribers
-324 hours
-227 days
-15030 days
Posts Archive
🚀 Explore AI News with Us! 🤖
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LeGrad: Layerwise Explainability GRADient method for large ViT transformer architectures
Explore More:
💻DEMO: you may use demo
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
Relevance: #AI #machinelearning #deeplearning #computervision
join our community:
👉 @MachineLearning_Programming
AiOS: The Future of Human Shape & Pose Recovery
Discover AiOS, the cutting-edge, unified framework by SenseTime, HKU, IDEA, S-Lab, and Shanghai AI Lab. AiOS redefines state-of-the-art expressive pose and shape recovery, seamlessly integrating advanced features without the need for separate human detection steps.
Highlights:
✅First-of-its-Kind: Single-stage EHPS with zero extra detection networks.
✅Innovative Design: Unique "Human-as-Tokens" concept for deeper insights.
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✅Comprehensive Analysis: Unified feature system for unparalleled whole-body understanding.
✅Unmatched Performance: Top-tier results sans ground truth bounding boxes.
Explore More:
Project Page
Read the Paper
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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8️⃣ programming Languages
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This channels is for Programmers, Coders, Software Engineers.
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
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Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with Conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
👉 @MachineLearning_Programming
🌴🌴Direct-a-Video: driving Video Generation🌴🌴
👉Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion. Authors: City University of HK, Kuaishou Tech & Tianjin.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Decoupling camera/object motion in gen-AI
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✅Novel temporal cross-attention for cam motion
✅Training-free spatial cross-attention for objects
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👉Channel: @MachineLearning_Programming
👉Paper https://arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/
Repost from Artificial Intelligence && Deep Learning
EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction
1. Channel: @deeplearning_ai
2.Source Code: https://github.com/mit-han-lab/efficientvit
3. Paper: https://arxiv.org/abs/2402.05008
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
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8- programming Languages
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Tracking Any Point (TAP)
Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid Dataset, our top-performing TAPIR model, and our RoboTAP extension.
Source code: https://github.com/google-deepmind/tapnet
Google Colab: https://github.com/google-deepmind/tapnet
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InstantID : Zero-shot Identity-Preserving Generation in Seconds
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Repost from Computer Science and Programming
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