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Github Top Repositories
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Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
Show more📈 Analytical overview of Telegram channel Github Top Repositories
Channel Github Top Repositories (@githubre) in the English language segment is an active participant. Currently, the community unites 13 354 subscribers, ranking 15 230 in the Education category and 31 848 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 354 subscribers.
According to the latest data from 18 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 431 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.06%. Within the first 24 hours after publication, content typically collects 0.78% reactions from the total number of subscribers.
- Post reach: On average, each post receives 142 views. Within the first day, a publication typically gains 104 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as repository, fork, programming, statistic, description.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Thanks to the high frequency of updates (latest data received on 19 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.
13 354
Subscribers
+1324 hours
+757 days
+43130 days
Posts Archive
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Imagine an AI assistant for data scientists that builds an entire Jupyter notebook on request
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Some projects don’t scream for attention — they quietly change the way we learn🧑🎓.
Python Data Science Handbook is one of them📖.
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A powerful open-source GitHub repository by Jake VanderPlas. It turns his bestselling book into interactive Jupyter notebooks that teach: 🧮 NumPy → fast array operations 📊 Pandas → data cleaning & analysis 📈 Matplotlib → clear visualizations 🤖 Scikit-learn → machine learning made simple 🛸 IPython → efficient coding environment🎚️Run everything instantly via Binder or Google Colab — no setup needed⚙.
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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🚀 Master Python with Ease!
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💃 GENMO: Generalist Human Motion by NVIDIA 💃
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Paper:
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Repost from Machine Learning with Python
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13 353
🔥Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
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yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml
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Sure! Here's the English version of your post, translated naturally and professionally:
---
🔥 Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
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yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml13 353
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
4️⃣ Artificial Intelligence
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6️⃣ Statistics
7️⃣ Deep Learning
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✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
13 353
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Repost from Machine Learning with Python
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Repost from Python Courses & Resources
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Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
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🚀 The new HQ-SAM (High-Quality Segment Anything Model) has just been added to the Hugging Face Transformers library!
This is an enhanced version of the original SAM (Segment Anything Model) introduced by Meta in 2023. HQ-SAM significantly improves the segmentation of fine and detailed objects, while preserving all the powerful features of SAM — including prompt-based interaction, fast inference, and strong zero-shot performance. That means you can easily switch to HQ-SAM wherever you used SAM!
The improvements come from just a few additional learnable parameters. The authors collected a high-quality dataset with 44,000 fine-grained masks from various sources, and impressively trained the model in just 4 hours using 8 GPUs — all while keeping the core SAM weights frozen.
The newly introduced parameters include:
* A High-Quality Token
* A Global-Local Feature Fusion mechanism
This work was presented at NeurIPS 2023 and still holds state-of-the-art performance in zero-shot segmentation on the SGinW benchmark.
📄 Documentation: https://lnkd.in/e5iDT6Tf
🧠 Model Access: https://lnkd.in/ehS6ZUyv
💻 Source Code: https://lnkd.in/eg5qiKC2
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