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 14 500 subscribers, ranking 8 595 in the Technologies & Applications category and 28 001 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 500 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -117 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 9.99%. 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 27 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.
14 500
Subscribers
-1024 hours
-157 days
-11730 days
Posts Archive
NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.
source code:https://github.com/NVIDIA-AI-IOT/nanosam
Note: source code includes 👍 Usage - ⏱ Performance - 🛠 Setup - 🤸 Examples - 🏋️ Training
- 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also
join us: @MachineLearning_Programming
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join us: @MachineLearning_Programming
Repost from Artificial Intelligence && Deep Learning
Stanford University has just opened full access to CS224U. One of their immensely popular graduate-level Natural Language Understanding course taught by Professor Christopher Potts.
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YouTube: https://youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp
@deeplearning_ai
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MLOps Masterclass
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Repost from Artificial Intelligence && Deep Learning
Segment Anything in High Quality
We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Refer to our paper for more details. Our code and models will be released in two weeks. Stay tuned!
https://github.com/syscv/sam-hq
@deeplearning_ai
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Repost from Artificial Intelligence && Deep Learning
ImageBind: One Embedding Space To Bind Them All.
PyTorch implementation and pretrained models for ImageBind. For details, see the paper: ImageBind: One Embedding Space To Bind Them All.
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
Source code: https://github.com/facebookresearch/imagebind
Paper: https://arxiv.org/pdf/2305.05665v1.pdf
@deeplearning_ai
Machine Learning Operations (MLOps) Masterclass
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▪️MLOps Introduction
▪️Getting started with AWS for Machine Learning
▪️AWS SageMaker Studio
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▪️AWS MLOps Tools
▪️AWS MLOps - Build, Train & deploy ML Model
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☎️ Contact:
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Text2Video-Zero
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Paper: https://arxiv.org/abs/2303.13439
Video Result: video result link
Source code: https://github.com/picsart-ai-research/text2video-zero
join us: @MachineLearning_Programming
MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.
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👉 @deeplearning_ai
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▪️Cloud ML Services 101
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☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930
