Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Computer Science and Programming
Kanalga Telegram’da o‘tish
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:
Ko'proq ko'rsatish📈 Telegram kanali Computer Science and Programming analitikasi
Computer Science and Programming (@machinelearning_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 14 851 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 8 724-o'rinni va Hindiston mintaqasida 29 599-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 14 851 obunachiga ega bo‘ldi.
03 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -150 ga, so‘nggi 24 soatda esa -3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 14.63% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 0 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 0 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, github, engineer, quantization, detection kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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:”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
14 851
Obunachilar
-324 soatlar
-227 kunlar
-15030 kunlar
Postlar arxiv
+1NanoSAM 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
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.
GitHub: https://github.com/cgpotts/cs224u
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@deeplearning_ai
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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
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▪️AWS MLOps Tools
▪️AWS MLOps - Build, Train & deploy ML Model
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☎️ Contact:
Sarath Kumar
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