اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Artificial Intelligence && Deep Learning
رفتن به کانال در Telegram
Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:
نمایش بیشتر📈 تحلیل کانال تلگرام Artificial Intelligence && Deep Learning
کانال Artificial Intelligence && Deep Learning (@deeplearning_ai) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 58 023 مشترک است و جایگاه 2 289 را در دسته فناوری و برنامهها و رتبه 6 003 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 58 023 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 24 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -193 و در ۲۴ ساعت گذشته برابر 17 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 9.42% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 5 467 بازدید دریافت میکند. در اولین روز معمولاً 0 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 16 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند github, learning, estimation, dataset, engineer تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact:”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 25 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
58 023
مشترکین
+1724 ساعت
-237 روز
-19330 روز
آرشیو پست ها
Hey if you are interested in learning data science, machine learning, deep learning or Big data. You will also get to know how to do career transition to data science field.
In this telegram group, you will get daily practice quizzes on interview questions, study materials, cheatsheets, blogs, books for free.
Here is the telegram group that you can join
https://t.me/dataspoof
Swin Transformer for Object Detection
PAPER:
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
VIDEO:
https://www.youtube.com/watch?v=FQVS_0Bja6o
CODE:
https://github.com/SwinTransformer/Swin-Transformer-Object-Detection
@deeplearning_ai
Free Hands-on Demo Session organized by Management Career Institute
Topic: Application of AI in price prediction.
Deliverables:
-Basic of data handling part.
-Introducing to forecasting models.
-Introducing Deep learning based forecast.
-Explaining detailed architecture of the DL model.
-Implementing live on real time dataset.
Duration - 2 hours
Date - Thursday | 29th July
Time - 6.30 PM IST (Evening Slot)
Live Hands-on Session
Get Register Now: https://forms.gle/fkRv6dJnymew6sk6A
Who will be Instructor?
Instructor is Lead of Automation (Data Science & Artificial Intelligence) at Wipro. He has solved 60+ Research use cases, published 45+ Patents (received 23 grants) across various countries. He is also a Member of Advisory council at India ka AI.
A simpler design but better performance! It aims to bridge the gap between research and industrial communities.
Paper:
https://arxiv.org/pdf/2107.08430v1.pdf
Github:
https://github.com/Megvii-BaseDetection/YOLOX
t.me/deeplearning_ai
YOLOX: Exceeding YOLO Series in 2021
Anchor-free version of YOLO series
Won the 1st Place on Streaming
Perception Challenge (Workshop on Autonomous Driving
at CVPR 2021)
t.me/deeplearning_ai
Deep Learning with PyTorch by Microsoft 🔥🔥.
100% free !
Course covers:
* An Introduction to PyTorch
* Computer Vision with PyTorch
* NLP with PyTorch
* Audio Classification with PyTorch
https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/
https://t.me/DeepLearning_ai
Woww! This is really awesome!! A new course to learn the fundamentals of Deep Learning with PyTorch by Microsoft 🔥🔥.
https://t.me/DeepLearning_ai
Review — Non-local Neural Networks (Video Classification)
https://sh-tsang.medium.com/review-non-local-neural-networks-video-classification-object-detection-segmentation-pose-ac42fe57d0e4
https://t.me/DeepLearning_ai
💡AI Workshop: Bring Your Use Case Ideas to Life 💡
👍In this online workshop, we present how you can use AI services and start prototyping in minutes.
👍We cover some of the most popular AI verticals such as Computer Vision and Natural Language Processing.
After this workshop, you will know:
1. What is AI as a Service, and how does it work?
2. What are Computer Vision, NLP, and Speech?
3. How can you prototype your AI products before actual implementation?
Requirements:
✅ Free Registration
✅ Basic Python knowledge
👉16 July, 17:00 - 18:00 CET👈
https://www.linkedin.com/events/6814928657631473664/
GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
[Blog] https://autonomousvision.github.io/giraffe/
[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf
[Source] https://github.com/autonomousvision/giraffe
[Interactive slides] https://m-niemeyer.github.io/slides/#/4
[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html
https://t.me/DeepLearning_ai
GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf
[Source] https://github.com/autonomousvision/giraffe
[Blog] https://autonomousvision.github.io/giraffe/
[Interactive slides] https://m-niemeyer.github.io/slides/#/4
[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html
https://t.me/DeepLearning_ai
GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf
[Source] https://github.com/autonomousvision/giraffe
[Blog] https://autonomousvision.github.io/giraffe/
[Interactive slides] https://m-niemeyer.github.io/slides/#/4
[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html
https://t.me/DeepLearning_ai
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t.me/deeplearning_ai
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Probabilistic two-stage detection
Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.
github:
https://github.com/xingyizhou/CenterNet2?utm_source=catalyzex.com
paper:
https://arxiv.org/pdf/2103.07461.pdf
https://t.me/DeepLearning_ai
