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 58 019 مشترک است و جایگاه 2 290 را در دسته فناوری و برنامهها و رتبه 5 977 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 58 019 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -204 و در ۲۴ ساعت گذشته برابر -8 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 9.58% 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 5 556 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 26 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.
58 019
مشترکین
-824 ساعت
-287 روز
-20430 روز
آرشیو پست ها
Algorithms online Course from PRINCETON UNIVERSITY
About this Course
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. It does not offer a certificate upon completion
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@DeepLearning_AI
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https://www.coursera.org/learn/algorithms-part1?ranMID=40328&ranEAID=SAyYsTvLiGQ&ranSiteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&siteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ
DeepMind & Google Graph Matching Network Outperforms GNN
DeepMind and Google researchers have proposed a powerful new graph matching network (GMN) model for the retrieval and matching of graph structured objects. GMN uses similarity learning for graph structured objects and outperforms graph neural network (GNN) models on graph similarity learning (GSL) tasks.
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@DeepLearning_AI
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https://medium.com/syncedreview/deepmind-google-graph-matching-network-outperforms-gnn-c277d3ca6f75
On Choosing a Deep Reinforcement Learning Library
As Deep Reinforcement Learning is becoming one of the most hyped strategies to achieve AGI (aka Artificial General Intelligence) more and more libraries are developed.
And choosing the best for your needs can be a daunting task…
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@DeepLearning_AI
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https://medium.com/data-from-the-trenches/choosing-a-deep-reinforcement-learning-library-890fb0307092
Fun with Snapchat's Gender Swapping Filter
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@DeepLearning_AI
How to become an expert in NLP in 2019 (1)
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@DeepLearning_AI
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https://medium.com/@kushajreal/how-to-become-an-expert-in-nlp-in-2019-1-945f4e9073c0
My Top 5 Recommended Places to Learn about Deep Learning and Machine Learning
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@DeepLearning_AI
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https://medium.com/datadriveninvestor/my-top-5-recommended-places-to-learn-about-deep-learning-and-machine-learning-f95153a847e
Live Object Detection – Towards Data Science
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@DeepLearning_AI
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https://towardsdatascience.com/live-object-detection-26cd50cceffd
Edge Detection in Opencv 4.0, A 15 Minutes Tutorial
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@DeepLearning_AI
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https://blog.sicara.com/opencv-edge-detection-tutorial-7c3303f10788
A Step-by-Step Introduction to the Basic Object Detection Algorithms
Table of Contents
A Simple Way of Solving an Object Detection Task (using Deep Learning)
Understanding Region-Based Convolutional Neural Networks
1. Intuition of RCNN
2. Problems with RCNN
Understanding Fast RCNN
1. Intuition of Fast RCNN
2. Problems with Fast RCNN
Understanding Faster RCNN
1. Intuition of Faster RCNN
2. Problems with Faster RCNN
Summary of the Algorithms covered
Deep Learning Explained (FREE COURSE)
Welcome to course
1 | Introduction and Overview
2 | Multi-class Classification using Logistic Regression
3 | Multi-Layer Perceptron
4 | Convolution Neural Network
5 | Recurrent Neural Network and Long Short Term Memory
6 | Text Classification with RNN and LSTM
Wrap-up and Post-Course Survey
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@DeepLearning_AI
China is about to overtake America in AI research
China will publish more of the most-cited 50 percent of papers than America for the first time this year
https://www.theverge.com/2019/3/14/18265230/china-is-about-to-overtake-america-in-ai-research
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@DeepLearning_AI
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Machine Learning is Fun!
The world’s easiest introduction to Machine Learning
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
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@DeepLearning_AI
TOP 10 FREE DEEP LEARNING MASSIVE OPEN ONLINE COURSES :
1. Deep Learning by Google
2. Neural Networks and Deep Learning
3. Algorithms: Design and Analysis
4. Machine Learning
5. Improving Deep Neural Networks
6. Deep Learning Lecture
7. Neural Networks for Machine Learning
8. Creative Applications of Deep Learning with TensorFlow
9. Introduction to Deep Learning
10. Deep Learning for Self-Driving Cars
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@DeepLearning_AI
https://edgy.app/top-10-free-deep-learning-moocs
Initializing neural networks
Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes can accelerate training, but they require care to avoid common pitfalls. In this post, we’ll explain how to initialize neural network parameters effectively.
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@DeepLearning_AI
https://www.deeplearning.ai/ai-notes/initialization/
