متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Artificial Intelligence && Deep Learning
الذهاب إلى القناة على 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:
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Artificial Intelligence && Deep Learning
تُعد قناة Artificial Intelligence && Deep Learning (@deeplearning_ai) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 58 029 مشتركاً، محتلاً المرتبة 2 289 في فئة التكنولوجيات والتطبيقات والمرتبة 6 003 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
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بحسب آخر البيانات بتاريخ 24 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -193، وفي آخر 24 ساعة بمقدار 17، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 9.42%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A% من ردود الفعل نسبةً إلى إجمالي المشتركين.
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- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل github, learning, estimation, dataset, engineer.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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:”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 25 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
58 029
المشتركون
+1724 ساعات
-237 أيام
-19330 أيام
أرشيف المشاركات
Must-read papers on GNN
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@DeepLearning_AI
.https://github.com/thunlp/GNNPapers
Review: FSRCNN (Super Resolution)
What Are Covered
1. Brief Review of SRCNNFSRCNN Network
2. ArchitectureExplanation of 1×1 Convolution Used in
3. Shrinking and Expanding
4. Explanation of Multiple 3×3 Convolutions in Non-Linear Mapping
5. Ablation Study
6. Results
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@DeepLearning_AI
.https://towardsdatascience.com/review-fsrcnn-super-resolution-80ca2ee14da4
Review: G-RMI — Winner in 2016 COCO Detection (Object Detection)
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@DeepLearning_AI
.https://towardsdatascience.com/review-g-rmi-winner-in-2016-coco-detection-object-detection-af3f2eaf87e4
The project is about predicting coronary heart disease by using three different ML algorithms
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https://blog.goodaudience.com/heart-disease-prediction-aa656f2db585
The 5 Feature Selection Algorithms every Data Scientist should know
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.https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2
Deepfakes, FaceGANS, and Synthetic Data: Welcome to the Reality Illusion of 2020
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.https://medium.com/swlh/deepfakes-facegans-and-the-rise-of-synthetic-data-welcome-to-2020-a54b88eecdf9
2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging
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@DeepLearning_AI
.https://towardsdatascience.com/2d-or-3d-a-simple-comparison-of-convolutional-neural-networks-for-automatic-segmentation-of-625308f52aa7
A 2019 Guide to Object Detection
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.https://heartbeat.fritz.ai/a-2019-guide-to-object-detection-9509987954c3
Everything You Need to Know About Autoencoders in TensorFlow
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.https://towardsdatascience.com/everything-you-need-to-know-about-autoencoders-in-tensorflow-b6a63e8255f0
The Best Machine Learning Research of 2019 So Far - ODSC - Open Data Science - Medium
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.https://medium.com/@ODSC/the-best-machine-learning-research-of-2019-so-far-954120947794
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library 1st Edition
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@DeepLearning_AI
Who This Book Is For
This book contains descriptions, working code examples, and explanations of the
C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be
helpful to many different kinds of users:
Professionals and entrepreneurs
For practicing professionals who need to rapidly prototype or professionally
implement computer vision systems, the sample code provides a quick frame‐
work with which to start. Our descriptions of the algorithms can quickly teach or
remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐
ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly
taking advantage of a variety of hardware platforms.
Students....
Teachers....
Hobbyist....
Video classification with Keras and Deep Learning
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.https://www.pyimagesearch.com/2019/07/15/video-classification-with-keras-and-deep-learning/
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
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.https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html
FREE COURSE Intro to TensorFlow for Deep Learning
This course is a practical approach to deep learning for software developers
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@DeepLearning_AI
.https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
Your State of AI Report 2019
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@DeepLearning_AI
.https://medium.com/@NathanBenaich/state-of-ai-2019-733c81aeb04b
Landmark Assisted CycleGAN for Cartoon Face Generation
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@DeepLearning_AI
.https://deepai.org/publication/landmark-assisted-cyclegan-for-cartoon-face-generation
The Secret to Mastering ML
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.https://medium.com/technomancy/the-secret-to-mastering-ml-9e328d5f06a
500+ awesome bookmarks for Data Science & Machine Learning
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.https://towardsdatascience.com/500-free-high-quality-online-resources-for-data-science-machine-learning-7eda5bf33872
Computer Vision: A Study On Different CNN Architectures and their Applications
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.https://medium.com/alumnaiacademy/introduction-to-computer-vision-4fc2a2ba9dc
AI Scholar: Human vs Machine Attention in Neural Networks
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.https://medium.com/ai%C2%B3-theory-practice-business/ai-scholar-human-vs-machine-attention-in-neural-networks-69a8b59e577f
