Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر
شبکه:📈 تحلیل کانال تلگرام Machine Learning
کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 398 مشترک است و جایگاه 3 324 را در دسته فناوری و برنامهها و رتبه 225 را در منطقه سوريا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 398 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 13 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 421 و در ۲۴ ساعت گذشته برابر 25 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.65% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.74% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 1 070 بازدید دریافت میکند. در اولین روز معمولاً 701 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 4 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند distance, insidead, gpu, learning, degree تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 14 ژوئیه, 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.
40 398
مشترکین
+2524 ساعت
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آرشیو پست ها
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✨ Face detection with dlib (HOG and CNN) ✨
📖 In this tutorial, you will learn how to perform face detection with the dlib library using both HOG + Linear SVM and CNNs. The dlib library is arguably one of the most utilized packages for face recognition. A Python package…...
🏷️ #dlib #FaceApplications #Tutorials
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✨ NeRFs Explained: Goodbye Photogrammetry? ✨
📖 Table of Contents NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps…...
🏷️ #3DComputerVision #3DReconstruction #DeepLearning #NeuralNetworks #Photogrammetry #Tutorial
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✨ Building a Multimodal Gradio Chatbot with Llama 3.2 Using the Ollama API ✨
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🏷️ #AIApplications #Chatbots #DeepLearning #Gradio #LargeLanguageModels #OpenSource #Tutorial
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✨ Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection ✨
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🏷️ #AttentionMechanisms #DeepLearning #RealTimeObjectDetection #Tutorial #YOLOSeries
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✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
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✨ Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection ✨
📖 Table of Contents Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection The YOLO Evolution (Quick Recap) YOLOv8: Introducing the C2f Module and OBB Support YOLOv9: Programmable Gradient Information and GELAN YOLOv10: NMS-Free Training and Dual Assignments YOLOv11:…...
🏷️ #AttentionMechanisms #DeepLearning #RealTimeObjectDetection #Tutorial #YOLOSeries
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✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
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