Machine Learning
前往频道在 Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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网络:📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 403 名订阅者，在 技术与应用 类别中位列第 3 324，并在 叙利亚 地区排名第 225 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 40 403 名订阅者。
根据 13 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 421，过去 24 小时变化为 25，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 2.65%。内容发布后 24 小时内通常能获得 1.74% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 070 次浏览，首日通常累积 701 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 4。
- 主题关注点： 内容集中在 distance, insidead, gpu, learning, degree 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 14 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
40 403
订阅者
+2524 小时
+1547 天
+42130 天
帖子存档
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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 ✨
📖 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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✨ 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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