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Machine Learning

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

Ko'proq ko'rsatish

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 398 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 324-o'rinni va Suriya mintaqasida 225-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 398 obunachiga ega bo‘ldi.

13 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 421 ga, so‘nggi 24 soatda esa 25 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 2.65% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.74% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 070 marta ko‘riladi; birinchi sutkada odatda 701 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Yuqori yangilanish chastotasi (oxirgi ma’lumot 14 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

40 398
Obunachilar
+2524 soatlar
+1547 kunlar
+42130 kunlar
Postlar arxiv
✨ Face detection with dlib (HOG and CNN) ✨ 📖 In this tutorial, you will learn how to perform face detection with the dlib li
✨ 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

✨ NeRFs Explained: Goodbye Photogrammetry? ✨ 📖 Table of Contents NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?
✨ 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

✨ Building a Multimodal Gradio Chatbot with Llama 3.2 Using the Ollama API ✨ 📖 Table of Contents Building a Multimodal Gradi
✨ Building a Multimodal Gradio Chatbot with Llama 3.2 Using the Ollama API ✨ 📖 Table of Contents Building a Multimodal Gradio Chatbot with Llama 3.2 Using the Ollama API What Is Gradio and Why Is It Ideal for Chatbots? The Chatbot You’ll Build Today 🚀 What Is Ollama and the Ollama API Functionality Ollama…... 🏷️ #AIApplications #Chatbots #DeepLearning #Gradio #LargeLanguageModels #OpenSource #Tutorial

✨ Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection ✨ 📖 Table of Contents Breaking the CNN Mold:
✨ 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

✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨ 📖 Table of Contents Training YOLOv12 for Detectin
✨ 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

✨ Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection ✨ 📖 Table of Contents Breaking the CNN Mold:
✨ 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

✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨ 📖 Table of Contents Training YOLOv12 for Detectin
✨ 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

Get top-tier market analysis: world events meet technical trading. I'm Michael 👋. My team and I share our market insights da
Get top-tier market analysis: world events meet technical trading. I'm Michael 👋. My team and I share our market insights daily on our Telegram channel. Over the past weekend, our strategies delivered up to +39% gains. We will tell you everything on the channel, even for beginners. Join the channel below! 👇 https://t.me/+9mZure1faNRkOTE8

🔥 Trending Repository: mcp 📝 Description: AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP. 🔗 R
🔥 Trending Repository: mcp 📝 Description: AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP. 🔗 Repository URL: https://github.com/awslabs/mcp 🌐 Website: https://awslabs.github.io/mcp/ 📖 Readme: https://github.com/awslabs/mcp#readme 📊 Statistics: 🌟 Stars: 5.6K stars 👀 Watchers: 55 🍴 Forks: 747 forks 💻 Programming Languages: Python - Shell - Dockerfile - HTML - Jinja - TypeScript - CSS 🏷️ Related Topics:
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================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: UI-TARS 📝 Description: No description available 🔗 Repository URL: https://github.com/bytedance/UI-T
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🔥 Trending Repository: GhostTrack 📝 Description: Useful tool to track location or mobile number 🔗 Repository URL: https://
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================================== 🧠 By: https://t.me/DataScienceM

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================================== 🧠 By: https://t.me/DataScienceM

“Monday. 8AM. My phone rings—another alert: BTC’s flying, GOLD breaking records. My VIP group? Already in the profit. But wan
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🔥 Trending Repository: papermark 📝 Description: Papermark is the open-source DocSend alternative with built-in analytics and custom domains. 🔗 Repository URL: https://github.com/mfts/papermark 🌐 Website: https://papermark.com 📖 Readme: https://github.com/mfts/papermark#readme 📊 Statistics: 🌟 Stars: 7.1K stars 👀 Watchers: 23 🍴 Forks: 976 forks 💻 Programming Languages: TypeScript 🏷️ Related Topics:
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================================== 🧠 By: https://t.me/DataScienceM

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================================== 🧠 By: https://t.me/DataScienceM

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================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: awesome-n8n-templates 📝 Description: Supercharge your workflow automation with this curated collecti
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================================== 🧠 By: https://t.me/DataScienceM