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

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Tarmoq:Machine Learning with Python Suriya225 Texnologiyalar & Aralashmalar3 324...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 403 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, 40 403 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.

40 403

Obunachilar

+2524 soatlar

+1547 kunlar

+42130 kunlar

1 070

Post ko'rishlar

~ 70124 soatlar

~ 96048 soatlar

2.65%

Muloqot nisbati

~ 2

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40 403

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📌 MobileNetV1 Paper Walkthrough: The Tiny Giant 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-04 | ⏱️ Read time: 26 min read Understanding and implementing MobileNetV1 from scratch with PyTorch

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📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

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📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

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📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

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📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Read time: 13 min read How to detect it and which model to choose.

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📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:00 | ⏱ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

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📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

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📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12:30:00-05:00 | ⏱ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

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Repost from Github Top Repositories

🔥 Trending Repository: ML-From-Scratch 📝 Description: Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. 🔗 Repository URL: https://github.com/eriklindernoren/ML-From-Scratch 📖 Readme: https://github.com/eriklindernoren/ML-From-Scratch#readme 📊 Statistics: 🌟 Stars: 27.8K stars 👀 Watchers: 951 🍴 Forks: 4.8K forks 💻 Programming Languages: Python 🏷️ Related Topics:

#data_science #machine_learning #data_mining #deep_learning #genetic_algorithm #deep_reinforcement_learning #machine_learning_from_scratch

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

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No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep secret. Want to know how? The real strategies are hidden right here — and only a few catch on before it’s too late! #إعلان InsideAds

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“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But nobody talks about the brutal truths I learned along the way. Want to see what everyone’s missing? 👉 See it here #إعلان InsideAds