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

Tarmoq:Machine Learning with Python Suriya225 Texnologiyalar & Aralashmalar3 332...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 323 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 332-o'rinni va Suriya mintaqasida 225-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 323 obunachiga ega bo‘ldi.

09 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 378 ga, so‘nggi 24 soatda esa 30 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 2.23% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.95% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 897 marta ko‘riladi; birinchi sutkada odatda 788 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 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 10 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.

40 323

Obunachilar

+3024 soatlar

+1067 kunlar

+37830 kunlar

897

Post ko'rishlar

~ 78824 soatlar

~ 1 02448 soatlar

2.23%

Muloqot nisbati

~ 2

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40 323

📌 Create Your Supply Chain Analytics Portfolio to Land Your Dream Job 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-31 | ⏱️ Read time: 9 min read A guide for students and professionals to build real-world projects and showcase their skills using…

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📌 A Simple Implementation of the Attention Mechanism from Scratch 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-03-31 | ⏱️ Read time: 10 min read How attention helped models like RNNs mitigate the vanishing gradient problem and capture long-range dependencies…

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📌 Graph Neural Networks Part 3: How GraphSAGE Handles Changing Graph Structure 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-01 | ⏱️ Read time: 9 min read And how you can use it for large graphs

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📌 Agentic AI: Single vs Multi-Agent Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-01 | ⏱️ Read time: 14 min read Demonstrated by building a tech news agent in LangGraph

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📌 AI in Social Research and Polling 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-01 | ⏱️ Read time: 13 min read What do we do when nobody answers the phone?

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📌 The Case for Centralized AI Model Inference Serving 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-01 | ⏱️ Read time: 11 min read Optimizing highly parallel AI algorithm execution

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📌 PyScript vs. JavaScript: A Battle of Web Titans 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-02 | ⏱️ Read time: 5 min read Can Python really replace JavaScript for web development?

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📌 The Art of Noise | Diffusion Model 🗂 Category: DEEP LEARNING 🕒 Date: 2025-04-02 | ⏱️ Read time: 36 min read Understanding and implementing a diffusion model from scratch with PyTorch

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📌 Agentic GraphRAG for Commercial Contracts 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-02 | ⏱️ Read time: 26 min read Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph…

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📌 Kernel Case Study: Flash Attention 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-03 | ⏱️ Read time: 16 min read Understanding all versions of flash attention through a triton implementation

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📌 Linear Programming: Managing Multiple Targets with Goal Programming 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-03 | ⏱️ Read time: 12 min read Part 6: Balancing multiple objectives using the weights and preemptive goal programming approaches

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📌 Are We Watching More Ads Than Content? Analyzing YouTube Sponsor Data 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-03 | ⏱️ Read time: 21 min read Exploring if sponsor segments are getting longer by the year

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📌 Creating an AI Agent to Write Blog Posts with CrewAI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-04 | ⏱️ Read time: 12 min read How to assemble a crew of AI agents with CrewAI and Python

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📌 On-Premise Computing, Data Career Switches, AI File Readers, and Other March Must-Reads 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-04 | ⏱️ Read time: 3 min read A selection of our most-read and -shared articles of the past month.

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📌 How I Would Learn To Code (If I Could Start Over) 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-04 | ⏱️ Read time: 10 min read How to learn to code in 2025

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📌 Let’s Call a Spade a Spade: RDF and LPG — Cousins Who Should Learn to Live Together 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-07 | ⏱️ Read time: 20 min read An objective comparison of the RDF and LPG data models

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📌 How to Optimize your Python Program for Slowness 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-07 | ⏱️ Read time: 20 min read Write a short program that finishes after the universe dies

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Nobody told me ETF investing could be this easy—until I saw the real power of sector rotation. I ignored it for years… and lost out on simple, steady income that almost runs on autopilot. You want to see the setup? It’s right here. #إعلان InsideAds

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📌 Avoiding Costly Mistakes with Uncertainty Quantification for Algorithmic Home Valuations 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-07 | ⏱️ Read time: 10 min read The simple tricks for using AVMU, or Automated Valuation Model Uncertainty, to make your home…

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📌 Circuit Tracing: A Step Closer to Understanding Large Language Models 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-08 | ⏱️ Read time: 7 min read Reverse-engineering large languages models’ computation circuit to understand their decision-making processes