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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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📈 Análisis del canal de Telegram Machine Learning

El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 365 suscriptores, ocupando la posición 3 329 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 365 suscriptores.

Según los últimos datos del 11 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 393, y en las últimas 24 horas de 17, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.74% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 924 visualizaciones. En el primer día suele acumular 702 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
  • Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

40 365
Suscriptores
+1724 horas
+1237 días
+39330 días
Archivo de publicaciones
📌 Creative Canvas: Using AI to Paint, Edit, and Stylize Images 🗂 Category: 🕒 Date: 2024-07-08 | ⏱️ Read time: 22 min read
📌 Creative Canvas: Using AI to Paint, Edit, and Stylize Images 🗂 Category: 🕒 Date: 2024-07-08 | ⏱️ Read time: 22 min read I explored commercial and open-source photo editing systems for the creative use of AI image…

🤖🧠 Artificial Intelligence: A Modern Approach — The Ultimate Number 1 Guide to Learning AI by Stuart Russell and Peter Norv
🤖🧠 Artificial Intelligence: A Modern Approach — The Ultimate Number 1 Guide to Learning AI by Stuart Russell and Peter Norvig 🗓️ 12 Oct 2025 📚 AI News & Trends When it comes to learning artificial intelligence (AI), few resources hold as much authority as “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig. Often regarded as the “Bible of AI”, this textbook has become the most widely used academic reference in the field adopted by over 1,500 universities and institutions worldwide. Published ... #ArtificialIntelligence #AIModernApproach #StuartRussell #PeterNorvig #AIBible #AIEducation

📌 Is LLM Performance Predetermined by Their Genetic Code? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-08 | ⏱️ Read
📌 Is LLM Performance Predetermined by Their Genetic Code? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-08 | ⏱️ Read time: 9 min read Exploring phylogenetic algorithms to predict the future of large language models

📌 NLP: Text Summarization and Keyword Extraction on Property Rental Listings – Part 1 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 NLP: Text Summarization and Keyword Extraction on Property Rental Listings – Part 1 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-08 | ⏱️ Read time: 13 min read A practical implementation of NLP techniques such as text summarization, NER, topic modeling, and text…

📌 TensorFlow Transform: Ensuring Seamless Data Preparation in Production 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-08 |
📌 TensorFlow Transform: Ensuring Seamless Data Preparation in Production 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-08 | ⏱️ Read time: 10 min read Leveraging TensorFlow Transform for scaling data pipelines for production environments

📌 Implementing Neural Networks in TensorFlow (and PyTorch) 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-08 | ⏱️ Read time: 6
📌 Implementing Neural Networks in TensorFlow (and PyTorch) 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-08 | ⏱️ Read time: 6 min read Step-by-step code guide on building a Neural Network

📌 Doping: A Technique to Test Outlier Detectors 🗂 Category: 🕒 Date: 2024-07-09 | ⏱️ Read time: 18 min read Using well-craf
📌 Doping: A Technique to Test Outlier Detectors 🗂 Category: 🕒 Date: 2024-07-09 | ⏱️ Read time: 18 min read Using well-crafted synthetic data to compare and evaluate outlier detectors

📌 Tracking in Practice: Code, Data and ML Model 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 13 min rea
📌 Tracking in Practice: Code, Data and ML Model 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 13 min read A guide to tracking in MLOps

📌 Spicing up Ice Hockey with AI: Player Tracking with Computer Vision 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-
📌 Spicing up Ice Hockey with AI: Player Tracking with Computer Vision 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-09 | ⏱️ Read time: 35 min read Using PyTorch, computer vision techniques, and a CNN, I worked on a model that tracks…

📌 Perception-Inspired Graph Convolution for Music Understanding Tasks 🗂 Category: 🕒 Date: 2024-07-09 | ⏱️ Read time: 12 mi
📌 Perception-Inspired Graph Convolution for Music Understanding Tasks 🗂 Category: 🕒 Date: 2024-07-09 | ⏱️ Read time: 12 min read This article discusses MusGConv, a perception-inspired graph convolution block for symbolic musical applications.

Ever wondered why some wines taste like liquid silk while others burst with crisp, electric freshness? Unlock the secrets of
Ever wondered why some wines taste like liquid silk while others burst with crisp, electric freshness? Unlock the secrets of terroir, barrel, and grape—without the snobbery. Simply Wine brings you honest reviews, hidden gems, and stories that make every sip unforgettable. Want to truly taste what’s in your glass? Explore with us and savor wine like never before. #ad InsideAds

📌 Deep Dive into LSTMs & xLSTMs by Hand 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 13 min read Explore t
📌 Deep Dive into LSTMs & xLSTMs by Hand 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 13 min read Explore the wisdom of LSTM leading into xLSTMs - a probable competition to the present-day LLMs

📌 Conversational Analysis Is the Future for Enterprise Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-09 | ⏱️ Read
📌 Conversational Analysis Is the Future for Enterprise Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-09 | ⏱️ Read time: 9 min read LLMs won’t replace data scientists, but they will change how we collaborate with decision makers

📌 Scaling Law Of Language Models 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 8 min read How language mode
📌 Scaling Law Of Language Models 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-09 | ⏱️ Read time: 8 min read How language models scale with model size, training data, and training compute

📌 AI-Proof Your Data Science Skill Set by Embracing Four Timeless Concepts 🗂 Category: CAREER ADVICE 🕒 Date: 2024-07-09 |
📌 AI-Proof Your Data Science Skill Set by Embracing Four Timeless Concepts 🗂 Category: CAREER ADVICE 🕒 Date: 2024-07-09 | ⏱️ Read time: 5 min read And stay competitive in a saturated job market

📌 Delta Lake Optimistic Concurrency Control: To Lock or Not to Lock? 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-10 | ⏱️
📌 Delta Lake Optimistic Concurrency Control: To Lock or Not to Lock? 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-10 | ⏱️ Read time: 12 min read Delta Lake Concurrency Management and its relevance

📌 Language Models and Spatial Reasoning: What’s Good, What Is Still Terrible, and What Is Improving 🗂 Category: PSYCHOLOGY
📌 Language Models and Spatial Reasoning: What’s Good, What Is Still Terrible, and What Is Improving 🗂 Category: PSYCHOLOGY 🕒 Date: 2024-07-10 | ⏱️ Read time: 22 min read A review of capabilities as of July 2024

📌 Heat Diffusion in a Thin Metal Rod 🗂 Category: PHYSICS 🕒 Date: 2024-07-10 | ⏱️ Read time: 7 min read What would happen i
📌 Heat Diffusion in a Thin Metal Rod 🗂 Category: PHYSICS 🕒 Date: 2024-07-10 | ⏱️ Read time: 7 min read What would happen if you heated a small section of an insulated metal rod and…

📌 How to Test Machine Learning Systems 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-10 | ⏱️ Read time: 18 min read From Concep
📌 How to Test Machine Learning Systems 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-10 | ⏱️ Read time: 18 min read From Concepts to Practical Code Snippets for Effective Testing

📌 Exploring Medusa and Multi-Token Prediction 🗂 Category: 🕒 Date: 2024-07-10 | ⏱️ Read time: 12 min read This blog post wi
📌 Exploring Medusa and Multi-Token Prediction 🗂 Category: 🕒 Date: 2024-07-10 | ⏱️ Read time: 12 min read This blog post will go into detail on the “MEDUSA: Simple LLM Inference Acceleration Framework…