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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
Mostrar más📈 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 40 151 suscriptores, ocupando la posición 3 380 en la categoría Tecnologías y Aplicaciones y el puesto 228 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 151 suscriptores.
Según los últimos datos del 29 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 380, y en las últimas 24 horas de 3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.08%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.91% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 837 visualizaciones. En el primer día suele acumular 766 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- 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 30 junio, 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.
40 151
Suscriptores
+324 horas
+1157 días
+38030 días
Archivo de publicaciones
40 150
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40 150
📌 Towards Generalization on Graphs: From Invariance to Causality
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-18 | ⏱️ Read time: 19 min read
This blog post shares recent papers on out-of-distribution generalization on graph-structured data
40 150
📌 A Python Engineer’s Introduction to 3D Gaussian Splatting (Part 3)
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-07-18 | ⏱️ Read time: 9 min read
Part 3 of our Gaussian Splatting tutorial, showing how to render splats onto a 2D…
40 150
📌 YOLO inference with Docker via API
🗂 Category: DEEP LEARNING
🕒 Date: 2024-07-19 | ⏱️ Read time: 16 min read
Learn how to orchestrate object detection inference via a REST API with Docker
40 150
📌 Product Quasi-Experimentation: Statistical Techniques When Standard A/B Testing Is Not Possible
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-19 | ⏱️ Read time: 6 min read
A guide to the most popular techniques when randomized A/B testing is not possible
40 150
📌 Constrained Sentence Generation Using Gibbs Sampling and BERT
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-07-19 | ⏱️ Read time: 11 min read
A fast and effective approach to generating fluent sentences from given keywords using public pre-trained…
40 150
📌 Evaluating ChatGPT’s Data Analysis Improvements: Interactive Tables and Charts
🗂 Category: CHATGPT
🕒 Date: 2024-07-19 | ⏱️ Read time: 11 min read
Is ChatGPT becoming a BI tool?
40 150
📌 Streamlining Object Detection with Metaflow, AWS, and Weights & Biases
🗂 Category:
🕒 Date: 2024-07-19 | ⏱️ Read time: 18 min read
How to create a production-grade pipeline for object detection
40 150
📌 Battling Open Book Exams with Open Source LLMs
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-19 | ⏱️ Read time: 10 min read
In the age where everyone uses ChatGPT for work and school, I am taking advantage…
40 150
📌 Understanding Positional Embeddings in Transformers: From Absolute to Rotary
🗂 Category: DEEP LEARNING
🕒 Date: 2024-07-20 | ⏱️ Read time: 19 min read
A deep dive into absolute, relative, and rotary positional embeddings with code examples
40 150
📌 Three Mind-Blowing Ideas in Physics: The Stationary Action Principle, Lorentz Transformations, and…
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 31 min read
How mathematical innovations yield increasingly more accurate models of the physical world
40 150
📌 Counterfactuals in Language AI
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-07-23 | ⏱️ Read time: 34 min read
with open source language models and LLMs
40 150
📌 Line By Line, Let’s Reproduce GPT-2: Section 1
🗂 Category:
🕒 Date: 2024-07-23 | ⏱️ Read time: 26 min read
This blog post will go line-by-line through the code in Section 1 of Andrej Karpathy’s…
40 150
📌 I Used to Hate Overfitting, But Now I’m Grokking It
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 9 min read
The surprising generalisation beyond overfitting
40 150
📌 Summer Olympic Games Through the Lens of Data
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 13 min read
Using Python and Wikipedia to draw geographical and network maps of the medal-winning countries.
40 150
📌 From Ephemeral to Persistence with LangChain: Building Long-Term Memory in Chatbots
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 8 min read
A detailed walkthrough on transforming simple chatbots into sophisticated AI assistants with long-term memory and…
40 150
📌 Evolution of Data Science: New Age Skills for the Modern End-to-End Data Scientist
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 27 min read
From Python scripting to data engineering, MLOps, and GenAI
40 150
📌 Organizations’ Machine Learning Investment Is (or Should Be) Incremental
🗂 Category: DATA SCIENCE
🕒 Date: 2024-07-23 | ⏱️ Read time: 8 min read
Embedding ML systems into production is still a hard thing to do (for most companies)
40 150
📌 Monocular Depth Estimation with Depth Anything V2
🗂 Category: DEEP LEARNING
🕒 Date: 2024-07-24 | ⏱️ Read time: 11 min read
How do neural networks learn to estimate depth from 2D images?
40 150
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