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

Red:Machine Learning with Python Siria228 Tecnologías y Aplicaciones3 352...

📈 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 40 205 suscriptores, ocupando la posición 3 352 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 40 205 suscriptores.

Según los últimos datos del 02 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 343, y en las últimas 24 horas de 10, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.99%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.28% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 800 visualizaciones. En el primer día suele acumular 915 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 03 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.

40 205

Suscriptores

+1024 horas

+837 días

+34330 días

800

Visitas de la publicación

~ 91524 horas

~ 1 23548 horas

1.99%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 210

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40 210

I thought I’d read every secret manga out there… but last night I stumbled onto a title so wild it blew my mind. I can’t believe no one is talking about it. Want to know the name? Find it right here before it disappears. #ad InsideAds

40 210

📌 Uncertainty in Markov Decisions Processes: a Robust Linear Programming approach 🗂 Category: MATH 🕒 Date: 2024-09-18 | ⏱️ Read time: 8 min read Theoretical derivation of the Robust Counterpart of Markov Decision Processes (MDPs) as a Linear Program…

40 210

📌 Principal Component Analysis – Hands-On Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 13 min read Dimensionality reduction through Principal Component Analysis (PCA).

40 210

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40 210

📌 A Visual Exploration of Semantic Text Chunking 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2024-09-19 | ⏱️ Read time: 22 min read Use embeddings and visualization tools to split text into meaningful chunks

40 210

📌 Emerging Tech Is Nothing Without Methodology 🗂 Category: ANALYTICS 🕒 Date: 2024-09-19 | ⏱️ Read time: 6 min read Or: a Hundred Ways to Solve a Complex Problem

40 210

📌 A Closer Look at Scipy’s Stats module – Part 1 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-19 | ⏱️ Read time: 7 min read Let’s learn the main methods from scipy.stats module in Python.

40 210

📌 A Closer Look at Scipy’s Stats Module – Part 2 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-19 | ⏱️ Read time: 6 min read Let’s learn the main methods from scipy.stats module in Python.

40 210

📌 How to Build Your Own Roadmap for a Successful Data Science Career 🗂 Category: CAREER ADVICE 🕒 Date: 2024-09-19 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

40 210

📌 The Evolution of Text to Video Models 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-19 | ⏱️ Read time: 10 min read Simplifying the neural nets behind Generative Video Diffusion

40 210

📌 AdEMAMix: A Deep Dive into a New Optimizer for Your Deep Neural Network 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-19 | ⏱️ Read time: 15 min read A better and faster option than the ADAM optimizer, from Apple Research

40 210

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40 210

📌 Shared Nearest Neighbors: A More Robust Distance Metric 🗂 Category: 🕒 Date: 2024-09-19 | ⏱️ Read time: 36 min read A distance metric that can improve prediction, clustering, and outlier detection in datasets with many…

40 210

📌 Improving Code Quality with Array and DataFrame Type Hints 🗂 Category: 🕒 Date: 2024-09-19 | ⏱️ Read time: 12 min read How generic specification permits powerful static and runtime validation

40 210

📌 Through the Uncanny Mirror: Do LLMs Remember Like the Human Mind? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-19 | ⏱️ Read time: 10 min read Exploring the Eerie Parallels and Profound Differences Between AI and Human Memory

40 210

📌 Mastering t-SNE: A Comprehensive Guide to Understanding and Implementation in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-20 | ⏱️ Read time: 26 min read Unlock the power of t-SNE for visualizing high-dimensional data, with a step-by-step Python implementation and…

40 210

📌 Choosing Between LLM Agent Frameworks 🗂 Category: 🕒 Date: 2024-09-20 | ⏱️ Read time: 15 min read Thanks to John Gilhuly for his contributions to this piece. Agents are having a moment.…

40 210

📌 Paper Walkthrough: U-Net 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-20 | ⏱️ Read time: 16 min read A PyTorch implementation on one of the most popular semantic segmentation models.