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 344...

📈 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 40 208 suscriptores, ocupando la posición 3 344 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 40 208 suscriptores.

Según los últimos datos del 03 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 338, y en las últimas 24 horas de 9, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.04%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.42% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 822 visualizaciones. En el primer día suele acumular 973 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 04 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.

40 208

Suscriptores

+924 horas

+727 días

+33830 días

822

Visitas de la publicación

~ 97324 horas

~ 1 32148 horas

2.04%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 215

📌 Understanding KL Divergence, Entropy, and Related Concepts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-08 | ⏱️ Read time: 8 min read Important concepts in information theory, machine learning, and statistics

40 215

📌 Nine Rules for Running Rust in the Browser 🗂 Category: PROGRAMMING 🕒 Date: 2024-10-08 | ⏱️ Read time: 25 min read Practical lessons from porting range-set-blaze to WASM

40 215

📌 Graph Neural Networks Part 2. Graph Attention Networks vs. GCNs 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read A model that pays attention to your graph

40 215

📌 Still Manually Reviewing All User Interactions For Your AI Solutions? 🗂 Category: BUSINESS 🕒 Date: 2024-10-08 | ⏱️ Read time: 7 min read Discover how to use cosine similarity to save hours and streamline your AI systems

40 215

📌 TDS Newsletter: To Better Understand AI, Look Under the Hood 🗂 Category: THE VARIABLE 🕒 Date: 2025-09-25 | ⏱️ Read time: 3 min read AI-powered tools tend to generate extreme reactions: on one side we have the “It’s magic!” and…

40 215

📌 Make the Switch from Software Engineer to ML Engineer 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read 7 steps that helped me transition from a software engineer to Machine Learning engineer

40 215

📌 How to Improve Model Quality Without Building Larger Models 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 12 min read Going into the Google DeepMind’s “Scaling LLM Test-Time Compute Optimally can be More Effective than…

40 215

📌 A Deeper Dive into Odds Ratios Using Logistic Regression 🗂 Category: STATISTICS 🕒 Date: 2024-10-08 | ⏱️ Read time: 21 min read A comprehensive guide on how to extract and explore odds ratios from a Logistic Regression…

40 215

📌 From Set Transformer to Perceiver Sampler 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-08 | ⏱️ Read time: 4 min read On multi-modal LLM Flamingo’s vision encoder

40 215

📌 ITT vs LATE: Estimating Causal Effects with IV in Experiments with Imperfect Compliance 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 11 min read Intuition, step-by-step script, and assumptions needed for the use of IV

40 215

📌 Embracing Uncertainty: The Power of Fuzzy Logic in Decision-Making 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 13 min read Exploring how fuzzy logic enhances AI, systems thinking, and real-world applications

40 215

📌 5 AI Projects You Can Build This Weekend (with Python) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 8 min read From beginner-friendly to advanced

40 215

📌 From Newton to LLM’s 🗂 Category: PHYSICS 🕒 Date: 2024-10-09 | ⏱️ Read time: 17 min read A new approach to AI reasoning optimization

40 215

📌 Mathematics I Look for in Data Scientist Interviews 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 18 min read Let’s rebuild our data science foundation.

40 215

📌 Keep the Gradients Flowing 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 27 min read Optimizing Sparse Neural Networks: Understanding Gradient Flow for Faster Training, and Better Performance in Deep…

40 215

📌 Mastering Sample Size Calculations 🗂 Category: 🕒 Date: 2024-10-09 | ⏱️ Read time: 19 min read A/B Testing, Reject Inference, and How to Get the Right Sample Size for Your Experiments

40 215

📌 The Easiest Way to Learn and Use Python Today 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 9 min read Google Colab and its integrated Generative AI, a powerful combination

40 215

📌 The Most Valuable LLM Dev Skill is Easy to Learn, But Costly to Practice. 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-09 | ⏱️ Read time: 18 min read Here’s how not to waste your budget on evaluating models and systems.

40 215

📌 Fine-Tune Llama 3.2 for Powerful Performance on Targeted Tasks 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-10 | ⏱️ Read time: 13 min read Learn how you can fine-tune Llama3.2, Meta’s most recent Large language model, to achieve powerful…

40 215

📌 Forecasting with NHiTs: Uniting Deep Learning + Signal Processing Theory for Superior Accuracy 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-10 | ⏱️ Read time: 12 min read A high-performance DL model for all forecasting cases