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Artificial Intelligence
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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
Mostrar más📈 Análisis del canal de Telegram Artificial Intelligence
El canal Artificial Intelligence (@artificial_intelligence_com) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 70 390 suscriptores, ocupando la posición 1 845 en la categoría Tecnologías y Aplicaciones y el puesto 4 788 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 390 suscriptores.
Según los últimos datos del 12 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 1 141, y en las últimas 24 horas de 11, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.42%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.10% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 221 visualizaciones. En el primer día suele acumular 1 476 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- Intereses temáticos: El contenido se centra en temas clave como learning, linkedin, linux, udemy, 040k|.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 13 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.
70 390
Suscriptores
+1124 horas
+2017 días
+1 14130 días
Archivo de publicaciones
70 390
🔅 Machine Learning with Python: Logistic Regression
📝 Get an introduction to logistic regression by exploring how to build supervised machine learning models with Python.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 19m
📋 Topics: Logistic Regression, Machine Learning, Python
🔗 Join Machine Learning for more courses
70 390
From automating repetitive tasks to boosting creativity, the best AI tools are essential for improving productivity in 2026 ✌️
70 390
RAG was supposed to make LLMs smarter.
Ground them in facts. Give them memory.
But the truth?
Most RAG systems today are just fancy search engines—fetching chunks and hoping the model figures it out.
That’s not intelligence.
The real upgrade is Agentic RAG.
Tools like Glean, Perplexity, and Harvey don’t just retrieve... they reason.
They decide what to fetch, when to fetch, or whether they should fetch anything at all.
This changes everything:
• No blind embeddings
• No random chunk dumps
• Real, layered memory
• APIs, search, and tools inside the reasoning loop
The LLM stops guessing and starts thinking.
70 390
📱Artificial Intelligence and Machine Learning
📱Introduction to Large Language Models
70 390
📱Artificial Intelligence and Machine Learning
📱Introduction to Large Language Models
70 390
🔅 Introduction to Large Language Models
📝 Learn about large language models—what they are, what they can do, and how they work.
🌐 Author: Jonathan Fernandes
🔰 Level: Intermediate
⏰ Duration: 1h 17m
📋 Topics: Large Language Models
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70 390
🔰 Python library for finetuning Gemma 3
Includes papers on finetuning, sharding, LoRA, PEFT, multimodality, and tokenization in LLM.
pip install gemma70 390
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70 390
⭐️ 5 Techniques to Fine-Tune Large Language Models (LLMs)
With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help:
1⃣ LoRA (Low-Rank Adaptation)
LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient.🔢 LoRA-FA (LoRA with Feature Augmentation)
This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead.🔢 Vera (Virtual Embedding Regularization Adaptation)
Vera helps regularize model embedding during fine-tuning, preventing over-fitting and improving generalization across different domains.🔢 Delta LoRA
An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness.🔢 Prefix Tuning
Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.
70 390
📱Artificial Intelligence and Machine Learning
📱Machine Learning Foundations: Statistics
70 390
🔅 Machine Learning Foundations: Statistics
📝 Learn how statistics can help you troubleshoot issues, optimize performance, and innovate, creating new machine learning models that are more efficient.
🌐 Author: Terezija Semenski
🔰 Level: Beginner
⏰ Duration: 1h 20m
📋 Topics: Statistical Analysis, Machine Learning
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70 390
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70 390
📱Artificial Intelligence and Machine Learning
📱Building a Recommendation System with Python Machine Learning and AI
70 390
🔅 Building a Recommendation System with Python Machine Learning and AI
📝 Discover how to use Python to build programs that can make recommendations. This hands-on course explores different types of recommendation systems, and shows how to build each one.
🌐 Author: Lillian Pierson, P.E.
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Machine Learning, Recommender Systems
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