¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Artificial Intelligence && Deep Learning
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Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:
Mostrar más📈 Análisis del canal de Telegram Artificial Intelligence && Deep Learning
El canal Artificial Intelligence && Deep Learning (@deeplearning_ai) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 58 024 suscriptores, ocupando la posición 2 297 en la categoría Tecnologías y Aplicaciones y el puesto 6 023 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 58 024 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -218, 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 8.90%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 163 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 15.
- Intereses temáticos: El contenido se centra en temas clave como github, learning, estimation, dataset, engineer.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact:”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 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.
58 024
Suscriptores
-1024 horas
-557 días
-21830 días
Archivo de publicaciones
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ best channels on Telegram:
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✅ Data Science Books:
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StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
paper: https://arxiv.org/pdf/2312.12491v1.pdf
source code: https://github.com/cumulo-autumn/streamdiffusion?tab=readme-ov-file
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Generative Models by Stability AI
source code: https://github.com/stability-ai/generative-models
paper: https://static1.squarespace.com/static/6213c340453c3f502425776e/t/655ce779b9d47d342a93c890/1700587395994/stable_video_diffusion.pdf
provided a streamlit demo for text-to-image and image-to-image sampling in scripts/demo/sampling.py. They provide file hashes for the complete file as well as for only the saved tensors in the file ( see Model Spec for a script to evaluate that).
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
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* PIPNet's precision in facial landmark detection with a record 2.6% NME on the 300W benchmark. Explore the model and its capabilities by visiting our GitHub repository.
Watch the performance showcase on YouTube. Support and star our project on GitHub for future advancements.
video: https://www.youtube.com/watch?v=cxi1WQr-HKE
source code: https://github.com/Shohruh72/PIPNet/tree/main
join our community:
👉 @deeplearning_ai
PIPNet Facial Landmark Detection
PIPNet model has achieved a significant milestone on the 300W dataset, one of the most challenging benchmarks in facial landmark detection. Successfully attained a minimum Normalized Mean Error (NME) of 2.6%, demonstrating the model's high accuracy and robustness in complex facial recognition tasks.
video: https://www.youtube.com/watch?v=cxi1WQr-HKE
source code: https://github.com/Shohruh72/PIPNet/tree/main
+1VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
🤗🤗🤗 VideoCrafter is an open-source video generation and editing toolbox for crafting video content.
It currently includes the Text2Video and Image2Video models:
https://github.com/ailab-cvc/videocrafter
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ best channels on Telegram:
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✅ Data Science Books:
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Harvard CS50 – Free Computer Science Course (2023 Edition)
Here are the lectures included in this course:
Lecture 0 - Scratch
Lecture 1 - C
Lecture 2 - Arrays
Lecture 3 - Algorithms
Lecture 4 - Memory
Lecture 5 - Data Structures
Lecture 6 - Python
Lecture 7 - SQL
Lecture 8 - HTML, CSS, JavaScript
Lecture 9 - Flask
Lecture 10 - Emoji
Cybersecurity
https://www.freecodecamp.org/news/harvard-university-cs50-computer-science-course-2023/
join our community:
👉 @deeplearning_ai
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