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Computer Science and Programming

Computer Science and Programming

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Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

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📈 Análisis del canal de Telegram Computer Science and Programming

El canal Computer Science and Programming (@computer_science_and_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 140 416 suscriptores, ocupando la posición 811 en la categoría Tecnologías y Aplicaciones y el puesto 88 en la región Italia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 140 416 suscriptores.

Según los últimos datos del 01 septiembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -772, y en las últimas 24 horas de -64, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.15%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.97% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 11 442 visualizaciones. En el primer día suele acumular 2 771 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 13.
  • Intereses temáticos: El contenido se centra en temas clave como sellerflash, github, developer, pricing, waybienad.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 02 septiembre, 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.

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140 416
Suscriptores
-6424 horas
-2617 días
-77230 días
Archivo de publicaciones
Introducing PyTorch3D: An open-source library for 3D deep learning. PyTorch3D: Faster, flexible 3D deep learning research
Introducing PyTorch3D: An open-source library for 3D deep learning. PyTorch3D: Faster, flexible 3D deep learning research

End to End Machine Learning: From Data Collection to Deployment. - Collect and scrape data with Scrapy / Selenium - Train a deep character CNN for (English) sentiment analysis using PyTorch - Build an interactive web app with Dash to serve the model in real-time - Put everything in Docker Compose - Deploy to AWS on a custom domain name

More than 200 NLP datasets - this is gold (last update 21.01.202) https://quantumstat.com/dataset/dataset.html and also Google provided dataset search tool for publicly available datasets: https://datasetsearch.research.google.com/

Paper: https://arxiv.org/pdf/2001.05613.pdf Project page: http://www.ynl.t.u-tokyo.ac.jp/research/vmocap-syn/ Dataset will be available publicly soon

Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild

Everybody’s Talkin’: Let Me Talk as You Want This paper presents a method to edit a target portrait footage by taking a seque
Everybody’s Talkin’: Let Me Talk as You Want This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.

YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation Results are impressive, above 30 FPS on COCO test-de
YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation Results are impressive, above 30 FPS on COCO test-dev

However, great resource from data-flair team and there are waiting you 240+ Python Tutorials from scratch (under advanced, intermediate, beginner categories): https://data-flair.training/blogs/python-tutorials-home/ and you'll also follow their telegram channels for fresh news from original source: https://t.me/dataflair

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Happy new year. I would like to share channel's progress for 2019 and we have +29 874 new members for this year. Thank you fo
Happy new year. I would like to share channel's progress for 2019 and we have +29 874 new members for this year. Thank you for all members of channel.

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Due to your interest and some specific points, you will read about in more detail from the report: https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf?fbclid=IwAR228NxD7QCdksNYkSPZ2vcpm5Jzk5zCGx9v0NpsAkQVOspv85MvG3LK3wE

2019 is also finishing with great achievements in AI field. Thanks to the extended report from 'artificial intelligence index
2019 is also finishing with great achievements in AI field. Thanks to the extended report from 'artificial intelligence index' which I highlighted more specific ones (Of cource is just my choise only): 👉 AI Research went crazy. Between 1998 and 2018, there’s been a 300% increase in the publication of peer-reviewed papers on AI. 👉 Attendance at conferences went crazy too, for eg. NeurIPS, got some 13,500 attendees this year, up 800% from 2012. 👉 Education too bumped up, a lot of folks took up MSc / PhD with something in Machine Learning 👉 USA still leads in AI, no matter what other countries say 👉 AI algorithms are becoming cheaper and mainstream 👉 self driving vehicles market is coming of age and raking in a lot of investments

I think, every AI lovers are waiting for AI debate: Yoshua Bengio and Gary Marcus, which a decade that has revived the field
I think, every AI lovers are waiting for AI debate: Yoshua Bengio and Gary Marcus, which a decade that has revived the field of AI