¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Artificial Intelligence && Deep Learning
Ir al canal en Telegram
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
5TH UG2+ PRIZE CHALLENGE CVPR 2022
$10K PRIZES
http://cvpr2022.ug2challenge.org/
https://docs.google.com/forms/d/e/1FAIpQLSeK0j4cPRNFQbm27qMfaTr27wRQ6tXMV2gmohjaJlbn2fAX0A/viewform
https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FUG2CHALLENGE2022
invite your friends 🌹🌹
@deeplearning_ai
+1#—————CVPR_2021—————
RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)
[paper] : download paper and enjoy
source: use source code and get awesome result
invite your friends and get latest news and sources on AI
RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)
[paper] : download paper and enjoy
source: use source code and get awesome result
invite your friends and get latest news and sources on AI
—————— ConvNeXt ——————--
Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.
Github: https://github.com/facebookresearch/ConvNeXt
Paper: https://arxiv.org/abs/2201.03545
invite your friends 🌹🌹
@deeplearning_ai
Papers with Code 2021 : A Year in Review.
Papers with Code indexes various machine learning artifacts — papers, code, results — to facilitate discovery and comparison. Using this data we can get a sense of what the ML community found useful and interesting this year. Below we summarize the top trending papers, libraries and datasets for 2021 on Papers with Code.
https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b
👉👉@deeplearning_ai
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@deeplearning_ai
Dive into Deep Learning
Interactive deep learning book with code, math, and discussions
Implemented with NumPy/MXNet, PyTorch, and TensorFlow
Adopted at 300 universities from 55 countries
@deeplearning_ai
HAYAI - Artificial Intelligence in the Water Industry https://lnkd.in/dpWdyKin
Please Vote for this project - it's a matter of One-Click. https://aiqom.ai/dashboard/challenge-project/6
This project participates in the Certified AI Entrepreneur (CAIE) Program provided by AIQOM and Khalifa Fund for Enterprise Development
NeurIPS 2021—10 papers you shouldn’t miss
2334 papers, 60 workshops, 8 keynote speakers, 15k+ attendees. A dense landscape that’s hard to navigate without a good guide and map, so here are some of our ideas!
https://towardsdatascience.com/neurips-2021-10-papers-you-shouldnt-miss-80f9c0793a3a
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@deeplearning_ai
Hello Everyone, A StartUp out of California is finally delivering AiNews to the masses. AiNews.com, well funded and will be delivering Ai News to a level not seen. It’s Free to sign up at https://www.ainews.com/newsletter/.
Review — DeepFace: Closing the Gap to Human-Level Performance in Face Verification
DeepFace for Face Verification After Face Alignment
https://sh-tsang.medium.com/review-deepface-closing-the-gap-to-human-level-performance-in-face-verification-973442ad7850
https://t.me/DeepLearning_ai
Join the channel of researchers and programmers, the channel includes a huge encyclopedia of programming books and scientific articles in addition to the most famous scientific projects
t.me/datascience_books
Welcome to the Code Programmer community.
Our community offers many software projects with source code attached to explanations about the codes
In addition, we support both Arabic and English languages at the same time.
https://t.me/CodeProgrammer
Deep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Deep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Summary
Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
What's Inside:
* Deep learning from first principles
* Setting up your own deep-learning environment
* Image-classification models
* Deep learning for text and sequences
* Neural style transfer, text generation, and image generation
@Deeplearning_aiDeep Learning with Python (2021)
Invite your friends 🌹🌹
@deeplearning_ai
Summary
Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
What's Inside:
* Deep learning from first principles
* Setting up your own deep-learning environment
* Image-classification models
* Deep learning for text and sequences
* Neural style transfer, text generation, and image generation
@Deeplearning_ai
An important collection of the 15 best machine learning cheat sheets.
مجموعة مهمة الافضل ١٥ ورقة غش في مجال التعلم الآلي.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
@deeplearning_ai
