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:

India5 977 Tecnologías y Aplicaciones2 290...

📈 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 58 018 suscriptores, ocupando la posición 2 290 en la categoría Tecnologías y Aplicaciones y el puesto 5 977 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 58 018 suscriptores.

Según los últimos datos del 25 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -204, y en las últimas 24 horas de -8, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.58%. 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 5 556 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 16.
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 26 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.

58 018

Suscriptores

-824 horas

-287 días

-20430 días

5 556

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

9.58%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

58 018

. @DeepLearning_AI 👆👆👆👆👆 https://medium.freecodecamp.org/these-are-the-best-free-courses-to-learn-data-structures-and-algorithms-in-depth-4d52f0d6b35a

58 018

. @DeepLearning_AI 👆👆👆👆👆 MY FAVORITE FREE COURSES TO LEARN DATA STRUCTURES AND ALGORITHMS IN DEPTH * Free Courses to Learn Data Structures and Algorithms * Easy to Advanced Data Structures * Data Structure Concepts in C * Algorithms Part 1 - Coursera * Data Structure in Java 10 Algorithm Books Every Programmer Should Read 10 Books to Prepare Technical Programming/Coding Job Interviews

58 018

https://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9 👇👇👇👇👇 @DeepLearning_AI

58 018

@DeepLearning_AI 👆👆👆👆👆 Gentle Dive into Math Behind Convolutional Neural Networks... * Autonomous driving, healthcare or retail are just some of the areas where Computer Vision has allowed us to achieve things that, until recently, were considered impossible.

58 018

. @DeepLearning_AI 👆👆👆👆👆 https://medium.freecodecamp.org/more-project-ideas-to-improve-your-coding-skills-99f48d09bb4b

58 018

More project ideas to improve your coding skills an article containing 15 project ideas that you can build to level up your coding skills, and people were very excited about that resource. Also, the app-ideas repository has gotten almost 3000 stars since I published that article. That’s insane! 😱 👇👇👇👇👇 @DeepLearning_AI

58 018

@DeepLearning_AI 👆👆👆👆👆 YouTube Channel aimed at teaching Machine Learning Channel link - https://www.youtube.com/BhaveshBhatt8791/

58 018

👇👇👇👇👇 @DeepLearning_AI

58 018

Mastering OpenCV 3 (2nd edition) Get hands-on with practical Computer Vision using OpenCV 3 This book covers : Chapter 1, Cartoonifier and Skin Changer for Raspberry Pi Chapter 2, Exploring Structure from Motion Using OpenCV Chapter 3, Number Plate Recognition Using SVM and Neural Networks Chapter 4, Non-Rigid Face Tracking Chapter 5, 3D Head Pose Estimation Using AAM and POSIT Chapter 6, Face Recognition Using Eigenfaces or Fisherfaces Chapter 7, Natural Feature Tracking for Augmented Reality 👇👇👇👇👇 @DeepLearning_AI

58 018

@DeepLearning_AI 👆👆👆👆👆 https://medium.freecodecamp.org/how-to-be-a-great-programmer-34939494996d

58 018

How to be a great programmer What sets apart the really great programmers? 5min read...

58 018

@DeepLearning_AI 👆👆👆👆👆 https://medium.com/@rockyxu399/three-models-for-kaggles-flowers-recognition-dataset-bc2ff732cf4e

58 018

Three models for Kaggle’s “Flowers Recognition” Dataset (6 min read) 👇👇👇 @DeepLearning_AI

58 018

@DeepLearning_AI 👆👆👆 https://medium.com/mathematical-beauty/deep-learning-for-cosmetics-2d1b427bbfa2

58 018

Deep Learning for Cosmetics In this blog post, how we can use computer vision to solve a particularly poignant instance of this problem: finding influencers, images and videos that address a specific eye shape and complexion. Along the way, we’ll illustrate how three simple yet powerful ideas — geometric transformations, the triplet loss function and transfer learning — allow us to solve a variety of difficult inference problems with minimal human input. 👇👇👇 @DeepLearning_AI

58 018

@DeepLearning_AI 👆👆👆 https://medium.com/@a.mirzaei69/adversarial-autoencoders-on-mnist-dataset-python-keras-implementation-5eeafd52ab21

58 018

Adversarial Autoencoders on MNIST dataset Python Keras Implementation 👇👇👇 @DeepLearning_AI

58 018

This book covers: Chapter 1, Getting Started with OpenCV. Chapter 2, An Introduction to the Basics of OpenCV. Chapter 3, Learning the Graphical User Interface and Basic Filtering. Chapter 4, Delving into Histograms and Filters. Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection. Chapter 6, Learning Object Classification Chapter 7, Detecting Face Parts and Overlaying Masks, Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations, Chapter 9, Learning Object Tracking Chapter 10, Developing Segmentation Algorithms for Text Recognition, Chapter 11, Text Recognition with Tesseract 👇👇👇 @DeepLearning_AI

58 018

👇👇👇 @DeepLearning_AI

58 018