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Artificial Intelligence && Deep Learning

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:

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πŸ“ˆ Analytical overview of Telegram channel Artificial Intelligence && Deep Learning

Channel Artificial Intelligence && Deep Learning (@deeplearning_ai) in the English language segment is an active participant. Currently, the community unites 58 018 subscribers, ranking 2 290 in the Technologies & Applications category and 5 977 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 58 018 subscribers.

According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -204 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 9.58%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 5 556 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 16.
  • Thematic interests: Content is focused on key topics such as github, learning, estimation, dataset, engineer.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œChannel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:”

Thanks to the high frequency of updates (latest data received on 26 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

58 018
Subscribers
-824 hours
-287 days
-20430 days
Posts Archive
. @DeepLearning_AI πŸ‘†πŸ‘†πŸ‘†πŸ‘†πŸ‘† 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

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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! 😱 πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

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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 πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

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

Three models for Kaggle’s β€œFlowers Recognition” Dataset (6 min read) πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

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 β€” geometric transformations, the triplet loss function and transfer learning β€” allow us to solve a variety of difficult inference problems with minimal human input. πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

Adversarial Autoencoders on MNIST dataset Python Keras Implementation πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

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 πŸ‘‡πŸ‘‡πŸ‘‡ @DeepLearning_AI

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