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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:
Больше📈 Аналитический обзор Telegram-канала Artificial Intelligence && Deep Learning
Канал Artificial Intelligence && Deep Learning (@deeplearning_ai) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 58 024 подписчиков, занимая 2 297 место в категории Технологии и приложения и 6 023 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 58 024 подписчиков.
Согласно последним данным от 23 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -218, а за последние 24 часа — -10, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 8.90%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 5 163 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 15.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как github, learning, estimation, dataset, engineer.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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:”
Благодаря высокой частоте обновлений (последние данные получены 24 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
58 024
Подписчики
-1024 часа
-557 дней
-21830 день
Архив постов
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
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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
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2- Machine Learning
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4- Artificial Intelligence
5- Data Analysis
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8- programming Languages
✅ best channels on Telegram:
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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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✔️ 10 hours session consisting of theory + Hands-on.
✔️One-on-One Debugging Session (Optional)
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▪️Getting started with AWS for Machine Learning
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▪️AWS MLOps - Build, Train & deploy ML Model
🔥 Limited Seats Available!
☎️ Contact:
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+918940876397 / +918778033930
