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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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📈 Аналітичний огляд Telegram-каналу Artificial Intelligence && Deep Learning

Канал Artificial Intelligence && Deep Learning (@deeplearning_ai) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 58 024 підписників, посідаючи 2 297 місце в категорії Технології та додатки та 6 023 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 58 024 підписників.

За останніми даними від 23 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -218, а за останні 24 години на -10, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 8.90%. Протягом перших 24 годин після публікації контент зазвичай збирає N/A% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 5 163 переглядів. Протягом першої доби публікація в середньому набирає 0 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 15.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як github, learning, estimation, dataset, engineer.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
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:

Завдяки високій частоті оновлень (останні дані отримано 24 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

58 024
Підписники
-1024 години
-557 днів
-21830 день
Архів дописів
This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
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: https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ Data Science Books: https://t.me/DataScienceM

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 Visualiza
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: https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ Data Science Books: https://t.me/Codeprogrammer

* 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

VideoCrafter1: Open Diffusion Models for High-Quality Video Generation 🤗🤗🤗 VideoCrafter is an open-source video generation
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VideoCrafter1: 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

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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

MLOps+Masterclass+November+2023.pdf6.11 MB

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