Artificial Intelligence && Deep Learning

Открыть в 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:

Больше

Индия5 977 Технологии и приложения2 290...

📈 Аналитический обзор Telegram-канала Artificial Intelligence && Deep Learning

Канал Artificial Intelligence && Deep Learning (@deeplearning_ai) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 58 019 подписчиков, занимая 2 290 место в категории Технологии и приложения и 5 977 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 58 019 подписчиков.

Согласно последним данным от 25 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -204, а за последние 24 часа — -8, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 9.58%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 5 556 просмотров. В течение первых суток публикация набирает 0 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 16.
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 26 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.

58 019

Подписчики

-824 часа

-287 дней

-20430 день

5 556

Просмотры поста

Нет данных24 часа

Нет данных48 часов

9.58%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

58 019

650 Free Online Programming & Computer Science Courses You Can Start This July join👇👇👇 @DeepLearning_AI . https://www.freecodecamp.org/news/650-free-online-programming-computer-science-courses-you-can-start-this-summer/

58 019

Best Training & Certification Courses for Professionals | Edureka * PGP in AI & Machine Learning * Data Scientist Master Program * Cloud Architect Masters Program * ..... join👇👇👇 @DeepLearning_AI . https://www.edureka.co/all-courses

58 019

Review: FCN — Fully Convolutional Network (Semantic Segmentation) Covered: * From Image Classification to Semantic Segmentation * Upsampling Via Deconvolution * Fusing the Output * Results join👇👇👇 @DeepLearning_AI . https://towardsdatascience.com/review-fcn-semantic-segmentation-eb8c9b50d2d1

58 019

Implement Back Propagation in Neural Networks join👇👇👇 @DeepLearning_AI . https://medium.com/coinmonks/implement-back-propagation-in-neural-networks-ed09897593e7

58 019

sticker.webp0.15 KB

58 019

Free 6-Hour Data Science Course for Beginners This course covers: * foundations of data science * data sourcing * coding for data scientists * mathematics for data scientists * statistics join👇👇👇 @DeepLearning_AI . https://www.freecodecamp.org/news/data-science-course-for-beginners/

58 019

1. 10 New Things I Learnt from fast.ai v3 2. 2019 deep learning course Practical Deep Learning for Coders, v3. 10 learning points as such: 1. The Universal Approximation Theorem 2. Neural Networks: Design & Architecture 3. Understanding the Loss Landscape 4. Gradient Descent Optimisers 5. Loss Functions 6. Training 7. Regularisation 8. Tasks 9. Model Interpretability 10. Appendix: Jeremy Howard on Model Complexity & Regularisation join👇👇👇 @DeepLearning_AI https://towardsdatascience.com/10-new-things-i-learnt-from-fast-ai-v3-4d79c1f07e33

58 019

TOP 15 PYTHON RESOURCES 1) Learn Python the Hard Way (free ebook) https://learnpythonthehardway.org/book/ 2)Codecademy (free code tutorials) https://www.codecademy.com/learn/learn-python 3)Google's Python class https://developers.google.com/edu/python/ 4)A Byte of Python (free ebook) https://python.swaroopch.com 5)TutsPlus tutorial https://code.tutsplus.com/articles/the-best-way-to-learn-python--net-26288 6)LEARN PYTHON ONLINE: BEST PYTHON https://mikkegoes.com/learn-python-online-best-resources/ 7)Best Python Resources for Beginners and Professionals https://pythontips.com/2013/09/01/best-python-resources/amp/ 8)TutorialsPoint http://www.tutorialspoint.com/python/ 9)Learning Python https://docs.python-guide.org/intro/learning/ 10)Full Stack Python https://www.fullstackpython.com/best-python-resources.html 11)Python For Beginners https://www.python.org/about/gettingstarted/ 12)Codementor community https://www.codementor.io/community/topic/python 13)How should I start learning Python? https://www.quora.com/How-should-I-start-learning-Python-1 14)Codeconquest https://www.codeconquest.com/blog/the-50-best-websites-to-learn-python/ 15)Python for Beginners https://www.pythonforbeginners.com join👇👇👇 @DeepLearning_AI .

58 019

SEVEN NEW COURSES that cover Python, R, and SQL. First up is Analyzing Business Data in SQL, where you’ll learn how to write SQL queries to calculate key business metrics and produce report-ready results. Plus our Introduction to Text Analysis in R course, where you’ll learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models. Courses : 1. Writing Functions and Stored Procedures in SQL Server 2. Analyzing Business Data in SQL 3. Feature Engineering for Machine Learning in Python 4. Introduction to Seaborn (in Python) 5. Advanced Dimensionality Reduction in R 6. Introduction to Text Analysis in R 7. Intermediate Interactive Data Visualization with plotly in R 1. https://www.datacamp.com/courses/writing-functions-and-stored-procedures-in-sql-server?utm_medium=email&utm_source=customerio&utm_campaign=course_7996 2. https://www.datacamp.com/courses/analyzing-business-data-in-sql?utm_medium=email&utm_source=customerio&utm_campaign=course_15268 3. https://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python?utm_medium=email&utm_source=customerio&utm_campaign=course_14336 4. https://www.datacamp.com/courses/introduction-to-seaborn?utm_medium=email&utm_source=customerio&utm_campaign=course_15192 5. https://www.datacamp.com/courses/advanced-dimensionality-reduction-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_10590 6. https://www.datacamp.com/courses/introduction-to-text-analysis-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_14290 7. https://www.datacamp.com/courses/intermediate-interactive-data-visualization-with-plotly-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_7193 join channel 👇👇👇 @DeepLearning_AI .

58 019

Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule 👇👇👇👇👇 @DeepLearning_AI . https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

58 019

Diving deeper into Reinforcement Learning with Q-Learning Join👇👇👇 @DeepLearning_AI . https://medium.com/free-code-camp/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe

58 019

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models paper — arxiv👇👇👇 https://arxiv.org/pdf/1905.08233.pdf video — youtube👇👇👇 https://www.youtube.com/watch?v=p1b5aiTrGzY join channel 👇👇👇 @DeepLearning_AI .

58 019

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models 👇👇👇👇👇 @DeepLearning_AI

58 019

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models 👇👇👇👇 @DeepLearning_AI

58 019

Stanford Machine Learning Content 01 and 02: Introduction, Regression Analysis and Gradient Descent 03: Linear Algebra - review 04: Linear Regression with Multiple Variables 05: Octave[incomplete] 06: Logistic Regression 07: Regularization 08: Neural Networks - Representation 09: Neural Networks - Learning 10: Advice for applying machine learning techniques 11: Machine Learning System Design 12: Support Vector Machines 13: Clustering 14: Dimensionality Reduction 15: Anomaly Detection 16: Recommender Systems 17: Large Scale Machine Learning 18: Application Example - Photo OCR 19: Course Summary http://www.holehouse.org/mlclass/ 👇👇👇👇👇 @DeepLearning_AI

58 019

Deep Learning lecture The full deck of (600+) slides, by Gilles Louppe: 👇👇👇👇👇 @DeepLearning_AI . https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

58 019

Deep learning lecture

58 019

Not just another GAN paper — SAGAN – Towards Data Science 👇👇👇👇👇 @DeepLearning_AI . https://towardsdatascience.com/not-just-another-gan-paper-sagan-96e649f01a6b

58 019

Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3 👇👇👇👇👇 @DeepLearning_AI . https://sthalles.github.io/deep_segmentation_network/