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:

India5 977 Technologies & Applications2 290...

📈 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 58 019 subscribers, ranking 2 290 in the Technologies & Applications category and 5 977 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 58 019 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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:”

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.

58 019

Subscribers

-824 hours

-287 days

-20430 days

5 556

Post views

No data24 hours

No data48 hours

9.58%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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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/

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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

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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

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

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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/

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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

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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 .

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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 .

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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/

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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

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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 .

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

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

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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

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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

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Deep learning lecture

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

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