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 Tecnologías y Aplicaciones2 290...

📈 Análisis del canal de Telegram Artificial Intelligence && Deep Learning

El canal Artificial Intelligence && Deep Learning (@deeplearning_ai) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 58 019 suscriptores, ocupando la posición 2 290 en la categoría Tecnologías y Aplicaciones y el puesto 5 977 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 58 019 suscriptores.

Según los últimos datos del 25 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -204, y en las últimas 24 horas de -8, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.58%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 5 556 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 16.
Intereses temáticos: El contenido se centra en temas clave como github, learning, estimation, dataset, engineer.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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:”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 26 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

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Suscriptores

-824 horas

-287 días

-20430 días

5 556

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

9.58%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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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/