اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Artificial Intelligence && Deep Learning
رفتن به کانال در 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:
نمایش بیشتر📈 تحلیل کانال تلگرام Artificial Intelligence && Deep Learning
کانال Artificial Intelligence && Deep Learning (@deeplearning_ai) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 58 019 مشترک است و جایگاه 2 290 را در دسته فناوری و برنامهها و رتبه 5 977 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 58 019 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -204 و در ۲۴ ساعت گذشته برابر -8 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 9.58% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 5 556 بازدید دریافت میکند. در اولین روز معمولاً 0 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 16 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند github, learning, estimation, dataset, engineer تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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:”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 26 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
58 019
مشترکین
-824 ساعت
-287 روز
-20430 روز
آرشیو پست ها
650 Free Online Programming & Computer Science Courses You Can Start This July
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https://www.freecodecamp.org/news/650-free-online-programming-computer-science-courses-you-can-start-this-summer/
Best Training & Certification Courses for Professionals | Edureka
* PGP in AI & Machine Learning
* Data Scientist Master Program
* Cloud Architect Masters Program
* .....
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https://www.edureka.co/all-courses
Best Training & Certification Courses for Professionals | Edureka
* PGP in AI & Machine Learning
* Data Scientist Master Program
* Cloud Architect Masters Program
* .....
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@DeepLearning_AI
Review: FCN — Fully Convolutional Network (Semantic Segmentation)
Covered:
* From Image Classification to Semantic Segmentation
* Upsampling Via Deconvolution
* Fusing the Output
* Results
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https://towardsdatascience.com/review-fcn-semantic-segmentation-eb8c9b50d2d1
Implement Back Propagation in Neural Networks
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https://medium.com/coinmonks/implement-back-propagation-in-neural-networks-ed09897593e7
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
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@DeepLearning_AI
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https://www.freecodecamp.org/news/data-science-course-for-beginners/
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
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https://towardsdatascience.com/10-new-things-i-learnt-from-fast-ai-v3-4d79c1f07e33
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
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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
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Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule
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https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/
Diving deeper into Reinforcement Learning with Q-Learning
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https://medium.com/free-code-camp/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe
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
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@DeepLearning_AI
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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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@DeepLearning_AI
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/
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Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:
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https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf
Not just another GAN paper — SAGAN – Towards Data Science
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https://towardsdatascience.com/not-just-another-gan-paper-sagan-96e649f01a6b
Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3
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https://sthalles.github.io/deep_segmentation_network/
