🏙 دوره #علم_داده در #تحلیل_کسب_و_کار (Business Analysis) با نرم افزار R

👤 با تدریس #دکتر_فرزاد_مینویی فارغ التحصیل دکتری مدیریت از دانشگاه کلورادو امریکا (مجری پروژه‌های علم داده در امریکا و ایران)

مخاطبان دوره:
🔸 علاقمندان به حوزه علوم داده
🔸 علاقمندان به کار در صنایع، استارتاپ‌ها، بانک، بیمه و ...
🔸 تحلیلگران کسب و کار

🔗 جزئیات و رزرو از طریق: https://bit.ly/2JqFMNk

💬 @tiheac
🌐 tihe.ac.ir
📞 021-86741 (EXT: 120-125-127)

16 منبع آموزشی الگوریتمهای هوش مصنوعی〰️〰️〰️

♾ 1. Speech and Language Processing by Dan Jurafsky and James Martin

🔊 2. Deep Learning for Natural Language Processing by Richard Socher (Stanford University)

🔔 3. Natural Language Processing (NLP) by Microsoft

🔸 4. Andrew Ng’s course on Machine Learning

🔸 5. The video lectures and resources for Stanford’s Natural Language Processing with Deep Learning

🔰 Part 2

🔹6. Sequence Models for Time Series and Natural Language Processing

🔺 7. Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford.

🔺 8. Natural Language Processing Fundamentals in Python by Datacamp

🔺 9 Natural Language Processing by Higher School of Economics

🔸 10 How to Build a Chatbot Without Coding by IBM

🔸 11. CS 388: Natural Language Processing by University of Texas

🔸 12. Natural Language Processing with Python

⚡️ 13. CSEP 517: Natural Language Processing by University of Washington

🔰 14. Dan Jurafsky & Chris Manning: Natural Language Processing

📘 15. NATURAL LANGUAGE PROCESSING by Carnegie Mellon University

📘 16. CS224n: Natural Language Processing with Deep Learning by Stanford University

#منابع #یادگیری_ماشین #فیلم #کلاس_آموزشی #الگوریتمها #پردازش_زبان_طبیعی #هوش_مصنوعی #یادگیری_عمیق

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#یادگیری_عمیق دقیقا چیه؟

طی چند روز گذشته، بعضی از محقق‌های بزرگ هوش مصنوعی مانند #یان_لکان و #فرانسوا_شوله تعریف خودشون رو از دیپ لرنینگ توی سال 2019 اعلام کردند.

یان لکان:

"Some folks still seem confused about what deep learning is. Here is a definition:

DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization. That's it.

This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.

Don't say "DL can't do X" when what you really mean is "supervised learning needs too much data to do X"

Extensions (dynamic networks, differentiable programming, graph NN, etc) allow the network architecture to change dynamically in a data-dependent way."
https://www.facebook.com/722677142/posts/10156463919392143/

فرانسوا شوله:

"What's deep learning?

The "common usage" definition as of 2019 would be "chains of differentiable parametric layers trained end-to-end with backprop".

But this definition seems overly restrictive to me. It describes *how we do DL today*, not *what it is*."
https://twitter.com/fchollet/status/1210031900695449600

اندری بورکوف:

"Looks like in late 2019, people still need a definition of deep learning, so here's mine: deep learning is finding parameters of a nested parametrized non-linear function by minimizing an example-based differentiable cost function using gradient descent."
https://www.linkedin.com/posts/andriyburkov_looks-like-in-late-2019-people-still-need-activity-6615377527147941888-ce68/

#deep_learning

#ویدئو

ویدئو نشست تخصصی خرداد ماه آزمایشگاه سپهر

موضوع ارائه: موضوعات رونددار در شبکه‌های اجتماعی

ارائه‌کننده: مهرداد رنجبر خدیوی

https://aparat.com/v/DfEe0

@cominsys_channel

Transform any hands-drawn design into a #HTML code with #AI.

https://sketch2code.azurewebsites.net/

✅@ai_python

MIT lecture series on deep learning in 2019
MIT lecture series on deep learning:Basics:
https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

MIT lecture series on deep learning: State of the Art:
https://www.youtube.com/watch?v=53YvP6gdD7U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

MIT lecture series on deep learning: Introduction to Deep RL:
https://www.youtube.com/watch?v=zR11FLZ-O9M&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources

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دیپ لرنیگ در آینده به چه صورت خواهد بود؟
می‌تونید سخنرانی یوشوآ بنجیو در NeurIPS 2019 در این باره را دنبال کنید.

From System 1 Deep Learning to System 2 Deep Learning

Video: https://www.youtube.com/watch?v=FtUbMG3rlFs
Slide: https://drive.google.com/file/d/1zbe_N8TmAEvPiKXmn6yZlRkFehsAUS8Z/view

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