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1:00 awesome

Thank you so much for this fantastic course! I really enjoyed the way you explained everything-it felt clear and approachable. I’ve learned a lot, and I just wanted to let you know how much I appreciate the time and effort you put into making this. Looking forward to more videos from you!

These lectures of yours are absolutely gold. Thanks a lot!

I really enjoy your teaching.

thanks a lot for your sharing.❤

What is true distribution?

Thank you, Sir.

The formula for the neural network in this video should be a 2 level summation instead of one level.

یاشا آذربایجان ثروتی ❤

At 20:20 why does Phi(Q_i) not cancel out in the numerator and denominator?

Thank you for sharing this lecture, prof. Great quality and easy to understand!

من با شما قلمچی اردبیل بودم،بعد سالها توی کشورهای نزدیک بهم اسمتون رو دیدم،خوشحالم از موفقیت های هم دوره ایی ها

your explanation was very nice and easy to understand. Thank you!

Wonderful lecture!! Thank you for sharing

Can you please share the slide

Thank you for the lecture! ☺️

@1:58:57 You said you will explain different procedures to generate different responses later. I did not find it till you start discussing Step 3. Could you illustrate further?

When you upload the video, could you set the speed to 1.5? Right now, I'm setting it to 2X, it is still very very slow.

Can you please share the slides? (2)

Can you please share the slides

Thank you Soheil for the great content and the clear way of explanation! Could you also share the final written notes of each session for download?

Massage lesson! Tnx

Thanks a lot for providing such an excellent lecture. Would it be possible to release the notes for study? Thanks~

Many thanks for your excellent lectures, particularly those on diffusion models. I do have a few inquiries regarding models of conditional diffusion. Can we think of text vectors as the query (Q) and image vectors as the key (K) and value (V) in cross-attention instead of image vectors as the query (Q)?

Hi, could u please introduce a good course in the basics of machine learning and deep learning for beginners?

Thanks for sharing.

Real quality content

I just want to thank you 🤗

This here is the best video on SWIN transformers

The handwriting is difficult to read, maybe write slower or with a different brush?

Thanks for uploading these lectures.

❤

Thanks for uploading the lectures.

Thanks so much for sharing this updated version of the course!!

It's minute 30 of the video and I have watched +8 ads. Really attention is all we need!

Thank Soheil for sharing the updated deep learning theory courses. I ever followed Sohail's former lectures in 2020, where I learned the theoretical knowledge of deep learning in terms of representation, generalization, and optimization. I found that Soheil's course schedule this year has substantially changed to state-of-the-art transformer-based technologies, such as large language models, etc. I plan to catch up with Sohail's updated deep learning foundation course this year and really appreciate the new lecture videos.

Thank you for sharing it

Hi professor, I was also wondering that if you plan to to add some contents related to distanglement learning? like nonlinear ICA which I think is very theoretically interesting and important.

Thanks for updating this really amazing course. I've read the syllabus of this semester, and find it is really interesting, especially the generative models and multi-modal models part. Hope to see more latest course videos. Thanks a lot for your effort of sharing the contents of this amazing course.

Thank you for sharing the updated version of the course🙏

Really nice lecture with a very even pace. Thank you for sharing.

Great lecture. Thank you for sharing

explanation of: "Loss landscapes and optimization in over-parameterized non-linear systems and neural networks"

Great lecture on the highly ignored ground! thanks

Thanks for the great lectures, Prof. Soheil.

25:00 (Bookmark)

24:18 The loss function is sometime defined by an L and sometime edfines by the caligraohic L, are they the same? thank you very much !

42:30 one option could be KL divergence loss?

Such a great lecture I've seen in TH-cam related to self-supervised learning. So many thanks!

great explanation, just Subscribed!