Пікірлер

• 
  @JabirKhan-le7xq6 күн бұрын

  What is true distribution?

• 
  @JabirKhan-le7xq6 күн бұрын

  Thank you, Sir.

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  @freerockneverdrop123615 күн бұрын

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

• 
  @KittyCat-lp3zyАй бұрын

  یاشا آذربایجان ثروتی ❤

• 
  @MonkkSoori3 ай бұрын

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

• 
  @janesun90083 ай бұрын

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

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  @NavaAbdolalipour3 ай бұрын

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

• 
  @simaranjbari3 ай бұрын

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

• 
  @fierydino94024 ай бұрын

  Wonderful lecture!! Thank you for sharing

• 
  @PradeepKumar-zy6cd4 ай бұрын

  Can you please share the slide

• 
  @prabhavkaula96974 ай бұрын

  Thank you for the lecture! ☺️

• 
  @ax53444 ай бұрын

  @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?

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  @ax53444 ай бұрын

  @2:15:30 Found it. Thanks!

• 
  @ax53444 ай бұрын

  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.

• 
  @sabujchattopadhyay4 ай бұрын

  Can you please share the slides? (2)

• 
  @miquelnogueralonso25764 ай бұрын

  Can you please share the slides

• 
  @bardiasafaei4574 ай бұрын

  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?

• 
  @ai__765 ай бұрын

  Massage lesson! Tnx

• 
  @amiltonwong5 ай бұрын

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

• 
  @parhamsalar38265 ай бұрын

  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)?

• 
  @parisaemkani57305 ай бұрын

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

• 
  @Stealph_Delta_30035 ай бұрын

  Thanks for sharing.

• 
  @sdiabr67925 ай бұрын

  Real quality content

• 
  @mozhganmomtaz81695 ай бұрын

  I just want to thank you 🤗

• 
  @INSTIG8R5 ай бұрын

  This here is the best video on SWIN transformers

• 
  @MrNoipe5 ай бұрын

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

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  @naeemkhoshnevis5 ай бұрын

  Thanks for uploading these lectures.

• 
  @miladkhademinori27095 ай бұрын

  ❤

• 
  @naeemkhoshnevis5 ай бұрын

  Thanks for uploading the lectures.

• 
  @Nerraruzi6 ай бұрын

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

• 
  @shayanmohammadizadeh1726 ай бұрын

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

• 
  @Umar-Ateeq4 ай бұрын

  you can use "adblock for youtube" extension to avoid ads.

• 
  @junqi70506 ай бұрын

  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.

• 
  @mohammadshahbazhussain20296 ай бұрын

  Thank you for sharing it

• 
  @user-pz5nn2kg2j6 ай бұрын

  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.

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  @user-pz5nn2kg2j6 ай бұрын

  Sorry, there's a typo. It should be 'disentanglement'.

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  @user-pz5nn2kg2j6 ай бұрын

  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.

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  @hesamce6 ай бұрын

  Thank you for sharing the updated version of the course🙏

• 
  @AyushSharma-ie7tj Жыл бұрын

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

• 
  @StratosFair Жыл бұрын

  Great lecture. Thank you for sharing

• 
  @hamedgholami261 Жыл бұрын

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

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  @mojtabakolahdoozi2418 Жыл бұрын

  Great lecture on the highly ignored ground! thanks

• 
  @quanguyenang1615 Жыл бұрын

  Thanks for the great lectures, Prof. Soheil.

• 
  @sylus121 Жыл бұрын

  25:00 (Bookmark)

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  @Thaumast Жыл бұрын

  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 !

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  @bryanbocao4906 Жыл бұрын

  42:30 one option could be KL divergence loss?

• 
  @mskang009 Жыл бұрын

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

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  @sinaasadiyan Жыл бұрын

  great explanation, just Subscribed!

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  @sumitsah6092 Жыл бұрын

  How can we guranttee that w_t lies within the ball??? Because if that is not the case then we can't apply PL inequality. Please comment.

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  @zonghua Жыл бұрын

  The handwriting is unclear.

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  @user-cp8uy9om7o Жыл бұрын

  Amazing video. Thanks!

• 
  @luisjacobson4375 Жыл бұрын

  ✌️ ρ尺ｏΜ𝐎ᔕᗰ

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  @jfjfcjcjchcjcjcj9947 Жыл бұрын

  Thanks for the lecture! I think that there are a couple of points that would make the lecture more digestible if they are explained a bit more in depth. First, is the definition of interpolation, different people imply different things when referring to interpolation. I presume that Misha here by interpolation refers to the ability of estimators to achieve zero training error and still not overfit to the hold-out test set? The second aspect which might require some more explanation is @40:05 when Misha is describing the difference between interpolation accuracy and interpolation in the l2-norm. I presume that what is meant by l2-norm interpolation is that 2 estimators with the same l2-norm on their parameters exhibit the same generalisation capabilities, is that right?