MIT 6.S191 (2023): Deep Generative Modeling

don't know why, but i could not breath listening to this lecture. she's so clear without any redundancy, without any hmmm, urgggg,... how come. she is so amaizing . i would have practiced 1000 times to be able to lecture like this

I really like this lecture, what keeps me sleepless is the question: "Can we learn the true (if so) explanatory factors from purely observational data ?"

Can you give me extra resources

Hi Alexander & Ava, thanks for this video.

Thousands of people watch these videos and learn from them. So any mistakes you make will impact them directly. If/when you do find errors or someone points them out to you, it is your utmost responsibility to update about it to your viewers. Please look into the loss functions for GAN. They are incorrect.

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Ingenious.

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- Bud Bundy

i love this woman. she will be a good wife

Hey, I was going through this video with a beautiful explanation on working of GANS. I just want to ask that whether we can say that idea behind working of GANs is to have some sort of overfitting which is usually avoided in traditional ML approaches. Not exactly overfitting but in a way we want to overfit it in a sense that the points are in the probability distribution region of actual points???

Thank you for sharing the info... ❤❤

Where to find code for this?

Wow! Can't wait to learn the coming lectures!

Excellent Content Ma'am Truly unnbelievable 😊😊😊😊😊

Pure engineering.

i don't know why i see the loss of GAN is not correct.

Wow, such clarity of thought and ideas. I guess that's the MIT advantage! Well done :)

Greatly appreciate the knowledge sharing.

A lot of appreciations from my side to your Team who build such a excellent course on Deep Learning

whats basically taking place is Image processing.

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Great! Love these Videos. They help me alot.

Hold up - this is your wife or sister?

Thank you so much Alexander and Amini.......

Gen folding

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You can fine underling leadership

Never disappointing👌🏻

I love how she apologizes when displaying math...😂😂. Its as if she understands the math struggles we all go through. Nevertheless, Its apparent that math is an important aspect of understanding the architecture of machine learning models and developing new ones.

HOW YOU WILL DRIVE A SYSTEM WHEN MAXIMUM STRIVE TO ATTAIN MINIMUM TO BALANCE ENTROPY?

Is there a non-intro deep learning course after this course?

Incroyable !!!

Is there a lecture that deals with generative language models ?

Is it still relevant to teach GANs and autoencoders, instead on just focusing on diffusion models?

Universities create things like covid-19, corporations create things like chatGPT. The death of the university is coming soon.

Highly recommended series for AI enthusiasts. This MIT series is by far the most intuitive videos covering all aspects of deep learning. Well done on that.

haha, tao noi roi, so AI lam, cao sieu lam, tao ko du kha nang dau, bien di cho khac

Introduce myself my name is Ariful Islam leeton im software engineer and software developer and website development and data analytics

I really appreciate these lectures, but I never could absorb lectures that are simply a script read aloud. I can read the material myself. She's MUCH more effective when she explains concepts from memory without reading from a text.

Perfect lecture! Congratulations

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Brilliant presentation. World-class.

😁😁😁😁😁☺️☺️☺️☺️❤️❤️❤️❤️

I'd like to see something about AI that can adjust its code and observe how it changes its functioning.

What's great about this instructor is that they are very careful and particular about what they say, and how they phrase it. There's no fluff, nothing that could cause confusion. Straight to the point and very intentional.