This is the Best resource fo gan i've come across so far. Very detailed explanation with complicating the terms. You are a life saver! . thank you

Truly an exceptional and informative video! Literally made me understand the concept very well!!

This is a well made and well explained video, one can be extremely grateful to you for this

This video is a perfect and most explanatory video on this topic, absolutely love it.

Hey Sujan, I just started learning GANs and I happen to stumble upon your video & your channel ! Great work :) You have a bright future ahead :)

This is exactly what I wanted. Your explanation was amazing and very clear.

I almost never leave comments... this is an amazing summary of the mathematical aspect and quirks of the paper. Thank you!

Thank you! Very well explained. Mentioned all the underlying mathematical concepts on which GAN is based on.

I'm really thankful. Great explanation!

Such a good explanation, man! Thank you so much!!

Great explanation for something as complex as GAN.

This is a great explanation! I'd love to see more in depth videos! If you could cover autoencoders that'd be really cool too!

The first well explained GAN I've ever seen, thanks

Thanks for the great effort in making the videos. God bless you

Amazing.. Just no words. Standing ovation to you.

Incredibly well made !

Thanks for explaining this advanced topic!

Congratulation....one of best marh explanations of GAN ever🎉🎉

Brilliant presentation - simplifies the math using intuitive explanations and examples (same for the video about the binary cross entropy ) - thank you for this

Thanks for such a great explanation

Very informative. Thanks for this clear explanation!

was very easy to understand. thank you

Good explanation. Thank you!

you deserve Best explanation award

Thank you for posting this!

Woah !!!! I consider myself bad at math, but this video was like a hot knife in my dense but butterish brain! thank you !

Great work thank you very very much❤

This is the best explanation I’ve found on GANs, thank you!! I’m currently training a DCGAN, however in terms of theory, are there any differences between DCGAN vs GAN as from my understanding, the difference is DCGAN utilizes deep convolution networks and GAN utilizes fully connected layers, however is the theory described in this video also applied to DCGAN? Thanks for your help, appreciate it!

Great job!!

Wow wow Thank you! Well explained.

Wow.this was very educational. Please make more videos on gans.

Very nice explanation

Great, dont stop! Keep making such nice videos.

Outstanding content

Amazing video man!

Clear mathematical explanation

This was a fantastic video, Im might actually pass my class now!

Excelent video!

Best explanation ever!

Loved that intro 👌

Extremely good explanation!!!!

Great Explanation!!

This is so.. GOOD! Thank you so much!!!!

thank you. Great explanation

I like how he pronounce "z" as "zed" but "G of Z" as "G of zee".

Dude. Awesome!! Literally you explain better than medium "how to"'s :) expecting awesome content

Really nice, I really like the part at 11:52, made that part so much easier to understand with that visual example

great job ! thank so much

You are my favorite person on the internet right now

Spectacular

Awesome explaination

Thank you

great video!

Thanks Man

It was great explanation

Amazing, keep up the good work (y)

Cool video, thank you very much :)

Why is your channel so underrated ?!!!

Nicely explained. Do make a playlist of regression classification nlp deep learning so that we can easily follow up. Great job 👌👌👌

Very well explained. Can you once try gan inversion as well?

Thank you!

Amaizng video bro..Could you please make a playlist for Deep learning(include this video) and/or reinforcement learning.

Nice tutorial. Which software you are using for writing on the board here ?

Well, thanks for the transfer learning🤭... You have explained it in a very crisp manner.. Keep up the good work 👍

Thank you so much for this video! *I think the JS divergence equation needs a second ln sign after ...+1/2 Ex pg ? The equation appears at 12:53. Thank you again!

Great video, thanks! The label of 0 for the reconstructed image. Is that correct? According to another reference I have, it should set the labels to 1 to fool the discriminator into thinking the image is real? Edit, my bad. Yes, you are correct, feeding y = 0 into the discriminator is correct. The label 1 is then used to train the generator :)

How do you get the equation of binary cross entropy from the cross entropy definition?

SOOO GOOOD

Hey buddy / guru ! Great Lecture in as simple form as possible. I really appreaciate your effort in making this video. Am actually learning this lecture a day before my uni exam about a subject whuch is remotely connected to it. I would be humbled to know how you prepared this video by going throuh that Research Paper

wow, gan er gan beregelo amar thanks for that.

🎯 Key Takeaways for quick navigation: 00:00 *🧠 Overview of Generative Adversarial Networks (GANs)* - GANs consist of two models: a generative model (G) and a discriminative model (D). - Generative models learn the joint probability distribution of input and output variables, while discriminative models learn the conditional probability of the target variable given the input variable. - GANs use an adversarial setup where the generator produces fake data points, and the discriminator distinguishes between real and fake data, leading to both models improving over time. 02:13 *📊 Structure and Components of GANs* - GANs consist of multi-layered neural networks representing the generator (G) and discriminator (D). - Theta G and theta D represent the weights of the respective networks. - GANs utilize a noise distribution as input to the generator to produce data points similar to the original distribution. 05:26 *🔢 Understanding the Value Function of GANs* - The value function represents the objective of G (minimize) and D (maximize) in the GAN setup. - The value function resembles the binary cross-entropy function, crucial for training GANs. - Expectation (E) is used to calculate the average value over the entire dataset, essential for continuous distributions. 08:37 *🔄 Training Process and Optimization of GANs* - GAN training involves an iterative process where the generator and discriminator alternate updates. - Stochastic gradient descent is used to optimize the loss function. - The discriminator is updated to maximize the value function, while the generator is updated to minimize it. 10:42 *🎯 Convergence and Guarantee of GANs* - The goal is to prove that the generator's distribution converges to the original data distribution. - Jensen-Shannon divergence is a method used to measure the difference between two distributions. - At the global minimum of the value function, the generator's distribution becomes indistinguishable from the original data distribution. 14:48 *⚙️ Phases of GAN Training* - GAN training progresses through phases where initially, both generator and discriminator perform poorly. - As training continues, the discriminator becomes adept at distinguishing real and fake data, while the generator's distribution approaches that of the original data. - At convergence, the discriminator cannot differentiate between real and generated data, achieving the desired outcome. Made with HARPA AI

Thank you very much for the video! Can someone help me and explain why 1 - was dropped at 12:30

Thanks! Wonderful explanation. But it seems that 9:58 needs to adding a sum sign in front of the square brackets.

Amazing video. I'm still confuse that what is difference between normal GAN vs GAN CLS? Can you explain a little bit

Do you provide personal lessons? @normalized nerd

Awesome explanation thank you very much. Subscripted

What about the sign he told us to forget? should it not be considered at the end? why?

thanks bro

Hey ! What does it mean, when people say, data points/ images/ texts (on which we train our model) belong to a distribution ? What is its inuitive meaning about belonging to a distribution and how are they sure about the real life data belonging to a distribution ?

Would like to see Use of Regularization functions/terms in loss function through equations....Plz make VDO on this

sera porali bhai

What would really help is if you add links to your videos if some concepts in a video have been discussed more in depth. So if I don't understand some concept that is glossed over here I can scroll down and click the video that explains it in more depth.

I didn't manage to understand starting from Binary Crossentropy Function :(

What platform you're using for the videos?

So I heard someone saying it's easier for the discriminator to predict a fake data than It is for the generator to create a fake data who could pass as original. That would make the system unbalaced. Is It true and If so, Is there a way to fix It?

what whiteboard software is that ?

Killer!!

Good initiative but very similar to Ahlad Kumar's explanation.

Do you use tablet to make these notes?

9:29 in gradient update part, why would generator try to make good images.....as D(g(z)) will be close to zero imples nothing to learn for generator...I dint get ascent and descent at all from any video

Sorry please teach me, I don’t understand how the function V(G,D) corresponds to the loss of generator and discriminator…

mathematically convincing

AWW, I'm disappointed..... 22,322 views...... so close to 22,222 =]

"geee izz da virgin ass" lol

It's not clear why you replace p_z(z) by p_g(x) when showing global optimality. x and z are on a different space.

at 0.22, you pronounced content wrong. sound of "con" in content should be like "con" in con man

I believe the presenter is knowledgeable. However, some details are not well explained and not consistent, such as 11:46, he mentioned that this formula and there is no intuitive explanation, as this type of KZfaq presentation is for the general public who has no in-depth of understanding of either maths or deep-learning.

Apni ki Bengali?

One doesn't become cool by repeating the phrase - "What the hell is that?"

speak slowly my dude. Noone is chasing you.