Deep Learning 28: (2) Generative Adversarial Network (GAN) : Loss Derivation from Scratch

[@MLDawn]

Please indulge me! The two people who have disliked this video, what were they expecting to see that they could not find here! I genuinely cannot understand what they disliked about this lecture! This is just Gold!

[@abdelghanikadouri1626]

I can't believe that I just find out about this wonderful tutorial I never thought I'd understand the gan like this easy thank you so much

[@MLDawn]

You are killing me here! This is too amazing of a lecture to be true! Well done a million times

[@AhladKumar]

Thanks for the feedback...watch out for more

[@muhammadyaqoob9129]

I have seen your complete playlist of Brain MRI tumor detection. Those were also too good. Love you from depth of the heart.

[@NR_Tutorials]

Sir jaan lena hai jaan le lo is lecture ke liye ... kya padhya hau aap ne great sir salute

[@shahriarshayesteh8602]

Fantastic explanation! we need such detailed and in-depth tutorials. I hope you can go over transformers and BERT in such detail. Thanks!

[@ziauldba]

Seriously my feeling is like im in a classroom . Thanks professor. god bless u .

[@syeddanish7055]

Very nicely explained... was able to learn all in one go. Thanks a lot, sir!!!

[@hamzaameer2213]

Thanks Sir , i wasted 4-Days in reading research papers . You have done it in just 4 Lectures. You are pure Gold.

[@iliasaarab7922]

This channel deserves a million+ subs! Amazing explanation!

[@learner3539]

Thanks for your lecture. Your lecture makes all complicated topic much easier . You are doing great work.

[@soumambanerjee1816]

Ian Goodfellow will be shocked to see such a great lecture. Thank you for choosing to be a professor. ❤️

[@jyotideshwal337]

There are not enough words to thank you for all that you do! I am lucky enough to call you my teacher

[@surayuthpintawong8332]

Thanks for sparing your time for teaching us

[@DoctorPataMedicast]

Hmmm this is one of the best I have seen.You are a born teacher.

[@gioxc88]

Words are not enough to thank you for this!!!

[@mozhganrahmatinia1656]

your description is perfect. thank you.

[@nikhilkumawat1797]

Very well explained, Amazing.

[@zeinabawad9317]

thanks a lot the best explanation ever for GANs thanks again

[@youtubecommenter5122]

Bloody good explaination!

[@Afshaanjum-ge7dt]

Bravo.... your efforts are appreciated.

[@shaurovdas5842]

Isn't there supposed to be a negative sign in the binary cross entropy formula?

[@BJ-gj2mv]

this is the best lectures i seen on deep learning . Great work . Thank you . Keep it up.

[@madhuvarun2790]

Your videos are a gold mine. Thank you so much sir.

[@zainabkhan5859]

This is such a classy explanation Thank you so much

[@shineshine9599]

your videos literally saved me while working on Gan. can't thank you enough.

[@submagr]

Thank you, Sir. I got a much better understanding of the loss function now!

[@Vishal-mf2db]

Thank you so much for this video. Helped me a lot.

[@girishmishra]

Awesome pick....no other lecture is needed. Thanks a lot

[@gauravsahani2499]

Really interesting video Sir!, Thank you for this Playlist!

[@ZERU326]

at 31:18, when we are training the generator fake data label is 1, thus we get directly L = -log(D(G(z))) as loss function for generator.

[@go64bit]

This explanation is pure art. Brilliant!

[@AhladKumar]

thanks

[@minhajansari8272]

Wow! Thank you very much. I slept through all my lectures and tomorrow is my exam (which is going to be tough)

[@anubhavgupta4917]

Sir, I am from India. Your lecture is diamond i.e. kohinoor hera. It deserves millions of likes and i am shocked why only 445 ??

[@zeppelinpage861]

because justin bieber is more important to us than GANs

[@RaviKumar-yu8xf]

Simply Awesome!. Thank you very much sir!

[@aeigreen]

great explanation

[@danielenrique7184]

Thank you so much for the exceptional explanation! :)

[@armankamal3694]

Very very very good explanation. Thanks for the lecture it helped me a lot

[@digvijaymahamuni7722]

Very Impressive Lecture!

[@tarmiziizzuddin337]

Your videos are gems sir, thank you for the effort!

[@rajeshraman1980]

Awesome .the best on GAN.Thanks a lot

[@adminai9450]

awesome explanation sir thank you

[@SW-ud1wt]

Dear Sir, very good elaboration. I need to ask question, at 4:44 time, you said weights need to be adjusted after calculating error and we take another samples from distribution Z, why should we not work with previous random points from z? Why we take new points? Because we only want only weights to be updated. Please guide . Thanks

[@karimmache4018]

Thank you for this wonderful tutorial, It's any tutorial about autoencoder and variational autoencoder?

[@praslisa]

I am watching all the ads on these videos...this person deserves $$$$...come up with an udemy course...you will do great :)

[@haztec.]

I don't think that gets them any more money though.

[@soumyasarkar4100]

awesome lectures....these lectures are so good that I want them to keep going for hours .... I was wondering if the minimisation objective is ill defined because it can go arbitary low towards negative infinity

[@arjunmajumdar6874]

The binary cross-entropy loss is: -y.log(yhat) -(1-y).(1-yhat). In your derivation, this isn’t written explicitly. Is it because you have modified the BCE loss ?

[@BiranchiNarayanNayak]

Thanks. Very well explained.

[@ankurbhatia24]

the cross entropy loss function used here does not have a -ve sign. −(ylog(p)+(1−y)log(1−p)) This is the cross-entropy loss function. So every time we maximized or minimized the thing would be reversed. Right? Please correct me if I am wrong.

[@prashant_canada]

i actually started following you when i saw your first Deep Learning Video series where you were discussing about kaggle google colab, setting up an environment or getting ready to dive in data science. I found it very informative. but, as Tensflow 1.0 is completely out and we have tensorflow 2.0 instead. so many syntax/concepts does play a role like session, placeholder, summary and many more. so, how can fulfill this gap as i had to discontiue to follow rest of the series.. is there any other way or you have another video series for Tensorflow2.0. please update sir

[@unchaineddreameralpa]

Excellent tutorial

[@raghavsharma2658]

thank you again, you made this tough subject very easy...

[@bosepukur]

if you minimize G(z) doesn't it make the cost function ill defined because it can go to arbitary low value ?

[@홍성-w4g]

What software are you using for this teaching?

[@douglasamoo-sargon5049]

Awesome explanation.

[@microcosmos9654]

Thank you so much for the lectures, they help me a lot!

[@PavanKumar-yk5mq]

excellent

[@aakanksha7877]

thanks alot for this.

[@StrawHatLuffy750]

Thank you so much :) pls keep explaining things :)

[@madhavpr]

Hi Ahlad, Awesome tutorial. I am wondering if we can interchange max and min in the expression for the loss function of a GAN. More precisely, if L is the loss function, does min max L = max min L where the minimizer and the maximizer are with respect to G and D respectively ?

[@zahrakhalid4731]

Please make a video on stylegan.

[@sharmaannapurna]

Hi, very nice explanation. Thank u for sharing this. I would like u to check the first slide and the definition of generator. I think the last word is a typo.

[@ammarkhan2611]

At 0:57, isn't the ides of the generator to fool the discriminator ?

[@hamidrezanabati]

Thanks for the great explanation!

[@sangeethbalakrishnan9177]

Nice lectures. But equation for binary cross entropy has negative sign , i think

[@ambujmittal6824]

Yes, it should be negative and the loss is always to be minimized. (not maximized) The log graphs will also be drawn reflected. The instructor has taught the concept completely wrong there. (My reason for disliking the video since this concept is very trivial in ML)

[@nisaratutube]

Thanks Sir, for this great stuff. One request, if you can put some refrence links alongwith the description of the videos, would be very helpful.

[@danielmathew5008]

These videos are probably the best explanation of these machine learning concepts, but I have one problem.I don't want to complain about ads since I know you work hard to put these on and have to earn, but please try to put all the ads at the end and not 2 or 3 in the middle of the video since it disrupts the thought flow. Just a suggestion. Thanks for your work.

[@prelimsiscoming]

Can we have the lecture slides ??

[@ankurgupta2806]

Sir at 0:50 I think it should be "to fool the discriminator".

[@ogsconnect1312]

Thanks, Excellent!

[@vasukapoor6423]

Sir in first slide it should be that it can fool the discriminator in the place of fool the generator.

[@priyansukushwaha5195]

Hey everyone, please note that sir made a mistake at 28:00, mistake that he clarified in next video . The expectation terms will also include minG and maxD on RHS of equation

[@nirajpudasaini4450]

legend

[@venkatbabu186]

A feeder loop string instruments are the modern hardware artificial intelligence for both machine learning and clustered deep learning reduction. Modulators demodulators. Weighed proportional clustering of deep learning. Advisory is error rectification and retuning. The higher the sensory perception the more new kind and a bit slower. Sometimes much faster because of new methods. That's why AI is able to remember almost the entire world and extras. Even gone with the wind. Color sensory says it is red. Smell sensory says it is attractive. Shape sensory says it is conical. So red rose.

[@arabnaouel289]

thank you very much for this amazing serie, can you please activate the subtitle in another language please ??

[@pasinduekanayaka8023]

Hi, first of all, thanks for the explanation but I have some questions regarding the training cycle which you explained. so in the explanation, it seems like the discriminator is already trained to a maximum level before the generator starts to train. isn't that a problem? because during the research related to GANs many people have said that if the discriminator is stronger than the generator the model is not gonna perform well. It's somewhat logical as well because the main objective of GANs is to perform a min-max game between the generator and the discriminator so if the discriminator is stronger than the generator, it's not a fair game at all. can you please give an explanation about this? Thank you.

[@dkmoni17]

Wow. Just One Question - At Slid 08:38, looks like you are Training GENERATOR to correct its Errors during Training. In PART 1, you said to train a GENERATOR Model, we should keep Label = 1 to Fool the DISCRIMINATOR. How come you have shown y = 0 in this Video for Training of GENERATOR. If DISCRIMINATOR is already Trained on Fake and Real Samples, then to Fool it the Generator should put Label = 1 else it will not be Fooled. Please pass your comments. Can someone reply on this please.

[@MustafaCoban-hm2ov]

Around 2:40, you say that "z is sampled from a random distribution and we cannot say it is gaussian or some other distribution". I don't think this is correct. First of all, what is random distribution? In GAN implementations, researchers determine a prior distribution such as Normal distribution and then they sample z vector from this normal distribution. This way, generator learns the mapping from normal distribution to the training data distribution. Therefore, what is random is the z vector, not the distribution itself.

[@shineshine9599]

i agree

[@amitkumarshrivastava7437]

What is z , is it some kind of image or anything else - I understand its something from distribution but is it images ? Also, what kind of noise we are basically adding -It may be some kind of vector but is it images ? I understand that discriminator will have real images like shoe, cloth etc . in the same context I am asking about z

[@abhishekprasad7030]

I wanted to know that how many vedios are left in this series to complete DL

[@AhladKumar]

four more left

[@vikramnimma]

At 0:50, u said the objective of generator is to create data so that it can fool the generator, but in ur previous lecture u said objective is to fool the descriminator, correct me if I am wrong.

[@priyabratdash8964]

This might have been a mistake, the discriminator needs to be fooled

[@adnanhashem98]

I think that in 0:48 you meant to write and say that the role of the generator is to "...can fool the *discriminator* " instead of the generator.

[@sgrimm7346]

I believe he meant to say the Generator's role is to create data so that it can fool the " Discriminator". Just pointing it out. Good video, however.

[@makting009]

why fake dataset become circular?

[@alibahari4217]

totally Unique but there are a lot of disturbing Ads

[@AhladKumar]

you can take youtube premium to avoid them

[@rachittsharmaaa]

@@AhladKumar 😂😂😂

[@miranbaban9554]

Dear Ahlad, It should be Discriminator in your first slide, it fools discriminator not generator

[@NitishRaj]

1:06 Fool the discriminator. Kindly correct it

[@ttreza5922]

Anyone from 2024 watching this?

[@dddd-rf1xy]

Please open translate

[@aadityasingh4911]

very confusing....u say one thing and then contradict it 5 seconds later

[@VR-fh4im]

Generator fools the Discriminator.

[@NandakishanRajagiri]

very interesting but boring...

[@MrSushantsingh]

Even Ian Goodfellow will not be able to explain that well, anyways I heard he's pretty arrogant.

[@NR_Tutorials]

i saw the lecture iangoodfellow also.. not better than this