A Friendly Introduction to Generative Adversarial Networks (GANs) 

As a beginner in ML a lot of this still went over my head but it's the most accessible video I've found yet on GANs! Thank you so much

You sir are a fantastic teacher. No fancy gimmicks, no catch phrases. Just pure talent. Hoping to collaborate with you!

This is one of the best explanations I have ever seen. You manage to cover the goal and the method intuitively, mathematically, and programmatically, and you did it with a concrete example that was simple enough to work out by hand. I also appreciate that you showed how we might code the rules for a solution, and then showed how are would program a machine learning approach to come up with a similar solution. I hope you continue to make more excellent videos like this!

My fellow data scientists were all about GANs, so I went to learn something about it so I know where I stand in regards of synthetic data. And I'm glad I stumbled upon your video. What a great introduction to the topic! I feel I understand a lot more of what has been said and done about GANs now. Thank you!

This channel’s been a gem of a find, always a go to source to refresh seemingly complex algorithms in an absurdly intuitive way. Thank you, Luis.

Thank you for the clear explanation. Just a couple of comments: a) In 6:35, I think it should be -1.5 (not -0.5) b) When creating the discriminator, if the bias is -1 then the threshold between good/bad images should be lower than 1. Otherwise some of the real faces would be labelled as false…

I love his teaching, he makes complex things seem simple.

Amazing summary of GANs with the simplest but concise explanations. Thank you!

Thank you Luis for the simplified and very clear explanation. Finally, I feel that I can confidently understand the how GANs work. I also really liked the idea of the simple toy examples that you usually start to explain the complicated concepts.

mad respect to you for explaning neural network so clear in 20 minutes, actually amazing

Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!

You really have a special talent to generalize and explain complex concept. You go through every questions that we could think about during your explanations and all of them are answered with such pedagogy.

No words would appreciate this rich explanation. I do like the visuals, mathematics and codes when they come together. Also, Your language was easy and smooth. You made the complex topic so easy to comprehend. Great thanks.

Love that you broke down of concepts to micro level. Made the understanding of GAN's so simple and yet detailed. Appreciate it.

very nice explanation... I started learning GAN from zero, only have basic understanding about CNN. and from this video, I now understand how GAN works. Thank you

The most informative and intuitive explanation of GANs, for a beginner this video is priceless as all other resources aren't so patient with the critical details and steps which doesn't help with the learning process

brilliance is the ability to take the complex and reduce it to simplicity. Brilliant work!

Mr Serrano thank you for existing.

What a great explanation! It's amazing how you teach complex concepts in such an easy way. I have learned a lot from your videos. Mil gracias!

Senor Luis Serrano: Ud. ha hecho una excelente demostración pedagógica sobre un tema que otros han convertido en mistificación. Trabajo en la interpretación de Edipo Rey, de Sófocles, y estoy tratando de traducir mis conocimientos sobre la comunicación hecha por un texto literario clásico que "pinta" los rasgos de sus personajes mediante relaciones opuestas, principalmente, análogas a las de los slanted portraits de los "retratos" usados por Ud. Muchos de esos "retratos" se acercan a las imágenes que Ud. llamaría "noisy" hasta el extremo de ser desechables por su policía ... y por mí. Quiero decir que, no solo en esto, sino en otros muchos aspectos, encuentro un acercamiento entre la herramienta matemática por Ud. expuesta y la que yo quiero usar ... pero no soy matemático. A pesar de ello, gracias a mi búsqueda serendípica, he logrado encontrarme con su Serrano.Academy, y seguiré orientándome por sus lecciones, con agradecimiento.

getting to see this after a heavy day at work is refreshing.. Thank you so much for sharing

Best video on GAN explanation hands down

One of the best explanations of the subject I have ever seen, congratulations, you are an excellent teacher!

By far, the best explanation of GANS ever! Well done, sir. Many, many thanks!!!!

Thanks for the slow transition and yeah i am not really good in maths and not yet much into more harder python. Its really useful than other videos out there.

THIS IS SOOOOOOOOOOO DAMN GOOD. thanks a lot man. totally understood gans without even a computer vision background

Love your explanations and visuals! Thanks again for all you do here Luis, you sir are a gentleman and a scholar🎓🍻

So well explained that makes it easy to understand GAN. Thank you. Big thumb up.

ooooh I knew your voice sounded familiar! I'm doing your pytorch course

plz sir, make more videos frequently your way of teaching is the blow of the mind it is osm....

Loved the Slanting people demo... Thanks Luis.

Awsome, you make neural networks easy and interesting. Thank you 🙏🏻

This is a wonderful video, very easy to understand. You have presented such a complex part of deep learning in such a way that it looks so easy! Thank you so much.

BRAVO! BRAVO! You are really the Grand Master Explaining Machine Learning! Let me tell you why? about 2 years ago, when I was looking to learn python I also was looking to learn the process of coding a machine learning python script. By that time, I remember searching watching a lot of videos of machine learning, but things were very fuzzy since most people the people just talk and talk and talk and repeat what others said, but no one explained the real roots of the process of it until I found your Videos explaining the real process in calculating the weights, and until that day my head was able to understand the REAL process of creating the code. And here I am 2 years later, breaking my head with how a GAN really works or how is made, and BANG! I just finished washing about 40 videos and NO ONE BUT YOU EXPLAINED SO WELL, that is why you are the MASTER in explaining Machine Learning! BRAVO! BRAVO! God Bless You, Luis!!!! What could be my life without you!!! Thank you a million times!.

Wow, I never understood the error functions until now! Thank you Luis!

Excellent video. Teaches the basics in a very clear manner. Thank you very much!

Thank you so much for this video! I haven't found any good video explaining GANs as you did!

Your explanations are so simple that anyone can understand !!! Thank so much

Dear Serrano. Thank you for your very interesting video. In the generator equation (image generator_math.png), I think there is a missing W_i. Note in your code in function "def derivatives(self, z, discriminator)", you have the line: "factor = -(1-y) * discriminator_weights * x *(1-x)". Parameter "discriminator_weights" represents W_i in the equation, although I believe it is missing in the generator equation. Please, let me know if I'm wrong. Thanks.

Interesting example of GAN. Really enjoy your video. Keep a good works

I didn't expect this video until the end of the video. This is really helpful! Thank you

Uau! Amazing! Thanks for the simplest explanation I've ever seen 🙂

This video is really helpful to understand about GAN. The way of teaching is really awesome.I liked it a lot.

Luis truly "KISS" (keep it sweetie simple) machine learning and re-ignites my love of scratching math!!! Whenever i find anything hard to crack i just search it in your channel...... Minor typos at Generator's Derivatives 00:19:00 where D weights are missing and the notations of G weights and D weights get mixed up, though everything is correct in your codes. Kind of cool to work on the simple math and detect those typos... Thanks again Luis for democratizing complex knowledge!! =)

Great explanation! Cheers to all slantland people!!

This is one of the best explanation i ever read/watched

Thank you very much for this awesome explanation, Luis. Very, very well done!

This is how the teachers explain, thanks a lot

Great Job Man. I understood the basics of GANs and now i can work with my StackGan project

the best in GAN tutorials

Simple and easy narration. Thank you sir

Amazing explanation.... Love from India.....

Amazing presentation with high quality slides!

Excellent video - great way to explain a quite complex concept! I learned about your video and github from MIT class "Designing and Building AI Products and Services." Hope that you are getting proper credits from MIT;-)

You provide by far some of the most descriptive explanations of Neural Network architecture, Machine Learning & statistics out there! Thank you! By the way, I think you forgot to subtract the bias from the result of the second, noisy image at 6:32. It should be -1.5 instead of -0.5 :)

best video for GANs thanks

Very informative for learning GAN's! Congratulations!

Best explanation of GAN in YTB

This is the best explanation on the internet. Thanks a lot!

OMG this is sooooooo friendly and easy to understand!!!!!! Thank you so much!!!!

This is a wonderful introduction to GANs. Many thanks for this - TRIPLE BAM!!!!!!!!! No wait, this is not a StatQuest video, my bad 😁😁😁😁

Absolutely stunning explanation!

So good & Easy to understand. Ty ! So generous w your knowledge

You are such a good teacher

Hi Luis! Great Videos! I'm very impressed and happy to see my old friend on RU-vid!

Thank you very much for this nice and very helpful explanation of GANs.

Best ML videos in the Net for beginners

Love your video. I have a question about what you say at 16:32: Shouldn't the discriminator network only be updated with weights from the real images? In other word: why do we use back propagation to update the weights after feeding it an image from the generator? isn't the generator making a fake image, and therefore, if you update the weights on a discriminator network, shouldn't the discriminator network then be learning how to detect a fake image?

I finally understand the lost key of GAN! Thank you a lot!

really nice illustrations!! Understand the gan now

Such a simple and great explanation. Thank you!

Thanks a lot sir for such an easy explanation...

WOW! best tutorial on machine learning ever! Thank you

You're the best man. u deserve a million subscribers

Its really wonderful explanation, amazing video for beginners, thank you:)

All support for your channel

Outstanding explanation!!!!

Great vid also for your generate_random_image() function in your code, all you have to do is this --> np.random.random(4)

simply amazing. Thank you so much for your efforts🙏

I find it a great vid. But a question: shouldn't the bias to be the same for all the weights of the same neuron? I would imagine that you have 1.7 for the diagonal values and 0.3 for the non-diagonal ones, since I would use a bias equal. Where am I wrong?

You rock Luis!! Inspired me to release my own video about RL & GANs soon :)

First of all, great video! And a short question, what do you use to animate your video? The transitions of the arrows and so on all look so smooth.

Wonderfully explained, thank you for making this video!

Dear Sir, When are you writing a book on Deep Learning, also it would be great if you can post more videos on speech to text from scratch, transformers, attention mechanism the latest state of art. Your videos and explanation are/is awesome. Thanks a ton once again.

Hello! This is a great video, thanks! But I have a question, why is the bias +/-1 ? And is it necessary to have a bias in the model?

We owe you a lot sir...!

Simply amazing explanation👍🙌

A bit confused at 19:01 How the weights v1, v2, v3, v4, c1,c2, c3 and c4 are adjusted by retropropagation? Their derivatives do not show up in the derivation.

Great video, very clear!

such a good explanation

Thank you a lot. I am always impressed by your videos.

I wrote code for this example in Octave/Matlab, but the training error of the generator diverged to ever increasing values. How do you avoid this problem?

Melhor explicação que encontrei!

I don't know why in Generator class function derivatives line: factor = -(1-y) * discriminator_weights * x *(1-x), why we must multiply by discriminator_weights, There is no mention of this in the formula above???

Wow, Amazing explanation. Learnt alot.

You are very good at explaining this thanks!

Very good explained video. thanks

This video is beautiful !! Liked and Subscribed !!!

Thank you Luis Serrano this video helps explain some of what may be happening with the Mandela effect. This is a very important video. Do you have any thoughts on the Mandela effect? Do you know why all the videos on you tube have been hijacked. Why we can't find residue of non deep fake living manuscript? Why GAN's is becoming the majority basic reality of many of our memories of the past. What did you tube do with all the real videos? This seems to only explain some things but it is important none the less.

amazing tutorial! I was looking into the maths and was wondering why you multiplied the discriminator weights into the generator derivative function. Is it because the generator output needs to pass through the discriminator to get its prediction?