A Friendly Introduction to Generative Adversarial Networks (GANs)

What is the simplest pair of GANs one can build? In this video (with code included) we build a pair of ONE-layer GANs which will generate some simple 2x2 images (faces).

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[dyoolyoos]

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

[DodaGarcia]

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

[DienTran-zhkj]

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

[reverse_engineered]

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!

[carolinagijon]

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…

[YWCKOK]

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

[denismoura]

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!

[tianqilong]

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

[glowish]

Best video on GAN explanation hands down

[blesucation]

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

[acidtears]

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 :)

Very nice and clear explaination. Everytime I'd need a recap on GANs, I come to this video. Having no code but simple math makes it more meaningful -- comparing the other channel's which includes ML libraries, that places as obstacles on our way to understand!

[meghanaiitb]

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.

[sairamakurti]

If you know the basics of CNN or ML and you are looking to learn basics of GAN this video is for you....very well explained thankyou

[amirnasser]

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.

[jamespaladin]

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

[KuliahInformatika]

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

[localexpert]

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.

[gunamrit]

getting to see this after a heavy day at work is refreshing..

Thank you so much for sharing

[kanabana]

Finally Gan math explained is the most elegant way..Thank you Sir