Generator is responsible to generate from the given data and discriminator has to say if it is fake or not
the practice and learn continues till the discriminator is unable to tell if generated data is fake or real
a really fascinating video about GAN with an interesting example of building a GAN from scratch
the code of the example in this video: https://github.com/luisguiserrano/gans/blob/master/GANs_in_Slanted_Land.ipynb?short_path=28407f0
GitHub
gans/GANs_in_Slanted_Land.ipynb at master · luisguiserrano/gans
GANs in slanted land. Contribute to luisguiserrano/gans development by creating an account on GitHub.
AI Scope
https://www.youtube.com/watch?v=eyxmSmjmNS0&t=1170s
watch this to feel smart
Forwarded from Machine Learning
Mathematics_for_Machine_Learning .pdf
1.4 MB
MATHEMATICS FOR MACHINE LEARNING
A Comprehensive Guide to Building Mathematical Foundations for AI and Data Science
@machine_learning_and_DL
A Comprehensive Guide to Building Mathematical Foundations for AI and Data Science
@machine_learning_and_DL