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GANs are a model architecture for training a generative model
first introduced in 2014
GAN uses two sub-models
Generator and Discriminator
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
holy shit the math
and this is just a beginning and the simplest form of math
a comprehensive guide to GAN
real simple blog
a really advanced approach to GAN
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