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I mean stuff that doesn't use Python, Keras or TensorFlow. I have been looking for an in-depth explanation on how to implement a DCGAN from the ground up so I can have a complete understanding of the concept. Even some literature on GANs from the ground up would be useful.

All of the stuff I have read thus far leaves out a lot of the details, falling back on third party libraries, hiding the implementation.

Is my best bet to read papers? Trudge through references until it makes sense? Is there not a comprehensive text because of the novelty?

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Checkout the playlist of Ahlad Kumar on YouTube. He has a wonderful playlist for theory and implementation of Gans. Although the implementation is using tensorflow, it will satisfy your curiosity of understanding the concept without getting your hands dirty with coding from scratch without using deep learning framework.

Conditional GANs: https://youtu.be/7Tlk3Gql-Wg

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Recently, deeplearning.ai has released its GAN specialization on Coursera. It may help you.

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