Generative Adversarial Networks (GANs) implementation using Tensorflow

Generative Adversarial Networks (GANs) are a model framework in which two models are trained concurrently, one learns to generate data from the same distribution as the training set and the other learns to distinguish true data from generated data. In this video, you will learn how to implement a basic GANs model using TensorFlow on the MNIST dataset.

Code Link -
https://github.com/ConvolutedAi/Basic-GANs-using-Tensorflow-on-MNIST-dataset.git

Reference Links -
https://blog.paperspace.com/implementing-gans-in-tensorflow/
https://towardsdatascience.com/build-a-super-simple-gan-in-pytorch-54ba349920e4
https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-an-mnist-handwritten-digits-from-scratch-in-keras/

Music Link -
https://www.youtube.com/watch?v=ZVZqVySjzGY&ab_channel=Amazing-GrandCross

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