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Understanding Generative Adversarial Networks (GANs)

Learn about Generative Adversarial Networks (GANs), a type of deep learning model that consists of a generator and a discriminator. Explore key components such as convolutional layers, Leaky ReLU activation, batch normalization, and transposed convolutions. Discover how GANs can be used for tasks like image generation and how to implement GANs using TensorFlow. Dive into training techniques, loss functions, and optimization methods for GANs. Experiment with GANs on the MNIST dataset and explore conditional generation and image interpolation.

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Understanding Generative Adversarial Networks (GANs)

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