GitHub / Shakib-IO / Variational_Autoencoder_MNIST_Dataset

Variational autoencoder (VAE), one of the approaches to unsupervised learning of complicated distributions. A VAE could do compressing data, reconstructing noisy or corrupted data, interpolating between real data, and are capable of sourcing new concepts and connections from copious amounts of unlabelled data. VAEs are built on top of neural networks.

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Topics: autoencoder, ipython-notebook, nuralnetwork, variational-autoencoder

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