## Reduce MNIST dimensionality using autoencoders

Dimensionality reduction is useful in compressing the representation of your data. In my last post, how not to reduce dimensionality for clustering, I show a neural network that reduces the 784 dimensions of the MNIST dataset into 3, which are constrained by the k-means algorithm to capture the essence of the data. This week we’ll […]

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