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The ipynb file contains the solution to the attached Problem Statement.
It contains the implementation of an AutoEncoder using the MNIST Dataset.
About AutoEncoder:
Autoencoders are a specific type of feedforward neural networks where the input is the same as the output. They compress the input into a lower-dimensional code and then reconstruct the output from this representation. The code is a compact “summary” or “compression” of the input, also called the latent-space representation.
You can read more about AutoEncoders here
About the MNIST Dataset:
The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
You can read more about the dataset as well as download the dataset from this link.