Use a feedforward architecture to train and predict on the CIFAR-10 and Fashion-MNIST dataset. Here, you may need to use dropout to train better and reduce overfitting—find out how to implement this technique. We discussed dropout very briefly in class, so you may want to look up Dropout techniques online for more background.
Project the MNIST dataset into as small a manifold as possible. Meaning, you should come up with two transformations (we will call them encoder and decoder). The encoder should represent each (28\times 28) test image into a small vector (you can have this vector have less than 10 coordinates, but it is ok if your output is slightly larger too), but that doesn’t lose information—namely the decoder can reconstruct the original image (with negligible loss) from the small vector output from the encoder.