Deep auto encoders applied on handwritten digits using Keras

Deep auto encoders are explained with same handwritten digits data to show the comparison of how this non-linear method differs to linear methods like PCA and SVD. Non-linear methods generally perform much better, but these methods are kind of black-box models and we cannot determine the explanation behind that. Keras software has been utilized to build the deep auto encoders here, as they work like Lego blocks, which makes it easy for users to play around with different architectures and parameters of the model for better understanding:

# Deep Auto Encoders 
>>> import matplotlib.pyplot as plt 
>>> from sklearn.preprocessing import StandardScaler 
>>> from sklearn.datasets import load_digits 
 
>>> digits = load_digits() 
>>> X = digits.data 
>>> y = digits.target 
 
>>> print (X.shape) 
>>> print (y.shape) 
>>> x_vars_stdscle = StandardScaler().fit_transform...