I am reading the book, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and came across the following paragraph -
You can recognize the basic structure of all autoencoders, with an encoder followed by a decoder (in this example, they both have two hidden layers), but there is a twist: instead of directly producing a coding for a given input, the encoder produces a mean coding μ and a standard deviation σ. The actual coding is then sampled randomly from a Gaussian distribution with mean μ and standard deviation σ. After that the decoder decodes the sampled coding normally.
I do not understand how sampling is conducted over here and have the following question -
I understand it is trivial to sample from a univariate Gaussian distribution. However, for a dataset with n features, we won't be able to use a univariate Gaussian. How does that work?