of data you corrupt it which means you remove some pieces you run it through a few layers of neuron that there's a latent variable which is implicit in those models which is like which of the outputs is picked as a function of the of the probability distribution on the output and then you compare this with the the actual data that you observed and you train the entire system to minimize the reconstruction error and in continued space the conceptually what that does is that if you imagine that your data manifold is this okay those