Lossy, noisy, or missing data are a common phenomena in many areas of statistics ranging from sampling to statistical learning. Instead of just ignoring these missing values, it can be useful to somehow attempt to recover or impute them. Meanwhile, deep learning is increasingly shown to be adept at learning latent representations or distributions of data. These patterns or representations can often be too complex to be recognized manually or through classical statistical techniques. We will discuss practical deep learning approaches to the problem of lossy data restoration or imputation with examples of several different types of datasets. We will compare the results to classical techniques to see if deep learning can really be used to perform higher quality imputation.