In this section, we are going to train an auto-encoder in R and show you that it can be used as a dimensionality reduction technique. We will compare it with the approach we took in Chapter 2, Training a Prediction Model, where we used PCA to find the principal components in the image data. In that example, we used PCA and found that 23 factors was sufficient to explain 50% of the variance in the data. We built a neural network model using just these 23 factors to classify a dataset with either 5 or 6. We got 97.86% accuracy in that example.
We are going to follow a similar process in this example, and we will use the MINST dataset again. The following code from Chapter8/encoder.R loads the data. We will use half the data for training an auto-encoder and the other half will be used to build a classification model to evaluate how good the auto-encoder...
