Training an auto-encoder in R

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 Modelwhere 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 ...

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