Java Deep Learning Cookbook

Loading and transforming data

After the data extraction phase, we need to transform the data before loading it into a neural network. During data transformation, it is very important to ensure that any non-numeric fields in the dataset are transformed into numeric fields. The role of data transformation doesn't end there. We can also remove any noise in the data and adjust the values. In this recipe, we load the data into a dataset iterator and transform the data as required.

We extracted the time series data into record reader instances in the previous recipe. Now, let's create train/test iterators from them. We will also analyze the data and transform it if needed.