We have to build a pipeline that includes the following components:

• Data preprocessing
• Balancing data
• Anomalous data
• Data augmentation

These four concepts make up the cornerstone of this chapter. As we work through examples in each of these domains, you'll see that each contribution is important to ensuring your model is learning the right traits and qualities.

Let's start with a simple example of how we could apply all of these basic technologies. We'll focus on introducing the concepts, then the rest of this chapter will focus on the practical implementation of these concepts. Basically, this is going to be the template that we are going to fill in piece-by-piece:

These are the steps for producing a data processing pipeline:

1. Read in data as a NumPy array
2. Check the distribution of the data for anomalous indices
3. Balance the dataset for the learning step
4. Throw out the anomalous data
5. Augment our data in an structured and intelligent manner