Handling Imbalanced Datasets in Deep Learning

Not all data is perfect. In fact, you'll be extremely lucky if you ever get a perfectly balanced real-world dataset. Most of the time, your data will have some level of class imbalance, which is when each of your classes have a different number of examples. Before committing time to any potentially lengthy task in a Deep Learning project, it's important to understand why we should do it so that we can be sure it's a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.