Given different problems, the minimum requirements to successfully apply deep learning vary. Unlike benchmark datasets, such as MNIST or CIFAR-10, real-world data is messy and evolving. However, data is the foundation of every machine learning-based application. With higher quality data or features, even fairly simple models may provide better and faster results. For deep learning, similar rules apply. In this section, we will introduce some common good practices that you can do to prepare your data.
Massaging your data
Data cleaning
Before jumping into training, it’s necessary to do some data cleaning, such as removing any corrupted samples. For example, we can remove short texts, highly distorted images, spurious...
