Data preparation

Machine learning is about training a model to generalize on the cases it sees so that it can make predictions on unseen data. Therefore, the data used to train the deep learning model should be similar to the data that the model sees in production. However, at an early product stage, you may have little or no data to train a model, so what can you do? For example, a mobile app could include a machine learning model that predicts the subject of image taken by the mobile camera. When the app is being written, there may not be enough data to train the model using a deep learning network. One approach would be to augment the dataset with images from other sources to train the deep learning network. However, you need to know how to manage this and how to deal with the uncertainty it introduces. Another approach is transfer learning, which we will cover in Chapter 11...