This tutorial will focus on Deep Learning for image classification, adopting a pragmatic perspective and dealing with data scarcity, a scenario where training models from scratch leads to overfitting. We are going to tackle these problems by learning from practical examples. We will show in code examples, using Jupyter notebooks, how to deal with model selection with an example dataset. We will show how the theory of approximation-generalization works in practice, by producing and interpreting the learning curves for different models and estimating the amount of data necessary to obtain a given performance. Finally, we will introduce the transfer learning technique and show how it allows to obtain better performance with less data and limited resources.
André Panisson, ISI Foundation, Turin, Italy
Alan Perotti, ISI Foundation, Turin, Italy