Throughout the book we have covered the theoretical aspects of models and introduced the reader to a number of frameworks for Deep Learning. In this chapter we will cover the process of training deep learning models.
Performance Metrics
The model development process typically starts by formulating a crisp problem definition. This basically involves defining the input and the output of the model and the impact (usefulness) such a model can deliver. An example of such a problem definition is the classification of product images into product categories, the input to ...
