In this chapter, we have learned about how transfer learning helps to achieve high accuracy, even with a smaller number of data points. We have also learned about the popular pre-trained models, VGG and ResNet. Furthermore, we understood how to build models when we are trying to predict different scenarios, such as the location of key points on a face and combining loss values while training a model to predict for both age and gender together, where age is of a certain data type and gender is of a different data type.
With this foundation of image classification through transfer learning, in the next chapter, we will learn about some of the practical aspects of training an image classification model. We will learn about how to explain a model and also learn the tricks of how to train a model to achieve high accuracy and finally, learn the pitfalls that a practitioner needs to avoid while implementing a trained model.
