Summary

In this chapter, we have learned how transfer learning helps to achieve high accuracy, even with a smaller number of data points. We have also learned about the popular pretrained models VGG and ResNet. Furthermore, we understood how to build models when we are trying to predict different scenarios, such as the location of keypoints on a face and combining loss values when 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 how to explain a model, tips and tricks for training a model to achieve high accuracy, and finally, the pitfalls that a practitioner needs to avoid when implementing a trained model.