Summary

In this chapter, we discussed overfitting in image classification and explored the different techniques to overcome it. We started by examining what overfitting is and why it happens, and we discussed how we can apply different techniques such as early stopping, model simplification, L1 and L2 regularization, dropout, and data augmentation to mitigate against overfitting in image classification tasks. Furthermore, we applied each of these techniques in our weather dataset case study and saw, hands-on, the effects of these techniques on our case study. We also explored combining these techniques in a quest to build an optimal model. By now, you should have a good understanding of overfitting and how to mitigate it in your own image classification projects.

In the next chapter, we will dive into transfer learning, a powerful technique that allows you to leverage pre-trained models for your specific image classification tasks, saving time and resources while achieving impressive...