Handling Overfitting
One major challenge in machine learning (ML) is overfitting. Overfitting occurs when a model is trained too well on the training data but fails to generalize on unseen data, resulting in poor performance. In Chapter 6, Improving the Model we witnessed firsthand how overtraining pushed our model into this overfitting trap. In this chapter, we will probe further into the nuances of overfitting, striving to unpack both its warning signs and the underlying reasons behind it. Also, we will explore the different strategies we can apply to mitigate the dangers overfitting presents to real-world ML applications. Using TensorFlow, we will apply these ideas in a hands-on fashion to overcome overfitting in a real-world case study. By the end of this chapter, you should have a solid understanding of what overfitting is and how to mitigate it in real-world image classification tasks.
In this chapter, we will cover the following topics:
- Overfitting in ML
- Early...