In this chapter, you learned about two very important groups of techniques for improving the accuracy of your deep learning models: regularization techniques and hyperparameter-tuning techniques. You learned about how regularization helps address the overfitting problem, and had an introduction to different regularization methods. Among those methods, L1 and L2 norm regularization and dropout regularization were covered in detail, since they are very important, commonly used regularization techniques. You also learned about the importance of hyperparameter tuning for machine learning models and saw how performing hyperparameter tuning is highly challenging for deep learning models in particular. You learned how to perform hyperparameter tuning on Keras models more easily using scikit-learn optimizers.
In the next chapter, you will learn about the limitations of accuracy metrics when evaluating model performance. You will also learn about other metrics, such as precision, sensitivity...
