The TensorFlow Workshop

6. Regularization and Hyperparameter Tuning

Overview

In this chapter, you will be introduced to hyperparameter tuning. You will get hands-on experience in using TensorFlow to perform regularization on deep learning models to reduce overfitting. You will explore concepts such as L1, L2, and dropout regularization. Finally, you will look at the Keras Tuner package for performing automatic hyperparameter tuning.

By the end of the chapter, you will be able to apply regularization and tune hyperparameters in order to reduce the risk of overfitting your model and improve its performance.

Introduction

In the previous chapter, you learned how classification models can solve problems when the response variable is discrete. You also saw different ...