Introduction

Optimizing and tuning a deep learning architecture can be challenging. Sometimes, small changes in hyperparameters can have a big impact on training, resulting in worse performance. On the other hand, overfitting the training data is a well-known problem in machine learning. In this chapter, we'll demonstrate how to combat both. Also, we'll demonstrate the solutions and steps for hyperparameter tuning.