Applied Deep Learning with Keras

Chapter 5

Improving Model Accuracy

Learning Objectives

By the end of this chapter, you will be able to:

• Explain the concept of regularization
• Explain the procedures of different regularization techniques
• Apply L1 and L2 regularization to improve accuracy
• Apply dropout regularization to improve accuracy
• Describe grid search and random search hyperparameter optimizers in scikit-learn
• Use hyperparameter tuning in scikit-learn to improve model accuracy

In this chapter, we will learn about the concept of regularization and different regularization techniques. We will then use regularization to improve accuracy. We will also learn how to use hyperparameter tuning to improve model accuracy.

Introduction

Deep learning is not only about building ...