Previous chapters introduced individual regularization methods (like L1/L2, Dropout), normalization techniques (Batch Normalization), and optimization algorithms (SGD, Adam, etc.). Building on this foundation, this chapter focuses on integrating these methods effectively within a practical deep learning workflow.

You will learn about:

• The interactions between different regularization and optimization techniques.
• Structuring a typical training process incorporating these methods.
• Monitoring training progress using loss curves and metrics.
• Applying early stopping as an additional regularization strategy.
• Specific considerations for combining Dropout and Batch Normalization.
• How data augmentation contributes to model generalization.
• Guidelines for selecting appropriate combinations of techniques.
• Troubleshooting common training issues related to optimization and regularization.

The chapter concludes with a hands-on exercise where you'll build and tune a model using several of these combined strategies.