Having constructed models like RNNs, LSTMs, and GRUs, the next logical step is to assess their effectiveness and refine them for optimal performance. Simply building a model isn't enough; understanding its strengths and weaknesses through rigorous evaluation and systematic tuning is essential for practical application.

This chapter provides the tools and techniques for this process. You will learn:

• How to select and interpret appropriate evaluation metrics for different sequence modeling tasks:
  • Classification: Accuracy, precision, recall, F1-score.
  • Prediction (e.g., time series): Mean Squared Error ($MSE$), Mean Absolute Error ($MAE$).
  • Generation (e.g., language modeling): Perplexity.
• Methods for visualizing model behavior to aid understanding and debugging.
• Strategies for hyperparameter tuning, including adjusting learning rates, network architecture (units, layers), and sequence lengths.
• Techniques to mitigate overfitting in recurrent networks, such as dropout and its recurrent-specific variants.
• Guidance on diagnosing and resolving common issues that arise during the training of sequence models.

By the end of this chapter, you'll be equipped to measure your sequence models' performance accurately and apply standard techniques to improve their results on specific tasks.