Having established the fundamental architectures of Recurrent Neural Networks (RNNs), including LSTMs and GRUs, and learned how to implement and train them, we now turn our attention to their practical application. Understanding the mechanics of these models is essential, but their value lies in solving specific sequence modeling problems.

This chapter covers common techniques for applying RNNs to various tasks. You will learn about:

• Sequence Prediction: Strategies for forecasting future values in a sequence, with a focus on time series analysis. We will examine model setups like many-to-one and many-to-many predictions.
• Sequence Classification: Methods for assigning a category label to an entire sequence, such as determining the sentiment of a sentence or classifying the topic of a document.
• Sequence Generation: Approaches for creating new sequences based on learned patterns, often applied in text generation tasks.
• Advanced Concepts: A brief introduction to the encoder-decoder architecture (sequence-to-sequence models) and attention mechanisms, which enhance the capabilities of sequence models for more complex tasks.

We will discuss the model architectures and output handling specific to each application type, preparing you to build models for diverse sequence-based challenges. Practical examples will illustrate these techniques using the frameworks introduced earlier.