Advancing Language Understanding and Generation with the Transformer Models
In the previous chapter, we focused on RNNs and used them to deal with sequence learning tasks. However, RNNs may easily suffer from the vanishing gradient problem. In this chapter, we will explore the Transformer neural network architecture, which is designed for sequence-to-sequence tasks and is particularly well suited for Natural Language Processing (NLP). The key innovation is the self-attention mechanism, allowing the model to weigh different parts of the input sequence differently, and enabling it to capture long-range dependencies more effectively than RNNs.
We will learn two cutting-edge models utilizing the Transformer architecture and delve into their practical applications, such as sentiment analysis and text generation. Expect enhanced performance on tasks previously covered in the preceding chapter.
We will cover the following topics in this chapter:
- Understanding self-attention...
