2 An in-depth look into the soul of the Transformer Architecture
This chapter covers
- The advantage of the Transformer Architecture over Recurrent Neural Networks
- Dive deep into the key components of the Transformer architecture: Self-Attention Mechanism, Multi-Head Attention, and Positional Encoding
- A comprehensive understanding of the Encoder and Decoder models and their roles within the Transformer architecture
- Real-world use cases and examples showcasing the power and versatility of the Encoder-Decoder model over existing architectures.
In the last chapter, we embarked on a journey to explore the transformative impact of Large Language Models (LLMs) and the remarkable advancements they have brought forth. Now, it's time to peel back the layers and dive into the core architecture that sets LLMs apart from their predecessors. Allow me to introduce you to the Transformers Architecture. The key takeaways from this chapter revolve around a trio of essential concepts: the Self Attention Mechanism, Encoder models, and Decoder models. We will thoroughly examine each of these elements and the significant roles they play. While Self Attention Mechanism is only covered in this chapter, Encoder and Decoder models will be explored in greater depth throughout the rest of the book, as they form the cornerstones of building powerful LLM Applications such as semantic search, machine translation, summarization and many more.