This module explores the Transformer architecture, including attention mechanisms, encoder-decoder components, and positional encoding, with practical exercises to reinforce understanding.

Developing Large Language Models

In this module, we’ll explore the architecture and components of Transformer models, starting with an introduction to their foundational concepts. We’ll dive into the attention mechanism, focusing on self-attention, multi-head attention, and their role within the encoder and decoder. Additionally, we’ll examine positional encoding, feedforward networks, and the integration of key components in both the encoder and decoder. To solidify our understanding, we’ll engage with exercises on the Transformer and its decoder architecture, concluding with a quiz to reinforce key concepts.

Transformers

Learn about the other encoder’s components, including the feedforward component and the add and norm component. 

Basics of Transformers

A Deep Dive into Attention Mechanism

Understanding Encoder and Decoder in Transformers

Exercises

Conclusion

Encoder: Feedforward and Add and Norm Component

Feedforward network