Transformers: The Heart of Large Language Models
- Generative AI
This lesson continues the Large Language Models course by explaining the foundation concepts behind LLM architecture, starting with what foundation models are and why they matter. It covers neural networks and deep learning, including forward propagation and backpropagation, as the mechanics that let models learn from text. The lesson then introduces the Transformer architecture, the design that replaced older sequential models, and walks through its core terms: attention, encoder, decoder, self-attention, and positional encoding. It sets up the next lessons on pre-training and fine tuning.
Key takeaways
- What a foundation model is: a large, general purpose model pre-trained on vast text data to learn language patterns
- How neural networks learn through forward propagation and backpropagation, adjusting weights to reduce prediction error
- Why the Transformer architecture replaced older sequential models by using self-attention to capture relationships between distant words
- The core Transformer building blocks: encoder, decoder, multi-head attention, positional encoding, and masking
- How positional encoding gives the model a sense of word order since Transformers do not process text sequentially
Start owning your AI