EP 31 | CME295: Transformer Architecture
from AI Bites: The Academic Series Hosted by Jack Lakkapragada
Now that we know why Transformers work, it’s time to look under the hood. In this episode, we strip the architecture down to its studs, exploring the specific structural components that allow these models to understand complex language. Key Topics: Encoders and Decoders: The difference between "reading" the input data and "writing" the output prediction. Self-Attention & Multi-Head Attention: How the model looks at the same sentence through multiple different "lenses" simultaneously to grasp nuance and grammar. Positional Encoding: Since Transformers process everything at once, how they use math to remember the actual order of the words. Note: This is an AI-generated study resource created via NotebookLM based on the CME295 curriculum and personal study notes.
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