Lesson 10 of 38 · Written learning
Neural networks and representations
Follow how learned layers transform numbers into useful internal representations.
A layer combines inputs with weights and biases, then often applies a nonlinear activation. Stacking layers lets a network represent more complex relationships than a single linear transformation. During the forward pass, each layer transforms the current values until the model produces its output.
Parameters and embeddings
Parameters are learned values such as weights and biases. An embedding is a learned vector associated with an item such as a token. In language models, embeddings give tokens a numerical starting representation; later layers contextualize them based on neighboring tokens. The vector itself is not a human-readable definition.
- A linear layer changes representation using matrix multiplication and a bias.
- A nonlinear activation helps stacked layers represent more than one large linear map.
- More parameters increase capacity and memory use; they do not automatically improve data quality or reliability.
- Inspect tensor shapes after each layer to catch mismatched dimensions early.
Return to the Machine learning and deep learning stage · Suggested timing is flexible; move at your own pace.