AI engineering glossary · Deep learning core
What is input layer?
Input layer: The first layer of a network, which simply receives the raw feature values. It performs no learning of its own; it defines the shape of the data the model accepts, such as one slot per pixel or per tabular feature.
Input layer: an example
For a house-price model with 12 features (area, bedrooms, locality score and so on), the input layer has 12 slots.
Related terms
More Deep learning core terms
- Instance normalisation
- Iteration
- Label smoothing
- LAMB optimiser
- Layer
- Layer normalisation
- Leaky ReLU
- Learning rate finder
Where you learn it
Input layer is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.