Glossary term
Glossary term
Foundations
A layer in a neural network between the input layer (the features) and the output layer (the prediction). Each hidden layer consists of one or more neurons. For example, the following neural network contains two hidden layers, the first with three neurons and the second with two neurons:
A deep neural network contains more than one hidden layer. For example, the preceding illustration is a deep neural network because the model contains two hidden layers.
See Neural networks: Nodes and hidden layers in Machine Learning Crash Course for more information.
Created for this library
A retail forecasting team experiments with hidden-layer widths from 64 to 512 to find the smallest network that meets accuracy targets.
An NLP team adds extra hidden layers to its classifier head to capture interactions between pooled token embeddings.
A medical imaging team uses several hidden layers in its diagnostic model to capture multi-scale features in chest X-rays.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License