What are the hidden layers in a neural network responsible for?
Identifying patterns
Explanation:
Hidden layers in a neural network are responsible for processing information and finding patterns within the data. They transform the input data into useful features that help the model make predictions.
Example:
In image recognition:
- Early hidden layers → Detect edges and shapes
- Deeper hidden layers → Recognize complex patterns like faces or objects
Why not the others?
- a) Uploading data → Done by the input layer.
- c) Tagging friends in pictures → This is an application of the trained model, not the role of hidden layers.
- d) Handling input and output → Input and output layers handle those tasks; hidden layers process information in between.
