Three-Layer Neural Network: The Fruit Sorting Factory

Imagine you now have a fruit sorting factory with three layers of workers. Each layer specializes in part of the sorting process.

How the Layers Work

1. First Layer: Initial Sorting (Shape) The first layer of workers checks the shape of the fruit and sends it to the next layer based on this feature:
2. Second Layer: Detailed Inspection (Color) In the second layer, workers further inspect the fruits from the first layer:
3. Third Layer: Final Decision (Texture) The third layer makes the final call based on texture:

How It’s Different from Fewer Layers:

• More Layers = More Detail: Each layer refines the understanding of the fruit.
• The three-layer neural network can combine shape, color, and texture to handle complex decisions, like distinguishing an apple from a cherry or a banana from a cucumber.

In Neural Network Terms:

1. Input Layer (First Layer): Takes raw data (features like shape).
2. Hidden Layer 1 (Second Layer): Processes color based on input.
3. Hidden Layer 2 (Third Layer): Refines the decision with texture.
4. Output Layer: Gives the final result (Apple, Banana, Cherry, Cucumber).

Real-Life Analogy of Workers to Neural Network Layers:

Key Benefit of Three Layers:

A three-layer network can identify more nuanced patterns. For example:

• A two-layer network might confuse apples and cherries because they are both round and red.
• A three-layer network can use texture to differentiate them.