Multilayer Perceptrons: Adding Depth to Learning
1980s: The Revival
In the 1980s, researchers found a way to overcome the limitations of the Perceptron by adding more layers, leading to the creation of Multilayer Perceptrons (MLPs). These networks could handle more complex problems by learning features at different levels.
How They Work:
- Input Layer: Takes in the raw data.
- Hidden Layers: Each layer learns different features. Imagine recognizing a face: one layer might learn to detect eyes, another the nose, and so on.
- Output Layer: Gives the final prediction, like recognizing a person’s face.
Backpropagation: Learning from Mistakes
What is Backpropagation?
Backpropagation is a learning process where the network adjusts its weights based on the error of its predictions, much like how you learn from your mistakes. This allowed networks to learn more accurately and handle complex tasks.