What Propagation Meaning, Applications & Example
The process of passing information through a network.
What is Propagation?
Propagation in machine learning refers to the process of passing information through a network or model . In neural networks, this typically involves forward propagation, where input data is passed through layers of the network to generate an output. The term can also refer to backpropagation , which is the process of adjusting the weights of the network based on the error between the predicted and actual outputs, during the training phase.
Types of Propagation
- Forward Propagation: Involves moving the input data through each layer of the neural network to compute the final output.
- Backpropagation: After forward propagation, backpropagation computes the error at the output and propagates this error backward through the network to adjust weights using gradient descent .
Applications of Propagation
- Neural Network Training: Forward and backward propagation are fundamental to the training process of deep learning models, allowing them to learn from data and improve their accuracy.
- Signal Processing: Propagation can refer to the transmission of signals, such as sound or electromagnetic waves, through a medium or system.
- Optimization: In optimization algorithms, propagation is used to adjust parameters based on feedback from a loss function.
Example of Propagation
In neural network training, during forward propagation, an input image of a cat is passed through layers of neurons, and the model produces a classification output (e.g., “cat” with 90% confidence). If the actual label is “cat”, but the model incorrectly predicted “dog”, backpropagation is used to calculate the error and propagate it backward through the network to update the weights, making the model more accurate for future predictions.