What is Forward Propagation?
Forward propagation is the process of passing input data through the neural network to generate predictions. Signals flow from input to output, layer by layer.
The Journey of Data:
Input → Weighted Sum → Activation → Next Layer → ... → Output
Key Operations:
- Matrix multiplication (weights × inputs)
- Addition of bias terms
- Application of activation functions
- Sequential layer processing
The Forward Pass Steps
1
Input Reception
Receive and normalize input features
2
Weighted Sum
Calculate z = Wx + b for each neuron
3
Activation Function
Apply non-linear transformation a = f(z)
4
Layer Output
Pass activations to next layer or final output