Zufallsinitialisierung
Zufallsinitialisierung refers to the process of assigning random values to the parameters of a neural network at the beginning of its training. This is a crucial step because if all weights were initialized to the same value, the network would not be able to learn effectively. During backpropagation, all neurons in a layer would receive the same gradient and update their weights identically, leading to a situation where all neurons in a layer would perform the same function.
The choice of initialization strategy can significantly impact the training process and the final performance of
The goal of Zufallsinitialisierung is to break symmetry and allow different neurons to learn different features.