What Does Xavier Initialization Mean?
Xavier initialization is an attempt to improve the initialization of neural network weighted inputs, in order to avoid some traditional problems in machine learning. Here, the weights of the network are selected for certain intermediate values that have a benefit in machine learning application.
Techopedia Explains Xavier Initialization
Some experts explain that Xavier initialization helps machine learning technologies to converge, because the neuron activation functions are in a decent range — in the words of some data scientists, not in “saturated” or “dead” regions: balanced in weighting in a way that facilitates better results.
