  A particle filtering approach is suggested for the training of multi-layer
neural networks without utilizing gradients calculation. The network weights
are considered to be the components of the estimated state-vector of a noise
driven linear system, whereas the neural network serves as the measurement
function in the estimation problem. A simple example is used to provide a
preliminary demonstration of the concept, which remains to be further studied
for training deep neural networks.
