  We describe an alternative learning method for neural networks, which we call
Blind Descent. By design, Blind Descent does not face problems like exploding
or vanishing gradients. In Blind Descent, gradients are not used to guide the
learning process. In this paper, we present Blind Descent as a more fundamental
learning process compared to gradient descent. We also show that gradient
descent can be seen as a specific case of the Blind Descent algorithm. We also
train two neural network architectures, a multilayer perceptron and a
convolutional neural network, using the most general Blind Descent algorithm to
demonstrate a proof of concept.
