Hands-On Java Deep Learning for Computer Vision

Learning neural network weights

To understand this section, let us assume that the person in question will eventually and indefinitely be affected by a heart disease, which directly implies that the output of our sigmoid function is 0.

We begin by assigning some random non-zero values to the weights in the equation, as shown in the following diagram:

We do this because we do not really know what the initial value of the weights should be.

We now do what we have learned in the previous section: we move in the forward direction of our network, which is from the input layer to the output layer. We multiply the features with the weights and ...