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Chapter 1
Figure 39: Backward step in backpropagation
The process of forward propagation from input to output and the backward
propagation of errors is repeated several times until the error gets below a
predefined threshold. The whole process is represented in Figure 40:
Figure 40: Forward propagation and backward propagation
The features represent the input, and the labels are used here to drive the learning
process. The model is updated in such a way that the loss function is progressively
minimized. In a neural network, what really matters is not the output of a single
neuron but the collective weights adjusted in each layer. Therefore, the network
progressively adjusts its internal weights in such a way that the prediction increases
the number of correctly forecasted labels. Of course, using the right set of features
and having quality labeled data is fundamental in order to minimize the bias during
the learning process.
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