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The Math Behind Deep Learning
The easiest way to think about backtracking is to propagate the error back (see
Figure 10), using an appropriate optimizer algorithm such as a gradient descent to
adjust the neural network weights with the goal of reducing the error (again for
the sake of simplicity only a few error values are represented here):
Figure 10: Backward step in backpropagation
The process of forward propagation from input to output and backward propagation
of errors is repeated several times until the error goes below a predefined threshold.
The whole process is represented in Figure 11. A set of features is selected as input
to a machine learning model, which produces predictions. The predictions are
compared with the (true) label and the resulting loss function is minimized by the
optimizer, which updates the weights of the model:
Figure 11: Forward propagation and backward propagation
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