Practical Deep Learning by Ron Kneusel

Author:Ron Kneusel [Ron Kneusel]
Language: eng
Format: epub
Publisher: No Starch Press
Published: 2021-03-15T16:00:00+00:00

Note we’ve introduced a new notation for the parameters:

This represents the weight that links the j-th input, an output of the i – 1 layer, to the k-th node of layer i. We also have

to represent the bias value for the k-th node of the i-th layer. Here layer 0 is the input layer itself. The parentheses on the exponent are a label, the layer number; they should not be interpreted as actual exponents. Therefore,

is the weight from the third output of the first layer to the first node of the second layer. This is the highlighted weight in Figure 9-4. Remember that we always number nodes top to bottom, starting with 0.

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