April 2017
Intermediate to advanced
320 pages
7h 46m
English
Content preview from Deep Learning with TensorFlow
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Weights and biases
Besides considering the state of the neuron and the way it is linked to others, we should consider the synaptic weight, which is precisely, the influence of that connection within the network. Each weight has a numerical value indicated by Wij, which is the synaptic weight connecting the neuron i to neuron j.
Depending on the point where a neuron is located, it will always have one or more links, which correspond to relative synaptic weights.
The weights and output function determine the behavior of an individual neuron and the network in general.
They should be correctly changed during the training phase, to ensure the correct behavior of the model.
For each unit, i is defined an input vector xi= (x1, x2,...,xn) and a ...
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