April 2017
Intermediate to advanced
320 pages
7h 46m
English
Content preview from Deep Learning with TensorFlow
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Introducing feed-forward neural networks
A feed-forward neural network (ffnn) consists of a large number of neurons, organized in layers: one input layer, one or more hidden layers, and one output layer. Each neuron is connected to all the neurons of the previous layer; the connections are not all the same, because they have a different weight. The weights of these connections encode the knowledge of the network.
Data enters at the inputs and passes through the network, layer by layer, until it arrives at the outputs; during this operation there is no feedback between layers.
Therefore, these networks are called feed-forward neural networks.
An ffnn with enough neurons in the hidden layer is able to approximate with arbitrary precision:
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