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The network generates the appropriate outputs according to the noise density and kind (Figure 5).
Marr's discussion suggests a functional conception of computation, on which computation is a matter of transforming inputs into appropriate outputs.
A typical multi-layer perception is a feed forward ANN model that maps sets onto a set of appropriate outputs.
MLPs are feed-forward ANN models mapping the sets of input data onto a set of appropriate outputs.
Approximate search and the stream extraction of data combine to allow the processing of huge amounts of data, while simultaneously extracting the most frequent and appropriate outputs from the search.
MLP is a type of feed-forward neural network (FFNN) model that maps the input data onto a set of appropriate outputs.
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They are trained using thousands of examples, and a "learning" algorithm that alters the strength of the connections in the network so that it gives the appropriate output value (whether or not a misfire has occurred) depending on the input values (engine speed, acceleration, cylinder position, and so forth).
Each property element describes how a program should be executed to create appropriate output(s).
out_velocities receives velocity vectors from velocities and processes them to generate the appropriate output.
The add-on executes the authorized request decision algorithm and produces the appropriate output.
MLP is a feed-forward ANN that maps the input data to the appropriate output.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com