correlation between input and output layer vs correlation between inputs and output

Both phrases are correct, but they are used in slightly different contexts. The first phrase 'correlation between input and output layer' refers to the relationship between the input and output layers in a neural network or similar system. The second phrase 'correlation between inputs and output' is more general and can refer to the relationship between multiple inputs and a single output in any context.

Last updated: March 17, 2024 • 755 views

correlation between input and output layer

This phrase is correct and commonly used in the context of neural networks or similar systems.

This phrase is used to describe the relationship or connection between the input and output layers in a neural network or similar system.

Examples:

The correlation between the input and output layer is crucial for the performance of the neural network.

correlation between inputs and output

This phrase is correct and can be used in a general context to describe the relationship between multiple inputs and a single output.

This phrase is used to describe the correlation or connection between multiple inputs and a single output in any context, not limited to neural networks.

Examples:

The correlation between the inputs and output of the system determines its overall performance.

Show examples from the web [+]

print.tornado, Computes Correlation between Inputs and Output in a mc Object ( tornado) in the Variability Dimension;. print.tornadounc, Computes Correlation ...
Data in Table 3 indicates the correlation between inputs and output used in broiler production in the studied area. The value of a correlation coefficient can vary ...
... do not influence much on the final CGPA8 based on the value of R in Model B. As for model C, there is poor correlation between inputs and output parameter.
strong correlation between inputs and output. The analysis has revealed that only Split. Airport and Dubrovnik Airport are relative efficient performers in ...

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correlation between input and output layer vs correlation between inputs and output