network with single hidden layer vs network with a single hidden layer

Both phrases are correct, but they are used in slightly different contexts. "Network with single hidden layer" is more technical and concise, suitable for technical documentation or discussions. On the other hand, "network with a single hidden layer" is more commonly used in general writing or speech where clarity and completeness are important.

Last updated: March 17, 2024 • 495 views

network with single hidden layer

This phrase is correct in technical contexts where brevity is preferred.

This phrase is commonly used in technical discussions or documentation to refer to a neural network architecture with only one hidden layer.

Show examples from the web [+]

feedforward neural network with single-hidden layer containing. S neurons with arbitrarily chosen input weights can learn S distinct observations with arbitrarily ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): For high dimensional pattern recognition problems, the learning speed of ...
Every boolean function can be represented by network with single hidden layer; But might require exponential (in number of inputs) hidden units. Continuous ...
Sigmoid network with single hidden layer can approximate any continuous function". ▫ Therefore, additional sigmoid layers are unnecessary". ▫ Typical sigmoid ...

Alternatives:

network with a single hidden layer
neural network with single hidden layer
neural network with a single hidden layer
network having single hidden layer
network having a single hidden layer

network with a single hidden layer

This phrase is correct and more commonly used in general writing or speech.

This phrase is suitable for general writing or speech where clarity and completeness are important, such as in articles, reports, or presentations.

Show examples from the web [+]

... + η×oErr[o]×hid[h] 33: until termination 34: return w0,...,wn. Figure 7.12: Back- propagation for learning a neural network with a single hidden layer ...
In the mathematical theory of artificial neural networks, the universal approximation theorem states that a feed-forward network with a single hidden layer ...
A neural network with no hidden layers cannot solve the XOR problem (top curve ), but a neural network with a single hidden layer can solve the problem with a ...
Number of Hidden layers : Theoretically, a Neural Network with a single hidden layer can fit majority of the hypothesis functions and rarely there arises any need ...

Alternatives:

network with single hidden layer
neural network with single hidden layer
neural network with a single hidden layer
network having single hidden layer
network having a single hidden layer

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network with single hidden layer vs network with a single hidden layer