### Final Answer
**True.**

The ReLU (Rectified Linear Unit) activation function does indeed introduce non-linearity to the model. It is defined as $ \text{ReLU}(x) = \max(0, x) $, which means that it outputs zero for any input value less than or equal to zero and outputs the input value itself for any positive input. This characteristic allows neural networks to approximate complex functions and capture non-linear relationships in the data. By incorporating non-linearity, ReLU helps improve the learning capability of neural networks, making it a popular choice in deep learning architectures.

Question

### Final Answer
**True.**

The ReLU (Rectified Linear Unit) activation function does indeed introduce non-linearity to the model. It is defined as $ \text{ReLU}(x) = \max(0, x) $, which means that it outputs zero for any input value less than or equal to zero and outputs the input value itself for any positive input. This characteristic allows neural networks to approximate complex functions and capture non-linear relationships in the data. By incorporating non-linearity, ReLU helps improve the learning capability of neural networks, making it a popular choice in deep learning architectures.

Knowee AI · Accepted Answer

### Final Answer
**True.**

The ReLU (Rectified Linear Unit) activation function does indeed introduce non-linearity to the model. It is defined as $ \text{ReLU}(x) = \max(0, x) $, which means that it outputs zero for any input value less than or equal to zero and outputs the input value itself for any positive input. This characteristic allows neural networks to approximate complex functions and capture non-linear relationships in the data. By incorporating non-linearity, ReLU helps improve the learning capability of neural networks, making it a popular choice in deep learning architectures.

The ReLU activation function can introduce non-linearity to the model.Group of answer choicesTrueFalse

Question

The ReLU activation function can introduce non-linearity to the model.

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