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Is Rectified Linear Unit a good activation function?

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Is Rectified Linear Unit a good activation function?
What are your thoughts on using Rectified Linear Unit as an activation function compared to other activation functions?
Do you believe Rectified Linear Unit is the best activation function to use in deep learning models?
In your opinion, how does Rectified Linear Unit perform as an activation function?
Could you discuss the advantages and disadvantages of using Rectified Linear Unit as an activation function?
How does Rectified Linear Unit compare to other popular activation functions in terms of model performance?
What is your experience with using Rectified Linear Unit as an activation function and its impact on model accuracy?
When working on deep learning models, how do you decide whether to use Rectified Linear Unit or another activation function?
Can you provide examples of scenarios where Rectified Linear Unit may not be the best activation function to use?
In your experience, have you found Rectified Linear Unit to be a reliable activation function for various deep learning tasks?
Can you provide some insights on the effectiveness of Rectified Linear Unit as an activation function?

Interview question asked to Data Scientists interviewing at Netflix, Juul Labs, Ironclad and others: Is Rectified Linear Unit a good activation function?.

Is Rectified Linear Unit a good activation function?

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