APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Input is any real number
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Output is a real number in range between 0 to 1
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Nearly linear when input is close to zero
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Slope is close to zero when input is close to zero
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Detailed explanation-1: -Properties. In general, a sigmoid function is monotonic, and has a first derivative which is bell shaped. Conversely, the integral of any continuous, non-negative, bell-shaped function (with one local maximum and no local minimum, unless degenerate) will be sigmoidal.
Detailed explanation-2: -At x = 0, the logistic sigmoid function evaluates to: This is useful for the interpretation of the sigmoid as a probability in a logistic regression model, because it shows that a zero input results in an output of 0.5, indicating equal probabilities of both classes.
Detailed explanation-3: -The sigmoid function is bound in the range of (0, 1). Hence it always produces a non-negative value as output. Thus it is not a zero-centered activation function.
Detailed explanation-4: -A standard sigmoid dose-response curve (previous equation) has a Hill Slope of 1.0. When HillSlope is less than 1.0, the curve is more shallow. When HillSlope is greater than 1.0, the curve is steeper.