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sigmoid activation function

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Innocent Iguana answered on February 12, 2023 Popularity 10/10 Helpfulness 3/10

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sigmoid_output = 1.0 / (1.0 + np.exp(-z))

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Contributed on Apr 27 2023

Innocent Iguana

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σ(x) = 1 / (1 + e^(-x))

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Contributed on Aug 10 2023

Clean Chinchilla

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sigmoid function

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Sigmoid or logistic function is a step function that calculates the
probability of "(the transpose of θ) times x"
It is used in logistic regression to tell which class a point belongs to.  
Remember what "(the transpose of θ) times x" is?
X is the dataset and θ is the weight-vector or co-efficient for each feature in
the dataset. Each value of θ signifies the importance of each feature in the data. 
When "(the transpose of θ) times x" is large for a point, sigmoid is close to 1.
This means that probability of that point belonging to class 1 is almost 1 (most likely).
When "(the transpose of θ) times x" is small for a point, sigmoid is close to 0.
This means that probability of that point belonging to class 1 is almost 0 (least likely).

def sigmoid_activation(weighted_sum):
    return 1.0 / (1.0 + np.exp(-1 * weighted_sum))

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Contributed on Feb 12 2023

Innocent Iguana

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sigmoid activation function

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