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    • #260
      Abhishek TyagiAbhishek Tyagi

      Before using np.exp(), you will use math.exp() to implement the sigmoid function. You will then see why np.exp() is preferable to math.exp().

      Exercise: Build a function that returns the sigmoid of a real number x. Use math.exp(x) for the exponential function.

      Reminder: sigmoid(x)=11+e−xsigmoid(x)=11+e−x is sometimes also known as the logistic function. It is a non-linear function used not only in Machine Learning (Logistic Regression), but also in Deep Learning.

      # GRADED FUNCTION: basic_sigmoid
      import math
      def basic_sigmoid(x):
          Compute sigmoid of x.
          x -- A scalar
          s -- sigmoid(x)
          ### START CODE HERE ### (≈ 1 line of code)
          s = 1 / (1 + math.exp(-x))
          ### END CODE HERE ###
          return s
      basic_sigmoid(4) #4 is an example
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