Logistic_Regression

1. Is there any derivation for the log loss function (from where did
   this function came)?

2. How does gradient descent work in logistic regression?
   Like in linear regression our y was (y = b0 + b1x) and what we did
   there was randomly select a value for b1 and then kept updating it
   until we get a minimum MSE or the global minima.
   Here, in Logistic regression, our (y = sigmoid function), just wanted
   to know how gradient descent will work here? In linear regression we
   used a random value for b1, what will be that random value in logistic
   regression?

3. After reading tons of articles I also got to know that logistic
   regression uses MLE(Maximum likelihood estimation). I couldn’t figure
   out where exactly we use MLE in this algorithm

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