Machine learning - Logistic regression

[@harry1357931gmail]

Excellent Lecture on Logistic Regression....must go through it twice to thrice to absorb it completely....

[@JackSPk]

Maybe I'm misunderstading the 3D plot, but shouldn't it be Y=0={RED} and Y=1={BLUE} at 12:40 ?

[@18amarage]

I really love your lecture sir...

[@mrf145]

Thank you. The lecture is quite easy to understand. You delivered it in very good way.

[@Jinex2010]

22:00 I think J comes Jacobian and H from Hessian

[@Raven-bi3xn]

But the question is about the J of cost function.

[@brianclark4796]

thank you for making this lecture available. very helpful.

[@anynamecanbeuse]

This is just blowing my mind I should say.

[@karthiks3239]

This was a nice lecture.. Thank you..
I was also looking for lectures on Constrained Optimization and Support Vector Machines.. Are these available?

[@flamingxombie]

Great lecture. By 'simulating' thetas, I am reckoning that we are respawning thetas from regions in proportion to their probabilities?

[@vermajiutube]

Awesome lecture. How do we estimate w(thetha_i) using MC in bayesian ?

[@Romis008]

The prof repeatedly mentions that entropy is the opposite of information... I always thought entropy was expected shannon information content. Am I missing something? By the way, amazing content!

[@Cindy-md1dm]

Entropy measures the uncertainty, it is negative of information. When the probability goes close to 1, which means more information you get, less uncertainty left.

[@ar_rahman_90]

Superlike!