Batch Gradient Descent

I happened to stumble on Prof. Andrew Ng’s Machine Learning classes which are available online as part of Stanford Center for Professional Development. The first lecture in the series discuss the topic of fitting parameters for a given data set using linear regression.  For understanding this concept, I chose to take data from the top…

Read More

GATE-2012 ECE Q52 (electromagnetics)

Question 52 on electromagnetics from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. An infinitely long uniform solid wire of radius  carries a uniform dc current of density . Q52. The magnetic field at a distance  from the center of the wire is proportional to (A)  for and for (B)  for  and  for  (C)  for  and  for  (D)  for  and  for  Solution…

Read More

GATE-2012 ECE Q24 (math)

Question 24 on math from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q24. Two independent random variables X and Y are uniformly distributed in the interval [-1, 1]. The probability that max[X,Y] is less than 1/2 is (A) 3/4 (B) 9/16 (C) 1/4 (D) 2/3

Read More

Books

Happy holidays! 🙂 Wishing every one merry Christmas and a great year 2009 and beyond. I will list down some of the books which I have on my desk. They help me with the math and simulations Digital Communication: Third Edition, by John R. Barry, Edward A. Lee, David G. Messerschmitt

Read More

Closed form solution for linear regression

In the previous post on Batch Gradient Descent and Stochastic Gradient Descent, we looked at two iterative methods for finding the parameter vector  which minimizes the square of the error between the predicted value  and the actual output  for all  values in the training set. A closed form solution for finding the parameter vector  is possible, and in this post…

Read More