Hadoop

April 26, 2014 Leave a comment

Screen Shot 2014-04-27 at 12.04.31 AMRecruiters are spellbound by this incantation – Hadoop. So I took the plunge and started working with it. I haven’t made much progress but now I use this Sandbox VM and follow the tutorial steps.

Screen Shot 2014-04-27 at 1.24.09 AM

Filed under Hadoop

Machine learning on Big data

April 15, 2014 Leave a comment

I just viewed the webinar conducted by SkyTree. This particular slide has some information about the evolution of Machine Learning.

Screen Shot 2014-04-15 at 9.14.24 PM

Filed under Machine Learning

Gradient checking quiz

April 15, 2014 2 Comments

Let J(\theta) = \theta^3. Furthermore, let \theta = 1 and \epsilon=0.01. You use the formula

(J(\theta+\epsilon )- J(\theta-\epsilon))/2\epsilon

to approximate the derivative. What value do you get using this approximation ?(When \theta=1,the true, exact derivative is
d/d\theta J(\theta) = 3 ).

The Octave code that I used to solve this is 

((1+0.01)^3-(1-0.01)^3)/(2*0.10)

3.0001

Filed under Machine Learning

Logistic Regression

April 6, 2014 Leave a comment

I just finished the code for a Logistic Regression classification model. I worked on a Card transaction processing system and there was a requirement for identifying whether a card transaction is fradulent or not. We did not use any classification model but if we had had a training set of historical data we could have used this learning algorithm.
Most of the time it is the lack of skills that affects software projects that I have been involved with.

Cost function for Logistic Regression

J(\theta) = 1/m \sum_{i=1}^{m}[-y^{(i)} log( h_\theta(x^{(i)}) - (1-y^{(i)})log( 1 - h_\theta(x^{(i)}))] ;

Gradient of the cost

(\partial J(\theta)/\partial \theta_j ) = 1/m \sum_{i=1}^{m} ( h_\theta(x^{(i)}) - y^{(i)}) x^{(i)}_j

This is the plot generated by the course material based on my code. The line is a decision boundary that shows which exam scores ensure admittance. We can use historical card transaction data in place of this.

Screen Shot 2014-04-06 at 9.12.09 PM

This classificatio model can predict which card transactions could be fradulent based on a probability calculated by the algorithm. So here instead of plotting exam scores on the axes we can plot card transaction data.

Filed under Machine Learning

Varargs

April 2, 2014 Leave a comment

Is this confusing code ? I was stumped for a few seconds.

public class PassThreeValues {
     static void calculateSomeThing(int one, int... values){}

     public static void main( String... argv ){                 
         calculateSomeThing(1,2,3);
    }

Filed under Uncategorized