# Blog Archives

## Monitoring Productivity II – the Others

September 30, 2011
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In previous Monitoring Productivity Experiment post I looked into the hours I spent in computer, now will look into the hours Others spend in computer, which is far more interesting :) To find things like what day people spend more time on computer, ho...

## Machine Learning Ex5.2 – Regularized Logistic Regression

March 20, 2011
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Exercise 5.2 Improves the Logistic Regression implementation done in Exercise 4 by adding a regularization parameter that reduces the problem of over-fitting. We will be using Newton's Method. Data Here's the data we want to fit. # linear regression # load the data mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydHZPN2pFbkZGd1RKeU81OFY3ZHJldWc&output=csv", header = TRUE) # plot the data plot(mydata\$u, mydata\$v,, xlab="u", ylab="v") points(mydata\$u,...

## Machine Learning Ex5.1 – Regularized Linear Regression

March 18, 2011
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Exercise 5.1 Improves the Linear Regression implementation done in Exercise 3 by adding a regularization parameter that reduces the problem of over-fitting. Over-fitting occurs especially when fitting a high-order polynomial, that we will try to do here. Data Here's the points we will make a model from: # linear regression mydata = read.csv("http://spreadsheets.google.com/pub?hl=en_GB&hl=en_GB&key=0AnypY27pPCJydGhtbUlZekVUQTc0dm5QaXp1YWpSY3c&output=csv", header = TRUE) # view data plot(mydata)

## Machine Learning Ex4 – Logistic Regression and Newton’s Method

March 16, 2011
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Exercise 4 is all about using Newton's Method to implement logistic regression on a classification problem. For all this to make sense i suggest having a look at Andrew Ng machine learning lectures on openclassroom. We start with a dataset representing 40 students who were admitted to college and 40 students who were not admitted, and their corresponding...

## Machine Learning Ex3 – multivariate linear regression

March 8, 2011
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Exercise 3 is about multivariate linear regression. First part is about finding a good learning rate (alpha) and 2nd part is about implementing linear regression using normal equations instead of the gradient descent algorithm. Data As usual hosted in google docs: mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydExfUzdtVXZuUWphM19vdVBidnFFSWc&output=csv", header = TRUE) # show last 5 rows tail(mydata, 5) area bedrooms price 43 2567 ...

## Machine Learning Ex2 – linear regression

February 24, 2011
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Andrew Ng has posted introductory machine learning lessons on the OpenClassRoom site. I've watched the first set and will here solve Exercise 2. The exercise is to build a linear regression implementation, I'll use R. The point of linear regression is to come up with a mathematical function(model) that represents the data as best as possible, that is done...

## Weight Loss Predictor

February 5, 2011
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Got for 2010 Xmas a very cool book called the "4 Hour Body"(thanks Jose Santos) written by Tim Ferriss who write a previous favorite of mine about productivity, the 4 hour work week. Its an interesting book, because it has a scientific approach, it ...

## Monitoring Productivity Experiment

October 20, 2010
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For over a year now, i've been collecting how much time i spend in computer and how much of it is actually used in creative/productive activities. By productive activity i mean that the time spent in text editor(emacs), terminal, excel or a datab...