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Showing posts from July, 2012

Music Data Hackathon 2012 - Beginner's view

When I first heard of the existence of Hackathons (receive a data set, predict the response as good as possible, win money. All within 24 hours), I had two thoughts:
1. Wow, that sounds greats. Like a huge game for intelligent people.
2. My skills are not good enough to participate.

That was one or two years ago. Now I have finished my bachelor degree in statistics and also gained a little experience with some machine learning techniques (boosting and neural networks). So I felt confident enough to try it out. Then I read about the EMI Music Data Science Hackaton and decided to take part. The cool thing about it was, that it was hosted by kaggle, so you did not have to be in London to participate.

The week before

The next step was to find a team. As a statistics student it is easy to find other statisticians. So I started to ask people around me if they were interested. To my surprise the euphoria to be part of such a competition was huge. My first plan to spend the 24 hours of data ha…

Getting started with neural networks - the single neuron

Neural networks can model the relationship between input variables and output variables. A neural networks is built of artificial neurons which are connected. For the start it's the best to look at the architecture of a single neuron.

They are motivated by the architecture and functionality of neuron cells, of which brains are made of.  The neurons in the brain can receive multiple input signals, process them and fire a signal which again can be input to other neurons. The output is binary, so the signal can be fired (1) or not be fired (0) which depends on the input.

The artificial neuron has some inputs which we call \(x_1, x_2, ... x_p\). There can be an additional input \(x_0\), which is always set to \(1\) and is often referred to as bias. The inputs can be weighted with weights \(w_1, w_2, ..., w_p\) and \(w_0\) for the bias.  With the input and the weights we can calculate the activation of the neuron \[  a_i = \sum_{k = 1}^p w_k x_ik + w_0 \].
The output of the neuron is …

What is it all about?

This blog will be about machine learning and all the stuff which comes along with it. You will read about statistics, informatics, mathematics and software.

I am not an expert in machine learning and I am not even close. Why would I write a blog about machine learning then?

One of the most useful things I experienced in the web are blogs or homepages, where people write about things they've learned. They let other people be part of their learning progress, which is a benefit to both the blogger and the reader.

Next question: Why machine learning?

I want to search for cats in the web, thats why!