New design profile

Today we at Expert Analytics have renewed our design profile. It includes a new logo, a set of colors and some preferred fonts. It has already been introduced on our homepage, Linkedin and Github.

The logo, named "folded ribbon" consisting of an X and an A merged together. The colors are #87aade, #5f8dd3 and #3771c8 on darker backgrounds, and #5f8dd3, #2c5aa0 and #214478 on lighter backgrounds.

The font in use is the Ubuntu font, developed as an open source font for the Ubuntu operating system. The logo uses monospace, titles uses condenced, where regular is used for normal text bodies.

Various versions of the logo, can be downloaded from github.com/expertanalytics/logo.git.

Machine Learning

Among our fields of expertise, Expert Analytics offers consultancy services in the field of machine learning - ranging from courses to implementation of machine learning solutions. This is a large field with endless possibilities, and we will here give you small taste of what it's all about.

As a small demonstration of the power of machine learning, we have programmed a neural network that can predict the weather in Oslo, based solely on the weather on the last few days.  You can read more about this below, or if you are impatient, you can try it out yourself here!

What is machine learning?

The field of study that gives computers the ability to learn without being explicitly programmed
— Arthur Samuel, machine learning pioneer.

The field of machine learning revolves around programs and algorithms that have the ability to learn. Algorithms that can be trained on a data sets, learning their features, and can use their "experience" to make predictions on data they have never seen before.

These algorithms excel at solving problems that are difficult or cumbersome to model.  A prime example is the problem of recognizing and classifying animals in pictures. While this is a task a human can perform rather easily, it is extremely difficult to create an algorithm that can find and identify animals in varying positions and postures, and with different individual appearances. Such an algorithm would first of all need a very detailed model of how different animals look from different angles, and in different poses. It must, of course, also take the lighting in the picture into account, and be able to recognize an animal even when parts of it are hidden.

Because of the immense complexity, constructing such algorithms by hand is not really an option; tasks like these require machines that can be taught to recognize the shapes and patterns in a picture as an animal. Google uses machine learning algorithms called neural networks in their image recognition software — see this article for some interesting and enjoyable reading on the subject. These neural networks have been trained on large sets of correctly classified images, to the point at which they can correctly classify images they have never been exposed to before.

Neural networks

Neural networks are programs with a structure that resembles the nervous tissue of the brain. Similar to a brain, a neural network consists of a number of artificial neurons that transmit information between each other, and whose output signals depend on the inputs from other neurons. Using specialized training algorithms, a neural network can made to recognize patterns in a data set, and based on what one could call experience, make predictions for data points it has never been seen before. Neural networks are very flexible, and have a multitude of uses - from function estimation and data classification to image and speech recognition.

A weather prediction neural network

As a small demonstration, we have programmed a neural network that will predict the weather in Oslo on a given day, based on the weather on the preceding five days. Through training on weather data since 1950, the network has learned to recognize patterns in the way the weather develops. When given the weather data from five consecutive days, the network will use its experience with how the weather has behaved in the past to guess what the weather will be the following day.
Read more, and try it out yourself here!


Weather symbols from yr.no.