Category Archives: development

Confidence Intervals in Python

Suppose you are interested in finding out the mean weight of all Sumo wrestlers in Japan. Or the average gas consuption of Korean made automobiles… Why…? No idea, but that sort of statistics might be of interest, for someone, at … Continue reading

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How many North (or South) poles are there…?

https://www.scientificamerican.com/article/the-earth-has-more-than-one-north-pole/

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Estimation of tides

Tide estimation made simple by using “rule of 12th’s”.

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Central Limit Theorem illustrated

A fundamental theorem of statistics is the Central Limit Theorem, which basically states that the averages (means) of a large number of samples drawn from any population converges towards a normal distribution. The graph above demonstrates this theorem: the blue … Continue reading

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Wind,Temperature,Air Pressure statistics

The above graphs show statistics from SMHI, the swedish meteorological and hydrographical institute, on air pressure, temperature and wind speed.  For each categrory, (pressure,temp,wind speed) there are two graphs: the first a frequency chart, the second a Q-Q-chart, showing the … Continue reading

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Wind Speed Distribution – is it Normal…?

As a sailor, I’ve become obsessed by wind. Trying to understand its behavior has kept me busy for decades, and still, I can’t claim I fully understand wind behavior. Anyways, one of the things I’ve wondered is whether wind is … Continue reading

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Map Projections using Python & Basemap

The numerous external libraries available for Python continue impressing me, in scope, numbers and ease of use.  A couple of hours ago I googled for chartographic tools for Python, and found the Basemap-library, an add-on to Matplotlib. For whatever reason, … Continue reading

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Great Circle Navigation

The shortest distance between two points is a straight line, right…? Nope, not on the surface of a sphere, like our earth. On a sphere, the shortest distance between two points is a Great Circle.  Let’s say you are a … Continue reading

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Machine Learning – results

So, I reached the point where my Neural Network , after a couple of thousand training iterations, is capable of correctly identifying and separating different categories of input into their corresponding classes. More specifically, as “targets”, I’ve chosen a number … Continue reading

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Machine Learning – normalizing your inputs

Often it pays off  to pre-process/normalize your input data before passing it further down the line of your Neural Network, eg. to get the range of input values into a scale more suitable for your network. Im experimenting with images, … Continue reading

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