Machine Learning has been successfully applied in the areas such as recommendation systems and visual object identification in the recent past. Most of the machines learning algorithms, including deep learning, are in the final analysis nothing but regression systems and classification systems.

A few weeks ago, we saw how nature’s preference of perceiving things in log-scale and of growing things in gnomons leads to occurrences of Fibonacci numbers in the data-sets. This week, we will discover another fundamental reason why Fibonacci numbers may emerge in a dataset.

George Kingsley Zipf (1902-1950) observed an interesting phenomenon in natural languages. This phenomenon, now known as Zipf’s law, which Wikipedia defines as follows “Zipf’s law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc”

Aristotle described Gnomon as adding an “L” shaped figure to an existing square, such that the resulting figure is still a square. Euclid extended the concept to a parallelogram, and finally Hero extended the concept to any figure that, after being added by any figure, retains its original form.

Gustav Fechner (1801-87) discovered that the way humans perceive the intensity of sensory inputs is logarithmically proportional to the absolute magnitude of stimulus as measured by non-living measuring devices.

In 1837 Charles Babbage described “Analytical Engine” (AE for short). Had he succeeded in actually building AE, it would have the first Turing Complete computer designed by humans.