There is a father of all, In to which there is a creator, curator and creative Intellect, in which the beginnings, begin.
As we begun the history on deep learning, naturally, as that is the best part of the story, from where the beginning began, we must tell…
Deep Learning, has a creative antecedent, as this would be a gentleman and his name was Alexey Grigoryevich, oh, so suave name there sir.
As he was a Ukraine, Russian Empire – well then; we shall start with that. I may think to myself, ‘dream boat,’ naturally, as I am a lady and beyond intrigued to the sounds of an Empire, I mean, fairly why would we not crave the intellectual beings, though, as we all are, truly and yes, no high pedestal here, for mister Empire, I know.
He was a soviet and Ukrainian mathematician and he was most famous for developing the Group Method of Data Handling (GMDH), a method of inductive statistical learning.The first general, working learning algorithm for supervised deep feed forward multi layer perceptrons was published by this mister, Alexey, himself in 1965.
These ideas were implemented in a computer identification system “Alpha”, which demonstrated the learning process. Other Deep Learning working architectures, specifically those built from artificial neural networks (ANN), date back to the Neocognitron introduced by Kunihiko Fukushima in 1980.
Alexey, was an outstanding scientist in the field of automatic control, cybernetics and informatics, whose scientific results have received a worldwide recognition. He has developed new principles and devices for automatic control of speed of electric engines and for computing of systems with magnetic amplifiers. He has also written the first domestic monograph on technical cybernetics which was republished abroad several times. Boy oh, boy, ‘mister empire,’ I guess we can praise you for a moment.
The main brainchild of Alexey, was the Group Method of Data Handling (GMDH), which is actively developing as a method of inductive modelling and forecasting of complex processes and systems and In 1984. The collective monograph of American and Japanese scientists with the delineation and examples of effective application of GMDH in various applied areas was published in the USA. Even dozens of publications on the subject of GMDH appear each year in scientific magazines of the world and on the websites in Internet.
To capture all of this scientific, methodological, GMHD information, know that this is not permitted entirely to your understanding or may it be, just as we love a beautiful back story and a good one for mad inspiration as to our understanding on the deep learning, and where he or she was sourced from. I say he or she as ‘ deep learning’ is a being too, right?
I found all of his work rather charming, beyond brilliant myself and the information I read upon perspicacious.
The biggest thing for us, with this article and in going to one single man, vs. starting with facts and other information that could have been distributed, was that our first reason, was in knowing, that to find answers and or better inspiration, is to lie with the deep root of our source and to being inspired by that alone. This and that deep root, was behind deep learning, as the man of it all, well he was not the only human, just one in which we looked at, to start.
Alexey Ivakhnenko serves as an example of scientist with a heightened sense of novelty and outstanding scientific intuition.
If I could be more fulfilled and hungry for learning more than with this gentleman’s achievements, and to further my knowledge on deep learning, then I will start here.
If you would like more information, a taste more on his academics, or any other ways of contribution to our society, to deep learning, look at our source list there.
Follow along, on our ride of deep learning and all of it’s spectacular ways into our universe.
From our galaxy to yours,