Neural Networks in computer system are quite analogues to what
the neurons we have in our brain. Our brain that is the collection of millions
of neurons can be considered as the most advanced system. The decision taken by
brain are impeccable and are accompanied with a lot of decision making that
involved a lot of permutation and combination.
Scientist were working day and night to produce an AI enabled
system that can solve majority of problem. Few AI system were developed also
with the capability to solve the problem in a formal way but do not have a
broader prospect. The IBM Deep Blue was one of such invention that defeated World
Chess Champion Garry Kasprov.
Initially the AI system based their knowledge on some set of
rules and regulation. Such kind of system were not so robust and had certain
limitation. The human need to feed every possible situation which a cumbersome
job was. Such approach was called Knowledge Base approach. The scientist
devised a new methodology which does not require any specific rules and work
according to the raw data fed into the system.
The scientist works upon the machine learning approaches and
have many successes, one of them was logistic regression which was able to
predict the cesarean delivery by feeding the system with certain inputs which
was called feature extraction.
Similarly, Naïve Bayes was used to classify the email as Spam and Non-Spam. The
feature extraction was quite a tough job and require lots of human effort and time,
moreover it requires a top-level domain expertise.
The ground-breaking innovation came into the year 1960 when
Frank Rosenblatt has discovered an artificial neuron called Perceptron. Later,
the evolution take place in such a perceptron and lead the way for the Multilayer Perceptron (MLP).
We will cover about Perceptron and Multilayer Perceptron in
our next blog post.
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