need of yet another learning algorithm when
we already have linear regression and logistic
regression. The reason is that a simple logistic
regression together with adding in quadratic or
the cubic features to provide a reliable answer,
will end up with millions of features and that’s
just too much. This is not a good way to do it.
We need something better and that’s were
Neural Network steps in. Another reason for the
emergence of Neural Networks is the emergence
of big and fast computer that makes large scale
Neural Networks to work economically. Neural
Networks are actually very effective state of
the art technique for modern day machine
learning applications and get them to work well
on problems.
Without diving into the technical details
and avoiding mathematical definition, let us
understand how the neural network looks.
At a very simple level, neurons are basically
computational units that take a number of
inputs (dendrites) as electrical inputs (called
“spikes”) and does some computation and then
are channeled to outputs (axons) to other nodes
or to other neurons in the brain. An example of
neural network is given in figure 1.
Armed with above knowledge, one is in a position
to understand the terms machine learning and
Neural Network. It also emerges from above
that the scope of machine learning seems to
be unlimited. In general, there is wide scope for
people with good knowledge of mathematics and
particularly IT and electrical engineers to cash
in on this opportunity. Management knowledge
would be an added advantage.