The term AI was coined in 1956 by John McCarthy, who is also referred to as the Father of Artificial Intelligence. The idea behind AI is fairly simple yet fascinating, which is to make intelligent machines that can take decisions on their own. You may think of it as science fantasy, but with respect to recent developments in technology and computing power, the very idea seems to come closer to reality day by day.
Machine Learning
A Step Towards Artificial Intelligence Now, that you are familiar
with AI, let us talk briefly about Machine Learning and understand what it
means when we say that we’re programming machines to learn. Let us begin with a the very famous definition of Machine Learning:
“A computer program is said to learn from experience E with respect
to some task T and some performance measure P, if its performance on T, as
measured by P, improves with experience E.”
So, if you want your program to predict, traffic patterns at a busy
intersection (task T), you can run it through a machine learning algorithm with
data about past traffic patterns (experience E). Now, the accuracy of the
prediction (performance measure P) will depend on the fact that whether the
program has successfully learned from the data set or not (experience E).
Basically, Machine Learning is referred to as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed by exposing them to vast amounts of data. The core principle behind Machine Learning is to learn from data sets and try to minimize error or maximize the likelihood of their predictions being true.
Deep Learning
Deep learning is one of the only methods by which we can overcome
the challenges of feature extraction. This is because deep learning models are
capable of learning to focus on the right features by themselves, requiring
little guidance from the programmer. Basically, deep learning mimics the way
our brain functions i.e. it learns from experience. As you know, our brain is
made up of billions of neurons that allow us to do amazing things. Even
the brain of a one-year-old kid can solve complex problems which are very
difficult to solve even using super-computers. For example:
Recognize the face of their parents and different objects
as well.
Discriminate different voices and can even recognize a particular
person based on his/her voice.
Draw inference from facial gestures of other persons
and many more.
Actually, our brain has subconsciously trained itself to do such
things over the years. Now, the question comes, how deep learning mimics
the functionality of a brain? Well, deep learning uses the concept of
artificial neurons that functions in a similar manner as the biological neurons
present in our brain. Therefore, we can say that Deep Learning is a
subfield of machine learning concerned with algorithms inspired
by the structure and function of the brain called artificial neural networks.
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning is the one category of machine learning that emphasizes training the computer about the basic instincts of human beings. It is a prime technology behind the concept of virtual assistants, facial recognition, driverless cars, etc. The working of deep learning involves training the data and learning from the experiences
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