Let’s resolve the biggest confusion between all comprehensive technical terms like artificial intelligence, machine learning and deep learning.
Artificial intelligence is a broader space under which Machine Learning and Deep Learning are subsets of it.
Let’s understand briefly about each of them to get a better picture.
The concept of artificial intelligence came into existence in 1956. But data at that time was not sufficient to calculate accurate results.
Artificial Intelligence is a technique by which machines demonstrates intelligence or behavior like humans.
In AI, machine can learn from experience, like new born kids do. So from new input data, machine can adjust new responses.
We can consider artificial intelligence as a project of creating Huge Monument which can take centuries to build. So the one who started building it, could not even see it fully built.
AI researchers started working on bricks and bases of the project by creating learning algorithms so that future researchers will use it to build smart intelligence system.
Example of Artificial Intelligence: Apple Siri, Microsoft Cortana, Tesla Self Driving Cars and many more.
In Artificial intelligence, it was difficult to train complex decision making operation models of the Human brain.
“Machine Learning is an application Artificial intelligence which enables machine to learn from statistical data to improve with experience.”
The designed algorithms in Machine Learning are developed in such a way, that it can learn and improve the results when new data is provided.
Example of Machine Learning: Netflix, Google Maps
Elaborate Examples: Netflix – Depending upon what type of movies and series you watch, Netflix will suggest you same type of movies and series to you in Recommended section.
Google Maps – Google map analyses the traffic and suggests you the fastest routes to your destination.
Deep learning is a part of a broader family of Machine Learning that is inspired by the functionality of our brain cells called artificial neural network.
It takes data connection between all the artificial neurons and adjust it according to the data parent. With the increase in the size of data parent ,more neurons is added .
You can relate Deep Learning as rocket a rocket engine which uses huge amount of data a fuel to process the algorithms.
Deep Learning concept is not new but recently it’s hype has increased and getting a lot of attention.
How Deep Learning Works at simple scale:
In above example, machine will validate all the criteria to check if the rectangle is a square. When it is nothing but nested hierarchy of conditions and checks.
Deep learning does the same thing but a larger scale
Above blog gives the brief difference of difference between different types of AI.
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