Following article guides you through Machine Learning vs Deep Learning.
Artificial Intelligence has so much to offer and it seems quite overwhelming learning about it. However, whenever you come across Machine Learning vs Deep Learning, it really takes a toll to understand the terms! You’re sure to have heard about machine learning and deep learning as both are interchangeably used over time. Hence, it is important to understand Machine Learning vs Deep Learning.
One of the godfathers of Artificial Intelligence, John McCarthy defined AI as “the science and engineering of making intelligent machines that can achieve goals like humans do” in 1955. It may seem profoundly overwhelming that how Artificial Intelligence, however it comes down to two concepts – Machine Learning and Deep Learning.
Many assume that both the term are same and usually use the terms interchangeably, but the fact is that they are different. The way Machine Learning and Deep learning are utilised to describe intelligent machines have always been changing.
What is Machine Learning?
Machine Learning is a large sub-field of Artificial Intelligence that gives the system the ability to learn and improve from experiences without being explicitly programmed automatically. They are designed to work like virtual personal assistants. And as a matter of fact, they work quite well.
What is Deep Learning?
In Deep Learning, neural networks have more than three layers, which mean more than one hidden layers. These neural networks used in it are called Deep Neural Networks. It is a particular type of Machine Learning that is apprehensive about algorithms enthused by the structure and function of the brain that is called as artificial neural networks. And because of all the build-up, Deep Learning is getting more attention.
Machine Learning vs Deep Learning
For organisations, it is essential to understand the difference between both the terms.
Machine Learning uses types of automated algorithms which learn to predict future decisions and model functions using the data fed to it.
Deep Learning interprets data features and its relationships using neural networks which pass the relevant information through several stages of data processing.
In Machine Learning, various algorithms are directed by the analysts to examine the different variables in the datasets.
In Deep learning, once they are implemented, the algorithms are usually self-directed for the relevant data analysis.
Machine Learning – Usually, there are a few thousands of data points used for the analysis.
Deep Learning – There are a few millions of data points used for the analysis.
The output in Machine Learning is usually a numerical value, like classification or a score.
The output in Deep Learning can be anything – an element, a score, free text or sound and more.
Future of Machine Learning and Deep Learning
After knowing the difference between the two concepts, it is essential to know what future they have in the industry.
- Machine Learning, a must for survival – There is perpetual growth in the popularity of machine learning and deep learning. For surviving in the industry, it is becoming increasingly competitive for organisations to be a part of the bandwagon soon.
- Research to prosper – In today’s time, research has been blooming in academics and industry both. It is no more restricted to academia only. Research in every field is expanding as many funds are being invested.
- Continue to amaze us – Machine Learning and Deep Learning have the capabilities to do wonders and surprise us each day. And it looks like that these will continue to do so in future as well. The latter is proving to be one of the best techniques in the industry and providing with high-quality performance.
So to summarize everything:
- Artificial Intelligence is machines exhibiting human intelligence
- Machine Learning is a way to achieve Artificial Intelligence
- Deep Learning is a technique for putting Machine Learning into practice
It is easy to get carried away with the hype and exaggeration, which is often used when such cutting-edge technologies are discussed. However, the truth is that these concepts deserve the attention they are getting. It is very unlikely to hear data scientists say that they have technology and tools available to them which they had not expected to see this soon. It is all happening because of the advances that Machine Learning and Deep Learning have made possible.
Well, with such advancements, we can only expect to see more innovative applications of deep learning in the near future and expect machines to provide even better-customized assistance.