Since the introduction of artificial intelligence (AI) at the completion of the 20th century, technology has grown greatly. During its development, it has lived golden times and during one of those golden moments, a part of AI was born: machine learning (ML). Machine learning is an analytical method of solving difficulties through identification, prediction, and classification.
Already in the 21st century, in 2011, the domain of machine learning called deep learning (DL) emerged. The prevalence of machine learning and the growth of the computing capacity of computers equipped this new technology. Deep learning as a theory is very similar to machine learning but employs different algorithms. While machine learning uses regression algorithms or decision trees, deep learning works with neural networks that function similarly to the biological neural connections of our brain.
In this blog, we will discuss the similarity as well as the difference between deep learning & Machine Learning.
What Is Machine Learning?
ML algorithms are mathematical algorithms that enable machines to learn by imitating the manner humans learn, although machine learning is not only algorithms, it is also the way from which the problem is approached. If you are interested in learning more about this technology, you can join our machine learning online course.
What Is Deep Learning?
Deep learning (DL) is a component of machine learning. In fact, it can be defined as the latest evolution of machine learning. It is an automatic algorithm that mimics human perception caused by our brain and the association between neurons. DL is the method that comes nearest to the way humans learn.
Mostly deep learning methods use neural network architecture. That is why deep learning is often regarded to as “deep neural networks.”
What’s The Difference Between The Two?
Simply described, both machine learning, as well as deep learning, mimic the manner the human brain learns. Its principal difference is hence the kind of algorithms used in each scenario, although deep learning is more related to human learning as it works with neurons. Machine learning regularly uses decision trees and deep learning neural networks, which are more developed. If you are interested in learning more about this technology, you can join our Deep learning online training. Apart from that, both can learn in a supervised or unsupervised manner.
How Machine Learning & Deep Learning Are Interrelated?
Deep learning is a subsection of machine learning. The difference between deep learning vs machine learning is related to the difference between your fingers and your thumbs. As in, all thumbs are fingers, but not all fingers are thumbs.
In this similarity, deep learning is the thumb, machine learning the finger. Total deep learning is machine learning, but not every machine learning is deep learning.
But what exactly is it that discriminates the two? The answer lies in how they work.
Machine Learning: Supervised Vs Unsupervised
It demands certain quantities of data to develop a machine on how to learn. From here, there are two types of learning: supervised and unsupervised.
Supervised learning is the more traditional of the two. This is where an individual gives the machine example data labeled with accurate answers. The machine can then learn to spot the patterns and apply the steps to new data information.
Unsupervised learning is less widely used. But, it opens the opportunity of a machine finding new solutions to new questions — ones we humans don’t yet recognize ourselves. This is the category that deep learning falls under.
So, a different way to look at the deep learning vs machine learning question is in the type of data they learn from.
Still Confused? Join CETPA
Despite the similarities between machine learning and deep learning, they can be quite clearly separated when approached in the right way. If you still confused regarding the two, or want to learn in detail about these technologies; their application, uses, how they work etc. take a look at our online summer training programs & contact us for the same.