Advancements in artificial intelligence are taking place at lightning speed which makes it quite overwhelming. But fundamentally, it trickles down to two main concepts; Deep Learning vs Machine Learning. Even though these concepts are used interchangeably at times but believe us, they are as different as chalk and cheese! This confusion makes it critical to clarify the difference.
Let us mention some everyday examples for you. Have you ever wondered how Facebook or Google recognize your face in the photos, how Netflix recommends the next show you’d be interested in, or how self-driving cars are turning into a reality? You see, these platforms are more intelligent today than ever before due to artificial intelligence in general and machine learning and deep learning in particular.
So if you’ll stick with us for a while, we’ll explain the differences between these two subsets of AI: Deep Learning vs Machine Learning. Let’s learn!
What is Machine Learning?
Machine learning can simply be described as a branch of artificial intelligence that learns from data and improves its accuracy without human intervention. Algorithms are made self-sufficient in finding the patterns in a large amount of data to make predictions and decisions. Over time, the more data is processed, the more the system gets intelligent in making accurate decisions and predictions. As an example, digital assistants play music by our voice command.
What is Deep Learning?
Deep learning is a subset of machine learning in artificial intelligence that can itself learn from unlabeled and unstructured data without human supervision. It is sometimes also referred to as deep neural learning. In simple words, it imitates the working of human brains in making decisions based on patterns and experiences.
Deep Learning vs Machine Learning!
Practically speaking, deep learning is a subset of machine learning in AI. As a matter of fact, deep learning functions just like machine learning, but they both have widely different capabilities. Machine learning models do get better with the enormous amount of data they deal with, but they still rely on human intervention if some algorithms make inaccurate forecasts. Engineers then have to step in to make some adjustments. On the contrary, in deep learning, an algorithm can itself conclude whether a prediction is accurate or not by utilizing its own neural networks.
Let’s take an example to understand Deep Learning vs Machine Learning pivotal differences.
Suppose you have a system that switches on the light after hearing the word ‘dark.’ Now, as time progresses, it might continue to switch the light on after hearing the sentence that has the word ‘dark’ in it. Now, if this system has deep learning, it will figure out to switch on the light after hearing, ‘I can’t see’ or ‘why the light isn’t on.’ You got the gist, right?
How Does Deep Learning Actually Work?
Deep learning continues to analyze data with numerous logical structures and works like the human brain’s decision-making process. This is made possible by the presence of layered structures of multiple algorithms called neural networks. It takes plenty of time to reach a stage where it can make human-like decisions. But once it gets there, it is undoubtedly a true marvel of artificial intelligence.
In a Nutshell
In Deep Learning vs Machine Learning, the former is considered an advanced form of machine learning. It is helpful when the data to be dealt with is unstructured and unlabeled.
And as deep learning becomes more intelligent over time, we’ll see even more advanced applications of artificial intelligence in everyday life.
