Deep learning vs machine learning: what’s the difference?
Machine learning is perhaps one of the most interesting subsections of artificial intelligence. The emerging ability of machines to learn as they go unlocks possibilities once thought of as outlandish science fiction.
But there’s a question when it comes to terminology. Deep learning vs machine learning, what’s the difference?
Surely all instances where a machine learns counts as machine learning? If that was your thought, you would have been right. But that doesn’t mean that there’s no distinction between deep learning and machine learning.
Thumbs and fingers
Deep learning is a subsection of machine learning. The difference between deep learning vs machine learning is akin to the difference between your fingers and your thumbs. As in, all thumbs are fingers, but not all fingers are thumbs.
In this analogy, deep learning is the thumb, machine learning the finger. All deep learning is machine learning, but not all machine learning is deep learning.
This is the simplest possible starting point for unravelling the deep learning vs machine learning question. But what exactly is it that differentiates the two? The answer lies in how they work.
Machine learning: supervised vs unsupervised
It takes absolute masses of data to teach a machine how to learn. (Regardless of which type in the deep learning vs machine learning question.) From here, there are two types of learning: supervised and unsupervised.
Supervised learning is the more common of the two. This is where a human gives the machine example data labelled with the correct answers. The machine can then learn to spot the patterns and apply the steps to new data input.
Unsupervised learning is less widely used. But, it opens the possibility of a machine finding new answers to new questions — ones we humans don’t yet know ourselves. Unsupervised learning involves messy, unstructured data, and no other input from humans. This is the category that deep learning falls under.
So, another way to look at the deep learning vs machine learning question is in the type of data they learn from.
Layers of algorithms
Deep learning also works in a specific way — one different to general machine learning.
All machine learning uses an algorithm to parse data, learn from the data, and decide an answer. (By comparing it to its bank of examples.) Non-deep machine learning tends to use linear reasoning — applying each process to the data step-by-step.
Deep learning, meanwhile, does this using an artificial neural network (ANN). An ANN is a computer system that aims to mimic the human brain. Rather than a linear, step by step process, the data filters through many layers of processes to find patterns on its own. (Without help from humans.) This allows for a deeper analysis of the data in question — and outcomes humans may not foresee.
In short, the deep learning vs machine learning question relates to how each processes input. Deep learning uses many layers of processes to look for patterns, mimicking the human brain. Non-deep machine learning, meanwhile, is more linear, comparing input to example data.
Deep learning vs machine learning
The question of deep learning vs machine learning is misleading. Deep learning is, after all, a type of machine learning.
The differences between the two terms are a question of detail. Machine learning is a catch-all term for any machine able to learn from data. Deep learning is a specific method of enabling a machine to learn and make decisions.