Deep Learning vs. Machine Learning: Simplified
People have been embarking upon buzz terms in the tech industry lately. This is because there’s a specific trend that arises once a word or name is coined. Many people nowadays use terms without fully getting to understand them, which causes confusion, misinformation, and most of the time, fake news.
Every often a new app or tool is developed, a new term follows. Now, raise your right hand if you’ve been caught in the mix-up of distinguishing machine learning (ML) vs. deep learning (DL). Bring down your hand, pal, we can’t see it anyway!
Even though people typically use the two jargons interchangeably, they do not denote the same thing. Machine learning and deep learning are two subcategories of artificial intelligence (AI), which have reaped a lot of attention over the past years. Business gurus, marketers, and techies love these terminologies and throw them around whether or not they comprehend the differences.
Data is at the Core of the Matter
AI or artificial intelligence is a branch of computer science that deals with the simulation of intelligent behavior in our computers. In short, robots.net shared that it is the ability of a machine to emulate intelligent behavior – speech recognition, translation, decision making, and visual perception, for instance.
Understanding modern developments in AI can seem overwhelming. However, if it’s learning the fundamentals that you’re concerned about, you can boil down numerous AI inventions to two concepts: deep learning and machine learning.
Now, what are these concepts that govern the discussions about artificial intelligence and how precisely they differ?
Machine learning investigates data. It crunches numbers, absorbs from it, and utilizes that to make truth, determination, or prediction, reliant on the scenario. We train machines on how to accomplish a task properly after they learn from all the data examined. In short, it is creating its own logic and solutions.
Machine learning encompasses a lot of complicated math and coding, which functions a mechanical purpose, the same method as a flashlight does.
When we say something is proficient of this machine learning, it’s something that achieves a function with the data provided to it and gets gradually better over time. It’s as if you had a flashlight that turns on every time you said, “it’s dark,” so it would identify different phrases that contain the word “dark.”
The way machines can acquire new tricks gets exciting (and engaging) when we start discussing deep learning.
Deep learning crunches more data than machine learning. So, if we have a little bit of data, machine learning is the way to go, but if we’re sinking in lots of data, deep learning is the solution.
Deep learning algorithms are powerful and influential, and they require a lot of data to provide the best outcome. Deep learning needs powerful machines, but machine learning doesn’t.
Going back to the flashlight, it could be automated to turn on when it identifies the audible cue of a person saying the word “dark.” As it continues learning, it might ultimately turn on with any phrase that contains that word.
When the flashlight had a deep learning model, it could discover that it should turn on with the cues “I can’t see,” perchance together with a light sensor. Overall, a deep learning model can absorb intelligence through its system of computing—a method that makes it as if it has its brain.
The Comparison of two Learnings
a. Hardware Dependencies
Deep learning rests on high-end machines, whereas traditional machine learning depends on low-end devices.
b. Data Dependencies
Performance is the core difference between the two algorithms. Although, when the data is not that large, deep learning algorithms can’t perform well, unlike machine learning can. This is the main reason why DL requires a large amount of data to comprehend it seamlessly.
c. Problem Solving Approach
We use the machine learning algorithm to solve problems. However, it necessitates breaking a problem into various parts to solve them independently. To get a result, you can combine them all.
d. Feature Engineering
Domain knowledge is put into the formation of feature extractors to lessen the intricacy of the data and create patterns more noticeable to study the process. It’s very challenging to process, though; hence, it’s time-consuming.
e. Execution Time
Typically, deep learning takes more time to train when likened to machine learning. The focal reason is that there are several parameters in a deep learning algorithm. In machine learning, it takes much less time, reaching from a few seconds to a few hours.
Machine learning and deep learning are here to stay only for the advantage of humanity. They are two different things which are composed of the same shared core of AI. They are good to use in various scenarios, yet one shouldn’t be used over the other except if there is an absolute need.