What is Deep Learning and Machine Learning?
Machine learning and deep learning are both hot topics and buzzwords in the tech industry. You’ll hear these topics in the context of artificial intelligence (AI), self-driving cars, computers beating humans at games, and other newsworthy technology developments. If you’re new to the AI field, you might wonder what the difference is between the two.
Deep learning vs machine learning
The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning.
More specifically, deep learning is considered an evolution of machine learning. It uses a programmable neural network that enables machines to make accurate decisions without help from humans.
What is machine learning?
Here’s a basic definition of machine learning: “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”
An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations.
Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant—something that they do quite well.
Machine learning involves a lot of complex math and coding that, at the end of the day, serves a mechanical function the same way a flashlight, a car, or a computer screen does. When we say something is capable of “machine learning”, it means it’s something that performs a function with the data given to it and gets progressively better over time. It’s like if you had a flashlight that turned on whenever you said “it’s dark,” so it would recognize different phrases containing the word “dark.”
Now, the way machines can learn new tricks gets really interesting (and exciting) when we start talking about deep learning and deep neural networks. To learn about Machine Learning Get Certification in ML.
The difference between deep learning and machine learning
In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different.
While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network.
Let’s go back to the flashlight example: it could be programmed to turn on when it recognizes the audible cue of someone saying the word “dark”. As it continues learning, it might eventually turn on with any phrase containing that word. Now if the flashlight had a deep learning model, it could figure out that it should turn on with the cues “I can’t see” or “the light switch won’t work,” perhaps in tandem with a light sensor. A deep learning model is able to learn through its own method of computing—a technique that makes it seem like it has its own brain.