Machine learning and deep learning are vital to daily Internet activities everywhere.
It’s how Netflix knows which shows you are interested in, how Facebook knows whose face is in a photo and how self-driving cars know when to turn.
Since machine learning and deep learning have become so prevalent in almost everyone’s lives, it is important to understand the differences between the two.
Deep learning is machine learning.
Deep learning essentially is a product or evolution of machine learning. While machine learning requires more human interaction to provide data and create algorithms to mimic the way humans learn, deep learning takes it a step further and uses a programmable neural network that enables machines to make accurate decisions without the help of humans.
Machine learning is used when there is one specific task that needs to be done, where deep learning will continue to look for more solutions on its own.
For example: A light could be designed with machine learning to turn on when someone says the word dark. The same light designed with deep learning instead, may start to turn on with other words or phrases, such as, “I can’t see” or “I need light” because it may hear those phrases said in tandem with “dark” and overtime learn that those terms also mean that someone needs light.
A deep learning model continually stores and analyzes data to further develop its learning. It is designed to think logically like a person would. These deep learning applications use a layered structure of algorithms called an artificial network, which was actually inspired by the biological neural network of the human brain.
What is tricky is training the AI to draw correct conclusion but when it works as it’s intended to, it doesn’t rely on people anymore and continues to learn on its own.
Deep learning is considered by many to be the backbone of AI. While we are still learning more about it every day, it is expected that in the next 10 years we will start to see innovations that we cannot even fathom yet.
In cybersecurity, the neural networks of deep learning are the tipping point for an evolved security platform, focusing threat protection on observed behavior instead of signatures.
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