This article will discuss the differences of deep learning vs. machine learning and their main characteristics. Furthermore, we will discuss what problems deep learning and machine learning can solve and how they relate to computer vision.
Difference Between Machine Learning and Deep Learning
Machine learning and deep learning both fall under the category of artificial intelligence, while deep learning is a subset of machine learning. Hence, the differences between machine learning and deep learning are based on the fact that deep learning is a part of machine learning, but machine learning is not necessarily deep learning.
Similar to how every square is a rectangle while not every rectangle is a square, there are differences between machine and deep learning which allow deep learning to have a certain set of rules that not every machine learning implementation follows.
The varying degrees of reliance on data inputs differentiate machine learning and deep learning and alter what problems they can be used to solve. Differences between deep learning vs machine learning that are based on their algorithmic backbones also distinguish them from each other, since one depends on layers while the other depends on data inputs to learn from itself.
Deep learning is what powers the most human-resemblant artificial intelligence (including Computer Vision), such as that which allows us to unlock our phones using face recognition.
Machine Learning and Artificial Intelligence
Machine learning encapsulates multiple subsets of artificial intelligence, including deep learning. Machine learning takes in a set of data inputs and then learns from that inputted data.
Many automated recommendations are created using machine learning. This is because machine learning algorithms are commonly used to identify trends, like what music you would want to listen to next as part of an on-demand music streaming service. Another example of ML based recommendations is the personalized movie recommendation system of Netflix.
Comparison of Deep Learning and Machine Learning
Deep learning is specifically designed to continually analyze data with a logic structure similar to how a human would draw conclusions. Deep learning is best characterized by its layered structure, which are usually done by creating an artificial neural network.
Deep learning is seen commonly when solving computer vision related problems, where images or parts of images need to be specifically classified. Applications of computer vision include Tesla’s automated driving system, which is currently making breakthroughs in the electric car industry.
Machine Learning vs. Deep Learning for Computer Vision
Machine learning is not usually the ideal solution to computer vision problems or problems that require “eyesight.” Machine learning makes decisions with minimal human intervention. Because of that capability, machine learning algorithms have the ability to constantly improve upon their accuracy. With each additional data point provided, machine learning algorithms become more able to identify trends.
Deep learning allows computer vision to be a reality because of its incredibly accurate neural network architecture, which isn’t seen in machine learning as a general field. While machine learning requires hundreds if not thousands of augmented or original data inputs to produce valid accuracy rates, deep learning as a subset of machine learning requires little to none.
The novel ability of deep learning to learn and make intelligent decisions without multitudes of data makes it a unique approach to solving problems in artificial intelligence. Without deep learning, computer vision would not be nearly as accurate as it is today.
If you enjoyed this article and want to know more about machine learning, we recommend you check out the following articles:
- Read about the differences of ANN and CNN
- Overview of five Deep Learning Frameworks.
- What is Computer Vision? 8 Questions You Need to Ask