What Is the Difference Between Deep Learning and Machine Learning?
Deep learning is a subset of machine learning (ML), which is, in turn, a subset of artificial intelligence (AI). ML employs algorithms that parse data, learn from it, then use the new information to make informed decisions, similar to human thinking. ML enables a wide range of automated tasks, from queuing up the next song on a streaming music service to recommending a new product for a customer to buy.
Deep learning has additional abilities compared to basic ML algorithms. It automatically learns representations from data, such as images, video, or text, without introducing hand-coded rules or human domain knowledge. For example, to detect oranges on a production line using machine learning, you would include instructions such as, “oranges are round” and “oranges are orange” to help the machine to learn. Deep learning needs no such instructions; it can work out the characteristics for itself.
Both deep learning and machine learning hold tremendous promise for almost every business sector. They can help organizations make more accurate predictions about consumer behavior, patient needs, marketing gaps, and more. Because deep learning is designed to continually, automatically analyze data, it should be applied to specific tasks that require the highest level of accuracy.