Deep learning is becoming a commonly heard word in business, science and programming circles. However, the concept is still quite new to many people. This important type of algorithm is a subset of machine learning. It uses neural networks and pattern recognition to develop deep insights that would ordinarily be indecipherable by other types of programs. Due to increased computing power and strides in deep learning programming, this technology is driving a true technical revolution.
The easiest example of deep learning to understand is visual search algorithms. Imagine a photo of a tree. Now think of the millions of potential photos of trees and thousands of different types of trees. How is the computer supposed to know that they should all be labeled “tree” and be indexed that way in visual search? The answer is deep learning algorithms which input a “training set” of data. The program becomes generally familiar with what a tree looks like. When a new photo of a tree is inserted, it can accurately describe and categorize the image.
While advanced deep learning programs are still in the works, there are many exciting possibilities. For example, many researchers believe deep learning technology could produce ground breaking medical drugs that would cure disease and make humans much healthier. Today, scientists move to slowly with trials and experimentation to try the full range of options. However, a deep learning program may be able to create a realistic simulation that produces amazing results.
Other opportunities for breakthroughs based on deep learning include weather forecasting, quality control in manufacturing (getting mistakes below one in a million), economic models and improving machine vision systems.
No matter what, the steady advance of this technology is already having profound impacts on society and technology.
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Learn more about Deep Learning Applications and Functions below.
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