Deep Learning: What does the Data Say?

April 2021

Deep learning is a subset of machine learning that has enabled dramatic gains in artificial intelligence, and it's one of the most powerful tools available to companies wishing to deploy AI.

But what is deep learning, exactly? And how does it differ from other types of machine learning? Deep Learning teaches computers how to identify patterns in data by showing them lots and lots of examples - think millions or even billions. These machines are able to learn on their own without needing input from humans.

Deep learning is one of the most exciting areas in AI today, potentially enabling computers to spot patterns within imagery and speech that they couldn't before. It's also been heavily employed by companies looking to deploy AI to solve business problems rather than simply for research purposes.

Technically, deep learning is a branch of machine learning. Machine learning algorithms learn how to train themselves as data gets fed in and out of the system - this allows them to "learn" from training data alone, without having external experts program them how. Deep learning is a subset of machine learning because it focuses on neural networks - a type of computer program that's loosely modelled after how the human brain works. Neural networks are made up of multiple layers, with each layer representing an abstraction layer of information. Deep learning involves building systems with multiple hidden layers, and that take advantage of artificial neural networks to solve complex problems.

In some ways, deep learning is similar to the machine learning algorithms used by companies like Netflix for its movie recommendations engine or Amazon when recommending items to customers who have shopped for certain products before - both companies use deep-learning models to provide products and services that are relevant to their customers. The difference is that deep learning models work at a much more complex level. They can help try to spot patterns in images and speech, for example. They can also be used to make predictions about information like the likelihood of an event happening based on data that has been collected in the past.

Deep-learning models are able to help companies use their data more effectively to get ahead of their competitors. That's why so many companies - from insurance providers looking for new ways to assess risk, to healthcare organizations struggling with costly overhead problems - are looking closely at how deep learning could impact them, and transform how their businesses operate.

Deep-learned systems can be used for various tasks including image recognition which can be applied to robotic systems as well as face recognition by applying modern deep-learning techniques on images of human faces. In the future, with these techniques we may not need sign languages or other types of aids for deaf people who cannot hear at all. Deep-learning systems are able to recognize images much better than other systems can. This chart shows results of a deep-learning system's image recognition and a traditional system's. Although the traditional method is also image recognition, using deep learning allows it to perform even better. Deep learning's strength lies within its structure, as I will explain later in the article.

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