Right now, one of the hottest topics in artificial intelligence is deep learning. This field of research is growing rapidly and arouses a lot of discussions. While some say it is just another “buzzword,” a new label for a worn-out AI, biggest companies such as Google or Facebook became increasingly involved in this technology.
Deep learning is a segment of machine learning, which involves the creation of complex, multi-level neural networks, that will allow powerful computer systems to recognize structures, patterns, and other elements by analyzing vast amounts of data.
An important feature of the deep learning is that it is not based on the linear logic. Instead, the program tries to mimic the human brain by forming a tangled layer of interconnected nodes. The system learns through constant reorganization of connections between nodes. This dynamic nature of deep learning methods allows it to be used for many purposes, such as speech recognition, images classification, advanced analysis, predictions, and much more.
Deep learning algorithms are arranged hierarchically according to the increasing complexity and abstraction. The data will then pass through several layers of processing, which is why it was decided to use the term “deep” learning. The output of one layer is used as an input for another, so each successive level of analysis is more accurate. This process continues until the desired level of accuracy is reached.
The process of deep learning can be divided into two phases. First of them will be training. In this phase, the neural network will be given a large amount of data to learn how to classify it – for example, thousand of images of animals.
In the next phase, the system will receive new, unlabeled data – in our example a picture of a dog. Then, it will be processed trough several layers. Initial layers will cover basic features like shapes. The next will focus on more and more complex and abstract attributes until the system can predict what the object in the picture most likely is. As a result of this analysis, it will be able to assign the appropriate label to input data.
The concept of deep learning and neural networks was known to researchers for many years. However, the development of this technology was restrained by technological limitations. Today, scientists have at their disposal massive computing power and enormous amounts of data required to create and train neural networks.
In recent years, the funds allocated for the development of deep learning have increased significantly. None of the big IT players want to be left behind. Google is currently working on more than 1,000 deep-learning projects. Since 2011 they are also working on the Google Brain, a project focused on research of deep learning and neural networks. Microsoft introduced deep learning into many of its products including Bing and X-Box. Facebook uses neural networks to translate posts into different languages or search and organize photos. They are also working on features to help visually impaired use their services.
Assistance in choosing the show to watch, diagnosing diseases, real-time translations – possible applications of deep learning are very broad. In the coming years, it will certainly be one of the fastest developing technologies, which will affect numerous areas of business, industry and our daily life.
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